Probability Thresholds and Equity Values
Marc Badia
Columbia Business School
Columbia University
6A Uris Hall, 3022 Broadway
New York, NY 10027
mb2323@columbia.edu
December 20, 2007
Preliminary, not for distribution
I gratefully acknowledge the helpful and unconditional support of Bjorn Jorgensen. I also
thank Divya Anantharaman, Sid Balachandran, Colin McGee, Nahum Melumad, Partha
Mohanram, Doron Nissim, Gil Sadka, Du Nguyen and participants in the Brown Bag
Ph.D. Seminar and the Brown Bag Seminar 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, and to Nellie Kim for editing
assistance. All errors are mine.
Abstract
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 and also with prospect theory. Lower measurement error in past
reserves estimates and the presence of a reserves committee strengthen these results. The
market value weight of proved reserves tends to be larger for small size firms with a
lower ratio of proved to probable reserves. The market value weight of probable reserves
tends to be larger for large size firms with a higher ratio of proved to probable reserves.
This study is relevant to regulators and investors. The FASB and the IASB are currently
discussing the role of probability thresholds in their joint Conceptual Framework project.
My findings offer support for the use and disclosure of probability thresholds in asset
measurement to inform investors. The incremental value relevance of the new oil and gas
reserves classification is also informative for the IASB in their on-going development of
a new standard for the extractive industries.
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 before, 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.
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.
1
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 two tests. First, an incremental value relevance
analysis addresses the question on whether investors attach a different market value
weight to proved and probable reserves. Second, a relative value relevance analysis
investigates whether the breakdown in reserves is more informative about 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
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, the most similar study is Campbell et al. (2003),
which examines 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
2
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 first study that compares the valuation of an accounting item
across different probability categories.
The results of my work 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 tries to address 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.
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
1
The SEC does not allow the voluntary disclosure of additional reserves categories.
3
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)
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.
4
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
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
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.
5
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
present asset retirement obligations whose settlement amount and timing may be
uncertain 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 criterion 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 develops a fault and the customer seeks repair
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 (¶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.
6
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 (¶17). 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
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.
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).
7
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
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.
8
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 socalled 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.
9
utilizes asset/liability recognition to communicate with risk-averse investors about the
uncertainty of future benefits/obligations. Like in my setting, the “recognition hurdle”
(i.e., probability threshold) is exogenously determined and the investor learns about the
distribution of future cash flows. 9 In his model, investments that generate future benefits
with an uncertainty level lower than the “recognition hurdle” are capitalized. Otherwise,
they are expensed. So, conceptually, he 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 level. 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
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, we can find a
few studies that investigate the differential information of recognition versus disclosure
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.
10
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 accruals (i.e.,
amounts recognized in the primary financial statements) versus disclosures 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.
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
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
comprehensive set of disclosures on reserves quantities and values in the footnotes. Other
international GAAPs mandate similar disclosures. 11
The majority of studies on disclosures of reserves quantity and value find this
information relevant to investors, creditors and management. However, evidence is
mixed on whether this information is incrementally relevant to the primary financial
statements figures from the point of view of stock investors. Contrary to what historical
accounting critics would expect, Harris and Ohlson (1987) find no evidence that book
values are less relevant than the present value measures. The rest of measures required by
SFAS 69 –future net cash flows, direct profit margin, and quantity of proved reserves–
are not significant in explaining the market value of O&G properties. In a subsequent
paper (Harris and Ohlson, 1990), the authors reject a plausible “functional fixation”
hypothesis –i.e. investors placing 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 When a returns model is used, the overall
change in reserves value is not found to be 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
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.
12
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.
12
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 SFAS No. 69 required 10% discount rate.
However, historical measures appear to be noisier than present value estimates as
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 oil and gas reserves estimates by 20%. 14 This was not an isolated event
though. Companies such as Forest Oil, El Paso, Penn West Petroleum, BP Plc 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
definition presents a major problem. Under the “reasonable certainty” pretext firms might
13
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
Plc 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 19th 2004.
13
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. However, there is a constant
need for revision since the technologies employed in petroleum exploration,
development, production and processing continue to evolve and improve. The most up to
date and commonly accepted classification of reserves can be found in the Petroleum
Resources Management System document, 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). In this document, the definitions of reserves include a numerical probability
threshold according to the level of recovery uncertainty.
It remains to be seen how long regulators will take to 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.
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
16
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.
14
stated by the ASC, is “to enhance the quality, consistency, timeliness and comparability
of public disclosure by reporting issuers concerning their upstream oil and gas
activities.” 17 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. 18 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.
In Canada, the tipping point that set in motion a reform process was an alleged
fraud that consternated the industry and the financial community. In 1998, soon after
acquiring Blue Range Resource Corporation through a hostile takeover, Big Bear
Exploration Ltd. had to write down 32% of the reserves of the new subsidiary and place it
under bankruptcy protection. That same year, 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 consistent with other international initiatives.19 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
17
Canadian Securities Administrators Notice, September 26th 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% (www.bea.gov). 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
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.
18
15
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. 20 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
pioneering in the unequivocal probabilistic 21 definition of reserves taken from the
COGEH: 22
-
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.
20
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
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.
22
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.
16
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.
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.
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:
•
Forecast prices and costs: in the U.S., 23 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
23
In the UK, the disclosure provisions in the 2001 SORP do not include reserves values, only quantities.
17
(4) natural 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. 24 Reserves Committees are recommended for the purpose
of hiring the evaluators and supervising their numbers before they are officially approved
by the Board of Directors.
3.3.4 Other regulation projects
The IASB is working on a new standard specific to extractive activities. 25 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, consistently with NI 51-101.
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−P90) should have
a probability between 50% and 90% of being recovered (see appendix 1 for an example).
24
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.
25
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.
18
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, i.e., each type of reserve is
incremental to the other. So my first hypothesis stated in its null form is as follows:
H1: Investors value proved reserves the same as probable preserves.
In my setting, if proved reserves receive a value significantly different from
probable reserves, we can infer that the breakdown is more informative about market
values than an aggregate measure including both proved and probable reserves (P50). In
my analysis, I will examine this question separately to confirm that this is the case.
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 have to do with the
19
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 property) 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 (P90) figure and a smaller proved plus probable plus
possible reserves (P10) figure. The proved plus probable reserves (P50) will still be the
same. The example from exhibit 3 illustrates by means of a Monte Carlo simulation how
the impact of entity probabilistic aggregation on P90 and P10 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).
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 real estimate P90. P50 should not be affected. But if P90
is higher, then probable reserves, the difference between P90 and P50, should have lower
real estimates. 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
20
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 proved to probable reserves. The lower is 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 low proportion of proved to probable reserves, i.e., higher
variance. In addition, difference levels of variance can also affect the valuation of proved
reserves relative to probable reserves. A lower ratio of proved to probable 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 higher ratio of proved to probable 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.
21
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
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.
22
Canadian royalty trusts are different from U.S. royalty trusts. The U.S. trusts pay
out the cash flow generated by their O&G properties, but they do not acquire new
properties. Consequently, their cash flow declines over time as their assets are depleted.
Canadian trusts, by contrast, try to replenish depleted properties with new acquisitions.
Since royalty trusts distribute most of their income to unitholders, they must raise cash to
fund acquisitions either by borrowing or by selling more units.
On average I expect trusts to be larger, to own more mature properties, to have a
higher dividend yield and a narrower difference between market values and reserves
estimations (since they mainly receive O&G royalties). I expect this factor to behave in
the same way as size. If corporations are smaller and less diversified than trusts, I expect
that they will receive a relative higher market value for proved reserves and lower market
value for probable reserves.
5. Methodology
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. 26 A corollary 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:
V0it
= α + β ( p0it − c0it ),
it
R0
(1)
where i indexes companies, t indexes time, 0 signifies the then current values as of
sample date t, V is the market value of reserves, R is the quantity of reserves, p is the spot
26
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.
23
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.
Similar to the studies aforementioned, I also use an Imputed Value model. The
idea is not any 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
rearranging equation (2) and substituting by market values I obtain the following
regression model:
24
MVEit = α + β1 NOGAit + β 2TLit + β 3 PVOGit + ε it .
(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): 27
MVEit = α + β1 NOGAit + β 2TLit + β 4 Provedit + β 5 Probableit + ε it . (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 H0: β4 = β5, that is,
investors value proved reserves and probable reserves in the same way. Rejecting the null
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 H0: β4 = β5 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
27
Note that expression (4) is equivalent to a specification that included Proved reserves (P>90%) and
Proved plus Probable reserves (P>50%).
25
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.28 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 a returns specification in section 6.3.1.
5.2 Data Collection
Under NI 51-101 all reporting issuers in Canada with O&G activities 29 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. 30 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
28
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).
29
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.
30
The database is accessible at www.sedar.com 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.
26
quoted in the Toronto Stock Exchange (TSX). 31 This results in a total number of 422
firm-years. 32
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, 33
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
243 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 7 firm years because of mergers and acquisitions, 13 firm
years that use SE, and 13 firm years with market values per barrel higher than Cdn$60.
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$40 of
imputed value per barrel (note that IV=MV+L−NOGA and that the average exchange rate
for the period of my study was 1.26 Cdn$/US$), consistent with the crude nominal price
level of their study period. My final sample contains 210 firm-year observations, from
2003, year in which the Instrument became effective, to 2006.
31
I 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.
32
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
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.
33
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.
27
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 210 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 (133,837 and 95,375 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, like for example
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 BOEs. The mean of the variables tends to be higher than the
median because of the asymmetry in the variables distribution (lower bound at zero and
upper bound unlimited). In the case of Net Income Before Extraordinary Items, where
there is not such an asymmetry, the mean equals the median. 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 pair this less conservative method with a growing
level of investment, like the one experienced in recent years –oil price increases made
28
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+L−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 L and
NOGA are close to fair value. The mean MV+L−NOGA=21.19+5.79−2.98=
Cdn$24.0/Barrel. If we compare it to the case of estimated reserves using forecast prices
and costs, after taxes, and at 10% discount rate, 34 we will find that the mean Proved plus
Probable reserves is only Cdn$13.88/Barrel –without weighting reserves by the
probability of recovery– and hence the difference is Cdn$10.12/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$23.93/Barrel.
Comparing the quantity of proved and probable reserves, we observe that the
former is on average twice as much as the latter (80,163 vs. 41,725 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
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
34
We consider this case (forecast, before taxes, 10% discount rate) because it is consistent with the
assumptions used to estimate the quantities of proved plus probable reserves in the deflator.
29
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
correlations, since they are part of an identity that must hold. Although regressions might
experience some multicolinearity, it does not seem 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, in addition to the main OLS
estimation, I also run a firm and time fixed-effects regression as suggested by Boone
(2002). The purpose of the fixed-effects regression is only to show that the coefficients
relations and their magnitudes are consistent, so I do not provide the results. Obviously,
the amount of degrees of freedom used up by this method lessens its power. I also rerun
the OLS regression excluding the 6 outliers identified using an MM robust estimation
35
MM estimation addresses the three classes of problems with outliers: 1) outliers in the y-direction
(response direction), 2) multivariate outliers in the covariate space (x-space), 3) outliers in both the ydirection and the x-direction.
30
technique (Yohai, 1987) and results still hold. So my analysis will primarily focus on the
OLS results with the whole sample.
The coefficients are as predicted, not only in signs, but also in magnitudes. Assets
(NOGA) and liabilities (TL) present coefficients close to 1 and -1, respectively. In the
restricted regression, proved plus probable reserves (PVOG) has a highly significant
coefficient close to 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, assuming
no bias in the estimates and a symmetric distribution 36 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 H0: β4 = β5 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 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 increases thanks to diversification, probable reserves decreases. In the contextual
analysis I explore more in depth this last possibility.
Comparing the results under different valuation assumptions I find that the
coefficients for proved and probable reserves estimated before taxes are more significant
36
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 in the forecast case.
31
than the ones in the after taxes case. Overall, adjusted R2 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 we have to use before
taxes estimates when we 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 small to draw strong conclusions.
The second question I need to answer is whether the breakdown in reserves is
more informative about market values than an aggregate reserve measure. I compare the
coefficients of determination of the restricted and unrestricted regressions through an Ftest. Table 3 shows that the F-test statistics are significant at 0.05 for all the valuation
scenarios. Thus, the breakdown of reserves is significantly more informative than the
aggregate measure of reserves.
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
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
32
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 make partitions of the sample according to size, ratio of proved to probable
reserves, precision, legal form, and the presence of a reserves committee, and then I
compare coefficients. 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
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 in terms of discovered reserves and also the still low accumulated depreciation of the
first years. 37
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 (1.34 vs. 0.83) and lower to probable
37
Firms follow the unit of production depreciation method, that is, O&G assets are depreciated
proportionally to production.
33
reserves (-0.04 vs. 1.62). So it seems that they are pricing in the diversification effect. For
large firms, the coefficient of determination is substantially greater and the intercepts
much lower. Both facts 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 young firms
are a mix of small start-ups and large amalgamations of firms that had already been in
operation for a long time.
6.2.2 Ratio of Proved to Probable Reserves
The second partition is based on the proportion of proved to probable reserves
(PV/PB), which is a proxy for variance. Again I have formed two subsamples, one with
PV/PB above the median (High) and the other one below the median (Low). In Table 4,
we observe that firms with high PV/PB are on average significantly more leveraged (0.40
vs. 0.34) and profitable (7.2% vs. 2.9%) than firms with low PV/PB. This higher
profitability is also reflected in the higher present value estimates of O&G reserves per
BOE. Since probable reserves tend to be extracted later than proved reserves, those firms
with a higher proportion of probable reserves (i.e., low PV/PB) will get lower present
values.
The multivariate analysis (Table 5) indicates that those firms with a higher
proportion of probable reserves (i.e., low PV/PB or 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., high
PV/PB) the effect is the opposite. Surprisingly, their probable reserves receive a very
high market value (β5=2.19, t-stat=3.74), higher than for proved reserves (β4=0.58, tstat=2.72). 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 not a significant difference in size between
firms with high and low PV/PB. Furthermore, size and PV/PB exhibit a correlation of
34
only 0.14 (see Table 2). Accordingly, I do not expect the PV/PB 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 similar significant coefficients for proved and probable
reserves (1.18 and 1.15, respectively). This result would suggest that for firms with
higher quality of estimates investors do not make a distinction between proved and
probable reserves and hence, they do not care about the second moment. Investors value
much more the probable reserves of firms with low revisions, consistently with having
lower information risk. For proved reserves the difference is also consistent but 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, firms that are
more diversified (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
(0.50 vs. 0.33).
Next, I distinguish between those firms with reserves committee and those
without it. Only 30 firms out of 210 do not have a reserves committee, so results should
be interpreted cautiously. In general, firms with reserves committee have a higher
dividend yield and higher estimates of reserves per BOE. This latter trait might be
explained by the higher proportion of proved reserves –less affected by the discount rate–
over probable reserves. Looking at the evidence from the multivariate analysis we do not
find significant differences in the valuation of reserves between the two subsamples. In
general though, firms with reserves committee exhibit more significant estimates and a
higher coefficient of determination. Still, the reason for this difference in significance can
be the lack of power –i.e. only 30 firms without committee.
Finally, I have run an evaluator fixed-effects model to control for the effects of
different evaluators (not reported). Results remain robust.
35
6.2.4 Legal Form
Partitioning by legal form I obtain 137 corporations and 73 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. This is just a reflection of the
higher proportion of probable reserves for corporations.
In the regressions (Table 5) we see that for trusts the coefficient for proved
reserves is 0.90 (t-stat=4.02). Considering that trusts are very large and well diversified
the magnitude of this coefficient makes total sense. In contrast, the coefficient for proved
reserves in corporations is significantly larger (β 4=1.53 and t-stat=8.64).
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 supposition (β4=1.52 and
β5=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
36
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
changes on my results. First, natural gas prices did not follow the same trend as crude oil
prices. In particular, in 2006, lower winter heating demand, growth of onshore natural gas
production and above average storage supplies led to dramatic price decreases compared
to 2005. In the same period, liquid oil prices kept increasing. Second, although most
firms’ announcing dates are concentrated in the same periods, market values are taken for
each firm in different days, as opposed to taking all market values on December 31st or
3.5 months after fiscal year end.
Exhibit 2 graphs the evolution of oil and gas 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 significantly different. 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. As mentioned in Section 5.1,
utilizing a returns model poses some limitations and results must be taken cautiously.
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, also divided by the price at the beginning of the return
37
period. For my analysis, I introduce the change in the estimation of reserves. The final
specifications I run are the following:
Rit = α + β1 NI it + β 2 ΔNI it + β 3ΔPVOGit + ε it .
(5)
Rit = α + β1 NI it + β 2 ΔNI it + β 3ΔProvedit + β 4 ΔProbableit + ε it .
(6)
where Rit is the market return of firm i at year t, NI is net income before extraordinary
items, ∆NI is the change in NI, ∆PVOG is the change in proved plus probable reserves,
∆Proved is the change in proved reserves, and ∆Probable 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
are lower than in the levels specification. Further analysis should look into the
components of reserves changes. The F-tests for relative value relevance under the
returns specification confirm that the reserves breakdown is significantly more
informative than the aggregate amount.
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. 38 When impairments take place, the
38
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
38
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
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 mcf of gas to one BOE, based on the equivalence in
energy units. However, this conversion rate might not be consistent with the real
economic equivalence. It is frequently suggested 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
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.
39
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 only worth noticing that
firms with a lower proportion of oil present less significant coefficients and adjusted Rsquared. This result would partially contradict the evidence of Berry and Wright (1997)
that 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 find 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. gas). The market
40
value weight of probable reserves tends to be larger for large size firms with a higher
ratio of proved to probable reserves.
My setting specifically looks at the application of probability thresholds for assets
estimations at the measurement stage. One should exercise caution when trying to
generalize the results of this study to other contexts. The first question is whether
investors make the same interpretation of thresholds for assets and liabilities. For
instance, prospect theory would predict that for decisions involving losses, investors
might shift from risk-averse to risk-seeking behavior. In such a case, investors might give
a premium to slightly probable liabilities and a discount to highly probable liabilities.
Second, the use of probability thresholds at the definition, recognition, or measurement
stages is an interesting conceptual distinction, but I do not believe it has practical
consequences for the interpretation of investors.
Future research could examine a regime with voluntary disclosure of uncertainty
as presented in some theoretical models (Jorgensen and Kirschenheiter, 2003). Although
the disclosure of possible reserves (note that P[Proved + Probable + Possible] >10) is
voluntary in Canada, my sample did not include enough observations from disclosers to
perform this test. Alternatively, one could investigate early voluntary adoption of the new
reserves classification in accounting regimes that might possibly incorporate it (e.g.,
IFRS future standard for the extractive industries). For the specific case of O&G
companies, regulators might be interested in whether the classification according to
uncertainty of recovery is incrementally relevant to other existing classifications (e.g.,
Producing vs. Non-Producing; Developed vs. Undeveloped).
Another topic that deserves further analysis is the role of evaluators and
governance in the estimation of reserves. As standard setters push for the recognition and
disclosure of fair value measurements, auditors and specialized appraisers assume
additional responsibilities and incentives that we need to understand.
41
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44
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 are estimates that follow a probability distribution. In Canada,
O&G firms have to disclose different points of the distribution of reserves: proved
(P[X≥proved]=90%), proved + probable (P[X≥prov.+prob.]=50%), and proved +
probable + possible (P[X≥prov.+prob.+poss.]=10%). The following inverse cumulative
distribution function shows these point estimates:
Prob.%
100
90
50
10
Proved
Proved
+ Probable
+ Possible
Proved
+ Probable
Reserves
Quantity
Example: Firm A has 5 wells whose reserves estimates (X) present the following discrete
probability distributions:
Well 1: X1= 50 with a 95% probability and X1= 10 with a 5% probability.
Well 2: X2= 70 with 60%, X2= 40 with 30%, and X2= 0 with 10%.
Well 3: X3= 100 with 90% and X3=20 with 10%.
Well 4: X4= 60 with 60% and X4= 40 with 40%.
Well 5: X5= 30 with 30%, X5= 20 with 30% and X5= 10 with 40%.
For this specific example, and assuming there is no diversification effect (we could
assume the reserves production of the wells has a high positive correlation), the estimates
of each reserves category would be as follows:
45
Proved (P90) = 50 + 100 = 150
Proved + Probable (P50) = 50 + 100 + 70 + 60 = 280
Probable (P50 − P90) = 70 + 60 = 130
Proved + Probable + Possible (P10) = 50 + 70 + 40 + 100 + 20 + 60 + 40 + 30 +
20 + 10 = 440
Possible (P10 − P50) = 40 + 20 + 40 + 30 + 20 + 10 = 160
Obviously, this example is not realistic at all and it is only meant to illustrate the
definition of each reserves category. In reality, the reserves of each well follow a
continuous distribution and there is a diversification effect (see exhibit 3) that increases
with the number of wells and decreases with the level of correlation of the wells.
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%.
Source: Crew Energy Corporation, Annual Information Form 2006
46
1998
2001
2002
2003
2004
2005
Oc
t.
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c. a m
18 e n
, a dm
me en
nd ts
. e pu
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e
Exhibit 1: Timeline of National Instrument 51-101
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
90
80
70
60
50
40
30
20
10
WTI
Edmonton
Henry Hub
Sep-07
Jun-07
Mar-07
Dec-06
Sep-06
Jun-06
Mar-06
Dec-05
Sep-05
Jun-05
Mar-05
Dec-04
Sep-04
Jun-04
Mar-04
Dec-03
Sep-03
Jun-03
Mar-03
Dec-02
0
AECO USD
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.
47
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: μ = 100
and σ = 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
0.04
0.03
0.02
0.01
0.00
25
60
95
130
165
Reserves
Quantity
Chart B: Inverse Cumulative Distribution Function
Prob.
1.0
0.9
1 well
0.8
2 wells
0.7
10 wells
0.6
100 wells
0.5
0.4
0.3
0.2
0.1
0.0
25
60
95
48
130
165
Reserves
Quantity
Table 1: Sample Descriptive Statistics (2003-2006)
MVE
NOGA
TL
OGA
NI
BOE Proved
BOE Probable
Mean
Std.Dev.
Q1 25%
Median
Q3 75%
21.19
2.98
5.79
12.24
0.58
55,138
30,517
11.96
4.27
4.33
7.20
1.39
133,837
95,375
12.26
1.04
2.48
7.45
0.08
4,372
2,469
19.46
1.76
5.40
11.97
0.57
14,589
7,270
26.87
3.05
7.48
16.07
1.25
50,946
26,478
6.82
2.60
6.07
1.93
7.63
2.74
7.10
2.15
10.92
5.97
9.74
4.97
12.29
6.58
11.19
5.33
11.18
3.53
9.91
2.90
11.45
3.74
10.63
3.09
16.47
8.44
15.03
6.73
18.28
8.57
16.18
7.17
14.42
5.22
13.73
4.03
14.86
5.48
13.26
4.43
22.17
11.54
20.15
9.49
22.95
11.95
21.23
9.79
Reserves Value Estimates (under different assumptions)
10% discount rate
Prices
Constant
Taxes
Before
After
Before
Forecast
After
Undiscounted
Before
Constant
After
Before
Forecast
After
Classif.
Proved
Probable
Proved
Probable
Proved
Probable
Proved
Probable
Proved
Probable
Proved
Probable
Proved
Probable
Proved
Probable
10.83
4.17
9.79
3.32
11.42
4.56
10.34
3.54
16.76
9.56
15.14
7.73
18.10
9.96
16.29
7.96
5.11
2.49
4.73
2.04
5.47
2.83
5.00
2.03
7.57
5.21
7.07
4.25
8.31
5.05
7.71
3.94
Statistics for a sample of 210 firm-years.
Variable definitions: MV=Market Value, NOGA=Non-O&G Assets, L=Liabilities, OGA=O&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$.
49
Table 2: Correlation Matrix (Pearson above diagonal and Spearman below)
MVE
NOGA
TL
OGA
PVOG
PROV
PROB
TRUST
SIZE
REVI
PV/PB
RECOM
MVE
1.000
NOGA
0.492
<.0001
0.279
<.0001
0.419
<.0001
0.570
<.0001
0.562
<.0001
0.310
<.0001
-0.064
0.356
-0.232
0.001
0.001
0.993
0.178
0.010
0.123
0.075
0.377
<.0001
1.000
0.240
0.000
0.598
<.0001
1.000
0.401
<.0001
0.442
<.0001
0.795
<.0001
1.000
0.545
<.0001
0.239
0.001
0.439
<.0001
0.585
<.0001
1.000
0.525
<.0001
0.205
0.003
0.444
<.0001
0.587
<.0001
0.897
<.0001
1.000
0.215
0.002
0.142
0.040
0.131
0.058
0.183
0.008
0.521
<.0001
0.091
0.191
1.000
-0.114
0.099
0.005
0.945
0.204
0.003
0.069
0.322
0.097
0.161
0.278
<.0001
-0.320
<.0001
1.000
-0.294
<.0001
-0.250
0.000
-0.144
0.037
-0.282
<.0001
-0.226
0.001
-0.094
0.173
-0.327
<.0001
0.570
<.0001
1.000
0.033
0.684
0.178
0.026
0.019
0.814
0.004
0.958
-0.078
0.333
-0.198
0.014
0.224
0.005
-0.391
<.0001
-0.388
<.0001
1.000
0.140
0.043
0.090
0.196
0.245
0.000
0.211
0.002
0.219
0.001
0.450
<.0001
-0.375
<.0001
0.470
<.0001
0.143
0.039
-0.214
0.008
1.000
0.109
0.115
0.084
0.227
0.175
0.011
0.242
0.000
0.225
0.001
0.242
0.000
0.038
0.584
0.155
0.025
0.027
0.695
-0.084
0.298
0.163
0.018
1.000
TL
OGA
PVOG
PROV
PROB
TRUST
SIZE
REVI
PV/PB
RECOM
0.485
<.0001
0.537
<.0001
0.340
<.0001
0.311
<.0001
0.228
0.001
-0.076
0.270
-0.363
<.0001
0.177
0.028
0.056
0.423
0.068
0.323
0.878
<.0001
0.570
<.0001
0.587
<.0001
0.208
0.002
0.207
0.003
-0.128
0.063
-0.052
0.519
0.267
<.0001
0.199
0.004
0.632
<.0001
0.629
<.0001
0.291
<.0001
0.094
0.177
-0.240
0.000
0.012
0.878
0.221
0.001
0.236
0.001
0.891
<.0001
0.600
<.0001
0.112
0.106
-0.197
0.004
-0.081
0.319
0.232
0.001
0.235
0.001
0.249
0.000
0.286
<.0001
-0.069
0.319
-0.209
0.009
0.451
<.0001
0.247
0.000
-0.321
<.0001
-0.336
<.0001
0.199
0.013
-0.374
<.0001
0.070
0.309
0.570
<.0001
-0.391
<.0001
0.470
<.0001
0.155
0.025
-0.388
<.0001
0.143
0.039
0.027
0.695
-0.214
0.008
-0.084
0.299
0.163
0.018
For each pair of variables the top figure is the correlation and the bottom one is the probability value under H0: ρ=0. Variable definitions: MVE=Market Value of
Equity per BOE; NOGA=Non-Oil and Gas Assets per BOE; TL=Total Liabilities per BOE; OGA=O&G Assets per BOE; PVOG=Present Value Estimate of Proved
plus Probable Reserves per BOE, assuming a 10% discount rate, before taxes, and constant prices and costs; PROV=Proved Reserves portion of PVOG;
PROB=Probable Reserves portion of PVOG; 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=1 if the firm has a reserves committee, 0 otherwise.
50
Table 3: Results from Basic Regressions
Intercept
NOGA
TL
PVOG
Proved
Probable
N
R2
R 2 F-test
βˆ = βˆ
5
6
Restricted Model:
MVEit = α + β1 NOGAit + β 2TLit + β 3 PVOGit + ε it .
(3)
Unrestricted Model:
MVEit = α + β1 NOGAit + β 2TLit + β 4 Provedit + β 5 Probableit + ε it .
(4)
Constant Prices and Costs
Before Taxes
After Taxes
(3)
(4)
(3)
5.0688
5.6362
6.3624
3.19***
2.88***
3.56***
1.0724
1.1191
1.0967
5.90***
5.60***
5.65***
-0.6422
-0.7165
-0.6112
-3.17***
-3.52*** -2.91***
1.0417
1.0888
9.18***
8.20***
1.1915
9.02***
0.6056
2.62***
210
210
210
0.3825
0.3934
0.3438
8.48***
5.37**
4.68**
(4)
7.4285
4.06***
1.1445
5.87***
-0.7000
-3.31***
1.2597
8.34***
0.3928
1.19
210
0.3572
5.28**
Forecast Prices and Costs
Before Taxes
After Taxes
(3)
(4)
(3)
4.6798
5.4439
6.4507
2.73***
3.21***
3.70***
1.1553
1.2227
1.1477
6.18***
6.64***
5.89***
-0.9649
-1.0839
-0.8400
-4.55***
-5.15*** -3.83***
1.2442
1.2351
9.76***
8.46***
1.4977
10.16***
0.5187
2.02**
210
210
210
0.4049
0.4309
0.3540
10.48***
7.24***
10.41***
(4)
7.1317
4.06***
1.2080
6.18***
-0.9202
-4.17***
1.4070
8.46***
0.6086
1.84*
210
0.3645
4.43**
Pooled regressions estimated following Ordinary Least Squares (OLS). The numbers below the coefficient estimates are t-statistics. The symbols *, ** and *** denote significance at the 0.10, 0.05
and 0.01 levels (two-tailed) respectively.
Pit= 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.
TLit = Total Liabilities.
Provedit= Estimation of Proved reserves value reported by the firm (P>90% of being extracted).
Probableit= 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 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$.
51
Table 4: Contextual Analysis: Univariate Analysis
PV/PB
SIZE
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
105
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
High
104
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
107
985
0.34
2.9%
3.2%
58.1
45.1
5 0%
19.79
2.59
4.85
10.95
9.04
5.71
7.93
4.05
High
102
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
REVI
Low
80
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
RECOM
High
80
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
179
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
TRUST
Corp.
137
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
73
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
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=1 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=O&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;
52
Table 5: Contextual Analysis: Multivariate Analysis (SEC Case)
MVEit = α + β1 NOGAit + β 2TLit + β 4 Provedit + β 5 Probableit + ε it .
PARTITIONS
(4)
α
β1
β2
β4
β5
N
Adj.R2
β 4=β5
9.1322
2.80***
3.3741
2.04**
-5.7581
-8.12***
1.0084
4.08***
0.987
2.43**
-0.0214
-0.08
-0.6683
-2.32**
-0.49
-1.77*
0.1783
0.76
1.3412
6.47***
0.8325
5.51***
-0.5087
-2.71***
-0.0434
-0.13
1.6158
5.33***
1.6592
6.67***
105
0.3624
12.39***
105
0.4502
4.72**
4.5355
2.07**
8.4555
3.00***
3.9200
5.60***
2.1914
5.55***
0.8708
4.35***
-1.3206
-5.36***
-1.2341
-3.92***
-0.6104
-2.41**
0.6237
2.65***
1.6317
7.79***
0.576
2.72***
-1.0557
-5.22***
0.0902
0.30
2.1898
3.74***
2.0996
6.98***
105
0.4862
15.87***
105
0.3627
5.13**
0.8790
0.36
13.1019
3.90***
12.2229
15.00***
0.7457
1.45
1.8207
4.07***
1.075
3.26***
-0.1568
-0.33
-0.9794
-2.60**
-0.8226
-2.65***
1.1793
4.91***
1.0124
4.51***
-0.1669
-0.73
1.1509
2.58**
-0.2501
-0.61
-1.401
-4.51***
77
0.4987
0.00
78
0.329
7.56***
2.0200
0.44
5.479
2.76***
3.4590
3.42***
3.6872
2.87***
1.0645
5.59***
-2.6227
-7.11***
-1.7484
-2.34**
-0.6757
-3.17***
1.0727
3.12***
1.6177
3.39***
1.2464
8.87***
-0.3713
-1.33
0.4315
0.75
0.4546
1.76*
0.0231
0.06
30
0.3238
2.40
180
0.4110
6.87***
6.2262
2.80***
4.2646
2.00**
-1.9616
-2.79***
1.2682
4.46***
0.4436
2.18**
-0.8246
-3.36***
-0.8402
-3.21***
0.2054
0.79
1.0456
4.32***
1.5314
8.64***
0.8994
4.02***
-0.632
-3.08***
0.123
0.42
0.0435
0.06
-0.0795
-0.26
137
0.4488
17.11***
73
0.5045
0.83
SIZE
Small
Large
Large−Small
PV/PB
Low
High
High−Low
REVI
Low
High
High−Low
RECOM
No
Yes
Yes−No
TRUST
Corporations
Trust
Trust−Corp
Pooled regressions estimated following Ordinary Least Squares (OLS). The numbers below the coefficient estimates are t-statistics. 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=1 if the firm has a reserves committee, 0 otherwise.
Regression Variables: Pit= 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 ; TLit = Total Liabilities; Provedit= Estimation
of Proved reserves value reported by the firm (P>90% of being extracted); Probableit= 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$.
53
Table 6: Yearly Regressions (SEC Case)
Restricted Model:
MVEit = α + β1 NOGAit + β 2TLit + β 3 PVOGit + ε it .
(3)
Unrestricted Model: MVEit = α + β1 NOGAit + β 2TLit + β 4 Provedit + β 5 Probableit + ε it . (4)
2003
Intercept
NOGA
TL
PVOG
Proved
5
2006
(3)
(4)
(3)
(4)
(3)
(4)
-2.6797
-0.82
0.7726
2.15**
-0.6166
-1.05
1.5647
4.99***
-0.5871
-0.17
0.7772
2.24**
-0.8717
-1.46
8.1197
1.81*
1.3952
2.18**
-0.1258
-0.21
0.8764
2.47**
8.9262
1.89*
1.4509
2.28**
-0.2401
-0.38
-0.9480
-0.29
1.1377
2.84***
-0.6872
-2.00*
1.2267
7.13***
0.6089
0.18
1.3391
3.19***
-0.7823
-2.25**
10.8777
2.43**
1.3220
4.32***
-1.1944
-2.93***
0.8752
3.02***
8.9949
2.07**
1.3748
4.69***
-1.4308
-3.55***
0.5014
49
6
2005
(4)
Probable
R2
N
βˆ = βˆ
2004
(3)
1.7334
5.31***
0.7024
1.12
0.5176
49
2.51*
0.2138
54
0.9815
2.46**
0.5260
0.75
0.2033
54
0.34
0.5542
58
1.3198
7.26***
0.7407
1.98**
0.5633
58
2.13
0.3110
49
1.5176
3.85***
0.0782
0.18
0.3708
49
5.28**
Regressions estimated with Ordinary Least Squares (OLS). The numbers below the coefficient estimates are t-statistics.
The symbols *, ** and *** denote significance at the 0.10, 0.05 and 0.01 levels (two-tailed) respectively.
Pit= 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. TLit = Total Liabilities. Provedit= Estimation of Proved
reserves value reported by the firm (P>90% of being extracted). Probableit= 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, constant prices and costs as of fiscal year-end, and before taxes.
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$.
B
54
Table 7: Returns Model
Intercept
NI
∆NI
∆PVOG
∆Proved
∆Probable
N
R2
R 2 F test
βˆ 4 = βˆ 5
Rit = α + β1 NI it + β 2 ΔNI it + β 3ΔPVOGit + ε it .
(5)
Rit = α + β1 NI it + β 2 ΔNI it + β 3ΔProvedit + β 4 ΔProbableit + ε it .
(6)
Constant Prices and Costs
Before Taxes
After Taxes
(5)
(6)
(5)
1.0644
1.0428
1.0585
17.40***
17.48*** 17.02***
2.4966
2.3537
1.9918
2.29**
2.22**
1.77*
-1.2173
-2.3943
-0.7007
-1.15**
-2.21***
-0.66
0.3545
0.4442
5.21***
5.37***
0.7309
5.64***
-0.1289
-0.81
171
171
167
0.1747
0.2239
0.1814
10.93***
6.83**
11.28***
(6)
1.0362
16.80***
1.9685
1.78*
-1.8260
-1.61
0.7675
5.17***
0.0036
0.02
167
0.2097
6.72**
Forecast Prices and Costs
Before Taxes
After Taxes
(5)
(6)
(5)
1.0135
1.0128
1.0059
15.29***
15.36*** 15.01***
2.8058
2.5103
2.6033
2.56**
2.20**
2.40***
-1.3565
-1.9462
-0.7619
-1.27
-1.80*
-0.70
0.4528
0.4024
5.02***
4.89***
0.6517
5.02***
-0.0123
-0.07
171
171
165
0.1666
0.1892
0.1709
6.30**
2.45*
5.99**
(6)
1.0045
14.90***
2.4733
2.18***
-1.2822
-1.13
0.6264
4.31***
0.1480
0.68
165
0.1781
2.37
Pooled regressions estimated following Ordinary Least Squares (OLS). The symbols *, ** and *** denote significance at the 0.10, 0.05 and 0.01 levels (two-tailed) respectively.
Rit= 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.
NIit= Net Income Before Extraordinary Items (data #18).
∆NIit=Change in Net Income Before Extraordinary Items.
∆Provedit= Change in Proved reserves value reported by the firm (P>90% of being extracted).
∆Probableit= 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).
55