The effect of supplemental reserve-based accounting data on the market microstructure

The Effect of Supplemental Reserve-Based Accounting Data on the Market Microstructure K. K. Raman and Niranjan Tripathy”

At the present time, considerable attention is being devoted to the merits of accounting information obtained from valuation bases other than historical cost. Alternative valuation bases could provide relevant information in the context of firms with impaired or appreciated assets. In this article, we focus on the extractive petroleum industry where serious reservations about the usefulness of historical cost information and the need for supplemental reserve-based present value data were expressed by the Securities and Exchange Commission (SEC) and the Financial Accounting Standards Board (FASB) as far back as the 1970s (FASB 1976; SEC 1978). From a societal perspective, usefulness is a necessary but not a sufficient condition for regulation (Lev 1988, p. 2); rather, financial disclosure regulation is motivated primarily by the need to reduce information asymmetry or inequity in the capital markets. In this paper, we evaluate the reserve-based present value disclosures for a sample of oil- and gas-producing firms by investigating the effects of disclosure on the informed trading component of bid/ask spreads.

1. Introduction The accounting profession is devoting considerable attention to the merits of information obtained from valuation bases other than historical cost (Berton and Baker 1991; Salwen 1992; Washington Update 1992; FASB 1990). Proponents for alternative valuation bases suggest that the historical cost basis is largely irrelevant and that alternative bases (e.g., current cost, current market value, net realizable value, or the present value of future cash flows) would provide useful information. However, usefulness

*Authors’ names are in alphabetical order. Address reprint requests to: Professor K. K. Raman, Department Business, University of North Texas, Denton, Texas 76203-3677. Journal of Accounting and Public Policy, 12,113-133 D 1993 Elsevier Science Publishing Co., Inc.

of Accounting,

College of

113

(1993) 0278.4254/93/$6.00

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of financial information is a necessary but not a sufficient condition for regulation (Lev 1988, p. 21. In the current context, it is appropriate to focus on required present value-based supplemental disclosures in the oil and gas industry which remains a major sector of the economy. In the oil and gas industry, serious reservations about the usefulness of historical cost-based accounting data, as noted below, were expressed by the FASB and the SEC as far back as the 1970s. In most other industries, the cost in a transaction is generally a fair measure of value received; however, there is generally little correlation between costs incurred in exploring for oil and gas, and the value of discovered mineral reserves. Consequently, historical costs reflect expenditures for finding and developing the principal asset-proved reserves of hydrocarbons-rather than the fair value of these reserves. Moreover, the discovery of new reserves as a result of current-year efforts are included in income not in the current year but in subsequent years (often with considerable lag) as the reserves are produced and sold. Hence, historical cost-based accounting data for oil and gas firms are viewed as less useful relative to similar data for firms in other industries (FASB 1976). For this reason, accounting policy makers have concluded that present value-based supplemental data on mineral reserves, although potentially very imprecise, are relevant in valuing equity securities in the extractive petroleum industry (Accounting Series Release (ASR) No. 253 (SEC 1978); FASB 1982). These supplemental disclosures report present values based on estimated future cash flows rather than the costs incurred in finding and developing the reserves. In our article, we investigate these supplemental data from the perspective of the market microstructure. As noted by Morse and Ushman (1983, p. 2491, the bid/ask spread in dealer markets is influenced by the information asymmetry between the dealer and informed traders. The spread serves as a defensive mechanism to minimize losses from trading with investors who possess asymmetric information (Lev 1988, p. 8). The social costs of information asymmetry can be high in terms of large transaction costs, thin markets, and reduced liquidity; voluntary disclosure alone cannot be relied on to mitigate these adverse social effects (Glosten and Milgrom 1985, pp. 72-74; Lev 1988, p. 8). According to Lev (1988, p. 9>, financial disclosure regulation is motivated primarily by the need to maintain public confidence in the securities markets by reducing information asymmetry or inequity. He (p. 5) indicates that regulatory changes can be assessed by the effects of the mandated disclosure on “observable variables affected by information asymmetries” such as the size of the spreads. The purpose of this paper is to evaluate the present value-based supplemental disclosures in the extractive petroleum industry by investigating the association between changes in bid/ask spreads and the supplemental data around the times of their release.

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Reserve-Based

Accounting

Data

115

2. Prior Research Useful accounting information generally involves an appropriate balance between relevance and reliability. Under either of the two traditional accounting methods used in the extractive petroleum industry (the successful efforts method or the full cost method), historical cost-based values bear little or no relationship to the market values of oil and gas reserves even at the time of their discoveT (for a discussion of the two methods, see FASB (1976)). Initially, ASR Nos. 253 (SEC 1978) and ASR 269 (SEC 1979) sought to develop a value-based reporting and income measurement system known as reserve recognition accounting (RRA) and required publicly-traded firms to disclose a present value estimate of their proved oil and gas reserves at fiscal year-end in their Form 1OKs. Under RRA, the performance of oil and gas firms would have been measured based on the present value of future net revenues from current period discoveries of new proven reserves, current oil and gas price changes, and revisions in estimates of recoverable reserve quantities; in contrast, under historical cost accounting income is recognized when existing reserves are produced and sold (Deakin and Deitrick 1982, p. 63). The reserve-based present value measures were criticized as lacking reliability. Estimates of proved oil and gas reserve quantities (especially new discoveries) can be highly variable, uncertain, and imprecise because they are a function of numerous unknown factors such as the quality, concentration, accessibility, and recoverability of the reserves (Connor 1978, p. 98; Cooper et al. 1979, p. 86). Similarly, although the SEC (see ASR No. 269 (SEC 1979)) mandated a standardized (uniform) ten percent discount rate, standardized present value measures are subject to uncertain future economic conditions and projected rates of production (Connor 1979, p. 97; Cooper et al., 1979, p. 85). For these reasons, ASR No. 289 (SEC 1981) deemed the uncertainty and imprecision inherent in reserve estimates to be too unreliable for use in primary financial statements. However, relative to historical cost financial statements, supplementary reserve data were viewed by the SEC 0981) as useful in depicting, on a more timely basis, firms’ underlying economic conditions and changes in future cash flows. Deakin and Deitrick (1982, p. 69) and Lilly (1983, p. 97) reported survey results which both suggest that financial and credit analysts consider reserve value information to be useful despite its imprecision. In Statement No. 69, the FASB (1982, para. 7) required the continuation of the SEC-mandated supplementary disclosures but dropped the requirement for a reserve-based income summary; although useful, reserve estimates were perceived by the FASB (1982) as too unreliable for periodic income measurement. Prior capital market tests of reserve disclosures have been in the context

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of valuation and/or return models involving security prices. Harris and Ohlson (1987, p. 663) reported the counterintuitive result that book (historical cost) values dominate the supplementary present value measures in a valuation model of oil and gas properties. They (p. 652) suggest that the importance of historical cost accounting data might potentially be explained by its “objectivity.” In contrast to the cross-sectional valuation analysis in Harris and Ohlson (19871, Doran et al. (1988, pp. 392-393) examined the information value of changes in historical cost earnings and three reserve-based measures in explaining cross-sectional differences in security returns of oil and gas firms. The three reserve-based measures were constructed from the supplemental disclosures and consisted of: 1) the change in the present value of estimated future oil and gas net revenues (net of related future development and production costs) resulting from new discoveries and extensions of proved reserves (“PVDIS”); 2) the change in the present value of estimated future oil and gas net revenues from proved reserves due to revisions in price and quantity estimates (“PVRPQ”), and 3) the total change for the year in the present value of future net revenues after omitting changes from purchases and sales of reserves (“PVTCH”). Their results (1988, p. 411) indicate that the reserve-based measures had incremental explanatory power (over the change in historical cost earnings) in explaining cross-sectional return differences during the period 1979-1981 but were relatively non-informative during the period 1982-1984. Harris and Ohlson (1990, p. 779) suggest that the results of Harris and Ohlson (1987) and Doran et al. (1988) challenge “those who argue that the (traditional historical cost) accounting principles presently in use for oil and gas properties are inadequate and that other accounting principles would provide superior information.” As described in the next section, we evaluate the usefulness of reservebased measures from the perspective of the market microstructure. Our capital market analysis is therefore different from, yet complementary to, the prior research discussed in this section.

3. Model Development

and Data

3.1 Model Development As noted by Bagehot (1971, p. 13), the role of the securities dealer (market maker) is to provide liquidity (immediacy) by standing ready to trade at his/her declared bid and ask prices with sellers and buyers as they come to the market. The dealer has to trade both with those who are liquidity motivated (i.e., seek to convert securities into cash and vice versa> and those who possess private (asymmetric) information. Liquidity-motivated

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transactors typically lose, as they are trading against the dealer’s bid/ask spread; at the same time, due to adverse selection, the dealer always loses to those traders who possess asymmetric information (Jaffe and Winkler 1976, p. 52; Copeland and Galai 1983, p. 1458; Glosten and Milgrom 1985, p. 72). Typically, dealers’ inventories increase (decrease) before a decrease (increase) in the equilibrium price, suggesting a loss due to the lag in adjusting the bid/ask spread (St011 1976, p. 372). Thus, parties with private (asymmetric) information profit at the expense of the dealer who, in turn, profits from those who are liquidity motivated. As the dealer cannot distinguish between the two types of transactors, to stay in business his/her gains from dealing with the liquidity motivated must exceed the losses from dealing with the information motivated. A testable implication of the Copeland and Galai (1983, p. 1468) and Glosten and Milgrom (1985, p. 73) models is that if a corporate event decreases (increases) information asymmetry, the post-event spread should be smaller (larger). Venkatesh and Chiang (1986, p. 10901 suggest that dealers widen the spread as a defensive measure when they expect higher losses (due to a suspected increase in the differential information possessed by informed traders) prior to easily predictable firm-specific information events such as earnings or dividend announcements. In their study, Venkatesh and Chiang (1986, p. 1097) compared the average spread over a five-da;y trading period preceding each announcement with the average spread over the approximately 250 trading days in a calendar year. Their results (1986, p. 1101) suggest an increase in the spread preceding only those earnings (or dividend) announcements which are nonroutine in the sense that they follow dividend (or earnings) announcements by more than ten days. These findings are consistent with Morse and Ushman (1983, p. 257) who reported no significant change in the average daily spread around quarterly earnings announcements but indicated an increase in the spread on days of significant price changes. Assuming that an increase in price variability is indicative of an information event, they concluded (1983, p. 257) that an association exists between information events and changes in bid/ask spreads. In our study, we attempt a more direct examination of the change in spread around the release of the Form 10K (and the supplemental reserve data reported therein) for a sample of over-the-counter (OTC) oil and gas firms by comparing the average spread before and after the release of the 10K. Specifically, we compare the average spread over the 20 trading days following the 10K filing date with the average spread over the 20 trading days ending five trading days before the filing date; we omit the five trading days preceding the 10K filing from our comparison period as prior research (e.g., Aharony and Swary 1980, p. 8) suggests substantial information leakage and price adjustments approximately two days prior to public

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information announcements. The primary question addressed in our study is whether the change in spreads surrounding the public release of the Form 10K is associated with the reserve measures disclosed therein. Several studies have examined the theoretical and empirical determinants of the bid/ask spread (e.g., Demsetz 1968, pp. 40-45; Benston and Hagerman 1974, pp. 354-356; Stoll 1978a, p. 1145; 1978b, p. 1161). The factors influencing the spread include not only the anticipated loss to information-motivated traders but also inventory-holding and orderprocessing costs. Stoll (1989, p. 132) indicated that the information trading (adverse selection) component is about 43% of the total spread for stocks traded on the NASDAQ. Hence, in examining the change in spread, it is desirable to control for changes in holding and order-processing costs around the information event being examined. Prior research (e.g., Benston and Hagerman 1974, pp. 354-356; Stoll 1978b, p. 1170) suggests that the inventory-holding and order-processing costs may be proxied by the trading volume, price variance, and the price of the stock. The basic model used in our study attempts to relate the changes in bid/ask spreads surrounding the Form 10K filing date to: 1) changes in the trading volume, price variance, and the price of the stock, and 2) the magnitude of the reserve disclosures.’ The model is expressed as follows: CHSP = f( CHVOL, CHVAR, CHPRC, CHHCNI, PVDIS, PVRPQ, PVTCH) The dependent variable CHSP is the natural log of the change in the market-adjusted spread surrounding the Form 10K filing date; the change in the raw spread for each stock is adjusted to control for possible intertemporal shifts in market-wide spreads.’ Although prior studies (e.g., Venkatesh and Chiang 1986, p. 1097) have focused largely on the percentage spread, for completeness we calculate both the percentage spread (CHPSP) and the dollar spread (CHDSP); hence, our dependent variable is analyzed in two alternate forms (CHPSP and CHDSP)-see Table 1. For the sake of convenience, the explanatory variables may be classified as either finance or accounting variables. The first three independent variables (CHVOL, CHVAR, and CHPRC) are the finance (control) variables and are, respectively, the natural logs of the change in trading

‘Our approach in examining the change in spreads (the change form) may be contrasted with earlier cross-sectional studies (e.g., Demsetz 1968, pp. 46-47; Benston and Hagerman 1974, pp. 355-356; Stall 1978b, p. 1163; Hagerman and Healy 1992, p. 238) which are in the level form and attempt to explain the level of spreads in terms of the levels of trading volume, price variability, and the price of the stock. ‘We thank an anonymous reviewer for pointing out the need to control for the effects of market-wide shifts in spread on the raw bid/ask spreads of individual stocks. The process we used for adjusting the raw spreads is similar to that described in Fedenia and Grammatikos (1992, p. 343).

Supplemental

Reserve-Based

Table 1. Description

Accounting

Data

119

of Variables Description

Variable Dependent Variable:” CHPSP

Change in market-adjusted percentage spread defined as the natural log of the ratio of the change in percentage spread for the firm divided by the concurrent change in percentage spread for the market. The numerator (change in percentage spread for the firm) is defined as the ratio of the average percentage spread over the 20-day period following the Form 10K filing date divided by the average percentage spread over the 20-day period ending five trading days prior to the filing date. The denominator (change in percentage spread for the market) is defined similarly except that it is computed for all NASDAQ stocks with available quotes over that same time period. The percentage spread is defined as: [ask price-bid price]/[(ask price + bid price) X OS];

CHDSP

Change in market-adjusted dollar spread defined as the natural log of the ratio of the change in dollar spread for the firm divided by the concurrent change in dollar spread for the market. The numerator (change in dollar spread for the firm) is defined as the ratio of the average dollar spread over the ZO-day period following the Form 10K filing date divided by the average dollar spread over the 20-day period ending five trading days prior to the filing date. The denominator (change in dollar spread for the market) is defined similarly except that it is computed for all NASDAQ stocks with available quotes over that same time period. The dollar spread is defined as the ask price minus the bid price;

Independent

Variables:

Finance Variablesb CHVOL

Natural logarithm of the ratio of the average daily share trading volume after and before the Form 10K filing date;

CHVAR

Natural logarithm of the ratio of the variance of daily returns after and before the Form 10K filing date;

CHPRC

Natural logarithm of the ratio of the average closing price after and before the Form 10K filing date;

Accounting VariablesC CHHCNI

Natural log of the change in historical cost net income from continuing operations deflated by the beginning of the year market value of common stock;

PVDIS

Natural log of the change in the present value of estimated future net revenues resulting from new discoveries and extensions of proved reserves deflated by the beginning of the year market value of common stock;

PVRPQ

Natural log of the change in the present value of estimated future net revenues from proved reserves due to revisions in price and quantity estimates deflated by the beginning of the year market value of common stock;

PVTCH

Natural log of the total change for the year in the present value of future net revenues (after omitting changes in present value from purchase and sales of reserves) deflated by the beginning of the year market value of common stock.

=As discussed in Section 3.1, our dependent variable (change in spread) was analyzed in two alternate forms (CHPSP and CHDSP). bFor the finance variables (CHVOL, CHVAR, and CHPRC), the after and before periods are defined the same way as for the dependent variables; “after” represents the 20 trading days following the 10K filing date (t, through t,) and “before” represents the 20 trading days ending five trading clays prior to the filing date (1_25 through t_s). ‘For the accounting variables (CHHCNI, PVDIS, PVRPQ, and PVTCH), the natural log is taken of the absolute values; as in Doran et al. (1988, p. 393), these values are deflated by the beginning of the year market value of common stock. In Doran et al. (1988, p. 393) CHHCNI is called “AHCNI.”

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volume, price variance, and the stock price surrounding the Form 10K filing date. Variables CHHCNI, PVDIS, PVRPQ, and PVTCH are the accounting (experimental) variables. Variable CHHCNI is the change in historical cost income. The reserve-based variables PVDIS, PVRPQ, and PVTCH are the same as those examined in Doran et al. (1988, p. 393) and discussed earlier in Section 2. Doran et al. (p. 390) noted that the PVDIS and PVRPQ measures are similar under both ASR No. 269 (SEC 1979) and the FASB’s Statement No. 69 (FASB 1982); they (p. 3901 also noted that the PVTCH measure disclosed under FASB Statement No. 69 “roughly approximates” the RRA earnings number previously required by ASR No. 253 (SEC 1978) and ASR No. 269 (SEC 1979). A complete definition of all the variables in our basic model is provided in Table 1. 3.2 Hypotheses As a supplier of immediacy, the dealer is required to maintain an inventory of shares Wenkatesh and Chiang 1986, p. 1092). The costs of holding this inventory are related to the opportunity cost of funds tied up and the risk that the price may fall. Holding other things constant, the greater the volume of trading, the shorter the dealer’s holding period since higher volume should make it easier for the dealer to move toward his/her preferred inventory level (Venkatesh and Chiang 1986, p. 1092). Consequently, the spread may be expected to fall with increases in the daily dollar trading volume. In Table 1, variable CHVOL is intended to be a control variable and has an anticipated negative sign. The change in the variance of daily returns (CHVAR) is a measure of the change in price variability. The greater the fluctuation in prices, the greater the price risk which, as noted earlier, is a component of holding costs. Consequently, variable CHVAR is expected to be positively correlated with the change in spread. Increased price variability or increased trading volume could itself represent the effects of an information event (Beaver 1968, p. 91). However, as pointed out by Venkatesh and Chiang (1986, p. 1092), inventotyholding and order-processing costs, and information-trading costs are conceptually separate and distinguishable cost components of the spread. By controlling for the change in price variability and trading volume, our regression tests are conservative in that a portion of the informationtrading costs are likely to be subsumed in holding and order-processing costs. Hence, the possibility of wrongly rejecting the null hypothesis of no association between the change in spread and the public release of the reserve measures in the 10K is likely to be small. Variable CHPRC is included in the model to control for the effects of any trend in the price of the stock. For stocks which suffer a price decline, the same dollar spread will give the impression of an increase in the

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percentage spread. At the same time, a decrease in stock price implies lower holding costs as the same number of shares may now be held in inventory with fewer dollars invested; in turn, lower holding costs imply a lower dollar spread. For these reasons, the anticipated sign of CHPRC is negative (positive) in explaining the change in the percentage (dollar) spread. The variables discussed up to this point represent our control variables obtained from the finance literature (e.g., Venkatesh and Chiang 1986, p. 1093). The experimental (accounting) variables in our model (CHHCNI, PVDIS, PVRPQ, and PVTCH) are the same as those used by Doran et al. (1988, p,. 393) and discussed earlier in Section 2. The first variable, CHHCNI, represents the change in the traditional historical cost earnings of the firm. Prior research (e.g., Ball and Brown 1968, p. 168; Beaver et al., 1979, p. 339; Doran et al. 1988, p. 412) has demonstrated unambiguously a positive association between changes in historical cost earnings and abnormal stock returns. However, as discussed earlier in this section, both Morse and Ushman (1983, p. 257) and Venkatesh and Chiang (1986, p. 1101) indicated no change in the spread surrounding routine earnings announcements. Their (Morse and Ushman 1983, p. 257; Venkatesh and Chiang 1986, p. 1101) results suggest that the information uncertainty about earnings is resolved prior to its public announcement due potentially to leakage from alternative sources of information. Moreover, earnings announcements typically precede (rather than follow) the formal filing of the 10K with the SEC. For these reasons, we would not expect the change in the spread around the filing of the 10K to be associated with the change in historical cost earnings. Variable CHHCNI is included in our model for completeness. Unlike historical cost earnings, oil and gas reserve disclosures are nor publicly announced prior to the release of the 10K. Moreover, unlike earnings which are reported quarterly, under ASR No. 253 (SEC 19781, ASR No. 269 (SEC 19791, and the FASB’s Statement No. 69 (FASB 1982) the reserve value disclosures have to be reported only annually. Hence, we anticipate a greater resolution of information asymmetry with respect to these reserve disclosures at the time of the filing of the 10K. The reserve variables in our model (PVDIS, PVRPQ, and PVTCH) were constructed as in Doran et al. (1988, p. 393) from the supplementary disclosures in the 10K. As noted in our earlier discussion in Section 2, these variables measure changes in values since the preceding year. As discussed in Glosten and Milgrom (1985, pp. 97-981, the adverse selection component of the spread is based on the revision in the dealer’s expectation of the equilibrium value of a stock. Informed traders are expected to observe the dealer’s quotes and trade only if they expect to earn abnormal returns (Glosten and Milgrom, 1985, pp. 97-98). In advance of reasonably predictable firm-specific information events (such as

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earnings announcements or the release of the lOK), the dealer can be expected to revise his/her expectation of the stock value upward (downward) conditional on a buy (sell) order for the stock. As a defensive measure, the dealer is expected to incorporate his/her revised expectation of a higher (lower) stock price into a higher ask (lower bid) price. Holding other things constant, an increase in the ask price or a decrease in the bid price both constitute an increase in spread. Thus, the magnitude of the change in spread is expected to be associated with the magnitude of the anticipated revision in the equilibrium price of the stock. Prior accounting research (e.g., Beaver et al. 1979, p. 339) suggests that both the sign and magnitude of accounting data constitute relevant information. The association between the change in the equilibrium price of a stock and accounting data (such as earnings changes) is not merely one of sign (as demonstrated in the work by Ball and Brown (1968, p. 168)) but also one of magnitude. Beaver et al. (1979, p. 339) indicated that to ignore the magnitude of the earnings data is to throw away information. For similar reasons, assuming that the reserve-based disclosures are relevant in valuing oil and gas firms, both the sign and magnitude of the reserve-based accounting measures should be relevant in revising expectations about the equilibrium stock price. In examining cumulative abnormal returns (as in Doran et al. 1988, p. 3921, the sign of the abnormal returns can be expected to be influenced by the sign of the reserve measures; that is, the direction of the cumulative abnormal returns will depend on whether the reserve disclosures represent good news or bad news. In contrast, in our model the reserve measures are expressed as the natural log of their absolute values as public disclosure can be expected to reduce information asymmetry irrespective of whether the disclosure represents good news or bad news. Changes in bid/ask spreads resulting from the public release of reserve data are unlikely to be related to the sign of the disclosures. However, holding other things constant, the greater the size of the upward (downward) revision in the value of the oil and gas reserves, the greater the anticipated increase (decrease) in the value of the firm, and the greater the upward (downward) pressure on the ask (bid) price due to an order imbalance generated by buy (sell> orders from informed traders. Hence, changes in bid/ask spreads can be expected to be related to the magnitude of the disclosures. If the public disclosure of the supplemental reserve data in the 10K has the effect of reducing the informed trading component of the spread, the change in the spread (over and above that explained by changes in inventory-holding and order-processing variables) should be related empirically to the magnitude of the absolute values of the reserve variables with an anticipated negative sign. Our research hypotheses, stated in the null form, are that there are no associations between the change in the spread and the magnitude of each of the three reserve variables.

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3.3 Data Our study focuses on a sample of 31 oil- and gas-producing firms traded on the OTC market which had the requisite price and volume data on the CRSP NASDAQ tapes for the years 1980-1987.3 We selected producing (rather than refining) firms because, as discussed earlier in Section 2, the reserve-based performance measures examined in this study relate primarily to oil and gas producers. For our sample firms, bid/ask data were not available for all years. Moreover, trading volume data were not available prior to October 1982. Consequently, we estimated our basic model separately with trading volume (over the years 1982-87) and without trading volume (over the years 1980-87).4 See the Appendix for the listing of the firms in our sample. We chose NASDAQ firms because information asymmetry (prior to the public release of accounting data) is expected to be greater for the smaller firms which are typical of OTC securities. Firms listed on the NYSE or the AMEX typically exhibit greater institutional ownership and closer monitoring by professional analysts relative to NASDAQ firms (Blume and Friend 1987, p. 196). Consequently, market participants (including dealers) may ascribe greater information content to public disclosures (such as the 1OK) for NASDAQ firms. Also, the OTC market is characterized by multiple dealers as opposed to the single market maker (specialist) system which prevails in the national exchanges. The specialist has privileged access to information about demand and supply conditions provided by the accumulation of limit orders. This non-public information may partially mitigate the effects of transacting against informed traders on the bid/ask spread. Hence, the resolution of information asymmetry as reflected in the change in the spread may be dissimilar in the OTC and specialist markets.5

4. Results and Discussion Descriptive data are provided in Table 2; these statistics are shown in raw form for convenience although the logarithmic form is used in the regressions. In Panel A, both the percentage and dollar spreads exhibit on

3NASDAQ is the computerized OTC trading system operated by the National Association of Security Dealers (NASD). ‘The reserve-based accounting data used in this study are not available in machine readable form and had to be hand collected from supplementa footnote disclosures in annual reports and Form 1OKs. In addition, we were constrained by the limited availability of these annual reports and Form 1OKs for NASDAQ firms on our campus. Hence, we were not able to obtain complete data for all the sample firms over the entire sample period. Together with the unavailability of bid/ask and trading volume data for all firms for all years, these constraints limited our number of observations (64 for the model with trading volume and 93 for the model without trading volume). sGlosten and Harris (1988, p. 139) estimated that the informed trading component as a percentage of the total bid/ask spread on the NYSE is approximately 20% relative to the 43% estimated by Stall (1989, p. 132) in the OTC market.

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K. K. Raman

Table 2. Descriptive

and Niranjan

Tripathy

Statistics’

Panel A Variable” Percentage spread-before Percentage spread-after Market-adjusted percentage spread-before Market-adjusted percentage spread-after Dollar spread-before Dollar spread-after Market-adjusted dollar spread-before Market-adjusted dollar spread-after Trading volume-before Trading volume-after Return variance-before Return variance-after Price level-before Price level-after CHHCNI PVDIS PVRPQ PVTCH

Mean

Standard Deviation

Number of Observations

8.550 9.040

7.706 9.038

93 93

1.009

0.961

93

1.123 0.212 0.213

1.360 0.151 0.149

93 93 93

0.411

0.237

93

0.426 16,044.531 21,488.984 16.476 16.396 6.157 5.992 0.356 0.211 0.690 0.536

0.309 16,972.428 37,997.443 23.313 21.494 7.570 7.138 1.315 0.214 1.881 1.012

93 64 64 93 93 93 93 93 93 93 93

Number of Increases

Number of Decreases

Number of Observations

48

4.5

93

47 46

46 47

93 93

44 38 45 39

49 24 48 54

93 64 93 93

Panel B Variable Percentage spread Market-adjusted percentage spread Dollar spread Market-adjusted dollar spread Trading volume Return variance Price level

measures are shown for descriptive purposes only; the logarithmic form is used in the The number of observations for trading volume (n = 64) is lower because, as discussed in Section 3.3 and footnote 4, volume data were not available prior to October 1982 as well as due to other constraints. ‘Before represents the 20-day period ending live trading days prior to the 10K filing date kZS through 1~~); after represents the 20-trading-day period following the filing date (rl through ?&. “Raw

regressions.

Supplemental Reserve-Based Accounting Data

125

average a marginal increase subsequent to the filing of the lOK, the mean percentage spread exhibits an increase from 8.55% to 9.04%, while the dollar spread shows an increase from $0.212 to $0.213. After adjusting for concurrent changes in market spreads, the mean percentage spread exhibits an increase from 1.009% to 1.123%, while the dollar spread shows an increase from $0.411 to $0.426. Panel B indicates that in terms of raw observations, the percentage (dollar) spread decreased for 45 (47) of the 93 observations; at the same time, the market-adjusted percentage (dollar) spread decreased for 46 (49) of the 93 observations. However, as discussed earlier in Section 3.1, these changes in spreads could potentially be explained by concurrent changes in trading volume, price variance, and the price level; in Table 2, trading volume appears to have increased (from a daily average of 16,045 shares to 21,489 shares) while the price variance and the price level appear to have decreased slightly on average during the same time period. In our regression analyses discussed below, we control for these changes in examining the association between changes in spread and the accounting disclosures. Table 3 presents the correlation matrix. Some of the correlations are rather large, although considerably less than the 0.8 suggested by Judge et al. (1980, p. 459) as indicative of a serious collinearity problem. Potentially, collinearity may result in inflated standard errors for the coefficients of the independent variables, i.e., variables which are statistically insignificant according to the t test may be significant for alternative specifications. We employed a heuristics test known as the variance inflation factor (or VIF) to help determine whether a coefficient was insignificant due to collinearity or due to lack of explanatory power in the associated variable. The VIF has been computed for each independent variable and is the reciprocal of one minus the R square when the particular independent variable is regressed on all the other independent variables. An examination of the VIFs in the various multivariate regression models revealed the highest VIF (in any model) to be only 2.37, far below the level of 10.0 generally regarded as indicating a significant collinearity problem

Table 3. Pearson Correlation Matrix Variable

CHVOL

CHVAR

CHPRC

CHHCNI

PVDIS

PVRPQ

CHVAR CHPRC CHHCNI PVDIS PVRPQ PVTCH

0.453* * - 0.049 0.040 0.218 - 0.021 0.013

- 0.208 0.197 0.264’ 0.191 0.213

0.002 0.025 0.079 0.023

0.107 0.524* * 0.343**

0.171 0.131

0.642* *

*Correlations significant at 0.05 (Two-tailed) **Correlations significant at 0.01 (Two-tailed)

126

K. K. Raman and Niranjan Tripathy

(Marquardt 1970, p. 610; Neter et al. 1983, p. 392). The low VIFs (and the additional tests reported in Section 4.1, footnote 6) suggest that collinearity does not appear to be a serious problem in interpreting our empirical findings.

4.1 OLS Regression Results Table 4 presents the OLS regression results in which the change in market-adjusted percentage spread (CHPSP) was the dependent variable. These regressions examine whether, as hypothesized, the magnitude of the reserve disclosures in the 10K is associated with the change in bid/ask spreads surrounding the release of the 10K. As trading volume data were unavailable prior to October 1982, the regression results are presented both with and without trading volume as an independent variable for years 1982-1987 and 1980-1987, respectively. All of the significant finance (control) variables are associated with changes in spread in the expected direction. These results are consistent with the prior literature (e.g., Benston and Hagerman 1974, p. 363; Stoll 1978b, p. 1170) on the determinants of bid/ask spreads. Our main interest is whether or not the accounting variables are associated with changes in spread. As anticipated, the first accounting

Table 4. OLS Regression Models with Change in Market-Adjusted

Spread as Dependent

Predicted Sign

Variable

?

Intercept Finance

(Control)

With Trading Volume (n = 64) Coefficient - 0.087

t statistic - 1.293

Without Trading Volume (n = 93) Coefficient - 0.088

t statistic - 1.657

Variables -

CHVOL CHVAR CHPRC Accounting

Percentage

Variable

+ -

-0.149 0.133 - 0.659

-3.258*** 4.821*** - 3.998***

0.082 - 0.629

3.986*** -5.184***

_ -

0.007 - 0.046 - 0.035 0.017

0.427 - 2.023* * - 1.117 0.625

0.004 - 0.051 - 0.009 0.005

0.307 -2.856*** - 0.420 0.269

Variables

CHHCNI PVDIS PVRPQ PVTCH Adjusted R square Model F value Levels of Significance

*p < 0.10 **p < 0.05 ***p < 0.01

0.458 8.012***

0.389 9.922** *

Supplemental

Reserve-Based

Accounting

Data

127

variable CHHCNI is not significant. Information about historical cost earnings is typically announced prior to the filing of the 10K; consequently, no resolution of information asymmetry is expected with regard to this variable at the time of the release of the 10K. Variable CHHCNI was

included in our model for completeness. Hence, our focus is on the reserve variables (PVDIS, PVRPQ, and PVTCH) which are also disclosed in the 10K but are not announced earlier. Moreover, these reserve measures are required to be disclosed only annually and are not reported quarterly. Hence, if these reserve measures are relevant and useful in valuing the securities of oil- and gas-producing firms, a reduction of information asymmetry is to be expected around the time the 10K is filed. Our results show clear support for the hypothesis that the reserve disclosures are associated with changes in bid/ask spreads. Variable PVDIS (see Table 1 for explanation) is significant in both models (with and without trading volume) with the anticipated negative sign. Thus, present value information relating to new discoveries appears to be relevant in security pricing. Although variable PVRPQ (see Table 1 for explanation) has the anticipated negative sign, it is not significant in either model (with or without trading volume). As information about last year’s proven reserves and current year changes in oil and gas prices are publicly available, the market can presumably estimate with fair accuracy the

Table 5. OLS Regression Models with Change in Market-Adjusted Spread as Dependent Variable

Variable

Predicted Sign

Intercept

?

With Trading Volume (n = 64) Coefficient - 0.098

t statistic - 1.345

+ +

- 0.133 0.108 0.238

-2.683*** 3.605*** 1.330*

_ -

0.016 - 0.045 - 0.058 0.017

0.882 - 1.804** - 1.680** 0.597

Dollar

Without Trading Volume (n = 93) Coefficient - 0.095

t statistic - 1.708*

Finance (control) Variables CHVOL CHVAR CHPRC

_

0.065 0.253

3 031*** 1:997**

Accounting Variables CHHCNI PVDIS PVRPQ PVTCH Adjusted R square Model F value Levels of Significance :p,<,o&

***p <

0.01

0.181 2.827* *

0.009

- 0.047 - 0.024 0.005

0.645 -2.553*** - 1.064 0.294

0.103 2.609**

128

K. K. Raman and Niranjan Tripathy

impact on present values of price changes during the year. Finally, the third reserve variable PVTCH (see Table 1 for explanation) is not significant in any of the models in our study. The PVTCH measure is a rough approximation of the periodic RRA earnings number previously required by ASR No. 253 (SEC 1978) and ASR No. 269 (SEC 1979). The lack of explanatory power in the PVTCH variable is consistent with the FASB position in Statement No. 69 (FASB 1982, para. 84) that reserve estimates, although useful, are too unreliable for periodic income measurement. Although empirical analysis in the finance literature has focused almost exclusively on the percentage spread, for completeness we also analyzed the change in dollar spread. Table 5 presents the regression results where the change in dollar spread is the dependent variable; as noted earlier in Section 3.2, the only change in anticipated signs is for the variable CHPRC which is now expected to be positive. The overall findings in Table 5 are consistent with the results discussed above for Table 4; once again, variable PVDIS is significant with the anticipated negative sign.6 Our results are consistent with the notion that the supplemental reserve disclosures are relevant in security pricing and that the required release of these data reduces information asymmetry and the informed trading component of bid/ask spreads.

5. Summary and Policy Implications Although market or fair values are necessarily uncertain, imprecise, and often very volatile, the accounting profession appears to be viewing fairvalue measurements as increasingly relevant over the traditional historical cost model (Berton and Baker 1991; Salwen 1992; Washington Update 1992; FASB 1990). However, usefulness is a necessary but not a sufficient condition for required disclosure. Rather, disclosure regulation is moti-

6Given the rather large correlations in Table 3 among the three accounting variables CHHCNI, PVRPQ, and PVTCH, we performed additional analyses to see if the lack of significance in CHHCNI and PVTCH in Tables 4 and 5 was due to collinearity or due to lack of explanatory power. First, we examined a revised basic model which included only the finance (control) variables and the accounting (RRA) variable PVDIS. The results of this revised basic model were the same as in Tables 4 and 5 in that the finance variables and PVDIS were significant with anticipated signs. Then, of the three correlated accounting variables (CHHCNI, PVRPQ, and PVTCH), no more than one variable at a time was included in the revised basic model. As collinearity has the effect of inflating standard errors, the inclusion of no more than one of these three correlated variables in the model should minimize the standard error and increase the chances of finding the variable significant. However, for the models with the market-adjusted percentage spread as the dependent variable, none of these three variables was significant, which is consistent with the findings in Table 4. For the models with the market-adjusted dollar spread as the dependent variable, the results were similar except that PVRPQ was significant (with the anticipated negative sign) in the model with trading volume, which is also consistent with the findings in Table 5. This additional evidence is consistent with our earlier discussion of VIFs and suggests that collinearity is not a serious problem in interpreting our empirical findings. All these various regression models were overall statistically significant at 5%.

Supplemental Reserve-Based Accounting Data

129

vated largely by the need to preserve public confidence in the integrity of the capital markets by reducing information asymmetry (inequality of opportunity) between informed and uninformed investors (Lev 1988, p. 9). In our study, we examined the usefulness of the reserve-based present value disclosures for firms in the extractive petroleum industry from the perspective of the bid/ask spread which is an easily observable variable affected by information asymmetry. Specifically, we examined the association between reserve-based measures and changes in bid/ask spreads surrounding the filing of the 10K for a sample of oil- and gas-producing firms. Our results indicate, as hypothesized, that the magnitude of the reserve-based disclosures is associated with changes in bid/ask spreads with an anticipated negative sign. Moreover, the reserve variables appear to possess explanatory power over and beyond the variables (such as price variability and trading volume) discussed in the finance literature as determinants of bid/ask spreads. By controlling for changes in price variability and trading volume, which in themselves could represent the effects of an information event (Beaver 1968, p. 911, our regression tests are conservative. Our empirical findings suggest that the supplemental disclosure of present values of petroleum reserves constitutes relevant information; the public release of these disclosures reduces information asymmetry or inequity in the securities markets by reducing the informed trading component of bid/ask spreads. Appendix-List OBS”

2 3 4 6

7 8 9 10 11 12 13 14 15 16 17 18 19 20 21

of Sample Observations

with CUSIP No. and 10K Filing Date

Firm Name Alfa Res Inc Altex Oil Corp Altex Oil Corp Amarex Inc Aztec Res Corp Barret Res Corp Barret Res Corp Barret Res Corp Basic Earth Science Sys Inc Basic Earth Science Sys Inc Basic Earth Science Sys Inc Bellwether Expl Co Bellwether Expl Co Bellwether Expl Co Brock Expl Corp Brock Expl Carp Chaparral Res Inc Chaparral Res Inc Great Estn Energy & Dev Corp Great Estn Energy & Dev Corp Great Estn Energy & Dev Corp

CUSIP No.

10K Dateb

01539610 02145610 02145610 02300610 05500310 06848020

820903 811228 821130 830810 860630 860110 861229 871229

06984210 06984210 06984210 07989510 07989510 07989510 11162810 11162810 15942020 15942020 39032310 39032310 39032310

860711 86033 1 870401 870928 860630 870629 860227 870227 850415 860409 870331

130

K. K. Raman

22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71

Gulf Energy Corp Gulf Energy Corp Hadson Corp Hadson Corp Hadson Corp Hadson Corp Hadson Corp Hadson Corp Hamilton Oil Corp Hamilton Oil Corp Hamilton Oil Corp Hamilton Oil Corp Hamilton Oil Corp Hamilton Oil Corp Hamilton Oil Corp Hamilton Oil Corp Harcor Energy Co Harken Oil & Gas Inc Harken Oil & Gas Inc Harken Oil & Gas Inc Harken Oil & Gas Inc Harken Oil & Gas Inc Houston Oil Fields Co K R M Pete Corp K R M Pete Corp K R M Pete Corp K R M Pete Corp K R M Pete Corp K R M Pete Corp K R M Pete Corp Lexicon Res Corp Louisiana LD Offshore Expl Inc Louisiana LD Offshore Expl Inc Magic Circle Energy Corp Magic Circle Energy Corp Magic Circle Energy Corp Magic Circle Energy Corp Magic Circle Energy Corp Marion Corp Marion Corp Marion Corp Marion Corp Marion Corp May Pete Inc May Pete Inc May Pete Inc May Pete Inc May Pete Inc Maynard Oil Co Maynard Oil Co

40227410 40227410 40501810 40501810 40501810 40501810 40501810 40501810 40784810 40784810 40784810 40784810 40784810 40784810 40784810 40784810 41162810 41255210 41255210 41255210 41255210 41255210 44190710 48266220 48266220 48266220 48266220 48266220 48266220 48266220 52887310 54627210 54627210 55911610 55911610 55911610 55911610 55911610 56869520 56869520 56869520 56869520 56869520 57778810 57778810 57778810 57778810 57778810 57844410 57844410

and Niranjan

Tripathy

810729 820729 800331 83033 1 840329 850327 860331 870324 800331 810331 820331 830331 840330 850328 860327 870330 870331 820330 83033 1 850329 860328 870331 850325 80033 1 810331 820330 830323 840319 850321 860328 810729 820329 830329 820415 830427 840501 850416 86033 1 800429 810427 820430 830429 840524 810331 820331 840331 840330 850401 830330 840329

Supplemental

72 73 74 75 74 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93

Reserve-Based

Accounting

Data

Maynard Oil Co Maynard Oil Co McFarland Energy Inc McFarland Energy Inc McFarland Energy Inc McFarland Energy Inc McFarland Energy Inc Mid Amer Pete Inc Mid Amer Pete Inc Nugget Oil Corp Nugget Oil Corp Nugget Oil Corp Nugget Oil Corp Nugget Oil Corp Nugget Oil Corp Oakridge Energy Inc Oakridge Energy Inc Oakridge Energy Inc Ridgeway Exco Inc Texas Energies Inc Vanderbilt Energy Corp Western Nat Gas Co

131

57844410 57844410 58043210 58043210 58043210 58043210 58043210 59521510 59521510 67051810 67051810 67051810 67051810 67051810 67051810 67382820 67382820 67382820 76617910 88241110 92178410 95885120

850329 860331 800328 810325 82033 1 840330 &50401 821129 840119 810723 820728 830729 840730 850729 860609 810615 820610 830531 830630 820730 830628 860327

‘Observation number ?OK filing date

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Experts urge