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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 INFORMATION TO USERS The quality of this reproduction is dependent upon the quality of the copy submitted. Broken or indistinct print, colored or poor quality illustrations and photographs, print bleed-through, substandard margins, and improper alignment can adversely affect reproduction. In the unlikely event that the author did not send a complete manuscript and there are missing pages, these will be noted. Also, if unauthorized copyright material had to be removed, a note will indicate the deletion. ® UMI UMI Microform 3333300 Copyright 2008 by ProQuest LLC. All rights reserved. This microform edition is protected against unauthorized copying under Title 17, United States Code. ProQuest LLC 789 E. Eisenhower Parkway PO Box 1346 Ann Arbor, Ml 48106-1346 ©2008 Marc Badia All Rights Reserved 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 ongoing 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 2.2 Previous Research 6 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 uncertaintyreducing 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 probabilityweighted 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 nonaccountants (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 reservesare 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 firmspecific 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. 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). 19 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 riskaverse 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 riskaversion 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. 30 5. Methodology 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. 32 1 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 ydirection 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 rateover 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 31 st 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!. R.t = p{NIlt + j32ANIit + j33AProvedit + j34AProbableit + sit. (5) (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). 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Annals of Statistics 1:253-270. 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 NET PRESENT \ ALVES OF FUTURE : NET REV~E.yr.jE AFTER IXC OME TAXES DISCOUNTED AT BEFORE I M . O M TAXE^:DISCOUNTED VF ell >' > ve.ii! It.', 2i.-„ 2.i J c 1" . 5% MM •tMbl i.M$^ •M$i IM: MS i MS i IMS ;MS. iMS RESERVES CATEGORY P3.0YED PtOfllKlUC Developed No:iPioil'.icui: Undeveloped TOTAL PROVED 2 1 . ••>: i .in r : 1"S * -3 ftU4 0 2 .02 "••>"• 'lJpfS TOTAL PROBABLE 1 TOTAL PROVED PLUS PROBABLE 4 ^ 1 >& P24 2 'f 6 . i ( ^>P: ^c"4:o"* -UP 41* 25t42 i : 124 323 ±i> l l 4 -t, "2? "421 1"1 '"(•(: 274 -a» 10" i" i ^ 4 0 us: 241 Zii :os& . 2W 14. i i l -»C" 1% 24 21 C • 4SST -»_ 42~ ' _ib J 4 M L , ^1M i^S 22*i ->f,X^. .•4^~ -1 !l _• """ZTTu"" 2- i C P4-. " tP J T 4 £ 2»C ^«U 23S 3ou 20^ n.4 ."'416 Source: Crew Energy Corporation, Annual Information Form 2006 60 Exhibit 1: Timeline of National Instrument 51-101 ^ *# *" ?VV-9 "v '"9 ^ ^ # V # r 4^ & 1998 2001 2002 2003 + 2005 2004 2006 2007 NI 51-101 regime Sample period 2003-2006 NP 2-B regime Exhibit 2: Oil and Gas Prices (2003-2007) US$/BOE 100- /V A/ to -wn 10 CD ~ 9 9 EdrrDrton - - - Henry HLJD \ /•*""«./ \ / CD N fc; I"- 1 2 ? 2 2 * '« 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). Min Q10% Median Mean Q90% Max 1 well 26.8 65.6 95.6 100.0 139.9 381.5 2 wells 38.3 74.7 97.8 100.0 128.0 254.4 10 wells 63.9 88.1 99.5 100.0 112.3 150.2 100 wells 88.7 96.2 100.0 100.0 103.9 114.5 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 • 1 well 0.07 2 wells 0.06 -10 wells 0.05 100 wells 0.O4 0.03 0.02 0.01 Reserves Quantity 0.00 Chart B: Inverse Cumulative Distribution Function Prob. 1.0 0.9 1 well 2 wells 10 wells 100 wells Reserves Quantity 62 Table 1: Sample Descriptive Statistics (2003-2006) MVE NOGA TL OGA NI BOE Proved BOE Probable Mean Std.Dev. Ql 25% Median Q3 75% 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 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 Reserves Value Estimates (under different assumptions) Prices I Constant 4-» Before After | 0% disci Taxes Before Forecast After Before Constant After Undi o o Before Forecast 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 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 m O N N ( N ^ O — l o o © ©'© O ^ t - m O N o o © O N — n O N © ^ H © T ) O O O ^ O - J t O f N O r J - O ^ t - m t s - — l O c - j O f N i O f N o o m '©' ©'©'©' ©'©'©' © ' 0 0 ' © 6° - r < - ) C s a N O N N o o o ^ © ^ r O O t ^ O O t ^ - O ^ O — i o © a \ © f S r t r t © ' © ' © ' © © © © ' © ' © ' ^ o >p ; 3 in O N ON 1> © cs © © T-I © © © © C-m cs © O © © © 00 i-< N O © NO •* © © o © N O © r O © 0 O © t © t — ^ © r - H ©©'©'©©©oo'©'©'©'o'o © © © ©' © o oo ><"> © ON cs © m ©'©'©' NO CS ro <-; ~ H ro •*t © ON © ~H © © © <-; © vo © ©' o oo © t-~ © ^H © © ' © © © oo © © © ' r~ ro •— © ' © o © © i—1 © ON © © T « p oo © © O © -l Aw o © © p^ © iu T •°& - H c-~ © © © - H© © © r•* m <-i —< © © © o S > » o- fa 060 NOP"--—<t-~i^~^Hi--oo©'^-ONr-r~ NO -* - 089 .276 m i—I >-; ©' 000 oo 00 i-< © 424 ON OO © © 347 a" oo r^H © - oo © © ©- cs O N © t© ON ©- © o .3 a-S ii 9 > <« o V I? E I 1 O N m ^ t — < © i n ^ o o — < N O © p xr cs © © ^t © cs cs © ' © ' © ' © ' ©° © ' © ' © ' © ' o ^ © © © © © C-~ — < ' t © o © © P ~ r-H Tf — i© NO © •* ^t" © ^H O T3 — a l! h H i o ( S h < t h H w i r t m o i o C © S ' © © - H © © © ^ © H ' © © C ' S © © I © O © © P ^ H © © NO P~ © ©' oo ci m © m © cs t-cs © © © --H <r> NO CN ©' V 00 © m r H C S © C S © ' © in ^t CS © © O l O ^ t ^ t ' © H ^ f r H " © O N " © i n © ©P © P © V v" t— •—< m © ON r f © in ON oo ,—i © © © V - H © © CS . - i CS © NO O ©• p V CS NO © CS NO oo ©• © OO I - I © © —< © © © N O © © © _; ©'©'©' V cs a ON © [^ © © © t ^ © © ° . ©' ° . ^ V V cr> t> CS ©' f r t O © © © N © © P - ; V © © ^ O ^ H l — ^ - C T t - H © CS © m ^ - © ^ ©' p ©• V ^ © O © h © NO cs m ©' H © © © o P V o H r~ ,© ! ot—o o©o m m © rNO r-H © ^ H rt -5Jcs © ©' © © ' © © ' © ' © ' V a ON © 4 m ^ ^H © •—> m © © r - NO i n r^ m NO o t i m ©' © © ' •<t -<t © O N © © in >n o m o © ON CS O CS © © © © ' © ' © ' © " —H i n © m © © P © ' V T-t m © in m cs © ' © ' - H t ^ ^ H N O ^ H O O r O N O © O O C S ^ H © t ^ © ^ © O N © ^ © ^ H C S ^ H .-H © © p V © © © © m ©© c ©s ©o ^ ©- i o© ©c s o© ©© o© o ©m © cs © ^r © cs © ' © ' ^H ^H © © © © prices ' V to a S3 O £ w T3 2° a o cd t-H O 3 c« rt • S i •a g o a o w ^ ^-t-J a 2 53 'fee ^§ •Sirf* cd cfl 3 s.B I 3I^ 'c3 ^8 ' > Pi o =3 - s J3 o B v u H g S IS rn ill ^ | ii „ go H = GO 5 a-S s © © © © cs © © © © fl V V c! CO s probi a rese: ON rm . O ON in -t ©' © © © i-i ^ © © P V © © © ~ ON © i n >—i t-~ © •* © © P V ^H © © © O N O ^ H C S C S ^ O N ^ | - 0 0 © © t - ~ ^ o o o n M o i - t o - H o o n c s © © c s ^ t © © i n ^ H ^ H © c s • © © © © © © © © © © © © V © © o .—I N O i—< i n i—i O N O \ m H © r^ © ^ © N O © O N © C S © i n © o-> © O m N O O © ' © ' © ' P © i i v © --< cs © "St © © © © cs V ©'©'©' • © V o o h « i n m o \ C S N O ^ H C < - i ^ t h - H O i H O ' © ' © ' © ' © ' © ' i i > a- o* - • 9 s ^ Pi ta i " —-i Pi n O •a oa W < a o £ < j H o o a o > (X > O Pi PH H pa 00 o pi (* PH H W N w > W Pi o « 9 PH PS PH W .a E rt O O PH MH O s° pQ _o o J2 5 o '-S t» O w a OHPH pa o, 06 0.8847 0.4620 2.0** 0.8924 s 72*** 0.8752 4.21** 0.3992 1.1 0.8791 Constant Prices and Costs Before Taxes After Taxes (3) (4) (3) (4) 1.0302 1.2042 1.0052 1.1206 a 9*** ^ <*** 3 3### 3 s*** -0.6942 -0.8579 -0.5827 -0.7052 _2 9*** _2 g*** -2 6*** -3 1*** 1.0607 1.0750 g Q*** 1.2038 1.2806 r=03 (4) (3) 0.8926 j*** 8.51*** 0.8989 0.6552 2 5*** j0 0.8817 4.32** 0.5651 1.6 0.8851 Forecast Prices and Costs Before Taxes After Taxes (3) (4) (3) (4) 1.0360 1.1956 0.9649 1.0777 3 Q*** 2 3*** 3 2*** 3 6*** -0.8104 -0.9837 -0.6506 -0.7956 _3 3*** _4 4*** 1.2878 1.2400 9.00*** 9.16*** 1.5037 1.4045 MVEit = ]T at + /3lNOGAit + fi2TLu + j34Provedit + J35Probableit + sit. 06 t=03 MVEit = £ a, + /3,NOGAit + j32TLit + P,PVOGit + eit. Pooled regressions estimated following Ordinary Least Squares (OLS) and time fixed effects (not reported). Sample of 219 firm-years. 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: 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 NI 51-101 forms. NOGAit= Non-O&G Assets. TLH = Total Liabilities. PVOGj,= Present Value of proved plus probable reserves (Prob>50% of being extracted). Provedit= 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$. R2 Probable Proved PVOG TL NOGA Unrestricted Model: Restricted Model: Table 3: Results from Basic Regressions High 110 2,025 0.39 7.1% 6.2% 105.7 57.9 53% 17.95 1.91 5.20 10.27 10.97 3.67 9.63 3.00 Low 110 1,260 0.40 7.2% 5.7% 52.4 15.3 46% 23.07 3.40 6.82 13.68 13.99 3.39 11.77 2.56 High 109 985 0.34 2.9% 3.2% 58.1 45.1 50% 19.79 2.59 4.85 10.95 9.04 5.71 7.93 4.05 PB/PV Low 76 1,593 0.38 3.2% 4.3% 83.4 47.8 45% 21.9 2.24 5.18 11.00 13.10 4.16 10.37 2.92 REVI High 11 444 0.35 7.1% 4.1% 15.1 8.3 50% 23.4 3.00 5.18 11.10 10.56 5.18 8.32 3.34 No 30 1,504 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 189 1,055 0.38 4.7% 5.0% 45.7 21.3 47% 21.95 3.13 6.12 13.00 12.00 4.63 10.28 3.36 RECOM Corp. 145 540 0.30 1.6% 1.5% 22.4 15.4 45% 22.15 2.97 5.18 11.94 10.35 5.25 8.62 3.54 Trust 74 2,197 0.42 7.7% 9.9% 116.8 58.9 53% 19.98 3.00 7.00 12.92 13.50 3.32 12.02 2.91 TRUST Figures in bold indicate that the difference of means is significant at 0.05 (Satterthwaite). Partition Variables: TRUST=1 if firm is an energy trust, 0 if it is a corporation; 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. Other Variables: N=number of observations; Market Value=Total Market Value of the firm; Leverage=TL / Total Assets; ROA=Net Income/Total Assets; Dividend Yield=Total Dividends / Market Value; BOE Proved= Barrels of Oil Equivalent of Proved Reserves in millions; BOE Probable=Barrels of Oil Equivalent of Probable reserves in millions; MVE=Market Value of Equity per BOE; NOGA=Non-Oil and Gas Assets per BOE; TL=Total Liabilities per BOE; OGA=0&G Assets per BOE; Pv/B/C (Pb/B/C)= Present Value Estimate of Proved (Probable) Reserves per BOE, assuming a 10% discount rate, before taxes, and constant prices and costs; Pv/A/F (Pb/A/F)= Present Value Estimate of Proved (Probable) Reserves per BOE, assuming a 10% discount rate, after taxes, and forecast prices and costs. N Market Value Leverage ROA Dividend BOE Proved BOE OILMIX MVE NOGA TL OGA Pv/B/C Pb/B/C Pv/A/F Pb/A/F Low 109 222 0.35 2.9% 2.7% 5.6 3.5 43% 24.79 4.04 6.41 14.26 11.94 5.48 9.98 3.64 SIZE Table 4: Contextual Analysis: Univariate Analysis 66 Table 5: Contextual Analysis: Multivariate Analysis (SEC Case) 06 MVEU = Y,a, + P\ NOGAit + j32TLit + ^Proved,, + J35 Probableu + su. (4) 1=03 PARTITIONS Pi h P4 h 1.3133 5.06*** 1.7981 2.27** 0.4848 1.44 -1.1611 -3 05*** -1.3414 -2.54** -0.1803 -0.61 1.7860 (y 79*** 1.3562 g j j*** 0.1342 0.27 0.8937 2.00** 0.7595 2.53** 1.0121 7 20*** 2.7103 4.56*** 1.6982 5.96*** -0.4326 -1.37 -2.0450 -5 93*** -1.6124 -6.43*** 1.1103 2.59** 2.2672 Q 79*** N Adj.R2 PrPs 108 0.8654 15.07*** 111 0.8944 0.80 111 0.9047 0.06 108 0.8638 35.47*** 76 0.8986 5.94** 76 0.8898 24 13*** 30 0.8739 0.07 189 0.8695 8.10*** 145 0.8600 20.25*** 74 0.9462 0.19 SIZE Small Large Large—Small -0.4298 -2.00** PB/PV Low High High-Low 1.1569 4.50*** 1.4277 1.38 0.0441 0.17 -1.3836 -3.63*** RE VI 97*** 1.6625 5 93*** 1.6213 7 34*** 0.8242 2.63*** -0.0412 -0.17 2.1948 2.37** 1.4653 5.90*** -0.7295 -1.94* -0.0416 -0.03 -1.2703 -3 78*** -1.2287 -2 77*** 1.5943 3 Q7*** 1.6053 8.26*** 0.0110 0.03 1.3000 1.94* 0.5947 1.76* -0.7053 -1.69* Corporations 1.6147 2 go*** Trust 0.8909 5.87*** -0.7238 -2.16** -1.4522 -4.16*** -0.0446 -0.12 1.4076 5.00*** 1.9984 10.85*** 0.8510 2.09** -1.1474 -4 88*** 0.2476 0.76 0.3164 0.33 0.0688 0.20 Low High High-Low 1.5483 4 79*** 1.3386 6.81*** -0.2097 -0.85 -1.8100 -3 42*** -0.9858 ji 0.9019 2 82*** 0.0196 0.07 -0.8823 -3 37*** RECOM No Yes Yes-No TRUST Trust-Corp 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 2003 Intercept NOGA TL PVOG k=k 2006 (3) (4) (3) (4) (3) (4) -3.8207 -0.82 1.5613 2.09** 0.2035 0.23 1.3893 2.53** -1.9661 -0.42 1.5837 2.05** -0.0060 -0.01 2.7036 0.65 1.5706 1.44 -1.4426 -2.03** 1.6845 4.I9*** 4.4590 0.97 1.6658 1.58 -1.6672 -2.34** -1.7561 -0.53 1.3624 6.44*** -0.9210 -1.90* 1.3188 5.90*** -0.5162 -0.15 1.4065 6.95*** -0.9894 -2.36** 10.8777 2.35** 1.3220 4 49*** -1.1944 -2 76*** 0.8752 4.40*** 8.9949 2.10** 1.3748 5.03*** -1.4308 -3.43*** Probable N 2005 (4) Proved I2 2004 (3) 0.5338 53 1.5350 2.53** 0.6151 0.93 0.5445 53 1.81 0.3607 56 1.9205 444*** 0.8985 1.20 0.3768 56 1.70 0.6841 61 1.4604 6.81*** 0.8158 2.00** 0.5633 61 2.80* 0.3541 49 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$. 178 0.5113 0.0977 0.33 178 0.5599 6.13** and Costs After Taxes (5) (6) 1.5229 1.3536 1.73* 1.67* 1.5440 0.7428 1.58 0.66 0.8904 7.05*** 1.1938 7 25*** 0.4249 1.28 171 171 0.5476 0.5680 2.93* Pooled regressions estimated following Ordinary Least Squares (OLS). The numbers below the coefficient estimates are t-statistics corresponding to clustered adjusted errors at the firm level. The symbols *, ** and *** denote significance at the 0.10, 0.05 and 0.01 levels (two-tailed) respectively. Variables definitions: Ru— Annual Market Return of firm i in period 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. N1H= Net Income Before Extraordinary Items (data #18). AM;,=Change in Net Income Before Extraordinary Items. AProvedit- Change in Proved reserves value reported by the firm (P>90% of being extracted). AProbable^ Change in Probable reserves value reported by the firm. Calculated as the difference between change in 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 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). P* = Ps N R2 AProbable AProved APVOG AW NI Constant Prices and Costs Before Taxes After Taxes (6) (5) (5) (6) 2.9304 3.9703 3.6616 3.1732 3 97*#* 7.04*** 5.06*** 5.55*** -1.6057 -1.3568 -2.8211 -0.3046 -1.34 -1.47 -0.29 -2.55** 0.5298 0.6759 6.13*** 6.64*** 1.0546 1.1422 7ig*** 8.34*** -0.1288 0.0661 0.28 -0.73 165 165 161 161 0.4958 0.5630 0.5060 0.5513 17 39*** 8.38*** Forecast Prices Before Taxes (6) (5) 2.1145 1.7662 2.20** 2.31** 0.2323 -0.7325 0.24 -0.59 0.7254 g 40*** 1.1661 7i<*** (6) R„ = px NIit + J32 ANIit + J33 AProvedit + J34 AProbable,, + eu. ' (5) Rlt = JS.NI,, + J32ANIit +.&APVOG,, + s,r Table 7: Returns Model a-* oo