an examination of the value relevance and bias in …...an examination of the value relevance and...

An Examination of the Value Relevance and Bias in the

Accounting Treatment of Intangible Assets in

Australia and the US Over the Period 1994-2003 Using

the Feltham and Ohlson (1995) Framework

Firas Naim Dahmash

This thesis

is presented for the degree of

Doctor of Philosophy

at

The University of Western Australia

2007

i

Table of Contents

Table of Contents........................................................................................................... i

List of Tables............................................................................................................... iv

Acknowledgments ....................................................................................................... vi

Abstract ................................................................................................................. vii

Chapter 1: Introduction................................................................................................ 1

1.0 Background to the Study.................................................................... 1

1.1 The Research Questions and their Importance.................................... 4

1.2 Method .............................................................................................. 5

1.3 Findings............................................................................................. 6

1.4 Outline of the Remaining Chapters .................................................... 8

Chapter 2: Accounting for Intangibles: Comparing Australian and US GAAP ............. 9

2.0 Introduction ....................................................................................... 9

2.1 Goodwill Treatment under Australian and US GAAP ........................ 9

2.1.1 Defining Goodwill ................................................................. 9

2.1.2 Recognising Goodwill.......................................................... 10

2.1.3 Useful Life and Amortisation of Goodwill ........................... 10

2.1.4 Impairment Testing Goodwill............................................... 11

2.2 Identifiable Intangible Asset Treatment under Australian and US GAAP ............................................................................................. 11

2.2.1 Defining Identifiable Intangible Assets................................. 11

2.2.2 Recognising Acquired Identifiable Intangible Assets............ 11

2.2.3 Recognising Internally Generated Identifiable Intangible Assets .................................................................................. 12

2.2.4 Revaluing Identifiable Intangible Assets .............................. 12

2.2.5 Useful Life and Amortisation of Identifiable Intangible Assets .................................................................................. 13

2.2.6 Impairment Testing Identifiable Intangible Assets................ 13

2.3 Summary ......................................................................................... 13

Chapter 3: Related Literature ..................................................................................... 15

3.0 Introduction ..................................................................................... 15

3.1 The Value Relevance of Australian and US GAAP........................... 15

3.1.1 Empirical Studies on the Value Relevance of Australian GAAP.................................................................................. 15

3.1.2 Empirical Studies on the Value Relevance of US GAAP ...... 16

3.1.3 Empirical Studies Comparing the Value Relevance of Australian and US GAAP..................................................... 17

3.2 Studies on the Value Relevance of Intangible Assets in Australia and the US....................................................................................... 18

3.2.1 Studies on the Value Relevance of Intangible Assets in Australia .............................................................................. 18

ii

3.2.2 Studies on the Value Relevance of Intangible Assets in the US.................................................................................. 21

3.3 Studies on Value Relevance Using the Feltham and Ohlson (1995) Valuation Framework (Model). ............................................ 25

3.4 Summary ......................................................................................... 29

Chapter 4: Hypotheses ............................................................................................... 30

4.0 Introduction ..................................................................................... 30

4.1 The Value Relevance and Bias in Reported Goodwill and Identifiable Intangible Assets in Australia........................................ 30

4.2 The Value Relevance and Bias in Reported Goodwill and Identifiable Intangible Assets in the US ........................................... 32

4.3 Comparing the Value Relevance and Bias in Reported Goodwill and Identifiable Intangible Assets in Australia and the US. .............. 34

4.4 Summary ......................................................................................... 35

Chapter 5: Method and Data ...................................................................................... 37

5.0 Introduction ..................................................................................... 37

5.1 The Feltham and Ohlson (1995) Valuation Framework (Model)....... 37

5.2 Operationalising Feltham and Ohlson (1995) to Address the Hypotheses in this Dissertation........................................................ 42

5.3 Estimating the Model: Practical Considerations ............................... 46

5.4 The Discount Rate ........................................................................... 48

5.5 Data ................................................................................................. 49

5.6 Summary ......................................................................................... 55

Chapter 6: Results for Australia ................................................................................. 57

6.0 Introduction ..................................................................................... 57

6.1 Preliminary Analysis........................................................................ 57

6.2 The Value Relevance and Bias in the Reporting of Goodwill and Identifiable Intangible Assets in Australia........................................ 60

6.3 Australian Linear Information Models: Are Australian Accounting Practices Biased?.......................................................... 70

6.4 Conclusion....................................................................................... 79

Chapter 7: Results for the US..................................................................................... 80

7.0 Introduction ..................................................................................... 80

7.1 Preliminary Analysis........................................................................ 80

7.2 The Value Relevance and Bias in the Reporting of Goodwill and Identifiable Intangible Assets in the US ........................................... 83

7.3 US Linear Information Models: Are US Accounting Practices Biased?............................................................................................ 90

7.4 Conclusion......................................................................................100

Chapter 8: Comparing the Results for Australia and the US ......................................101

8.0 Introduction ....................................................................................101

8.1 Do Australian and US Investors Value Goodwill Differently?.........101

iii

8.2 Do Australian and US Investors Value Identifiable Intangible Assets Differently? .........................................................................104

8.3 Conclusion......................................................................................105

Chapter 9: Conclusion ..............................................................................................107

9.0 Introduction ....................................................................................107

9.1 Summary of Findings......................................................................107

9.2 Limitations .....................................................................................109

9.3 Future Research ..............................................................................110

9.3 Conclusion......................................................................................111

Bibliography..............................................................................................................112

Appendices ................................................................................................................120

iv

List of Tables

Table 5.1: Selection of the Australian sample......................................................... 51

Table 5.2: Selection of the US sample .................................................................... 52

Table 5.3: Variable definitions/calculations for the Australian data ........................ 53

Table 5.4: Variable definitions/calculations for the US data ................................... 54

Table 6.1: Descriptive statistics for the variables in the Australian pooled sample (1994-2003)............................................................................... 58

Table 6.2: Pearson correlation matrix for the variables in the Australian pooled sample N = 2,611 (1994-2003)............................................................... 59

Table 6.3: Expanding the model: Regression results for the basic Feltham and Ohlson (1995) valuation model and its extensions (1994-2003) ............. 62

Table 6.4: Regression results for the disaggregated valuation model for the Australian pooled sample and sub-samples (1994-2003) ........................ 64

Table 6.5: Is the analysis robust? Estimating the disaggregated valuation model with industry dummies for the Australian pooled sample and sub-samples (1994-2003)....................................................................... 68

Table 6.6: Is the analysis robust? Estimating the disaggregated valuation model where the variables are scaled by the number of shares on issue for the Australian pooled sample and sub-samples (1994-2003) .... 70

Table 6.7: Estimating the disaggregated LIMs for the Australian pooled sample and sub-samples (1994-2003) ................................................................ 72

Table 6.8: Are the estimates for the disaggregated LIMs robust? Estimating the disaggregated LIMs with industry dummies for the Australian pooled sample and sub-samples (1994-2003)......................................... 74

Table 6.9: Are the estimates for the disaggregated LIMs robust? Estimating the disaggregated LIMs where the variables are scaled by the number of shares on issue for the Australian pooled sample and sub-samples (1994-2003) ............................................................................. 78

Table 7.1: Descriptive statistics for the variables in the US pooled sample (1994-2003)........................................................................................... 81

Table 7.2: Pearson correlation matrix for the variables in the US pooled sample N = 4,584 (1994-2003) ......................................................................... 82

Table 7.3: Regression results for the disaggregated valuation model for the US pooled sample and sub-samples (1994-2003)......................................... 84

Table 7.4: Is the analysis robust to the choice of discount rate? Estimating the disaggregated valuation model where the variables have been derived using a 12% cost of capital for the US Sample (1994-2003) ...... 87

Table 7.5: Is the analysis robust? Estimating the disaggregated valuation model with industry dummies for the US pooled sample and sub-samples (1994-2003) ............................................................................. 88

Table 7.6: Is the analysis robust? Estimating the disaggregated valuation model where the variables are scaled by the number of shares on issue for the US pooled sample and sub-samples (1994-2003) ............... 90

v

Table 7.7: Estimating the disaggregated LIMs for the US pooled sample and sub-samples (1994-2003)....................................................................... 92

Table 7.8: Is the analysis robust to the choice of discount rate? Estimating the LIMs where the variables have been derived using a 12% cost of capital for the US Sample (1994-2003) .................................................. 94

Table 7.9: Are the estimates for the disaggregated LIMs robust? Estimating the disaggregated LIMs with industry dummies for the US pooled sample and sub-samples (1994-2003) .................................................... 95

Table 7.10: Are the estimates for the disaggregated LIMs robust? Estimating the disaggregated LIMs where the variables are scaled by the number of shares on issue for the US pooled sample and sub-samples (1994-2003) ............................................................................. 99

Table 8.1: Comparison of the estimated goodwill coefficient for Australia and the US ..................................................................................................103

Table 8.2: Comparison of the estimated identifiable intangible assets coefficient for Australia and the US......................................................105

vi

Acknowledgments

I would like to thank all the people who have helped me to finish this dissertation.

Firstly, I am deeply indebted to my father who supported me financially during my

academic journey. My father, who passed away three months before I completed this

dissertation, was a real model in terms of his good morals, the sacrifices he made to

give me this opportunity and his wisdom. Indeed, without his support I would not have

been able to continue my academic journey.

Next, I would like to acknowledge and thank my supervisors, Associate Professor John

Watson and Associate Professor Robert Durand, who have patiently and generously

advised and guided me and without whose support I would not have completed this

dissertation.

I would also like to express my deep appreciation to my wife for her sacrifices and

patient endurance during this long journey.

I am indebted to my good mother for her understanding and encouragement during my

academic journey.

I am also highly grateful to our family friend, Maureen Bertram, for her support over all

these years.

Finally, but not least, I would like to thank my fellow PhD student, Tarmizi Achmad.

vii

Abstract

The primary aim of this study was to examine, and compare, the value relevance and

any bias associated with the reporting of intangible assets in Australia and the US over

the ten-year period 1994 to 2003. The study adopts a disaggregated form of the Feltham

and Ohlson (1995) valuation model and associated linear information models (LIMs) to

allow goodwill and identifiable intangible assets to be separately examined using

unbalanced panel regression analysis.

The results for the Australian sample suggest that the adaptation of the Feltham and

Ohlson (1995) valuation model used in this study is particularly useful in examining

Australian equity securities. For example, the pooled sample analysis results in an

adjusted R2 of 71%, which is consistent with similar US studies by Ahmed, Morton and

Schaefer (2000) and Amir, Kirscenheiter and Willard (1997). Further, the results from

the disaggregated Feltham and Ohlson (1995) valuation models suggest that the

information presented with respect to intangible assets (both goodwill and identifiable

intangible assets) under Australian GAAP is value relevant. However, the results from

the valuation models also suggest that (for the average Australian company) the market

believes goodwill is reported conservatively and identifiable intangible assets

aggressively. This finding, while consistent with Godfrey and Koh (2001) and

Shahwan (2004), appears to conflict with Wyatt (2005) who concluded that identifiable

intangible assets were more highly valued by investors than goodwill. While the results

from the valuation models suggest that the market perceives both goodwill and

identifiable intangible assets to be reported with bias, this was not confirmed by the

LIM results. The results for the LIMs suggest that the accounting treatment adopted by

the average Australian company for the recognition of intangibles is unbiased.

The Australian results support the views of Barth and Clinch (2001), Wyatt (2005), and

Ritter and Wells (2006) that Australian GAAP has allowed managers appropriate

discretion to convey (through financial statements) their (potentially value relevant)

private information concerning the value of identifiable intangible assets. However, and

despite the LIM results suggesting the accounting treatment adopted by the average

Australian company for identifiable intangible assets is unbiased, the market appears to

systematically discount the values reported for such assets.

viii

The results for the US sample also suggest that the adaptation of the Feltham and

Ohlson (1995) valuation model employed in this study is particularly useful in

examining US equity securities. For example, the pooled sample analysis results in an

adjusted R2 of 54%, which, again, is consistent with similar US studies by Ahmed et al.

(2000) and Amir et al. (1997). The results for the US again suggest that the information

presented with respect to intangible assets (both goodwill and identifiable intangible

assets) is value relevant. However, the results from the valuation models also suggest

that the market believes that the average US company reports both goodwill and

identifiable intangible assets conservatively. This finding is consistent with previous

US studies such as: Wilkins et al. (1998); Choi et al. (2000); Chauvin and Hirschey

(1994); and Jennings et al. (1996). While the results from the valuation models suggest

that the market perceives both goodwill and identifiable intangible assets to be reported

with bias, this was not confirmed by the LIM results, which (consistent with the

Australian findings) suggest that the accounting treatment adopted by the average US

company for the recognition of intangibles is unbiased.

The results for Australia and the US were generally consistent except that (as expected

because of the significant differences in accounting treatment) the US market appears to

believe that identifiable intangible assets are conservatively valued by the average US

company, while the Australian market appears to believe that such assets are

aggressively valued by the average Australian company. However, for both countries

the LIM results indicate that the actual accounting treatment adopted for the recognition

of intangibles is unbiased.

The findings reported in this study, particularly for Australia, suggest that the limits

placed by International Accounting Standards (IASs) on the recognition and

revaluation of certain identifiable intangible assets (such as brands) might not only be

unwarranted, but might result in the market being provided with less value relevant

(more biased) information. As noted earlier, the increasing importance of intangible

assets in the ‘new-economy’ suggests that (wherever possible having regard to the

measurement difficulties) all intangible assets should be recognised in financial

statements to maximise the value relevance of those statements. It should be noted,

however, that there was some evidence to suggest that certain Australian companies

(that is, those not consistently reporting positive abnormal operating earnings) might be

ix

reporting goodwill and/or identifiable intangible assets aggressively and this is an area

that standard setters might need to carefully consider in future.

I trust that the findings presented in this study will prove helpful to both researchers and

those involved with formulating international accounting standards in this particularly

difficult area of intangible assets. I also hope the results will help to allay any fears

regulators (and others) might have that providing managers with accounting discretion

will (necessarily) lead to biased reporting practices; based on the findings of this study

for the majority of Australian and US companies, any such fears appear unwarranted.

1

Chapter 1: Introduction

1.0 Background to the Study

Accounting for intangible assets is one of the most controversial topics that standard

setters have had to confront (Wines and Ferguson 1993; Jennings et al. 1996; Choi et al.

2000; Godfrey and Koh 2001; Ritter and Wells 2006). Statement of Accounting

Concept SAC 2: Objective of General Purpose Financial Reporting (Australian

Accounting Research Foundation 1990, para 26) states that ‘the objective of general

purpose financial reporting is to provide information to users that is useful for making

and evaluating decisions about the allocation of scarce resources.’ It has been

suggested, given the increasing importance/materiality of intangible assets and the

limitations placed on their recognition, that traditional financial statements do not

capture the ‘value drivers that dominate the new economy’ (Jenkins and Upton 2001,

p.4), thereby reducing their value relevance. This is particularly so given the standard

setters change in focus in recent times away from the profit and loss statement and

towards the balance sheet. Under Statement of Accounting Concept SAC 4: Definition

and Recognition of the Elements of Financial Statements (Australian Accounting

Research Foundation 1992), profit (loss) is defined as the increase (decrease) in net

assets during the period (excluding owner contributions/distributions). This definition

places the spotlight on the balance sheet, and more particularly on a firm’s assets,

highlighting the need for assets to be appropriately valued. Statement of Accounting

Concept SAC 3: Qualitative Characteristics of Financial Information (Australian

Accounting Research Foundation 1990, para 7) notes that, to achieve the objective of

providing useful information, the qualities of relevance and reliability ‘may need to be

balanced against each other’. This is particularly true for intangible assets where

obtaining reliable estimates of value can be problematic. Intangible assets fall into two

broad categories: identifiable (for example, patents and brand names) and un-

identifiable (generally referred to as goodwill and might include, for example, market

penetration and good customer relations). As with tangible assets, intangible assets can

be both purchased and internally generated.

There are two primary issues confronting standard setters with respect to the accounting

treatment for intangible assets. First, should intangible assets be recognised (capitalised

rather than expensed) in financial statements and, if so, should purchased intangible

assets be treated differently to internally generated intangible assets. Second, if

2

intangible assets are recognised, should all intangible assets be systematically amortised

(including those with indefinite lives); or should intangible assets with indefinite lives

simply be subjected to impairment testing and, if so, on what basis.

Although there are currently significant differences in the way intangible assets are

treated in various countries, the real importance of these differences can only be

appreciated if accounting for intangibles can be shown to affect real economic

decisions. In Australia, Statement of Accounting Concepts SAC 2: Objective of

General Purpose Financial Reporting (Australian Accounting Research Foundation

1990, para 13) states that the:

Efficient allocation of scarce resources will be enhanced if those who

make resource allocation decisions ... have the appropriate financial

information on which to base their decisions.

Given the increasing importance of intangible assets in the ‘new-economy’, it could be

argued that (wherever possible having regard to the measurement difficulties) all

intangible assets should be recognised in financial statements to maximise the value

relevance of those statements (Chauvin and Hirschey 1994; Jenkins and Upton 2001).

Given that the value relevance of a financial statement is its ability to confirm, or

change, investors' expectations of firm value, if shares are traded amongst investors the

market price of those shares should summarize investors’ consensus expectations of

value. The value relevance of financial statements could, therefore, be measured by the

response in market price when accounting numbers are published (Høegh-Krohn and

Knivsflå 2000; Barth et al. 2001). This is not to imply that equity valuation is the sole

purpose of financial statements (Holthausen and Watts 2001)1 but, as noted by Barth et

al. (2001, p.89): ‘the dominant focus of the SEC and, thus, the FASB is on equity

investors.’

Currently the standard setting bodies in most leading capital markets are endeavouring

to internationally harmonise accounting standards. As part of this process the subject of

intangible assets, and the proper accounting treatment for intangible assets, is being

1 For example, Holthausen and Watts (2001, p.26) note that ‘creditors and lenders are more interested in valuing a firm’s debt and default probability’.

3

heavily debated (Ritter and Wells 2006). This debate is likely to continue for some time

because improving the accounting treatment for intangibles is a sensitive subject that

represents a real challenge for standard-setting organizations (Alfredson 2001).

The growing importance of intangible assets, and the fact that current accounting

practice might significantly understate the economic value of intangible assets, means

that for many companies there is a wide divergence between their book and market

values (Godfrey and Koh 2001; Jenkins and Upton 2001); this potentially undermines

confidence in the reliability and relevance of accounting data. Indeed, Lev and

Sougiannis (1996) and Lev and Zarowin (1999) suggested that the decrease in the value-

relevance of accounting numbers in the US in recent times (as evidenced by the

declining association between those numbers and either share prices or market returns)

could be attributed to the lack of proper recognition of intangibles by US companies.

This suggests that, for the foreseeable future, accountants and financial economists are

likely to be concerned with finding better ways to recognise and value intangible assets

(Chauvin and Hirschey 1994).

It should also be noted that, because of the inherent difficulty in valuing intangible

assets (and the fact that all intangible assets might not decline in value, or the decline in

value might not be consistent across all companies), providing some discretion to

managers in the way they treat intangible assets could be beneficial in terms of reducing

the error/bias with which intangible assets are reported in financial statements (Choi et

al. 2000). That is, managers could use this discretion to accurately convey their private

information to the capital market, thereby reducing the information asymmetry between

management and investors (Godfrey and Koh 2001). This is provided, of course, that

managers’ financial reporting incentives are aligned with those of financial statement

users (Jennings et al. 1996).

In the past, Australian generally accepted accounting principles (GAAPs) have typically

provided more flexibility than most other jurisdictions, particularly the US, with respect

to the reporting of identifiable intangible assets and, potentially, less flexibility with

respect to the reporting of goodwill. Therefore, the primary aim of this study is to

examine (and compare) the value relevance (and any bias) in the reporting of intangible

assets within the Australian and US contexts over the ten-year period 1994 to 2003.

4

Note that it is one thing for information provided under a particular accounting method to

be shown to be value relevant, but if it cannot be demonstrated that such information can

be reported reliably (that is, without bias) then it is arguable that the provision of this

information will be useful (Holthausen and Watts 2001). Therefore, this study aims to

examine both value relevance and reliability (bias) with respect to the reporting of

intangible assets in Australia and the US. Given the significant differences in the

accounting treatment of intangibles in these two jurisdictions, the findings from such an

examination should provide useful input to policy makers and international standard

setters involved in any future review of the accounting treatment of this important class

of assets.

As far as I am aware, this is the first study to compare the value relevance of reported

goodwill and identifiable intangible assets within the Australian and the U.S contexts

using the Feltham and Ohlson (1995) valuation methodology (framework). While

Feltham and Ohlson’s (1995) model has met with mixed empirical support (as outlined

in Richardson and Tinaikar 2004), using this model allows both the value relevance of

the accounting treatment for intangible assets and the bias with which intangible assets

are perceived to be reported by parties external to the firm (the financial community) to

be considered. Further, estimation of Feltham and Ohlson’s (1995) ‘Linear Information

Dynamic’ also permits an examination of the reporting of intangible assets from the

perspective of company insiders and their attempts to reflect a firm’s economic reality;

that is, whether the practices adopted for the recognition of intangible assets are

consistent with accounting conservatism or whether they result in unbiased, or

aggressive reporting.

1.1 The Research Questions and their Importance

This study investigates two main research questions:

1. Are the amounts reported for goodwill and identifiable intangible assets value

relevant and unbiased in both Australia and the US?

2. Does the level of conservatism (bias), if any, with respect to the reporting of

goodwill and identifiable intangible assets vary significantly between Australia and

the US?

5

I believe that the results of this study will provide useful input to policy makers and

international standard setters involved in any future review of the accounting treatment

of this important class of assets.

I trust the findings will prove helpful to both researchers and those involved with

formulating international accounting standards in this particularly difficult area of

intangible assets. I also hope the results will help to allay any fears regulators (and

others) might have that providing managers with accounting discretion will

(necessarily) lead to biased reporting practices.

1.2 Method

Australian and US companies were selected for examination because of the significantly

contrasting GAAPs (Generally Accepted Accounting Principles) applying to the

treatment of goodwill and other identifiable intangible assets in these two jurisdictions.

This study covers the ten-year period from 1994 to 2003.

The data covers all companies listed on the Australian Stock Exchange (ASX) and the

New York Stock Exchange (NYSE) for the period 1994-2003. The Australian data

includes 2,611 company-years while the US data includes 4,584 company-years. The

Australian company financial data was taken from Fin Analysis and SIRCA/Aspect

Financial databases and the market capitalisation data was taken from the Share Price

and Price Relative Database (SPPR) compiled by the Centre for Research in Finance

and the Australian Graduate School of Management at the University of New South

Wales. The US data was taken from DATASTREAM and MERGENTONLINE.

The study analyses a disaggregated version of the Feltham and Ohlson (1995) valuation

model and associated linear information models (LIMs) to allow goodwill and

identifiable intangible assets to be separately examined. Unlike many previous studies,

the analysis in this dissertation uses a model that has a theoretical foundation. While

previous studies have typically used well-known variables established in the empirical

literature, the Feltham and Ohlson (1995) framework used in this study is grounded in a

theory that seeks to explain the determinants of firm value. Use of a model grounded in

theory will avoid potential specification bias in the analyses.

6

Unbalanced panel regressions are used to analyse the disaggregated models and the

associated LIMs in order to test whether goodwill and identifiable intangible assets are

value relevant (with the null hypothesis being that the coefficient for each of these two

variables is equal to zero) and whether the market values goodwill and identifiable

intangible assets in an unbiased manner (with the null hypothesis being that the

coefficient for each of these two variables is equal to one).2

1.3 Findings




adjusted R2 of 71%, which is consistent with similar US studies by Ahmed, Morton and

Schaefer (2000) and Amir, Kirscenheiter and Willard (1997). Further, the results from

the disaggregated Feltham and Ohlson (1995) valuation models suggest that the

information presented with respect to intangible assets (both goodwill and identifiable

intangible assets) under Australian GAAP is value relevant. However, the results from

the valuation models also suggest that the market believes that goodwill is reported

conservatively and identifiable intangible assets aggressively, by the average Australian

company. This finding, while consistent with Godfrey and Koh (2001) and Shahwan

(2004), appears to conflict with Wyatt (2005) who concluded that identifiable intangible

assets were more highly valued by investors than goodwill. While the results from the

valuation models suggest that the market perceives both goodwill and identifiable

intangible assets to be reported with bias, this was not confirmed by the LIM results.

The results for the LIMs suggest that the accounting treatment adopted by the average

Australian company for the recognition of intangibles is unbiased.





(2000) and Amir et al. (1997). Further, the results from the disaggregated Feltham and

Ohlson (1995) valuation models suggest that the information presented with respect to

2 Section 5.3 discusses in more detail the justification for the measure of bias used in this study.

7

intangible assets (both goodwill and identifiable intangible assets) under US GAAP is

value relevant. However, the results from the valuation models also suggest that the

market believes that the average US company reports both goodwill and identifiable

intangible assets conservatively. This finding is consistent with previous US studies

such as: Wilkins et al. (1998); Choi et al. (2000); Chauvin and Hirschey (1994); and

Jennings et al. (1996). While the results from the valuation models suggest that the

market perceives both goodwill and identifiable intangible assets to be reported with

bias, this was not confirmed by the LIM results, which (consistent with the Australian

results) suggest that the accounting treatment adopted by the average US company for

the recognition of intangibles is unbiased.

The results for Australia and the US were generally consistent in all respects except that

(as expected because of the significant differences in accounting treatment) the US

market appears to believe that identifiable intangible assets are conservatively valued by

the average US company, while the Australian market appears to believe that such

assets are aggressively valued by the average Australian company. However, for both

countries the LIM results indicate that the actual accounting treatment adopted for

identifiable intangible assets is unbiased.





(more biased) information. As noted earlier, the increasing importance of intangible

assets in the ‘new-economy’ suggests that (wherever possible having regard to the

measurement difficulties) all intangible assets should be recognised in financial



(that is, those not consistently reporting positive abnormal operating earnings) might be

reporting goodwill and/or identifiable intangible assets aggressively and this is an area

that standard setters might need to carefully consider in future.

I trust that the findings presented in this study will prove helpful to both researchers and

those involved with formulating international accounting standards in this particularly

8

difficult area of intangible assets. I also hope the results will help to allay any fears

regulators (and others) might have that providing managers with accounting discretion

will (necessarily) lead to biased reporting practices; based on the findings of this study,

any such fears appear unwarranted.

1.4 Outline of the Remaining Chapters

The remaining chapters are organised as follows. Chapter 2 discusses (and compares)

the accounting treatment for intangible assets (goodwill and other identifiable

intangibles) under both Australian and US GAAP. Chapter 3 reviews the accounting

literature relevant to the study. The hypotheses are then developed in chapter 4. Chapter

5 describes the data sources and method of analysis used in the study. The results for

Australia and the US are presented and interpreted in chapters 6 and 7, respectively.

Chapter 8 then examines the differences between the two countries. Conclusions,

limitations and suggestions for extending this line of research are set out in chapter 9.

9

Chapter 2: Accounting for Intangibles: Comparing Australian and US GAAP

2.0 Introduction

This chapter identifies the key differences over the period of this study (1994-2003)

between Australian and US GAAP with respect to the treatment of intangible assets. The

purpose of undertaking this comparison is to identify important differences between

Australian and US GAAP likely to affect the value relevance of, and any bias in, the

accounting information published by companies in these two jurisdictions. It should be

noted that it is not the intention of this chapter to identify each and every difference

between Australian and US GAAP but, rather, to highlight significant points of

departure that will help to frame the hypotheses developed in chapter 4. The

information sources used for this chapter include: Parker and Porter (1998, pp. 85-92);

PricewaterhouseCoopers (2001, pp. 8-10, 32-37, 48-50); Parker and Porter (2003, pp.

158-199); and Delaney, Epstein, Nach and Budack (2003, pp. 361-386).

It should be noted that during the period of this study there was no single standard

governing the accounting treatment for intangible assets in Australia but, rather, there

were a number of standards that impacted the treatment of intangible assets (AASB

1010: Recoverable Amount of Non-Current Assets; AASB 1011: Accounting for

Research and Development Costs; AASB 1013: Accounting for Goodwill; AASB 1015:

Accounting for the Acquisition of Assets; AASB 1021: Depreciation; and AASB 1041:

Revaluation of Non-Current Assets).3

2.1 Goodwill Treatment under Australian and US GAAP

2.1.1 Defining Goodwill

Purchased goodwill can be defined as the difference between the cost of an acquisition

and the fair value of the acquired identifiable assets and liabilities. Goodwill is

considered a separate and unique, but unidentifiable, intangible asset and is perhaps the

3 In 1989 the Australian Accounting Research Foundation (AARF) issued a draft standard dedicated to identifiable intangible assets in which it advocated that all recorded identifiable intangible assets be amortised over a finite period. However, this proposed standard was so controversial that the AARF withdrew it in 1992 (Wines and Ferguson 1993).

10

most common form of intangible asset found in company annual reports. Examples of

unidentifiable intangible assets include: market penetration; effective advertising; good

labour relations; and a superior operating team.

2.1.2 Recognising Goodwill

Australian GAAP (AASB 1013, AAS 18)

Goodwill is recognised as an asset only when it satisfies the following asset recognition

criteria:

- it is probable that the future benefits embodied in the unidentifiable assets will

eventuate; and

- it possesses a cost or other value that can be measured reliably.

AASB 1013 prohibited both the recognition of internally generated goodwill and the

upward revaluation of purchased goodwill.

US GAAP (APB 17, SFAS 121, SFAS 141, SFAS 142)

The rules for recognising purchased goodwill under US GAAP were essentially the same

as those under Australian GAAP. This was also the case for internally generated

goodwill and the upward revaluation of purchased goodwill.

2.1.3 Useful Life and Amortisation of Goodwill


Under Australian GAAP, goodwill had to be amortised using the straight-line method

over a period not exceeding 20 years.

US GAAP (APB 17, SFAS 121, SFAS 141)

Under US GAAP prior to the introduction of SFAS 142 in 2001, goodwill had to be

amortised using the straight-line method over its estimated useful life, which could not

exceed 40 years. With the introduction of SFAS 142 there was no longer a requirement in

the US to systematically amortise goodwill but, instead, goodwill was subjected to an

impairment test at the reporting unit level at least once each financial year.

11

2.1.4 Impairment Testing Goodwill


Under Australian GAAP, the unamortised balance of purchased goodwill had to be

reviewed at each reporting date, and to the extent that future benefits were no longer

probable, goodwill had to be expensed. However, Australian GAAP provided little

guidance as to the form this annual review should take.

US GAAP (SFAS 142)

Prior to the introduction of SFAS 142 in 2001, the requirement to review the unamortised

balance of goodwill under US GAAP was virtually the same as that applying under

Australian GAAP. However, with the introduction of SFAS 142, US GAAP provided

more detailed guidelines about the conduct of impairment testing. Specifically, goodwill

was required to be reviewed for impairment annually or whenever there were signs

casting doubt on the recoverability of the carrying amount of goodwill. Further, the

impairment test required a comparison of the fair value of the cash-generating unit to

which the goodwill was attached and the carrying amount, including goodwill, of that

cash-generating unit. If the carrying amount was higher than the fair value amount,

goodwill was considered to be impaired and had to be written down appropriately.

2.2 Identifiable Intangible Asset Treatment under Australian and US GAAP

2.2.1 Defining Identifiable Intangible Assets

Identifiable intangible assets can be defined as identifiable non-monetary assets with no

physical substance. There are many types of identifiable intangible assets with the more

common types being: intellectual property; copyrights; patents; and trademarks.

Identifiable intangible assets can be acquired or internally generated.

2.2.2 Recognising Acquired Identifiable Intangible Assets

Australian GAAP

While there was no specific standard relating to identifiable intangible assets under

Australian GAAP over the period of this study, there were a number of standards that

impacted the treatment of intangible assets (AASB 1010: Recoverable Amount of Non-

Current Assets; AASB 1011: Accounting for Research and Development Costs; AASB

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1015: Accounting for the Acquisition of Assets; AASB 1021: Depreciation; and AASB

1041: Revaluation of Non-Current Assets). Under Australian GAAP, acquired

identifiable intangible assets had to be recognised separately if the general criteria

relating to asset recognition were met; that is, if the cost of the asset could be reliably

measured and it was probable that the future benefits from the use of the asset would

flow to the entity.

US GAAP (APB 17, SFAS 142)

The recognition of acquired identifiable intangible assets under US GAAP was

essentially the same as under Australian GAAP.

2.2.3 Recognising Internally Generated Identifiable Intangible Assets

Australian GAAP

There was no particular guidance under Australian GAAP clarifying the recognition of

internally generated identifiable intangible assets. However, under AASB 1011:

Accounting for Research and Development costs, R&D expenditures should be

capitalised (and amortised) if they were recoverable beyond any reasonable doubt. In

practical terms this meant that early stage research was normally expensed but once the

research could be shown to have the potential for generating future cash flows,

subsequent outlays could be capitalised and amortised. However, any research and

development costs previously expensed could not subsequently be capitalised.

US GAAP (SFAS 142, SFAS 86, SOP-98)

Under US GAAP, the recognition of internally generated identifiable intangible assets

was basically limited to the development of computer software; again provided the costs

were recoverable.

2.2.4 Revaluing Identifiable Intangible Assets


Under Australian GAAP over the period of this study there was scope to revalue all

types of identifiable intangible assets, including: brands, patents and trademarks.

Where revaluations were undertaken entities were encouraged, but not required, to keep

the valuations up to date.

13

US GAAP (APB 17, SFAS 121, SFAS 141, SFAS 142)

Under US GAAP over the period of this study, identifiable intangible assets could not be

revalued.

2.2.5 Useful Life and Amortisation of Identifiable Intangible Assets

Australian GAAP (AASB 1011, AASB 1021)

There was no presumed useful life for identifiable intangible assets under Australian

GAAP. However, most assets were assumed to have limited useful lives.

US GAAP (SFAS 86, APB 17, SFAS 142)

Prior to 2001, US GAAP required all purchased identifiable intangible assets to be

amortised over a maximum period of 40 years. After 2001, with the introduction of

SFAS 142, the assumed maximum useful life for identifiable intangible assets was

removed.

2.2.6 Impairment Testing Identifiable Intangible Assets


Under Australian GAAP, there was an expectation that the carrying value of non-

current assets should be reviewed annually to ensure their carrying value did not exceed

their recoverable amount. However, there was no specific requirement requiring a

formal annual impairment test.

US GAAP (SFAS 121, SFAS 142)

Prior to the introduction of SFAS 142 in 2001, US GAAP required all intangible assets to

be amortised over a maximum 40-year period and these assets had to be reviewed for

impairment whenever events or changes in circumstances indicated that the carrying

amount of the asset might not be recoverable. After 2001, intangible assets that were

considered to have indefinite useful lives were no longer required to be amortised but,

rather, were required to be tested at least annually for impairment.

2.3 Summary

This chapter has outlined the key differences between Australian and US GAAP over the

period of this study (1994-2003) with respect to the accounting treatment for intangible

assets (goodwill and other identifiable intangible assets). In terms of the accounting

14

treatment for goodwill, it appears that Australian GAAP has been far more restrictive than

US GAAP. For example, under Australian GAAP goodwill was required to be amortised

over a maximum period of 20 years. By comparison, under US GAAP the maximum

amortisation period was 40 years prior to 2001 and after 2001 there was a requirement to

test for impairment.

In terms of identifiable intangible assets the position appears reversed, with Australian

GAAP being far less restrictive than US GAAP. For example, US GAAP limited the

recognition of internally generated identifiable intangible assets to certain software

development costs. There was no such limit under Australian GAAP. Further, Australian

GAAP permitted the revaluation of identifiable intangible assets; something not permitted

under US GAAP. Finally, up until 2001, US GAAP required identifiable intangible

assets to be amortised over a maximum forty-year period, while there was no such limit

under Australian GAAP.

The comparison between Australian and US GAAP will be helpful in developing the

hypotheses in chapter 4.

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Chapter 3: Related Literature

3.0 Introduction

The purpose of this chapter is to place the study in context and to inform the study by

reviewing relevant recent literature related to the value relevance of generally accepted

accounting principles (GAAP), particularly as they relate to goodwill and identifiable

intangible assets. Chapter 2 highlighted the main differences between Australian and

US GAAP. This chapter will focus on recent empirical studies grouped under the

following three leadings: the value relevance of Australian and US GAAP; the value

relevance of intangible assets in Australia and the US; and studies that have used a

methodological approach similar to the Feltham and Ohlson (1995) valuation

framework adopted in this study.

The review begins in section 3.1 with empirical studies that have examined the potential

change in the value relevance of Australian and US GAAP over time. Section 3.2 then

provides a review of those studies that have specifically examined the value relevance

of intangible assets in Australia and the US. Finally, section 3.3 provides a review of

studies that have used the Feltham and Ohlson (1995) valuation framework.

3.1 The Value Relevance of Australian and US GAAP

It has been suggested that historic cost financial statements have declined in value

relevance in recent times with the growing importance of the ‘new economy’ (Collins et

al. 1997). Sections 3.1.1 and 3.1.2 present a review of Australian and US studies,

respectively, that have specifically set out to test this belief. Section 3.1.3 then reviews

studies that have compared the value relevance of Australian and US GAAP.

3.1.1 Empirical Studies on the Value Relevance of Australian GAAP

Goodwin and Ahmed (2006) examined the 25 year period from 1975 to 1999 to

determine whether the value relevance of financial statements prepared under

Australian GAAP had declined over this period. Their results indicated that while

earnings value relevance had declined over this period the decline was primarily driven

by loss-making companies. Further, Goodwin and Ahmed (2006) found that the value

relevance of financial statement data presented by profitable firms that had capitalised

intangible assets had improved considerably over time, while the opposite was true for

profitable non-capitalisers.

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The results presented by Goodwin and Ahmed (2006), for firms that capitalised

intangible assets, supported the earlier results of Barth and Clinch (1998) who

investigated the extent to which different types of revalued assets were correlated with

share prices and non-market-based estimates of firm value. Using a sample of 350

Australian companies for the period 1991 to 1995, Barth and Clinch (1998) reported that

revalued financial, tangible, and intangible assets were value relevant. While the result

for financial and tangible assets did not surprise Barth and Clinch (1998), they did express

surprise at the strength and consistency of their finding with respect to intangible assets

which appeared to contradict the generally held view that such estimates were unreliable.

Further, Barth and Clinch (1998) reported that there was little evidence to indicate that

investors viewed independent valuations differently to directors’ valuations. Barth and

Clinch (1998) suggested that this last finding indicated that directors’ private information

enhanced value estimates; despite the potential for directors to use the discretion afforded

them under Australian GAAP for self-interest purposes.

3.1.2 Empirical Studies on the Value Relevance of US GAAP

Many empirical studies in the US have documented a decrease in the value relevance of

US GAAP over the past few decades (see, for example, Collins et al. 1997; Aboody and

Lev 1998; Brown et al. 1999; Ely and Waymire 1999; Francis and Schipper 1999; Lev

and Zarowin 1999). Lev and Zarowin (1999) argued that this decline in the value

relevance of US GAAP was the result of the inappropriate treatment of intangibles. Lev

and Zarowin (1999) noted that their findings suggested that reporting inadequacies with

respect to intangible assets might be adversely affecting the welfare of both investors and

firms. Somewhat contrary to the findings of Lev and Zarowin (1999), Francis and

Schipper (1999) provided mixed evidence concerning the decline over time in the value

relevance of financial statements presented under US GAAP. Francis and Schipper

(1999) noted that while the explanatory power of earnings had reduced significantly

over time the same was not true of the relationship between market and book values.

However, Francis and Schipper (1999) also noted that their findings did not imply that

the value relevance of financial statements could not be improved. Collins et al. (1997)

argued that it was premature to conclude that the conventional historic cost accounting

model had lost its value relevance because their findings, similar to Francis and

Schipper (1999), suggested that while the value relevance of earnings had declined the

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value relevance of balance sheet book values had increased. Further, Collins et al.

(1997) noted that the declining value relevance of earnings was largely related to a

significant increase in: the frequency of one-time items; the prevalence of negative

earnings; and the importance of intangible assets.

Using Form 20-F reconciliations for a sample of 61 foreign companies traded in the US

during the period 1987 to 1992, Chan and Seow (1996) compared the relative information

content of earnings based on foreign GAAP with earnings adjusted to US GAAP by

conducting association tests (for each firm) between stock returns and the two sets of

earnings numbers. Their results indicated a higher association between stock returns and

reported earnings based on foreign rather than US GAAP. Chan and Seow (1996)

concluded, therefore, that foreign GAAP earnings provided more useful (value relevant)

information than earnings derived using US GAAP. However, Chan and Seow (1996)

also noted that their results might have been affected by institutional factors specific to

foreign markets.

Similarly, using Form 20-F reconciliations for a sample of 89 non-US companies traded

in the US during the period 1992 to 1996, Harris and Muller (1999) investigated the

association between market values and accounting numbers (earnings and book value

amounts) prepared under IASs and US-GAAP. Harris and Muller’s (1999) findings

suggested that IAS numbers were more highly associated with share prices than US

GAAP numbers, but that US GAAP numbers appeared more highly associated with

share returns than IAS numbers.

3.1.3 Empirical Studies Comparing the Value Relevance of Australian and

US GAAP

Norton (1995) examined the quantitative differences between Australian financial

reporting practices and US GAAP using data based on form 20-F filings for thirteen

Australian incorporated companies quoted on US stock exchanges for the period 1985-93.

To assess differences in the levels of conservatism (bias) between Australian and US

GAAP, Norton (1995) used the ‘index of conservatism’ developed by Gray (1980) for

measuring differences in profits and equity. Norton’s (1995) results did not support the

expectation that US GAAP was more conservative than Australian GAAP with respect to

reported profits. However, Norton (1995) did find support for the proposition that US

18

GAAP was more conservative than Australian GAAP with respect to shareholders’

equity.

Barth and Clinch (1996) examined differences between US GAAP and the GAAPs for

Australia, Canada and the UK for the period 1985 to 1991. Their sample consisted of

firms that: (1) were incorporated in the UK, Australia, and Canada; (2) whose equity

shares were traded in a US securities market and were included in Compustat as of

January, 1992; and (3) who presented a reconciliation of their accounts from domestic to

US GAAP. Barth and Clinch (1996) adopted both price and returns models and their

final sample included 98, 22 and 229 observations for the UK, Australia and Canada,

respectively. A number of interesting findings emerged from the study by Barth and

Clinch (1996). First, both Australian and US GAAP appeared to result in too little

goodwill amortisation expense and, therefore, goodwill appeared to be priced at a

discount relative to other assets. Second, revaluations under Australian GAAP did not

appear to be viewed by investors as assets (that is, they were not value relevant);

however, Barth and Clinch (1996) noted that this finding was hampered by the

aggregation of tangible and intangible asset revaluations and their small sample size.

Third, US and Australian tax accounting methods appeared to not recognise sufficient tax

expense or liability. Fourth, accrual pension accounting and, in some cases, interest

capitalisation, required under US GAAP added explanatory power beyond Australia’s

cash-based method of accounting for pensions. Finally, Barth and Clinch (1996) noted

that their results suggested that the GAAP reconciliation required by the US Securities

and Exchange Commission (SEC) reflected information useful to Australian investors.

3.2 Studies on the Value Relevance of Intangible Assets in Australia and the US

A number of recent studies, using a variety of methodologies (valuation models), have

examined the value relevance of intangibles in both Australia and the US. These studies

will now be reviewed.

3.2.1 Studies on the Value Relevance of Intangible Assets in Australia

Wines and Ferguson (1993) investigated the accounting treatment adopted for both

goodwill and identifiable intangible assets for 150 listed Australian companies over the

period 1985 to 1989. Wines and Ferguson (1993) found evidence to suggest that

Australian companies were increasingly recording identifiable intangible assets in

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takeover situations (presumably to reduce the amount of goodwill reported) and electing

not to amortise those identifiable intangible assets (presumably to overcome the impact on

reported profits of the requirement to systematically amortise goodwill over a maximum

period of only 20 years). Of course, it is equally possible that managers were simply

attempting to convey (to financial statement users) the relevant value for the various types

of intangible assets under their control. In a subsequent study over the period 1993 to

1997, and consistent with the substantial discretion managers of Australian firms had with

respect to capitalising intangible assets, Wyatt (2001) found a wide range of capitalisation

practices across firms and classes of intangible assets. While some might suggest that this

diversity in observed practice is an indication of managers attempting to manipulate

reported earnings, it is also possible that this diversity is a reflection of managers’ best

attempts to find ways to report the most value relevant information, within the constraints

imposed by Australian GAAP.

Abrahams and Sidhu (1998) specifically examined the value relevance of capitalised

R&D (research and development) expenditures; an area where Australian managers have

typically had considerably more discretion than their counterparts in most western

countries. Abrahams and Sidhu (1998) collected data for a sample of 167 firm-years over

the period 1994 to 1995 for industries that normally involved high levels of R&D

expenditures. The results of their analysis indicated that capitalised R&D expenditures

were value relevant (had a significant association with market values) and improved

accounting based measures of firm performance. As will be seen in the following section,

this result is consistent with the results reported by Aboody and Lev (1998) for the

capitalisation of software development costs in the US.

More recently, Godfrey and Koh (2001) tested the value relevance of reported goodwill,

capitalised R&D, and other identifiable intangibles for a sample of 172 firms selected

from the top 500 Australian companies for the year 1999. Godfrey and Koh’s (2001)

results indicated that, taken as a group, intangible assets are value relevant over and above

other information contained in financial statements (such as total tangible assets and total

liabilities). When intangible assets were disaggregated into goodwill, capitalised R&D,

and identifiable intangible assets, Godfrey and Koh’s (2001) results indicated that both

goodwill and identifiable intangible assets are value relevant, but not capitalised R&D.

Further, investors appear to attach greater value to goodwill than to other balance sheet

20

items, including identifiable intangible assets. Godfrey and Koh’s (2001) result with

respect to R&D contrasts with the earlier finding of Abrahams and Sidhu (1998).

However, given that Godfrey and Koh’s (2001) study focussed on intangible assets in

general (not specifically R&D), their sample included relatively few firms with

capitalised R&D and this (they acknowledge) might have led to their finding that

capitalised R&D is not value relevant. A subsequent study by Ke, Pham and Fargher

(2004), focussing on a larger sample of R&D intensive firms, supported the earlier

finding of Abrahams and Sidhu (1998) indicating that capitalised R&D is value relevant.

The results provided by Godfrey and Koh (2001) with respect to goodwill and identifiable

intangible assets were subsequently supported by Shahwan (2004) who examined a larger

sample of 993 companies for a four-year period from 1997 to 2000. Shahwan (2004) also

found a positive and significant relationship between the market value of equity and both

goodwill and identifiable intangible assets; with goodwill having the highest coefficient in

Shahwan’s (2004) model.

Wyatt (2005) investigated the extent to which the reporting of intangible assets is related

to the strength and cycle time of the technology affecting a firm’s operations and also to

property rights affecting a firm’s ability to appropriate any expected benefits. Three

important findings emerged from Wyatt’s (2005) study, which examined between 225

and 296 firms per year across the period 1993-1997. First, the results suggested that a

firm’s underlying economics is more important to the recording of intangible assets than

contracting or signalling factors. Second, intangible assets where management had

greater reporting discretion (identifiable intangible assets) are more highly correlated with

a firm’s underlying economics than intangible assets where management has less

discretion (R&D and goodwill). Third, intangible assets where management has greater

reporting discretion (identifiable intangible assets) are more highly valued by investors

than intangible assets where management has less discretion (R&D and goodwill). Wyatt

(2005, p.1000) concluded, therefore, that ‘limiting management’s choices to record

intangible assets would tend to reduce, rather than improve, the quality of the balance

sheet and investor’s information set.’

Bugeja and Gallery (2006) examined a sample of 475 firm-years between 1995 and 2001

using six different disaggregated regression models to determine whether the value

relevance of purchased goodwill holds as it ages. Their findings suggest that recently

21

acquired goodwill has information content but this does not apply to ‘older’ goodwill.

However, Bugeja and Gallery (2006) noted that their result could reflect the fact that over

time the benefits associated with purchased goodwill are increasingly reflected in normal

earnings. As a result of this, the share market value associated with goodwill will more

likely be associated with earnings rather than the goodwill asset itself.

Finally, Ritter and Wells (2006) examined the relationship between voluntarily

recognised and disclosed identifiable intangible assets, stock prices and future earnings in

Australia across the period 1979 to 1997 using a sample of 1,078 firm-year observations

selected from the largest 150 firms (by market capitalisation) listed on the Australian

Stock Exchange (ASX). Their findings revealed a positive relationship between stock

prices and voluntarily recognised and disclosed identifiable intangible assets. Further,

Ritter and Wells (2006) noted that identifiable intangible assets and income were not

substitutes and that the value relevance of disclosed identifiable intangible assets goes

beyond income. Finally, Ritter and Wells (2006) reported a positive relationship between

identifiable intangible assets and realised future period income. Based on their findings,

Ritter and Wells (2006) argued that proposed regulatory reforms in Australia limiting

the recognition (and revaluation) of identifiable intangible assets is likely to lead to a

reduction in the value relevance of this important class of assets.

In summary, the available Australian evidence suggests that amounts reported for both

goodwill and identifiable intangible assets are likely to be value relevant for the average

Australian company; however, goodwill is likely to be more highly valued by the market

than identifiable intangible assets.

3.2.2 Studies on the Value Relevance of Intangible Assets in the US

Chauvin and Hirschey (1994) used Compustat data for the period 1989 to 1991 to

develop a model that related US market values to: net income; goodwill; advertising;

R&D expenditure; market share; intangible assets; tangible assets; leverage; growth; and

beta. Their results indicated that accounting goodwill numbers were a useful proxy for

the size and duration of economic goodwill and were, therefore, value relevant. Further,

Chauvin and Hirschey (1994) argued that their results suggested that efforts to separately

identify, and value, the many components of goodwill might be a useful exercise.

22

Similarly, Jennings et al. (1996) examined the extent to which accounting goodwill and

the related amortisation expense reported by a sample of US firms from 1982 to 1988

were valued by investors. Their results indicated that, after controlling for other net

assets, there was a strong positive association between a firm’s market value and its level

of purchased goodwill. This suggests that accounting goodwill reported under US GAAP

is value relevant. Jennings et al. (1996) also reported a negative (but weaker) association

between a firm’s market value and its goodwill amortisation expense, after controlling for

other components of accounting earnings. Jennings et al. (1996) suggested that this

weaker association for goodwill amortisation expense could indicate that goodwill might

not be declining in value for many firms and/or, where it is declining in value, the rate of

decline might vary significantly across firms. Again, these results suggest that further

examination of the accounting treatment applied to (mandated for) goodwill is warranted.

In a later, more extended study (1978 to 1994), Choi, Kwon, and Lobo (2000) also found

that intangible assets were valued by the US capital market and that the requirement to

systematically amortise all intangible assets might not be the most appropriate accounting

treatment for all firms. However, and consistent with the greater levels of uncertainty

associated with intangible assets, Choi, Kwon, and Lobo (2000) noted that the market

value per dollar of intangible assets was less than the market value per dollar of tangible

assets. Similarly, based on Compustat data for a sample of 8,230 firm-years between

1988 and 1996, Wilkins, Swanson, and Loudder (1998) reported a significant positive

association between market values and both goodwill and, to a lesser extent, other

intangible assets; again indicating that intangible assets are priced positively in the US

capital market.

Henning, Lewis and Shaw (2000) examined whether investors distinguished between

the various components of purchased goodwill in the year of acquisition. These

components included: going concern goodwill; synergy gains resulting from the

acquisition; and overpayment for the target. Their study included 1,576 firm-year

observations where there was a goodwill asset recorded in any year between 1990 and

1994. Henning, Lewis and Shaw’s (2000) results suggested that the going concern and

synergy components of goodwill were significantly positively valued by the market,

while investors placed a significant negative value on the over payment component.

Henning, Lewis and Shaw (2000, p.385) argued that their results suggested that either

23

‘the going-concern and synergy components are non-wasting assets or that the assumed

amortisation rule does a poor job of capturing the declines in the values of these assets.’

Hirschey and Richardson (2002) used an event study methodology to analyse the impact

on market values of goodwill write-off announcements during the period 1992 to 1996.

The results presented by Hirschey and Richardson (2002) confirmed the earlier findings

of Chauvin and Hirschey (1994) and Jennings et al. (1996) that accounting goodwill

numbers are value relevant. Hirschey and Richardson (2002) found that goodwill write-

off announcements were typically associated with significant negative share price

adjustments; although the market appears to, at least partially, anticipate these goodwill

write-offs.

Taking a different approach, Churyk (2005) examined the appropriateness of the

requirement to systematically amortise goodwill based on the Exposure Draft (ED)

guidelines that preceded the issuance of SFAS 142. SFAS 142 eliminated the mandatory

requirement to systematically amortise goodwill and replaced this requirement with an

annual impairment test. Churyk (2005) examined 162 firms with reported goodwill that

were involved in a merger or takeover during the period 1996 to 1998. The results

presented by Churyk (2005) indicated that goodwill is typically not overvalued (as the

result of possible overpayment for the acquired firm) when initially recorded but for some

firms goodwill does subsequently show signs of impairment. Churyk (2005) argued that

the results provide support for the move by US regulators to replace the mandatory

amortisation of goodwill with an annual impairment test.

While the above studies have typically focused on intangible assets as a whole, or at

best a breakdown of intangibles into goodwill and other intangibles, there have also

been a number of notable US studies that have examined various components of

intangible assets. For example, Lev and Sougiannis (1996) estimated the value relevance

of R&D expenditures; Aboody and Lev (1998) examined the value relevance of

capitalised software development costs; Barth et al. (1998) examined the value

relevance of brand names; and Kohlbeck (2004) examined the value relevance and

reliability of four customer-based intangible assets for the banking sector. Following is a

brief discussion of these studies.

24

Lev and Sougiannis (1996) examined the relationship between R&D outlays (which,

except for software development costs, are required to be expensed in the US) and

subsequent earnings for a large cross-section of R&D intensive firms to estimate each

firm’s R&D capital and periodic R&D amortisation. These estimates were then used to

adjust individual firm’s accounts (both earnings and book values) to determine the

likely reliability, objectivity and value relevance associated with capitalising (rather

than expensing) R&D outlays. Lev and Sougiannis (1996) found that the adjusted

earnings and book values were significantly associated with security prices and returns;

indicating that such information, were it to be incorporated into financial statements,

would be value relevant. The results reported by Lev and Sougiannis (1996) supported

the earlier findings of Hirschey and Weygandt (1985) suggesting that advertising and

R&D expenditures should be capitalised and amortised rather than expensed. The results

reported by Lev and Sougiannis (1996) were, in turn, supported by Hirschey,

Richardson, and Scholz (2001), and, to a lesser extent, by Kothari, Laguerre and Leone

(2002) and Callen and Morel (2005). Hirschey, Richardson, and Scholz (2001) found

that the disclosure of scientific information on patent quality in conjunction with the

traditional R&D expenditure disclosures gave investors a useful basis on which to judge

the merits of a firm’s R&D efforts. Kothari, Laguerre and Leone (2002) compared the

relationship between current expenditures on R&D and property plant and equipment

(PP&E) and a firm’s future earnings variability. Based on an examination of 50,000 firm-

year observations from 1972 to 1992, Kothari, Laguerre and Leone (2002) concluded that

while R&D investments generate positive future benefits these benefits are far more

uncertain than those associated with PP&E investments. Kothari, Laguerre and Leone

(2002) noted that (although R&D is positively associated with firm value) they were

unable to make any policy recommendations on the accounting treatment that should be

adopted for R&D expenditures because they had no knowledge of the weights to be

assigned in assessing the value relevance versus the uncertainty of the future benefits

associated with R&D expenditures. Similarly, using an earnings-based time series

valuation model, rather than the cross-sectional or panel data regressions typically used in

prior studies, Callen and Morel (2005) were only able to find weak empirical support for

the value relevance of R&D expenditures at the firm level.

Aboody and Lev (1998) examined the value relevance of capitalised software

development costs for a sample of 163 firms during the period 1987 to 1995. Aboody

25

and Lev (1998) found that annually capitalised software development costs were

positively associated with security returns and that balance sheet values for capitalised

software development costs were positively associated with security prices. Based on

their results, Aboody and Lev (1998) concluded that the capitalisation of software

development costs provides value relevant information to investors.

Similarly, for a sample of 1,204 brands held by 183 publicly listed US firms across the

period 1992 to 1997, Barth, Clement, Foster and Kasznik (1998) found that brand value

estimates were significantly associated with equity market values. Barth et al. (1998)

argued that their findings indicate that brand values could be reliably estimated and,

further, they suggested that international standard setters should consider allowing firms

to recognise (in their financial statements) the value associated with internally generated

(as well as purchased) brands.

Finally, Kohlbeck (2004) examined financial statement and market data over the period

1994 to 1998 for 216 bank holding companies and found evidence to suggest that his

estimates of four customer based intangible assets (mortgage servicing right, credit card

intangible, core deposit intangible and trust operations intangible) were value relevant and

increased the explanatory power of his valuation models. Given his findings, Kohlbeck

(2004) concluded that the existing financial reporting requirements for the banking sector

provide useful information with respect to intangible assets.

In summary, the available US evidence suggests that amounts reported for both goodwill

and identifiable intangible assets are value relevant for the average US company,

however, these amounts are likely to be conservatively (under) valued by US companies.

Further, because US GAAP is far more restrictive than Australian GAAP with respect to

the recognition of internally generated identifiable assets it is expected that identifiable

intangible assets are likely to be reported more conservatively by US companies

compared to Australian companies.

3.3 Studies on Value Relevance Using the Feltham and Ohlson (1995) Valuation Framework (Model).

Feltham and Ohlson (1995) presented a model that focused on the valuation of

companies. The fundamental difference between Feltham and Ohlson (1995) and Ohlson

(1995) lies in Feltham and Ohlson’s (1995) inclusion of the expected growth of a firm’s

26

operating assets in the valuation equation (Callen and Morel 2005). The Feltham and

Ohlson (1995) framework has been adapted to not only predict the value of companies,

but to also model the relationship between company value and various accounting

variables. Therefore, the present study is one of many to have used the Feltham and

Ohlson (1995) framework to analyse the value relevance of accounting information. This

section highlights key studies that have used the Feltham and Ohlson (1995) framework

in order to demonstrate how this framework has been adapted to answer many interesting

questions in accounting research.

Amir et al. (1997) examined the value relevance of deferred taxes based on the theoretical

framework of the Feltham and Ohlson (1995) model. A sample of 1,114 firm-years for

the period between 1992 and 1994 were selected from Fortune 500 companies covered by

the Compustat database. The researchers adopted three valuation models by dividing

Feltham and Ohlson’s (1995) book value of equity variable into two variables comprising

net operating assets (NOA) and net financial assets (NFA) and then adding additional

variables for net deferred taxes (DT) and current abnormal operating earnings (AOE). The

results for their three disaggregated valuation models revealed high values of R2 ranging

from 0.36 to 0.46. Based on significant coefficient values, Amir et al. (1997) concluded

that their operating, financial, and deferred tax variables were value relevant in

explaining cross-sectional variations in equity values. Further, they found that (on

average): a dollar of net operating assets was valued as a dollar by the market; a dollar

of net financial assets was valued at less than a dollar by the market; and a dollar of net

deferred taxes was valued at slightly more than a dollar by the market.

Bauman (1999) used the Feltham and Ohlson (1995) framework to empirically examine

valuation-related issues concerning the level of conservatism (bias) inherent in book

values reported under US GAAP. Bauman’s (1999) sample comprised 665 US firms

(6,171 firm-years) over the period 1980-1994. Bauman (1999) noted that the coefficients

in the Feltham and Ohlson (1995) model will be greater than one as abnormal earnings

load onto conservatively stated book values. Bauman’s (1999) results suggested that the

age of fixed assets and R&D intensity were economically the most significant sources

of accounting conservatism (bias). Further, using Feltham and Ohlson’s (1995) linear

27

information models (LIMs)4, Bauman (1999) found only a weak association between

accounting methods and firm specific conservatism parameters and suggested, therefore,

that there was a need for additional modelling of the relationship between conservative

accounting and equity value.

Dechow, Hutton, and Sloan (1999) provided an empirical assessment of the residual

income valuation model proposed by Ohlson (1995) for a sample of 50,133 observations

over the period 1976 to 1995. Dechow, Hutton, and Sloan’s (1999, p.32) results

suggested that ‘a simple valuation model that capitalizes analysts’ earnings forecasts in

perpetuity is better at explaining contemporaneous stock prices’ than the Ohlson (1995)

model. However, Dechow, Hutton, and Sloan (1999, p.32) noted ‘that the superior

explanatory power of the simple capitalization model may arise because investors over-

weight information in analysts’ earnings forecasts and under-weight information in

current earnings and book value.’ Dechow, Hutton, and Sloan (1999, p.32) also

suggested that the Ohlson (1995) model provided a useful framework for empirical

research for three reasons. First, it provides a unifying framework highlighting the

relationship between current accounting variables and future abnormal earnings.

Second, it serves as a basic framework (model) on which subsequent research can build,

and in this respect Dechow, Hutton, and Sloan (1999, p.32) noted that Feltham and

Ohlson (1995) ‘generalize the model to incorporate growth and accounting

conservatism.’ Third, Dechow, Hutton, and Sloan (1999, p.32) argued that ‘the focus of

the model on the relation between current information variables and future abnormal

earnings is heuristically appealing.’

Myers (1999) examined the LIMs of Ohlson (1995) and Feltham and Ohlson (1995)

using a sample of 44,980 firm-years across the period 1975 to 1996. Myers (1999)

found that the LIMs provided value estimates no better than book values alone. Further,

Myers (1999) found that the median conservatism parameter of Feltham and Ohlson

(1995) was significantly negative, which is contrary to the model’s prediction and,

therefore, Myers (1999) concluded that the LIMs did not capture the effect of

conservatism particularly well. Myers (1999) also cautioned against modifying the LIMs

(for example by including analyst’s forecasts) as this can create internal inconsistencies.

4 Linear information models (LIMs) are discussed in the following chapter.

28

For a sample of 1,074 US firms listed on the 1997 Compustat database with at least 15

years of continuous data, Ahmed et al. (2000) examined the relationship between

accounting conservatism and market valuation using the Feltham and Ohlson (1996)

valuation model.5 Their findings supported previous research documenting that, on

average and against expectations, the LIM conservatism parameter was negative.

However, Ahmed et al. (2000) provided evidence suggesting that the firms with negative

values for the LIM conservatism parameter were significantly smaller, less profitable

and had lower growth rates compared to firms with positive values for the LIM

conservatism parameter.

Ota (2002, p.160) noted that the ‘LIM was originally proposed in Feltham and Ohlson

(1995) and Ohlson (1995)’ and ‘is an information dynamics model that describes the

time-series behavior of abnormal earnings.’ Ota (2002) attempted to improve the LIM

(without tackling the difficult task of specifying the ‘other information’ variable) by using

generalised least squares to rectifying the problem associated with having a significant

level of serial correlation in the error term. Based on a sample of 674 firms from the

Tokyo and Osaka Stock Exchanges, Ota’s (2002) results suggest that the modified LIM

(using generalised least squares) was generally superior to LIMs that omit the ‘other

information’ term.

Callen and Segal (2005) also tested the Feltham and Ohlson (1995) model and their

findings led them to reject the Ohlson (1995) model in favour of the Feltham and Ohlson

(1995) model. Callen and Segal (2005, p.426) argued that this result highlighted ‘the

importance of conservatism in accounting valuation.’ However, Callen and Segal (2005)

noted that although the Feltham and Ohlson (1995) model incorporated conservatism it

abstracted from a number of other important issues likely to affect security prices (for

example, bankruptcy costs and taxes). Callen and Segal (2005, p.426) indicated that

their empirical evidence ‘suggests that any model that fails to account for these frictions

is unlikely to perform well in explaining security prices.’

From the above it can be seen that while previous empirical studies have generally

supported the use of the Feltham and Ohlson (1995) valuation framework, the results

5 Note that Feltham and Ohlson (1996) is an adaptation/extension of the Feltham and Ohlson (1995) model.

29

from empirical tests of the framework have been mixed. In particular, past research with

respect to the LIMs appears to have been unable to confirm the expected conservatism

bias in accounting variables.

3.4 Summary

This chapter has reviewed prior studies concerned with: the value relevance of Australian

and US GAAP; the value relevance of goodwill and other intangible assets in Australia

and the US; and the appropriateness of the Feltham and Ohlson (1995) valuation

framework for examining value relevance.

In summary, prior research suggests that with the growing importance of intangible assets

over time both Australian and US GAAP appear to have had an adverse affect on the

value relevance of firm financial statements. However, the decline in value relevance

appears to have primarily affected reported income numbers with some authors

suggesting that the value relevance of balance sheet book values has actually increased

over time. In terms of comparing Australian and US GAAP, there is some evidence to

suggest that US GAAP is more conservative (biased) in terms of equity balances but not

in terms of reported income. With respect to goodwill and other identifiable intangibles,

the prior research consistently indicates that these items are value relevant and suggests

that limiting management choice in terms of the reporting of these items might be

counter-productive. Finally, there seems to be general acceptance in the literature for the

use of the Feltham and Ohlson (1995) valuation framework, although it is not without its

problems.

This thesis extends prior value relevance studies by focusing on a comparison of the value

relevance and level of bias in the reporting of goodwill and identifiable intangible assets

under both Australian and US GAAP using a modified version of the Feltham and Ohlson

(1995) valuation framework as discussed in chapter 5.

An understanding of the main accounting differences between Australian and US GAAP,

particularly as it relates to the treatment of goodwill and other identifiable intangible

assets, was provided in chapter 2. A discussion of the prior empirical literature relevant to

the current study has been provided in this chapter. The hypotheses that explore the

research questions of interest in this study can now be stated. This is the task of chapter 4.

30

Chapter 4: Hypotheses

4.0 Introduction

Chapter 2 highlighted the main differences between Australian and US GAAP. Chapter

3 highlighted prior studies related to: the value relevance of Australian and US GAAP;

the value relevance of intangible assets in Australia and the US; and the appropriateness

of the Feltham and Ohlson (1995) methodology.

The purpose of this chapter is to develop a set of testable hypotheses for this study. The

proposed testable hypotheses are grouped under the following three headings: the value

relevance and level of conservatism (bias) in reported goodwill and identifiable

intangible assets within the Australian context; the value relevance and level of

conservatism (bias) in reported goodwill and identifiable intangible assets within the US

context; and expected differences between Australia and the US in terms of the value

relevance and level of conservatism (bias) with respect to reported goodwill and

identifiable intangible assets.

4.1 The Value Relevance and Bias in Reported Goodwill and Identifiable Intangible Assets in Australia

The two key features of the various Australian standards that related to intangible assets

over the period examined by this study (1994-2003), as discussed in chapter 2, can be

summarized as follows:

1. Purchased goodwill (an un-identifiable intangible asset) had to be recorded at the

amount paid and then systematically amortised over a period not exceeding 20

years. In determining the amount of purchased goodwill, companies were required

(wherever possible) to separately record the fair value of any identifiable intangible

assets acquired. Further, the unamortised balance of goodwill had to be reviewed at

each reporting date and recognised as an expense to the extent that future benefits

were no longer probable. Purchased goodwill could not be revalued (upwards) and

internally generated goodwill could not be recognised.

2. Acquired identifiable intangible assets were required to be recorded at fair value at

the date of acquisition. Costs incurred (including research and development

expenditures) that gave rise to internally generated identifiable intangible assets

were required to be capitalised to the extent that those costs were expected (beyond

31

any reasonable doubt) to be recoverable. Identifiable intangible assets (acquired or

internally generated) with finite lives were required to be systematically amortised

over their useful lives, with no arbitrary limit applied to the determination of useful

life. There was no requirement to amortise identifiable intangible assets with

indefinite lives; however, the unamortised balance of identifiable intangible assets

had to be reviewed at each reporting date and recognised as an expense to the

extent that future benefits were no longer probable. Identifiable intangible assets

(both purchased and internally generated) could be revalued (upwards).

As can be seen from the above summary, the accounting treatment for intangible assets

under Australian GAAP over the period of this study was characterised by both very

restrictive (for unidentifiable intangible assets, that is goodwill) and very flexible (for

identifiable intangible assets) requirements. It has been suggested that the very

restrictive requirements that applied to unidentifiable intangible assets (goodwill) might

have caused the reported values for this class of assets to be far removed from their

economic reality (Miller 1995). It has also been suggested that the very flexible

treatment allowed by Australian GAAP with respect to identifiable intangible assets

might have led to the opportunistic treatment of such assets that again did not reflect their

economic reality (Godfrey and Koh 2001).

Indeed it has been suggested that the significant flexibility allowed with respect to the

reporting of identifiable intangible assets under Australian GAAP might have caused

this class of assets to be reported with considerable bias (Godfrey and Koh 2001;

Shahwan 2004). However, it could also be argued that Australian GAAP has allowed

managers more discretion to convey (through financial statements) their (potentially

value relevant) private information concerning the value of identifiable intangible assets

leading to less bias in the reporting of this important class of assets.

The Australian empirical evidence available to date, as discussed in chapter 3, suggests

that the amounts reported for both goodwill and identifiable intangible assets are likely to

be value relevant for the average Australian company. However, goodwill (because it is

likely to be more conservatively reported in financial statements) is likely to be more

highly valued by the market than identifiable intangible assets. Given that managers in

Australia have had more discretion with respect to the reporting of identifiable intangible

32

assets than they have had with respect to goodwill, this thesis assumes that managers will

have used their discretion to appropriately reflect the value of identifiable intangible

assets and, therefore, that identifiable intangible assets will be reported without bias in

Australia. Conversely, this thesis assumes that goodwill will be conservatively (under)

valued (reported with bias) by the average Australian company.

Therefore, based on the above arguments and the prior research findings within the

Australian context reported in chapter 3, it is hypothesised that:

H1a: Amounts reported for goodwill and identifiable intangible assets will be

value relevant for the average Australian company.

H1b: Reported goodwill for the average Australian company is likely to be

conservatively (under) valued (reported with bias).

H1c: Reported identifiable intangible assets for the average Australian company is

likely to be fairly valued (reported without bias).

4.2 The Value Relevance and Bias in Reported Goodwill and Identifiable Intangible Assets in the US

The two key features of the various US standards that related to intangible assets over

the period examined by this study (1994-2003), as discussed in chapter 2, can be

summarized as follows:

1. Prior to 2001, purchased goodwill had to be recorded at the amount paid and then

systematically amortised (using the straight-line method) over a maximum

estimated useful life of 40 years. Companies were required (wherever possible) to

separately record the fair value of any identifiable intangible assets acquired.

Goodwill had to be reviewed for impairment annually or whenever there were any

signs suggesting that the recoverability of the carrying amount of goodwill was in

doubt. In 2001, SFAS 142 was introduced abolishing the requirement to amortise

goodwill and instead replacing it with a requirement to test for impairment at least

annually. Throughout the period of this study, purchased goodwill could not be

revalued (upwards) and internally generated goodwill could not be recognised.

33

2. Acquired intangible assets were required to be recognised if: the general criteria for

asset recognition were met; the intangible asset could be measured reliably; and it

was probable that future benefits would flow to the entity from the use of the

intangible asset. Prior to 2001, acquired identifiable intangible assets had to be

systematically amortised over a maximum period of 40 years. After 2001, with the

introduction of SFAS 142, intangible assets with a finite life had to be amortised

(but with no prescribed maximum period) while intangible assets with an indefinite

useful life were not required to be amortised but had to be tested for impairment at

least annually. Throughout the period of this study intangible assets in the US

could not be revalued (upwards) and internally generated intangible assets, with the

exception of certain software development costs, could not be recognised.

US studies have found that reported goodwill is value relevant (Chauvin and Hirschey

1994; Jennings et al. 1996; Wilkins et al. 1998; Choi et al. 2000; Churyk 2005) and that

reported goodwill is typically not overvalued when initially recorded (Churyk 2005).

US studies have also found that reported identifiable intangible assets (as a total) are

value relevant in the US but less so than reported goodwill alone (Wilkins et al. 1998;

Choi et al. 2000). Many US studies have drawn attention to the inappropriateness of

expensing (rather than capitalizing) expenditures on various identifiable intangible

assets such as R&D, brands and patents, and the resultant reduction in the value

relevance of US financial statements. For example, Lev and Zarowin (1999) argued

that the decline in the value relevance of earnings over the past two decades is directly

related to the mistreatment (expensing rather than capitalising) of intangible assets.

Their results indicated that firms with an increasing intensity of intangibles had the

greatest decline in value relevance and firms with a decreasing intensity of intangibles

had the greatest increase in value relevance. Lev and Sougiannis (1996) argued that a

policy of selective R&D capitalisation would be better viewed by the market compared

to a policy of expensing all R&D outlays. In support of the argument advanced by Lev

and Sougiannis (1996), Aboody and Lev (1998) found that annually capitalised software

development costs were positively associated with stock returns and that balance sheet

values for capitalised software development costs were positively associated with stock

prices. Similarly, Barth et al. (1998) found that brand value estimates were significantly

associated with equity market values. The finding by Barth et al. (1998) indicates that

brand values can be reliably estimated and suggests that international standard setters

34

should consider allowing firms to recognise (in their financial statements) the value

associated with internally generated (as well as purchased) brands.

In summary, the available US evidence suggests that the amounts reported for both

goodwill and identifiable intangible assets are likely to be value relevant for the average

US company, however, both amounts are likely to be conservatively (under) valued

(reported with bias).

Therefore, based on the above arguments and the prior research findings within the US

context reported in chapter 3, it is hypothesised that:


value relevant for the average US company.

H2b: Reported goodwill for the average US company is likely to be conservatively

(under) valued (reported with bias).

H2c: Reported identifiable intangible assets for the average US company is likely

to be conservatively (under) valued (reported with bias).

4.3 Comparing the Value Relevance and Bias in Reported Goodwill and Identifiable Intangible Assets in Australia and the US.

Purchased goodwill had to be recorded at the amount paid and then systematically

amortised during its useful life over a period not exceeding 20 years in Australia and,

until 2001, over a period not exceeding 40 years in the US. After 2001, US companies

were no longer required to systematically amortise goodwill but, instead, were required

to test goodwill at least annually for impairment.

In terms of identifiable intangible assets, Australian companies have been able to

recognise a far greater range of identifiable assets than has been the case in the US.

Australian companies have also been permitted to revalue (upwards) identifiable

intangible assets; something not permitted in the US. Finally, the amortisation of

identifiable intangible assets has been less restrictive in Australia compared to the US.

35

The more flexible treatment of goodwill over the period of this study under US GAAP

compared to Australian GAAP (particularly after 2001) should have permitted US

managers to provide better signals (than Australian managers) with respect to their

expectations of future cash flows from this important asset (Wilkins et al. 1998). By

way of contrast, the more flexible treatment of identifiable intangible assets permitted

under Australian GAAP compared to US GAAP should have permitted Australian

managers to provide better signals (than US managers) with respect to their

expectations of future cash flows from such assets.

Based on the above arguments, it is hypothesised that:

H3a: Reported goodwill for the average Australian company is likely to be more

conservatively (under) valued (reported with more bias) than the reported

goodwill for the average US company.

H3b: Reported identifiable intangible assets for the average Australian company

are likely to be less conservatively (under) valued (reported with less bias)

than reported identifiable intangible assets for the average US company.

4.4 Summary

This chapter developed eight hypotheses to explore the research questions identified in

chapter 1.

The first three hypotheses explore the research questions within the Australian context.

It is predicted that: both goodwill and identifiable intangible assets will be value

relevant (H1a); reported goodwill will be conservatively (under) valued (H1b); and

reported identifiable intangible assets will be fairly valued (H1c).

The second three hypotheses explore the research questions within the US context. It is

predicted that: both goodwill and identifiable intangible assets will be value relevant

(H2a); reported goodwill will be conservatively (under) valued (H2b); and reported

identifiable intangible assets will also be conservatively (under) valued (H2c).

The final two hypotheses explore the research questions with respect to expected

differences between Australia and the US. It is predicted that: reported goodwill will be

36

more conservatively valued under Australian compared to US GAAP (H3a); and,

conversely, reported identifiable intangible assets will be more conservatively valued

under US compared to Australian GAAP (H3b).

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Chapter 5: Method and Data

5.0 Introduction

This dissertation examines questions that can only be settled empirically. In order to

address these questions, it is necessary to establish an appropriate experimental design.

This chapter discusses the research methodology used to address the hypotheses stated

in chapter 4. In order to assess the value relevance of the variables in the study, this

study adopts the Feltham and Ohlson (1995) valuation framework; this framework

(model) will be introduced in section 5.1. However, use of the ‘pure’ model is not valid

for this study and, therefore, how the model is operationalised will be discussed in

section 5.2. Section 5.3 discusses the appropriateness of the statistical techniques used

to estimate the model. The model requires an estimate of the appropriate cost of capital

(required rate of return) for firms in the sample. Estimating the cost of capital for

individual firms is problematic and this is discussed in section 5.4. Section 5.5

discusses the sources and specification of the data used to estimate the model and,

finally, section 5.6 concludes the chapter.

5.1 The Feltham and Ohlson (1995) Valuation Framework (Model)

The usefulness of financial information to investors can be best measured by the

statistical association between accounting data and capital market values (stock prices

or returns). Such an association reflects the consequences of investors’ actions rather

than just their opinions and beliefs which can be determined on the basis of

questionnaires and interviews (Lev and Zarowin 1999).

This study aims to assess the usefulness (value relevance) of published accounting

information relating to goodwill and identifiable intangible assets within the Australian

and US contexts over the period 1994 to 2003. To do this requires a model that has two

features.6 First, the model must facilitate a methodologically rigorous analysis of firm

value. Second, the model must use accounting information (which, after all, is the focus

of this study). This section will discuss the Feltham and Ohlson (1995) valuation model

6 Unlike many of the studies discussed in chapter 3, the analysis in this dissertation uses a model that has a theoretical foundation. While the studies discussed in chapter 3 typically use well-known variables established in the empirical literature, the Feltham and Ohlson (1995) framework used in this study is grounded in a theory that seeks to explain the determinants of firm value. Use of a model grounded in theory will avoid potential specification bias in the analyses presented.

38

used to assess the value relevance of, and any bias associated with reported goodwill

and identifiable intangible assets in Australia and the US.

The Feltham and Ohlson (1995) framework represents a rigorous valuation

methodology with strong theoretical and empirical implications suitable to the analysis

of firm value.7 The Feltham and Ohlson (1995) model, while focusing on the prediction

of future earnings and future growth in book values, can also be used to explore the

association between current published accounting information and stock prices (or

returns). This view was supported by Dechow, Hutton, and Sloan (1999) who argued

that Feltham and Ohlson (1996), Feltham and Ohlson (1995) and Ohlson (1995) provide

a unifying framework for assessing the models typically used in empirical studies.

Dechow et al. (1999) highlighted the fact that most studies completed prior to Ohlson

(1995) have been conducted without any clearly supported theory underlying the

analyses.

The Feltham and Ohlson (1995) valuation model is a modification of the residual

income model which represents a restatement of the classical dividend discount model.8

In the Feltham and Ohlson (1995) valuation model the value of a company equals its

book value of equity plus any unrecorded goodwill. Feltham and Ohlson (1995) show

that, by using accounting data following the clean surplus relation assumption (that is,

the change in book value of equity is equal to net income minus dividends), unrecorded

goodwill is equal to the present value of expected future abnormal earnings (Amir et al.

1997). The net operating asset and abnormal operating earnings variables are adjusted

to reflect the firm’s cost of capital and coefficients capturing: accounting conservatism

and persistence in abnormal operating earnings; and asset growth (taken from Feltham

7 There are other models that might have been utilised in the analysis. For example, Pope and Wang (2005) present extensions of Feltham and Ohlson (1995) which allow for a distinction between ‘core’ and ‘abnormal’ earnings. Such a model is interesting if ‘abnormal’ earnings equates with Feltham and Ohlson’s (1995) ‘abnormal’ earnings but, even if such a distinction is interesting, it is difficult to operationalise ‘abnormal’ in a systematic way (whereas Feltham and Ohlson’s (1995) model has been generalised using a systematic cost of capital adjustment which we discuss in the following two sub-sections). Additionally, alternate models such as Pope and Wang (2005) have not attracted the empirical support of Feltham and Ohlson’s (1995) model.

8 The dividend discount model states that a firm’s value is equal to the present value of its expected future dividends (Dechow et al. 1999).

39

and Ohlson’s (1995) linear information models (LIMs), as discussed later in this chapter

and which cannot be observed directly).

The Feltham and Ohlson (1995) model has analytically four straightforward

assumptions which can be explained for simplicity as follows:

1. There is no arbitrage. Therefore, firm value will represent the present value of the

expected dividends conditional on the LIMs (which are discussed below);

2. Accounting data and dividends satisfy the clean surplus assumption so dividends

will reduce the book value of equity without affecting current earnings. The model

assumes that the clean surplus relation assumption (CSR) operates only with

abnormal operating earnings. In practice, the restricted CSR cannot be applied

perfectly; for example, the ability for a subsequent revaluation of property, plant

and equipment within Australian GAAP violates CSR. That is, revaluations of the

book values of such assets will more closely reflect the market values of those

assets during their useful lives and, therefore, subsequent revaluations of the book

values for those assets should explain more of the variation in firm market values.

However, the restricted CSR assumption is a relatively minor issue when the

empirical power of the model is considered (King and Langli 1998);

3. Net financial assets have a zero net present value. Feltham and Ohlson (1995)

explained that the book value of equity is equal to net operating assets and net

financial assets. Net financial assets are assumed to be traded in relatively perfect

markets so, by construction, net financial assets will generate zero abnormal

earnings. Therefore, only net operating assets will be valued (Feltham and Ohlson

1995; Stromann 2002). It should be noted that the assumption that net financial

assets should not be valued is relaxed in operationalising the Feltham and Ohlson

(1995) model for the purposes of this study (this issue will be discussed in section

5.2); and

40

4. Abnormal operating earnings and net operating assets change through time

following a modified autoregressive process9 that may be captured in LIMs. The

LIMs of the Feltham and Ohlson (1995) valuation model can be presented as

follows (with some simplification for the sake of clarity):

AOEt +1 = !11 AOEt + !12 NOAt + "ıt + #1t+1 (1) NOAt+1 = !22 NOAt + "2t + # 2t+1 (2) "1t+1 = $1"1t + # 3t+1 (3) "2t+1 = $2 "2t + # 4t+1 (4)

Where:

AOEt +1 is abnormal operating earnings, time t+1 (defined as actual operating earnings in the current period minus expected operating earnings equal to the firm’s weighted average cost of capital (WACC) times NOA in the previous period);

NOAt is net operating assets, time t; !11 is the persistence parameter of abnormal earnings, (0! "ıı < 1); !12 is the conservatism parameter in the accounting for net operating asset, (0!

"ı2); !22 is the growth parameter of net operating assets, (0! "22 < 1+r); "1t, "2t, are ‘other information’ variables, time t; $1, $2 capture the persistence parameter of #ıt and #2t respectively, (0! $1, $2 < 1);

and # jt+1 are independently and identically distributed error terms (j=1,2,3,4)

(Callen and Segal 2005).

The above LIMs are a function of three accounting-specific parameters, namely:

1. the persistence in abnormal operating earnings;

9 An autoregressive process is one where the current value of a variable is a function of its historical value. The linear information process defined in equations 1 to 4 is not a ‘pure’ autoregressive process. In equations 1 to 4, not only are the lagged (historical) values of the dependent variables included in the equation, the lagged values of other variables are also admitted.

41

2. the growth in both net operating assets and abnormal operating earnings; and 3. the level of conservatism in the accounting treatment for net operating assets.

Persistence ("11) and growth ("22) are affected by both the underlying economics of the

firm and the accounting procedures employed, while the conservatism parameter is only

a function of the accounting procedures employed (Stromann 2002). Abnormal

operating earnings are a function of the two parameters of persistence and conservatism.

Persistence represents monopoly rents enjoyed by a firm. In the long run, these

monopoly rents will fall towards the cost of capital through market competition.

Therefore, the persistence parameter is assumed to have a value within the range of 0!

"11 < 1; a value of ‘0’ implies no persistence and value of ‘1’ implies full persistence.

The second parameter is conservatism. Accounting conservatism reduces the reported

value of assets to below their market value, and such a procedure will necessarily create

abnormal earnings; assets with lower values will appear to generate higher returns

ceteris paribus. Following this reasoning, it is assumed that conservative accounting

practices will result in a value for the conservative parameter that is greater than zero

(that is, 0 < "12). If accounting practices are unbiased, the conservative parameter

should equal zero (that is, 0 = "12). If aggressive accounting practices are followed, the

conservative parameter should be less than zero (that is, 0 > "12) (Feltham and Ohlson

1995; Amir et al. 1997; Ota 2002; Callen and Segal 2005).

From these assumptions, and using the LIMs, Feltham and Ohlson (1995) demonstrate

that the value of the firm can be expressed as following:

MVEt = BVEt+ %1NOAt+ %2AOEt+ &1"t+ &2"t+ # t (5)

Where:

MVEt is the market value of the firm’s equity, time t; !V"t is the book value of equity, time t;

%1 is "11 / {(1+r)- "11} % 0; %2 is {"12 (1+r)} / {[(1+r)- "11][(1+r)- "22]} % 0;

&1 is {1+r} /{[(1+r)-"11] [(1+r)-$1]} % 0;

&2 is &2 /{(1+r)-$2} % 0; and all other variables are as defined previously.

42

The other information variable, #t1, is used to predict future abnormal earnings, and the

persistence of these earnings, while #t2 is used to predict growth in net operating assets.

Both variables can use non-accounting information. Details of the predicted values for

these parameters can be found in Feltham and Ohlson (1995). The version of the model

adopted in this study (derived in the following section) assumes that the ‘other

information’ variables, "1 and "2, can be discarded from the model and, therefore, the

results of the tests are conditional on that assumption. The reasons for not including

these variables are discussed in section 5.2.

The Feltham and Ohlson (1995) model presents a framework for determining the market

value of a firm using accounting information. The model provides a framework,

discussed in the following section, for determining the value relevance of accounting

variables. Furthermore, as this study will argue, this framework allows the level of bias

with which the market incorporates accounting information into share prices to be

assessed. Feltham and Ohlson’s (1995) LIMs also provide a means of assessing the

level of accounting conservatism present in the information reported by firms.

5.2 Operationalising Feltham and Ohlson (1995) to Address the Hypotheses in this Dissertation

The hypotheses specified in chapter 4 pose questions about the reported goodwill (GW)

and identifiable intangible assets (ID) in the Australian and US markets. GW and ID

are components of both the book value of equity (BVE) and net operating assets (NOA),

as shown by the following equation:

!

BVEt= NOA " INT( )

t+ GW

t+ ID

t+ NFA

t (6)

where:

BVEt is book value of equity, time t;

(NOA-INT)t is net operating assets – total intangible assets (GW + ID), time t; GWt is goodwill, time t; IDt is other (identifiable) intangible assets, time t; and NFAt is net financial assets, time t.

43

The four independent variables of interest will be included in the analysis by estimating

a disaggregated model based on equation 5. Simply estimating equation 5 with an

expanded definition following equation 6 will, by definition, introduce multicollinearity

into the proposed econometric analysis. This problem was recognised by Amir et al.

(1997) and Callen and Segal (2005) who adopted a disaggregated variation of equation

5 that is consistent with Feltham and Ohlson (1995) while avoiding multicollinearity.

This is achieved by omitting NOA and BVE in favour of using the constituents of BVE

(as shown in equation 6). As such, this study will follow Amir et al. (1997) and Callen

and Segal (2005) and estimate a variation to the Feltham and Ohlson (1995) valuation

model, as shown below in equation 7. This model separates out from net operating

assets the two components of intangible assets (goodwill and other identifiable

intangible assets) and also considers a potential role for net financial assets, but

excludes the ‘other information’ variables.

MVEt = %0 + %1 (NOA – INT)t + %2(GW)t + %3(ID)t + %4NFAt + %5AOEt + # t (7)

where:

MVEt is the market value of the firm’s equity, date t (where t is sometime after the financial year end as will be discuss later in this chapter);

#0 to #5 are estimated regression coefficients; and all other variables are as

defined previously.

Equation 7 is estimated for Australia in chapter 6 (see Table 6.3) and for the US in

chapter 7 (see Tables 7.3 and 7.4). Estimating equation 7 allows the value relevance of

the accounting treatment for intangible assets, and the bias10 with which investors

impound the value of intangible assets into market values (under both Australian and

US GAAP) to be considered. In particular, estimating Feltham and Ohlson’s (1995)

LIMs permits a consideration of whether (over the period of this study) the practice of

recognising intangible assets under both Australian and US GAAP was consistent with

accounting conservatism or whether it resulted in unbiased, or aggressive reporting.

As discussed in section 5.1, Feltham and Ohlson (1995) permit ‘other information’

without specifying what this ‘other information’ might include. The ill-defined nature

10 The question of how bias is addressed is dealt with in more detail in the following section.

44

of Feltham and Ohlson’s (1995) ‘other information’ might result in empirical studies

having an omitted variable. The analysis in this dissertation departs from Feltham and

Ohlson (1995) in that ‘other information’ is not operationalised. It should be noted that

while some US studies have used analysts’ forecasts as a proxy for ‘other information’,

the Australian data is simply not rich enough to provide acceptable information on

analyst’s forecasts for the large data set of Australian firms examined in this study.

Having said that, however, it is believed that making use of analysts’ forecasts to proxy

for ‘other information’ defeats the purpose of Feltham and Ohlson (1995). Feltham and

Ohlson (1995) attempt to model market values based on past accounting information. If

this study is to incorporate analysts’ forecasts into the model it might as well use

analyst’s forecasts to estimate market values, making Feltham and Ohlson (1995)

redundant.

It is also open to debate whether analysts’ forecasts add value to the model, Frankel and

Lee (1998) found that (in US studies) the inclusion of analysts’ forecasts provided only

a marginal improvement in the model. Pre-empting the findings of this study, the high

goodness-of-fit statistics (adjusted R2 values) presented in the results for both Australia

and the US suggest there is little room in the model for further information (including

analysts’ forecasts).

A further reason for not including ‘other information’ in this study is that it might

detract from the core purpose of the analysis. The primary aim of this study is to assess

the usefulness (value relevance) of published accounting information relating to

goodwill and identifiable intangible assets in Australia and the US. Market values in

this study will be a function of how investors, including analysts, impound such

information into share prices for the companies under examination. Including analysts’

forecasts in the information set would introduce circularity into the research design and

could bias the findings with respect to how accounting numbers are impounded into

share prices.

In addition to the valuation model (equation 7) which addresses the question of the

valuation of intangible assets, this study also adapts Feltham and Ohlson’s (1995) LIMs.

These LIMs permit the regression coefficients "11, "12 and "22 to be estimated, which in

turn allows the pervading accounting policy choices within Australia and the US (over

45

the period of this study) to be captured. Accordingly, the analysis in this dissertation

will estimate disaggregated LIMs derived from the ‘basic’ functional forms presented in

equations 8 and 9:

!

AOEt

="11AOE

t#1 +"12NOA

t#1 + $1,t

(8)

!

NOAt

="22NOA

t#1 + $2,t

(9)

The modification then continues in the spirit of the disaggregated valuation model and

expands the conservatism and growth parameters to be consistent with equation 7.

These disaggregated LIMs facilitate further examination of the components of net

operating assets (NOA-INT, GW and ID) to determine which (if any) influence the

findings, particularly with respect to accounting conservatism. Accordingly, the

following equations (10 to 13) are estimated:

!

AOEt

="11AOE

t#1 +"12(NOA # INT)

t#1 +"13GW

t#1 +"14ID

t#1 + $1,t

(10)

!

(NOA " INT)t

=#22(NOA " INT)

t"1 + $2,t

(11)

!

GWt

="23GW

t#1 + $3,t

(12)

!

IDt

="24ID

t#1 + $4,t

(13)

where:

!12, !13,

and !14 are estimated regression coefficients indicating the level of

conservatism in the accounting treatment for (NOA-INT), GW and ID, respectively;

!22, !23,

and !24 are estimated regression coefficients associated with the growth

parameters for (NOA-INT), GW and ID, respectively; $1,t, $2,t,

$3,t, and $4,t are the respective error terms for equations 10 to 13; and the other

variables are as defined previously.

While it is believed that the Feltham and Ohlson (1995) framework provides a sound

theoretical framework for the analysis of the hypotheses presented in chapter 4, there

are, never the less, some challenges that need to be addressed in estimating models 7,

10, 11, 12 and 13. In the following section, these challenges and the solutions adopted

in this study are discussed.

46

5.3 Estimating the Model: Practical Considerations

To estimate models 7, 10, 11, 12 and 13, this study uses firm-level data from Australia

and the US. Many companies will appear in the data over a number of years and this is

recognised by using an econometric technique appropriate for panel-data. A panel-data

set is simply one that includes a sample of observations from firms, or individuals,

where those observations are taken over a period of time (see Greene, 2003, Chapter

13).11 Recognising that not all companies will be present in the data for all years, this

study uses unbalanced panel analysis.

All the independent variable coefficients will be tested to see if they differ significantly

from both one and also from zero (except AOE which will be tested only to see if it

differs significantly from zero). In an ideal world, where all information known about a

company is captured in its financial reports at fair value (including internally generated

goodwill), the coefficients for all independent balance sheet variables should be one.

That is to say, a dollar of value on the balance sheet should be worth $1 of market

capitalisation. If the null that a coefficient equals one can be rejected, this means that a

dollar of value on the balance sheet is worth more, or less, than a dollar of market

capitalisation. If the null that a coefficient equals one is rejected, it can be argued that

the market is biased in its valuation of the accounting asset. If all assets are recorded at

their fair value (reflecting the expected returns from those assets) there should be no

AOE for the average firm, that is, AOE should have a coefficient of zero (Feltham and

Ohlson 1995). Testing the null hypothesis that a coefficient equals zero allows the

study to address the question of whether the variable is value relevant. If the coefficient

is not significantly different from zero there is a presumption that the variable is not

value relevant.

Note that hypotheses H1b (introduced in section 4.1), H2b and H2c (introduced in section

4.2 of chapter 4) imply a direction in the findings (for example, that a variable will be

under valued). However, rather than using one-tailed levels of significance, two-tailed

tests are reported. Use of two-tailed tests facilitates consideration of whether the market’s

incorporation of accounting data is biased, but biased in a direction different to that

11 A seemingly standard textbook example is one where families are sampled over a number of years to examine their patterns of consumption. Failure to recognise that the same family is being observed repeatedly might lead to false inferences being made.

47

hypothesised. Foreshadowing the results that will be presented in subsequent chapters,

the analysis finds that this is indeed the case.

It should also be noted that in this study none of the variables are scaled.12 Given that

the analysis in this study focuses on tests of null hypotheses that coefficients equal one,

distortions in the data caused by scaling would preclude the study addressing the

questions it set out to answer. This, in itself, is perhaps the most compelling reason for

not scaling in this study. Scaling might also introduce spurious correlations into the

analyses. Kim (1999) demonstrated that scaling by a common divisor can result in

spurious correlations and, therefore, scaling should not be undertaken lightly.

Yet the question of whether or not to scale variables in a regression is an important

issue. It has been argued that the use of unscaled data might result in flawed inferences

due to the impact of heteroskedasticity and influential observations (Easton and

Sommers 2003) and the issue is one that still attracts considerable attention (see, for

example, Akbar and Stark 2003; Easton and Sommers 2003; Barth and Clinch 2005).

However, Barth and Kallapur (1996) argued that the use of heteroskedasticity-consistent

standard errors (White 1980) permits valid inferences if heteroskedasticity is present.

Therefore, the tables presented in this study will report t-statistics and p-values adjusted

following White (1980). Furthermore, rather than scaling individual variables, Barth

and Kallapur (1996) recommend the inclusion of a scale proxy as an independent

variable in the regressions. I suggest that NOA-INT might be an appropriate proxy for

scale and, therefore, the inclusion of NOA-INT as an independent variable in models 7,

10, and 11 is consistent with Barth and Kallapur’s (1996) recommendation.

Additionally, any effect of heteroskedasticity and of potentially influential observations

(the issues of concern to papers advocating scaling) is mitigated by two further features

of the analysis in this study. First, any scale effect is reduced by the exclusion of the

top and bottom 2.5% of observations measured by market capitalization (see tables 5.1

and 5.2 below); omitting such data is in keeping with many other studies (see, for

example, Amir et al. 1997; Landry and Callimaci 2003; Lev and Nissim 2004). Second,

the creation of firm and time dummy variables for the panel estimation has the effect of

12 Scaling involves dividing the variables in the regression by a common scale-related factor (for example, net operating assets).

48

‘soaking up’ any potentially anomalous and/or influential observations that might

otherwise bias the analysis.13 Perhaps this point is usefully illustrated by analogy to

ordinary-least-squares regression. Consider an ordinary-least-squares regression with

an outlier. One might remove the outlier and estimate the regression to obtain the true

coefficient for the dependent variable. Conversely, one might identify the observation

with a dummy variable; in this case, the true coefficient for the dependent variable

would also be produced using ordinary-least-squares regression. The panel estimation

procedure used in the analysis presented in this dissertation incorporates dummy

variables to identify firms through time (as well as specific years). The firm and time

dummies will systematically control for outliers and any potentially influential

observations.14

5.4 The Discount Rate

As can be seen in section 5.1, where the definitions of the variables used in this study

are presented and discussed, the Feltham and Ohlson (1995) framework requires an

appropriate discount rate to be estimated in order to calculate abnormal operating

earnings (AOE). The issue of what is an appropriate discount rate to use in determining

AOE is problematic (Morel 2003). Feltham and Ohlson (1995) provide no guidance on

how to operationalise this construct and there has been considerable debate in the

literature about which model of expected returns applies to Australian equity. For

example, Durand, Limkriangkrai, and Smith (2006) argued that the ‘conventional’

three-factor model does not apply in Australia and that, in fact, no model applies to the

entire Australian market.15 A number of US studies have used a simplified discount rate

of 12% which represents the long-run average realised return on U.S equities (Dechow

et al. 1999; Ahmed et al. 2000; Nissim and Penman 2001). But simply following this

approach would appear to be inappropriate for Australian data. In this study (following

13 In treating individual years using a dummy variable approach, panel data estimation allows for any shifts in the intercept associated with the timing of events such as the US adoption of SFAS 142 (discussed in Chapter 2).

14 Despite the arguments advanced against the need to scale, for the sake of robustness this dissertation will follow the suggestion by Barth and Clinch (2005) and also present regressions scaled by the number of shares on issue at the end of the financial year being considered. The findings from the robustness tests suggest that concerns in the literature about scaling might not be warranted; although further research in this area is needed before such a conclusion could be drawn definitively.

15 If it cannot be determined which, if any, model applies to Australian firms it would be incorrect to calculate a firm-specific cost of capital.

49

the prior US practice) a discount rate of 13.3% representing the nominal rate of return

on equity for Australian companies during the period 1900-2000 was initially chosen

(Dimson et al. 2002). However, preliminary analysis adopting this approach for the

Australian sample resulted in around 80% of companies having negative AOE; an

outcome that is clearly inappropriate.

Recognising that both equity and debt providers fund the assets of a firm (Modigliani

and Miller 1958), this study uses a discount rate based on the estimated WACC for the

Australian and US companies in this study. To calculate the average WACC for both

Australia and the US, the annual cost of equity capital was found by analysing the

monthly value-weighted returns of stocks listed on the Australian and New York Stock

Exchanges. The expected cost of debt was then calculated from monthly ten-year bond

rates for both Australia and the US (taken from Datastream) and company tax rates for

Australia (Deutsch et al. 1999; Barkoczy 2001; Gilders et al. 2004) and the US (taken

from the OECD16). This procedure resulted in an estimated WACC of approximately

8% and 6.63% for the Australian and US samples, respectively.17 In using long run

averages, the analysis in this dissertation assumes that, in the long run, ex post

observations will equal ex ante expectations; such an assumption is consistent with

investors holding rational-expectations of future returns. The following section

discusses the sources of the company data used in this study and provides detailed

definitions of the variables of interest.

5.5 Data

In section 5.2, the study presented models 7, 10, 11, 12 and 13, which will be estimated

in chapters 6 and 7 for Australia and the US, respectively. In order to estimate these

models, the study needs to source both financial statement data and data on the market

capitalisation (value) of firms in Australia and the US. The sources for this data will be

discussed in this section. This section will also discuss how the data was manipulated

to produce the variables used in the analysis.

16 The data were downloaded from:

http://www.oecd.org/home/0,2987,en_2649_201185_1_1_1_1_1,00.html.

17 The robustness of the inferences made to the choice of discount rate are discussed at length in sub-sections 7.2 and 7.3 of this dissertation where results contrary to expectations are found for the US market. The further analysis presented in these sub-sections indicates that the results are robust to the choice of discount rate.

50

The Australian market capitalisation data was taken from the Share Price and Price

Relative Database (SPPR) compiled by the Centre for Research in Finance and the

Australian Graduate School of Management at the University of New South Wales. All

the Australian financial statements data was taken from Fin Analysis and

SIRCA/Aspect Financial databases. The US market capitalisation and financial

statements data were taken from the DATASTREAM INTERNATIONAL and

MERGENT ONLINE databases. This study covered the ten-year period from 1994 to

2003 for both Australia and the US.

The final sample of 2,611 Australian firm-years was derived from an initial sample of

11,279 observations. Similarly, the US sample of 4,584 firm-years was derived from a

potential sample of 34,780 observations. The filtering process used in arriving at the

final samples for both Australia and the US is discussed below and summarised in

Tables 5.1 and 5.2, respectively.

1. Financial, resources and mining firms were excluded because of the unique

characteristics (operations) associated with these industries (Jennings et al. 1996;

Barth and Clinch 1998). This step resulted in the removal of 5,209 observations

from the Australian sample and 18,280 from the US sample.

2. Observations with missing book and/or market value data were excluded. If the

data does not exist, it simply cannot be analysed. This step resulted in the removal

of 2,407 observations from the Australian sample and 7,844 from the US sample.

3. Companies which did not have a 30 June year-end for Australia or a 31 December

year-end for the US were excluded. This ensured that all sample firms were at the

same stage in the financial reporting process for any given valuation date (Jennings

et al. 1996). Additionally, suspended, delisted and any unmatched firms were

excluded from both samples. This step resulted in the removal of 751 observations

from the Australian sample and 3,647 from the US sample.

4. Firms with negative book values were excluded. It is likely that the future for these

firms is highly uncertain and the inclusion of such firms might add noise to the

51

analysis (Frankel and Lee 1998; Myers 1999; Ahmed et al. 2000). This step

resulted in the removal of 141 observations from the Australian sample and 160

from the US sample.

5. Firms in the Australian sample not applying Australian GAAP and firms in the US

sample not applying US GAAP were excluded. Inclusion of such companies would

be inconsistent with a study such as this that seeks to analyse and compare the

effects of Australian and US GAAP. This step resulted in the removal of 28

observations from the Australian sample and 31 from the US sample.

6. The top and bottom 2.5% of observations are excluded to prevent any undue

influence of outliers on the regression analysis (Amir et al. 1997; Landry and

Callimaci 2003; Lev and Nissim 2004). This step resulted in the removal 132

observations from the Australian sample and 234 observations from the US sample.

After following these six steps there were 2,611 and 4,584 observations remaining for

Australia and the US, respectively (as shown in Tables 5.1 and 5.2).

Table 5.1: Selection of the Australian sample

Year 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 1994-2003

Starting number of firm years 759 817 841 970 1096 1251 1331 1381 1394 1439 11279

Less:

Financial, resourses and mining firms -391 -438 -424 -468 -494 -553 -579 -604 -616 -642 -5209

Firms with missing market and book values -275 -239 -113 -187 -278 -305 -330 -293 -220 -167 -2407

Firms with other than June 30 year ends -29 -40 -35 -41 -63 -136 -151 -86 -82 -88 -751

and suspended, delisted or unmatched companies

Firms with negative book value of equity -8 -11 -9 -8 -5 -11 -10 -10 -24 -45 -141

Firms not applying Australian GAAP 0 0 -1 -2 0 0 0 -6 -7 -12 -28

56 89 259 264 256 246 261 382 445 485 2743

Less:

Top and bottom 2.5% observations -2 -4 -12 -14 -12 -12 -12 -18 -22 -24 -132

Final number of firm years 54 85 247 250 244 234 249 364 423 461 2611

52

Table 5.2: Selection of the US sample

Year 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 1994-2003

Starting number of firm years 3478 3478 3478 3478 3478 3478 3478 3478 3478 3478 34780

Less:

Financial, resourses and mining firms -1828 -1828 -1828 -1828 -1828 -1828 -1828 -1828 -1828 -1828 -18280

Firms with missing market and book values -912 -912 -978 -978 -944 -665 -613 -602 -608 -632 -7844

Firms with other than Dec 31 year ends -387 -386 -328 -253 -281 -534 -413 -389 -346 -330 -3647

and suspended, delisted or unmatched companies

Firms with negative book value of equity -20 -20 -11 -10 -9 -15 -16 -20 -21 -18 -160

Firms not applying US GAAP -2 -2 -2 -3 -3 -3 -3 -4 -4 -5 -31

329 330 331 406 413 433 605 635 671 665 4818

Less:

Top and bottom 2.5% observations -16 -16 -16 -20 -20 -22 -30 -30 -32 -32 -234

Final number of firm years 313 314 315 386 393 411 575 605 639 633 4584

Tables 5.3 and 5.4 provide the definitions for each of the variables used in the

estimation of equations 7, 10, 11, 12 and 13. In essence, the calculations follow Nissim

and Penman (2001) with some minor adjustments to reflect the use of the different

Australian and US databases. Due to the delay in reporting financial results, and to

better reflect the market response to the latest information available in the financial

reports, this study uses the market capitalisation for each firm three months after the

financial year-end date.18

This study departs from Dechow et al. (1999) and Amir et al. (1997) in that it does not

exclude extraordinary items from earnings. This procedure is followed because, as

noted by Dechow et al. (1999), excluding extraordinary items violates the clean surplus

assumption underlying the theoretical development of the residual income valuation

model which, in turn, underpins the Feltham and Ohlson (1995) framework.

It should also be noted that from 1st January 2005, Australian GAAP (consistent with

International Accounting Standards) was changed to prohibit companies from disclosing

extraordinary items, either on the face of the income statement or in the notes. While

this change in Australian GAAP does not affect the current study, if this study excluded

extraordinary items from earnings, it would prohibit a comparison of the results from

this study with any subsequent Australian study.

18 Although, Abrahams and Sidhu (1998) reported little difference using market capitalisation at balance date compared with three months after balance date.

53

Table 5.3: Variable definitions/calculations for the Australian data

Variable Description Definition/calculation

MVE Market Value of Equity = Market Capitalisation at the end of September

NOA Net Operating Assets = Operating assets – Operating liabilities Operating Assets = Current Assets – Cash –

Short Term Investments + (Plant, Property and Equipment – Accumulated Depreciation) + Long term investments using the equity method + Intangible assets + Future income tax benefits

Operating Liabilities = Total liabilities + Preference Shares – Financial Liabilities

Financial Liabilities = Long Term Debt + Debt in Current Liabilities + Preference Shares

NFA Net Financial Assets = Financial Assets – Financial Liabilities Financial Assets = Total assets – Operating

assets Financial Liabilities = As above

AOE Abnormal Operating Earnings = OEt - (r*NOAt-1)

OEt = Operating earnings for year t, calculated as: reported Net Profit after Tax + Interest Expense after Tax – Interest Income after Tax.

r = The weighted average cost of capital which is assumed to be 8%

NOAt-1 = Net operating assets for year t-1

GW Goodwill = Reported goodwill

ID Identifiable Intangibles = Reported identifiable intangible assets

Minority interest has not been excluded in the above calculations.

54

Table 5.4: Variable definitions/calculations for the US data

Variable Description Definition/calculation

MVE Market Value of Equity = Market Capitalisation at the end of March

NOA Net Operating Assets = Operating Assets – Operating Liabilities

Operating Assets = Total Assets (#392) – Financial Assets

Financial Assets = Total Cash and Equivalents (#375)19 + Total Investments (Excluding Associates) (#350).

Operating Liabilities = Total Liabilities – Financial Liabilities

Total Liabilities = Total Assets (#392) – Equity Capital and Reserves (#305)

Financial Liabilities = Total Debt (#1301)20

NFA Net Financial Assets = Financial Assets – Financial Liabilities

Financial Assets = Total Assets (#392) – Operating Assets

AOE Abnormal Operating Earnings = OEt - (r*NOAt-1)

OEt = Operating earnings for year t, calculated as: Published Profit after Tax (#623) + Interest Expense after Tax (#536) – Interest Income after Tax (#143).

r = The weighted average cost of capital which is assumed to be 6.63%

Minority interest has not been excluded in the above calculations. Note that DATASTREAM, which is used to calculate the variables for the US sample,

does not separate the goodwill (GW) and identifiable intangible assets (ID) in the

calculation of intangible assets (INT). This is in contrast to Fin Analysis and

SIRCA/Aspect, which provide these items separately for the Australian data. In order

to address this issue and ensure the data used in the analysis of the US variables is

19 Total cash and equivalents (#375) are represented by cash and short-term investments.

20 Total debt (#1301) includes the value of preferred shares.

55

strictly comparable to that used for the Australian analysis, the following procedures

were followed:

1. MERGENT ONLINE was contacted and a comprehensive set of net goodwill

values, net identifiable intangible asset values, net total intangible asset values and

the full company name for all companies listed on the NYSE was obtained.

2. Company names were matched manually for the two databases (DATASTREAM

and MERGENT ONLINE).

3. In some cases, companies did not have identical values for net total intangible

assets in both databases; these companies were excluded from the study.

4. Furthermore, after matching (in step 2) and checking (in step 3), a few companies

were found to have GW greater than total intangible assets; these companies were

also excluded prior to undertaking the filtering process used to derive the final US

sample as reported in Table 5.2.

5.6 Summary

This chapter presented and discussed the data and methodology used in this study. It

presented an overview of the nature, importance and contribution of the Feltham and

Ohlson (1995) valuation framework which provides the basis of the models estimated in

chapters 6 and 7 for the Australian and US firms, respectively. While providing a

thorough theoretical framework for the analysis, the Feltham and Ohlson (1995)

valuation framework requires adaptation in order to appropriately test the hypotheses

specified in chapter 4. In particular, this study will analyse a disaggregated version of

the Feltham and Ohlson (1995) valuation model and associated LIMs where the basic

model is varied to directly include goodwill and identifiable intangible assets.

This chapter also presented and discussed some potential complications in estimating

the various models examined in this study and the proposed solutions to such

complications. In particular, issues arising from testing whether variables are value

relevant (cases where the null hypothesis is that the coefficient is equal to zero) and

whether the market valuation of accounting numbers is biased (cases where null

hypothesis is that the coefficient is equal to one) were discussed. To test the hypotheses

56

of interest to this study necessitated an approach that is at odds with some of the

literature in capital markets research; that is, the data is not scaled. However, the

experimental design adopted in this dissertation facilitates robust inferences concerning

the hypotheses this study address.

The data sources and selection procedures for the Australia and US data (which covers

all listed companies on the Australian and NYSE Stock Exchanges for the period 1994

to 2003) were also discussed. Special care has been taken to ensure that data from both

countries is robust so that inferences made about any similarities and differences are as

sound as possible.

57

Chapter 6: Results for Australia

6.0 Introduction

This chapter analyses Australian data to address the hypotheses presented in chapter 4.

In section 6.1, summary statistics are considered before estimating the relationship

between the accounting variables of interest to this study and market capitalisation (firm

value) in section 6.2. The findings reported in section 6.2 show that goodwill (GW) and

identifiable intangible assets (ID) are value relevant but that the market incorporates

these accounting numbers into prices with bias; that is, a dollar of GW or ID on the

books will not translate into a dollar of market capitalisation. Section 6.3, presents the

analyses for the disaggregated linear information models (LIMs). The findings reported

in section 6.3 suggest that the accounting treatment adopted by the average Australian

company with respect to GW and ID is unbiased. Clearly there is a conflict in the

results reported in section 6.2 from the markets perspective and those reported in

section 6.3 from an accounting perspective.

6.1 Preliminary Analysis

Table 6.1 reports descriptive statistics for each variable used in the analysis of the

pooled Australian sample and Table 6.2 reports the correlations between those

variables. The appendix provides details concerning the summary statistics (Table

6.1.1) and correlations (Tables 6.2.1, 6.2.2, 6.2.3 and 6.2.4) for various sub-samples of

the Australian data. The rationale for examining these various sub-samples is discussed

in section 6.2.

Table 6.1 indicates that the Australian data covers a wide spectrum of firms (the market

capitalisation ranges from a minimum of only $300,000 to a maximum of nearly $22

billion). The data are clearly skewed and indicate that there is the potential for the error

terms in the regression analysis to be heteroscedastic. Therefore, to ensure that the

inferences from the study are robust to departures from normality, heteroskedasticity-

consistent estimators are used in the analysis. The minimum value for GW and ID is

zero, indicating that at least one firm does not have any reported GW or ID (in fact there

are many firms that do not report any intangible assets).

58

Table 6.1: Descriptive statistics for the variables in the Australian pooled sample

(1994-2003)

Pooled Sample n=2,611

Variable (millions) Mean Median Std. Dev. Min Max

MVE 246 28 818 0.3 21,900

NOA-INT 117 10 556 -3,250 11,300

GW 18 0 107 0 3,490

ID 41 0 416 0 19,600

NFA -46 0 450 -9,100 2,080

AOE -3 0 105 -4,140 615

AOE t-1 -2 0 112 -4,140 712

NOA-INT t-1 132 10 730 -1,190 24,500

GW t-1 17 0 109 0 3,490

ID t-1 51 0 733 0 24,900 The definitions and construction of the variables in this table are detailed in Table 5.3.

Table 6.2 explores the correlations between all the variables used in the regression

analysis for the Australian pooled sample (as noted earlier, the correlations for each of

the four sub-samples are reported in the appendix). The independent variables have a

reasonable correlation with each other but the correlations do not appear to be of such

magnitude to cause concerns about multicollinearity in any of the analyses reported in

this chapter.

59

Table 6.2: Pearson correlation matrix for the variables in the Australian pooled sample N = 2,611 (1994-2003)

MVE NOA-INT GW ID NFA AOE AOEt-1 NOA-INTt-1 GWt-1 IDt-1

MVE 1.000 0.556 0.261 0.580 -0.137 0.106 0.239 0.385 0.198 0.389

P-Value 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

NOA-INT 0.556 1.000 0.476 0.336 -0.797 0.029 0.151 0.329 0.111 0.203

P-Value 0.000 0.000 0.000 0.000 0.000 0.136 0.000 0.000 0.000 0.000

GW 0.261 0.476 1.000 0.125 -0.593 -0.002 0.057 0.097 0.253 0.065

P-Value 0.000 0.000 0.000 0.000 0.000 0.934 0.004 0.000 0.000 0.001

ID 0.580 0.336 0.125 1.000 -0.144 -0.107 0.123 0.209 0.099 0.672

P-Value 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

NFA -0.137 -0.797 -0.593 -0.144 1.000 0.019 -0.041 -0.105 -0.042 -0.055

P-Value 0.000 0.000 0.000 0.000 0.000 0.333 0.035 0.000 0.031 0.005

AOE 0.106 0.029 -0.002 -0.107 0.019 1.000 0.061 -0.715 -0.342 -0.662

P-Value 0.000 0.136 0.934 0.000 0.333 0.000 0.002 0.000 0.000 0.000

AOEt-1 0.239 0.151 0.057 0.123 -0.041 0.061 1.000 0.080 0.023 0.073

P-Value 0.000 0.000 0.004 0.000 0.035 0.002 0.000 0.000 0.244 0.000

NOA-INTt-1 0.385 0.329 0.097 0.209 -0.105 -0.715 0.080 1.000 0.409 0.639

P-Value 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

GWt-1 0.198 0.111 0.253 0.099 -0.042 -0.342 0.023 0.409 1.000 0.129

P-Value 0.000 0.000 0.000 0.000 0.031 0.000 0.244 0.000 0.000 0.000

IDt-1 0.389 0.203 0.065 0.672 -0.055 -0.662 0.073 0.639 0.129 1.000

P-Value 0.000 0.000 0.001 0.000 0.005 0.000 0.000 0.000 0.000 0.000 The definitions and construction of the variables in this table are detailed in Table 5.3.

60

6.2 The Value Relevance and Bias in the Reporting of Goodwill and Identifiable Intangible Assets in Australia

Section 4.1 presented three hypotheses that will be tested in the analyses reported in this

section:


value relevant for the average Australian company.

H1b: Reported goodwill for the average Australian company is likely to be

conservatively (under) valued (reported with bias).

H1c: Reported identifiable intangible assets for the average Australian company

are likely to be fairly valued (reported without bias).

This section reports and discusses the coefficients for the unbalanced panel regression

results for the disaggregated Feltham and Ohlson (1995) valuation model (see sections

5.1 and 5.2 for details). As noted in chapter 5, testing the null hypothesis that a

coefficient equals zero allows the study to address the question of whether the variable

is value relevant and, therefore, allows H1a to be addressed; if a variable is not

statistically significant, the null hypothesis that the variable is not value relevant cannot

be rejected.

H1b and H1c will be examined by testing the null hypotheses that the coefficients equal

one. This test will allow a consideration of whether a dollar of value on the balance

sheet equates to a dollar of market capitalisation. If the null that a coefficient equals

one is rejected, this means that a dollar of value on the balance sheet is worth more, or

less, than a dollar of market capitalisation and, therefore, it can be argued that the

market believes that the accounting policy adopted for a particular asset is biased.

However, before the three hypotheses noted above are addressed, the question as to

whether the disaggregated model used in this study adds explanatory value to the basic

Feltham and Ohlson (1995) valuation model is considered. To do this, the values of

61

adjusted R2 and Akaike’s Information Criterion (AIC),21 based on unbalanced panel

regression analyses, are compared for three models. Note that this study does not use a

likelihood ratio test, as the models are not strictly nested.22 The three models compared

are:

Model 1: MVE = !0 + !1NOAt + !2NFAt + !3AOEt + "t

Model 2: MVE = !0 + !1(NOAt – INT)t + !2(GW+ID)t + !3NFAt + !4AOEt + "t

Model 3: MVEt = !0 + !1 (NOA – INT)t + !2(GW)t + !3(ID)t + !4NFAt + !5AOEt + "t

Model 1 is the basic Feltham and Ohlson (1995) valuation model, model 2 is the model

where NOA is disaggregated into NOA-INT and INT (that is, GW and ID), and model 3

is the primary equation (7) studied in this dissertation, as discussed in section 5.2.

The results presented in Table 6.3 indicate that the adjusted R2 is highest for model 3,

indicating that this is the most preferred model using this criterion. Similarly, the AIC

is higher for model 3 than model 1 (although model 2 appears the most preferred model

under this criterion). Note that models typically improve with the inclusion of

additional variables and, therefore, to account for such improvement in explanatory

power both the adjusted R2 and the AIC introduce a penalty when new variables are

added. However, the penalty imposed by the AIC is somewhat ‘harsher’ than that

imposed by the adjusted R2 and this explains the somewhat contradictory results

concerning which model is the most preferred. In support of model 3, it should be

noted that the additional variables in this model, compared to model 2, are all

statistically significant; it would appear, therefore, that these variables have significant

explanatory power with respect to the dependent variable (market capitalisation).

Therefore, it appears that the disaggregated model (as shown in model 3 above and

equation 7 in section 5.2) adds value to the basic Feltham and Ohlson (1995) valuation

21 The AIC is N

k

N

i 2log

2

+!!

"

#

$$

%

&'(

where ! 2

i" is the sum of squared residuals, N is the number of observations and k is

the number of independent variables in the equation.

22 That is, the additional models are not simply introducing new variables; they are also redefining them. A likelihood ratio test is invalid in this case.

62

model, although the improvement is somewhat marginal. It is on this disaggregated

model that the following analysis will now focus.

Table 6.3: Expanding the model: Regression results for the basic Feltham and Ohlson

(1995) valuation model and its extensions (1994-2003)

Model 1 Model 2 Model 3

Pooled Sample n = 2,611 Pooled Sample n = 2,611 Pooled Sample n = 2,611

Constant 101.000 Constant 98.831 Constant 85.376

t-statistic (Ho: 0) 4.961 t-statistic (Ho: 0) 9.792 t-statistic (Ho: 0) 9.158

p-value 0.000 p-value 0.000 p-value 0.000

NOA 1.056 NOA-INT 1.444 NOA-INT 1.473





NFA 0.995 GW+ID 0.731 GW 1.873



t-statistic (Ho: 1) -0.028 t-statistic (Ho: 1) -2.275 t-statistic (Ho: 1) 2.784


AOE 1.046 NFA 1.372 ID 0.662



t-statistic (Ho: 1) 0.068 t-statistic (Ho: 1) 3.424 t-statistic (Ho: 1) -3.560


AOE 0.805 NFA 1.553

t-statistic (Ho: 0) 1.493 t-statistic (Ho: 0) 13.560

p-value 0.136 p-value 0.000

t-statistic (Ho: 1) 4.826

p-value 0.000

AOE 0.748

t-statistic (Ho: 0) 1.448

p-value 0.148

Adj R Sq 0.705 Adj R Sq 0.696 Adj R Sq 0.712

F-Statistic 520.013 F-Statistic 13.648 F-Statistic 14.580


Akaike info criterion 42.668 Akaike info criterion 42.854 Akaike info criterion 42.803 The definitions and construction of the variables in this table are detailed in Table 5.3.

Table 6.4 reports the unbalanced panel regression results for model 3 (equation 7) for

the Australian pooled sample. In addition, to examine the robustness of the findings,

Table 6.4 reports the results for four sub-samples of the data. Given that the focus of

this study is on intangible assets, the first sub-sample includes only observations (1,527)

63

where GW and/or ID are reported23 and the second sub-sample includes all observations

(1,081) where GW is reported (and ID might, or might not, be reported). To further

examine the relationship between firm value and both AOE and GW, a further two sub-

samples of the data where a positive abnormal earnings amount is reported in both the

current and prior period (indicating some degree of persistence in AOE and, therefore,

the potential for unrecorded internally generated GW) are examined. The third sub-

sample (938) includes all firms with positive AOE in both the current and prior period,

while the fourth sub-sample (503) only includes those firms that also report a GW

amount. The firms in these last two sub-samples are referred to as high performers and

it is expected that these firms (which report positive AOE in, at least, two consecutive

periods) might have significant amounts of (unrecorded) internally generated GW. It

should also be noted that, not unexpectedly given that GW is only recorded in takeover

situations, the firms in each of the sub-samples are significantly larger (on average) than

the firms in the pooled sample.

The regression results for the pooled sample (presented in the first column of Table

6.4), and for each of the four sub-samples, confirm previous Australian studies (Godfrey

and Koh 2001; Shahwan 2004; Wyatt 2005; Bugeja and Gallery 2006; Ritter and Wells

2006) indicating that intangible assets are value relevant; that is, the coefficients for

GW and ID are significantly greater than zero (P < 0.05). This finding supports H1a

and indicates that both GW and ID are value relevant for the average Australian

company (and also for various sub-samples of Australian companies).

23 Godfrey and Koh (2001), Godfrey (2001), and Shahwan (2004), for example, all focus on the effect of intangibles by selecting only companies with intangibles.

64

Table 6.4: Regression results for the disaggregated valuation model for the Australian

pooled sample and sub-samples (1994-2003)

Firms with Firms with Firms with

Pooled GW and/or Firms with Positive Positive

Sample ID GW AOE AOE & GW

n = 2,611 n = 1,527 n = 1,081 n = 938 n = 503

Av. Total Assets (millions) 301 425* 499** 488** 649**

Constant 85.376 116.000 95.113 60.953 81.423

t-statistic (Ho: 0) 9.158 9.133 6.678 3.164 3.188

p-value 0.000 0.000 0.000 0.002 0.002

NOA-INT 1.473 1.413 1.459 0.968 1.006

t-statistic (Ho: 0) 15.070 14.272 16.351 8.657 8.772

p-value 0.000 0.000 0.000 0.000 0.000

t-statistic (Ho: 1) 4.838 4.172 5.141 -0.283 0.053

p-value 0.000 0.000 0.000 0.777 0.958

GW 1.873 1.592 1.675 1.077 1.056

t-statistic (Ho: 0) 5.974 4.903 4.715 3.486 3.173

p-value 0.000 0.000 0.000 0.001 0.002

t-statistic (Ho: 1) 2.784 1.823 1.901 0.250 0.169

p-value 0.005 0.069 0.058 0.803 0.866

ID 0.662 0.721 0.670 0.588 0.515

t-statistic (Ho: 0) 6.977 9.009 12.860 3.186 2.435

p-value 0.000 0.000 0.000 0.002 0.015

t-statistic (Ho: 1) -3.560 -3.484 -6.345 -2.230 -2.294

p-value 0.000 0.001 0.000 0.026 0.022

NFA 1.553 1.406 1.436 1.013 1.026

t-statistic (Ho: 0) 13.560 11.743 10.122 6.939 6.737

p-value 0.000 0.000 0.000 0.000 0.000

t-statistic (Ho: 1) 4.826 3.393 3.075 0.091 0.173

p-value 0.000 0.001 0.002 0.927 0.863

AOE 0.748 2.245 2.649 10.818 10.103

t-statistic (Ho: 0) 1.448 2.960 2.666 5.288 4.744

p-value 0.148 0.003 0.008 0.000 0.000

Adj R Sq 0.712 0.756 0.798 0.748 0.783

F-Statistic 14.580 16.720 21.257 19.207 19.479

p-value 0.000 0.000 0.000 0.000 0.000

Akaike information criterion 42.803 43.067 43.130 43.121 43.328 *, ** Significantly different to pooled sample at 0.05 and 0.01, respectively.

The definitions and construction of the variables in this table are detailed in Table 5.3.

The results in Table 6.4 also indicate that the reporting of intangible assets by the

average Australian company might not be seen (by the market) as unbiased. That is,

while the coefficients for GW and ID are significantly greater than zero (indicating that

these variables are value relevant), for the pooled sample and the majority of the sub-

samples the coefficients are also significantly different from one (indicating that these

variables are not seen by the market as representing unbiased estimates of value).

65

Consistent with expectations, the coefficient for GW is significantly greater than one (P

< 0.05) for the pooled sample, indicating that (on average) the market believes GW is

significantly understated (conservatively valued) in the financial reports of Australian

companies. This finding supports hypothesis H1b suggesting that reported GW for the

average Australian company is likely to be conservatively (under) valued. This finding

might reflect the fact that internally generated GW cannot be recognised and purchased

GW has to be systematically amortised over a maximum period of only twenty years.

Before examining the sub-samples, it should be noted that all p-values reported in Table

6.4 (and all subsequent tables) are for a two-tailed test. Strictly speaking, hypothesis

H1b indicates that a one-tailed test should be used as the hypothesis suggests the

direction that any bias might take. However, as noted in section 5.3, the use of two-

tailed tests facilitates a consideration of whether the market’s incorporation of

accounting data into market prices is biased, but biased in a direction different to that

hypothesised. Therefore, two-tail tests will be reported in the tables and, where

required, the implications of any one-tailed test will be developed in the text. With this

in mind, the results for the four sub-samples can now be examined.

The results in Table 6.4 with respect to GW suggest that for the first two sub-samples

the results (using a one-tailed test) are consistent with those for the pooled sample.

However, the results for the last two sub-samples are inconsistent with those of the

pooled sample (and the first two sub-samples). However, it should be noted that for the

last two sub-samples the coefficient for AOE is large in comparison to the other groups.

Examination of the two sub-sets of high performing firms reveals that the AOE variable

has a coefficient that is remarkably consistent with what might be considered an average

price/earnings (PE) ratio for an Australian company. This indicates that the market

believes the abnormal operating earnings reported by these high performers is likely to

persist and, therefore, there is a reasonably persuasive case for arguing that these firms

have unrecorded internally generated GW that is being reflected through the AOE

variable (rather than GW). As noted in section 5.3, in an ideal world AOE should not

be priced because the value associated with any abnormal earnings should be captured

by NOA (including GW and ID). However, where a firm is achieving abnormal

earnings that are expected to persist and in the absence of firms being allowed to value

internally generated GW (and particularly given purchased GW must be amortised) the

market will, in effect, place a value on the internally generated GW by applying some

66

multiple to the abnormal earnings amount and adding this to the value of a firm’s NOA

and NFA (Feltham and Ohlson 1995). Therefore, it is reasonable to expect a highly

significant positive coefficient on the AOE variable for high performing companies.

This suggests that for the last two sub-samples any under valuation of GW (or non

recording of internally generated GW) is potentially being reflected in the AOE

variable. As noted in section 5.1, Feltham and Ohlson (1995) show (using accounting

data following the clean surplus relation assumption) that unrecorded GW is equal to

the present value of expected future abnormal earnings (Amir et al. 1997).

With respect to ID, the coefficients for the pooled sample and all four sub-samples are

significantly less than one (P < 0.05), indicating that (on average) the market believes

ID are significantly overstated (aggressively valued) in the financial reports of

Australian companies. This might be because Australian GAAP allows considerable

flexibility in terms of recognising, revaluing and amortising assets in this class. This

finding is inconsistent with the expectation articulated in H1c (which stated that

reported ID would be fairly valued for the average Australian company). This result,

indicating that a dollar of ID is worth less than a dollar of GW,24 is consistent with

Godfrey and Koh (2001) and Shahwan (2004), and supports the assertion that

Australian GAAP (which has permitted considerable flexibility in the treatment of ID)

has allowed managers to potentially over (aggressively) value ID. However, the result

is not consistent with the findings of Wyatt (2005) who reported that ID are more highly

valued by investors than GW.

Interestingly, the coefficients for the two tangible asset classes (NOA-INT and NFA)

are also significantly different from one for the pooled sample (and the first two sub-

samples), indicating that the market believes these classes of assets are also under

(conservatively) valued by the average Australian company. There are two potential

reasons for the result with respect to NOA-INT. First, some assets (such as land) might

appreciate in value but still be recorded at historical cost or, if they have been revalued,

the revalued amount might not reflect current market values. Second, some assets

might have been depreciated too heavily, thereby causing their book value to be below

24 To find the average impact of a reported accounting item on market capitalisation, you simply multiply the reported value of the item in the accounts by the regression coefficient.

67

their market value. The result for NFA is a little more puzzling as such assets (given

their nature) should be recorded at fair value. It is possible that the result for NFA is a

reflection of perceived future growth potential or lower perceived liquidity risk for

firms with significant net financial assets.

In summary, the results reported in Table 6.4 indicate that, for the majority of

Australian companies: the information presented for both GW and ID is value relevant

(supporting H1a); the market attaches significantly more than a dollar in value to each

dollar of reported GW (supporting H1b) and significantly less than a dollar in value to

each dollar of reported ID (supporting H1c). It should also be noted that (consistent

with prior studies using Feltham and Ohlson (1995) in the US) the regressions for the

pooled sample and each of the sub-samples have high explanatory power (adjusted R2

ranging from 0.712 to 0.798) indicating that the adaptation of the Feltham and Ohlson

(1995) framework used in this study is highly relevant for capturing Australian share

market values.

To test the robustness of the findings reported in Table 6.4 with respect to the value

relevance and level of bias associated with the reporting of GW and other ID two

further tests were undertaken. First, as reported in Table 6.5, potential industry effects

were controlled by introducing industry dummy variables into the analysis. The results

reported in Table 6.5 confirm the inferences made on the basis of Table 6.4 for the

pooled sample and for each of the four sub-samples.25

25 Note that industry data was not available for all companies in the sample and, therefore, the number of observations reported in Table 6.5 differ from those reported in Table 6.4.

68

Table 6.5: Is the analysis robust? Estimating the disaggregated valuation model with

industry dummies for the Australian pooled sample and sub-samples (1994-

2003)




n = 2,555 n = 1,493 n = 1,052 n = 917 n = 491

Constant 100.000 120.000 173.000 194.000 13.673

t-statistic (Ho: 0) 4.128 4.017 3.471 2.825 0.194

p-value 0.000 0.000 0.001 0.005 0.846

NOA-INT 1.494 1.437 1.469 1.028 1.151

t-statistic (Ho: 0) 14.896 14.394 15.643 9.394 10.150

p-value 0.000 0.000 0.000 0.000 0.000

t-statistic (Ho: 1) 4.926 4.377 4.996 0.260 1.335

p-value 0.000 0.000 0.000 0.795 0.182

GW 1.978 1.720 1.699 1.134 1.377

t-statistic (Ho: 0) 7.406 5.742 4.383 4.043 3.250

p-value 0.000 0.000 0.000 0.000 0.001

t-statistic (Ho: 1) 3.663 2.403 1.803 0.478 0.890

p-value 0.000 0.016 0.072 0.633 0.374

ID 0.658 0.718 0.680 0.631 0.636

t-statistic (Ho: 0) 7.452 8.801 13.095 3.866 3.769

p-value 0.000 0.000 0.000 0.000 0.000

t-statistic (Ho: 1) -3.880 -3.454 -6.162 -2.260 -2.161

p-value 0.000 0.001 0.000 0.024 0.031

NFA 1.578 1.458 1.469 1.093 1.230

t-statistic (Ho: 0) 14.936 13.090 10.504 8.717 7.409

p-value 0.000 0.000 0.000 0.000 0.000

t-statistic (Ho: 1) 5.469 4.114 3.355 0.739 1.385

p-value 0.000 0.000 0.001 0.460 0.167

AOE 0.715 2.098 2.866 10.339 7.782

t-statistic (Ho: 0) 1.412 2.598 2.940 5.902 5.034

p-value 0.158 0.010 0.003 0.000 0.000

69

Table 6.5 (cont.)




n = 2,555 n = 1,493 n = 1,052 n = 917 n = 491

Consumer Discretionary 28.972 51.472 -55.825 -79.798 159.000

t-statistic (Ho: 0) 0.910 1.337 -0.961 -1.034 1.776

p-value 0.363 0.181 0.337 0.301 0.076

Consumer Staples -75.209 -63.114 -83.305 -231.000 -32.108

t-statistic (Ho: 0) -1.867 -1.184 -0.905 -2.482 -0.282

p-value 0.062 0.237 0.366 0.013 0.778

Energy -69.515 -140.000 -240.000 -319.000 -539.000

t-statistic (Ho: 0) -1.664 -1.261 -1.621 -2.187 -1.387

p-value 0.096 0.208 0.106 0.029 0.166

Industrials -55.706 -61.872 -139.000 -183.000 65.897

t-statistic (Ho: 0) -1.740 -1.644 -2.191 -2.433 0.684

p-value 0.082 0.101 0.029 0.015 0.495

Information Techology -38.415 -29.193 -102.000 -121.000 61.970

t-statistic (Ho: 0) -1.183 -0.706 -1.707 -1.339 0.667

p-value 0.237 0.480 0.088 0.181 0.505

Telecommunications 62.898 123.000 299.000 416.000 5400.000

t-statistic (Ho: 0) 0.429 0.572 0.852 0.535 5.799

p-value 0.668 0.568 0.394 0.593 0.000

Utilities 201.000 195.000 107.000 -170.000 323.000

t-statistic (Ho: 0) 2.091 1.521 0.603 -1.137 1.484

p-value 0.037 0.129 0.546 0.256 0.139

Adj R Sq 0.714 0.765 0.805 0.758 0.820

F-Statistic 14.277 405.531 21.031 18.921 23.156

p-value 0.000 0.000 0.000 0.000 0.000

Akaike information criterion 42.813 42.879 43.121 43.111 43.200 The definitions and construction of the variables in this table are detailed in Table 5.3.

Second, following Barth and Clinch (2005), the analysis in Table 6.4 was re-run scaling

the variables by number of shares (NOS) on issue at the end of June (the end of the

Australian financial year).26 The results reported in Table 6.6 again suggest that GW is

value relevant for the average Australian company (except for the last sub-sample).

Similarly, the results in Table 6.6 for ID appear reasonably consistent with those

reported in Table 6.4, except that the p-values for the pooled sample and the first sub-

sample are marginally higher than the 0.05 cut-off. However, the p-values for these two

groups are not of such magnitude to warrant any major concern about differences

between the inferences that can be drawn from Table 6.6 and Table 6.4.

26 Note that data on the number of shares on issue was not available for all companies in the sample and, therefore, the number of observations reported in Table 6.6 also differ from those reported in Table 6.4.

70

Table 6.6: Is the analysis robust? Estimating the disaggregated valuation model where

the variables are scaled by the number of shares on issue for the Australian

pooled sample and sub-samples (1994-2003)




n = 2,104 n = 1,276 n = 908 n = 804 n = 432

Constant 1.148 1.065 0.877 0.697 -0.015

t-statistic (Ho: 0) 12.222 6.345 3.448 3.169 -0.048

p-value 0.000 0.000 0.001 0.002 0.962

NOA-INT/NOS 0.897 1.197 1.070 0.584 1.125

t-statistic (Ho: 0) 9.118 6.218 4.450 4.146 6.950

p-value 0.000 0.000 0.000 0.000 0.000

GW/NOS 0.922 1.397 1.180 0.658 1.541

t-statistic (Ho: 0) 7.343 4.354 4.533 4.431 1.466

p-value 0.000 0.000 0.000 0.000 0.144

ID/NOS 0.118 0.256 1.119 0.655 0.813

t-statistic (Ho: 0) 1.695 1.821 11.257 3.748 5.796

p-value 0.090 0.069 0.000 0.000 0.000

NFA/NOS 0.931 1.294 1.129 0.617 1.134

t-statistic (Ho: 0) 9.655 6.002 4.855 4.467 7.237

p-value 0.000 0.000 0.000 0.000 0.000

AOE/NOS 0.199 0.230 0.288 10.347 10.128

t-statistic (Ho: 0) 3.141 2.628 2.360 5.604 5.346

p-value 0.002 0.009 0.019 0.000 0.000

Adj R Sq 0.252 0.310 0.376 0.458 0.602

F-Statistic 2.525 2.964 3.641 5.465 8.005

p-value 0.000 0.000 0.000 0.000 0.000

Akaike info criterion 5.156 5.062 4.999 5.428 4.976 The definitions and construction of the variables in this table are detailed in Table 5.3.

Note that Table 6.6 does not include any test of bias (that is, are the coefficients

significantly different from 1) because, as noted in section 5.3, such a test would be

inappropriate with scaled variables.

6.3 Australian Linear Information Models: Are Australian Accounting Practices Biased?

As discussed in chapter 5, the Feltham and Ohlson (1995) framework incorporates

linear information models (LIMs) that capture three accounting-specific parameters that

reflect, at least in part, the actual level of bias in a firm’s accounting treatment. These

parameters include:

71

1. the persistence in abnormal operating earnings; 2. the growth in both net operating assets and abnormal operating earnings; and 3. the conservatism in the accounting treatment of net operating assets.

As explained in section 5.1, persistence and growth are affected by both the economics

of the firm and the accounting procedures adopted; however, the conservatism

parameter is only affected by the accounting procedures firms adopt (Stromann 2002).

In this section, the LIMs for the disaggregated equations (equations 10 to 13 in section

5.2) will be estimated to further examine any actual bias in the accounting treatment

(under Australian GAAP) for GW and ID. Accordingly, Table 6.7 presents the results

for estimating the LIMs for the Australian pooled sample and the same four sub-

samples examined in Table 6.4. It should be noted that while Table 6.4 reflects the

perceived level of bias in company annual reports from the market’s (or an external)

perspective, Table 6.7 reflects the actual level of bias from an internal (accounting)

perspective.

While Table 6.7 presents the results for all the LIMs, the focus of this study is on the

conservatism parameters (!12, !13, and !14), particularly the conservatism parameters

relating to the accounting treatment adopted for GW and ID (!13 and !14, respectively)

and, therefore, these will be the focus of the following discussion.

As can be seen from Table 6.7, the conservatism parameters for GW and ID are

unbiased for the pooled sample and the third and fourth sub-samples (the two high

performing sub-groups). However, for the first two sub-samples the conservatism

parameters indicate aggressive accounting treatment. This finding suggests there might

be many Australian firms with GW and/or ID on the books that are not consistently

earning the appropriate level of abnormal operating earnings (AOE) to support the

values attaching to their GW and ID assets. This finding suggests that regulators might

need to pay more attention to the procedures adopted by companies (particularly those

not achieving abnormal operating earnings) when testing intangible assets for

impairment.

72

Table 6.7: Estimating the disaggregated LIMs for the Australian pooled sample and

sub-samples (1994-2003)

Positive Positive Positive Positive

Pooled GW and/or GW AOE AOE & GW

Sample ID Firms Firms Firms Firms

n = 2,611 n = 1,527 n = 1,081 n = 938 n = 503

Av. Total Assets (millions) 301 425* 499** 488** 649**

Lim 1 (Equation 6)

11 (persistence) 0.12 0.28 0.35 0.76 0.73

t-statistic 1.62 3.15 3.04 6.45 4.90

p-value 0.10 0.00 0.00 0.00 0.00

12 (conservatism: NOA-INT) -0.06 -0.01 -0.01 0.00 0.01

t-statistic -2.56 -0.88 -0.53 0.54 0.70

p-value 0.01 0.38 0.59 0.59 0.48

13 (conservatism: GW) -0.10 -0.22 -0.21 0.00 0.01

t-statistic -1.57 -3.97 -4.40 -0.05 0.15

p-value 0.12 0.00 0.00 0.96 0.88

14 (conservatism: ID) -0.05 -0.03 -0.03 -0.01 -0.03

t-statistic -1.66 -5.07 -5.84 -0.52 -0.82

p-value 0.10 0.00 0.00 0.61 0.41

Lim 2 (Equation 7)

22 (growth: NOA-INT) 0.27 0.63 0.68 1.03 1.10

t-statistic 2.00 4.56 4.53 12.96 20.68

p-value 0.05 0.00 0.00 0.00 0.00

Lim 3 (Equation 8)

23 (growth: GW) 0.27 0.33 0.35 0.97 1.26

t-statistic 2.10 1.91 1.85 3.32 4.43

p-value 0.04 0.06 0.06 0.00 0.00

Lim 4 (Equation 9)

24 (growth: ID) 0.38 0.79 0.78 1.03 1.05

t-statistic 1.45 62.60 62.86 12.34 9.25

p-value 0.15 0.00 0.00 0.00 0.00 *, ** Significantly different to pooled sample at 0.05 and 0.01, respectively.


The results in Table 6.7 with respect to GW and ID appear to conflict with those

reported in Table 6.4. The results in Table 6.4 indicate that the market believes that

Australian firms, on average, account for GW conservatively (supporting H1b) and

account for ID aggressively (supporting H1b). However, the results for the LIMs

presented in Table 6.7 suggest that the accounting treatment adopted by the average

Australian firm for both GW and ID is unbiased (causing both H1b and H1c to be

73

rejected). To place these somewhat conflicting results in context it is again worth

noting that Table 6.7 captures the time series (accounting) properties of the information

contained in company annual reports, while the results in Table 6.4 reflect the value

placed by the market on the information contained in those reports. Reconciling these

differences is something that might be of interest to regulators and is, therefore, an area

that future research could usefully explore.

To test the robustness of the results presented in Table 6.7 the analysis was re-run

including industry control groups (as was done for the results reported in Table 6.4).

Again the results reported in Table 6.8 (after including the industry controls) confirm

the inferences made about the conservatism parameters reported in Table 6.7 with

respect to the accounting treatment adopted for both GW and ID (!13 and !14,

respectively). The analysis in Table 6.8 further supports the contention made in this

dissertation that industry controls are redundant if the model is adequately specified.

74

Table 6.8: Are the estimates for the disaggregated LIMs robust? Estimating the

disaggregated LIMs with industry dummies for the Australian pooled

sample and sub-samples (1994-2003)




n = 2,555 n = 1,493 n = 1,052 n = 917 n = 491

Lim 1 (Equation 6)

11 (persistence) 0.116 0.270 0.333 0.705 0.588

t-statistic 1.652 3.146 3.036 6.123 4.182

p-value 0.099 0.002 0.003 0.000 0.000


t-statistic -2.701 -1.149 -0.828 0.557 0.878

p-value 0.007 0.251 0.408 0.578 0.381

13 (conservatism: GW) -0.109 -0.227 -0.213 -0.016 -0.025

t-statistic -1.694 -3.875 -4.390 -0.361 -0.438

p-value 0.090 0.000 0.000 0.718 0.662

14 (conservatism: ID) -0.053 -0.026 -0.028 -0.015 -0.025

t-statistic -1.646 -4.682 -5.368 -0.571 -0.853

p-value 0.100 0.000 0.000 0.568 0.394

Consumer Discretionary 10.864 8.786 13.368 5.234 8.454

t-statistic (Ho: 0) 3.850 3.449 6.583 2.573 3.026

p-value 0.000 0.001 0.000 0.010 0.003

Consumer Staples 19.380 10.081 10.276 5.136 12.622

t-statistic (Ho: 0) 2.411 1.370 1.050 1.445 1.903

p-value 0.016 0.171 0.294 0.149 0.058

Energy 8.773 70.491 74.732 25.350 90.690

t-statistic (Ho: 0) 1.743 2.929 2.914 2.656 2.400

p-value 0.081 0.004 0.004 0.008 0.017

Industrials 12.386 8.532 10.620 3.956 6.353

t-statistic (Ho: 0) 4.968 4.107 4.439 2.182 3.111

p-value 0.000 0.000 0.000 0.029 0.002

Information Techology 1.884 9.061 10.222 6.057 7.464

t-statistic (Ho: 0) 0.923 2.367 2.436 1.997 1.665

p-value 0.356 0.018 0.015 0.046 0.097


t-statistic (Ho: 0) 0.585 0.765 0.887 1.430 4.136

p-value 0.559 0.444 0.375 0.153 0.000

Utilities 46.966 42.819 54.734 26.119 50.200

t-statistic (Ho: 0) 3.402 2.921 2.475 2.020 1.666

p-value 0.001 0.004 0.014 0.044 0.096

75

Table 6.8 (cont.)




n = 2,555 n = 1,493 n = 1,052 n = 917 n = 491

Lim 2 (Equation 7)

22 (growth: NOA-INT) 0.247 0.591 0.626 0.986 1.056

t-statistic 1.876 3.982 3.889 0.097 0.077

p-value 0.061 0.000 0.000 0.000 0.000


t-statistic (Ho: 0) 3.654 1.949 1.573 20.081 26.713

p-value 0.000 0.052 0.116 0.594 0.875


t-statistic (Ho: 0) 3.372 2.233 1.988 101.000 187.000

p-value 0.001 0.026 0.047 0.100 0.226

Energy 40.111 240.000 266.000 82.748 212.000

t-statistic (Ho: 0) 1.641 2.688 2.652 45.575 157.000

p-value 0.101 0.007 0.008 0.070 0.178

Industrials 109.000 85.470 110.000 98.896 118.000

t-statistic (Ho: 0) 4.142 2.819 2.735 40.434 63.163

p-value 0.000 0.005 0.006 0.015 0.062


t-statistic (Ho: 0) 1.570 0.382 0.547 67.829 121.000

p-value 0.117 0.703 0.585 0.234 0.184

Telecommunications 39.945 36.095 61.190 20.904 -80.117

t-statistic (Ho: 0) 1.205 0.955 0.994 78.454 224.000

p-value 0.229 0.340 0.320 0.790 0.721

Utilities 297.000 250.000 371.000 117.000 -25.912

t-statistic (Ho: 0) 2.668 1.604 2.552 131.000 103.000

p-value 0.008 0.109 0.011 0.369 0.802

76

Table 6.8 (cont.)




n = 2,555 n = 1,493 n = 1,052 n = 917 n = 491

Lim 3 (Equation 8)

23 (growth: GW) 0.251 0.303 0.308 0.877 1.098

t-statistic 1.987 1.780 1.692 2.892 3.154

p-value 0.047 0.075 0.091 0.004 0.002


t-statistic (Ho: 0) 4.413 3.504 3.565 1.239 0.490

p-value 0.000 0.001 0.000 0.216 0.625


t-statistic (Ho: 0) 2.984 2.460 2.408 1.948 1.511

p-value 0.003 0.014 0.016 0.052 0.131

Energy 6.172 49.153 56.215 11.892 31.373

t-statistic (Ho: 0) 2.493 2.821 2.826 1.369 0.875

p-value 0.013 0.005 0.005 0.171 0.382

Industrials 8.701 12.736 16.437 12.927 13.930

t-statistic (Ho: 0) 3.228 3.092 2.974 2.339 1.496

p-value 0.001 0.002 0.003 0.020 0.135


t-statistic (Ho: 0) 1.509 1.268 1.190 1.148 0.950

p-value 0.131 0.205 0.234 0.251 0.342


t-statistic (Ho: 0) 2.160 1.518 1.090 0.806 0.349

p-value 0.031 0.129 0.276 0.421 0.727

Utilities 14.493 20.514 36.192 20.256 43.359

t-statistic (Ho: 0) 1.855 1.779 1.816 1.341 1.223

p-value 0.064 0.076 0.070 0.180 0.222

77

Table 6.8 (cont.)




n = 2,555 n = 1,493 n = 1,052 n = 917 n = 491

Lim 4 (Equation 9)

24 (growth: ID) 0.380 0.786 0.783 1.000 1.020

t-statistic 0.265 0.012 0.013 0.090 0.122

p-value 0.151 0.000 0.000 0.000 0.000


t-statistic (Ho: 0) 20.768 0.000 7.995 8.443 11.951

p-value 0.009 0.000 0.002 0.009 0.095


t-statistic (Ho: 0) 32.944 8.124 10.864 14.112 23.802

p-value 0.053 0.229 0.229 0.175 0.503

Energy -39.932 35.219 40.618 23.726 57.845

t-statistic (Ho: 0) 33.231 26.339 30.165 18.577 68.657

p-value 0.230 0.181 0.178 0.202 0.400

Industrials 5.446 3.715 2.963 4.975 3.643

t-statistic (Ho: 0) 3.214 1.855 1.481 2.238 2.041

p-value 0.090 0.045 0.046 0.027 0.075


t-statistic (Ho: 0) 5.948 4.487 5.790 8.440 13.857

p-value 0.078 0.080 0.226 0.469 0.261

Telecommunications 5.950 14.117 -0.244 0.672 0.000

t-statistic (Ho: 0) 7.423 12.344 0.340 0.553 0.000

p-value 0.423 0.253 0.474 0.225 1.000

Utilities 53.661 40.619 51.009 52.720 70.745

t-statistic (Ho: 0) 26.411 24.144 27.226 30.133 53.493

p-value 0.042 0.093 0.061 0.081 0.187 The definitions and construction of the variables in this table are detailed in Table 5.3.

The results reported in Table 6.7 were also re-run scaling the variables by number of

shares on issue at the end of the Australian financial year (June 30). The results for this

second robustness test are presented in Table 6.9. Unfortunately, the results presented

in Table 6.9 with respect to the conservatism parameters for both GW and ID (!13 and

!14, respectively) appear to conflict with those in Table 6.7. The results in Table 6.7

indicate that the average Australian firm reports both GW and ID without bias.

However, the results in Table 6.9 (after scaling by number of shares) indicate that the

average Australian firm aggressively accounts for both GW and ID. The conflicting

findings in Table 6.4 might simply be a result of spurious correlation induced by scaling

(Kim, 1999). Scaling by the number of shares outstanding reduces the dispersion of the

dependent variables; in other words, the distribution of the dependent variables becomes

78

narrower. Such shrinkage results in lower standard deviations associated with the

estimated coefficients and, resulting from these lower standard deviations, estimates of

the t-statistics that are far greater than those reported in the preceding tables in this

chapter. Lower p-values than those reported earlier should therefore not be surprising.


disaggregated LIMs where the variables are scaled by the number of shares

on issue for the Australian pooled sample and sub-samples (1994-2003)




n = 2,104 n = 1,276 n = 908 n = 804 n = 432

Lim 1 (Equation 6)

11 (persistence) 0.004 0.007 0.013 0.041 0.026

t-statistic 1.352 2.389 1.457 1.739 2.229

p-value 0.177 0.017 0.145 0.083 0.026

12 (conservatism: NOA-INT/NOS) -0.077 -0.076 -0.074 0.049 0.047

t-statistic -117.856 -40.934 -18.279 6.078 5.276

p-value 0.000 0.000 0.000 0.000 0.000

13 (conservatism: GW/NOS) -0.083 -0.084 -0.087 0.103 0.184

t-statistic -47.561 -23.924 -12.760 1.807 3.092

p-value 0.000 0.000 0.000 0.071 0.002

14 (conservatism: ID/NOS) -0.077 -0.069 -0.071 0.045 0.047

t-statistic -21.362 -14.081 -5.468 4.864 4.523

p-value 0.000 0.000 0.000 0.000 0.000

Lim 2 (Equation 7)

22 (growth: NOA-INT/NOS) 0.036 0.045 0.048 1.366 1.330

t-statistic 4.509 2.544 2.049 4.815 4.465

p-value 0.000 0.011 0.041 0.000 0.000

Lim 3 (Equation 8)

23 (growth: GW/NOS) 0.013 0.013 0.014 1.300 1.127

t-statistic 1.384 1.319 1.308 1.890 21.739

p-value 0.167 0.188 0.191 0.059 0.000

Lim 4 (Equation 9)

24 (growth: ID/NOS) 0.027 0.067 0.120 0.934 1.229

t-statistic 1.603 1.394 1.171 5.235 9.330


In summary, the findings reported in Table 6.7 (and confirmed in Table 6.8 but not

Table 6.9) suggest that the average Australian company reports intangible assets (both

GW and ID) without bias. Comparing the results in Table 6.7 with those in Table 6.4

79

suggests the market might not always be valuing the various classes of net operating

assets appropriately, for either the pooled sample or the various sub-samples. For

example, in the pooled sample and all four sub-samples the market appears to be of the

view that ID are systematically over (aggressively) valued (see Table 6.4). However,

this view is not supported by the results in Table 6.7, which indicate that it is only in the

first two sub-samples (that is, not for the pooled sample nor for the two high performing

groups) where the tendency for ID to be aggressively valued exists. This finding

suggests that high performing companies (where, on average, ID is reported without

bias) need to do more to assure the market (which appears to believe that ID is

aggressively valued) about the appropriateness of the values attaching to their ID.

6.4 Conclusion

This chapter used the disaggregated market valuation model presented in section 5.2 of

chapter 5 to examine if, and how, the Australian securities market values intangibles

and whether that valuation is fair (unbiased). The analysis found that GW and ID were

value relevant, that is, the coefficients for GW and ID were significantly greater than

zero. However, for the average Australian company, the market appears to believe that

both GW and ID are reported with bias. Specifically, for each dollar of book value, the

market values GW as being worth more than a dollar and ID as being worth less than a

dollar. This perception held by the market of bias in the reporting of intangible assets

by Australian firms is not supported by the LIMs, which suggest unbiased reporting for

both GW and ID for the average Australian company. However, it should be noted that

this result appears to be driven by those companies consistently reporting positive AOE.

Therefore, it would appear that companies not consistently reporting positive AOE

might, indeed, be reporting their GW and/or ID aggressively and this is an area that

standard setters might need to examine more closely.

In the following chapter, the analysis presented in this chapter will be repeated for the

US sample. Chapter 8 will then compare the findings for the two countries.

80

Chapter 7: Results for the US

7.0 Introduction

This chapter analyses US data to address the hypotheses presented in chapter 4. In

section 7.1, summary statistics are considered before estimating the relationship

between the accounting variables of interest to this study and market capitalisation (firm

value) in section 7.2. The findings reported in section 7.2 show that goodwill (GW) and

identifiable intangible assets (ID) are value relevant but that the market incorporates

these accounting numbers into prices with bias; that is, a dollar on the books does not

translate into a dollar of market capitalisation. Section 7.3, presents the analyses for the

disaggregated linear information models (LIMs). The findings reported in section 7.3

suggest that the accounting treatment adopted by the average US company with respect

to GW and ID is unbiased. Again, as with the Australian data, there is clearly a conflict

in the results reported in section 7.2 from a market perspective and those reported in

section 7.3 from an accounting perspective.

7.1 Preliminary Analysis

Table 7.1 reports descriptive statistics for each variable used in the analysis of the

pooled US sample and Table 7.2 reports the correlations between those variables. The

appendix provides details concerning the summary statistics (Table 7.1.1) and

correlations (Tables 7.2.1, 7.2.2, 7.2.3 and 7.2.4) for various sub-samples of the US

data. The rationale for examining these various sub-samples was discussed in section

6.2 and will, therefore, not be repeated in this chapter.

Consistent with the Australian data, Table 7.1 indicates that the US data also covers a

wide spectrum of firms (the market capitalisation ranges from a minimum of only

$US60 million to a maximum of over $US36 billion). While the range in firm size

evident in this sample appears much larger than many other US studies (Chauvin and

Hirschey 1994; Hirschey and Richardson 2002; Churyk 2005) this wide range is

perhaps to be expected given that this sample is much larger than most of those previous

studies. As was the case with the Australian data the US data are clearly skewed, again

indicating the potential for any error terms in the regression analysis to not be

homoscedastic. Therefore, to ensure that the inferences from the study are robust to

departures from normality, heteroskedasticity-consistent estimators are also used in the

81

analysis of the US data.27 As with the Australian data, the minimum value for GW and

ID in the US sample is zero, again indicating that at least one firm in the sample does

not have any reported GW or ID (in fact there are many US firms that do not report any

intangible assets).

Table 7.1: Descriptive statistics for the variables in the US pooled sample (1994-2003)

Pooled Sample n = 4,584

Variable (millions) Mean Median Std. Dev. Min Max

MVE 3,434 1,446 4,904 60 36,108

NOA-INT 2,270 710 5,742 -23,036 208,000

GW 158 0 829 0 24,911

ID 425 18 1,629 0 40,493

NFA -1,378 -355 5,151 -216,000 21,058

AOE 17 24 563 -20,497 4,520

AOE t-1 9 18 468 -10,837 3,918

NOA-INT t-1 2,643 760 7,663 -4,674 262,000

GW t-1 112 0 648 0 14,431

ID t-1 380 14 1,512 0 40,493 The definitions and construction of the variables in this table are detailed in Table 5.4.

Table 7.2 explores the correlations among all the variables used in the regression

analysis for the pooled sample (1994-2003). While there are some significant

correlations between variables, as there was with the Australian data (see Table 6.2), the

relationships do not appear to be of a magnitude to cause concerns about

multicollinearity.

27 Use of heteroskedasticity-consistent estimators is also justified in order for the analysis to be consistent with the preceding chapter.

82

Table 7.2: Pearson correlation matrix for the variables in the US pooled sample N = 4,584 (1994-2003)


MVE 1.000 0.451 0.201 0.356 -0.345 0.088 0.032 0.414 0.182 0.316

P-Value 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.028 0.000 0.000

NOA-INT 0.451 1.000 0.100 0.195 -0.935 -0.291 -0.353 0.884 0.104 0.252

P-Value 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

GW 0.201 0.100 1.000 0.016 -0.151 -0.066 -0.096 0.174 0.749 0.193

P-Value 0.000 0.000 0.000 0.278 0.000 0.000 0.000 0.000 0.000 0.000

ID 0.356 0.195 0.016 1.000 -0.308 -0.301 -0.158 0.318 -0.009 0.815

P-Value 0.000 0.000 0.278 0.000 0.000 0.000 0.000 0.000 0.529 0.000

NFA -0.345 -0.935 -0.151 -0.308 1.000 0.320 0.416 -0.855 -0.135 -0.333

P-Value 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

AOE 0.088 -0.291 -0.066 -0.301 0.320 1.000 0.542 -0.491 -0.074 -0.417

P-Value 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

AOEt-1 0.032 -0.353 -0.096 -0.158 0.416 0.542 1.000 -0.555 -0.052 -0.252

P-Value 0.028 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

NOA-INTt-1 0.414 0.884 0.174 0.318 -0.855 -0.491 -0.555 1.000 0.130 0.411

P-Value 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

GWt-1 0.182 0.104 0.749 -0.009 -0.135 -0.074 -0.052 0.130 1.000 -0.025

P-Value 0.000 0.000 0.000 0.053 0.000 0.000 0.000 0.000 0.000 0.000

IDt-1 0.316 0.252 0.193 0.815 -0.333 -0.417 -0.252 0.411 -0.025 1.000


83

7.2 The Value Relevance and Bias in the Reporting of Goodwill and Identifiable Intangible Assets in the US

Section 4.2 presented three hypotheses that will be tested in the analyses reported in this

section:


value relevant for the average US company.

H2b: Reported goodwill for the average US company is likely to be conservatively

(under) valued (reported with bias).

H2c: Reported identifiable intangible assets for the average US company is likely

to be conservatively (under) valued (reported with bias).

This section reports and discusses the coefficients for the unbalanced panel regression

results for the disaggregated Feltham and Ohlson (1995) valuation model (see sections

5.1 and 5.2 for details). As noted in chapter 5, testing the null hypothesis that a

coefficient equals zero allows the study to address the question of whether the variable

is value relevant and, therefore, allows H2a to be addressed; if a variable is not

statistically significant, the null hypothesis cannot be rejected that the variable is not

value relevant.

H2b and H2c will be examined by testing the null hypotheses that the coefficients equal

one. This test will allow a consideration of whether a dollar of value on the balance

sheet equates to a dollar of market capitalisation. If the null that a coefficient equals

one is rejected, this means that a dollar of value on the balance sheet is worth more, or

less, than a dollar of market capitalisation and, therefore, it can be argued that the

market believes that the accounting policy adopted for a particular asset is biased.

Table 7.3 reports the unbalanced panel regression results for equation 7 (see section 5.2)

for the US pooled sample. In addition, for the sake of robustness and consistency,

Table 7.3 also reports the results for the same four sub-samples examined in the

analysis of the Australian data.

84

Table 7.3: Regression results for the disaggregated valuation model for the US pooled





n = 4,584 n = 3,040 n = 708 n = 2,605 n = 329

Avg Total Assets (millions) 4,917 5,230* 5,093* 4,765* 3,588*

Constant 1.146 1.362 0.454 0.849 0.104

t-statistic (Ho: 0) 13.350 11.518 3.473 7.400 0.619

p-value 0.000 0.000 0.001 0.000 0.536

NOA-INT 1.239 1.196 2.247 0.871 1.838

t-statistic (Ho: 0) 17.499 12.436 12.696 5.569 7.235

p-value 0.000 0.000 0.000 0.000 0.000

t-statistic (Ho: 1) 3.379 2.038 7.047 -0.826 3.299

p-value 0.001 0.042 0.000 0.409 0.001

GW 1.418 1.311 1.831 1.617 1.734

t-statistic (Ho: 0) 6.980 5.871 6.867 4.240 4.811

p-value 0.000 0.000 0.000 0.000 0.000

t-statistic (Ho: 1) 2.058 1.391 3.117 1.618 2.036

p-value 0.040 0.164 0.002 0.106 0.043

ID 1.523 1.445 7.448 1.886 6.998

t-statistic (Ho: 0) 9.143 8.792 3.705 8.602 2.665

p-value 0.000 0.000 0.000 0.000 0.008

t-statistic (Ho: 1) 3.140 2.709 3.208 4.040 2.284

p-value 0.002 0.007 0.001 0.000 0.023

NFA 1.048 0.991 2.264 0.701 2.186

t-statistic (Ho: 0) 11.131 8.392 8.552 2.940 5.783

p-value 0.000 0.000 0.000 0.003 0.000

t-statistic (Ho: 1) 0.509 -0.078 4.775 -1.255 3.137

p-value 0.610 0.938 0.000 0.210 0.002

AOE 2.823 2.785 4.065 7.698 14.858

t-statistic (Ho: 0) 9.983 7.827 3.436 5.710 10.418

p-value 0.000 0.000 0.001 0.000 0.000

Adj R Sq 0.541 0.508 0.640 0.628 0.734

F-Statistic 9.288 6.954 7.533 11.372 13.260

p-value 0.000 0.000 0.000 0.000 0.000

Akaike information criterion 33.001 33.267 32.938 32.878 32.695 * Significantly different to pooled sample at 0.05.


The regression results for the pooled sample (presented in the first column of Table 7.3)

and for each of the four sub-samples, confirm previous US studies (Wilkins et al. 1998;

Choi et al. 2000) indicating that intangible assets are value relevant, that is, the

coefficients for GW and ID are significantly greater than zero (P < 0.05). This finding

supports H2a and indicates that both GW and ID are value relevant for the average US

company (and also for various sub-samples of US companies).

85

The results in Table 7.3 also indicate that the reporting of intangible assets by the

average US company might not be seen (by the market) as unbiased. That is, while the

coefficients for GW and ID in the pooled sample are significantly greater than zero

(indicating that these variables are value relevant), the coefficients are also significantly

different from one (indicating that these variables are not seen by the market as

representing unbiased estimates of value).

Considering GW first, consistent with expectations the coefficient for GW in the pooled

sample is significantly greater than one (P < 0.05) indicating that (on average) the

market believes GW is significantly understated (conservatively valued) in the financial

reports of US companies. This finding supports hypothesis H2b suggesting that

reported GW for the average US company is likely to be conservatively (under) valued.

This finding might reflect the fact that, in the US, internally generated GW could not be

recognised and purchased GW had to be systematically amortised over a maximum

period of forty years for most of the period covered by this study. With respect to the

sub-samples, the results in Table 7.3 for the second and fourth sub-samples (that is, for

the two sub-samples that only include firms with reported GW) are consistent with

those for the pooled sample (and with H2b). However, although the results for the other

two sub-samples suggest that GW is reported without bias it should be noted that under

a one-tailed test the p-values come very close to the 5% cut-off.

Considering ID, the coefficients for the pooled sample and all four sub-samples are

significantly greater than one (P < 0.05), indicating that (on average) the market

believes ID are significantly understated (conservatively valued) in the financial reports

of US companies. Presumably this is the result of the significant restrictions in US

GAAP with respect to capitalising internally generated ID and revaluing purchased ID.

This finding is consistent with the expectation articulated in H2C (which stated that

reported ID would be conservatively valued by the average US company).

Consistent with the Australian results, the coefficient for NOA-INT was significantly

greater than one for the US pooled sample; indicating that the US market also believes

these assets are systematically under (conservatively) valued in the accounts of the

average US company. As noted in chapter 6, there are two potential reasons for the

86

result with respect to NOA-INT. First, some assets (such as land) might appreciate in

value but still be recorded at historical cost or, if they have been revalued, the revalued

amount might not reflect current market values. Second, some assets might have been

depreciated too heavily, thereby causing their book value to be below their market

value. However, and in contrast to the results reported by Choi et al. (2000), there is no

evidence in the US data that tangible assets are more highly valued by the market than

intangible assets.

In contrast to the Australian results, the coefficient for NFA was not significantly

different from one for the pooled US sample; indicating that the US market believes

these assets are fairly valued in the accounts of the average US company.

In summary, the results reported in Table 7.3 indicate that, for the majority of US

companies, the information presented for both GW and ID is value relevant (supporting

H2a). However, the market appears to attach significantly more than a dollar in value

to each dollar of reported GW and ID (supporting H2b and H2c). Consistent with prior

studies using Feltham and Ohlson (1995) in the US, the regressions for the pooled

sample and each of the sub-samples have high explanatory power (adjusted R2 ranging

from 0.508 to 0.734); indicating that the adaptation of the Feltham and Ohlson (1995)

framework used in this study is highly relevant for capturing US share market values.

To test the robustness of the findings reported in Table 7.3 with respect to the value

relevance and level of bias associated with the reporting of GW and ID, three further

tests were undertaken. First, this study departs from prior US studies in that, rather than

using a standard 12% discount rate (representing an estimated return on equity capital)

it uses an estimated weighted-average cost of capital (6.63% - see section 5.4 for

details). Therefore, to ensure that the findings reported in Table 7.3 are not simply the

result of using an inappropriate discount rate, Table 7.4 reports the results of using a

more customary 12% discount rate. The results reported in Table 7.4 are largely

consistent with those reported in Table 7.3 and, therefore, suggest that the choice of

discount rate is not driving the results with respect to the value relevance and level of

bias associated with the reporting of GW and ID for the average US company.

87

Table 7.4: Is the analysis robust to the choice of discount rate? Estimating the

disaggregated valuation model where the variables have been derived using

a 12% cost of capital for the US Sample (1994-2003)28




n=4,584 n=3,040 n=708 n=1,388 n=176

Constant 1.191 1.425 0.545 0.969 0.533

t-statistic (Ho: 0) 14.181 12.525 4.016 5.875 2.008

p-value 0.000 0.000 0.000 0.000 0.047

NOA-INT 1.355 1.304 2.376 1.012 3.251

t-statistic (Ho: 0) 18.093 12.938 11.515 3.914 7.328

p-value 0.000 0.000 0.000 0.000 0.000

t-statistic (Ho: 1) 4.743 3.017 6.669 0.046 5.074

p-value 0.000 0.003 0.000 0.964 0.000

GW 1.556 1.448 1.930 3.100 1.651

t-statistic (Ho: 0) 8.239 6.946 6.823 4.345 1.953

p-value 0.000 0.000 0.000 0.000 0.053

t-statistic (Ho: 1) 2.944 2.148 3.288 2.943 0.770

p-value 0.003 0.032 0.001 0.003 0.442

ID 1.597 1.509 7.618 2.741 13.931

t-statistic (Ho: 0) 9.629 9.356 4.102 6.038 2.913

p-value 0.000 0.000 0.000 0.000 0.004

t-statistic (Ho: 1) 3.599 3.154 3.563 3.835 2.704

p-value 0.000 0.002 0.000 0.000 0.008

NFA 1.048 0.981 2.256 0.279 3.066

t-statistic (Ho: 0) 10.417 7.802 8.146 0.664 4.002

p-value 0.000 0.000 0.000 0.507 0.000

t-statistic (Ho: 1) 0.478 -0.148 4.535 -1.714 2.696

p-value 0.633 0.883 0.000 0.087 0.008

AOE 2.118 2.101 2.543 8.290 10.434

t-statistic (Ho: 0) 8.018 6.673 3.057 3.249 4.346

p-value 0.000 0.000 0.002 0.001 0.000

Adj R Sq 0.525 0.491 0.621 0.760 0.760

F-Statistic 8.773 6.546 7.025 13.570 13.570

p-value 0.000 0.000 0.000 0.000 0.000


Second, as reported in Table 7.5, potential industry effects are controlled by introducing

industry dummy variables into the analysis. The results reported in Table 7.5 confirm

28 Note that the number of firms in the last two sub-samples in Tables 7.3 and 7.4

differ because the higher discount rate used in Table 7.4 results in fewer firms

consistently achieving abnormal operating earnings.

88

the inferences made on the basis of Table 7.3 for the pooled sample and for most of the

sub-samples.29

Table 7.5: Is the analysis robust? Estimating the disaggregated valuation model with

industry dummies for the US pooled sample and sub-samples (1994-2003)




n = 4,578 n = 3,037 n = 708 n = 2,605 n = 329

Constant 0.778 0.819 1.009 0.855 0.225

t-statistic (Ho: 0) 4.553 4.152 2.843 4.822 0.575

p-value 0.000 0.000 0.005 0.000 0.566

NOA-INT 1.300 1.194 2.053 0.907 1.862

t-statistic (Ho: 0) 17.425 12.221 9.658 5.785 7.409

p-value 0.000 0.000 0.000 0.000 0.000

t-statistic (Ho: 1) 4.023 1.981 4.953 -0.592 3.429

p-value 0.000 0.048 0.000 0.554 0.001

GW 1.416 1.382 1.459 1.522 1.624

t-statistic (Ho: 0) 7.741 7.509 4.664 4.029 4.414

p-value 0.000 0.000 0.000 0.000 0.000

t-statistic (Ho: 1) 2.272 2.074 1.467 1.383 1.697

p-value 0.023 0.038 0.143 0.167 0.091

ID 1.531 1.461 7.187 1.819 6.996

t-statistic (Ho: 0) 10.806 9.606 3.674 8.557 2.511

p-value 0.000 0.000 0.000 0.000 0.013

t-statistic (Ho: 1) 3.746 3.032 3.163 3.853 2.152

p-value 0.000 0.002 0.002 0.000 0.032

NFA 1.092 0.987 1.825 0.659 2.044

t-statistic (Ho: 0) 11.456 8.593 5.625 2.837 5.652

p-value 0.000 0.000 0.000 0.005 0.000

t-statistic (Ho: 1) 0.968 -0.114 2.542 -1.467 2.887

p-value 0.333 0.909 0.011 0.142 0.004

AOE 2.972 2.917 3.835 7.513 14.721

t-statistic (Ho: 0) 11.030 8.943 2.842 5.657 9.611

p-value 0.000 0.000 0.005 0.000 0.000

29 Note that industry data was not available for all companies in the sample and,

therefore, the number of observations reported in Table 7.5 differ from those

reported in Table 7.3.

89

Table 7.5 (cont.)




n = 4,578 n = 3,037 n = 708 n = 2,605 n = 329

Consumer Staples & Retailers 0.583 0.624 -0.197 0.445 0.396

t-statistic (Ho: 0) 2.689 2.430 -0.467 1.999 0.734

p-value 0.007 0.015 0.641 0.046 0.464

Energy -0.442 -0.541 -3.190 -0.913 -2.604

t-statistic (Ho: 0) -1.735 -1.222 -3.686 -3.111 -1.606

p-value 0.083 0.222 0.000 0.002 0.110

Health & Pharmaceuticals 1.283 1.414 0.496 1.068 0.720

t-statistic (Ho: 0) 4.588 4.326 0.905 2.920 1.051

p-value 0.000 0.000 0.366 0.004 0.294

Industry 0.066 0.216 -0.779 -0.495 -0.897

t-statistic (Ho: 0) 0.318 0.912 -1.779 -2.345 -1.758

p-value 0.750 0.362 0.076 0.019 0.080

Technology 0.525 0.544 -0.256 0.365 -0.481

t-statistic (Ho: 0) 2.171 2.022 -0.551 1.299 -0.811

p-value 0.030 0.043 0.582 0.194 0.418

Telecommunications 1.821 2.247 -1.458 0.702 -0.451

t-statistic (Ho: 0) 4.070 4.347 -2.589 1.186 -0.334

p-value 0.000 0.000 0.010 0.236 0.738

Transport -0.290 0.009 -1.202 -0.580 -0.641

t-statistic (Ho: 0) -1.215 0.021 -2.208 -2.111 -0.977

p-value 0.225 0.983 0.028 0.035 0.330

Utilities 0.345 0.910 0.181 -0.088 0.152

t-statistic (Ho: 0) 1.275 2.195 0.323 -0.322 0.217

p-value 0.202 0.028 0.747 0.748 0.828

Adj R Sq 0.557 0.524 0.632 0.638 0.740

F-Statistic 9.739 7.262 7.069 11.658 12.380

p-value 0.000 0.000 0.000 0.000 0.000


Finally, following Barth and Clinch (2005), the analysis in Table 7.3 was re-run scaling

the variables by number of shares (NOS) on issue at the end of December (the end of

the US fiscal year).30

The results reported in Table 7.6 (after scaling) again suggest that

both GW and ID are value relevant for US companies. It should be noted that Table 7.6

does not include any test of bias (that is, are the coefficients significantly different from

30 Note that data on the number of shares on issue was not available for all companies

in the sample and, therefore, the number of observations reported in Table 7.6 also

differs from those reported in Table 7.3.

90

1) because, as discussed in section 5.3, such a test would be inappropriate with scaled

variables.

Table 7.6: Is the analysis robust? Estimating the disaggregated valuation model where

the variables are scaled by the number of shares on issue for the US pooled





n = 4,583 n = 3,039 n = 708 n = 2,605 n = 329

Constant 21.316 19.542 17.466 21.236 21.002

t-statistic (Ho: 0) 11.014 9.453 14.367 2.220 14.123

p-value 0.000 0.000 0.000 0.000 0.000

NOA-INT/NOS 0.162 0.093 0.761 -0.104 0.456

t-statistic (Ho: 0) 0.964 0.493 7.181 -0.603 3.872

p-value 0.335 0.622 0.000 0.547 0.000

GW/NOS 0.507 0.597 0.776 0.526 0.382

t-statistic (Ho: 0) 4.466 3.866 6.166 3.128 2.623

p-value 0.000 0.000 0.000 0.002 0.009

ID/NOS 1.253 1.516 1.465 1.582 2.821

t-statistic (Ho: 0) 4.820 4.162 2.893 4.386 3.270

p-value 0.000 0.000 0.004 0.000 0.001

NFA/NOS 0.124 0.111 0.776 0.004 0.503

t-statistic (Ho: 0) 0.659 0.478 5.708 0.021 3.163

p-value 0.510 0.633 0.000 0.983 0.002

AOE/NOS 3.225 3.967 3.380 5.917 5.527

t-statistic (Ho: 0) 5.317 4.983 8.314 4.569 5.816

p-value 0.000 0.003 0.000 0.000 0.000

Adj R Sq 0.407 0.371 0.372 0.557 0.294

F-Statistic 5.833 4.391 3.180 8.731 2.901

p-value 0.000 0.000 0.000 0.000 0.000


7.3 US Linear Information Models: Are US Accounting Practices Biased?

The analysis in the previous section allows an examination of whether the market

believes the information provided by US companies with respect to intangible assets is

value relevant and reported without bias. However, as discussed in chapter 5 (and

section 6.3), the Feltham and Ohlson (1995) framework incorporates linear information

models (LIMs) that capture three accounting-specific parameters that reflect, at least in

part, the actual level of bias in a firm’s accounting treatment. These parameters are:

91

1. the persistence in abnormal operating earnings;

2. the growth in both net operating assets and abnormal operating earnings; and

3. the conservatism in the accounting treatment of net operating assets.

As explained in section 5.1, persistence and growth are affected by both the economics

of the firm and the accounting procedures adopted; however, the conservatism

parameter is only affected by the accounting procedures firms adopt (Stromann 2002).

In this section, the LIMs for the disaggregated equations (equations 10 to 13 in section

5.2) will be estimated to further examine any actual bias in the accounting treatment

(under US GAAP) for GW and ID. Accordingly, Table 7.7 presents the results for

estimating the LIMs for the US pooled sample and the same four sub-samples examined

in Table 7.3. It should be noted that while Table 7.3 reflects the perceived level of bias

in company annual reports from the market’s (or an external) perspective, Table 7.7

reflects the actual level of bias from an internal (accounting) perspective.

While Table 7.7 presents the results for all the LIMs, the focus of this study is on the

conservatism parameters (!12, !13, and !14), particularly the conservatism parameters

relating to the accounting treatment adopted for GW and ID (!13 and !14, respectively)

and, therefore, these will be the focus of the following discussion.

As can be seen from Table 7.7, the conservatism parameters for GW and ID are

unbiased for the pooled sample and for all four sub-samples. This finding suggests that

the accounting treatment adopted by the average US company over the period of this

study has been appropriate and should not cause regulators any concern.

92

Table 7.7: Estimating the disaggregated LIMs for the US pooled sample and sub-

samples (1994-2003)




n = 4,584 n = 3,040 n = 708 n = 2,605 n = 329

Av. Total Assets (millions) 4,917 5,230* 5,093* 4,765* 3,588*

Lim 1 (Equation 6)

11 (persistence) 0.506 0.433 0.562 0.758 0.763

t-statistic 6.238 4.545 4.565 0.060 0.113

p-value 0.000 0.000 0.000 0.000 0.000


t-statistic -1.774 -2.050 -1.647 0.003 0.010

p-value 0.076 0.040 0.100 0.000 0.042

13 (conservatism: GW) -0.018 -0.013 0.000 0.020 0.000

t-statistic -1.057 -0.718 -0.032 0.013 0.019

p-value 0.290 0.473 0.974 0.131 0.992

14 (conservatism: ID) -0.086 -0.084 -0.256 0.020 0.111

t-statistic -1.420 -1.366 -1.443 0.014 0.096

p-value 0.156 0.172 0.149 0.141 0.247

Lim 2 (Equation 7)

22 (growth: NOA-INT) 0.683 0.668 0.631 0.964 0.961

t-statistic 13.033 11.027 11.618 167.914 45.694

p-value 0.000 0.000 0.000 0.000 0.000

Lim 3 (Equation 8)

23 (growth: GW) 0.972 0.974 1.024 0.933 1.046

t-statistic 34.844 34.710 65.118 0.073 0.030

p-value 0.000 0.000 0.000 0.000 0.000

Lim 4 (Equation 9)

24 (growth: ID) 0.893 0.897 0.655 1.059 0.908

t-statistic 10.270 10.240 6.561 0.037 0.044

p-value 0.000 0.000 0.000 0.000 0.000 * Significantly different to pooled sample at 0.05.


The results in Table 7.7 with respect to GW and ID appear to conflict with those

reported in Table 7.3. The results in Table 7.3 indicate that the market believes that US

firms, on average, account for GW and ID conservatively (supporting H2b and H2c).

However, the results for the LIMs presented in Table 7.7 suggest that the accounting

treatment adopted by the average US company for both GW and ID is unbiased

(causing both H2b and H2c to be rejected). To place these somewhat conflicting results

93

in context it is again worth noting that Table 7.7 captures the time series (accounting)

properties of the information contained in company annual reports, while the results in

Table 7.3 reflect the value placed by the market on the information contained in those

reports. Reconciling these differences is something that might be of interest to

regulators and is, therefore, an area that future research could usefully explore.

To test the robustness of the results presented in Table 7.7 the analysis was re-run: using

a discount rate of 12%; including industry control groups; and scaling by the number of

shares on issue. The results of these robustness tests will now be discussed.

First, Table 7.8 presents the results of re-running the analysis using a 12% discount rate.

With respect to GW, the results presented in Table 7.8 are in stark contrast to those

reported in Table 7.7. While the results in Table 7.7 suggest that the accounting

treatment adopted by US companies for GW is unbiased, the results in Table 7.8 (using

a 12% discount rate) indicate that US companies account for GW aggressively for the

first two samples but, in contrast, the analysis for the last two samples (the high-

performing firms) suggests GW is treated conservatively. It seems that the choice of

discount rate might be having a significant effect on the results reported for GW.31

With respect to ID, for the pooled sample and all but one of the sub-samples, the results

reported in Table 7.8 were consistent with those reported in Table 7.7.

31 Using an inappropriate discount rate might also help explain why a number of

previous US studies have also reported negative values for the conservatism parameter in estimating Feltham and Ohlson’s (1995) LIMs (Bauman 1999; Myers

1999; Ahmed et al. 2000; Stromann 2002).

94

Table 7.8: Is the analysis robust to the choice of discount rate? Estimating the LIMs

where the variables have been derived using a 12% cost of capital for the

US Sample (1994-2003)




n = 4,584 n = 3,040 n = 708 n = 1,388 n = 176

Lim 1 (Equation 6)

11 (persistence) 0.390 0.303 0.448 0.084 0.186

t-statistic 4.682 3.074 3.656 9.704 3.557

p-value 0.000 0.002 0.000 0.000 0.001


t-statistic -6.466 -6.467 -3.566 15.048 6.294

p-value 0.000 0.000 0.000 0.000 0.000

13 (conservatism: GW) -0.046 -0.042 -0.029 0.027 0.024

t-statistic -2.495 -2.276 -1.625 3.037 3.846

p-value 0.013 0.023 0.105 0.002 0.000

14 (conservatism: ID) -0.110 -0.108 -0.285 0.025 0.178

t-statistic -1.790 -1.761 -1.470 3.433 0.698

p-value 0.074 0.078 0.142 0.001 0.486

Lim 2 (Equation 7)

22 (growth: NOA-INT) 0.683 0.668 1.077 0.044 0.118

t-statistic 13.033 11.027 24.933 24.473 10.077

p-value 0.000 0.000 0.000 0.000 0.000

Lim 3 (Equation 8)

23 (growth: GW) 0.972 0.974 0.839 0.059 0.023

t-statistic 34.844 34.710 4.411 16.697 47.339

p-value 0.000 0.000 0.000 0.000 0.000

Lim 4 (Equation 9)

24 (growth: ID) 0.893 0.897 1.091 0.025 0.060

t-statistic 10.270 10.240 27.826 44.355 15.579


Next, Table 7.9 presents the results controlling for industry. The results presented in

Table 7.9 confirm the inferences made about the conservatism parameters reported in

Table 7.7 with respect to the accounting treatment adopted for both GW and ID (!13 and

!14, respectively).

95


disaggregated LIMs with industry dummies for the US pooled sample and

sub-samples (1994-2003)




n = 4,578 n = 3,037 n = 708 n = 2,605 n = 329

Lim 1 (Equation 6)

11 (persistence) 0.451 0.370 0.514 0.730 0.659

t-statistic 5.437 3.855 4.134 10.847 4.839

p-value 0.000 0.000 0.000 0.000 0.000


t-statistic -2.737 -2.978 -2.667 3.064 2.140

p-value 0.006 0.003 0.008 0.002 0.033

13 (conservatism: GW) -0.031 -0.035 -0.007 0.016 -0.008

t-statistic -1.806 -1.952 -0.479 1.289 -0.440

p-value 0.071 0.051 0.632 0.198 0.660

14 (conservatism: ID) -0.089 -0.092 -0.275 0.015 0.040

t-statistic -1.433 -1.446 -1.579 1.128 0.357

p-value 0.152 0.148 0.115 0.259 0.722

Consumer Staples & Retail 0.099 0.120 0.079 0.031 0.032

t-statistic (Ho: 0) 4.603 4.162 3.825 3.900 1.865

p-value 0.000 0.000 0.000 0.000 0.063

Energy 0.167 0.319 0.237 0.037 -0.061

t-statistic (Ho: 0) 5.506 4.796 2.742 2.894 -0.578

p-value 0.000 0.000 0.006 0.004 0.564


t-statistic (Ho: 0) 2.565 2.336 3.353 3.929 1.640

p-value 0.010 0.020 0.001 0.000 0.102

Industry 0.076 0.105 0.051 0.021 0.056

t-statistic (Ho: 0) 2.705 2.727 2.706 2.757 3.101

p-value 0.007 0.006 0.007 0.006 0.002

Technology 0.056 0.064 0.027 0.024 0.020

t-statistic (Ho: 0) 2.856 2.744 1.216 3.061 0.952

p-value 0.004 0.006 0.224 0.002 0.342

Telecommunications 0.035 0.078 0.034 0.070 -0.029

t-statistic (Ho: 0) 0.589 1.106 0.996 2.381 -0.479

p-value 0.556 0.269 0.320 0.017 0.632

Transport 0.066 0.059 0.051 0.032 0.024

t-statistic (Ho: 0) 2.833 1.112 3.328 2.142 1.667

p-value 0.005 0.266 0.001 0.032 0.097

Utilities 0.018 0.003 0.036 0.015 0.013

t-statistic (Ho: 0) 0.707 0.081 1.928 2.336 0.646

p-value 0.479 0.936 0.054 0.020 0.519

96

Table 7.9 (cont.)




n = 4,578 n = 3,037 n = 708 n = 2,605 n = 329

Lim 3 (Equation 8)

23 (growth: GW) 0.958 0.955 0.999 0.921 1.031

t-statistic 33.641 33.062 60.924 12.385 31.264

p-value 0.000 0.000 0.000 0.000 0.000


t-statistic (Ho: 0) 1.893 1.702 2.349 2.344 2.184

p-value 0.058 0.089 0.019 0.019 0.030

Energy 0.046 0.132 0.405 0.019 -0.065

t-statistic (Ho: 0) 2.929 2.234 1.880 1.498 -0.380

p-value 0.003 0.026 0.061 0.134 0.705


t-statistic (Ho: 0) 3.510 3.453 0.506 2.277 1.520

p-value 0.001 0.001 0.613 0.023 0.130

Industry 0.058 0.074 0.065 0.028 0.059

t-statistic (Ho: 0) 3.600 3.516 2.813 3.513 1.684

p-value 0.000 0.000 0.005 0.001 0.093

Technology 0.050 0.059 0.063 0.028 0.055

t-statistic (Ho: 0) 2.961 2.953 1.706 3.313 2.656

p-value 0.003 0.003 0.089 0.001 0.008


t-statistic (Ho: 0) 3.121 2.837 1.057 1.748 0.669

p-value 0.002 0.005 0.291 0.081 0.504

Transport 0.026 0.073 0.037 0.008 0.057

t-statistic (Ho: 0) 1.820 1.694 1.879 1.947 1.454

p-value 0.069 0.090 0.061 0.052 0.147

Utilities 0.016 0.027 0.018 0.011 0.030

t-statistic (Ho: 0) 1.870 1.870 1.572 1.687 2.712

p-value 0.062 0.062 0.116 0.092 0.007

97

Table 7.9 (cont.)




n = 4,578 n = 3,037 n = 708 n = 2,605 n = 329

Lim 3 (Equation 8)

23 (growth: GW) 0.958 0.955 0.999 0.921 1.031

t-statistic 33.641 33.062 60.924 12.385 31.264

p-value 0.000 0.000 0.000 0.000 0.000


t-statistic (Ho: 0) 1.893 1.702 2.349 2.344 2.184

p-value 0.058 0.089 0.019 0.019 0.030

Energy 0.046 0.132 0.405 0.019 -0.065

t-statistic (Ho: 0) 2.929 2.234 1.880 1.498 -0.380

p-value 0.003 0.026 0.061 0.134 0.705


t-statistic (Ho: 0) 3.510 3.453 0.506 2.277 1.520

p-value 0.001 0.001 0.613 0.023 0.130

Industry 0.058 0.074 0.065 0.028 0.059

t-statistic (Ho: 0) 3.600 3.516 2.813 3.513 1.684

p-value 0.000 0.000 0.005 0.001 0.093

Technology 0.050 0.059 0.063 0.028 0.055

t-statistic (Ho: 0) 2.961 2.953 1.706 3.313 2.656

p-value 0.003 0.003 0.089 0.001 0.008


t-statistic (Ho: 0) 3.121 2.837 1.057 1.748 0.669

p-value 0.002 0.005 0.291 0.081 0.504

Transport 0.026 0.073 0.037 0.008 0.057

t-statistic (Ho: 0) 1.820 1.694 1.879 1.947 1.454

p-value 0.069 0.090 0.061 0.052 0.147

Utilities 0.016 0.027 0.018 0.011 0.030

t-statistic (Ho: 0) 1.870 1.870 1.572 1.687 2.712

p-value 0.062 0.062 0.116 0.092 0.007

98

Table 7.9 (cont.)




n = 4,578 n = 3,037 n = 708 n = 2,605 n = 329

Lim 4 (Equation 9)

24 (growth: ID) 0.871 0.870 0.607 1.034 0.866

t-statistic 9.616 9.368 6.179 26.363 16.751

p-value 0.000 0.000 0.000 0.000 0.000


t-statistic (Ho: 0) 2.932 2.765 4.765 2.276 1.052

p-value 0.003 0.006 0.000 0.023 0.293

Energy 0.060 0.180 0.023 0.033 0.033

t-statistic (Ho: 0) 2.575 2.395 1.766 1.465 1.289

p-value 0.010 0.017 0.078 0.143 0.198


t-statistic (Ho: 0) 1.622 1.348 2.839 1.551 1.982

p-value 0.105 0.178 0.005 0.121 0.048

Industry 0.093 0.105 0.034 0.060 0.018

t-statistic (Ho: 0) 2.094 1.781 3.140 3.809 1.747

p-value 0.036 0.075 0.002 0.000 0.082

Technology 0.057 0.064 0.014 0.052 0.006

t-statistic (Ho: 0) 1.960 1.811 2.186 2.735 1.953

p-value 0.050 0.070 0.029 0.006 0.052


t-statistic (Ho: 0) 2.086 2.076 0.906 1.532 0.715

p-value 0.037 0.038 0.365 0.126 0.475

Transport 0.052 0.113 -0.006 0.022 -0.020

t-statistic (Ho: 0) 1.817 1.466 -0.930 1.612 -1.061

p-value 0.069 0.143 0.353 0.107 0.289

Utilities 0.062 0.089 0.004 0.016 0.000

t-statistic (Ho: 0) 1.325 1.132 0.453 1.418 0.946


Finally, Table 7.10 presents the results scaling by number of shares outstanding. The

results presented in Table 7.10 also confirm the results reported in Table 7.7 indicating

that the accounting treatment adopted by US companies for GW and ID is unbiased.

99


disaggregated LIMs where the variables are scaled by the number of shares

on issue for the US pooled sample and sub-samples (1994-2003)




n = 4,583 n = 3,039 n = 708 n = 2,605 n = 329

Lim 1 (Equation 6)

11 (persistence) 0.630 0.649 0.526 0.545 0.465

t-statistic 13.424 10.520 8.179 6.364 4.385

p-value 0.000 0.002 0.002 0.000 0.000

12 (conservatism: NOA-INT/NOS) 0.015 0.018 -0.009 0.043 0.028

t-statistic 1.281 1.249 -1.470 4.299 4.686

p-value 0.200 0.212 0.142 0.000 0.000

13 (conservatism: GW/NOS) -0.029 -0.034 0.010 -0.012 0.004

t-statistic -1.367 -1.351 0.837 -0.727 0.245

p-value 0.172 0.177 0.403 0.467 0.807

14 (conservatism: ID/NOS) -0.009 -0.014 0.043 0.024 0.227

t-statistic -0.528 -0.651 0.790 1.783 2.792

p-value 0.598 0.515 0.430 0.075 0.006

Lim 2 (Equation 7)

22 (growth: NOA-INT/NOS) 0.758 0.720 0.643 0.927 1.044

t-statistic 29.190 20.072 9.393 14.225 29.740

p-value 0.000 0.000 0.000 0.000 0.000

Lim 3 (Equation 8)

23 (growth: GW/NOS) 0.996 0.998 1.010 1.020 1.041

t-statistic 86.638 86.396 91.069 49.986 40.914

p-value 0.000 0.000 0.000 0.000 0.000

Lim 4 (Equation 9)

24 (growth: ID/NOS) 0.931 0.933 0.922 1.077 0.755

t-statistic 25.186 25.153 20.587 39.869 4.680


In summary, the findings reported in Table 7.7 (and confirmed in Tables 7.9 and 7.10,

but not Table 7.8) suggest that the average US company reports intangible assets (both

GW and ID) without bias. Comparing the results in Table 7.7 with those in Table 7.3

suggests that the market might not always be valuing the various classes of net

operating assets appropriately. For example, in the pooled sample and all four sub-

samples, the market appears to be of the view that GW and ID are both conservatively

valued by US companies (see Table 7.3). However, this view is not supported by the

100

results presented in Table 7.7, which indicate that GW and ID are fairly valued in the

accounts of the average US company.

7.4 Conclusion

This chapter used the disaggregated market valuation model presented in section 5.2 to

examine if the US securities market values intangibles and whether that valuation is fair

(unbiased). The analysis found that GW and ID were both value relevant, that is, the

coefficients for GW and ID were significantly greater than zero. However, for the

average US company, the market appears to believe that both GW and ID are reported

with bias. Specifically, for each dollar of book value, the market values both GW and

ID as being worth more than a dollar. This perception held by the market of bias in the

reporting of intangible assets by US companies is not supported by the LIMs, which

suggest unbiased reporting for both GW and ID for the average US company.

In the following chapter, the analysis presented in this and the previous chapter (for the

US and Australian data, respectively) will be compared.

101

Chapter 8: Comparing the Results for Australia and the US

8.0 Introduction

The results in chapter 6 indicated that both goodwill (GW) and identifiable intangible

assets (ID) are value relevant for Australian investors but that the values reported in the

accounts appear to be incorporated into market prices with bias. Similarly, the results

in the previous chapter indicated that GW and ID are value relevant for US investors

but, again, these values appear to be incorporated into market prices with bias. This

chapter addresses the hypotheses presented in section 4.3. These hypotheses consider

the expected differences in the valuation of GW and ID by investors in the Australian

and US markets. The two hypotheses tested in this chapter are:

H3a: Reported goodwill for the average Australian company is likely to be more

conservatively (under) valued (reported with more bias) than the reported

goodwill for the average US company.

H3b: Reported identifiable intangible assets for the average Australian company

are likely to be less conservatively (under) valued (reported with less bias)

than reported identifiable intangible assets for the average US company.

In section 8.1, the analysis considers whether the coefficients estimated for GW in

Tables 6.3 for the Australian sample and Table 7.3 for the US sample are different. The

results indicate that, in all but the last sub-sample, there is no difference between the

coefficients in both markets. This evidence suggests that investors in the Australian and

US markets treat GW in much the same way. In Section 8.2, the question of whether

coefficients estimated for ID are different for Australia and the US is considered. It

appears that both markets incorporate ID into share prices with bias. However, while

Australian investors appear to believe that reported ID is overstated, US investors

appear to believe that ID is understated. Section 8.3 concludes the chapter.

8.1 Do Australian and US Investors Value Goodwill Differently?

This section considers whether reported GW for the average Australian company is

likely to be more conservatively (under) valued than reported GW for the average US

company (H3a). However, in order to consider if GW is valued differently in Australia

102

than it is in the US it is necessary to consider if the output of the regressions reported in

Table 6.4 and Table 7.3 are equal or different.

The coefficients reported in both tables are the most likely point estimate for each

coefficient. Such estimates, however, are made with error. It is this well-known fact

that the coefficients are reported with error that leads to the use of familiar tests such as

the t-test.32

Using the estimates of the coefficient for GW (as reported in Tables 6.4 and

7.3 and repeated in Table 8.1) to illustrate this point, the estimated coefficient for GW

for the Australian pooled sample is 1.873 while the equivalent estimate for the US is

1.418. The value of the t-statistic for both these coefficients allows rejection of the null

hypotheses that they equal zero (therefore, the variables are value relevant) and also that

they equal one (therefore, the variables are incorporated into prices with bias).33

However, to test H3a, a further issue needs to be examined; that is, are the estimates of

1.873 and 1.418 significantly different from each other? To consider whether the

estimate of 1.873 for Australia is significantly different to the estimate of 1.418 for the

US a Wald test is used to test the null hypothesis that 1.873 equals 1.418. The idea of

the Wald test is to impose a restriction on a parameter in a regression to see if the

resulting restricted regression is significantly different from the unrestricted version. If

the resulting !2-statistic is greater than the critical value, the restriction is rejected. The

unrestricted equation for the US is estimated34

and then the same equation is re-

estimated restricting the coefficient to have the same value as it had for the Australian

sample. If the resulting !2-statistic is less that the critical value, the restriction is

inconsequential and the null hypothesis that the Australian and US coefficients are the

same cannot be rejected. If the resulting !2-statistic is large, the coefficients will be

found to be significantly different and, as such, it can be inferred that Australian and US

investors respond to the accounting information they receive differently.

32 Remember that such tests examine the hypothesis that, given the estimation error,

the coefficient equals the value of the null.

33 Remember that if the null that a coefficient equals one can be rejected, this would

mean that a dollar of value on the balance sheet will be worth more (less) than a dollar of market capitalisation and, consequently, it can be argued that the market is

biased in its valuation of the accounting asset.

34 That is, the equations are estimated as they were for Chapter 7, letting the

coefficients take their most likely value.

103

For the pooled sample and the four sub-samples, Table 8.1 reports the estimated

coefficients and associated t-tests for GW for Australia (Panel A) and the US (Panel B).

These panels simply repeat the estimates reported in the two preceding chapters. Panel

C reports the !2-statistic for the Wald test of the null hypothesis that the GW coefficient

estimated for the US equals the coefficient estimated for the equivalent Australian

sample.

Table 8.1: Comparison of the estimated goodwill coefficient for Australia and the US




Panel A: Australia

GW 1.873 1.592 1.675 1.077 1.056

t-statistic (Ho: 0) 5.974 4.903 4.715 3.486 3.173

p-value 0.000 0.000 0.000 0.001 0.002

t-statistic (Ho: 1) 2.784 1.823 1.901 0.250 0.169

p-value 0.005 0.069 0.058 0.803 0.866

Panel B: US

GW 1.418 1.311 1.831 1.617 1.734

t-statistic (Ho: 0) 6.980 5.871 6.867 4.240 4.811

p-value 0.000 0.000 0.000 0.000 0.000

t-statistic (Ho: 1) 2.058 1.391 3.117 1.618 2.036

p-value 0.040 0.164 0.002 0.106 0.043

Panel C: Wald Tests

GW

Chi-square 2.103 0.748 0.192 3.052 5.299

P-value 0.147 0.387 0.661 0.081 0.022 The definitions and construction of the variables in this table are detailed in Table 5.3

(for the Australian data) and Table 5.4 (for the U.S. data).

Inspection of Table 8.1 reveals that the coefficient of 1.418 estimated for the US is not

significantly different from the coefficient of 1.873 estimated for Australia (the !2-

statistic for the test of the equality of the coefficients is 2.103 and its associated p-value

is 0.147). It would appear that, for the pooled sample, the market’s valuation of GW is

the same for both Australia and the US. Such a finding contradicts the expectations

articulated in this dissertation but it may be consistent with both countries having a

104

similar restricted treatment for GW; that is, neither country recognises any internally

generated GW even though the requirement to systematically amortise purchased GW

within the two countries is not the same. This finding holds for all but the last of the

sub-samples examined (the second group of high performing firms) where the !2-

statistic (5.299) is significantly different from zero (its p-value is 0.022).35

8.2 Do Australian and US Investors Value Identifiable Intangible Assets Differently?

This section considers whether ID for the average Australian company is less

conservatively (under) valued than is the case for the average US company (H3b).

For Australia, in all cases the coefficients estimated for ID in Table 6.3 were found to

be significantly less than one. These estimates are repeated in Table 8.2 for

convenience. For the US, the coefficients estimated for ID in Table 7.3 (also repeated

in Table 8.2) were all found to be significantly greater than one. This result indicates

that H3b is supported: that is, ID for the average Australian company is significantly

less conservatively valued than is the case for the average US company. These findings

are confirmed by the Wald test results reported in Table 8.2 where all the !2-statistics

reject the null that the coefficients for ID are equal for the Australian and US sample.

35 It should also be noted that the Wald test might appear misleading for the second

sub-sample where the value of the !2-statistic (0.192) is statistically insignificant. In

this case, the null hypothesis that the Australian coefficient for GW (1.675) equals one cannot be rejected (the p-value is 0.058) using the conventional 5% cut-off

level. For the US, the null hypothesis that the GW coefficient (1.831) equals one can be rejected; suggesting (contrary to the Wald test result) that the US GW

coefficient is greater than the Australian GW coefficient. However, it should be noted that the p-value for testing whether the Australian GW coefficient is

significantly different from one is a marginal 0.058 (using a two-tailed test) and, if the conventional 5% cut-off level was relaxed (or a one-tailed test was used), both

the Australian and US coefficients would be considered significantly greater than one, supporting the Wald test result suggesting no difference in the GW coefficient

for the two countries.

105

Table 8.2: Comparison of the estimated identifiable intangible assets coefficient for

Australia and the US




Panel A: Australia

ID 0.662 0.721 0.670 0.588 0.515

t-statistic (Ho: 0) 6.977 9.009 12.860 3.186 2.435

p-value 0.000 0.000 0.000 0.002 0.015

t-statistic (Ho: 1) -3.560 -3.484 -6.345 -2.230 -2.294

p-value 0.000 0.001 0.000 0.026 0.022

Panel B: US

ID 1.523 1.445 7.448 1.886 6.998

t-statistic (Ho: 0) 9.143 8.792 3.705 8.602 2.665

p-value 0.000 0.000 0.000 0.000 0.008

t-statistic (Ho: 1) 3.140 2.709 3.208 4.040 2.284

p-value 0.002 0.007 0.001 0.000 0.023

Panel C: Wald Tests

ID

Chi-square 82.273 81.798 16946.320 49.406 1469.010

P-value 0.000 0.000 0.000 0.000 0.000 The definitions and construction of the variables in this table are detailed in Table 5.3

(for the Australian data) and Table 5.4 (for the U.S. data).

8.3 Conclusion

Over the period of this study (1994-2003), as highlighted in chapter 2, Australian firms

had to record purchased GW at the amount paid and were then required to

systematically amortise GW over a period not exceeding 20 years. By comparison, US

firms were required to amortise GW over a period not exceeding 40 years prior to 2001,

with no maximum period stipulated after 2001 (only an annual impairment test). It was

expected, therefore, that the more flexible treatment of GW under US GAAP compared

to Australian GAAP (particularly after 2001) would have permitted US managers to

provide better signals with respect to their expectations of future cash flows from this

asset class (Wilkins et al. 1998). Accordingly, it was hypothesized (in H3a) that

reported GW for the average Australian company would be more conservatively (under)

valued than the reported GW for the average US company. Contrary to this

106

expectation, the results reported in section 8.1 suggest (for the pooled sample, and all

but one of the sub-samples) that the market attaches a similar premium to GW in both

Australia and the US.

With respect to ID over the period of this study (1994-2003), Australian GAAP appeared

to be far less restrictive than US GAAP (as highlighted in chapter 2). For example, while

US GAAP limited the recognition of internally generated ID to certain software

development costs there was no such restriction under Australian GAAP. Further,

Australian GAAP permitted the revaluation of ID; something not permitted under US

GAAP. Finally, up until 2001, US GAAP required ID to be amortised over a maximum

forty-year period, while there was no such limit under Australian GAAP. Accordingly, it

was hypothesized (in H3b) that reported ID for the average US company would be more

conservatively (under) valued than would be the case for the average Australian

company. The results reported in section 8.2 confirm this expectation for the pooled

sample and all sub-samples. These findings are in keeping with the expectation that the

more flexible treatment of ID permitted under Australian GAAP (compared to US

GAAP) has permitted Australian managers to provide better signals (than US managers)

with respect to their expectations of future cash flows from this important class of

assets.

107

Chapter 9: Conclusion

9.0 Introduction

As noted in chapter 1, accounting for intangible assets is one of the most controversial

topics that standard setters have had to confront. Jenkins and Upton (2001, p.4)

suggested that, given the increasing importance/materiality of intangible assets and the

limitations placed on their recognition, traditional financial statements do not capture

the ‘value drivers that dominate the new economy’, thereby reducing their value

relevance. In the past, Australian generally accepted accounting principles (GAAPs)

have typically provided more flexibility than most other jurisdictions, particularly the

US, with respect to the reporting of ID (and, potentially, less flexibility with respect to

the reporting of GW). However, as a consequence of regulatory reforms currently being

initiated as part of international convergence, Ritter and Wells (2006) suggested it was

likely that the recognition and disclosure of ID by Australian firms would cease.

Therefore, the primary aim of this study was to examine, and compare, the value

relevance and bias in the reporting of intangible assets in Australia and the US over the

ten-year period 1994 to 2003. It is anticipated that the results from such an analysis will

provide useful input to policy makers and international standard setters involved in any

future review of the accounting treatment of this important class of assets.

This chapter summarises the main conclusions from this study. Section 9.1 summarises

the key findings and their implications, section 9.2 deals with the study’s limitations

and section 9.3 sets out some future research possibilities.

9.1 Summary of Findings

The aim of the study was to seek answers to two research questions:

1. Are the amounts reported for goodwill and identifiable intangible assets value

relevant and unbiased in both Australia and the US?

2. Does the level of conservatism (bias), if any, with respect to the reporting of

goodwill and identifiable intangible assets vary significantly between Australia and

the US?

108

This study adopted a disaggregated form of the traditional Feltham and Ohlson (1995)

framework to examine the value relevance and any bias in the reporting of GW and ID

for a sample comprising 2,611 and 4,584 firm-years from Australia and the US,

respectively. Unbalanced panel regressions were used to analyse the disaggregated

valuation models and the associated linear information models (LIMs) in order to test

whether GW and ID were value relevant (with the null hypothesis being that the

coefficient for each of these two variables is equal to zero) and whether the market

values GW and ID in an unbiased manner (with the null hypothesis being that the

coefficient for each of these two variables is equal to one).




adjusted R2

of 71%, which is consistent with similar US studies by Ahmed et al. (2000)

and Amir et al. (1997). Further, the results from the disaggregated Feltham and Ohlson

(1995) valuation models suggest that the information presented with respect to

intangible assets (both GW and ID) under Australian GAAP is value relevant.

However, the results from the valuation models also suggest that the market believes

that GW is reported conservatively and ID aggressively, by the average Australian

company. This finding, while consistent with Godfrey and Koh (2001) and Shahwan

(2004), appears to conflict with Wyatt (2005) who concluded that ID were more highly

valued by investors than GW. While the results from the valuation models suggest that

the market perceives both GW and ID to be reported with bias, this was not confirmed

by the LIM results. The results for the LIMs suggest that the accounting treatment

adopted by the average Australian company for recognising intangibles is unbiased.

However, this result appears to be largely driven by those firms consistently reporting

positive AOE. It seems that firms that are not consistently earning positive AOE might,

indeed, be reporting GW and/or ID aggressively and this is an area that standard setters

might need to take a closer look at in the future.

The Australian results support the views of Barth and Clinch (2001), Godfrey and Koh

(2001), Wyatt (2005), and Ritter and Wells (2006) that Australian GAAP has allowed

managers appropriate discretion to convey (through financial statements) their

(potentially value relevant) private information concerning the value of ID. However,

109

and despite the LIM results suggesting the accounting treatment adopted by the average

Australian company for ID is unbiased, the market appears to systematically discount

the values reported for such assets.





(2000) and Amir et al. (1997). Further, the results from the disaggregated Feltham and

Ohlson (1995) valuation models suggest that the information presented with respect to

intangible assets (both GW and ID) under US GAAP is value relevant. However, the

results from the valuation models also suggest that the market believes that the average

US company reports both GW and ID conservatively. This finding is consistent with

previous US studies such as: Wilkins et al. (1998); Choi et al. (2000); Chauvin and

Hirschey (1994); and Jennings et al. (1996). While the results from the valuation

models suggest that the market perceives both GW and ID to be reported with bias, this

was not confirmed by the LIM results, which (consistent with the Australian findings)

suggest that the accounting treatment adopted by the average US company for the

recognition of intangibles is unbiased.

The results for Australia and the US were generally consistent except that (as expected

because of the significant differences in accounting treatment) the US market appears to

believe that identifiable intangible assets are conservatively valued by the average US

company, while the Australian market appears to believe that such assets are

aggressively valued by the average Australian company. However, for both countries

the LIM results indicate that the actual accounting treatment adopted for the recognition

of intangibles is unbiased.

9.2 Limitations

Although this study provides some useful insights concerning the value relevance of the

accounting treatment used to recognise intangible assets in Australian and the US,

several limitations should be noted.

110

First, while the data used in this study covers a wide range of industries from the

Australian and New York Stock Exchanges, it does not include the financial and mining

sectors which might, therefore, limit the generalisability of the results.

Second, the US data used in this study was restricted to companies listed on the New

York Stock Exchange and, therefore, this might further limit the generalisability of the

results within the US.

Third, although the disaggregated Feltham and Ohlson (1995) valuation models adopted

in this study appear to be particularly useful in examining both Australian and US

equity securities, the models do not (for reasons explained in chapter 5) incorporate the

‘other information’ variable included in the traditional Feltham and Ohlson (1995)

framework. Inclusion of the ‘other information’ variable might increase the predictive

power of the models.

Finally, while the results of this study apply to the Australian and US financial markets

they might not be generalisable to the financial markets of other countries.

9.3 Future Research

The results presented in this study suggest that the market believes that Australian

firms, on average, account for GW conservatively and account for ID aggressively.

However, the results for the LIMs suggest that the accounting treatment adopted by the

average Australian firm for both GW and ID is unbiased. As noted in chapter 6, the

LIM results capture the time series (accounting) properties of the information contained

in company annual reports, while the results of the regression analysis reflect the values

placed by the market on the information contained in those reports. Reconciling these

differences is something that might be of interest to regulators and is, therefore, an area

that future research could usefully explore.

Further research could also usefully replicate this study in different countries to

determine the robustness of the findings in different capital market and accounting

policy settings. Further, it might be helpful to standard setters if future research, using

a similar methodology to that adopted in this study, were to examine the value

111

relevance and the perceived bias in the reporting of individual components of

identifiable intangible assets (for example: brands, patents and trademarks).

9.3 Conclusion





(more biased) information. As noted in chapter 1, the increasing importance of

intangible assets in the ‘new-economy’ suggests that (wherever possible having regard

to the measurement difficulties) all intangible assets should be recognised in financial



(that is, those not consistently reporting positive AOE) might be reporting GW and/or

ID aggressively and this is an area that standard setters might need to carefully consider

in future.

I trust that the findings presented in this study will prove helpful to both researchers

and those involved with formulating international accounting standards in this

particularly difficult area of intangible assets. I also hope the results will help to allay

any fears regulators (and others) might have that providing managers with accounting

discretion will (necessarily) lead to biased reporting practices; based on the findings of

this study for the majority of Australian and US companies, any such fears appear

unwarranted.

112

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Appendices

Table 6.1.1 Descriptive statistics for all variables in the Australian sub-samples

(1994-2003)..........................................................................................121

Table 6.2.1 Pearson correlation matrix for all variables in the first Australian

sub-sample, N = 1,527 (1994-2003)......................................................122

Table 6.2.2 Pearson correlation matrix for all variables in the second Australian

sub-sample, N = 1,081 (1994-2003)......................................................123

Table 6.2.3 Pearson correlation matrix for all variables in the third Australian

sub-sample, N = 938 (1994-2003).........................................................124

Table 6.2.4 Pearson correlation matrix for all variables in the fourth Australian

sub-sample, N = 503 (1994-2003).........................................................125

Table 7.1.1 Descriptive statistics for all variables in the US sub-samples (1994-

2003) ....................................................................................................126

Table 7.2.1 Pearson correlation matrix for all variables in the first US sub-

sample, N = 3,040 (1994-2003) ............................................................127

Table 7.2.2 Pearson correlation matrix for all variables in the second US sub-

sample, N = 708 (1994-2003) ...............................................................128

Table 7.2.3 Pearson correlation matrix for all variables in the third US sub-

sample, N = 2,605 (1994-2003) ............................................................129

Table 7.2.4 Pearson correlation matrix for all variables in the fourth US sub-

sample, N = 329 (1994-2003) ...............................................................130

121

Table 6.1.1 Descriptive statistics for all variables in the Australian sub-samples (1994-2003)

Variable Panel A: First sub-sample n = 1,527 Panel B: Second sub-sample n = 1,081 Panel C: Third sub-sample n = 938 Panel D: Fourth sub-sample n = 503

(millions) Mean Med S.D. Min Max Mean Med S.D. Min Max Mean Med S.D. Min Max Mean Med S.D. Min Max

MVE 337 41 1,010 0 21,900 386 47 1,140 0 21,900 469 97 1,030 2 13,700 582 127 1,240 2 13,700

NOA-INT 170 18 677 -3,250 11,300 208 24 742 -2,400 11,300 189 30 733 -2,400 11,300 288 41 944 -2,400 11,300

GW 29 1 138 0 3,490 38 3 160 0 3,490 31 1 157 0 3,490 49 5 207 0 3,490

ID 66 1 540 0 19,600 62 0 622 0 19,600 52 0 199 0 3,090 62 0 231 0 3,090

NFA -81 -2 548 -9,100 1,800 -88 -4 574 -9,100 1,800 -61 -3 622 -9,100 1,800 -120 -8 809 -9,100 1,800

AOE 1 1 77 -1,060 615 3 1 78 -971 615 21 5 49 0 615 25 6 60 0 615

AOE t-1 3 1 75 -1,210 712 6 1 79 -1,210 712 21 5 51 0 712 26 6 63 0 712

NOA-INT t-1 161 17 608 -1,190 9,240 195 22 690 -907 9,240 125 20 484 -1,190 9,240 194 29 623 -9,070 9,240

GW t-1 26 1 127 0 3,490 34 3 147 0 3,490 14 1 47 0 672 24 4 54 0 502

ID t-1 66 1 666 0 24,900 64 0 778 0 24,900 38 0 164 0 2,510 47 0 188 0 2,510 Numbers are in millions of Australian dollars.


122

Table 6.2.1 Pearson correlation matrix for all variables in the first Australian sub-sample, N = 1,527 (1994-2003)


MVE 1.000 0.596 0.257 0.607 -0.160 0.142 0.434 0.598 0.217 0.592

P-Value 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

NOA-INT 0.596 1.000 0.488 0.346 -0.785 0.037 0.277 0.542 0.135 0.314

P-Value 0.000 0.000 0.000 0.000 0.000 0.153 0.000 0.000 0.000 0.000

GW 0.257 0.488 1.000 0.114 -0.619 -0.014 0.093 0.154 0.279 0.092

P-Value 0.000 0.000 0.000 0.000 0.579 0.000 0.000 0.000 0.000 0.000

ID 0.607 0.346 0.114 1.000 -0.137 -0.197 0.235 0.327 0.104 0.971

P-Value 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

NFA -0.160 -0.785 -0.619 -0.137 1.000 0.032 -0.080 -0.180 -0.054 -0.084

P-Value 0.000 0.000 0.000 0.000 0.000 0.217 0.002 0.000 0.036 0.001

AOE 0.142 0.037 -0.014 -0.197 0.032 1.000 0.185 -0.344 -0.461 -0.261

P-Value 0.000 0.153 0.579 0.000 0.217 0.000 0.000 0.000 0.000 0.000

AOEt-1 0.434 0.277 0.093 0.235 -0.080 0.185 1.000 0.225 0.036 0.203

P-Value 0.000 0.000 0.000 0.000 0.002 0.000 0.000 0.000 0.160 0.000

NOA-INTt-1 0.598 0.542 0.154 0.327 -0.180 -0.344 0.225 1.000 0.502 0.367

P-Value 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

GWt-1 0.217 0.135 0.279 0.104 -0.054 -0.461 0.036 0.502 1.000 0.106

P-Value 0.000 0.000 0.000 0.000 0..36 0.000 0.160 0.000 0.000 0.000

IDt-1 0.592 0.314 0.092 0.971 -0.084 -0.261 0.203 0.367 0.106 1.000


123

Table 6.2.2 Pearson correlation matrix for all variables in the second Australian sub-sample, N = 1,081 (1994-2003)


MVE 1.000 0.660 0.252 0.611 -0.185 0.162 0.470 0.634 0.206 0.597

P-Value 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

NOA-INT 0.660 1.000 0.498 0.363 -0.755 0.072 0.316 0.599 0.136 0.341

P-Value 0.000 0.000 0.000 0.000 0.000 0.019 0.000 0.000 0.000 0.000

GW 0.252 0.498 1.000 0.122 -0.678 -0.036 0.096 0.159 0.282 0.096

P-Value 0.000 0.000 0.000 0.000 0.000 0.241 0.002 0.000 0.000 0.002

ID 0.611 0.363 0.122 1.000 -0.118 -0.234 0.250 0.342 0.110 0.981

P-Value 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

NFA -0.185 -0.755 -0.678 -0.118 1.000 0.001 -0.083 -0.194 -0.069 -0.083

P-Value 0.000 0.000 0.000 0.000 0.000 0.975 0.006 0.000 0.023 0.007

AOE 0.162 0.072 -0.036 -0.234 0.001 1.000 0.251 -0.330 -0.484 -0.294

P-Value 0.000 0.019 0.241 0.000 0.975 0.000 0.000 0.000 0.455 0.000

AOEt-1 0.470 0.316 0.096 0.250 -0.083 0.251 1.000 0.257 0.023 0.218

P-Value 0.000 0.000 0.002 0.000 0.006 0.000 0.000 0.000 0.455 0.000

NOA-INTt-1 0.634 0.599 0.159 0.342 -0.194 -0.330 0.257 1.000 0.508 0.374

P-Value 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

GWt-1 0.206 0.136 0.282 0.110 -0.069 -0.484 0.023 0.508 1.000 0.109

P-Value 0.000 0.000 0.000 0.000 0.023 0.000 0.455 0.000 0.000 0.000

IDt-1 0.597 0.341 0.096 0.981 -0.083 -0.294 0.218 0.374 0.109 1.000


124

Table 6.2.3 Pearson correlation matrix for all variables in the third Australian sub-sample, N = 938 (1994-2003)


MVE 1.000 0.512 0.183 0.247 -0.092 0.794 0.791 0.708 0.355 0.246

P-Value 0.000 0.000 0.000 0.000 0.005 0.000 0.000 0.000 0.000 0.000

NOA-INT 0.512 1.000 0.501 0.114 -0.776 0.401 0.482 0.655 0.152 0.019

P-Value 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.571

GW 0.183 0.501 1.000 0.110 -0.693 0.168 0.162 0.101 0.259 0.034

P-Value 0.000 0.000 0.000 0.001 0.000 0.000 0.000 0.002 0.000 0.294

ID 0.247 0.114 0.110 1.000 -0.168 0.264 0.326 0.002 0.112 0.828

P-Value 0.000 0.000 0.001 0.000 0.000 0.000 0.000 0.953 0.001 0.000

NFA -0.092 -0.776 -0.693 -0.168 1.000 -0.076 -0.104 -0.166 -0.025 -0.046

P-Value 0.005 0.000 0.000 0.000 0.000 0.021 0.001 0.000 0.436 0.163

AOE 0.794 0.401 0.168 0.264 -0.076 1.000 0.764 0.470 0.166 0.153

P-Value 0.000 0.000 0.000 0.000 0.021 0.000 0.000 0.000 0.000 0.000

AOEt-1 0.791 0.482 0.162 0.326 -0.104 0.764 1.000 0.569 0.240 0.276

P-Value 0.000 0.000 0.000 0.000 0.001 0.000 0.000 0.000 0.000 0.000

NOA-INTt-1 0.708 0.655 0.101 0.002 -0.166 0.470 0.569 1.000 0.256 0.078

P-Value 0.000 0.000 0.002 0.953 0.000 0.000 0.000 0.000 0.000 0.016

GWt-1 0.355 0.152 0.259 0.112 -0.025 0.166 0.240 0.256 1.000 0.135

P-Value 0.000 0.000 0.000 0.001 0.436 0.000 0.000 0.000 0.000 0.000

IDt-1 0.246 0.019 0.034 0.828 -0.046 0.153 0.276 0.078 0.135 1.000


125

Table 6.2.4 Pearson correlation matrix for all variables in the fourth Australian sub-sample, N = 503 (1994-2003)


MVE 1.000 0.561 0.164 0.210 -0.121 0.802 0.811 0.755 0.367 0.201

P-Value 0.000 0.000 0.000 0.000 0.007 0.000 0.000 0.000 0.000 0.000

NOA-INT 0.561 1.000 0.517 0.096 -0.777 0.414 0.482 0.700 0.183 0.006

P-Value 0.000 0.000 0.000 0.031 0.000 0.000 0.000 0.000 0.000 0.888

GW 0.164 0.517 1.000 0.111 -0.731 0.143 0.147 0.104 0.288 0.031

P-Value 0.000 0.000 0.000 0.012 0.000 0.001 0.001 0.019 0.000 0.494

ID 0.210 0.096 0.111 1.000 -0.144 0.224 0.319 0.008 0.147 0.840

P-Value 0.000 0.031 0.012 0.000 0.001 0.000 0.000 0.859 0.001 0.000

NFA -0.121 -0.777 -0.731 -0.144 1.000 -0.084 -0.106 -0.184 -0.072 -0.029

P-Value 0.007 0.000 0.000 0.001 0.000 0.061 0.017 0.000 0.108 0.515

AOE 0.802 0.414 0.143 0.224 -0.084 1.000 0.765 0.516 0.179 0.117

P-Value 0.000 0.000 0.001 0.000 0.061 0.000 0.000 0.000 0.000 0.008

AOEt-1 0.811 0.482 0.147 0.319 -0.106 0.765 1.000 0.601 0.258 0.281

P-Value 0.000 0.000 0.001 0.000 0.017 0.000 0.000 0.000 0.000 0.000

NOA-INTt-1 0.755 0.700 0.104 0.008 -0.184 0.516 0.601 1.000 0.244 0.062

P-Value 0.000 0.000 0.019 0.859 0.000 0.000 0.000 0.000 0.000 0.168

GWt-1 0.367 0.183 0.288 0.147 -0.072 0.179 0.258 0.244 1.000 0.162

P-Value 0.000 0.000 0.000 0.001 0.011 0.000 0.000 0.000 0.000 0.000

IDt-1 0.201 0.006 0.031 0.840 -0.029 0.117 0.281 0.062 0.162 1.000


126

Table 7.1.1 Descriptive statistics for all variables in the US sub-samples (1994-2003)

Variable Panel A: First sub-sample n = 3,040 Panel B: Second sub-sample n = 708 Panel C: Third sub-sample n = 2,605 Panel D: Fourth sub-sample n = 329

(millions) Mean Med S.D. Min Max Mean Med S.D. Min Max Mean Med S.D. Min Max Mean Med S.D. Min Max

MVE 3,676 1,468 5,342 69 36,108 3,455 129 5,109 69 29,799 3,657 1,665 5,054 60 36,108 3,929 1,973 5,373 104 29,799

NOA-INT 2,129 605 6,536 -23,036 208,000 1,928 579 3,766 -6,331 32,054 1,860 632 3,292 -5,276 27,736 1,590 602 3,045 -691 20,856

GW 225 0 1,005 0 24,911 738 230 1,563 0 14,431 86 0 413 0 8,557 461 207 889 0 8,557

ID 615 98 1,963 0 40,493 55 0 142 0 988 306 15 784 0 8,741 28 0 74 0 401

NFA -1,434 -327 6,114 -216,000 21,058 -1,375 344 2,992 -21,095 6,400 -974 -279 2,075 -22,095 5,345 -992 -340 2,224 -17,911 989

AOE 2 23 638 -20,497 2,725 5 13 333 -3,412 1,544 150 65 250 0 4,520 138 632 195 1 1,544

AOE t-1 0 19 523 -10,837 2,665 4 13 270 -2,070 1,535 134 56 232 0 3,918 115 496 167 1 1,141

NOA-INT t-1 2,682 674 8,884 -4,674 262,000 2,475 766 5,243 -498 69,225 1,680 565 3,014 -4,674 26,540 1,484 593 2,694 -498 18,365

GW t-1 164 0 787 0 14,431 665 204 1,484 0 14,431 65 0 374 0 8,717 406 177 840 0 8,717

ID t-1 563 91 1,824 0 40,493 51 0 165 0 2,464 252 11 663 0 8,741 26 0 73 0 395 Numbers are in millions of US dollars.


127

Table 7.2.1 Pearson correlation matrix for all variables in the first US sub-sample, N = 3,040 (1994-2003)


MVE 1.000 0.409 0.212 0.386 -0.327 0.061 -0.002 0.382 0.194 0.344

P-Value 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

NOA-INT 0.409 1.000 0.105 0.216 -0.946 -0.335 -0.409 0.892 0.112 0.277

P-Value 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

GW 0.212 0.105 1.000 -0.004 -0.149 -0.063 -0.100 0.176 0.749 0.178

P-Value 0.000 0.000 0.000 0.832 0.000 0.000 0.000 0.000 0.000 0.000

ID 0.386 0.216 -0.004 1.000 -0.317 -0.324 -0.169 0.337 -0.029 0.817

P-Value 0.000 0.000 0.000 0.000 0.832 0.000 0.000 0.000 0.000 0.000

NFA -0.327 -0.946 -0.149 -0.317 1.000 0.349 0.455 -0.870 -0.135 -0.344

P-Value 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

AOE 0.061 -0.335 -0.063 -0.324 0.349 1.000 0.521 -0.526 -0.071 -0.449

P-Value 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

AOEt-1 -0.002 -0.409 -0.100 -0.169 0.455 0.521 1.000 -0.612 -0.052 -0.274

P-Value 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

NOA-INTt-1 0.382 0.892 0.176 0.337 -0.870 -0.526 -0.612 1.000 0.130 0.436

P-Value 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

GWt-1 0.194 0.112 0.749 -0.029 -0.135 -0.071 -0.052 0.130 1.000 -0.045

P-Value 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

IDt-1 0.344 0.277 0.178 0.817 -0.344 -0.449 -0.274 0.436 -0.045 0.013


128

Table 7.2.2 Pearson correlation matrix for all variables in the second US sub-sample, N = 708 (1994-2003)


MVE 1.000 0.664 0.475 0.261 -0.583 0.053 0.005 0.568 0.425 0.270

P-Value 0.000 0.000 0.000 0.000 0.000 0.156 0.895 0.000 0.000 0.000

NOA-INT 0.664 1.000 0.477 0.086 -0.933 -0.143 -0.227 0.833 0.411 0.087

P-Value 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

GW 0.475 0.477 1.000 0.266 -0.660 -0.246 -0.241 0.490 0.956 0.326

P-Value 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

ID 0.261 0.086 0.266 1.000 -0.139 -0.172 -0.039 0.181 0.285 0.715

P-Value 0.000 0.022 0.000 0.000 0.000 0.000 0.301 0.000 0.000 0.000

NFA -0.583 -0.933 -0.660 -0.139 1.000 0.140 0.292 -0.775 -0.580 -0.148

P-Value 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

AOE 0.156 -0.143 -0.246 -0.172 0.140 1.000 0.561 -0.492 -0.305 -0.272

P-Value 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

AOEt-1 0.005 -0.227 -0.241 -0.039 0.292 0.561 1.000 -0.469 -0.240 -0.146

P-Value 0.895 0.000 0.000 0.301 0.000 0.000 0.000 0.000 0.000 0.000

NOA-INTt-1 0.568 0.833 0.490 0.181 -0.775 -0.492 -0.469 1.000 0.472 0.192

P-Value 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

GWt-1 0.425 0.411 0.956 0.285 -0.580 -0.305 -0.240 0.472 1.000 0.339

P-Value 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

IDt-1 0.270 0.087 0.326 0.715 -0.148 -0.272 -0.146 0.192 0.339 1.000


129

Table 7.2.3 Pearson correlation matrix for all variables in the third US sub-sample, N = 2,605 (1994-2003)


MVE 1.000 0.601 0.153 0.455 -0.512 0.696 0.650 0.578 0.148 0.436

P-Value 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

NOA-INT 0.601 1.000 0.147 0.207 -0.885 0.560 0.540 0.911 0.146 0.182

P-Value 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

GW 0.153 0.147 1.000 -0.044 -0.272 0.087 0.086 0.164 0.835 0.048

P-Value 0.000 0.000 0.000 0.085 0.000 0.000 0.000 0.000 0.000 0.131

ID 0.455 0.207 -0.044 1.000 -0.323 0.374 0.347 0.225 0.015 0.867

P-Value 0.000 0.000 0.086 0.000 0.000 0.000 0.000 0.000 0.861 0.000

NFA -0.512 -0.885 -0.272 -0.323 1.000 -0.444 -0.394 -0.749 -0.270 -0.266

P-Value 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

AOE 0.696 0.560 0.087 0.374 -0.444 1.000 0.767 0.517 0.090 0.320

P-Value 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

AOEt-1 0.650 0.540 0.086 0.347 -0.394 0.767 1.000 0.537 0.071 0.359

P-Value 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

NOA-INTt-1 0.578 0.911 0.164 0.225 -0.749 0.517 0.537 1.000 0.162 0.226

P-Value 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

GWt-1 0.148 0.146 0.835 0.015 -0.270 0.090 0.071 0.162 1.000 -0.038

P-Value 0.000 0.000 0.000 0.861 0.000 0.000 0.000 0.000 0.000 0.114

IDt-1 0.436 0.182 0.048 0.867 -0.266 0.320 0.359 0.226 -0.038 1.000


130

Table 7.2.4 Pearson correlation matrix for all variables in the fourth US sub-sample, N = 329 (1994-2003)


MVE 1.000 0.638 0.335 0.205 -0.500 0.810 0.603 0.614 0.314 0.127

P-Value 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.022

NOA-INT 0.638 1.000 0.483 0.053 -0.925 0.575 0.490 0.943 0.492 -0.015

P-Value 0.000 0.000 0.000 0.350 0.000 0.000 0.000 0.000 0.000 0.785

GW 0.335 0.483 1.000 0.127 -0.699 0.297 0.252 0.544 0.969 0.078

P-Value 0.000 0.000 0.000 0.085 0.000 0.000 0.000 0.000 0.000 0.161

ID 0.205 0.053 0.127 1.000 -0.065 0.091 0.015 0.128 0.122 0.881

P-Value 0.000 0.330 0.022 0.000 0.239 0.099 0.784 0.020 0.027 0.000

NFA -0.500 -0.925 -0.699 -0.065 1.000 -0.464 -0.364 -0.880 -0.703 -0.014

P-Value 0.000 0.000 0.000 0.239 0.000 0.000 0.000 0.000 0.000 0.799

AOE 0.810 0.575 0.297 0.091 -0.464 1.000 0.660 0.496 0.258 0.034

P-Value 0.000 0.000 0.000 0.099 0.000 0.000 0.000 0.000 0.000 0.542

AOEt-1 0.603 0.490 0.252 0.015 -0.364 0.660 1.000 0.450 0.235 0.026

P-Value 0.000 0.000 0.000 0.784 0.000 0.000 0.000 0.000 0.000 0.635

NOA-INTt-1 0.614 0.943 0.544 0.128 -0.880 0.496 0.450 1.000 0.563 0.034

P-Value 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.838

GWt-1 0.314 0.492 0.969 0.122 -0.703 0.258 0.235 0.563 1.000 0.084

P-Value 0.000 0.000 0.000 0.027 0.000 0.000 0.000 0.000 0.000 0.127

IDt-1 0.127 -0.015 0.078 0.881 -0.014 0.034 0.026 0.034 0.084 1.000


an examination of the value relevance and bias in …...an examination of the value relevance and...

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