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8/13/2019 ssrn-Have IFRS Changed How Stock Prices Associate with Earnings and Book Value s- Evidence from Norway by it

1/43Electronic copy available at: http://ssrn.com/abstract=1334533

Have IFRS Changed How Stock Prices Associ-ate with Earnings and Book Values? Evidence

from Norway

by

Leif Atle BeislandUniversity of Agder,

4604 Kristiansand, Norway

Kjell Henry Knivsfl*NHH Norwegian School of Economics,

5045 Bergen, Norway

August,

2012

* Address correspondence to Kjell Henry Knivsfl, NHH, 5045

Bergen, Norway, or by e-mail to [email protected]. Thanks

to Frystein Gjesdal, John Christian Langli, Peder Fredslund

Mller, Ken Peasnell, Frank Thinggaard, and seminar partici-

pants at the 33rd EAA Annual Conference and the 1st Nordic

Conference on Financial Accounting for their helpful comments

on earlier versions of this study.


2/43Electronic copy available at: http://ssrn.com/abstract=1334533

Have IFRS Changed How Stock Prices Associ-ate with Earnings and Book Values? Evidence

from Norway

Abstract

Firms listed on European stock exchanges are required to report according to In-

ternational Financial Reporting Standards (IFRS). We use a Norwegian sample to

examine whether the adoption of IFRS in 2005 has changed the value relevance of

earnings relative to book values. IFRS are balance sheet-oriented with an empha-

sis on fair value; Norwegian GAAP are earnings-oriented with a strong focus on

transactional (historical) cost. IFRS also differ by recognizing more intangible as-sets, which further contributes to making IFRS less conservative than NGAAP.

We find that more fair value accounting increases the value relevance of book

values and decreases the value relevance of earnings. Interestingly, reduced bal-

ance sheet conservatism because of more recognition of intangible assets yields

associations opposite to those of reduced conservatism due to increased fair value

accounting. Fair value revaluations decrease the persistence of earnings, while

improved matching of expenditures on intangible assets with future economic

benefits increases the persistence of earnings and the value relevance of earnings

relative to book values.

Keywords: IFRS adoption, accounting conservatism, matching, earnings responsecoefficients

JEL Classification:M41, G15


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1. Introduction

The European Union (EU) required all exchange-listed firms within the European Economic

Area (EEA) to adopt International Financial Reporting Standards (IFRS) in their consolidated

financial statements from January 1st2005.1 IFRS adoption by the EEA is one of the most

significant events in the history of financial accounting, making IFRS the most widely accept-

ed financial reporting model in the world. Surely, it is necessary for financial statement users,

standard setters, and regulators to understand the implications of IFRS. We use this mandato-

ry transition to IFRS to study whether the usefulness of accounting information has changed

from an investor-oriented perspective. Specifically, we investigate whether the adoption of

IFRS has altered the associations between stock market values and summary accounting

measures from the balance sheet (the book value of equity) and the income statement (the

earnings).

The previous empirical evidence on the change in value relevance as a result of IFRS

adoption is mixed and depends on the Generally Accepted Accounting Principles (GAAP)

used prior to the adoption (Hung and Subramanyam, 2007; Gjerde et al. 2008; Paananen and

Parmar, 2008; Devalle et al., 2010; Barth et al., 2011). Most previous IFRS adoption studies

are not well connected to the differences between accounting methods when explaining ob-

served value relevance differences; differences are observed but rarely explained by tests. An

important contribution of this study is to choose one benchmark GAAP to more clearly being

able to identify the main accounting differences between IFRS and the domestic GAAP, and

to test the basic research question of whether the main accounting differences contribute to

explaining the observed differences in value relevance. As outlined below, our findings

demonstrate significant associations between the major accounting differences and the valua-

tion weights of book values and earnings.


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The common method of controlling for other effects than differences in accounting

methods potentially driving observed value relevance differences is to use matched samples

(Gjerde et al., 2008; Barth et al. 2011). But comprehensive matching limits the sample to the

year prior to IFRS adoption, for which financial statements according to both accounting re-

gimes can be obtained. When benchmarking on one domestic GAAP to more clearly identify

the major accounting differences, matched sampling may be very restrictive to the number of

observations and sensitive to short-term implementation effects of the IFRS adoption process,

as well as to restatements typically induced by discontinuous operations. Instead, we choose

to extend the previous IFRS literature on structural shifts between the pre- and post-adoption

periods (Hung and Subramanyam, 2007; Paananen and Parmar, 2008; Devalle et al. 2010) by

comprehensively controlling for other factors potentially varying between the IFRS and the

domestic GAAP sample, and explaining value relevance differences with differences in the

main accounting methods. Our findings are stable and robust for alternative specifications and

assumptions.

We choose the Norwegian GAAP (NGAAP) as the benchmark for evaluating the man-

datory IFRS adoption, a choice that is not random. IFRS are balance sheet-oriented with a

strong emphasis on measurement at fair value (Penman, 2007; Dichev, 2008). Consequently,

we seek domestic GAAP that are earnings-oriented with a strong focus on transactional (his-

torical) cost measurement as our benchmark.2This means that we exclude domestic GAAP

that have developed toward the balance sheet orientation prior to 2005, e.g., UK GAAP

(Paananen and Parmar, 2008), and domestic GAAP with gradual adoptions of IFRS prior to

2005, e.g., Swedish GAAP and Danish GAAP (Hamberg et al., 2011; Thinggaard and

Damkier, 2008). Importantly, the application of transactional cost accounting among the re-

maining domestic GAAP prior to 2005 differs according to whether the prescribed use of ac-

counting estimates, e.g., the remaining life of an asset, is biased or unbiased. Most Continen-


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fair value, suggest that NGAAP are more conservative than IFRS, according to Feltham and

Ohlsons (1995) definition.

As outlined in Section 3, theoretical as well as empirical studies suggest that the less

conservative accounting exhibited in its increased recognition of intangible assets and more

measurements of assets and liabilities at fair value will tend to increase the valuation weight

of the book value of equity and decrease the weight of earnings. Accordingly, we hypothesize

that the association between market value and book value will increase and that the earnings

response coefficients will decrease after the mandatory adoption of IFRS in Norway. Fur-

thermore, we hypothesize that the changes in the response coefficients can be explained by

the two major accounting differences between IFRS and NGAAP, i.e., more fair value ac-

counting and more capitalization of intangibles under IFRS than under NGAAP.

We find that more measurement at fair value according to IFRS increases the valuation

weight of book values. However, more fair value accounting tends to decrease the valuation

weight of earnings. More frequent and larger revaluations make reported earnings less persis-

tent and thereby less value relevant (Ohlson, 1995). Conversely, increased recognition of in-

tangible assets is found to decrease the valuation weight of the balance sheet and increase the

valuation weight of the income statement. Better matching of investment expenditures with

future economic benefits makes earnings more persistent (Dichev and Tang, 2008), which

increases the valuation weight of reported earnings. Because the book value effect of more

recognition of intangible assets is less significant than the effect of more fair value account-

ing, the net average effect of the mandatory adoption of IFRS in Norway from 2005 is an in-

creased association between market and book values. Our findings add to the existing re-

search by showing that the value relevance effect of IFRS adoption depends critically on the

accounting differences between IFRS and the chosen benchmark GAAP and that the net value


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relevance effect of IFRS adoption depends on a complex trade-off between various account-

ing effects.

The remainder of the study is organized as follows. Section 2 provides a short summary

of the differences between IFRS and NGAAP. Our hypotheses regarding how investors re-

spond to accounting information under IFRS relative to NGAAP are outlined in Section 3.

Section 4 presents the methodology for testing the hypotheses. The data and descriptive statis-

tics are discussed in Section 5. Section 6 performs the statistical tests of the hypotheses and

discusses the results. Stability and robustness tests are performed in Sections 7 and 8. Section

9 concludes the study.

2. IFRS vs. NGAAP

The International Financial Reporting Standards (IFRS) issued by the IASB in London are

based on a balance sheet-oriented conceptual framework.3This framework defines assets and

liabilities; the equity is the residual. IFRS have increasingly indicated fair values as the prin-

ciple of measurement after the initial recognition of assets and liabilities. However, transac-

tional cost accounting is widely accepted and is often an equal option, e.g., for property,

plants and equipment, intangible assets, and when there is no reliable measurement of fair

value. Under the balance sheet orientation, revenue is, in principle, an increase in assets or

decrease in liabilities; expenses are increases in liabilities or decreases in assets. Thus, com-

prehensive income, i.e., earnings plus other comprehensive income, is the change in equity

that is unrelated to net capital issues or dividends. Although some fair value revaluations are

recorded as other comprehensive income, other revaluation gains and losses are reported as

earnings, making earnings more nonrecurring or transitory than if they are governed by the

matching of transactional (historical) costs with earned revenues in the future (Penman, 2007;

Nissim and Penman, 2008).


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Norwegian Generally Accepted Accounting Principles (NGAAP) are the accounting

regulations in Norway. The most important regulations are the Accounting Act of 1998 and

accounting standards issued by the Norwegian Accounting Standards Board (Johnsen and

Eilifsen, 2003). NGAAP are based on an earnings-oriented conceptual framework in which

investment expenditures are matched with corresponding revenues to calculate a periods

earnings based on unbiased accounting estimates, e.g., for the economic lives of assets and

their residual values. The matching principle is, nevertheless, combined with prudence: the

cost value, net of the accumulated depreciation, is written off to the recoverable amount if

there is an impairment loss, and reversed maximum to initial cost if the value increases again.

In principle, there is no other revaluation, i.e., there is no write-up to fair value when it is

above cost. However, liquid financial instruments are recorded at fair value if they can be

measured reliably.

The difference between IFRS and NGAAP may appear considerable because their main

principles of measuring assets and liabilities in the balance sheet are fair value and past trans-

actional cost, respectively. Still, in practical terms, the two accounting regimes are not very

different for important classes of assets, such as most inventories, property, plants and equip-

ment, and some intangible assets. For example, the cost model is usually chosen for PP&E

and intangible assets by firms reporting according to IFRS (Christensen and Nikolaev, 2010);

although the revaluation model is an equally emphasized option in IAS 16 Property, Plant and

Equipment and IAS 38 Intangible assets. Table 1 lists the most important differences between

IFRS and NGAAP. The larger degree of fair value accounting under IFRS is related primarily

to financial instruments (IAS 39), investment property (IAS 40), and biological assets (IAS

41).

- INSERT TABLE 1 ABOUT HERE -


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As demonstrated in Table 1, we note another main difference between IFRS and

NGAAP; IFRS offer more recognition of assets than NGAAP. Increased recognition is related

to intangible assets (IAS 38), especially purchased goodwill, which is not amortized, and

capitalization of development expenditures. Overall, the two major accounting differences

may lead, on average, to significant deviations in key accounting figures between the two

reporting regimes. In general, assets are more often omitted from or understated on the bal-

ance sheet under NGAAP, meaning that NGAAP are expected to be more conservative than

IFRS.

3. Hypotheses Development

Based on Ohlson (1995), Penman (1998) develops a valuation model in which the stock price

is a linear combination of the book value of equity and the capitalized value of earnings per

share (see also Zhang, 2000):

PRICE = (1 - w) BVPS + w m EPS, (1)

where PRICE is the stock price of firm i at time t, BVPS is the book value of equity per

share, EPS is the periods earnings per share, w is the valuation weight of capitalized earn-

ings, i.e., m EPS, and m is the multiplier that capitalizes the earnings. In this simple model,

the choice of accounting methods affects the accounting variables and their weights, but not

the stock price; all accounting methods are predicted to have the same overall value relevance.

Because the valuation weights amount to one, an increase in the valuation weight of BVPS is

always compensated by a decrease in the valuation weight of EPS, and vice versa. Thus, the

effect of accounting differences is to change the valuation weights of respectively the balance

sheet and the income statement.

Conservative accounting is defined in accordance with Feltham and Ohlson (1995) as

accounting that understates the book value relative to the price: BVPS < PRICE. Increased

book value conservatism and earnings persistence tend to increase the valuation weight of


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earnings and residual earnings at the expense of the valuation weight of book values (Ohlson,

1995, 670-672; Feltham and Ohlson, 1996, 223-224; Penman, 1998, Equation (9)). Earnings

persistence could be increased, for example, by the better matching of expenses with reve-

nues, as occurs when investment expenditures are capitalized and not expensed as incurred

(Dichev and Tang, 2008).

In Section 2, we have identified two major accounting differences between IFRS and

NGAAP: more recognition of intangible assets and more fair value accounting under IFRS.

The overall value relevance between the two accounting regimes is expected to be equal, as

pricing according to (1) is unaffected. The analysis of how the two major accounting differ-

ences affect valuation weights is less straightforward.

First, increased recognition of intangible assets decreases conservatism and increases

earnings persistence due to better matching. Thus, the predicted net effects from more recog-

nition of intangibles on the valuation weights are unclear. Lev and Sougiannis (1996) examine

the value relevance of capitalizing research and development expenditures relative to expens-

ing them as incurred; see also Aboody and Lev (1998) and Lev and Zarowin (1999). Recog-

nizing R&D expenditures as assets is found to be more value relevant than expensing them as

incurred. Impairment testing with no amortization of goodwill under IFRS is also found to be

as value relevant as the combined amortization and impairment method consistent with

NGAAP because the absence of amortization of purchased goodwill functions as a proxy for

capitalizing internally generated goodwill (Stenheim, 2012). However, Hamberg and Beisland

(2012) find that the impairment only method deteriorates the value relevance of earnings rela-

tive to the combined method, as earnings becomes less recurring.

Second, increased fair value accounting has clearer predictions than the increased

recognition of intangible assets, as both balance sheet conservatism and earnings persistence

are reduced. The latter diminishes due to transitory revaluations in earnings, which means that


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the valuation weight of earnings is expected to decrease in favor of the valuation weight of

book values. Empirically, Hann et al. (2007) find that fair value accounting contributes to

impairing the value relevance of earnings; see also Khurana and Kim (2003), Stunda and

Typpo (2004), and Song et al. (2010).

Collectively, the two major accounting differences between NGAAP and IFRS, more

recognition of intangible assets and more fair value accounting, are expected to increase

BVPS relative to PRICE, meaning that the accounting becomes less conservative by Feltham

and Ohlsons (1995) definition. Therefore, the expected net effect of more recognition of in-

tangible assets and more measurement at fair values according to IFRS than according to

NGAAP is that, ceteris paribus, the weight on BVPS increases toward one and the weight on

earnings decreases toward zero. However, we cannot rule out that in particular, the effect of

the increased capitalization of intangible assets at costs could work in the opposite direction,

as the better matching of investment expenditures with corresponding future revenues in-

creases the persistence of earnings. Overall, empirical studies and insights from simple valua-

tion models suggest that more recognition of intangible assets and more fair value accounting

under IFRS can be expected to contribute to explaining the differences in the valuation

weights of book values and earnings, and we propose the following hypotheses (specified as

alternatives to their nulls and tested two-sided):

Hypothesis 1 (H1): The association between book values and market values is higher

and the earnings response coefficient is lower for firms reporting according to IFRS

than NGAAP.

Hypothesis 2 (H2): The changes in the valuation weights of book values and earnings

after the IFRS adoption are associated with increased recognition of intangible assets

and additional measurement at fair value.


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Are these hypotheses also substantiated by previous empirical research on the value

relevance effects of the 2005 IFRS adoption? Devalle et al. (2010) examine whether value

relevance has changed, using data from five major European countries. The findings are

mixed; they find structural breaks in the book value association and earnings response coeffi-

cient in several of the investigated countries, but the sign and magnitude vary across the coun-

tries. For example, the book value association decreased and the earnings response coefficient

increased in Germany after the IFRS adoption. This finding contrasts with an earlier study by

Hung and Subramanyam (2007), who study voluntary adoption of IAS/IFRS prior to the

mandatory adoption in Germany and report a significantly higher valuation weight for the

book value of equity and a significantly lower valuation weight for earnings in the IFRS than

in the German GAAP sample. For the UK, Devalle et al. (2010) note an increase in the book

value association, a finding consistent with Paananen and Parmar (2008). However, the latter

study also documents a decrease in the earnings response coefficient following the adoption

of IFRS in the UK. Collectively, these studies have one important feature in common: there is

typically, but not always, a trade-off between the valuation weight of book values and earn-

ings, as suggested by H1.

Hamberg and Beisland (2012) analyze the changes in value relevance following the

introduction of IFRS in Sweden. They report significantly lower earnings response coeffi-

cients under IFRS than under Swedish GAAP. Hamberg and Beisland conclude that the ap-

proach of only testing goodwill for impairment according to IAS 36 seems to have deteriorat-

ed the value relevance of the income statement. Gjerde et al. (2008) examine the value rele-

vance effects of the adoption of IFRS in Norway, focusing on the restated financial statements

from NGAAP to IFRS in 2004, the year prior to the mandatory adoption of IFRS on the Oslo

Stock Exchange. In 2005, comparable figures for 2004 had to be disclosed. The reconcilement


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adjustment is incrementally value relevant for both the balance sheet and the income state-

ment.

Overall, the IFRS adoption literature presents mixed results. Some of the diversity

can be attributed to the differences in domestic GAAP, the benchmarks for IFRS evaluation.

Moreover, one challenge in many studies is to properly control for the possible changes in

other factors in the pre- and post-adoption periods; another is that most studies are unable to

isolate the underlying causes of the observed changes in value relevance. Although some

studies propose, e.g., fair value accounting and intangible asset recognition as possible expla-

nations for their findings (Hung and Subramanyam, 2007; Gjerde et al., 2008; Paananen and

Parmar, 2008), they do not perform formal tests. An exception is Barth et al. (2011). Based on

eleven earnings components attributed to individual accounting standards adopted in fifteen

European countries, they are able to identify standards with incremental impacts on the value

relevance of earnings, simultaneously controlling for other effects by comparing the 2004

financial statements prepared both according to domestic GAAP in 2004 and restated for

IFRS in 2005.4

4. Test Methodology

H1 and H2 can be tested by expanding regression models commonly used in value relevance

studies to take two different financial reporting regimes, IFRS and NGAAP, into considera-

tion:5

SMT = 0 FIX + 1ACC + 2 IFRS + 3ATTR + 4ACC IFRS + (2)

5ACC ATTR + 6ATTR IFRS + 7ACC ATTR IFRS

+ ,

where SMT is the stock market valuation (price) or performance (return) of firm i at

time t or over period t. FIX = (INDU, YEAR) is a vector of indicator variables for potential

fixed industry and year effects. ACC is a vector of appropriate accounting variables, where


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the specific variables depend on whether a price or a return regression model is specified (fur-

ther explained below). IFRS is an indicator variable that equals 1 if firm i reports according to

IFRS during period t or 0 if the firm reports according to NGAAP during the same period.

ATTR is a vector of other attributes that influence SMT, either directly or through interactions

(further explained below). Finally, is the error term and the alphas are vectors of the coeffi-

cients.

Two SMT - variables are emphasized when examining the value relevance of the ac-

counting measures ACC (Easton and Harris, 1991; Collins et al., 1997; Francis and Schipper,

1999; Lev and Zarowin, 1999). In the price regression model, the stock price (PRICE) is re-

gressed on the book value of equity per share (BVPS) and earnings per share (EPS). Thus,

SMT = PRICE and ACC = (BVPS, EPS). In the return regression model, stock market return

(RET) is explained by the price-deflated earnings (EARN) and the price-deflated change in

earnings (DEARN). Thus, SMT = RET and ACC = (EARN, DEARN).6

We denote the sensitivity of the market valuation or performance to accounting varia-

bles, SMT/ACC, as the accounting association coefficient or simply the AAC. In (2),

the AAC equals

SMT/ACC = 1+ 4IFRS + 5 ATTR + 7ATTR IFRS. (3)

When ACC = BVPS, the accounting association coefficient AAC can be referred to as

the book value association (BVA), measuring the association between market and book values

per share. When ACC = EPS, EARN or DEARN, the AAC is commonly referred to as the

long-window earnings response coefficient (ERC).

In this study, we analyze the degree to which IFRS affects the AAC. The influence of

IFRS on the AAC is equal to

AAC /IFRS = 4+ 7 ATTR. (4)


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ATTR represents attributes that associate with the stock market (SMT) either directly or

through interactions with IFRS and ACC. Three types of variables are represented; test varia-

bles (INTAN, FAIR), other value relevance related variables (LOSS, SPEC, MVOL), and risk

and scale variables (BETA, SIZE, BTM); the three latter variables convert the return regres-

sion model into an abnormal return regression model (see, e.g., Easton and Harris, 1991).

INTAN is the extent of intangible assets recognized and capitalized in the balance sheet,

and it could be measured by the proportion of intangible assets reported or by an indicator

variable for intangible asset-intensive firms. Lev and Zarowin (1999) argue that the lack of

intangible asset recognition has been increasingly harmful to the value relevance of financial

reporting over time. Financial reporting becomes less informative for investors because value-

adding expenditures on intangibles, e.g., R&D, are not treated as investment expenditures and

capitalized. Expensing them as they are incurred creates transitory noise in earnings that are

not present if these expenditures were capitalized and then amortized (Lev and Sougiannis,

1996; Aboody and Lev, 1998).

FAIR is the extent of fair value accounting and can be measured by the proportion of

assets valued at fair value in the balance sheet or by an indicator variable for fair value-

intensive firms. A proxy could be the proportion of market-based financial instruments in the

balance sheet or an indicator variable for a firm with a high proportion of financial assets.

Because measurement at fair value leads to revaluation gains and losses in the income state-

ment, the extent of fair value measurement could also be approximated by an indicator varia-

ble for firms with a high proportion of financial nonrecurring items, e.g., gains and losses on

financial investments. Combining the indicator for financial asset-intensive firms with the

indicator for financial nonrecurring item-intensive firms also yields a variable indicating a

high degree of fair value measurement.


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To test H1, as specified in the previous section, we restrict 7 to 0 in Model (2) and

hence Equation (4). Consequently, (4) is simplified to AAC/IFRS = 4and unconditional

of ATTR. H1 implies that 4> 0 for the BVA and 4< 0 for the ERC. H2 contributes to ex-

plaining the unconditional effect of IFRS on the BVA and the ERC by the test variables IN-

TAN and FAIR, respectively. An approach that is conditional on ATTR, which includes IN-

TAN and FAIR, must be employed, making (4) appropriate. H2 implies, e.g., that 71< 0 and

72> 0 for the BVA and 71> 0 and 72< 0 for the ERC, where the last numbers of the sub-

scripts refer to the variable order in the ATTR - vector. Hence, AAC/IFRS is expected to

be associated with INTAN (the first variable in the ATTR - vector) and FAIR (the second

variable).

When evaluating the impact of INTAN and FAIR on the value relevance of IFRS versus

NGAAP, it is important to control for other variables documented to influence the value rele-

vance and for possible risk and scale differences. Thus, these control variables must be added

to ATTR, the vector of attributes beyond ACC and IFRS affecting SMT. LOSS is an indicator

variable for negative earnings. Hayn (1995) finds that the response coefficient of negative

earnings is lower than for positive earnings (see also Basu, 1997). When earnings relevance

deteriorates due to losses, book values become more important. SPEC is the proportion of

transitory, nonrecurring or special earnings or an indicator variable for a firm with a high pro-

portion of special earnings. The findings of, e.g., Elliott and Hanna (1996) suggest that nonre-

curring earnings, such as large asset impairments, are less value relevant than recurring earn-

ings (see also Francis et al., 1996; Hann et al., 2007). MVOL is market volatility, or an indica-

tor variable of high market volatility. Francis and Schipper (1999) argue that value relevance

depends on the variability of SMT. For example; the ERC is expected to fall when the stock

market becomes more volatile. BETA is a measure of systematic stock market risk, as in the

standard Capital Asset Pricing Model. According to Fama and French (1993), firm size


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(SIZE) and the book-to-market ratio (BTM) are relevant proxy risk factors on the cross-

section of firms. These proxy risk factors are lagged variables and are not related to infor-

mation revealed in the current years accounting variables. To some extent, the control varia-

bles may be correlated with INTAN and FAIR.

5. Data, Descriptive Statistics and Correlations

We have collected market and accounting data for all of the firms listed on the Oslo Stock

Exchange (OSE) from 2001 to 2008 that reported according to IFRS and NGAAP. The mar-

ket data are measured over or at the end of the financial year, or they are delayed by three

months to capture delayed disclosure when producing better explanatory power (Aboody et

al., 2002). Because the firms are required to report according to IFRS from 2005, IFRS are the

dominant accounting regime from 2005 to 2008. To obtain relatively equal sample sizes, we

also include four years of NGAAP financial statements: 2001-2004. According to Panel A of

Table 2, the total number of firm-year observations with a complete sample of test variables

and all control variables equals 1,264. Of these, 623 are IFRS observations and 641 are

NGAAP observations.


In Panel B of Table 2, all of the variables are defined and their computations explained

in detail. Alternative proxies are outlined for the test variables; they will all be used in our

main tests.

Table 3 lists distributional statistics for the total sample and for the IFRS and NGAAP

subsamples. The stock price, the book value per share, the earnings per share, the (excess)

stock market return, the price-deflated earnings per share and the price-deflated change in

earnings per share have been winsorized (so that the 1% lowest and highest values are re-

placed by, respectively, the 1stand 99thpercentile, for the IFRS and NGAAP samples sepa-

rately).


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Panel A shows that the average stock price in the combined sample equals NOK 86.837,

while the corresponding equity value per share is 66.304 and the corresponding earnings per

share is 6.114. As is usually the case, the stock price and the two key accounting numbers

have distributions skewed to the right. The mean price, book value and earnings are in the

interval between the median and the third quartile for the total sample and for the two sub-

samples. Focusing instead on the median, the price/book ratio is 1.536 and the price/earnings

ratio is 13.502. The difference in the median price/book ratios between the IFRS and the

NGAAP sample is 0.428; it is highly significant. The difference in the median price/earnings

ratio is -1.080, which is only weakly significant. The differences between the P/B and P/E

ratios are in accordance with H1. Panel B reveals that the average stock market return is

10.0%, measured in excess of the proxy for the risk-free rate. The median is -0.3%. The medi-

an excess return is negative in the IFRS sample (-5.5%), mainly because of the financial crisis

at the end of 2008. In the NGAAP sample, it is 3.9%. The median earnings yield is 4.9% of

the market value, while the median change in the earnings yield over the financial year is

0.5%.

Panel C presents the distributional statistics of the accounting attribute variables and

control variables. The first variable, IFRS, is an indicator variable, which equals 1 if the ob-

servation comes from the IFRS sample and 0 if it belongs to the NGAAP sample; see Panel B

of Table 2. The average value of IFRS is 0.492, meaning that the two subsamples are almost

balanced.

In Section 2, we have documented that the main accounting differences between IFRS

and NGAAP are more recognition of intangible assets and more measurement at fair value

according to IFRS. To represent these differences, we use several proxies for INTAN and

FAIR.


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INTAN1 is an indicator variable for highly intangible asset-intensive firms, represented

by firms with a ratio of intangible assets relative to their total assets that is above the 75thper-

centile. By construction, the mean value is 0.250 (32.9% in the IFRS sample and 17.3% in the

NGAAP sample). INTAN2 is an indicator variable for firms belonging to industries with an a

priori high intensity of intangible assets, defined as biotechnology, information technology or

communications. The percentage of new economy observations in the total sample is 23.2

(26.8% in the IFRS sample and 19.7% in the NGAAP sample). As shown in Panel D, the cor-

relation between INTAN1 and INTAN2 is 0.454 (highly significant). Because the two

measures are not perfectly correlated, they capture somewhat different aspects of the firms

intangible asset intensities. The industry-based measure (INTAN2) may be more related to the

capitalization of development expenditures than the balance sheet measure (INTAN1) be-

cause development is more common in high-tech industries, and to nonrecognized internally

generated goodwill, as the market-to-book value is typically higher in high-tech than in low-

tech industries.

FAIR1 is an indicator variable for firms whose financial assets (cash excluded) divided

by total assets are above the 75thpercentile. By construction, the percentage of financial asset-

intensive firms in the total sample becomes 25% (21.3% in the IFRS sample and 28.8% in the

NGAAP sample). FAIR2 is an indicator variable for firms with (scaled) financial nonrecur-

ring items above the 75thpercentile, where financial nonrecurring items include revaluations

of financial assets due to fair value measurement.7 The mean is 25% (22.8% and 27.1%).

FIN3 = FAIR1 FAIR2; the mean is 7.7% (7.5% and 7.8%). FIN4 is an indicator variable for

banks and other financial institutions. The mean is 17.3%. There are relatively fewer financial

institutions in the IFRS sample than in the NGAAP sample (13.2% and 21.4%, respectively),

as several minor savings banks continued to report according to NGAAP some years after

2004. The correlation between the fair value proxies is highest for FAIR1 and FAIR4 and


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18

equals 0.735; see Panel D. The difference in correlation between the FAIR measures suggests

that some measures could be noisier than others; those based on revaluations are likely to be

the noisiest measures.

The next three variables in Panel C of Table 3 are related to the properties of earnings

and book values and function as control variables for changes in the accounting association

coefficients (Hayn, 1995; Elliott and Hanna, 1996; Francis and Schipper, 1999). LOSS is an

indicator variable for negative earnings. In our sample, 30.3% of the observations are losses.

SPEC, the indicator variable for special earnings intensity, is equal to 1 if the absolute value

of the sum of operating nonrecurring items scaled by the ingoing market value of equity is

above the 75thpercentile, i.e., the most extreme observations, and zero otherwise.8Thus, the

proportion of observations related to extensive transitory earnings is constructed as 25.0%.

MVOL is an indicator variable for yearly market volatility above the 75thpercentile, meaning

that two years are considered high-volatility years (2002 and 2008). 27.5% of the observations

are from these two years.

The final three variables are risk factors or proxy risk factors expected to influence ex-

pected returns. BETA is the beta from the Capital Asset Pricing Model, and it is estimated by

the market model from the time series of monthly stock market returns. The average beta is

0.964, after winsorizing 1% in each tail. SIZE is the logarithm of the stock market value of

the firm at the beginning of the year and is a measure of firm size. After being winsorized, the

average value of the SIZE variable is 6.859. The average market value of equity is NOK

6.120 billion, equally weighted. BTM is the adjusted book-to-market ratio; the winsorized

average is 0.796. According to Fama and French (1993), both SIZE and BTM are proxy risk

factors.


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6. Main Tests of Hypotheses

To test H1 and H2, we begin by estimating Model (2) as a price regression model.9Panel A of

Table 4 reports the coefficient estimates for the restricted model used to test H1 and then for

the unrestricted model used to test H2 (with the exception of the coefficients for fixed indus-

try and year effects).


The effect of IFRS on the accounting association coefficients (AAC = (BVA, ERC))

is estimated by Model (2) with 7restricted to zero. Consequently, Equation (4) is reduced to

AAC/IFRS = 4. The impact of IFRS on the AAC is represented by the 4- vector alone,

with one coefficient for book values and one for earnings. According to the first regression of

Panel A, the effect of IFRS on book values, i.e., on BVA/IFRS, is estimated to be 0.472

and is found to be highly significant (with a p-value less than 0.010). The book value associ-

ation (BVA) is clearly higher in the IFRS sample than in the NGAAP sample. The effect of

IFRS on the long-window earnings response coefficient (ERC) is estimated to be -0.150; it is

negative, as predicted, but statistically insignificant (with a p-value above 0.100). Conse-

quently, we can only reject the null hypothesis in favor of our alternative, H1, for book val-

ues, and not for earnings.

There is little reason to believe that the specific influence of the IFRS adoption on the

balance sheet and the income statement is uniform across the different characteristics ex-

pected to vary between the pre- and post-IFRS samples. Thus, we estimate the unrestricted

model in accordance with Model (2). By letting 7be estimated freely, we are, as suggested

by Equation (4), able to test for factors contributing to an explanation of the average effects of

IFRS on the AAC.

From the second regression model of Panel A, we observe that the INTAN1, our first

measure of INTAN or intangible asset recognition, is associated with a decrease in BVA/


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20

IFRS, estimated to be -0.420. Therefore, even if the average effect of IFRS on the BVA is

positive and equal to 0.472, a separate negativeimpact of -0.420 on the BVA is included in

the average effect for firms with high intangible assets intensity. The impact of INTAN1 is,

however, insignificant. If we focus instead on the change in the ERC following IFRS, we note

that the partial effect of INTAN1 on ERC/ IFRS is 2.908 and significant (with a p-value

below 0.050). The influence of FAIR1, our first measure of FAIR or fair value accounting, on

the association between the BVA and IFRS is 0.587. This is a highly significant impact; it is,

of course, also highly significantly different from the intangible assets effect (-0.420). The

effect of FAIR1 on the association between ERC and IFRS is -2.223, but it is only weakly

significant (with a p-value between 0.050 and 0.100). However, it is highly significantly dif-

ferent from the intangible assets effect (2.908). Our findings are consistent with H2; the lack

of significance for two of the four coefficients means that we can only claim partial, not gen-

eral, support of H2.

Consequently, we may conclude that thus far, IFRS contributes to increasing the value

relevance of the balance sheet, i.e., the book value of equity, mainly because of increased fair

value accounting. The effect of increased intangible asset recognition is negative on the value

relevance of the balance sheet; therefore, it works in the opposite direction of the main effect

of fair value accounting. Although there are traces of reduced value relevance of earnings

according to IFRS and some evidence of the origin of this effect, we are unable to fully reject

the null hypotheses in favor of H1 and H2.

7. Robustness Tests

Price regression models have been criticized for potential scale effects (Barth and Kallapur,

1996; Brown et al., 1999; Easton and Sommers, 2003). Although we have cleaned for fixed

industry and year effects, we cannot exclude the possibility that the results thus far have been

driven by an omitted scale factor differing between the IFRS and NGAAP samples. To reduce


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21

the likelihood of scale differences, we employ two remedies. First, we use firm size and other

potential scale factors as additional control variables. Next, we expand the analysis with re-

turn regressions, which are deflated by the lagged price as a potential scale factor. We also

employ scale factors associated with expected returns, i.e., risk variables, as further control

variables. The model then becomes a model of abnormal returns with presumably negligible

scale effects.

Panel B of Table 4 reports the results of the price regression models with ATTR extend-

ed by BETA, SIZE and BTM to reduce potential scale effects and account for any risk differ-

ences.10We report only the main coefficients in relation to the impact of IFRS in line with

Equation (3) to limit the length of Panel B, and observe that the unconditional BVA/IFRS

decreases from 0.472 in Panel A to 0.449 in Panel B; both coefficients are highly significant.

Similarly, the unconditional ERC/IFRS decreases from -0.150 to -0.425; neither is signifi-

cant.11

The impact of INTAN1 on the BVAs relationship with IFRS, i.e., on BVA/IFRS,

increases in magnitude as the coefficient decreases from -0.420 in Panel A to -0.576 in Panel

B. The latter coefficient is significant. Similarly, the effect of INTAN1 on the ERCs associa-

tion with IFRS, i.e., on ERC/ IFRS, increases from 2.908 to 3.183; both coefficients are

significant. The influence of FAIR1 on the BVAs relationship with IFRS is reduced from

0.587 to 0.428; both are significant. Finally, the impact of FAIR1 on the ERCs association

with IFRS increases from -2.223 to -1.415; neither is significant. These findings indicate that

the predictions in H2 regarding intangible assets are strengthened by our extended control for

scale effects, while the predictions regarding fair value are slightly weakened, as the impact of

fair value on the earnings response coefficient moves from being weakly significant to be-

coming insignificant.


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22

Because INTAN1 and FAIR1 are only proxies for INTAN and FAIR, i.e., intangible as-

set recognition and fair value accounting, we also use alternative proxies (INTAN2, FAIR2,

FAIR3, and FAIR4) to capture possible undetected effects, although the proxies might capture

somewhat different aspects of INTAN and FAIR and some proxies may be noisier than oth-

ers. There are seven more combinations of INTAN and FAIR proxy pairs to study. Regarding

the overall effect on the AACs, the new seven pairs produce significantly positive effects of

IFRS on the BVA, while the effects on the ERC are insignificantly negative; the results are as

before, and these coefficients are, therefore, not tabulated. It should be noted that although the

significant levels vary, the prediction from the simple Model (1) that a positive change in the

BVA is always associated with a negative change in the ERC, is confirmed in all regression

analyses. Thus, all our empirical results support the general notion of a trade-off between the

value relevance of the balance sheet and the value relevance of the income statement (Ohlson,

1995; Penman, 1998; Nissim and Penman, 2008).

Panel C of Table 4 summarizes the impact of the additional seven pairs of INTAN and

FAIR proxies on the IFRS ability to alter the BVA and the ERC. We observe that all of the

coefficients have signs consistent with those reported in Panel B and consistent with the pre-

dictions in H2. The ability of the INTAN and FAIR proxies to explain the effect of IFRS on

the BVA is high (two out of sixteen coefficients are still weakly significant or insignificant),

while the effect of IFRS on the ERC is insignificant (six out of sixteen coefficients are still

significant or weakly significant).

Table 5 presents the corresponding results from return regression models.12 Although

they have the disadvantage of not including book values, the return regression models are less

affected by scale and are, therefore, used to further evaluate the impact of IFRS on the ERC of

the level and the ERC of the change in earnings. We focus the discussion on the combined

impact, i.e., the sum of the ERC of both the earnings change and the earnings level.


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23


According to Panel A, the unconditional impact of IFRS on the ERC is 0.129 and insig-

nificant. The analysis by the return regression models is consistent with that of the price re-

gression models: the impact of IFRS on the ERC is insignificant. Accordingly, we cannot

reject the null hypothesis in favor of H1 for earnings, only for book values by the price re-

gression model.

Furthermore, the combined impact of INTAN1 on ERC/IFRS in Panel A of Table 5

is 0.179 and insignificant, while the combined impact of FAIR1 is -2.646 and significant. The

signs of the estimated effects are consistent with those previously found by the price regres-

sion models: compare with Panel B of Table 4. However, the significance of the effects has

changed from INTAN1 to FAIR1, possibly due to the return regression models better ability

to eliminate scale effects.

According to Panel B of Table 5, the estimated signs of the impacts of the INTAN and

FAIR proxies are always consistent with H2, but only eight out of the coefficients have some

significance. Nevertheless, the impacts of INTAN2 and FAIR1 are always significant, and the

impact of FAIR3 is always at least weakly significant. Thus, the return model has documented

that IFRS make earnings more value relevant for firms in industries with high intangible asset

intensity, as represented by INTAN2, and less value relevant for firms with a high proportion

of financial assets on their balance sheets, as represented by FAIR1.

As discussed in Section 2, the main difference between IFRS and NGAAP for firms in

industries with presumably high intangible asset intensity (biotechnology, information tech-

nology, and telecommunications) is more recognition of development expenditures as assets

in the balance sheet. Capitalized development expenditures are the likely source of the in-

crease in the value relevance of IFRS earnings at the expense of the IFRS book value of equi-

ty; earnings become less transitory, while book values become softer.


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24

The finding that the value relevance of IFRS earnings is reduced for firms with a high

proportion of financial assets is consistent with a general fair value effect, as financial assets

are those most frequently measured at fair value, according to IFRS (and without an industry

effect related to financial institutions, as FAIR4 is never significant). However, using return

regression models we are unable to relate the effect directly to a high degree of revaluations in

the income statement with two exceptions: two cases in Panel B of Table 5, in which FAIR3

is significant or weakly significant. In the price regression models, however, a high degree of

revaluation contributes to reducing the value relevance of IFRS earnings on all occurrences of

statistical significance for the fair value proxies; see Panel C of Table 4, in which FAIR2 and

FAIR3 have some significance.

8. Stability and Additional Robustness Tests

We have performed stability tests, in which we have divided the test period 2001-2008 into

narrower and narrower periods around January 1st2005, when IFRS were introduced in Nor-

way. The narrowed periods are 2002-2007 with 927 firm-year observations, 2003-2006 with

592 observations, and 2004-2005 with 294 observations. The impact of IFRS on the BVA is

evaluated by price regressions, while the impact on the ERC is evaluated by return regres-

sions. Because none of the stability tests contrasts with the results presented in our main tests,

we do not tabulate the results. As in the main tests, the significance of the coefficients varies

depending on which INTAN and FAIR proxies are chosen. However, significant coefficients

are observed in both long and short windows around the introduction of IFRS. For example,

the effect of INTAN2 on the BVA difference between IFRS and NGAAP in 2004-2005 is -

2.011 and significant; the effect of FAIR1 on ERC difference is -4.077 and significant.

Next, we return to the whole sample and report some additional robustness tests. First

we run the most basic test model, PRICE = 0+ 11 BVPS + 12 EPS + 41 BVPS IFRS

+ 42 EPS IFRS + . The impact of IFRS on BVPS (41) is estimated to be 0.528 and high-


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25

ly significant, while the effect of EPS (42) is 0.102 and insignificant. Our conclusions about

H1 hold true in this simple model.

Then we return to the fully specified model with all the controls. As in the stability tests,

no robustness tests produce significant coefficients on the test variables contrary to those pre-

sented in Section 6. Therefore, we do not tabulate the results; we only list the major tests per-

formed. We start by limiting the sample to the firms that were present in all eight years so that

each firm will have four NGAAP observations and four IFRS observations. This limitation is

made to increase the similarity in the underlying operations between the IFRS and NGAAP

sample and to limit the possibility that operational differences will drive our findings. Second,

we replace the indicator variable for a high proportion of intangible assets with the ratio of

intangible assets to total assets, as we consistently replace the indicator for a high proportion

of financial assets with the ratio of financial assets to total assets. This broadening of the vari-

able definitions is performed to potentially catch continuous effects related to the variables,

not only the high-proportion effects. Third, the stock price is measured three months after the

end of the financial year, and the stock return is measured from nine months before until three

months after the end of the financial year, contrary to the main tests, which measured at the

end of the year or over the year. Fourth, we change how we measure the book value of equity

per share by not subtracting the current years earnings per share and how we measure the

price-deflated level of earnings by not subtracting the price-deflated change of earnings, as we

did in the main tests to avoid double counting and concentrate the change effects in single

variables. Finally, several additional robustness checks related to outliers and different statis-

tical techniques are conducted.13

As in the main tests, the significance of the coefficients in the robustness tests varies

with the pairs of INTAN and FAIR proxies used. Typically, INTAN2 and FAIR1 are the most

significant explanatory factors for the differences in book values and earnings between IFRS


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26

and NGAAP. In some more advanced statistical specifications, all the test variables within

most test pairs are significant in the direction of our alternative hypothesis H2: for example,

when using GLS instead of OLS.

Overall, the stability and robustness tests confirm and strengthen our conclusions from

the main tests in Section 6. The null hypothesis is rejected and we conclude in favor of H1

when considering whether the book value is more value relevant under IFRS. Furthermore,

the null hypothesis is rejected, and we conclude in favor of H2. Recognizing development

expenditures as assets under IFRS contributes to making the book values less value relevant

and the earnings more value relevant than under NGAAP. Conversely, more fair value ac-

counting under IFRS makes book values more and earnings less value relevant than under

NGAAP.

9. Conclusions

This study compares the association between stock market values and accounting information

under IFRS and Norwegian GAAP. IFRS generally allow more measurement at fair value and

recognize more intangible assets than NGAAP. Financial reports prepared according to

NGAAP are, therefore, expected to be more conservative than those prepared according to

IFRS. Consequently, we expect IFRS to exhibit a higher association between market and book

values and a lower earnings response coefficient than NGAAP. We find clear evidence sup-

porting our initial expectation regarding the association between market and book values; the

book values are more value relevant under IFRS than under GAAP. More surprisingly, earn-

ings response coefficients are not significantly different, meaning that earnings are as value

relevant under IFRS as under NGAAP.

However, as noted by Penman and Nissim (2008), pure correlations and general re-

sponse coefficients do not offer standard setters much of a grasp of specific policy questions;

for instance, questions related to recognition of intangible assets or the use of fair values.


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27

Thus, we analyze the sources of the identified value relevance differences, concentrating on

whether the two major accounting differences between IFRS and NGAAP contribute to the

changes in value relevance. More fair value accounting is shown to have a positive effect on

the value relevance of book values, while the increased recognition of intangible assets has a

negative effect. The increased value relevance of earnings compensates for the negative bal-

ance sheet effect of recognizing intangible assets under IFRS, while the positive balance sheet

effect of fair value measurement under IFRS is associated with a negative income statement

effect. Fair value revaluations are transitory in nature, leading to less persistent earnings

measurement and, therefore, reduced earnings response coefficients. Increased recognition of

intangible assets, by contrast, leads to the better matching of investment expenditures with

future revenues and, thus, to more persistent earnings measurements.

An important contribution of this study is to document and advance the understanding

of how reduced balance sheet conservatism in relation to IFRS adoption affects the valuation

weights of book values and earnings. Several theoretical studies (Ohlson, 1995; Penman,

1998) demonstrate that the choice between conservative transactional cost accounting versus

fair value accounting is a choice between a value relevant earnings statement versus a value

relevant balance sheet. Both fair values and transactional costs provide information about fu-

ture cash flows, but the methods are competing and mutually exclusive ways of conveying

information (unless both values are reported). Consistent with the commonly held assumption

that IFRS are more fair value-oriented than most domestic GAAPs, we find that the balance

sheets valuation weight increased following the IFRS adoption, and, at least partly, at the

expense of the earnings response coefficients. However, because we apply a unique test

methodology allowing us to split investors response coefficients to accounting information

according to accounting regime as well as several accounting attributes commonly recognized

to be important drivers of value relevance, we are able to identify the sources of the net aver-


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28

age effects. We instructively show that lower accounting conservatism in terms of more

recognition of intangible assets and more measurement at fair value, respectively, may have

opposite effects on the valuation weights.

Our findings suggest that the value relevance effects of the IFRS adoption may be high-

ly sensitive to firm characteristics and the choice of benchmarks to anchor the evaluation on

(Christensen et al., 2007). For instance, this study illustrates that intangible asset-intensive

companies with an insignificant use of fair value accounting may actually experience a de-

crease in the value relevance of their balance sheets and an increase in the value relevance of

their earnings after IFRS adoption. The findings also suggest that the effect of IFRS adoption

on valuation weights may differ throughout the EEA; e.g., the effect may depend on the rela-

tive impact of the changed recognition of intangible assets and measurement at fair value and

the effect of the changed bias in accounting estimates. Collectively, our results have implica-

tions for standard setting and accounting regulation, contributing to identifying the benefits

and costs of IFRS adoption in general and of intangible asset recognition and fair value ac-

counting in particular (Schipper, 2011).


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Notes

1EU Regulation No. 1606/2002. EU regulations also bind Norway, Iceland and Lichtenstein through their mem-

berships in the EEA.2A balance sheet-oriented framework starts with the definitions of assets and liabilities and then derives thedefinitions of revenues and expenses, as in IASBs Conceptual Framework for Financial Reporting. An earnings-oriented approach begins with the principles of income determination, usually in terms of the matching principle,and derives the balance sheet as a consequence of the matching. Although the conceptual orientation is in princi-ple independent of the valuation method for assets and liabilities, the balance sheet orientation fits well as thebasis for fair value measurement, while the earnings orientation is a better basis for transactional (historical) costmeasurement.3In 2010, the IASBs Framework for Preparation and Presentation of Financial Statements, published in 1989

and governing our sample period of 2001-2008, was superseded by the Conceptual Framework for FinancialReporting. The balance sheet focus in the 1989 Framework continues in the 2010 Framework; the definitions ofassets and liabilities (Paragraph 4.4) are the basis for recognizing revenues and expenses (Paragraph 4.25). For adiscussion of the balance sheet orientation of the IASB and the FASB relative to the earnings-oriented alterna-tive, see Dichev (2008).4A potential problem with the restated IFRS numbers is, however, that they were produced one year later thanthe corresponding local GAAP figures. Moreover, companies might have adapted to various temporary IFRSadoption regulations when preparing the restatements, and the numbers may be restated because of discontinuousoperations.5 In regression models with interactions, explanatory variables are multicollinear by construction. The main

problem with multicollinearity is that real explanatory variables may become insignificant because of the ac-companying variance inflation, typically producing regressions with high R2without many significant coeffi-cients. When a variable is found to be significant, collinearity has been taken into consideration by the varianceinflation and represents no further problems for the statistical inference (Wooldridge, 2009, 95-99).6The price model is more exposed to scale problems than the return model (Barth and Kallapur, 1996; Brown et

al., 1999; Easton and Sommers, 2003). However, both models are used to evaluate value relevance, as they ad-

dress different aspects of accounting.7Financial nonrecurring items are gains and losses on financial instruments, currency gains and losses, and other

transitory items reported among financial income and expenses.8Operational nonrecurring items are relatively large impairments and other transitory operating items such aslarge gains or losses on sale of operational assets, restructuring charges and special income from associatedcompanies.9Thus, SMT = PRICE, FIX = (INDU, YEAR), ACC = (BVPS, EPS), and ATTR = (INTAN1, FAIR1; LOSS,

SPEC, MVOL). The control variables for risk and scale (BETA, SIZE, BTM) will be added to ATTR in the nextstep of analyses.10The conclusions do not change if, e.g., the log of outstanding shares replaces SIZE, the log of market value, asour scale proxy.11Further, the persistence of earnings could be evaluated by EARNt= a FIXt + b1 EARNt-1+ b2 EARNt-1IFRSt-1+ et. We estimate b2to be -0.367 (the p-value equals 0.003). A significant drop in earnings persistence isconsistent with more nonrecurring items in IFRS earnings due to more fair value revaluations reported throughthe income statement, but the significance of the drop in persistence does not carry over to a significant drop in

the ERC after adequate controls.12

Model (2) has these specific variables: SMT = RET, FIX = (INDU, YEAR), ACC = (EARN, DEARN), andATTR = (INTAN, FAIR; LOSS, SPEC, MVOL; BETA, SIZE, BTM).13First, we return to the raw data, i.e., the data without winsorizing the 1% most extreme observations in eachtail. Second, we truncate 1% instead of winsorizing 1%. Third, we winsorize 1% as in the main tests and thenfurther down-weight the impact of extreme observations. Fourth, because we are dealing with skewed distribu-

tions, we focus on median effects through median regressions. Fifth, we replace the heteroskedasticity and auto-correlation - adjusted standard deviations, also referred to as Newey-West standard deviations, using multi-way

cluster robust standard deviations, with clustering on, e.g., firms and years or industries, audit firms, firms andyears, as suggested by Cameron et al. (2011). Sixth, instead of estimating the coefficients by ordinary leastsquares, we estimate them by general least squares, taking both panel specific heteroskedasticity and first-orderautocorrelation into account. Finally, we apply Model (1) with an error term directly and estimate the differencein the valuation weight of capitalized earnings between IFRS and NGAAP to be -0.416, when taking the fixedeffects into consideration and allowing the normal trailing price/earnings ratio to differ between the two samples.


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30

The firms reporting according to NGAAP have a significantly higher weight on capitalized earnings, which isconsistent with NGAAP being more conservative than IFRS.


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Table 2: Sample selection and variable definitions

Panel A: Sample selection on the Oslo Stock ExchangeFirm-year

observations

Firm-year observations of PRICE, BVPS and EPS 2001 - 2008 1,606- Lacking observations when lagging and calculating changes 176

= Firm-year observations of RET, EARN and DEARN 1,430

- Lacking observations of accounting attribute and control variables 166

= Sample with complete set of variables 1,264

- NGAAP observations 641

= IFRS observations 623

Number of companies involved in the selected sample 271

Panel B: Definition of variables

FIX Fixed effects in terms of industry (INDU) and year (YEAR) effects. Thus, FIX = (INDU,YEAR).

PRICE The stock price of firm i = 1, 2, , N = 271 at the end of financial year t = 2001, 2002, ,2008. The price three months after the end of the financial year is used as a robustnesscheck.

BVPS The book value of equity of firm i at the end of year t, including a provision for the pro-posed dividend and excluding noncontrolling interests, divided by the number of outstand-ing shares at year end. When BVPS is an explanatory variable together with EPS in theprice regression models, EPS is deducted from BVPS to capture EPS in a single variable,except in a robustness test.

EPS Firm is reported earnings per share in year t.RET The excess stock return of firm i in year t, where excess means return in excess of the

estimated risk free rate. Return is calculated as arithmetic return, adjusted for dividendpayments during the year. The risk-free rate of return is approximated by the three-monthNibor rate, which is an interbank borrowing rate. Nibor is adjusted for 28% tax and anaverage risk premium of 15% is deducted, which corresponds roughly to an average bankrating of AA. As a robustness check, RET is also measured over the year, running ninemonths before to three months after the end of the financial year or calculated logarithmic.

EARN The reported earnings per share of firm i during year t divided or deflated by the previousyears stock price at the years end. Thus, EARN = EPS/PRICEt-1. When EARN is anexplanatory variable together with DEARN in the return regression models, DEARN isdeducted from EARN to capture DEARN is a single variable. Then, EARN = EPS t-1/PRICE -1, except in a robustness test.

DEARN Price-deflated change in earnings per share. Thus, DEARN = (EPS - EPS-1)/PRICE -1.IFRS Indicator or dummy variable that equals 1 if the firm reports according to IFRS or 0 if firm

i reports according to NGAAP in year t.INTAN Indicator variable for intangible asset intensive firms. INTAN is measured in several ways.

INTAN1 is an indicator variable for firms with a high ratio of intangible assets relative tototal assets, i.e., above the 75

thpercentile. Alternatively, INTAN2 is an indicator variable

for firms in industries with presumably high intangible asset intensity, defined as the bio-technology, information technology and communications industries.

FAIR Proxy variable for fair value-intensive firms. FAIR is estimated in several ways. FAIR1 isan indicator variable for firms with a high ratio of financial assets, except cash, relative tototal assets, i.e., above the 75

thpercentile. FAIR2 is an indicator variable for firms with a

high absolute value of financial nonrecurring earnings relative to the lagged stock price;see endnote 7 for examples of nonrecurring financial items, including fair value revalua-tions. FAIR3 = FAIR1 FAIR2. Finally, FAIR4 is an indicator variable for banks and

other financial institutions.LOSS Indicator variable that equals 1 if EPS and EARN < 0 and 0 if EPS and EARN0.SPEC Indicator variable for firms with a high level of special or nonrecurring operational items.


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A high level is measured by a high absolute value of the ratio of operational nonrecurringearnings per share relative to the previous years lagged stock price, i.e., above the 75thpercentile. Endnote 8 lists examples of items classified as nonrecurring or special opera-tional items.

MVOL Indicator variable for years with high market volatility, i.e., above the 75t percentile.Market volatility is measured as the monthly standard deviation of the equally weighted

stock market index, based on monthly excess returns the 12 months before the end of thefinancial year and scaled by the square root of twelve.BETA Beta is an estimate of systematic risk according to the Sharpe-Lintner-Mossin Capital

Asset Pricing Model and is estimated from the time series 12 months before the end of thefinancial year.

SIZE Firm size is a proxy risk factor, according to Fama and French (1993). Firm size is laggedand measured as the logarithm of the previous years average market value of equity,where the average is calculated on a monthly basis.

BTM Book-to-market ratio is a proxy risk factor, according to Fama and French (1993). Thebook value of equity is lagged and calculated as the average over the previous yearsmonthly book-to-market ratios.


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Table 3: Descriptive statistics

Panel A: Price regression variablesObs Mean St. dev. Min Q1 Median Q3 Max

PRICE 1,264 86.837 136.631 0.140 9.000 39.950 116.000 1101.000BVPS 1,264 66.304 142.018 -0.700 5.299 22.823 87.281 1534.263EPS 1,264 6.114 19.026 -40.939 -0.232 1.531 8.581 139.379P/B 1,251 2.236 2.303 0.191 0.986 1.536 2.591 17.778P/E 880 28.374 62.372 1.139 9.057 13.502 21.852 561.734IFRS:PRICE 623 83.719 126.849 0.533 9.050 35.100 105.500 778.000BVPS 623 52.768 87.008 0.098 4.624 20.066 59.401 529.950EPS 623 6.391 17.527 -35.193 -0.121 1.404 8.255 100.362P/B 623 2.651 2.789 0.192 1.097 1.787 3.207 17.778P/E 447 32.551 77.801 1.139 8.379 12.824 22.022 561.734NGAAP:PRICE 641 89.868 145.544 0.140 8.950 43.500 122.500 1101.000BVPS 641 79.460 179.146 -0.700 6.612 26.568 104.782 1534.263EPS 641 5.845 20.388 -40.939 -0.421 1.639 8.786 139.379

P/B 628 1.824 1.586 0.191 0.935 1.358 2.137 9.147P/E 433 24.063 40.370 1.702 9.681 13.904 21.392 291.682

Panel B: Return regression variablesObs Mean St. dev. Min Q1 Median Q3 Max

RET 1,264 0.100 0.789 -1.000 -0.371 -0.003 0.369 4.781EARN 1,264 0.014 0.202 -0.853 -0.033 0.049 0.101 0.556DEARN 1,264 0.058 0.589 -2.422 -0.048 0.005 0.064 5.481IFRS:RET 623 0.026 0.685 -0.941 -0.445 -0.055 0.345 3.207EARN 623 0.036 0.133 -0.281 -0.021 0.049 0.096 0.556DEARN 623 -0.005 0.174 -0.732 -0.048 0.004 0.046 0.652

NGAAP:RET 641 0.172 0.873 -1.000 -0.292 0.039 0.394 4.781EARN 641 -0.009 0.249 -0.853 -0.057 0.049 0.105 0.530DEARN 641 0.120 0.805 -2.422 -0.048 0.006 0.095 5.481

Panel C: Accounting attributes and control variablesObs Mean St. dev. Min Q1 Median Q3 Max

IFRS 1,264 0.492 0.500 0.000 0.000 0.000 1.000 1.000INTAN1 1,264 0.250 0.433 0.000 0.000 0.000 1.000 1.000INTAN2 1,264 0.232 0.422 0.000 0.000 0.000 0.000 1.000FAIR1-2 1,264 0.250 0.433 0.000 0.000 0.000 1.000 1.000FAIR3 1.264 0.077 0.266 0.000 0.000 0.000 0.000 1.000FAIR4 1,264 0.173 0.379 0.000 0.000 0.000 0.000 1.000LOSS 1,264 0.303 0.460 0.000 0.000 0.000 1.000 1.000SPEC 1,264 0.250 0.433 0.000 0.000 0.000 1.000 1.000MVOL 1,264 0.275 0.447 0.000 0.000 0.000 1.000 1.000BETA 1,264 0.964 0.703 -0.347 0.409 0.859 1.362 3.491SIZE 1,264 6.859 1.684 2.933 5.662 6.695 7.921 11.649BTM 1,264 0.796 0.885 -0.001 0.349 0.581 0.902 7.313

P/B is the price/book ratio, i.e., PRICE/BVPS with BVPS > 0. P/E is the price/earnings ratio, i.e., PRICE/ EPS

with EPS > 0. Other variables are defined in Panel B of Table 2. Obs is the number of firm-year observations,mean is the sample average, St. dev. is the standard deviation, Min is the minimum, Q1is first quartile, Median isthe second quartile, Q3is the third quartile, and Max is the maximum observation. PRICE is registered at the endof the financial year, and RET is measured over the financial year. The PRICE, BVPS, EPS, RET, EARN,DEARN, P/B, P/E, BETA, SIZE and BTM are winsorized with cutoffs at the 1stand 99thpercentile separately

for the IFRS and NGAAP samples.


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Panel D: Correlation matrix for INTAN and FAIR proxiesINTAN1 INTAN2 FAIR1 FAIR2 FAIR3

INTAN2 0.454FAIR1 -0.253 -0.222 FAIR2 -0.156 -0.192 0.076FAIR3 -0.125 -0.137 0.499 0.499

FAIR4 -0.255 -0.252 0.735 -0.018 0.245

INTAN1 is an indicator variable for firms with a ratio of intangible assets relative to total assets above the 75th

percentile. INTAN2 is an indicator variable for firms in industries with high intangible asset intensity. FAIR1 isan indicator variable for firms whose ratio of financial assets, except cash, to total assets is above the 75thpercen-tile. FAIR2 is an indicator variable for firms with a high absolute value of financial nonrecurring earnings rela-tive to the lagged stock price. FAIR3 = FAIR1 FAIR2. FAIR4 is an indicator variable for banks and other fi-nancial institutions. The Pearson correlation coefficients are significant at the 1% level, tested two-sided, except-0.018, which is insignificant.


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Table 4: Tests of hypothesis 1 and 2 by price regression models

Panel A: Full specificationsVariable Coefficient

ssrn-have ifrs changed how stock prices associate with earnings and book value s- evidence from...

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