Probability Thresholds and Equity Values

Marc Badia

Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy

under the Executive Committee of the Graduate School of Arts and Sciences

COLUMBIA UNIVERSITY

2008

UMI Number: 3333300

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ABSTRACT

Probability Thresholds and Equity Values

Marc Badia

Some accounting standards specify probability thresholds to determine

recognition and measurement of assets and liabilities (e.g., SFAS No. 5). This

requirement is meant to communicate information to investors on the uncertainty of

future benefits and obligations. I identify a unique setting to test whether investors make

use of these probability thresholds for equity valuation.

A recent regulatory change in Canada requires oil and gas firms to break down

their estimates of natural reserves into proved and probable, dependent on the probability

of eventual production (i.e., P[proved]>90%, P[proved+probable]>50%). I find that

investors attach a higher market value to proved reserves consistently with a simple

expected value model. Lower measurement error in past reserves estimates and the

presence of a reserves committee strengthen these results. The market value weight of

proved reserves tends to be larger for small size firms with a lower ratio of proved to

probable reserves. The market value weight of probable reserves tends to be larger for

large size firms with a higher ratio of proved to probable reserves.

This study is relevant to regulators and investors. The FASB and the IASB are

currently discussing the role of probability thresholds in their joint Conceptual

Framework project. My findings offer support for the use and disclosure of probability

thresholds in asset measurement to inform investors. The incremental value relevance of

the new oil and gas reserves classification is also informative for the IASB in their on­

going development of a new standard for the extractive industries.

TABLE OF CONTENTS

1. Introduction 1

2. Probability Thresholds 6

2.1 The Use of Probability Thresholds in GAAP 6

2.2 Previous Research 10

2.2.1 Experimental Work 10

2.2.2 Theoretical Background 11

2.2.3 Lack of Empirical Evidence 13

3. Reserves Disclosures in the Oil and Gas Industry 14

3.1 Issues on Accounting for Reserves 15

3.2 Reserves Classification 17

3.3 Pioneering Regulation in Canada 19

3.3.1 Background 19

3.3.2 A new reserves definition 20

3.3.3 Other changes in disclosures and corporate governance 22

3.3.4 Other regulation projects 23

4. Hypotheses 24

4.1. Principal Hypothesis 24

4.2 Contextual Analysis 25

4.2.1 Size and Age 25

4.2.2 Ratio of Proved to Probable Reserves 26

4.2.3 Quality of Estimates: Technical Revisions, Evaluators and Reserves

Committee 27

i

4.2.4 Legal Form 28

5. Methodology 30

5.1 Model and Assumptions 30

5.2 Data Collection 34

5.3 Descriptive Statistics 36

6. Results 38

6.1 Value Relevance 38

6.2 Contextual Analysis 42

6.2.1 Size 42

6.2.2 Ratio of Probable to Proved Reserves 43

6.2.3 Quality of estimates 44

6.2.4 Legal Form 45

6.3 Sensitivity Analysis 46

6.3.1 Yearly Analysis 46

6.3.2 Oil and Gas Prices 46

6.3.3 Returns Model 47

6.3.4 Accounting Method: Full Cost vs. Successful Efforts 49

6.3.5 Discount Rate: Geographical Diversification 49

6.3.6 Product Mix: Oil vs. Gas 50

7. Conclusions and Future Research 51

REFERENCES 54

LIST OF APPENDICES, EXHIBITS AND TABLES:

Appendix 1: Reserves Classification 58

ii

Appendix 2: Reserves Disclosures 59

Exhibit 1: Timeline of National Instrument 51-101 60

Exhibit 2: Oil and Gas Prices (2003-2007) 60

Exhibit 3: Diversification Effect 61

Chart A: Probability Density Function 61

Chart B: Inverse Cumulative Distribution Function 61

Table 1: Sample Descriptive Statistics (2003-2006) 61

Table 1: Sample Descriptive Statistics (2003-2006) 62

Table 2: Correlation Matrix (Pearson above diagonal and Spearman below) 63

Table 3: Results from Basic Regressions 63

Table 3: Results from Basic Regressions 64

Table 4: Contextual Analysis: Univariate Analysis 65

Table 5: Contextual Analysis: Multivariate Analysis (SEC Case) 66

Table 6: Yearly Regressions (SEC Case) 67

Table 7: Returns Model 68

in

ACKNOWLEDGEMENTS

I thank Bjorn Jorgensen for his helpful and unconditional support. I also thank Divya

Anantharaman, Sid Balachandran, Donal Byard, Colin McGee, Natalie Mizik, Nahum

Melumad (Sponsor), Partha Mohanram, Doron Nissim (Chair), Du Nguyen, Gil Sadka,

and participants in the Brown Bag Seminars at Columbia University for their comments

and suggestions. Special thanks to David Elliott and Carrie Nermo from the Alberta

Securities Commission for providing valuable information. I gratefully acknowledge the

financial support from the Columbia University's Center for International Business

Education and Research (CIBER). All errors are mine.

IV

For my family and friends

v

1

1. Introduction

Regulators have long used probability thresholds in accounting standards as

criteria for the recognition and measurement of assets and liabilities. For instance, SFAS

No. 5 uses the verbal probability thresholds of probable, reasonably possible, and remote

to determine how to account for contingent liabilities. Estimable and probable liabilities

are recognized in the primary financial statements, whereas reasonably possible liabilities

are disclosed in the footnotes. Remote contingencies are not disclosed except for some

specific cases (e.g., guarantees). These probability thresholds are intended to help

financial statements users to infer the certainty of accounting estimates. This study

examines whether this is the case.

Attempts to investigate the effect of different probability thresholds on investor's

valuation face serious obstacles. First, as explained above, the probability thresholds are

often employed to determine whether an accounting estimate must be recognized,

disclosed in the footnotes, or can remain undisclosed. Consequently, it is impossible to

disentangle the valuation differences due to distinct probability thresholds from the ones

due to the place where the numbers are reported in the financial statements, i.e., primary

financial statements vs. footnotes. Second, regulators' probabilistic definitions are usually

verbal, and thus, subject to multiple interpretations (e.g., Schultz and Reckers, 1981;

Beaver, 1991; Amer et al., 1994, 1995; Aharony and Dotan, 2004). Third, often times, the

amounts recognized and disclosed are combined with other accounts for financial

statement presentation, either because they are not substantial or because firms have

incentives to disguise them. For example, firms might fear that a court perceives a

disclosure of an estimated liability for damages as an admission of guilt.

2

A recent regulatory change in the disclosure of oil and gas (O&G) activities in

Canada provides a unique setting to test the valuation implications of probabilistic

thresholds. Under the new regulation, Canadian issuers with O&G activities are required

to break down O&G reserves into proved, probable, and possible reserves -the disclosure

of the latter is voluntary- following explicit numeric probabilities of recovery:

1. Proved Reserves (P90): at least a 90% probability that the quantities actually

recovered will equal or exceed this estimate.

2. Proved + Probable Reserves (P50): at least a 50% probability.

3. Proved + Probable + Possible Reserves (P10): at least a 10% probability.

O&G firms use historical cost accounting in their primary financial statements.

The quantity and value estimates of their major asset, O&G reserves, are disclosed as

supplementary information in the footnotes.

I examine the information content of the breakdown in reserves according to

probability thresholds. By means of an incremental value relevance analysis, I address the

question on whether investors attach a different market value weight to proved and

probable reserves. A significant difference in the weights would imply that the

breakdown in reserves is more informative about the cross-sectional/time variation of

market values than an aggregate reserve measure. Finally, I study the variation in

reserves pricing across multiple valuation assumptions and firm characteristics.

I find that investors attach a higher market value to proved reserves than to

probable reserves. The magnitudes of the coefficients are consistent with investors

3

behaving rationally, in accordance with the tenets of a simple expected utility model or

even prospect theory, i.e., higher risk-aversion for gains. The result holds across different

valuation assumptions and is reinforced when firms' prior reserves estimates have been

more accurate and an independent reserves committee is in place. The market value

weight of proved reserves tends to be larger for small size firms that have a lower ratio of

proved to probable reserves. The market value weight of probable reserves tends to be

greater for large size firms with a higher ratio of proved to probable reserves.

This study contributes to the literature on quantitative and qualitative thresholds

to communicate GAAP and, in particular, to the extant research on probability thresholds

for estimates that are not defined contractually. Previous work is mostly experimental and

tries to elicit the interpretation of probability thresholds from financial statement

preparers/auditors (e.g., Schultz and Reckers, 1981; Harrison and Tomassini, 1989; Amer

et al., 1994, 1995), and users (e.g., Reimers, 1992; Kennedy et al., 1998; Aharony and

Dotan, 2004). In the empirical domain, Campbell et al. (2003) examine the uncertainty-

reducing role of accounting information in the context of SFAS No. 5 for the specific

case of contingent Superfund liability valuation. They find that information revealed

through accruals (i.e., amounts recognized in the primary financial statements) versus

disclosures (i.e., disclosure index) is differentially effective at reducing site and allocation

uncertainty for a sample of firms in the chemical industry. However, they do not estimate

the direct impact of amounts recognized and disclosed on market values. There are other

studies examining the difference in valuation between recognition and disclosure (e.g.,

Davis-Friday et al., 1999; Ahmed et al., 2006), but the accounting treatment of the

compared amounts is not driven by probability thresholds. To my knowledge, this is the

4

first study that compares the valuation of an accounting item across different probability

categories.

My results also contribute to the O&G accounting literature. Previous studies

have tested the value relevance of historical accounting in the presence of fair value

estimations in the footnotes. In a dynamic industry with new discoveries of reserves,

continuous changes in market prices, and constant innovation of exploration and

extraction techniques, one might expect the reserves value estimations to have higher

information content than historical book values. Yet, in the U.S. context, prior research

showed a weak association between security prices and oil valuation disclosures required

by SFAS 69 (e.g., Harris and Ohlson, 1987; Magliolo, 1986). Three plausible reasons

might explain these results: unreliable estimations of reserves quantities, flaws in the

valuation model, and model misspecifications (Clinch and Magliolo, 1992; Boone, 2002).

The new Canadian regulation arguably addresses the first two issues by introducing

probabilistic disclosure of reserves and requiring estimations under additional economic

assumptions (forecast prices and costs, different discount rates, etc.). In contrast, the SEC

only requires the disclosure of proved reserves,1 defined as those that can be recovered in

future years "with reasonable certainty" under existing economic and operating

conditions, i.e., constant prices and costs case. I specify a model based on Miller and

Upton (1985a), incorporating the suggestions of Boone (2002). This study offers

evidence on the specific assumptions under which reserves estimates values are

significantly more relevant than historical book values.

1 The SEC does not allow the voluntary disclosure of additional reserves categories.

5

The results of this study are relevant for standard-setters, auditors, investors and,

in particular, O&G industry participants. In the development of a new common

conceptual framework, the FASB and the IASB are questioning the role of probability

thresholds for recognition and measurement of assets and liabilities. The findings of this

paper are consistent with investors using numerical probability thresholds for valuation.

Although the implementation of similar numerical probability thresholds might not be

possible in other contexts where the estimations have higher uncertainty, regulators and

auditors can consider alternatives to increase consistency and comparability such as the

use of standard scales of probability phrases (Amer et al., 1994). Investors use probability

thresholds to estimate expected values and risk. For the specific case of O&G firms, I

show how these inferences can be potentially misleading if factors affecting the reliability

of estimates are not considered (e.g., prior accuracy of estimates, amount of O&G

properties aggregated in the estimation, etc.). Finally, I offer evidence on the significant

relevance of O&G reserves estimates under specific valuation assumptions (i.e., forecast

vs. constant prices and costs; before and after taxes) that can be relevant for regulators.

There has been a long debate in the industry among standard setters, professional

associations, and producers on how to harmonize the estimation and disclosure of

reserves. The new Canadian regulation is pioneering in this process of global

standardization. The IASB is currently working on a new standard specific to extractive

activities.

While accounting information can serve many users (Holthausen and Watts,

2001), I choose to focus on the value relevance for investors because such is the explicit

goal of the new disclosure standard. The Canadian Securities Administration (CSA)

6

states that the purpose of this new regulation is to help investors to make informed

investment decisions concerning securities of O&G producers by "enhancing the quality,

consistency, timeliness, and comparability of public disclosure."

The paper proceeds as follows. In Section 2 I review the previous literature on

probability thresholds. Section 3 provides some institutional knowledge on the O&G

industry and the new disclosure regulation in Canada. The research hypotheses are

presented in Section 4. Then, in Section 5, I describe the sample and the methodology.

Section 6 discusses the results of the main test and the contextual analysis. Section 7

concludes and suggests future related research.

2. Probability Thresholds

2.1 The Use of Probability Thresholds in GAAP

An accounting standard is the total body of principles and rules that apply to a

given accounting issue (Nelson, 2003). In a principles-based accounting system,

standards are written to operationalize the underlying conceptual framework. At the same

time, standards require rules to provide guidance, that is, specific criteria such as

thresholds, examples, exceptions, implementation guidance, etc. Probability thresholds

fall within this set of rules that helps to communicate GAAP and constraint aggressive

reporting.

To understand the use of probability thresholds in GAAP, we need to consider

two dimensions. First, at what stage probability thresholds are utilized in "the path to

7

recognition" of an amount resulting from a transaction or other events.2 For example,

probabilities could be used at the definition stage to determine whether an accounting

item is an asset/liability or not; or they could be used at the recognition stage to decide

whether that asset/liability must be recognized or just disclosed on the footnotes; or,

assuming we have an asset/liability that must be recognized or disclosed, probability can

be used to give a measurement of this asset, such as the best estimate or a range of values.

Second, how precise the probability threshold is. Probability statements in accounting

standards can vary from vague verbal statements, such as "probable" or "reasonably

possible", to "bright line" thresholds based on explicit numbers.

There exists a significant inconsistency, within and between GAAPs, in the role

of probability and uncertainty in defining, recognizing, and measuring assets and

liabilities.3 In US GAAP we find that, at the conceptual level, probability is used in

measurement (in particular, probability is embedded in the present value calculations

following Concept No. 7), whereas at the standard level it can be used in recognition. For

example, SFAS No. 5, issued 25 years before Concept No. 7, uses the probability

thresholds of probable, reasonably possible and remote to determine how to account for

contingent liabilities, that is, at the recognition stage. In contrast, standards issued after

the FASB's Conceptual Framework apply probability thresholds at the measurement

level. For example, SFAS No. 143, issued one year after Concept No. 7, states that

2 The FASB and the IASB, in the Exposure Draft of their new common conceptual framework, distinguish three stages in the "path to recognition" of an accounting item: 1) Definition: does the item meet the definition of an element of accrual-basis financial statements?; 2) Recognition: does the item meet the criteria for recognition (definition and measurability)?; 3) Measurement: what measurement attributes (historical cost, current cost, fair value, expected value, etc.) and methods can or should be used in order to calculate amounts to be recognized in the financial statements? 3 This inconsistency has been a major issue of discussion in the common project to elaborate a new conceptual framework started by the FASB and the IASB in October 2004. For more information on the nature of the debate, see the invitation to comment on "Selected Issues Relating to Assets and Liabilities with Uncertainties", FASB Financial Accounting Series No. 1235-001, September 30, 2005.

8

present asset retirement obligations whose settlement amount and timing may be

uncertain must be recognized as liabilities in the financial statements at fair value, unless

a reasonable estimate of fair value cannot be made. In the absence of market prices, net

present values techniques can be used. SFAS No. 143 encourages the use of probability

assessments for measurement.4 However, even if firms use these probabilities, they do

not need to disclose them.

The current predominant view in the FASB is to limit the presence of probability

thresholds to the measurement stage.5 The IASB seems to share this view. Although the

IASB's framework explicitly includes a probability threshold among its recognition

criteria (unlike the FASB's framework), a recent proposal hints a new direction. The

Exposure Draft of an amendment to IAS 37, the counterpart of SFAS No.5 on contingent

liabilities, plans to omit the use of probability as a recognition criterion for non-financial

liabilities and relegate it to the measurement stage. The conceptual implications of this

change are not trivial. The terms contingent assets and contingent liabilities should be

eliminated altogether since they do not meet the definition of an asset and a liability (i.e.,

not the result of past events and not controlled by the entity). What triggers recognition is

not the probability of this conditional or contingent rights/obligations, but the existence

of an underlying unconditional or non-contingent rights/obligations. The IASB provides

an example at case. The obligation of a firm issuing a warranty to repair or replace a

defective product is a conditional obligation because it depends on whether the product

4 FASB General Standards Section A50, paragraphs .143, .146, and the illustrative examples in .153, .156, .160 and .161. Similarly, SFAS No. 144 on impairment of long-lived assets recognizes that probability-weighted cash flows may be used to test the recoverability of long-lived assets fl[17). 5 Only three members of the FASB hold the alternative view that probability should also be used at the definition and recognition stages (Federal Accounting Standards Advisory Board's memo dated January 3, 2007). From reading the comment letters to the Exposure Draft, one can see that respondents are evenly divided regarding the use of probability thresholds at the definition level and at the recognition level.

9

develops a fault and the customer seeks repair or replacement under the warranty. The

unconditional obligation is to provide warranty coverage; that is, to stand ready to repair

or replace a defective product. Recognition is triggered by the existence of the

unconditional obligation. The probability assessment of the conditional obligation can be

helpful in the measurement of the liability.

My study focuses on a new disclosure standard that uses probability at the

measurement stage, consistent with the predominant view at the FASB and the IASB, and

requires the disclosure of probability assessments. Therefore, my research can inform

standard setters on the effect of probability thresholds at the measurement stage on

financial statement users. O&G reserves meet the main world standard setters' definition

of an asset: future economic benefits, controlled by the entity, as the result of past

transactions. What triggers recognition and disclosure is the mere determination of

whether a well drilling is successful or not, that is, whether we can extract some amount

of O&G profitably. The probability thresholds required by the new regulation are meant

to inform investors on the measurement of reserves estimates.6

Some accounting standards make use of "bright-line" probability thresholds. For

instance, SFAS No. 109 on income taxes specifies a probability threshold of 50% when

measuring the deferred tax asset valuation allowance (117). However, these are the

exceptions. Most probability statements are verbal (e.g., SFAS No. 5, No. 15, No. 19)7

and thus, subject to multiple interpretations. This ambiguity is compounded by the

6 Note that in terms of valuation it does not matter whether you apply the probability weights to the cash flows before or after discounting them. Thus, my setting is totally consistent with the Statement of Concepts No. 7. 7 The use of verbal probabilistic statements is also pervasive in the auditor's standards (e.g. AS No. 2, AS No. 5) and in the SEC's rules and regulations (e.g. Regulation S-K 229.303 on MD&A). In the IFRS we also find a great variety of verbal probabilistic statements (see appendix in Doupnik and Richter 2004).

10

inconsistency in the use of probabilistic statements. A clear example is the use of

"probable" with different meanings. FASB Concepts Statement No. 6, Elements of

Financial Statements, employs the term "probable" in the definition of assets and

liabilities to express not certain in a general sense (footnote 18). In contrast, SFAS No. 5

uses "probable" as a technical probability threshold, meaning "the future event or events

are likely to occur". Next section reviews the research on the interpretation of probability

statements among auditors, preparers and financial statement users.

2.2 Previous Research

2.2.1 Experimental Work

Most prior work on accounting probability thresholds is experimental and

investigates how auditors, preparers and financial statement users interpret probability

statements. Experiments and surveys present participants with probability statements

related to a specific accounting issue -many studies use SFAS No. 5 - and try to elicit

their probability assessments. Evidence from this research suggests that there is a

significant between-auditor variation in the interpretation of probability statements (e.g.,

Schultz and Reckers, 1981; Jiambalvo and Wilner, 1985; Harrison and Tomassini, 1989;

Amer et al., 1994, 1995), consistent with findings in the psychology literature using non-

accountants (e.g., Budescu and Wallsten, 1985). Although most studies focus on external

auditors, financial statements are primarily the responsibility of managers and are

addressed to investors, financial analysts and other users. Reimers (1992) and Aharony

and Dotan (2004) look at the degree of agreement between auditors, managers and users

11

in the interpretation of the probability thresholds of SFAS No. 5. In general, managers

and auditors share similar numerical interpretations whereas financial analysts tend to be

more conservative. At the international level, disparities in interpretation are accentuated

by the diversity in the language of likelihood (Price and Wallace, 2001) and cultural

contexts (Doupnik and Richter, 2004).

Some argue that numerical thresholds would avoid this reported variability in

interpretations among different constituents (e.g., Price and Wallace, 2001b).

Practitioners often complain about the costly negotiation processes between auditors and

preparers generated by ill-defined probability thresholds. Stone and Dilla (1994) find

evidence that consensus in auditor's risk judgment is higher for assessments based on

numerical probabilities. Although evidence in the psychological field using inexperienced

participants is mixed (Wallsten et al., 1993), one would expect that the variability in the

interpretation of numerical thresholds within and between groups is lower than the one of

verbal thresholds. Windschitl and Wells (1996) find numerical expressions to be less

influenced by context and framing than verbal expressions. Hence, the "bright-line"

probability thresholds of my study may make the results more relevant for other contexts.

2.2.2 Theoretical Background

Two lines of theoretical research are pertinent to this study: prospect theory and

the accounting models on risk disclosures.

It is well established in the psychology literature that people rely on a limited

number of heuristic principles to simplify the task of assessing probabilities and

predicting values (Tversky and Kahneman, 1974). Although useful, these heuristics can

lead to systematic biases. Some of these psychological biases were articulated in the so-

12

called Prospect Theory. In their seminal paper, Kanehman and Tversky (1979) present

several choice problems' experimental results that violate the axioms of expected utility

theory. First, people overweight outcomes that are considered certain, relative to

outcomes which are merely probable. This is called the certainty effect. Second, in the

case of losses, people overweight outcomes that are merely probable, relative to

outcomes that are considered certain. The preference between negative prospects is the

mirror image of the preference between positive prospects and this is why this

phenomenon is called the reflection effect. The final observation is that people tend to

discard components that are shared by all prospects under consideration, leading to

inconsistent preferences when the same choice is presented under different forms. This is

known as the isolation effect. Put together, these principles result in a value function that

is concave for gains (risk averse), convex for losses (risk seeking), and generally steeper

for losses than for gains.

Few theoretical studies address the communication of riskiness of investments or

uncertainty of future obligations.8 Jorgensen and Kirschenheiter (2003) present a model

where a manager can disclose the variance of his firm's future cash flows at a cost. They

find a partial disclosure equilibrium in which managers voluntarily disclose if their firm

has a low variance of future cash flows, but withhold the information if their firm has

highly variable future cash flows. However, in my setting disclosure is mandatory. One

model that better fits my study is Magee (2006). In his paper, a risk-neutral entrepreneur

8 The O&G reserves classification based on probability thresholds conveys information about the certainty of reserves extraction and the associated cash flows, so it can be understood as a risk disclosure. However, it does not say anything about the quality of the estimates. To assess the quality of the estimates we should examine the technical revisions reported in the reconciliation of reserves, but this would be a different question. Consequently, theoretical research on the disclosure of accounting estimates precision is not relevant for my study.

13

utilizes asset/liability recognition to communicate with risk-averse investors about the

uncertainty of future benefits/obligations. Similar to my setting, the "recognition hurdle"

(i.e., probability threshold) is exogenously determined and investors learn about the

distribution of future cash flows.9 In Magee's model, investments that generate future

benefits with an uncertainty level lower than the "recognition hurdle" are capitalized.

Otherwise, they are expensed. So, conceptually, Magee is dealing with probability

thresholds at the definition stage, that is, to determine whether an amount constitutes an

asset/liability or not. In my case, the use of probability thresholds is at the measurement

stage. In practice though, both cases are analogous, since thresholds help to distinguish

investments with future benefits of different level of uncertainty. Unlike my study,

Magee (2006) is concerned with the investment decision of the entrepreneur and just

assumes that investors will value the investments rationally. In my setting, I focus on this

latter assumption, that is, on whether investors are pricing assets of different uncertainty

rationally.

2.2.3 Lack of Empirical Evidence

Efforts to investigate how investors value accounting estimates corresponding to

different probability thresholds meet serious obstacles. When thresholds are used to

determine recognition, such as in SFAS No. 5, amounts corresponding to different

probability thresholds will receive different accounting treatment: recognition in the

primary financial statements, disclosure in the footnotes or non-disclosure. In such a case

it is impossible to disentangle those differences in valuation due to the degree of

9 In my case the present value of future cash flows from reserves is explicitly disclosed in the footnotes, whereas in Magee (2006) the cost of the investment is disclosed and the investor has a good idea of the distributional information of the returns. At the end of the day, in both cases investors can assess the value.

14

uncertainty from those due to the different position in the financial statements (functional

fixation hypothesis). When thresholds are used at the measurement stage, such as in

SFAS No. 143 and 144, the probability weights are embedded in the present value model

but they are not necessarily disclosed.

To my knowledge, this is the first empirical study that tries to look at the direct

net effect of accounting probability thresholds on market values. However, I find a few

studies that investigate the differential information of recognition versus disclosure to

assess uncertainty. Campbell et al. (2003) examine the uncertainty-reducing role of

accounting information in the context of SFAS No. 5 for the specific case of contingent

Superfund liability valuation. They find that information revealed through recognition

(i.e., amounts recognized in the balance sheet and income statements) versus disclosures

in the notes, is differentially effective at reducing site and allocation uncertainty for a

sample of firms in the chemical industry. However, they do not estimate the direct impact

of amounts recognized and disclosed on market values. Instead, they regress the dollar

amounts recognized on other public information and take the residual as an estimate of

the additional information of these accruals. They do the same for the disclosures but,

instead of dollar amounts disclosed, they use a disclosure index due to the heterogeneity

of disclosures. Then, they include these proxies in a valuation framework, interacting

them with the site and allocation uncertainty dummy variables.

3. Reserves Disclosures in the Oil and Gas Industry

This section analyzes the current situation of reserves accounting and how the

recent changes introduced in Canada provide a suitable setting for my research question.

15

3.1 Issues on Accounting for Reserves

O&G firms use historical accounting in their financial statements. The costs

incurred in the discovery and development of new reserves are capitalized following

either the full cost method (FC) or the successful efforts method (SE).10 Two problems

become immediately apparent. First, the amount of O&G reserves discovered does not

show in the balance sheet. So a reader of the financial statements could only find out how

much has been invested in exploration activity, but not how efficient these investments

have been. Second, the full value of the major asset of the firm, O&G reserves, is not

reported in the balance sheet. To overcome this shortcoming, SFAS No. 69 requires a

comprehensive set of disclosures on reserves quantities and values in the footnotes. Other

international GAAPs mandate similar disclosures.11

The majority of studies on disclosures of reserves quantity and value find this

information relevant to investors, creditors and management. However, evidence is

mixed on whether this information is incrementally relevant to the primary financial

statements figures from the point of view of stock investors. Contrary to what historical

accounting critics would expect, Harris and Ohlson (1987) find no evidence that book

values are less relevant than the present value measures. The rest of measures required by

SFAS 69 -future net cash flows, direct profit margin, and quantity of proved reserves-

are not significant in explaining the market value of O&G properties. In a subsequent

10 Under SE firms only capitalize those exploration and development costs that are associated to successful exploration, and expense those associated to unsuccessful projects. Under FC the majority of costs are capitalized. The underlying idea in FC is that all exploration costs are necessary to eventually lead to the discovery of reserves. 11 For example, in the U.K., the disclosure provisions in the 2001 Statements of Recommended Practice (SORP) are similar to those mandated by SFAS No. 69, except that the SORP do not require a reserve value disclosure. In Canada, the current disclosures are regulated by the National Instrument 51-101 that I will explain extensively in subsequent sections.

16

paper, Harris and Ohlson (1990) reject the plausible "functional fixation" hypothesis that

investors place more attention to the primary financial statements numbers than to the

footnote disclosures. Their evidence supports the validity and relevance of historical cost

accounting for O&G properties. Similarly, Magliolo (1986) fails to find a clear link

between market-determined value of reserves and Reserves Recognition Accounting

(RRA) disclosures.12 If a returns model is used, the overall change in reserves value is

not incrementally value relevant, although some of its components are (Alciatore, 1993).

Boone (2002) argues that previous studies (Magliolo, 1986; Harris and Ohlson,

1987; Shaw and Wier, 1993) suffer from model misspecification. The "Imputed Value

Model" assumes the same intercept for all firms and restricts the coefficients of other non

oil and gas assets and liabilities to be 1, implicitly assuming that the market values and

the book values of these items are equal. With an unrestricted, fixed-effects model, O&G

assets measured at present value exhibit a significantly greater explanatory power for

market values than the corresponding historical cost measure. Measurement error and

time-period idiosyncrasies are also presented as hypothesis to explain prior mixed results,

although the evidence is less compelling. The amount of measurement error seems to

increase in FC firms as revisions in reserve quantity estimates increase and as firm-

specific discount rates differ from the 10% discount rate required by SFAS No. 69.

However, historical measures appear to be noisier than present value estimates as

Unsatisfied with the historical approach of SFAS 19, Financial Accounting and Reporting by Oil and Gas Producing Companies, the SEC developed the so-called reserve recognition accounting (RRA), an alternative method of accounting for reserves that takes into consideration the estimated additions to proved reserves and the changes in valuation of estimated proved reserves at current prices and a discount rate of 10%. The SEC only required RRA as a supplemental disclosure. In 1982, the FASB issued SFAS 69, Disclosures about Oil and Gas Producing Activities, replacing SFAS 19 and the SEC's RRA disclosures.

17

measured by the error variance. Reserves appraisers' independence and oil prices

volatility are only weakly related to the amount of measurement error.

Although some have suggested that managers manipulate reserves quantity

estimates (e.g., Hall and Stammerjohan, 1997), an overwhelming number of empirical

studies indicate that estimates are unbiased based on the analysis of annual technical

revisions (e.g., Campbell, 1988; Spear and Lee, 1999; ASC O&G Review 2004, 2005,

2006).13 Nonetheless, most studies consider reserves estimates unreliable.

3.2 Reserves Classification

In January 2004, Shell shocked the business community with the revelation that it

had overstated its O&G reserves estimates by 20%.14 This was not an isolated event

though. Companies such as Forest Oil, El Paso, Penn West Petroleum, BP Pic and Baytex

Energy experienced downward revisions in 2003 that fall outside generally accepted

ranges.15 Although there were multiple explanations for the write-downs, this chain of

events revived an old debate on the regulators' approach to O&G reserves accounting.

The SEC requires the disclosure of proved reserves and defines them as "the

estimated quantities of crude oil, natural gas, and natural gas liquids which geological

and engineering data demonstrate with reasonable certainty to be recoverable in future

years from known reservoirs under existing economic and operating conditions". This

13 Technical revisions occur due to estimation procedures, resulting from moving reserves from one classification to another, obtaining new information, and due to poor geological and engineering reserves estimation practices. The ASC's studies use my same setting of Canadian firms post-NI 51-101 and poses an advantage with respect to the US samples from other studies. The technical revisions under NI 51-101 do not include the confounding effects of changes in prices and infill drilling. 14 This figure was later revised by a further 10%, amounting to a total reduction of 5.87 billion barrels in proved reserves from the 19.5 billion reported at the end of 2002. This incident prompted a sharp decline in the firm's stock price and two months later cost Royal Dutch/Shell's chairman his job. In April 2007, Shell Pic agreed to pay $352.6 million in settlement of claims by European investors related to the reserves overbooking ("Shell settles European Case - US Style", WSJ, April 12th 2007). 15 Herold Industry Insight, March 19lh 2004.

18

definition presents a major problem. Under the "reasonable certainty" pretext firms might

disclose any reserve estimate in the range between 50% and 99% level of certainty,16

hampering attempts to compare O&G firms. In addition, the SEC does not allow O&G

firms to disclose reserves categories with other levels of recovery uncertainty, such as,

probable and possible reserves. In the international arena, disparities in regulators'

reserves definitions and disclosure requirements magnify this inconsistency.

Efforts to standardize the definitions of reserves in the industry started as early as

in the 1930s. In the last decades this process has accelerated with the internationalization

of O&G producers, the general harmonization trend in financial markets and accounting,

and the creation of international associations in the industry. The continuous evolution of

the technologies employed in petroleum exploration, development, production and

processing creates a constant need for revision. The most up to date and commonly

accepted classification of reserves can be found in the Petroleum Resources Management

System document (PRMS).17 In this document, the definitions of reserves include a

numerical probability threshold according to the level of recovery uncertainty.

It remains to be seen when regulators will embrace more updated classifications.

The Canadian Securities Administration has made the first step forward by issuing a new

disclosure standard that provides clear definitions of reserves, based on numerical

probabilistic thresholds and the standardized guidelines of professional associations.

1 One way to gauge where those reserve estimates stand is by plotting the ratio of the annual positive revisions versus the positive plus negative revisions from the US Department of Energy US proved reserves (Laherrere 2004). The plot for oil shows that the probability was around 75% in the beginning of the 70s and that is trending towards 55% in 2005. 17 The PRMS was prepared by the Oil and Gas Reserves Committee of the Society of Petroleum Engineers (SPE), and reviewed and jointly sponsored by the World Petroleum Council (WPC), the American Association of Petroleum Geologists (AAPG), and the Society of Petroleum Evaluation Engineers (SPEE).

19

3.3 Pioneering Regulation in Canada

Effective September 30th 2003, all public Canadian O&G companies are subject

to National Instrument 51-101 (hereafter NI 51-101), a new reserves disclosure regulation

passed by the Alberta Securities Commission (ASC). The purpose of the Instrument, as

stated by the ASC, is "to enhance the quality, consistency, timeliness and comparability

of public disclosure by reporting issuers concerning their upstream O&G activities."18

The ASC considers information on O&G reserves essential "to enable investors to make

informed investment decisions".

3.3.1 Background

The debate on reserves classification and measurement is not foreign to Canada,

the second country in proved oil reserves after Saudi Arabia.19 But the wide range of

constituents to be satisfied in a multibillion dollar strategic sector with a highly technical

component makes harmonization a challenging endeavor.

As early as in 1998, the ASC established an O&G taskforce comprised of

representatives from a wide variety of professions and sectors of the O&G industry and

capital markets to study how to increase investor confidence and improve corporate

governance in the sector. At the same time, professional associations started working in

the development of new O&G reserves definitions and reserves evaluations standards

Canadian Securities Administrators Notice, September 26 2003. 19 Source: Oil & Gas Journal, Vol. 103, No. 47 (Dec. 19, 2005). In 2004, Canada was the 8th country in production of O&G. In 2006, the mining and petroleum sectors accounted for 3.7% of the Canadian GDP (www.statcan.ca), whereas in the US they accounted for 1.9% (wwWibcfugov). Almost half of the traded O&G firms in the world are listed in the Toronto Stock Exchange (166) and in the Toronto Stock Exchange Venture (266).

20

consistent with other international initiatives.20 This parallel process concluded with the

publishing of the Canadian Oil and Gas Evaluation Handbook (COGEH) in 2001, a much

needed prerequisite for any reserves disclosure reform that wants to guarantee reliability

and comparability.

When the taskforce issued its recommendations in January 2001, the ASC

assumed the primary responsibility for developing the Instrument. The first draft was

published and open for public comments in January 2002 and, one year later, the revised

version was already available for a new round of comments.21 The final standards were

published in July 18, 2003 and replaced the National Policy Statement No. 2-B, Guide for

Engineers and Geologists Submitting Oil and Gas Reports to Canadian Provincial

Securities Administrators. Exhibit 1 shows a timeline of the new regulation.

3.3.2 A new reserves definition

Under the new instrument firms must distinguish between proved and probable

reserves. Optionally they can also disclose possible reserves. We can find previous

distinctions between proved and probable reserves in Canada and with voluntary

character in the UK, but they are ambiguous and inconsistent. The Instrument is

The Petroleum Society of the Canadian Institute of Mining, Metallurgy and Petroleum (CIM) worked on the classification of reserves and the Calgary Chapter of the Society of Petroleum Evaluation Engineers (SPEE) on the evaluation standards. 21 In the first round, many commenters expressed their disagreement with the mandatory application of FASB standards (subsequently changed), and larger cross-border firms were reluctant to disclose more information different from their competitors in U.S. capital markets. Underlying this last criticism, there could be an implicit fear of having to reclassify reserves from proved to probable under the new definitions. In the second round, the ASC received 16 letters, the majority expressing general support for the Instrument. The main criticism was the excessive detail of some reserves data. Some commenters strongly opposed to the special exemptions for senior or cross-border reporting issuers.

21

pioneering in the unequivocal probabilistic22 definition of reserves taken from the

COGEH:23

Proved reserves (P90): at least a 90% probability that the quantities actually

recovered will equal or exceed the estimated proved reserves.

Proved + Probable reserves (P50): at least a 50% probability that the quantities

actually recovered will equal or exceed the sum of the estimated proved plus probable

reserves.

Proved + Probable + Possible reserves (P10): at least 10% probability that the

quantities actually recovered will equal or exceed the sum of the estimated proved plus

probable plus possible reserves.

Appendix 1 provides a graph and an example to illustrate how the classification is

done. Appendix 2 presents an example of reserves value disclosure. According to

COGEH, the "best estimate" of the reserves to be recovered should be the P50 estimate,

whereas the P90 and P10 definitions correspond to conservative and optimistic estimates,

respectively. The wider is the range between P90 and P10 the higher is the degree of

uncertainty. In general, uncertainty decreases with time, as more information on a

specific well or property becomes available.

22 The fact that the disclosure is probabilistic does not mean that the firm has employed probabilistic methods to calculate it. Actually, most firms, especially in the U.S., still use deterministic methods, that is, they select a single value for each parameter in the reserves calculation. In contrast, probabilistic methods first describe the full range of possible values for each unknown parameter, and then perform simulations (e.g., Monte Carlo) to generate the full range of possible outcomes and their associated probabilities. On average there should be no material difference between estimates prepared following either method. 23 The Companion Policy to NI 51-101 sets out, in Part 2 of Appendix 1, the reserves definitions derived from Section 5 of Volume 1 of the COGEH.

22

3.3.3 Other changes in disclosures and corporate governance

The instrument introduces other disclosures, some of them differing from the

SEC's requirements. The following are the most significant additions:

94

• Forecast prices and costs: in the U.S., the quantity and value of reserves is

calculated assuming the O&G prices and development and production costs from the

previous fiscal year-end. In increasing volatile O&G markets this requirement can cause

extreme valuations and a subsequent need for major reserves readjustments. Exhibit 2

illustrates graphically the recent volatile behavior of prices, in particular for natural gas.

The Instrument requires firms to value reserves using the evaluator's forecasts of O&G

prices and the costs of the firm, in addition to the constant prices case.

• More discount rates scenarios: in the US, the future expected cash flows from

reserves production are discounted at 0% and 10%. The Instrument also mandates the

5%, 15% and 20% for the forecast price case.

• Reconciliation of reserves: it separately identifies changes year-on-year of reserves

estimates due to extensions, improved recovery, technical revisions, discoveries,

acquisitions, dispositions, economic factors, and production. This reconciliation is similar

to the one required by the SEC.

• Future development costs for the next five years: these are the development costs

deducted in the estimation of net present values of reserves.

• Breakdown of reserves by major product type: for conventional O&G activities we

find (1) light and medium oil (combined), (2) heavy oil, (3) natural gas, and (4) natural

24 In the UK, the disclosure provisions in the 2001 SORP do not include reserves values, only quantities.

23

gas liquids. For non-conventional O&G activities the products are (1) synthetic oil, (2)

bitumen, (3) coal bed methane, and (4) hydrates.

To further guarantee the reliability and comparability of the estimates, the

Instrument requires firms to hire independent evaluators and to use the COGEH standards

to estimate reserves quantities. 5 Reserves Committees are recommended for the purpose

of hiring the evaluators and supervising their numbers before official approval by the

Board of Directors.

3.3.4 Other regulation projects

The IASB is working on a new standard specific to extractive activities.26 The

IASB Steering Committee on Extractive industries issued a paper for comment in

November 2000. Comments were submitted by 30th June, 2001. Since then, no new

version of the draft has been released. While all the members in the Steering Committee

agree in disclosing reserves quantities, they are divided with respect to reserves values. In

the disclosures the distinction should be made between proved and probable reserves, and

within proved, between developed and undeveloped reserves. The IASB plans to

incorporate the reserves definitions of the SPE-WPC, consistent with NI 51-101.

25 The CSA granted an exemption to large producers -more than 100,000 BOE per day throughout its most recent financial year- with demonstrated in-house reserves evaluation capabilities. 26 In the meanwhile, the IASB issued IFRS 6 in December 2004 with effective date January 1st 2006. This standard permits entities involved in extracting activities to continue using their existing accounting policies for exploration and evaluation assets. Regarding the disclosure of reserves, IFRS implicitly assumes that O&G firms will keep disclosing a standardized measure of reserves required by a local GAAP, such as the one required by the SEC, if that might potentially affect the financial statement numbers through ceiling tests and impairment for example.

24

4. Hypotheses

4.1. Principal Hypothesis

As explained before, O&G reserves are classified as proved or probable

according to their probability of recovery. Proved (P90) reserves have a probability of

90% or more of being produced, whereas proved plus probable (P50) reserves only have

a probability of 50% or more. This implies that probable reserves (P50 minus P90)

should have a probability between 50% and 90% of being recovered (see Appendix 1 for

an example). If we assume that investors are rational, based on expected utility theory

they should place a market value between $0.9 and $1 for each dollar of proved reserves,

and a market value between $0.5 and $0.9 for each dollar of probable reserves.

Alternatively, prospect theory would suggest that investors display a higher risk-

averse behavior in gains involving moderate probabilities (Kahneman and Tversky,

1979). For this reason, investors might value more a 0.9-1 probability of producing

proved reserves, than the 0.5-0.9 probability of producing probable reserves. If this is the

case, we would expect a coefficient of a higher range than 0.9-1 for proved reserves and a

lower range than 0.5-0.9 for probable reserves, in direct proportion to the degree of risk-

aversion of investors.

Despite suggesting slightly different coefficients, both theories mentioned above

imply that investors attach a higher market value weight to proved reserves. If that is the

case, we can conclude that probability thresholds are informative and thus the breakdown

of reserves into proved and probable is incrementally relevant. So my first hypothesis

stated in its null form is as follows:

25

HI: Investors value prove d reserves the same as probable preserves.

4.2 Contextual Analysis

The principal hypothesis investigates whether investors make use of the

accounting probability thresholds for valuation. The next question is which firm

characteristics make probability thresholds more or less relevant to investors. Thus I want

to focus on factors that affect the way investors value proved and probable reserves.

Specifically, I examine the effect of size, age, ratio of proved to probable reserves,

quality of estimates, and legal form. The first four factors relate to the probabilistic nature

of the estimates and their precision, and hence they are more likely to be generalized to

other settings. The last factor is more industry specific.

4.2.1 Size and Age

O&G reserves are estimated at the entity level (i.e., well or properly) and then

aggregated to the firm level. The final estimate factors in the diversification effect (also

called portfolio effect). As a result, those companies with more individual wells will see a

relatively larger proved reserve figure and a smaller proved plus probable plus possible

reserves figure. The proved plus probable reserves will still be the same. In other words,

as a result of diversification the reserves distribution narrows but the mean continues to

be the same. The example from Exhibit 3 illustrates by means of a Monte Carlo

simulation how the impact of entity probabilistic aggregation on reserve estimates can be

substantial. In addition, by the Law of Large Numbers, larger firms will have a better

behaved distribution with a lower level of technical revisions (measurement error).

26

For small firms, the reported average estimates P90 per well will be lower and

P10 higher than for large firms. However, if an investor in small firms is well-diversified,

he should obtain a real P90 higher than the reported estimates. Thus, investors might

attach a higher value to the P90 reserves estimates reported by small firms, knowing that

diversification can increase the P90 estimate. P50 should not be affected. If P90 is higher,

then probable reserves, the difference between P90 and P50, should have lower estimates

for investors. Hence, for small firms, I expect investors to attach a higher value per unit

of proved reserves and a lower value per unit of probable reserves than for large firms.

For each specific well, the level of uncertainty decreases with time as more

information becomes available. The reserves flow from possible to probable, and from

probable to proved. I would expect that firms of more recent creation tend to be smaller

and have a proportion of probable reserves larger than older firms. This is not necessary

the case, since many times new firms are the result of an amalgamation of preexisting

ones, especially in the case of trusts. So the impact on valuation of firms' age is unclear.

4.2.2 Ratio of Proved to Probable Reserves

The diversification effect brought to its last consequences implies that investors

only care about the mean. So theoretically, the second moment should not matter for

valuation. Yet, recent research suggests that idiosyncratic risk is actually priced (e.g.,

Goyal and Santa-Clara, 2003). Investors can experience constraints to diversification. In

the example at case, large investors might not be able to diversify among small firms

because these stocks are not liquid enough.

Since I do not know the exact distribution followed by reserves, I proxy the

variance of the distribution with the ratio of probable to proved reserves. The higher is

27

the ratio, the higher will be the variance. If this individual uncertainty is priced, I expect

investors to attach a lower market value weight to the proved and probable reserves

estimates of firms with a high proportion of probable to proved reserves, i.e., higher

variance. In addition, different levels of variance can also affect the valuation of proved

reserves relative to probable reserves. A higher ratio of probable to proved reserves might

indicate that the firm is operating new properties with potential but not proven results yet.

This lack of experience might place a discount on the probable reserves market value. In

contrast, firms with a higher proportion of proved reserves have already shown that they

can deliver and therefore, any probable reserves they have are a safer bet for future

growth. If this were the case, I would expect investors to place a market value premium

for the probable reserves of firms with a lower ratio of probable to proved reserves.

4.2.3 Quality of Estimates: Technical Revisions, Evaluators and Reserves Committee

Information risk is the likelihood that firm-specific information that is pertinent to

investor pricing decisions is of poor quality. Prior theoretical research shows that

information risk is a non-diversifiable risk (e.g., Easley and O'Hara, 2004; Lambert et al.,

2007). Francis et al. (2005) offer empirical evidence of different market pricing of

accruals depending on their quality.

We can see reserves estimates as a case analogous to accruals. One way to

measure the quality of reserves estimates is by analyzing the technical revisions in the

annual reserves reconciliation. I conjecture that firms with a larger technical revision (as

a percentage of total initial reserves) in the previous year are perceived as riskier because

their estimates are less reliable. Therefore, investors will assign lower market values to

the reserves of firms with larger relative amounts of technical revisions.

28

Following the same rationale, other factors that can affect the quality of reserves

estimates can also have an impact on their valuation. I identify two of them: independent

evaluators and reserves committees. Since I do not have information about the quality of

evaluators I will just control for them in one of the regressions as a fixed effect. More

important than the identity of evaluators, could be their incentives. Boards of directors

hire the independent reserves evaluator. This practice raises obvious concerns on possible

conflicts of interest. The Board might be interested in higher valuations and the

evaluators in keeping their business. Section 3.5 of NI 51-101 encourages O&G firms to

create an independent Reserves Committee to select the reserves evaluator and oversee

the evaluation process. The Board of Directors is still responsible for the final review and

approval of reserves evaluations. I expect those firms that voluntarily adopt Reserves

Committees to exhibit a lower information risk, i.e., higher quality of their reserves

estimates. I expect that investors will assign higher market values to the reserves

estimates of firms with Reserves Committees.

4.2.4 Legal Form

The legal form of O&G companies is not a random distinction. Firms adopt the

most convenient legal configuration according to their operational characteristics. For

example, Shaw and Wier (1993) examine how the organizational choice of US O&G

firms affects their market value. They find that exploration levels are similar for master

limited partnerships and corporations, but dividends and the present value estimation of

proved reserves are more relevant for master limited partnerships.

The two major legal forms in the Canadian O&G industry are trusts and

corporations. An energy trust is an investment vehicle that purchases royalties from its

29

wholly-owned subsidiaries that own producing O&G properties. The trust receives

income (which is essentially the subsidiaries' cash flow) and sells interests in the trust

(trust units) to investors (unitholders). The trust units generate regular cash distributions

for their unitholders. The key difference between trusts and corporations is that trusts are

structured so that they pay little or no corporate tax. So their income is taxed in the hands

of individual unitholders rather than at the corporate level.

Canadian royalty trusts are different from U.S. royalty trusts. The U.S. trusts pay

out the cash flow generated by their O&G properties, but they do not acquire new

properties. Consequently, their cash flow declines over time as their assets are depleted.

Canadian trusts, by contrast, try to replenish depleted properties with new acquisitions.

Since royalty trusts distribute most of their income to unitholders, they must raise cash to

fund acquisitions either by borrowing or by selling more units.

On average I expect trusts to be larger, to own more mature properties, to have a

higher dividend yield and a narrower difference between market values and reserves

estimations (since they mainly receive O&G royalties). I expect this factor to behave in

the same way as size. If corporations are smaller and less diversified than trusts, I expect

that they will receive a relative higher market value for proved reserves and lower market

value for probable reserves.

5. Methodology

30

5.1 Model and Assumptions

A good number of studies on O&G firms resort to valuation specifications based

on Hotelling's (1931) model for extractive industries.27 A special case of this model is

that, under the assumption that marginal cost equals average cost -i.e., constant returns to

scale in current as well in cumulative extraction - , the value of the total reserves depends

solely on the current spot price per unit, net of current extraction costs. Miller and Upton

(1985a, 1985b) test this simplified model with the following expression:

^ a + M-c"), (1)

where / indexes companies, t indexes time, 0 signifies the then current values as of

sample date t, Fis the market value of reserves, R is the quantity of reserves, p is the spot

price per unit, and c the current cost per unit. To proxy for the dependent variable, the

authors introduce the Imputed Value of O&G properties, calculated as the value of

equity, plus the value of liabilities, minus the value of non-O&G assets. This approach

has been followed by subsequent studies (e.g., Magliolo, 1986; Harris and Ohlson, 1987;

Shaw and Wier, 1993). For example, Harris and Ohlson (1987) regress the Imputed

Value of O&G on Book Value and different measures of reserves required by SFAS 69.

The so-called Hotelling's Principle states that the unit price of an exhaustible natural resource, less the marginal cost of extracting it, will tend to rise over time at a rate equal to the return on comparable capital assets. Obviously, this classic model relies on certainty and other restrictive assumptions, such as a production function with extraction costs per unit of output independent of cumulative output.

31

Similar to the aforementioned studies, I also use an Imputed Value model. The

idea is not different from the balance sheet approach pervasive in the capital markets

accounting literature. If we break down total assets (TA) into O&G assets (OGA) and

non-O&G assets (NOGA) in the basic balance sheet identity, we can express OGA as a

function of owners' equity (OE), total liabilities (TL) and NOGA:

OGA = OE + TL-NOGA (2)

The expression in the right-hand side of the equation is the same as the Imputed

Value from prior research. To implement the valuation model based on this expression,

ideally one should use market values for all the variables. First, I introduce the aggregate

-i.e. proved plus probable- estimation of O&G reserves reported in the footnotes

(PVOG) as a proxy for the market value of OGA. Next, OE can be substituted by the

market value of equity (MVE). Finally, in the case of TL and NOGA I will proxy market

values with book values. This last approximation presents some obvious caveats that I

will discuss in a subsequent section.

The dependent variable in my specification is MVE instead of the Imputed Value

(OE+TL-NOGA) from prior research. Boone (2002) argues that the "Imputed Value

Model" is misspecified because it assumes the same intercept for all firms and restricts

the coefficients of NOGA and TL to be 1, implicitly assuming that the market values and

the book values of these items are equal. With an unrestricted, fixed-effects model,

estimates change significantly. My model accommodates the suggestion of unrestricting

the variables and, in the sensitivity analysis, I also run a fixed-effects estimation. So

32

rearranging equation (2) and substituting by market values I obtain the following

regression model:

MVEit =a + PxNOGAit + fi2TLu + {33PVOGit + sit. (3)

In order to test my hypothesis I need the unrestricted form of this specification,

allowing different coefficients for estimations of reserves belonging to different

probability thresholds {Proved and Probable):29

MVEit =a + frNOGAu + /32TLit + p.Provedit + j35Probableit + e„. (4)

All the values in expressions (3) and (4) are scaled by units of Barrels Oil

Equivalent (BOE) of proved plus probable reserves. Barrels equivalents of reserves have

been often used as a deflator in previous O&G research (e.g., Magliolo, 1986; Harris and

Ohlson, 1987). It provides a natural deflator that allows a meaningful economic

interpretation of the variables and mitigates the scale effects (Barth and Kallapur, 1995;

Easton, 1998; Brown et al., 1999). Given the large range of firm sizes and share prices in

my sample, using the customary number of shares as a deflator might capture severe

scale effects. In my sensitivity analysis I also provide the results of estimating a returns

specification to further alleviate heterogeneity and scale effects.

The first hypothesis, stated in null form, can be expressed as Ho: p4 = Ps, that is,

investors value proved reserves and probable reserves in the same way. Rejecting the null

28 Note that expression (4) is equivalent to a specification that included Proved reserves (P>90%) and Proved plus Probable reserves (P>50%).

33

means that the decomposition according to probability thresholds is incrementally value

relevant. To assess the relative value relevance we should compare the coefficients of

determination of specifications (3) and (4). Since these two models are nested, by

rejecting Ho: p\ = Ps we could also conclude that model (4), with the breakdown of

reserves, fits significantly better that data than model (3). To test the rest of the

hypotheses I use partitions of my sample.

Several papers investigate the conceptual advantages and disadvantages of price

and return models (e.g., Lev and Ohlson, 1982; Christie, 1987). Kothari and Zimmerman

(1995) indicate that while price models normally exhibit less biased coefficients they are

more prone to econometric problems such as heteroscedasticity and/or model

misspecification. In this study I adopt a levels model for several reasons. First, because

the Imputed Value model is grounded in sound theory, a necessary condition for a levels

specification according to Gonedes and Dopuch (1974). Second, because with a returns

model I would lose many observations. Third, as mentioned before, the BOE deflator

solves some of the econometric problems characteristic of price models. Fourth, because

I lack a solid model of expected reserves.29 Finally, price models have been widely used

in previous research in O&G. In any case, I follow the advice of Kothari and Zimmerman

(1995) and also provide results following areturns specification in section 6.3.1.

29 In the O&G industry, models of expected reserves that include new discoveries are usually developed at the exploration play level. An exploration play (or petroleum zone) is any volume of rock-containing fields that have a common source, thermal, transport, and trapping history (Drew, 1990).

34

5.2 Data Collection

Under NI 51-101 all reporting issuers in Canada with O&G activities30 have to

file an electronic version of the following forms to their respective securities regulatory

authority:

Form 51-101F1: Statement of Reserves Data and Other Information

Form 51 -101F2: Report of Independent Qualified Reserves Evaluator or Auditor

Form 51-101F3: Report of Management and Directors

These forms are available in the System for Electronic Document Analysis and

Retrieval (SEDAR), the database of the CSA.31 Many times, these forms are included in

the Annual Information Form that O&G firms have to file every year with information on

their exploration and production operations.

I identify my initial sample doing a search for NI 51-101 documents including all

junior and senior O&G producers for the period 2003-2006. I only select public firms

quoted in the Toronto Stock Exchange (TSX).32 This results in a total number of 422

firm-years.33

Oil and gas activities are defined in the part 1.1 of NI 51-101 as those related to exploration, development, and production of hydrocarbons. This definition excludes transporting, refining or marketing of oil and gas, as well as activities related to the extraction of other natural resources. 31 The database is accessible at iYwwjswJaiuxtQi and it provides most public securities documents and information filed by public companies and investment funds with the CSA. It is the equivalent to Edgar database for the SEC. 321 purposely ignore those firms quoted in the TSX Venture because they tend to be less liquid and often times still at an exploration stage, with non-existent reserves. Actually the listing requirements are lower than in the TSX and one might argue that, as a whole, the TSX Venture is less efficient, undermining tests based on market efficiency. 33 To guarantee comprehensiveness, I compare my search with a dataset provided by the ASC with all the reserves estimations reported under NI 51-101 from 2003 to 2005. The ASC dataset contains 917 firm years with reserves different from zero. I find filings in SEDAR for 790 of these observations (86%). Out

35

Next, I drop 55 observations from firms that are not pure O&G producers

(mining, services, integrated oil, and others). The valuations of these firms might be

related with factors other than O&G reserves, potentially confounding my results. Then, I

remove from the sample those firm years with no stock price information. I use three

sources to get the stock prices and other market information: Yahoo Finance,34

Compustat Global, and Datastream, by this order. At this point, I start hand-collecting the

data I need from the NI 51-101 forms and Compustat (Canadian File). When data is not

available from Compustat I obtain it directly from the firms' Annual Reports. A total of

251 observations are left with the basic variables I need for the study: market value, book

value, liabilities, PP&E (as a proxy for OGA), total assets, net income before

extraordinary items, and all the measures of reserves estimates at 10% discount rate.

Finally, I eliminate 11 firm years because of mergers and acquisitions, 13 firm

years that use SE, and 8 firm years with market values per barrel higher than Cdn$80.

The latter criterion aims to eliminate firms whose main source of market value is not

O&G and other outliers. Harris and Ohlson (1986) apply a threshold of US$40 of

imputed value per barrel (note that IV=MV+TL~NOGA and that the average exchange

rate for the period of my study was 1.26 Cdn$/US$), consistent with the crude nominal

price level of their study period. My final sample contains 219 firm-year observations,

from 2003, year in which the Instrument became effective, to 2006.

of these, 343 are listed in the TSX, 335 in the TSX Venture, 22 in other exchanges, and 90 had issuances

other than equity. From conversations with the ASC, it seems plausible that the unidentified observations correspond to firms that were required to file again. In those cases, original filings were eliminated from SEDAR and the new filings were not available online. Large cross-listed firms might have also requested an exemption. Special thanks to David Elliott, Chief Petroleum Advisor of the ASC, and Carrie Nermo to make this information available. Although the information is public, all the firms and the evaluators in this dataset remain anonymous. Still, the dataset is useful as a cross-check for my sample. 34 Yahoo Finance is much more comprehensive and updated than the versions of Compustat Global and Datastream I am working with. The financial data provider for Yahoo is Hemscott Inc.

36

The dependent variable, market value (MVE), is calculated taking the stock price

and the outstanding shares three days after the filing of the Annual Report or NI 51-101

forms in SEDAR, whichever is filed later, to ensure that all the information is available to

investors. Using fiscal year end market values does not substantially alter the results of

this study.

5.3 Descriptive Statistics

The final sample contains 219 observations corresponding to 66 different firms.

The O&G exploration and production sector tends to be very fragmented with the

exception of a few large firms. This is reflected in my sample, as shown in Table 1. The

amount of Barrels Oil Equivalent (BOE) for proved and probable reserves present large

standard deviations (131,440 and 93,560 BOEs) and the mean is substantially greater

than the median. Note that a few large firms, such as Shell Canada Ltd., were removed

from the sample because they are integrated, that is, they also own transportation,

refinery and retail operations, potentially confounding the contribution of O&G reserves

to the market value. Some other large firms cross-listed in the US (e.g., Canadian Natural

Resources Ltd.) were eliminated because they were exempted from NI 51-101.

Table 1 includes the descriptive statistics for the whole sample. Figures are

expressed in Canadian Dollars per BOE, except for the quantities of proved and probable

reserves that are in units of BOE. Dividing the average Net Income by the average Total

Assets (NOGA + OGA) we obtain a ROA of 3.8%. This modest number partly reflects

the use of the FC method in Canada -the 5 firms that used SE in my sample have been

removed- combined with a sustained level of investment in recent years. Under FC,

O&G Assets are much higher and they are depreciated proportionally to production. If we

37

pair this less conservative method with a growing level of investment, like the one

experienced in recent years -oil price increases made investment projects more

attractive-, ROAs remain low -especially considering that it takes a while for O&G

investments to pay off.

The basic identity in the Imputed Value Model (e.g., Harris and Ohlson, 1987)

states that MVE+TL-NOGA=OGA, assuming that they all are at fair value. We can

substitute OGA with an estimation of reserves using present values and assume that TL

and NOGA are close to fair value. The mean MF+7Z-JV0Gy4=22.37+5.92-3.26=

Cdn$25.0/Barrel. If we compare it to the SEC case of estimated reserves using forecast

prices and costs, after taxes, and at 10% discount rate, we will find that the mean Proved

plus Probable reserves is only Cdn$16.2/Barrel -without weighting reserves by the

probability of recovery- and hence the difference is Cdn$8.8/Barrel. Multiple factors can

explain this gap: first, the fact that firms might have sources of revenue other than O&G

-i.e. non-conventional resources, minerals, or businesses in other parts of the O&G value

chain-; second, differences in O&G prices expectations between the end of the year -

when estimations of reserves are taken- and the reporting dates -when market values are

calculated; third, growth beyond the already discovered reserves priced in by investors;

fourth, overestimation of OGA; and finally, a too high discount rate for the reserves

estimations -actually, the undiscounted reserves estimation is Cdn$28.2/Barrel.

Comparing the quantity of proved and probable reserves, we observe that the

former is on average much larger than the latter (53,039 vs. 29,340 BOE). Since proved

reserves are those with probability 90% or more of being extracted and probable reserves

only have a probability between 50% and 90%, it would seem that probable reserves

38

should be larger. However, for each single well, uncertainty diminishes with time and

therefore most of the reserves move from probable to proved. These figures might reflect

an industry on average mature in Canada. Actually, in Section 6.2 we will see that the

ratio of proved to probable reserves is directly proportional to the age of the firms.

The identification of outliers and influential observations is particularly important

for the analysis of smaller cross-sections where the source of the data is known (Greene

2003, p.60). I identify outliers and influential observations using MM, a robust regression

technique (Yohai, 1987).35 A casual inspection of the outliers suggests that they are small

firms with very high market values per BOE, around $50Cdn on average. The amount of

assets and liabilities are also higher than the sample mean, but the estimation of reserves

are similar. This implies that these outliers might have large investments and the market

expects them to pay off in the future. Alternatively, these outliers might have other assets

unrelated to O&G with high fair values.

6. Results

6.1 Value Relevance

Table 2 presents the Pearson and Spearman correlations for the variables in this

study. The first seven variables in the table are the ones included in the two principal

regressions of table 3. The rest are dummy variables used in the contextual analysis that I

will discuss in the next section. As expected, assets and liabilities exhibit high positive

35 MM estimation addresses the three classes of problems with outliers: 1) outliers in the ̂ -direction (response direction), 2) multivariate outliers in the covariate space (x-space), 3) outliers in both the y-direction and the x-direction.

39

correlations, since they are part of an accounting identity that must hold. Regressions

might experience some multicolinearity, but not severe. The estimates of proved reserves

(PROV) are generally more correlated with the rest of the variables in the regression than

probable reserves (PROB). Table 1 shows that both proved and probable reserves

estimates experience high variation. However, the latter might be less precise due to their

more uncertain nature.

Table 3 shows the results of the main levels regression (4) under different

valuation assumptions: constant/forecast prices and before/after taxes. All the reserves

estimations employed in the analysis are discounted at 10% -which seems more plausible

than a scenario with undiscounted numbers. For each case, I run OLS estimations with

one-way clustered errors36. In addition, as robustness checks, I run the same regression

excluding outliers and a firm and time fixed-effects regression following the suggestion

of Boone (2002). In both cases, the coefficients relations and their magnitudes remain

consistent. Hereafter, my analysis focuses on the OLS results with the whole sample.

As predicted, liabilities (TL) present coefficients close to -1 . The coefficients for

non O&G assets (NOGA) are higher than 1. This suggests that market values of NOGA

are higher than book values and/or that the variable O&G assets (OGA) has been

overestimated and hence NOGA (Total Assets - OGA) has been underestimated. In the

restricted regression, proved plus probable reserves (PVOG) has a highly significant

coefficient slightly higher than 1. This is consistent with the COGEH's statement that the

"best estimate" of the reserves to be recovered should be the P50 estimate. Actually,

36 This approach allows for correlations among different firms in the same year (see e.g. Petersen, 2007; Gow et al. 2007). Cross-sectional correlation could be an issue because firms often form joint ventures to operate the same property. With only 4 years of data, it is not possible to run two-way clustered errors.

40

assuming no bias in the estimates and a symmetric distribution37 of reserves, a well

diversified O&G investor should expect to obtain this amount.

Regarding the variables of interest, the first observation is that we can reject the

null hypothesis Ho: p4 = (35 across the different valuation assumptions. This result is

consistent with investors placing a higher market value on proved reserves than on

probable reserves. Furthermore, the difference in coefficients suggests that the

breakdown in reserves is more informative about market values than an aggregate reserve

measure. The estimated coefficients are not far from the theoretical expected values.

According to the probability thresholds required by NI 51-101, $1 of proved reserves

should translate into roughly $0.95 of market value, whereas $1 of probable reserves

should increase market value by around $0.70. Results in table 3 show coefficients

significantly higher than 1 for proved reserves and apparently lower than 0.70 for

probable reserves. Still, these amounts are consistent with theory. Two factors mentioned

in the hypothesis section can account for the coefficient magnitudes. First, investors can

exhibit a high risk-aversion for gains as predicted by prospect theory and thus, they may

favor firms with high proved reserves relative to probable reserves. Second, through

diversification investors can increase the expected proved reserves, especially for firms

with very few O&G properties. However, no matter how much you diversify, proved plus

probable reserves remain equal. That implies that as "real" proved reserves increase

thanks to diversification, probable reserves decrease. In the contextual analysis I explore

more in depth this last possibility.

37 Robinson and Elliott (2005) claim that in the Western Canadian Sedimentary Basin only a few fields are likely to have significantly skewed distributions. Skewness to the right of the reserves distribution would imply a mean (expected value) higher than the median. This would also be consistent with the coefficients higher than one that we observe for PVOG.

41

Comparing the results under different valuation assumptions I find that the

coefficients for proved and probable reserves estimated before taxes are more significant

than those estimated after taxes. Overall, adjusted R are higher in the before taxes

regressions. A plausible explanation is that estimates before taxes are more informative

about firms because they make them comparable. In addition, the peculiarities of the two

scenarios before taxes might also increase their relevance. The first case, constant

prices/costs, might draw special attention from investors because is similar to the O&G

reserves estimate required by the SEC. The only difference with the SEC is in the

definition of proved reserves as mentioned in section 3.1. The second case, forecast

prices/costs, makes the same assumptions as the deflator. The quantities of proved and

probable reserves disclosed under NI 51-101 -and used as deflator- must assume

forecast prices/costs, before taxes, and 10% discount rate. Obviously, the higher relative

value relevance of the before taxes regressions does not imply that investors have to use

before taxes estimates when they value a particular company.

Estimates based on forecast prices and costs also seem slightly more relevant as

measured by the adjusted R2. On the one hand, firms forecast prices and costs might

reveal some inside information about future expected cash flows. On the other hand, this

result might just spuriously reflect the recent evolution of O&G prices as I will discuss in

a subsequent section. A sample of four years is too small to draw strong conclusions.

In order to examine the incremental value relevance of proved and probable

reserves to the historical accounting estimate of reserves from the balance sheet, I control

for OGA in my regressions. OGA is significant under all valuation assumptions except

for the case before taxes using forecast prices and costs. The magnitude of its coefficients

42

ranges from 0.30 to 0.50, much lower and less significant than the coefficients for the

proved reserves estimates. To make my study comparable to prior studies I also include

BV in substitution for NOGA, OGA, and TL. The coefficients for historical BV are

significant across different valuation assumptions. For cases before taxes, the coefficients

for BV are not significantly different from the coefficients for present value estimates of

reserves. This finding is consistent with Harris and Ohlson (1987).

6.2 Contextual Analysis

The study of the firm characteristics that drive my results will be helpful to

understand to what extent these results might be applicable to other accounting items and

industries. I compare the coefficients of sample partitions according to size, ratio of

proved to probable reserves, precision, legal form, and the presence of a reserves

committee. Tables 4 and 5 present the univariate and multivariate analysis, respectively.

6.2.1 Size

I have partitioned the sample in two subsamples above and below the median size.

I measure size as the sum of proved and probable barrels of oil equivalent (BOE). The

mean differences tests between small and large firms in Table 4 show that small firms

tend to be significantly more profitable (7.1% vs. 2.9%), more leveraged (0.39 vs. 0.35),

and have a higher proportion of oil (53% vs. 43%). Small firms also have a higher market

value per BOE. Two explanations seem plausible. First, small firms tend to be younger

and they might be working on recently discovered reservoirs. The costs of extraction tend

to be lower in the beginning and therefore margins for small firms might be higher for the

first years of operations. Second, small firms might receive a premium for potential

43

growth. In addition, their amount of assets per BOE is significantly higher than for larger

firms. This might reflect the fact that initial investments in exploration still have to pay

off and also the low accumulated depreciation of the first years.

In the multivariate analysis (Table 5), results are consistent with my predictions.

Because of the diversification effect, small firms will tend to underestimate proved

reserves and overestimate probable reserves. I find that for small firms investors attach a

market value significantly higher to proved reserves (1.79 vs. 1.35) and lower to probable

reserves (0.13 vs. 0.89). So it seems that they are pricing in the diversification effect. For

large firms, the coefficient of determination is substantially greater. This fact suggest that

the distributions of reserves are better behaved for large firms, consistent with the Law of

Large Numbers.

I also made a partition based on age (not reported), but differences were not

significant between subsamples. As mentioned before, the reason can be that some young

firms are amalgamations of firms that had already been in operation for a long time.

6.2.2 Ratio of Probable to Proved Reserves

The second partition is based on the proportion of probable to proved reserves

(PB/PV), which is a proxy for variance. Again I have formed two groups, one with

PB/PV above the median (High) and the other one below the median (Low). Table 4

shows that firms with low PB/PV are on average significantly more leveraged (0.40 vs.

0.34) and profitable (7.2% vs. 2.9%) than firms with high PB/PV. This higher

profitability is also reflected in the higher present value estimates of O&G reserves per

Firms follow the unit of production depreciation method, that is, O&G assets are depreciated proportionally to production.

44

BOE. Since probable reserves tend to be extracted later than proved reserves, those firms

with a higher share of probable reserves (i.e., high PB/PV) will get lower present values.

The multivariate analysis (Table 5) indicates that those firms with a higher

proportion of probable reserves (i.e., high variance) receive a much lower market value

for those probable reserves (actually no significantly different from zero) but their proved

reserves are highly valued. For firms with low variance (i.e., low PB/PV) the effect is the

opposite: surprisingly, their probable reserves receive a market value not significantly

different from the one received by proved reserves. This evidence is consistent with

investors placing significant discounts on the probable reserves (by nature more

uncertain) of firms with higher variance of reserves. So it may seem as if the second

moment matters.

It is important to note that there is no significant difference in size between firms

with high and low PB/PV. Furthermore, size and PB/PV exhibit a correlation of only

-0.18 (Table 2). Thus, I do not expect the PB/PV partition results to be explained by size.

6.2.3 Quality of estimates

The main partition in this section is based on technical revisions as a percentage

of initial proved plus probable reserves. In the multivariate analysis (Table 5), companies

with lower revisions present high significant coefficients for proved and probable

reserves (1.66 and 0.90, respectively). This result would suggest that, for firms with

higher quality of estimates, reserves receive a market value premium. Investors value

much more the probable reserves of firms with low revisions, consistently with having

lower information risk. For proved reserves the difference between high and low quality

45

is not significant. Overall though, it seems investors are pricing information risk. Future

studies using expected returns models can examine this issue further.

Firms with lower technical revisions are considerably larger. Again, more

diversified firms (more O&G properties) are obviously more accurate in their estimates.

Additional evidence of this is the greater adjusted R-squared for firms with low revisions.

Next, I distinguish between those firms with reserves committee and those

without it. Only 30 firm-years out of 219 do not have a reserves committee, so results

should be interpreted cautiously. In general, firms with reserves committee have a higher

dividend yield and higher estimates of reserves per BOE. This latter trait might be

explained by the higher proportion of proved reserves -less affected by the discount rate-

over probable reserves. Looking at the evidence from the multivariate analysis we do not

find significant differences in the valuation of reserves between the two subsamples.

Finally, I have run an evaluator fixed-effects model to control for the effects of

different evaluators (not reported). Results remain robust.

6.2.4 Legal Form

Partitioning by legal form I obtain 145 corporations and 74 energy trusts. Trusts

are on average larger, more leveraged, more profitable, and pay more dividends. The

present value of proved reserves per BOE is greater for trusts, whereas the present value

of probable reserves per BOE is greater for corporations. Trusts tend to be amalgamations

of firms with mature operations but no new investments, hence the higher amount of

proved reserves.

In the regressions (Table 5) we see that for trusts the coefficient for proved

reserves is 0.85 (t-stat=2.09). Considering that trusts are very large and well diversified

46

the magnitude of this coefficient makes total sense. In contrast, the coefficient for proved

reserves in corporations is significantly larger (fl 4=1.99 and t-stat=10.85).

6.3 Sensitivity Analysis

6.3.1 Yearly Analysis

Table 6 runs the regressions year by year. The smaller amount of observations

resulting of this partition lessens the power of the estimations. However, we still observe

patterns similar to the general findings. Proved reserves receive on average higher market

values than probable reserves as expected.

Shares of all Canadian royalty and income trusts took a big hit on November 1,

2006, after the Canadian Finance Minister proposed taxing them at regular corporate

rates. The tax rate change would affect new trusts that start trading after October 31, 2006

immediately, but would not affect existing trusts until 2011. This event might explain the

lower than average market value attached to PVOG (0.87 and t-stat=3.02). In addition,

we would expect investors to value more those trusts with a higher proportion of proved

reserves, since these reserves will most likely be extracted before 2011. The coefficients

from the unrestricted regression are consistent with this belief (J34=\.52 and/?j=0.08).

6.3.2 Oil and Gas Prices

Previous literature shows that the behavior of O&G prices during the study period

can have an impact on the estimated coefficients (e.g., Boone, 2002). In the case of my

sample period we find that crude oil prices have experienced an upward trend. It is not

entirely clear whether observed results under price increasing scenarios can be

generalized to price decreasing ones. However, two factors mitigate the impact of price

47

changes on my results. First, natural gas prices did not follow the same trend as crude oil

prices. In particular, in 2006, lower winter heating demand, growth of onshore natural gas

production and above average storage supplies led to dramatic price decreases compared

to 2005. In the same period, liquid oil prices kept increasing. Second, although most

firms' announcing dates are concentrated in the same periods, market values are taken for

each firm in different days, as opposed to taking all market values on December 31st or

3.5 months after fiscal year end.

Exhibit 2 graphs the evolution of O&G prices for the period of my sample. 2004

and 2005 are years with clear price increases for both oil and gas. In 2003 prices were flat

and in 2006 we see that gas prices fell whereas oil prices increased slightly. I have run

my tests with 2004 and 2005 only, and then with 2003 and 2006. I find that for both

subsamples the difference between the coefficients for proved reserves and probable

reserves is still significant. For 2003 and 2006 the coefficient for proved reserves is

significantly higher (B=1.56 vs. 1.04), perhaps in anticipation of the future price

increases already hinted in the first months of the upcoming year before reporting takes

place.

6.3.3 Returns Model

Kothari and Zimmerman (1995) suggest to implement both price and returns

models whenever possible. The use of both functional forms will help ensure that my

study's inferences are not sensitive to functional form and potential non-stationarity of

firm market values. Still, as mentioned in Section 5.1, utilizing a returns model poses

some limitations and results must be taken cautiously.

48

Easton and Harris (1991) test the earnings/return association with a model that

regresses stock returns on earnings divided by price at the beginning of the return period.

The model builds on two assumptions. First, if the "stock" variables of book value and

market value are related, so should their "flow" variables of returns and earnings. Second,

price is a multiple of earnings. In addition to earnings levels, their specification also

includes changes in earnings. For my analysis, I introduce the change in the estimation of

reserves. The final specifications I run are the following:

Ru=PxNIit+P2*NIu +j3iAPVOGit+ei!. (5)

R.t = p{NIlt + j32ANIit + j33AProvedit + j34AProbableit + sit. (6)

where Rit is the market return of firm / at year t, NI is net income before extraordinary

items, AM is the change in NI, APVOG is the change in proved plus probable reserves,

AProved is the change in proved reserves, and AProbable is the change in probable

reserves (all the independent variables deflated by the stock price at the beginning of the

period). Returns are calculated taking stock prices adjusted for dividends and stock splits

three days after the filing of the Annual Report or NI 51-101 forms in SEDAR,

whichever is filed later, to ensure that all the information is available to investors.

Table 7 presents the estimated coefficients under different valuation assumptions.

In general they are consistent with the levels regression. Proved reserves are valued more

than probable reserves. However, the coefficients for probable reserves changes are not

significantly different from zero across the board. The reason could be that probable

reserves changes are small and have low variation. The coefficients for proved reserves

49

are lower than in the levels specification. Further analysis should look into the

components of reserves changes.

6.3.4 Accounting Method: Full Cost vs. Successful Efforts

Prior research finds a significant difference in value relevance for book values and

net income obtained with each method (i.e., Harris and Ohlson 1987, Bryant 2003). FC is

a more aggressive accounting method and might yield higher book values. Higher book

values entail a higher probability of impairment.39 When impairments take place, the

book values are closer to the reserves values estimates. With the present value estimates

better approximated in the primary financial statements, these could be valued in a

different way consistently with the functional fixation hypothesis (Aboody, 1996).

In Canada, most companies follow FC. I only found 10 observations in my

sample that used SE. Including or excluding them does not affect my results.

6.3.5 Discount Rate: Geographical Diversification

The valuation of O&G reserves assumes a discount rate of 10% for all firms. Yet,

some firms run operations in geographical regions with higher political risk -mainly risk

of expropriation. Reserves located in these areas might be overstated in the reported

estimates. For this reason, investors might be applying a discount to the market value of

these reserves. To control for this factor, I distinguish between firms with operations in

39 O&G assets are evaluated on an annual .basis to determine that the costs are recoverable and do not exceed the fair value of the properties. The costs are assessed to be recoverable if the sum of the undiscounted cash flows expected from the production of proved reserves less unproved properties exceed the carrying value of the O&G assets. If the carrying value of the O&G assets is not assessed to be recoverable, an impairment loss is recognized to the extent that the carrying value exceeds the sum of the discounted cash flows expected from the production of proved and probable reserves less unproved properties. The cash flows are estimated using the future product prices and costs and are discounted using the risk-free rate.

50

North America, from firms with most of the operations in other countries. I only find 31

firm-years with operations in other countries. Running separate regressions, I find that

proved and probable reserves from North America have a higher market value than the

same estimates from other regions. The relationship between the valuation of proved and

probable reserves is not altered.

6.3.6 Product Mix: Oil vs. Gas

Proved and probable reserves can contain different proportions of O&G. Berry

and Wright (1997) find that, for FC firms, quantities of proved developed reserves of gas

are more value relevant than oil while, for SE firms, just the opposite is true.

In addition, the proved plus probable reserves quantity deflator assumes a

standard conversion factor of six thousand cubic feet of gas to one BOE, based on the

equivalence in energy units. However, this conversion rate might not be consistent with

the economic equivalence. Harris and Ohlson (1987), among others, suggest that the

relative market values should be in the neighborhood of a ten to one ratio (Harris and

Ohlson, 1987). If this were the case, firms with a higher proportion of gas will be

overdeflated. Similarly, at the firm level, if proved (probable) reserves possess a higher

proportion of gas they will also be overdeflated with respect to probable (proved)

reserves. The overall effect of the product mix on my results is uncertain.

I use the proportion of oil (including light oil and heavy oil) over gas as a

partitioning variable {OILMIX). I do not report significant differences between the

reserves coefficients of firms with low and high OILMIX. It is noteworthy that firms with

a lower proportion of oil present less significant coefficients and adjusted R-squared.

This result would partially contradict the evidence of Berry and Wright (1997) that

51

claims that quantities of proved reserves are more value relevant for gas than oil.

However, their study differs from mine because they use proved developed reserves and

quantity estimates. In addition, in my sample, firms with a low proportion of oil are

significantly smaller and therefore, their lower value relevance is most likely due to size.

7. Conclusions and Future Research

The FASB and the IASB are currently discussing the role of probability in the

accounting of assets and liabilities with uncertainties. The predominant view, as reflected

in recent standards, is to shift the use of probability thresholds from the recognition stage

(e.g., SFAS No. 5) to the measurement stage (e.g., SFAS 143, 144). In both cases,

probability thresholds are meant to inform investors about the uncertainty of future

benefits and obligations for the firm. Yet, no prior research examines this question.

I identify a unique setting to test how investors value assets estimates

corresponding to different levels of uncertainty. A recent regulation in Canada requires

all reporting issuers with O&G operations to break down their O&G reserves according

to the uncertainty of eventual production. Proved reserves are estimated to be recovered

with at least a 90% probability and Proved + Probable reserves with at least a 50%. I find

that investors use this information as intended by regulators, attaching a significantly

higher market value to proved reserves, around the magnitudes suggested by the

probability weights. These results are more significant for firms that have lower

measurement error in past reserves estimates and an independent reserves committee. The

market value weight of proved reserves tends to be larger for small size firms with a

lower ratio of proved to probable reserves and a higher proportion of oil reserves (vs.

52

gas). The market value weight of probable reserves tends to be larger for large size firms

with a higher ratio of proved to probable reserves.

My setting specifically looks at the application of probability thresholds for assets

estimations at the measurement stage. One should exercise caution when trying to

generalize the results of this study to other contexts. The first question is whether

investors make the same interpretation of thresholds for assets and liabilities. For

instance, prospect theory would predict that for decisions involving losses, investors

might shift from risk-averse to risk-seeking behavior. In such a case, investors might give

a premium to slightly probable liabilities and a discount to highly probable liabilities.

Second, the use of probability thresholds at the definition, recognition, or measurement

stages is an interesting conceptual distinction, but I do not believe it has practical

consequences for the interpretation of investors.

Future research could examine a regime with voluntary disclosure of uncertainty

as presented in some theoretical models (Jorgensen and Kirschenheiter, 2003). Although

the disclosure of possible reserves (note that P[Proved + Probable + Possible] >10) is

voluntary in Canada, my sample did not include enough observations from disclosers to

perform this test. Alternatively, one could investigate early voluntary adoption of the new

reserves classification in accounting regimes that might possibly incorporate it (e.g.,

IFRS future standard for the extractive industries). For the specific case of O&G

companies, regulators might be interested in whether the classification according to

uncertainty of recovery is incrementally relevant to other existing classifications (e.g.,

Producing vs. Non-Producing; Developed vs. Undeveloped).

53

Another topic that deserves further analysis is the role of evaluators and

governance in the estimation of reserves. As standard setters push for the recognition and

disclosure of fair value measurements, auditors and specialized appraisers assume

additional responsibilities and incentives that need to be studied.

54

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55

Christie, A. 1987. On Cross-Sectional Analysis in Accounting Research. Journal of Accounting and Economics 9: 231-258.

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57

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58

Appendix 1: Reserves Classification

Definition: Reserves are all the quantities of petroleum naturally occurring on or within the Earth's crust that have been discovered and are deemed to be economically recoverable. O&G reserves (X) are estimates that follow a probability distribution. In Canada, O&G firms have to disclose different points of the distribution of reserves:

Proved, such that P[ X> proved ] = 90%. Proved + Probable, such that P[ X> prov.+prob. ] = 50%. Proved + Probable + Possible, such that P[ X> prov.+prob.+poss. ] = 10%.

The following inverse cumulative distribution function shows these point estimates:

Prob.%

Proved Proved + Probable

Proved + Probable + Possible

Reserves Quantity

For each well, evaluators generate a range of reserves estimates and their associated probabilities based on known geological, engineering, and economic data. If evaluators follow a deterministic approach, they just give their best estimate for proved, probable and possible reserves. If they follow a probabilistic approach, they can generate the whole distribution function of reserves. In both cases, the data is aggregated at the firm level in a probabilistic manner. These estimates are adjusted every year based on production information, technological and economic changes, etc.

59

Appendix 2: Reserves Disclosures

NI 51-101 requires firms to disclose the quantities and dollar values of proved and probable reserves under different assumptions: constant / forecast prices and costs, before / after taxes, and different discount rates.

The example below corresponds to a disclosure of reserves values at constant prices and costs (firms also provide a similar table for forecast prices and costs). The amounts reported under proved and probable reserves are not multiplied by any probability weight (although they correspond to different probability thresholds). The encircled figures are the ones I use for my study. I use the 10% discount rate scenario for three reasons. First, to maximize the number of observations. For constant prices and costs, NI 51-101 only requires the disclosure of 0% and 10% discount rate scenarios, so not all firms disclose other discount rate scenarios like in this case. Second, the SEC only requires and allows the 0% and 10% discount rate scenarios, so investors might look at them for comparison. Third, a 10% discount rate is more plausible than 0%.

SUMMARY OF OIL AND GAS RESERVES AND NET PRESENT VALUES OF FUTURE NET REVENUE

as of December 51. 2CCK5 CONSTANT PRICES AND COSTS

RESERVES CATEGORY

P3.0YED PtOfllKlUC Developed No:i-Pioil'.icui: Undeveloped

TOTAL PROVED

TOTAL PROBABLE

TOTAL PROVED PLUS PROBABLE

NET PRESENT \

BEFORE I M . O M TAXE^: ell

I M :

2 1 . ••>: i

0 "••>"•

' l J p f S

1 - U P

4 ^ 1 >&

M M

.in r :

2 'f 6 . i (

4 1 *

I t . ' , MS

2 .02

^ c " 4 : o " *

25t42

ALVES OF DISCOUNTED VF

1" . i MS i

1 " S *

-3 ftU4 P 2 4

^ > P : i :

^ 4 0 u s :

241 Zii

2i.-„ •tMbl

124 323

±i> l l 4

1"1 '"(•(:

2W 14.

FUTURE : NET REV ~E.yr.jE AFTER IXC OME TAXES DISCOUNTED AT

>' > ve.ii!

IMS

:os& .

-t, "2?

"421

274 -a»

10" i" i

5% i.M$^

P 4 - . "

4 S S T ' _ib

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i i l -»C"

-»_ 42~

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^1M i^S

20^ n.4

2.iJc

•M$i

21 C •

. • 4 ^ ~ -1 !l _•

2- i C

. " ' 4 1 6

Source: Crew Energy Corporation, Annual Information Form 2006

60

Exhibit 1: Timeline of National Instrument 51-101

* "

# r

1998

?VV-9

^ * # ^ ^

"v '"9 # V

& 4 ^ +

2001 2002 2003 2004 2005 2006 2007

NP 2-B regime NI 51-101 regime Sample period 2003-2006

US$/BOE

1 0 0 -

Exhibit 2: Oil and Gas Prices (2003-2007)

A / \ \ /

/V / • * " " « . /

to 10 CD

~ 9 9 CD N fc; I"-

1 2 ? 2 2 * '«

-wn EdrrDrton - - - Henry HLJD AECOUSD

This graph plots the monthly prices for the most common oil and gas benchmarks in the U.S. and Canada: • WTI = West Texas Intermediate (or Texas Light Sweet) crude oil spot price at Cushing, Oklahoma. • Edmonton = Edmonton Par crude oil spot price (light, similar in quality to WTI). The price is

primarily based in the U.S. upper Midwest market, adjusted for quality and transportation costs from Edmonton, one of the two major Alberta hubs.

• Henry Hub = North American natural gas spot price at Henry Hub, Louisiana. • AECO USD = Canadian natural gas spot price at AECO Hub, Alberta. Prices translated to US$.

Source: Bloomberg.

61

Exhibit 3: Diversification Effect

Example of a probabilistic aggregation of reserves from independent wells using Monte Carlo simulation. Each well follows a lognormal distribution with parameters: /.i = 100 and a = 30. As the number of wells of a firm increases, the average parameters for each well converge to the mean (the distribution narrows).

1 well

2 wells

10 wells

100 wells

Min

26.8

38.3

63.9

88.7

Q10%

65.6

74.7

88.1

96.2

Median

95.6

97.8

99.5

100.0

Mean

100.0

100.0

100.0

100.0

Q90%

139.9

128.0

112.3

103.9

Max

381.5

254.4

150.2

114.5

Note: Q10=Proved reserves (or P90); Median=Proved + Probable (or P50); Q90=Proved + Probable + Possible (or P10).

Chart A: Probability Density Function

Prob. 0.09

0.08

0.07

0.06

0.05

0.O4

0.03

0.02

0.01

0.00

Chart B: Inverse Cumulative Distribution Function

Prob. 1.0

0.9

• 1 well

2 wells

-10 wells

100 wells

1 well

2 wells

10 wells

100 wells

Reserves Quantity

Reserves Quantity

Table 1: Sample Descriptive Statistics (2003-2006)

62

Mean Std.Dev. Ql 25% Median Q3 75%

MVE NOGA TL OGA NI BOE Proved BOE Probable

22.37 3.26 5.92

12.54 0.58

53,039 29,340

14.15 5.38 4.52 7.36 1.38

131,440 93,560

12.26 1.07 2.48 7.54 0.06

4,321 2,186

19.62 1.84 5.51

12.00 0.57

13,357 7,007

27.39 3.37 7.68

16.29 1.25

45,162 24,308

Reserves Value Estimates (under different assumptions)

I 4-»

|

0% d

isci

o o

Undi

Prices

Constant

Forecast

Constant

Forecast

Taxes

Before

After

Before

After

Before

After

Before

After

Classif. Proved Probable Proved Probable Proved Probable Proved Probable Proved Probable Proved Probable Proved Probable Proved Probable

11.59 4.58

10.42 3.56

10.90 4.19 9.86 3.29

18.19 9.96

16.32 7.94

16.75 9.49

15.15 7.62

5.43 2.77 4.95 2.02 5.06 2.47 4.70 1.90 8.21 5.04 7.60 3.92 7.45 5.15 6.98 4.13

7.93 2.76 7.22 2.18 6.87 2.61 6.08 1.93

12.41 6.58

11.32 5.33

10.95 5.90 9.95 4.89

11.57 3.84

10.70 3.16

11.21 3.64

10.02 2.90

18.29 8.70

16.17 7.17

16.46 8.40

15.00 6.63

14.90 5.48

13.45 4.46

14.47 5.22

13.73 4.04

23.00 11.95 21.23

9.79 22.12 11.42 20.15

9.45

Statistics for a sample of 219 firm-years. Variable definitions: MV=Market Value of Equity, NOGA=Non-0&G Assets, TL=Liabilities, OGA=0&G Assets, NI=Net Income Before Extraordinary Items, BOE Proved=Barrels of Oil Equivalent Proved, and BOE Probable=Barrels of Oil Equivalent Probable. Reserves Value Estimates are the discounted net revenues generated by the sales of O&G according to a firm's forecasted production schedule and the stated assumptions on discount rates (10% or 0), prices and costs (constant taken at the fiscal-year end or forecasted) , and taxes (before or after). Proved reserves have a probability of 90% or more of being extracted. Probable Reserves are calculated as Proved plus Probable (p>50%) minus Proved. All figures are expressed in Canadian Dollars per Barrel of Oil Equivalent (BOE), except for BOE Proved and BOE Probable which are stated in units (each barrel is equivalent to 1,000 ft.3 of Oil and a 6,000 ft.3 of Natural Gas). The average exchange rate for the period of my study was 1.26 US$/Cdn$.

63

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s. P

VO

Gj,=

Pre

sent

Val

ue o

f pr

oved

plu

s pr

obab

le r

eser

ves

(Pro

b>50

% o

f be

ing

extr

acte

d). P

rove

d it=

Est

imat

ion

of P

rove

d re

serv

es v

alue

rep

orte

d by

the

firm

(P>

90%

of

bein

g ex

trac

ted)

. Pro

babl

e/,-

Est

imat

ion

of

Prob

able

res

erve

s va

lue

repo

rted

by t

he f

irm.

Cal

cula

ted

as t

he d

iffer

ence

bet

wee

n P

VO

G a

nd P

rove

d re

serv

es.

All

thes

e re

serv

es v

alue

est

imat

es a

re t

he

disc

ount

ed n

et re

venu

es g

ener

ated

by

the

sale

s of

O&

G a

ccor

ding

to a

firm

's fo

reca

sted

pro

duct

ion

sche

dule

, a 1

0% d

isco

unt r

ate,

and

the

assu

mpt

ions

sta

ted

on to

p of

the

tabl

e on

pri

ces

and

cost

s (f

orec

ast

or c

onst

ant t

aken

at t

he fi

scal

-yea

r en

d), a

nd ta

xes

(bef

ore

or a

fter

). A

ll va

riab

les

are

expr

esse

d in

Can

adia

n D

olla

rs p

er B

arre

l of

Oil

Equ

ival

ent

(BO

E).

The

ave

rage

exc

hang

e ra

te f

or t

he p

erio

d of

my

stud

y w

as

1.26

C

dn$/

US$

.

Tab

le 4

: C

onte

xtua

l An

alys

is:

Un

ivar

iate

An

alys

is

SIZ

E

PB

/PV

R

EV

I R

EC

OM

T

RU

ST

N

Mar

ket

Val

ue

Lev

erag

e R

OA

D

ivid

end

BO

E

Pro

ved

BO

E

OIL

MIX

M

VE

N

OG

A

TL

O

GA

P

v/B

/C

Pb/

B/C

P

v/A

/F

Pb/

A/F

Low

10

9 22

2 0.

35

2.9%

2.

7%

5.6

3.5

43%

24

.79

4.04

6.

41

14.2

6 11

.94

5.48

9.

98

3.64

Hig

h 11

0 2,

025

0.39

7.

1%

6.2%

10

5.7

57.9

53

%

17.9

5 1.

91

5.20

10

.27

10.9

7 3.

67

9.63

3.

00

Low

11

0 1,

260

0.40

7.

2%

5.7%

52

.4

15.3

46

%

23.0

7 3.

40

6.82

13

.68

13.9

9 3.

39

11.7

7 2.

56

Hig

h 10

9 98

5 0.

34

2.9%

3.

2%

58.1

45

.1

50%

19

.79

2.59

4.

85

10.9

5 9.

04

5.71

7.

93

4.05

Low

76

1,

593

0.38

3.

2%

4.3%

83

.4

47.8

45

%

21.9

2.

24

5.18

11

.00

13.1

0 4.

16

10.3

7 2.

92

Hig

h 11

44

4 0.

35

7.1%

4.

1%

15.1

8.

3 50

%

23.4

3.

00

5.18

11

.10

10.5

6 5.

18

8.32

3.

34

No 30

1,50

4 0.

33

6.6%

1.

2%

113.

2 85

.7

55%

18

.04

2.10

3.

94

7.98

8.

19

4.30

6.

97

3.11

Yes

18

9 1,

055

0.38

4.

7%

5.0%

45

.7

21.3

47

%

21.9

5 3.

13

6.12

13

.00

12.0

0 4.

63

10.2

8 3.

36

Cor

p.

145

540

0.30

1.

6%

1.5%

22

.4

15.4

45

%

22.1

5 2.

97

5.18

11

.94

10.3

5 5.

25

8.62

3.

54

Tru

st

74

2,19

7 0.

42

7.7%

9.

9%

116.

8 58

.9

53%

19

.98

3.00

7.

00

12.9

2 13

.50

3.32

12

.02

2.91

Figu

res

in b

old

indi

cate

that

the

diff

eren

ce o

f mea

ns is

sig

nific

ant

at 0

.05

(Sat

tert

hwai

te).

Pa

rtiti

on V

aria

bles

: T

RU

ST=1

if

firm

is

an e

nerg

y tr

ust,

0 if

it i

s a

corp

orat

ion;

SIZ

E=1

if

firm

abo

ve m

edia

n pr

oved

plu

s pr

obab

le q

uant

ity o

f B

OE

, 0

belo

w

med

ian;

RE

VI=

1 if

firm

abo

ve m

edia

n te

chni

cal

revi

sion

div

ided

by

initi

al p

rove

d pl

us p

roba

ble

rese

rves

, 0

if b

elow

med

ian;

PV

/PB

=1 i

f ab

ove

med

ian

prop

ortio

n of

pro

ved

over

pro

babl

e re

serv

es q

uant

ities

, 0 if

bel

ow m

edia

n; R

EC

OM

=l i

f the

firm

has

a re

serv

es c

omm

ittee

, 0 o

ther

wis

e.

Oth

er V

aria

bles

: N

=num

ber

of o

bser

vatio

ns;

Mar

ket

Val

ue=T

otal

Mar

ket

Val

ue o

f th

e fir

m;

Lev

erag

e=T

L /

Tot

al A

sset

s; R

OA

=Net

Inc

ome/

Tot

al A

sset

s;

Div

iden

d Y

ield

=Tot

al D

ivid

ends

/ M

arke

t V

alue

; B

OE

Pro

ved=

Bar

rels

of

Oil

Equ

ival

ent

of P

rove

d R

eser

ves

in m

illio

ns;

BO

E P

roba

ble=

Bar

rels

of

Oil

E

quiv

alen

t of

Pro

babl

e re

serv

es i

n m

illio

ns; M

VE

=Mar

ket

Val

ue o

f E

quity

per

BO

E; N

OG

A=N

on-O

il an

d G

as A

sset

s pe

r BO

E; T

L=T

otal

Lia

bilit

ies

per

BO

E;

OG

A=

0&G

Ass

ets

per B

OE

; Pv

/B/C

(Pb

/B/C

)= P

rese

nt V

alue

Est

imat

e of

Pro

ved

(Pro

babl

e) R

eser

ves

per

BO

E,

assu

min

g a

10%

dis

coun

t ra

te, b

efor

e ta

xes,

an

d co

nsta

nt p

rice

s an

d co

sts;

Pv/

A/F

(Pb

/A/F

)= P

rese

nt V

alue

Est

imat

e of

Pro

ved

(Pro

babl

e) R

eser

ves

per

BO

E, a

ssum

ing

a 10

% d

isco

unt

rate

, afte

r ta

xes,

and

fo

reca

st p

rice

s an

d co

sts.

66

Table 5: Contextual Analysis: Multivariate Analysis (SEC Case) 06

MVEU = Y,a, + P\ NOGAit + j32TLit + ^Proved,, + J35 Probable u + su. (4) 1=03

PARTITIONS

SIZE

Small

Large

Large—Small

PB/PV

Low

High

High-Low

RE VI

Low

High

High-Low

RECOM

No

Yes

Yes-No

TRUST

Corporations

Trust

Trust-Corp

Pi

1.3133 5.06*** 1.7981 2.27** 0.4848

1.44

1.0121 7 20***

2.7103 4.56*** 1.6982

5.96***

1.5483 4 79***

1.3386 6.81*** -0.2097

-0.85

2.1948 2.37**

1.4653 5.90*** -0.7295 -1.94*

1.6147 2 go***

0.8909 5.87*** -0.7238 -2.16**

h

-1.1611 -3 05*** -1.3414 -2.54** -0.1803

-0.61

-0.4326 -1.37

-2.0450 -5 93***

-1.6124 -6.43***

-1.8100 -3 42***

-0.9858 ji 9 7 * * *

0.8242 2.63***

-0.0416 -0.03

-1.2703 -3 78***

-1.2287 -2 77***

-1.4522 -4.16*** -0.0446

-0.12

1.4076 5.00***

P4

1.7860 (y 79***

1.3562 g j j * * *

-0.4298 -2.00**

1.1103 2.59** 2.2672

Q 79***

1.1569 4.50***

1.6625 5 93***

1.6213 7 34***

-0.0412 -0.17

1.5943 3 Q7***

1.6053 8.26*** 0.0110

0.03

1.9984 10.85*** 0.8510 2.09** -1.1474

-4 88***

h

0.1342 0.27

0.8937 2.00** 0.7595 2.53**

1.4277 1.38

0.0441 0.17

-1.3836 -3.63***

0.9019 2 82*** 0.0196

0.07 -0.8823

-3 37***

1.3000 1.94*

0.5947 1.76*

-0.7053 -1.69*

0.2476 0.76

0.3164 0.33

0.0688 0.20

N

108

111

111

108

76

76

30

189

145

74

Adj.R2

0.8654

0.8944

0.9047

0.8638

0.8986

0.8898

0.8739

0.8695

0.8600

0.9462

PrPs

15.07***

0.80

0.06

35.47***

5.94**

24 13***

0.07

8.10***

20.25***

0.19

Pooled regressions estimated following Ordinary Least Squares (OLS) and time fixed effects (not reported). The numbers below the coefficient estimates are t-statistics corresponding to firm clustered adjusted errors. The symbols *, ** and *** denote significance at the 0.10, 0.05 and 0.01 levels (two-tailed) respectively. For each partition I test the difference in coefficients pooling standard errors. Partitioning Variables: SIZE=1 if firm above median proved plus probable quantity of BOE, 0 below median; REVI=1 if firm above median technical revision divided by initial proved plus probable reserves, 0 if below median; PV/PB=1 if above median proportion of proved over probable reserves quantities, 0 if below median; RECOM=l if the firm has a reserves committee, 0 otherwise. Regression Variables: P;,= Market Value of firm i at time t. The calculation uses the stock price and outstanding shares of the firm three days after the last filing of Annual Reports or Nl 51-101 forms. NOGA,,= Non-O&G Assets ; 7X„ = Total Liabilities; Provedu= Estimation of Proved reserves value reported by the firm (P>90% of being extracted); Probable,j= Estimation of Probable reserves value reported by the firm. Calculated as the difference between Proved+Probable reserves (P>50% of being extracted) and Proved reserves. Reserves Value Estimates are the discounted net revenues generated by the sales of O&G according to a firm's forecasted production schedule, a 10% discount rate, and the SEC assumptions: before taxes and constant prices and costs. All variables are expressed in Canadian Dollars per Barrel of Oil Equivalent (BOE). The average exchange rate for the period of my study was 1.26 Cdn$/US$.

67

Table 6: Yearly Regressions (SEC Case)

06

Restricted Model: MVEit = £ a, + ft NOGAH + ft2TLu + j33PVOGu + eu. (3)

(=03

06

Unrestricted: MVEU =YJa, + fiiNOGA« + fi2TLu + ^Proved,, + ft5Probableu +eu. (4)

(=03

Intercept

NOGA

TL

PVOG

Proved

Probable

I2

N

k=k

2003 (3)

-3.8207 -0.82

1.5613 2.09** 0.2035 0.23

1.3893 2.53**

0.5338 53

(4)

-1.9661 -0.42

1.5837 2.05**

-0.0060 -0.01

1.5350 2.53** 0.6151 0.93

0.5445 53

1.81

2004 (3)

2.7036 0.65

1.5706 1.44

-1.4426 -2.03** 1.6845 4.I9***

0.3607 56

(4)

4.4590 0.97

1.6658 1.58

-1.6672 -2.34**

1.9205 444***

0.8985 1.20

0.3768 56

1.70

2005 (3)

-1.7561 -0.53

1.3624 6.44*** -0.9210 -1.90* 1.3188 5.90***

0.6841 61

(4)

-0.5162 -0.15

1.4065 6.95*** -0.9894 -2.36**

1.4604 6.81*** 0.8158 2.00** 0.5633

61 2.80*

2006 (3)

10.8777 2.35** 1.3220 4 49*** -1.1944 -2 76*** 0.8752 4.40***

0.3541 49

(4)

8.9949 2.10** 1.3748 5.03*** -1.4308 -3.43***

1.5176 5.33*** 0.0782 0.19

0.4233 49

6.18**

Regressions estimated with Ordinary Least Squares (OLS). The numbers below the coefficient estimates are t-statistics corresponding to firm clustered adjusted errors. The symbols *, ** and *** denote significance at the 0.10, 0.05 and 0.01 levels (two-tailed) respectively. Variables definition: Pu= Market Value of firm i at time t. The calculation uses the stock price and outstanding shares of the firm three days after the last filing of Annual Reports or NI 51-101 forms. NOGAu= Non-O&G Assets. TLH = Total Liabilities. PVOGu= Present Value of proved plus probable reserves (Prob>50% of being extracted). Provedu~ Estimation of Proved reserves value reported by the firm (P>90% of being extracted). Probable^ Estimation of Probable reserves value reported by the firm. Calculated as the difference between PVOG and Proved reserves. All these reserves value estimates are the discounted net revenues generated by the sales of O&G according to a firm's forecasted production schedule, a 10% discount rate, and the assumptions stated on top of the table on prices and costs (forecast or constant taken at the fiscal-year end), and taxes (before or after). All variables are expressed in Canadian Dollars per Barrel of Oil Equivalent (BOE). The average exchange rate for the period of my study was 1.26 Cdn$/US$.

Tab

le 7

: R

etu

rns

Mod

el

Rlt

= J

S.N

I,,

+ J

32A

NI i

t +.&

AP

VO

G,,

+ s

, r

(5)

R„

= p

x NI i

t +

J3 2

AN

I it

+ J

3 3 A

Pro

ved

it +

J3 4

AP

roba

ble,

, +

eu.

(6)

NI

AW

AP

VO

G

AP

rove

d

AP

roba

ble

N

R2

P*

= P

s

' C

on

stan

t P

rice

s an

d C

osts

B

efor

e T

axes

(5)

3.9

70

3

5.5

5*

**

-1.3

56

8 -1

.47

0.5

29

8

6.1

3*

**

165

0.4

95

8

(6)

3.6

61

6

7.0

4*

**

-2.8

21

1 -2

.55

**

1.05

46

8.3

4*

**

-0.1

28

8 -0

.73

165

0.5

63

0

17 3

9*

**

Aft

er T

axes

(5)

3.1

73

2

3 9

7*

#*

-0.3

04

6 -0

.29

0.6

75

9

6.6

4*

**

161

0.5

06

0

(6)

2.9

30

4 5.

06**

* -1

.60

57

-1.3

4

1.14

22

7ig

**

*

0.0

66

1

0.28

16

1 0

.55

13

8.

38**

*

Fo

reca

st P

rice

s an

d C

osts

B

efor

e T

axes

(5)

2.11

45

2.3

1*

* 0

.23

23

0.

24

0.7

25

4

g 4

0*

**

178

0.5

11

3

(6)

1.76

62

2.2

0*

* -0

.73

25

-0.5

9

1.16

61

7i<

**

*

0.0

97

7

0.33

17

8 0

.55

99

6.1

3*

*

Aft

er T

axes

(5)

1.52

29

1.73

* 1.

5440

1.

58

0.8

90

4

7.0

5*

**

171

0.5

47

6

(6)

1.35

36

1.67

*

0.7

42

8

0.66

1.19

38

7 2

5*

**

0.4

24

9

1.28

17

1 0

.56

80

2.9

3*

Pool

ed r

egre

ssio

ns e

stim

ated

fol

low

ing

Ord

inar

y L

east

Squ

ares

(O

LS)

. The

num

bers

bel

ow th

e co

effic

ient

est

imat

es a

re t-

stat

istic

s co

rres

pond

ing

to c

lust

ered

ad

just

ed e

rror

s at

the

firm

leve

l. T

he s

ymbo

ls *

, **

and

***

deno

te s

igni

fican

ce a

t th

e 0.

10, 0

.05

and

0.01

leve

ls (

two-

taile

d) r

espe

ctiv

ely.

V

aria

bles

def

initi

ons:

Ru—

Ann

ual

Mar

ket R

etur

n of

firm

i in

per

iod

t. T

he c

alcu

latio

n us

es th

e st

ock

pric

e an

d ou

tsta

ndin

g sh

ares

of t

he f

irm t

hree

day

s af

ter

the

last

fili

ng o

f A

nnua

l R

epor

ts o

r N

I 51

-101

for

ms.

N1 H

= N

et I

ncom

e B

efor

e E

xtra

ordi

nary

Ite

ms

(dat

a #1

8).

AM

;,=C

hang

e in

Net

Inco

me

Bef

ore

Ext

raor

dina

ry I

tem

s. A

Pro

ved i

t- C

hang

e in

Pro

ved

rese

rves

val

ue r

epor

ted

by th

e fi

rm (P

>90%

of b

eing

ext

ract

ed).

AP

roba

ble^

C

hang

e in

Pro

babl

e re

serv

es

valu

e re

port

ed b

y th

e fi

rm. C

alcu

late

d as

the

diff

eren

ce b

etw

een

chan

ge in

Pro

ved+

Prob

able

res

erve

s (P

>50%

of b

eing

ext

ract

ed)

and

Prov

ed r

eser

ves.

R

eser

ves

Val

ue E

stim

ates

are

the

disc

ount

ed n

et re

venu

es g

ener

ated

by

the

sale

s of

O&

G a

ccor

ding

to

a f

irm

's f

orec

aste

d pr

oduc

tion

sche

dule

, a

10%

di

scou

nt r

ate,

and

the

assu

mpt

ions

sta

ted

on to

p of

the

tabl

e on

pri

ces

and

cost

s (f

orec

ast

or c

onst

ant t

aken

at t

he fi

scal

-yea

r en

d), a

nd ta

xes

(bef

ore

or a

fter

). a-*

oo