PENMANN_ZHANG Accounting Conservatism, Quality Earning & Stock Return

237

THE ACCOUNTING REVIEWVol. 77, No. 2April 2002pp. 237–264

Accounting Conservatism,the Quality of Earnings, and

Stock ReturnsStephen H. Penman

Columbia University

Xiao-Jun ZhangUniversity of California, Berkeley

ABSTRACT: When a firm practices conservative accounting, changes in theamount of its investments can affect the quality of its earnings. Growth ininvestment reduces reported earnings and creates reserves. Reducing invest-ment releases those reserves, increasing earnings. If the change in investmentis temporary, then current earnings is temporarily depressed or inflated, andthus is not a good indicator of future earnings. This study develops diagnosticmeasures of this joint effect of investment and conservative accounting. Wefind that these measures forecast differences in future return on net operatingassets relative to current return on net operating assets. Moreover, these mea-sures also forecast stock returns—indicating that investors do not appreciatehow conservatism and changes in investment combine to raise questionsabout the quality of reported earnings.

Keywords: earnings quality; conservative accounting; forecasting accountingrates of return; predicting stock returns.

Data Availability: Data are available from sources identified in the paper.

I. INTRODUCTION

This paper contributes to the stream of research on how accounting methods affectthe quality of earnings. While the academic and professional literature has notreached a consensus on the definition of quality of earnings, we define the term to

mean that reported earnings, before extraordinary items that are readily identified on theincome statement, is of good quality if it is a good indicator of future earnings. Thus we

The comments of participants in workshops at Barclays Global Investors, Baruch College of CUNY, ColumbiaUniversity, Cornell University, Georgetown University, the University of Michigan Quality of Earnings Conference,University of Minnesota, and Stanford University are appreciated, particularly those of Ilia Dichev, Tom Dyckman,John Elliott, Patricia Fairfield, Teri Yohn, and Paul Zarowin. We also appreciate comments of Mary Barth (associateeditor) and anonymous reviewers.

Submitted December 1999Accepted September 2001

238 The Accounting Review, April 2002

consider high-quality earnings to be ‘‘sustainable earnings,’’ as often referred to in financialanalysis (in Mulford and Comiskey [1996], for example). Correspondingly, when an ac-counting treatment produces unsustainable earnings, we deem those unsustainable earningsto be of poor quality.

Most of the research on the quality of earnings focuses on the effects of changes inaccounting estimates (see Healy and Wahlen [1999] for a recent review). Estimates areapplied in the accruals, so in order to identify poor-quality earnings, papers have investi-gated total accruals (as in Healy 1985), discretionary accruals (as in Jones 1991), andextreme accruals (as in Sloan 1996). This research characterizes managers as creating un-sustainable earnings, for example, by temporarily reducing estimates of bad debts or de-preciation (as in Teoh et al. 1998) or lowering loan loss reserves (as in Beaver and Engel1996)—or lowering current earnings (and increasing future earnings) by overestimatingrestructuring charges (as in Moehrle 2002). If the effect is temporary, and so reverses laterthrough a revision in the estimate, then reported earnings are of poor quality because currentearnings are not a good indicator of future earnings. Under this view, applying accountingprinciples consistently from period to period leads to more sustainable earnings, and soreduces quality concerns.

This paper shows empirically that quality concerns can also arise if firms apply con-servative accounting consistently without any change in accounting methods or estimates.

By conservative accounting we mean choosing accounting methods and estimates thatkeep the book values of net assets relatively low. Therefore, LIFO accounting for inventoriesis conservative relative to FIFO (if inventory costs are increasing); expensing research anddevelopment (R&D) expenditures rather than capitalizing and amortizing them is conser-vative; depreciation methods that consistently use short estimated asset lives (so as to recorddepreciation in excess of economic depreciation) are conservative; and policies that con-sistently overestimate allowances for doubtful accounts, sales returns, or warranty liabilitiesare conservative.

Conservative accounting affects not only the quality of the numbers reported on thebalance sheet, but also the quality of earnings reported on the income statement.1 Whenthe firm increases investment, conservative accounting leads to reported earnings that areindeed lower than they would have been had management made more liberal accountingchoices. These lower earnings, however, create unrecorded reserves that provide managerswith flexibility to report more income in the future. Management can increase these re-serves, and so reduce earnings, by increasing investment. Management can also release thereserves and create additional earnings, by subsequently reducing investment or reducingthe rate of growth in investment. While the effect of the interaction of conservatism andinvestment is a mechanical one, it is not necessarily temporary; the temporary extent of theeffect has to be ascertained. If the change in investment is temporary, then the inducedchange in earnings is also temporary and not indicative of subsequent earnings. Hence, thequality of earnings depends on the interaction between real activity and accounting policy,not merely on changes in accounting methods and estimates. These arguments suggest thatmanagers can use the joint effect of real activity and accounting policy to manage earnings.However, the effect arises, intended or not. The effect is perverse—reducing investment

1 Indeed, earnings quality issues are interesting only when balance sheets are imperfect indicators of value. Whenbalance sheets are marked-to-market so that book value is equal to value, earnings are of low quality as anindicator of future earnings; earnings are, in this case, simply changes in value, and changes in value are unin-formative about future earnings. Imperfect balance sheets shift the focus to the income statement as an indicatorof value. The quality of earnings therefore becomes more important as an indicator of the future earnings, andthus of value. That quality does, however, depend on how the accountant measures the balance sheet, as explainedbelow.

Penman and Zhang—Accounting Conservatism and Earnings Quality 239

reduces future earnings but, with conservative accounting, increases current earnings, mak-ing current earnings a poor indicator of future earnings.

We pose two research questions. First, does conservative accounting coupled withchanges in investment lead to temporary changes in earnings? Second, does the stockmarket price these temporary earnings as if they were indeed unsustainable? The answerto the first question appears to be yes; the answer to the second is no. In carrying out theanalysis, we develop metrics designed to assess whether the effect on earnings is temporary,and we suggest that these metrics can serve as tools for financial analysis and research intothe quality of earnings.

Additional investment typically yields additional future earnings; therefore, to examinethe sustainability of earnings, we focus on accounting rates of return—earnings relative tonet assets that deflates earnings for new net investment. Focusing on accounting rates ofreturn also serves our purpose of investigating the stock market’s pricing of earningsof varying quality. Accrual-accounting residual income valuation models dictate that fore-casted earnings are evaluated relative to the book values that generate them. Residual in-come models therefore describe intrinsic equity values as determined by anticipated ac-counting rates of return. Pricing errors occur if the market, relying on an unsustainablecurrent rate of return, forecasts future rates of return incorrectly.

Prior research has referred to this view of market inefficiency as ‘‘fixation’’ on reportedearnings. In those terms, our evidence suggests that the market fixates on reported earnings,unaware that conservative accounting may lead to reported earnings of doubtful quality.Our paper views the analysis of earnings quality—and the identification of market fixa-tion—as a matter of financial statement analysis that uncovers information in the financialstatements (beyond current earnings) about future earnings and equity values. Our studytherefore contributes to the emerging empirical research on financial statement analysis andfundamental analysis, such as Ou and Penman (1989), Sloan (1996), Penman and Sougian-nis (1998), Lee et al. (1999), and Nissim and Penman (2001).

The paper is organized as follows. As a background to the analysis, the next sectionoutlines the effects of conservative accounting and investment growth on accounting ratesof return. Section III develops metrics for scoring firms on the degree of their conservativeaccounting and the quality of their earnings, and Section IV summarizes the data. SectionsV and VI address the two research questions; Section V investigates whether we can usethe quality scores that capture the effect of conservative accounting and investment toidentify the sustainability of accounting rates of return, and Section VI documents thereturns to taking positions in stocks based on the earnings quality scores. Section VIIsummarizes the study’s conclusions.

II. CONSERVATISM, INVESTMENT, ACOCUNTING RATES OF RETURN, ANDTHE QUALITY OF EARNINGS

Definition of ConservatismResearchers have introduced a variety of definitions of conservative accounting. Some,

such as Basu (1997), define conservatism as the practice of reducing earnings (and writingdown net assets) in response to ‘‘bad news,’’ but not increasing earnings (and writing upnet assets) in response to ‘‘good news.’’ In the accounting-based valuation literature, re-searchers often refer to Feltham and Ohlson’s (1995) characterization of conservative or‘‘biased’’ accounting as an expectation that reported net assets will be less than marketvalue in the long run. That definition classifies the accounting for anticipated positive net-present-value investments at historical cost as conservative accounting, because the analystexpects those investments to be carried at less than their value. Zhang (2000) models


conservative accounting and valuation from this perspective. Beaver and Ryan (2000) sim-ilarly characterize conservatism (or bias) as a persistent difference between market valueand book value that is distinct from temporary differences due to economic gains and lossesthat are recognized in book value gradually over time. Gjesdal (1999) distinguishes ‘‘eco-nomic profitability’’ from accounting profitability. He characterizes accounting as conser-vative if it assigns investments a carrying value that yields an expected accounting rate ofreturn greater than the internal rate of return on their cost. Thus, for example, conservativeaccounting carries an asset whose value is equal to its historical cost (a zero net presentvalue investment) at less than historical cost, as is the case with the immediate expensingof R&D and advertising costs.

Our notion of conservatism follows the latter definition; that is, conservative accountingis a biased application of historical cost accounting.2 For our purposes, however, we neednot establish unbiased historical-cost carrying values of investments or unbiased allocationsof investment cost to match against revenues. Rather, we examine conservatism in a relativesense. We consider one accounting practice (e.g., LIFO accounting) more conservative thana second accounting practice (FIFO accounting) if the first yields lower accumulated earn-ings (because of the relatively accelerated expensing of costs) and consequently lowercarrying values than the second.

Effects of Conservatism and Investment on Rates of ReturnThe effect of conservative accounting on accounting rates of return is clear. If net assets

do not change over a period, then conservative accounting has no effect on earnings in thenumerator of a rate of return. (It is always the case, for example, that earnings are the sameunder accelerated depreciation and straight-line depreciation if property, plant, and equip-ment is unchanged over the period.) Because conservative accounting carries assets in thedenominator at lower amounts, the rate of return is higher, however. Greenball (1969) showsthat conservative accounting (bias) interacts with growth; conservative accounting reducesearnings when there is growth in net assets so that accounting rates of return are lowerthan in the no-growth case. Beaver and Ryan (2000) and Zhang (2001) model the interactionof conservatism and growth in a valuation context.

Our concern is not with the effects of conservatism and growth on accounting rates ofreturn, but with the effects of the interaction between conservatism and changes in growth.Conservatism and growth do not necessarily affect the quality of earnings as we definethe term. For example, if one expects a firm to maintain growth in net assets at thecurrent level, then one expects future rates of return, ceteris paribus, to be the same asthe current rate of return. However, should management reduce investment in assets thatare subject to conservative accounting, the firm’s rate of return will increase. If this reduc-tion in investment is temporary, then so is the increase in the rate of return. In this case,the current rate of return will not be a good indicator of future rates of return.

We illustrate the effect on earnings of the interaction between conservative accountingand changes in investment with the case of LIFO accounting for inventories. The dollareffect of this interaction is transparent in the LIFO reserve disclosure in footnotes to U.S.financial statements. LIFO accounting is conservative because it carries inventories on thebalance sheet at lower amounts than FIFO or at average cost methods if inventory prices

2 The Gjesdal (1999) concept is subsumed within the Feltham and Ohlson (1995) and Beaver and Ryan (2000)concepts. However, the latter also include in the definition of conservative accounting the practice of recordingpositive net-present-value investments at unbiased historical cost to yield an accounting rate of return equal tothe internal rate of return. Our characterization of conservatism does not involve this latter notion.


have risen in the past. Accordingly, accounting rates of return are typically higher underLIFO. The LIFO reserve is the difference between LIFO and FIFO carrying values.3 Cor-respondingly, the difference between LIFO and FIFO costs of goods sold is the change inthe LIFO reserve. If purchases of inventories increase relative to sales (with continuingrising purchase prices), then earnings are lower under LIFO than in the no-growth case,and the LIFO reserve increases. Except for the case of the effect of extreme increases inpurchase prices on cost of good sold, rates of return are also lower, for the denominatoreffect is typically greater than the earnings effect in the numerator. If dollar inventoriesdecline (so that lower LIFO inventory costs flow into cost of goods sold), then LIFO yieldshigher earnings and rates of return. The LIFO reserve decreases, and earnings increase asthe result of a real phenomenon: a decline in inventory. The buildup of the LIFO inventorycreates the reserve, and the decline in inventory—known as LIFO dipping—is a liquidationof the reserve.

The same phenomena arise, although less transparently, from all forms of conservativeaccounting. Accelerated depreciation from using short, estimated asset lives (that reportslower net asset values) has no effect on earnings if depreciable assets are not growing, butreduces earnings (and, like LIFO, creates reserves) if investments in the assets increase,ceteris paribus.4 If investments decline, then accelerated depreciation creates earningsthrough the liquidation of reserves. Immediate expensing of R&D expenditures and adver-tising is conservative (setting knowledge assets and brand assets to zero on the balancesheet), but has no effect on earnings relative to capitalizing and amortizing the expendituresif the expenditures are not growing. However, increasing R&D investments and advertising,while immediately expensing the related costs, depresses earnings and increases reserves.Slowing R&D and advertising increases earnings and reduces reserves because the benefitsfrom past expenditures are recognized without matching additional new expenditures.5 Be-cause the reserves created by conservative accounting are not recorded on the balance sheet,they are unrecorded ‘‘hidden’’ reserves that are released into earnings when investmentgrowth slows. We refer to them as ‘‘unrecorded reserves.’’6

If a temporary change in investment leads to a temporary change in a reported ac-counting rate of return, then the current earnings will be a poor indicator of future earningsand, thus, earnings quality will be low. If an analyst accepts the current book rate of returnas an unbiased indicator of future rates of return, then he will be misled. If investors valuefirms using current earnings as an input without appreciating that those earnings are un-sustainable, then market valuations will also be of low quality. But, if the analyst andinvestor penetrate the joint effect of conservative accounting and investment, then they willdiscover that reported earnings is a poor predictor of long-run ‘‘sustainable’’ profitability,and will value the firm appropriately.

Our empirical analysis, then, documents the incidence of temporary effects of the in-teraction of conservative accounting and investment on accounting rates of return, and askswhether the market pricing of stocks is consistent with investors’ appreciating the qualityof reported earnings in valuing stocks.

3 The SEC requires firms to calculate the LIFO reserve as the accumulated excess of the current cost of inventoriesover LIFO cost. With rapid inventory turnover, however, FIFO cost approximates current cost.

4 Conservative accounting reports lower earnings only in the growth case. Penman (2001, Chapter 17) givesexamples of the effects of conservative accounting and growth described here.

5 Baber et al. (1991) and Perry and Grinaker (1994) provide evidence that managers modify R&D expendituresto manage earnings.

6 The LIFO reserve is, of course, recorded in the footnotes of the 10-K, but not in the balance sheet.


III. INDEXES OF CONSERVATISM AND EARNINGS QUALITYWe use information from the financial statements to develop two indices of the quality

of earnings. The first index scores the degree to which the firm applies conservative ac-counting. The second index scores the quality of earnings that results from the joint effectof conservatism and changes investment activity.

Conservatism Index (C-Score)The C-score measures the effect of conservative accounting on the balance sheet. We

measure the C-score as the level of estimated reserves created by the conservatism, ER,relative to net operating assets:

ERitC � ,it NOAit

where i indicates firms and t indicates balance sheet dates.7 Net operating assets, NOA, isthe book value of operating assets minus operating liabilities. That is, NOA equals assetsminus liabilities, excluding financial assets and liabilities (financing debt and assets in whichthe firm invests excess cash). Balance sheets typically value these financial items at marketvalue, or close to market value if interest rates change little, so conservative accountingdoes not affect such financial items.8 We subtract operating liabilities from operating assetsin the NOA calculation to measure the net investment in operations. This netting recognizesthat conservative accounting can affect both operating assets and operating liabilities.

A complete C-score calculates unrecorded reserves created by all operating items inthe balance sheet—including bad-debt allowances, depreciation allowances, valuation al-lowances, deferred revenue, pension liabilities, and other estimated liabilities. We wish todistinguish earnings quality effects arising from changes in estimates (i.e., accounting ma-nipulation) from quality effects arising from permanent accounting policy and changes ininvestment. An allowance for bad debts, for example, might be ‘‘high’’ because of a per-manent policy of carrying net receivables at a conservative level or because of a temporaryincrease in the estimate of bad debts to reduce current income and increase future income.Because we cannot distinguish between the two effects for balance sheet items that are alsosubject to management’s estimates, we construct a C-score based on the accounting treat-ment of three investments for which the accounting is relatively immune from managerialdiscretion after the expenditure has occurred: inventories, R&D, and advertising. The ac-counting for these items follows mandates from accounting regulators or (in the case ofLIFO) an accounting choice that management (usually) does not change from period toperiod. Thus:

7 One might consider developing a C-score calculated as the estimated reserve relative to net operating assets plusthe estimated reserve—then the estimated reserve would be expressed as a percentage of net operating assetsthat would have been reported had the firm not practiced conservative accounting. In this paper, however, wewish to compare the effect of changes in reserves on return on net operating assets, and this return is, of course,denominated in net operating assets.

8 In terms of Compustat item numbers, net operating assets is calculated as Common Equity (60: Common Equity� 227: Preferred Treasury Stock � 242: Preferred Dividends in Arrears) � Financial Obligations (34: Debt inCurrent Liabilities � 9: Total Long-Term Debt � 130: Preferred Stock � 227: Preferred Treasury Stock � 242:Preferred Dividends in Arrears) � Financial Assets (1: Cash and Short-Term Investments � 32: Investments andAdvances � Other) � Minority Interest (38: Minority Interest on Balance Sheet). The procedures for reformu-lating financial statements to separate operating and financing activities are laid out in Penman (2001, Chapters8, 9, and 10).


res res resC � (INV � RD � ADV )/NOA ,it it it it it

where the calculation for the three reserve components is as follows:

● Inventory reserve equals the LIFO reserve reported in the financial statementres(INV )it

footnotes.● R&D reserve is calculated as the estimated amortized R&D assets that wouldres(RD )it

have been on the balance sheet if R&D had not been expensed. We capitalize R&Dexpenditures, then amortize them using the industry coefficients estimated by Levand Sougiannis (1996). In a sensitivity analysis, we also amortize using the sum-of-the-years-digits method over five years.

● Advertising reserve is the estimated brand assets created by advertisingres(ADV )it

expenditures. We capitalize advertising expenses and then amortize them using asum-of-the-year’s digits method over two years. Bublitz and Ettredge (1989) andHall (1993) indicate that advertising has a short useful life, typically one to twoyears.

We also calculate a subscore for each component of the reserve, the estimated reserve fromthat component relative to NOA.

Earnings Quality Indicator (Q-Score)Whereas the C-score measures the effect of conservative accounting on the balance

sheet, the Q-score measures the effect of conservative accounting on earnings in the incomestatement. Our Q-score is a combination of two measures. The first measure, , is cal-AQit

culated as:

ER ERit it�1AQ � �it NOA NOAit it�1

That is, is the change in the firm’s conservatism score, Cit. Thus, a firm’s QA score isAQit

positive if it builds up its reserve at a rate faster than that of the growth in net operatingassets, and negative if it builds its reserve at a slower rate. The second measure, ,BQit

compares a firm’s C-score to the median for its SIC two-digit industry code:

ER ERit itBQ � � Industry median .� �it NOA NOAit it

The Q-score combines these two measures:

A BQ � (0.5 � Q ) � (0.5 � Q )it it it

The Q-score does not ordinally rank firms on their earnings quality. Both ‘‘high’’ (positive)and ‘‘low’’ (negative) Q-scores can indicate that current earnings are of poor quality, anda Q-score of zero indicates good quality. We also calculate Q subscores from C-scores forinventories, R&D, and advertising.9

9 Both the C-score and Q-score might be adjusted for deferred tax effects of the alternative accounting. This isnot important for our purposes; Q is measured before tax. We use the score only to assign firms to high- andlow-Q groups.


The change in estimated reserves measures the effect of conservative accounting andchanges in investment on current earnings. To analyze the quality of the earnings, we focuson whether this effect is temporary. If a firm increased its reserve by growing investmentsbut continued that growth in the future, then the effect would not be temporary. The QA

score is the change in estimated reserves divided by net operating assets. Thus, the scoreis determined by the growth rate in the estimated reserves relative to the growth rate in netoperating assets (so that, if estimated reserves grow at the same rate as net operating assets,the score is 0). Therefore, we benchmark a change in a firm’s reserve against the changein the firm’s net operating assets in the QA measure. Accordingly, we posit that the growthin the reserve (unrecorded assets) is unusual if it differs from the growth in (recorded) netoperating assets. With the QB measure, we posit that if a firm’s buildup of reserves relativeto net operating assets—its C-score—differs from the industry median, then the firm’s C-score will likely revert toward the industry median in the future. That is, the portion of thefirm’s C-score that deviates from the industry median is likely to be temporary. If, forexample, a firm with a high C-score subsequently reverts toward the median, then, uponthis reversion, some of the firm’s estimated reserves will flow into earnings. Because it isnot clear ex ante which benchmark (the firm’s prior-period conservatism score or the cur-rent-period industry median score) might best capture temporary earnings effects, we weightthe two measures equally, and then, in sensitivity analysis, we change the weights from(0.5, 0.5) to (1, 0) and (0, 1), to investigate separately QA and QB. The overall Q-score, asweighted, is:

Q � 0.5 (C � C ) � 0.5 (C � Industry median C )it it it�1 it it

� C � (0.5 C � 0.5 Industry median C ).it it�1 it

The overall score can thus be interpreted as the ‘‘unexpected’’ C, where the expected C isa simple average of last period’s C and the median C for the industry.

Of course, our analyses are joint tests of the effect of the conservatism-investmentinteraction on earnings quality and of the ability of our scoring measures to capture con-servatism and earnings quality. Thus, evidence that Q-scores identify unsustainable earningswill also validate a useful diagnostic tool, as an aid to financial statement analysis.

IV. SAMPLE SELECTION AND DESCRIPTIONWe calculated C-scores and Q-scores for NYSE and AMEX nonfinancial firms on the

combined Compustat Annual Industrial and Research files (which include nonsurvivors) for1975–1997. We obtained monthly stock returns from the 1997 CRSP files. Our sampleperiod begins in 1975 because, prior to that year, accounting data needed to construct theindices were missing from Compustat for a significant number of firms.

We were able to calculate net operating assets for 46,854 of the 46,955 firm-years fornonfinancial NYSE and AMEX firms with share price, shares outstanding, and book valueof common equity data on Compustat at some time from 1975 to 1997. Of these, 46,122firm-years had positive NOA, and there were sufficient data to compute C-scores for 38,540firm-years. We deleted firm-years only if we could compute none of the three subscoresfrom the Compustat data. When just one or two subscores were missing, we used industrymedian subscores (for two-digit SIC industry groups) as substitutes to calculate the overallC-score for that firm-year. The 38,540 firm-years included the following:


TABLE 1Distribution of Conservatism Index (C-Score) and Earning Quality Index (Q-Score) over

Firm-Years; 1975–1997a

C-Score Q-Score

Number of Firm-Years 38,540 29,796Mean 0.313 0.099Percentiles

95 0.576 0.21990 0.413 0.13975 0.236 0.05960 0.153 0.025Median 0.114 0.00940 0.084 0.00025 0.048 �0.01010 0.014 �0.0465 0.004 �0.075

a Section III of the text describes C-score and Q-score calculations. The C-score estimates the degree of under-statement of net operating assets from the conservative practice of LIFO accounting for inventories and theexpensing of R&D and advertising expenditures. It is measured as the unrecorded reserve from conservativeaccounting relative to recorded net operating assets.

The Q-score indicates the extent to which earnings are affected by change of investment under conservative ac-counting. Positive Q-scores indicate the extent to which earnings are temporarily depressed by growth in investmentin inventories, R&D, and advertising. Negative Q-scores indicate the extent to which earnings are temporarilyinflated by a decline of investment in these three items.

Firm-years with one subscore 8,285

Firm-years with two subscores 19,258

Firm-years with three subscores 10,997

Total firm-years 38,540

Of these 38,540 firm-years, the LIFO reserve was available for 36,244, and 13,931reported using LIFO; therefore, there were 22,313 non-LIFO cases (where the reserve is0). We could calculate the R&D reserve for 25,357 firm-years, and the advertising reservefor 18,191 firm-years.

Table 1 summarizes the distribution of C-scores over firm-years. The median of 0.114indicates that NOA would have been 11.4 percent higher for the typical firm if the ac-counting treatment of the three items had not created reserves. The mean of 0.313, relativeto the median and upper percentile scores, indicates that C-scores are particularly large fora relatively small number of firms (i.e., they are positively skewed).

We were able to compute Q-scores for 32,343 firm-years, but we deleted 2,547 firm-years for firms with non-LIFO inventory methods and missing C subscores for both R&Dand advertising. Table 1 gives the distribution of the final sample of 29,796 Q-scores. Themedian is close to 0. The scores for the upper percentiles indicate that a nontrivial per-centage of firms noticeably increased reserves during this period of considerable assetgrowth. Low Q-scores are smaller in absolute magnitude than high scores, but 40 percentof the firm-years reported liquidations of reserves.


The mean first-order serial correlation of in the sample is 0.01, and that for isA BQ Qit it

0.61. In the cross-section, the mean Spearman rank correlation between and (inA AQ Qit it�1

the subsequent year) is 0.10, decaying to 0.01 five years ahead. The mean Spearman rankcorrelation of with (in the subsequent year) is 0.88, decaying to 0.63 five yearsB BQ Qit it�1

ahead. These statistics indicate that neither the QA nor the QB scores are permanent firmcharacteristics. However, they also indicate that, although the QB score does eventuallyrevert toward the industry median, the QA score is very much a ‘‘one-time’’ feature, andso is more likely to capture the temporary effect on earnings that we wish to identify.

V. ANALYSIS OF THE QUALITY OF BOOK RATES OF RETURNIf the Q-score identifies temporary effects on earnings, then a relatively high Q-score

should indicate current profitability that is lower than that expected in the future, and arelatively low Q-score should indicate current profitability that is higher than that expectedin the future. We examine how Q-scores predict changes in core return on net operatingassets from its current level. We define core return on net operating assets (RNOA) as:

Core RNOA � [Core operating income � (1 � statutory tax rate )]/average NOA .it it it it

Core operating income is operating income before interest, special items, extraordinaryitems, and discontinued operations. Core operating income therefore excludes transparentlytemporary components of earnings, along with interest expense (which is not generated byoperations and which is unaffected by conservative accounting). Thus, we focus on reportedincome that investors might identify as sustainable income from operations. We measureafter-tax core operating income without allocating taxes between operating and financingactivities. Our measure assumes that any difference between effective tax rates on operatingincome and statutory rates (calculated as the Federal rate plus 2 percent) is temporary.

To conduct the analysis, we sorted firms in each year, 1976–1996 and within each two-digit SIC industry, into ten equal-sized groups based on the core RNOA the firm reportedthat year. We retained only RNOA groups with at least three firms. Then, within eachRNOA group, we further divided firms into three equal-sized groups based on theirQ-scores. We refer to these as high, median, and low Q-score groups. Our analysis comparescore RNOA in the grouping year with core RNOA realized in subsequent years foreach Q group. The grouping by industry controls for the type of operations, so the differ-ences we observe in RNOA are more likely to be due to accounting effects than todifferences in operations. The grouping also controls for the mean reversion in RNOAdocumented in Nissim and Penman (2001), so the observed relation between changes inRNOA over time and the Q-scores does not reflect this typical RNOA mean reversion.

Table 2 summarizes median core RNOA for the five years before and after the year ofthe grouping (Year 0) for each of these Q-score groups and for all firms together. Thenumbers in the table are means of the medians computed over the 21 calendar years from1976 to 1996. Panel A of Figure 1 tracks these means for the high- and low-Q-score groups.Panel B tracks these same mean RNOAs after subtracting the median RNOA for all firmsin that year to adjust for trends in median RNOA over time.

The results in Table 2 and Figure 1 indicate that, on average, Q-scores do discriminateon future core RNOA. The RNOA for high-Q groups are declining prior to Year 0, con-sistent with conservative accounting’s increasing reserves and depressing earnings. Themean RNOA for these high-Q groups continues at about the same level, or slightly higher,after Year 0. The low-Q groups report lower mean RNOA than the high groups before Year0, but by Year 0 their mean RNOAs are approximately the same as the high-Q groups’

Penman

andZ

hang—A

ccountingC

onservatismand

Earnings

Quality

247

TABLE 2Summary of Median Core Return on Net Operating Assets (Core RNOA) in Years Relative to the Q-Scoring Year (Year 0), for Groups of

Firms with High, Medium, and Low Q-Scores1976–1996a

Year Relative to Year Q-Score is Calculated (Year 0)

�5 �4 �3 �2 �1 0 1 2 3 4 5

Core RNOAHigh Q 0.1231 0.1214 0.1179 0.1143 0.1116 0.1107 0.1147 0.1122 0.1090 0.1088 0.1065Medium Q 0.1165 0.1153 0.1136 0.1116 0.1097 0.1090 0.1075 0.1054 0.1026 0.1005 0.1013Low Q 0.1113 0.1108 0.1094 0.1092 0.1110 0.1094 0.1002 0.0976 0.0950 0.0939 0.0930All firms 0.1173 0.1159 0.1135 0.1117 0.1107 0.1096 0.1077 0.1052 0.1023 0.1012 0.1006

Differences in Core RNOAfrom Year 0High Q 0.0124 0.0106 0.0072 0.0036 0.0008 0 0.0039 0.0015 �0.0018 �0.0019 �0.0043Medium Q 0.0075 0.0064 0.0046 0.0026 0.0008 0 �0.0014 �0.0036 �0.0063 �0.0085 �0.0076Low Q 0.0020 0.0014 0.0000 �0.0002 0.0016 0 �0.0092 �0.0018 �0.0014 �0.0155 �0.0164

t-statistic on changes inCore RNOA from Year0High Q 1.47 1.33 1.01 0.59 0.21 0 0.96 0.32 �0.07 �0.04 �0.38Medium Q 0.97 0.91 0.74 0.47 0.22 0 �0.41 �0.61 �0.76 �0.90 �0.80Low Q 0.23 0.20 0.00 �0.04 0.44 0 �2.78 �2.24 �2.30 �2.11 �2.01

Differences in changes inCore RNOA from Year0 between High Q andLow Q groups

0.0104 0.0092 0.0071 0.0038 �0.001 0 0.0131 0.0132 0.0126 0.0136 0.0121

t-statistic on meandifferences in changesin Core RNOA

5.650 4.324 4.092 1.950 �0.051 7.379 7.172 6.477 5.256 3.835

(Continued on next page)

248T

heA

ccountingR

eview,

April

2002

TABLE 2 (Continued)


�5 �4 �3 �2 �1 0 1 2 3 4 5

Number of FirmsHigh Q 5,168 5,291 5,419 5,574 5,719 5,925 5,595 5,087 4,679 4,258 3,876Medium Q 6,153 6,353 6,527 6,697 6,939 7,178 6,816 6,197 5,711 5,198 4,731Low Q 4,184 4,324 4,486 4,683 4,897 5,193 4,928 4,475 4,094 3,728 3,398All firms 15,505 15,968 16,432 16,954 17,555 18,296 17,339 15,759 14,484 13,184 12,005

a The Q-score groups are based on a ranking of firms each year on Q-scores, within two-digit industry codes and within deciles of core RNOA. The high Q-score groupis the top third of firms in that industry-core-RNOA ranking, the medium Q-score group is the middle third, and the low Q-score group is the bottom third of firms inthe ranking on Q-score.

Core return on net operating assets (RNOA) is after-tax core operating income (exclusive of interest and unusual items) divided by average net operating assets.The core RNOA values reported in the table are the means of 21 yearly median core RNOAs computed over the years 1976 to 1996.


FIGURE 1Mean Core Return on Net Operating Assets (Core RNOA) for High and Low Q-Score Groups

over Five Years before and after the Q-Scoring Year, Year 0

Panel B: Tracks Core RNOA Adjusted for the Median Core RNOA of All Firms in that Year

Core RNOA

8.00%

9.00%

10.00%

11.00%

12.00%

13.00%

-5 -4 -3 -2 -1 0 1 2 3 4 5

High Q

Low Q

Relative Core RNOA - median Core RNIOA subtracted to detrend

-2.00%

-1.00%

0.00%

1.00%

2.00%

-5 -4 -3 -2 -1 0 1 2 3 4 5

Year

High Q

Low Q

Panel A: Tracks Core RNOA

Year

mean RNOAs (by construction of the tests). However, the mean RNOA for the low-Qgroups subsequently deteriorates. Accordingly, the Q-score identifies, ex ante in Year 0,firms with low-quality earnings whose core RNOAs on average decline by the next year.By liquidating their reserves, these firms achieve an average core RNOA in Year 0 that isindistinguishable from the average RNOAs of the high-Q group and of all firms as a whole.The Q-score indicates, however, that this creation of earnings is temporary.

Table 2 reports two sets of significance tests. For the first set, we calculated mediandifferences in core RNOA between Year 0 and each of the five years before and after Year0 for high-Q, medium-Q, and low-Q groups, for each of the 21 years. The table reportsthe mean of these median differences over the 21 years, along with a t-statistic on those


mean differences (based on a standard error of the mean estimated from the time series ofmedian differences). The second set of t-tests is on means of differences across high- vs.low-Q groups’ differences in core RNOA (computed between Year 0 and the relevant year).The two sets of t-statistics indicate that the mean RNOAs for the low-Q groups in the fiveyears after Year 0 are statistically significantly less than that in Year 0, and that the dropin core RNOA after Year 0 is statistically greater for the low-Q group than for the high-Qgroup. These differences—more than 1 percent of net operating assets—are economicallysignificant.10 Of the low-Q firms that survived through Year �1, untabulated results revealthat only 44.2 percent had increasing RNOA in Year �1, compared to 52.5 percent forhigh-Q firms.11 We repeated the analysis in Table 2 for the subperiods 1976–1982, 1983–1989, and 1990–1996, with similar results in each period.12

Sensitivity Tests and Further AnalysisWe performed further tests to provide additional controls, to document the robustness

of the findings over time and over Q subscores, and to explore the phenomenon further.We summarize these tests below; more detail is available on request.

Control for Mean Reversion in Core RNOAWe have attempted to control for the typical mean reversion of RNOA by comparing

the evolution of core RNOA across Q groups for firms first grouped on the level of coreRNOA. However, some of the differential behavior of core RNOA across Q-score groupsmight be attributable to differential behavior of different levels of RNOA if Q-scores arestrongly correlated with RNOA (and thus the ranking on Q-scores is effectively a rankingon RNOA). Untabulated analyses suggest this is not a problem. The median Spearmancorrelation between Q-scores and RNOA within RNOA groups is 0.00, the mean correlationis 0.10, and the 75th percentile of rank correlation is 0.42.13 We also repeated the analysisin Table 2, first with a ranking on core RNOA within each RNOA group, then splittinginto three RNOA groups rather than on Q-scores. In contrast to the results reported inFigure 1 and Table 2, which are based on Q-score partitions, we observed little differencein the RNOA dynamics across these three RNOA groups, so mean reversion in RNOAcannot explain the results.

Analysis of Inventory, R&D, and Advertising SubscoresWe performed an analysis identical to that in Table 2, except that we used separate Q-

scores for inventory, R&D, and advertising. Results (not tabulated here) are similar for eachsubscore, so the results are not due to one particular accounting item.

We next estimated 21 annual linear regressions of one-year-ahead core RNOA (in t� 1) on core RNOA and Q-scores in the scoring year, t. Panel A of Table 3 shows thatthe positive mean estimated coefficient for the Q-score and its t-statistic indicate that the

10 Using the residual income valuation formula, a revision of forecasted RNOA of 1.3 percent per year for fiveyears has considerable effect on the calculated value.

11 For a classification of high-Q and low-Q firms into those with increasing and decreasing core RNOA in Year1, the Chi-square statistic is 73.9, with a probability given no relationship between Q and subsequent profitabilityof less than 0.001.

12 Although firms in the same industry tend to have the same fiscal year-ends, they do vary. Thus, ranking firmsas if their RNOAs were available at the same time is subject to a peeking-ahead bias. However, we repeatedthe analysis on firms with (the same) December 31 fiscal year-ends, with similar results.

13 The mean annual Spearman rank correlations between the Q-score and core RNOA over all firms was 0.08, andthe mean for ranking within industry was 0.07. Thus, in addition to the low correlation between Q-score andcore RNOA within RNOA groups, correlation between the Q-score and core RNOA is minimal even over alllevels of RNOA.


TABLE 3Results of Regression Tests of the Ability of Q-Scores to Predict Year-Ahead Core RNOAa

Panel A: Overall Q-Score

RNOAt�1 � �0 � �1 RNOAt � �2Q � et�1

First ThirdMean Quartile Median Quartile

Intercept 0.016 0.009 0.018 0.022(6.51**)

RNOA coefficient 0.800 0.782 0.813 0.846(47.55**)

Q coefficient 0.096 0.054 0.103 0.136(7.17**)

Panel B: Three Subscores for Inventory, Research and Development, and Advertising

RNOAt�1 � �0 � �1 RNOAt � �2 Q inv � �3 Q rd � �4 Q adv � �t�1

First ThirdMean Quartile Median Quartile

Intercept 0.015 0.010 0.016 0.022(6.39**)

RNOA coefficient 0.802 0.765 0.823 0.866(43.56**)

Q inv coefficient 0.131 0.076 0.133 0.215(6.18**)

Q rd coefficient 0.106 0.080 0.130 0.149(5.15**)

Q adv coefficient 0.082 0.007 0.071 0.195(2.49*)

*, ** Denotes significance at the 0.05 and 0.01 levels, respectively, using a two-tailed t-test.a We estimated regression coefficients for 21 annual cross-sectional regressions, 1976 to 1996. The mean estimated

coefficients from the 21 regressions appear in the table, along with those at the 25th percentile, the median, andthe 75th percentile. The t-statistics (in parentheses) are calculated as the mean of the 21 estimated coefficientsrelative to their estimated standard errors.

RNOA is core return on net operating assets; Q is the Q-score that indicates temporary effects of conservatismand changes in investment on current earnings; Q inv is the inventory subscore; Q rd is the research and devel-opment subscore; Q adv is the advertising subscore.

Q-score improves forecasts of one-year-ahead RNOA over that from the current RNOAalone. Panel B reports the results of a similar analysis, after separating the Q-score into itsthree subscores. The results indicate that each subscore provides additional informationabout one-year-ahead RNOA, not only beyond current RNOA, but also relative to eachother.14

14 The mean Spearman rank correlation between the score for inventory (Q inv) and those for R&D (Q rd) andadvertising (Q adv), within industry, were 0.09 and 0.08, respectively. The correlation between Q rd andQ-adv was 0.11.


Analysis of QA and QB

The analyses in Tables 2 and 3 are based on a Q-score that weights QA and QB equally.As explained in Section III, we intended both of these measures to identify temporarychanges in reserves generated by conservative accounting. Additional tests reveal that bothmeasures, QA and QB, are effective in forecasting future core RNOA. However, QA appearsto be somewhat more effective than QB, consistent with our earlier conjecture that the lowerserial correlation in QA than in QB indicated that QA better captures the temporary effectof the conservative accounting on earnings.

Specifically, we repeated the tests in Table 2 for both QA and QB. Although QA producedslightly stronger results than QB, both discriminate on changes in future core RNOA. Themean changes in core RNOA from Year 0 to Years �1 to �5 for the low-QA group were�0.95 percent, �1.03 percent, �1.19 percent, �1.16 percent, and �1.11 percent, respec-tively. For the low-QB group the mean changes for these five years were �0.68 percent,�0.99 percent, �1.42 percent, �1.49 percent, and �1.70 percent, respectively. All the t-statistics were of the same order as those reported in Table 2. For the high-Q groups, resultswere stronger for the QA metric, with mean core RNOA increasing by 0.65 percent in Year�1 with a t-statistic of 1.78, significant at the 0.05 level for a one-tailed test. For QB, meancore RNOA increased by 0.26 percent in Year � 1 with a t-statistic of 0.65.

We also repeated the regression tests reported in Table 3 for QA and QB. The resultsare similar. The mean estimated coefficient for QA corresponding to that in Panel A ofTable 3 was 0.212 (with a t-statistic of 6.34) and that for QB was 0.030 (with a t-statisticof 4.70). Mean coefficient estimates on RNOA were similar to those in Table 3. For bothQ-scores, coefficients on all three subscores (corresponding to those in Panel B of Table3) were significantly positive. Again, however, QA measures provided more explanatorypower than QB measures.

Control for Growth in Net Operating AssetsThe QA-score captures the rate of change in estimated reserves relative to the rate of

change in net operating assets. By benchmarking growth in estimated reserves (from un-recorded assets) against growth in recorded assets, QA is designed to capture abnormalgrowth in estimated reserves. However, the QA growth measure is clearly affected by thegrowth in net operating assets.

Fairfield et al. (2001) show that growth in net operating assets reduces future profita-bility, and they argue that Sloan’s (1996) observation that high (low) accruals are followedby lower (higher) profitability is due to the positive correlation between growth and accru-als. The negative effect of growth of net operating assets on future profitability can beattributed to new investment earning at a lower rate or to the effect of conservative ac-counting; new investment, amortized rapidly, reduces subsequent earnings. Although thelatter explanation involves conservative accounting, we have a different feature of conser-vative accounting in mind.

Therefore, to ensure that our inferences are not solely attributable to differential growthin net operating assets, we estimated the cross-sectional regressions in Panel A of Table 3,adding the rate of growth in net operating assets as an additional explanatory variable.Growth in net operating assets indeed plays an incremental role in explaining one-year-ahead core RNOA (with a statistically significant negative mean estimated coefficient), butthe Q-score still provides incremental information, with a positive mean coefficient of 0.039and a significant t-statistic of 3.23.


Further Analysis of the RNOA ChangesOur analysis investigates whether core RNOA is temporarily affected by increases in

unrecorded reserves that depress earnings, and by the release of those reserves that increaseearnings. Core RNOA equals core profit margin (core operating income/sales) multipliedby asset turnover (sales/net operating assets), following the standard DuPont decomposi-tion. To confirm that the effect was indeed an effect on earnings (and thus the profit margin)and not just a turnover effect, we examined the two components separately.

Table 4 reports core profit margins and asset turnovers for each of the five years beforeand after the Q-scoring year, for both high- and low-Q groups. As the accounting forinventories, R&D, and advertising does not affect depreciation and amortization, profitmargins are before depreciation and amortization.

The average changes in profit margins from Year 0 to subsequent years are significantlydifferent for the low-Q groups than for the high-Q groups. Profit margins increase for thehigh-Q groups, and decrease for low-Q groups.15 We also performed this analysis for coreprofit margins before R&D, and advertising expense (and depreciation and amortization).For the low-Q group, these margins declined in Year 0 on average, whereas those afterR&D and advertising expenses increased. Thus, lower R&D and advertising expendituresturned otherwise decreasing mean margins into increasing margins.

Table 4 shows that mean asset turnovers increase after Year 0 for high-Q firms butdecline for low-Q firms; thus, the Q-score predicts differences in changes in asset turnovers,as well as margins. Note, furthermore, that because the mean asset turnover for low-Qgroups declines in Year 0 (reducing RNOA), the core RNOA created by low-Q firms inYear 0 appears to be a profit margin effect. The decline in asset turnovers for low-Q firmsin Year 0 is driven by sales’ increasing at a slower rate than net operating assets. Thus, thelow-Q firms are increasing profit margins and maintaining core RNOA in Year 0 at thelevel of high-Q firms, on average, even though their sales growth is declining relative tothe growth of their net operating assets. Furthermore, the decline in asset turnover subse-quently continues, on average. Asset turnovers for high-Q groups increase in Year 0. Theseresults indicate that changes in turnover are related to earnings quality, as in Fairfield andYohn (2001) but for different reasons, for that paper views changes in net operating assets(rather than Q) as a diagnostic of earnings quality.

Analysis of Changes in InvestmentTo corroborate that changes in Q are indeed due to changes in investment, we tracked

the investment in inventory, R&D, and advertising. For these three items, changes in re-serves due to conservative accounting result from changes in dollar investments, so wetracked cumulative (compounded) growth rates of estimated reserves for high- and low-Qgroups for years �5 to �5. The average growth rates for the low-Q groups declined inyears up to and including Year 0, and were lower than those for high-Q firms. However,they subsequently converged toward the high-Q growth rates indicating that the slowing ofinvestment growth up to Year 0 was indeed temporary, and thus increased earnings, profitmargins, and RNOA temporarily.

There is one qualification to our interpretation of the results. Firms that reduce invest-ment or the rate of growth in investment can do so in anticipation of lower profitability ofnew projects. Therefore, the lower RNOA after Year 0 for low-Q firms may reflect lower

15 Regression tests similar to those in Table 3 support the inferences from Table 4. Also, of the firms that survivedthrough Year �1, 49.8 percent of low-Q firms had increasing profit margins in Year �1, compared to 55.1percent of high-Q firms.

254T

heA

ccountingR

eview,

April

2002

TABLE 4Summary of Core Profit Margins (before Depreciation and Amortization) and Asset Turnovers in Years Relative to the Q-Scoring Year

(Year 0) for Groups of Firms with High, Medium, and Low Q-Scores 1976–96a


�5 �4 �3 �2 �1 0 1 2 3 4 5

Panel A: Core Profit MarginHigh Q 0.1215 0.1198 0.1173 0.1147 0.1148 0.1139 0.1164 0.1169 0.1167 0.1174 0.1172Medium Q 0.1201 0.1202 0.1214 0.1211 0.1212 0.1215 0.1215 0.1217 0.1218 0.1203 0.1200Low Q 0.1154 0.1168 0.1164 0.1200 0.1200 0.1251 0.1225 0.1204 0.1210 0.1209 0.1200All firms 0.1192 0.1192 0.1191 0.1187 0.1187 0.1196 0.1200 0.1194 0.1192 0.1187 0.1184

Differences in changes incore profit margins fromYear 0: High Q � Low Q

0.0173 0.0143 0.0121 0.0060 0.0027 0 0.0051 0.0072 0.0059 0.0066 0.0067

t-statistic on meandifferences in changes incore profit margins

8.01 6.61 7.53 4.19 2.25 3.82 5.29 4.40 4.45 4.07

Panel B: Asset TurnoverHigh Q 2.222 2.245 2.272 2.279 2.294 2.361 2.405 2.381 2.355 2.322 2.290Medium Q 2.123 2.116 2.119 2.105 2.101 2.111 2.093 2.094 2.086 2.065 2.070Low Q 2.054 2.069 2.058 2.068 2.080 2.015 1.961 1.958 1.953 1.948 1.973All firms 2.130 2.151 2.106 2.166 2.172 2.179 2.163 2.153 2.143 2.132 2.125

Differences in changes inasset turnovers from Year0: High Q � Low Q

�0.178 �0.170 �0.132 �0.135 �0.132 0 0.0974 0.0856 0.0783 0.0592 0.0165

t-statistic on meandifferences in changes inasset turnover

�5.92 �5.32 �5.12 �5.45 �6.49 5.25 4.00 3.05 2.22 0.68

a The Q-score groups are based on a ranking of firms each year on Q-scores, within two-digit industry codes and deciles of core RNOA. The high Q-score group consistsof the top third of firms in that industry-core-RNOA ranking, the medium Q-score group consists of the middle third, and the low Q-score group consists of the bottomthird of firms on Q-scores. Core profit margin is core operating income before depreciation and amortization divided by sales. Asset turnover is sales divided by netoperating assets. The core profit margin and asset turnover numbers in the table are the means over 21 yearly median numbers computed over the years 1976–1996.


project profitability. The finding that the decline in investment is temporary argues againstthis interpretation. Furthermore the conservatism effect is at work, ceteris paribus, by theconstruction of the accounting. In any case, an anticipated change in profitability from itscurrent level for any reason indicates unsustainable current earnings, and the results indicatethe Q-score identifies unsustainable earnings irrespective of the cause.

SurvivorshipWe have presented the core RNOA for high- and low-Q groups for Years �1 to �5 as

those that an investor might predict by observing Q-scores in Year 0. However, the analysisis of ex post outcomes, and we included firms in the analysis for Years �1 to �5 only ifthey survived to the respective years. Therefore, we do not observe realizations of coreRNOA for the nonsurvivors that would have been predicted ex ante. This is a problem onlyif survivorship differs over high- and low-Q groups.

The number of firms included in the analysis for each of these years appears at thebottom of Table 2.16 The survival rate for high-Q firms in Years �1 to �5 were 95.9percent, 92.5 percent, 89.3 percent, 86.5 percent, and 83.8 percent, respectively—very sim-ilar to the corresponding rates for low-Q firms of 96.3 percent, 92.7 percent, 89.6 percent,86.7 percent, and 83.8 percent, respectively. Of the high-Q firms that did not survive, 94.6percent failed to survive because of merger and acquisition; the corresponding percentagefor low-Q firms was only 82.5 percent. Thus, low-Q firms had a high percentage of firmsthat terminated for reasons other than mergers, such as liquidation, delisting, or suspensionof trading.

VI. THE RETURNS TO QUALITY ANALYSISWe now investigate whether the stock market prices stocks as if it appreciates the

differential quality of earnings indicated by our Q-score. We take investment positions instocks in the sample period based on their Q-scores and observe whether these positionsearn differential returns, adjusting for conjectured risk differentials.

In each year from 1976 to 1995, and within each two-digit SIC industry code, weranked the firms into ten equal-sized portfolios based on their Q-scores. Year 0 is the yearwe formed the portfolios. The portfolio formation date is three months after fiscal year-end, by which time the firm must file its annual reports with the SEC. We then calculatedmean buy-and-hold returns for each portfolio for each year, �2 to �5, relative to this date.The computed returns include delisting returns for nonsurvivors. Table 5 reports mean rawreturns and size-adjusted returns during the 20 years that the positions were taken. Thegrouping within industry controls for operating risk (to some degree), and the size adjust-ment controls for the stock return ‘‘size effect’’ that researchers (e.g., Fama and French1992) conjecture is a premium for risk. We computed the size-adjusted returns bysubtracting the raw (buy-and-hold) return on a size-matched, value-weighted portfolioformed from size-decile groupings supplied by CRSP.

The mean returns for years �1 to �5 in Table 5 are positively related to Q. The size-adjusted returns for portfolios 7–10 (the high-Q portfolios) are positive, and those for Year�1 (in boldface) are significantly different from 0. Those for the low-Q portfolios 1–4 areconsiderably lower, and many are negative. The difference between the mean raw returns

16 The number of firms at the bottom of Table 2 for Years �2 to �5 overstates survivorship. Because the dataperiod ended in 1997, data were not available for all of the Years �2 to �5 for firms with Year 0 rankings in1993–1996. The survivorship rates that follow exclude the observations unavailable at the end of the sampleperiod.


TABLE 5Mean Percentage Annual Stock Returns for Portfolios Formed on Q-Scores 1976–1995a

Year Relative to Year Q-Score Is Calculated (Year 0)

Q Portfolios �2 �1 0 1 2 3 4 5

Panel A: Raw Returns

Lowest Q 26.67 28.19 26.39 17.03 22.51 20.81 22.58 19.122 21.27 22.25 19.51 19.02 20.36 21.20 18.70 17.153 22.56 20.75 21.16 21.39 20.90 20.68 18.94 20.264 23.25 24.39 21.01 19.63 22.85 20.35 24.13 18.215 21.30 23.44 19.81 20.87 21.73 20.75 22.19 17.526 20.87 22.37 19.56 20.97 20.94 20.15 21.44 19.807 20.32 21.58 20.15 22.64 21.47 20.77 20.55 18.248 20.57 22.39 21.20 21.19 21.05 21.41 19.39 17.849 19.66 18.50 21.94 21.20 21.64 19.84 19.70 18.88

Highest Q 18.25 20.64 23.33 26.06 23.53 22.78 22.12 20.93

High – Low �8.41 �7.55 �3.06 9.03 1.02 1.98 �0.46 1.80

Significance 0.000 0.000 0.013 0.000 0.265 0.091 0.616 0.136

Panel B: Size-Adjusted Returns

Lowest Q 3.39 5.40 4.21 �3.17 �0.26 �0.09 �0.24 �0.062 0.08 0.72 �0.57 �0.66 0.48 1.58 �1.29 �0.223 2.33 0.72 1.21 1.72 1.35 1.05 �0.47 3.904 2.97 3.27 1.56 0.12 2.58 1.09 5.34 1.995 1.32 2.68 0.54 1.51 1.46 2.15 2.23 0.656 1.07 2.20 0.27 1.06 1.78 1.59 3.47 2.867 1.34 2.41 0.87 2.85 2.12 1.99 2.09 1.798 2.61 1.23 3.14 2.91 1.44 3.05 0.80 1.299 0.79 �0.12 2.10 2.76 2.40 1.02 0.39 2.06

Highest Q �0.42 0.19 2.34 5.78 1.80 2.38 2.63 2.90

High – Low �3.81 �5.21 �1.87 8.95 2.05 2.46 2.86 2.96

Significance 0.001 0.000 0.071 0.000 0.070 0.036 0.024 0.031

a We calculated buy-and-hold returns for each Q-score portfolio for each year, �2 to �5, in each scoring year,1976–1995. Panel A reports the mean returns over the 20 years. Panel B reports the mean size-adjusted returns,computed by subtracting the raw (buy-and-hold) return on a size-matched, value-weighted portfolio formed fromCRSP size-deciles.

The high-low return is the return from investing long in the highest Q portfolio and investing the same dollaramount short in the lowest Q portfolios, for 0 net investment.Significance tests are based on 5,000 replications of randomly assigning firms to Q portfolios. The significancelevels for the high-low return are the frequency of observing returns equal to the positive (negative) return on thehigh-low investment strategy, or higher (lower), in the 5,000 replications. For the size-adjusted returns in Panel B,returns in boldface are those where we observed returns equal to or greater than the return reported (or equal toor less than the return reported if negative) less than 5 percent of times in 5,000 replications with random assignmentof stocks to portfolios.


FIGURE 2Mean Size-Adjusted Return Differences between High-Q and Low-Q Portfolios in the Year

Following the Q-Scoring (Year t � 1), for Each Year, 1976–1995

Mean Size-Adjusted Return

-0.1

0

0.1

0.2

0.3

1976

1977

1978

1979

1980

1981

1982

1983

1984

1985

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

Year

Siz

e-ad

just

ed R

etu

rnfor the highest Q and lowest Q portfolios in Year �1 is 9.03 percent, or 8.95 percent on asize-adjusted basis. This is a return, before transaction costs, to a zero-net-investment strat-egy with the canceling of long and short positions in the highest- and lowest-Q portfolios.We assessed the statistical significance of these returns by randomly assigning firms to Qportfolios in 5,000 replications of the zero-net-investment strategy. The reported significancelevels are the relative frequencies of observing the actual mean differences, or higher, inthese replications. The results in Table 5 reveal that most of the return from going long onhigh-Q stocks and shorting low-Q stocks occurs in the first year after Year 0. Figure 2indicates that this strategy would have yielded a positive return before transaction costs inevery year except 1990.

Not only are the returns to the zero-net-investment strategy positive after Year 0, butthey are also negative prior to Year 0. The difference in returns between high and lowgroups in Years �2 to 0 and Years �1 to �5 correspond to differences in the changes incore RNOA graphed in Figure 1. Over Years –2 to 0, the average median-adjusted coreRNOA increases for low-Q firms (releasing reserves), compared to the decrease for high-Q firms (building up reserves), with a reversal of the pattern in Years �1 to �5. So thesereturn reversals are consistent with the market interpreting the firms’ reported earningsuncritically in Years �2 to 0, but correcting this mistake in subsequent years. It appearsthat the market fails, at least in part, to penetrate the earnings quality due to changes inreserves created by conservative accounting and changes in investment.

Sensitivity Tests and Further AnalysisImplementation of the Trading Strategy

The trading strategy, as implemented in Table 5, may suffer from a ‘‘peeking-ahead’’bias. We used Lev and Sougiannis’s (1996) estimated coefficients to capitalize and amortizeR&D, and these estimates use data from dates after we take positions in the stocks, at leastfor some years. Furthermore, in calculating the C-score, we substituted industry medianswhen certain subscores were missing, but median data are sometimes unavailable at the


ranking date due to different fiscal year-ends. The QB component of the Q-score alsoinvolves an industry comparison. Finally, because firms do not have the same fiscal year-ends, their Q-scores would not have been available at the same time for ranking relative toeach other.

Accordingly, we repeated the tests reported in Table 5 after modifying the researchdesign to eliminate each of these possible biases. We capitalized R&D, using a sum-of-the-year’s digits method over five years to amortize capitalized expenditures. We used the prioryear’s industry median for calculating C-scores, and, when forming portfolios, we comparedQ-scores only for firms with December 31 fiscal year-ends. With these modifications, thedifference in size-adjusted returns between high- and low-Q portfolios in Year �1 (withsignificance levels in parentheses) was 7.63 percent (0.000), and, for Years �2 to �5, thedifference was 3.36 percent (0.018), 2.97 percent (0.020), 0.36 percent (0.516), and 3.92percent (0.016), respectively, for the 58.8 percent of firm-years with December 31 fiscalyear-ends.17

Dealing with Possible Late FilersFor the analysis in Table 5 we took positions in stocks three months after fiscal year-

end, based on the assumption that annual reports are publicly available by that time. Al-though the SEC requires firms to file their financial reports within those three months, somefirms file late. Table 6 reports monthly mean size-adjusted returns for each month beginningfour months after fiscal year-end. The returns are for the low-Q and high-Q portfolios inTable 5, and for the difference between the two. It is clear from the returns for month 5onward that the Year �1 returns in Table 5 are not due solely to returns in the fourth monthafter the fiscal year-end, when firms may still be reporting.

Table 6 also reports the number of quarterly earnings announcements in each month.The returns to long positions in high-Q firms and the canceling of short positions in thelow-Q firms are relatively high in months 7 and 8 when second-quarter earnings for Year�1 would have been announced, and in months 10 and 11 when third-quarter earnings forthat year would have been announced. This, like similar investigations of how apparentmispricing corrects itself (e.g., Freeman and Tse 1989; Bernard et al. 1997), suggests themarket corrects its mispricing of forecasted earnings for Year �1 as those earnings arerealized. The association of the returns with earnings reporting also supports the interpre-tation that the predicted returns are attributable to mispricing rather than to rewarding forrisk. However, the association of the returns in Table 6 with earnings reporting months isimperfect; for months 4 and 5 when first quarter earnings would have been announced, thereturns are relatively low, and returns in some months with relatively few announcementsare positive and significant. Tests of returns around narrower windows surrounding subse-quent earnings announcements and analysts’ forecast revisions would be necessary to drawmore definitive conclusions.

Analysis of Potential Risk ProxiesRisk is nearly always a potential alternative explanation for predictable abnormal re-

turns. Most of the return differences between high-Q and low-Q groups occur in Year �1,

17 We also made a calculation for all firms irrespective of fiscal year-end, by forming portfolios every month basedon firms with fiscal year-ends three months before, and then weighting the monthly portfolios equally (investing$1 each month). The mean, size-adjusted return difference (significance level) between high- and low-Q port-folios in Year �1 was 15.27 percent (0.004) using equal weights, and 6.34 percent (0.072), 7.27 percent (0.056),�0.20 percent (0.460), and 5.79 percent (0.147) in Years �2 to �5, respectively. We obtained similar resultsby weighting monthly portfolios according to the number of firms in the month’s ranking.

Penman

andZ

hang—A

ccountingC

onservatismand

Earnings

Quality

259

TABLE 6Month-by-Month Size-Adjusted Returns after Fiscal Year-End for Portfolios Formed on Q-Scores, and the Number of Quarterly Earnings

Announcements in Each Montha

Month after Fiscal Year-End

4 5 6 7 8 9 10 11 12 13 14 15

Low-Q portfolio return 1.07 �0.14 0.39 0.02 �0.23 �0.45 �0.39 �0.50 �0.52 0.33 0.54 0.09High-Q portfolio return 1.16 0.09 0.03 0.94 0.50 0.05 0.71 0.49 0.38 1.33 0.91 0.28

High – Low return 0.09 0.23 �0.36 0.92 0.73 0.50 1.10 0.99 0.90 1.00 0.37 0.17

Number of quarterlyearnings announcements

2,330 1,127 36 2,138 1,133 32 2,190 1,072 22 687 1,739 693

a Returns are monthly size-adjusted returns for the lowest and highest Q portfolios in Table 5. The high – low return is the difference in the size-adjusted returns betweenthe high and low Q portfolios. Q is the Q-score. Returns in boldface are significantly different from 0, with significance assessed by observing less than 5 percent ofreturns greater than the reported amount (or less than the reported amount, if negative) in 5,000 replications with random assignment of stocks to portfolios. The numberof quarterly earnings announcements is the number of announcements made in the respective month after fiscal year-end.


suggesting a temporary correction to market inefficiency rather than a permanent effect thatone would expect if the differences were due to risk. However, the return differences arepositive in Years �2 to �5, and significantly so for the size-adjusted returns, so part of theobserved returns to the Q-strategy could be attributable to risk. The specification and mea-surement of risk premiums are elusive. Our design, which matches on industry and size,mitigates differences in risk across Q-groups. Furthermore, in untabulated analyses wefound that Q is not highly correlated with factors that Fama and French (1992) nominateas risk proxies: the mean, cross-sectional correlation between the Q-score and beta, book-to-market, leverage, and earnings-to-price are �0.00, �0.04, �0.09, and �0.02, respec-tively. Finally, in Table 7 we report the results of estimating Fama and Macbeth (1973)type cross-sectional regressions, using individual stocks, with a control for factors that Famaand French (1992) suggest are risk factors:

R � � � � � � � ln (M) � � ln(B/M) � � ln(LEV) � � (E(�) /P)i,t�1 0 1 i,t 2 i,t 3 i,t 4 i,t 5 i,t

� � (E/P dummy) � � Q � e ,6 i,t 7 i,t i,t

where:

Ri,t�1 � annual return in Year �1 after the Q-scoring (the year begins threemonths after fiscal year-end);

�i,t � Capital Asset Pricing Model beta;Mi,t � market value;

(B/M)i,t � book-to-market ratio;(LEV)i,t � leverage, calculated as book value of total assets to book value of

equity;(E(�) /P)i,t � earnings-to-price ratio, positive earnings only;

(E/P dummy)i,t � negative earnings dummy, 1 if earnings are negative, 0 otherwise;and

Qi,t � Q score in year t.

Results are in Table 7 with and without including the Q-score in the regression. Themean estimated coefficient on Q is significantly positive. We conclude that Q-scores forecastreturns in excess of those expected from commonly identified firm-risk factors.

Control for the Accrual AnomalyOne must be cautious in attributing abnormal returns to particular predictors; the cor-

relation between returns and a particular predictor may arise because that predictor is cor-related with another predictor. Sloan (1996) identified abnormal accruals as a predictor ofabnormal returns. Accruals are a different accounting phenomenon than the conservativeaccounting effect investigated here, but Q-scores and accruals might be correlated if man-agers use accruals as well as real investment to manage earnings.

Accordingly, we estimated the regressions in Table 7 including an accrual variable,defined (as in Sloan 1996) as the change in noncash current assets, less the change incurrent liabilities exclusive of short-term debt and taxes payable, less depreciation expense,all divided by average total assets. The estimated coefficient on the accrual variable was�0.424, with a significant t-statistic of �4.39 (consistent with Sloan’s [1996] analysis), andthe coefficient on the Q-score was 0.221, with a t-statistic of 2.65. When we defined theaccrual variable (more appropriately) as the difference between net income and cash from


TABLE 7Mean Coefficient Estimates for Regressions of Year-Ahead Returns on Q-Scores and Factors

to Control for Risk 1976–1995a

R � � � � � � � ln(M) � � ln(B/M) � � ln(LEV)i,t�1 0 1 i,t 2 i,t 3 i,t 4 i,t

� � (E(�) /P) � � (E/P dummy) � � Q � e5 i,t 6 i,t 7 i,t i,t

Variable Definition

Q-Score Includedin the Regression

Coefficients t-statistics

Q-Score Not Includedin the Regression

Coefficients t-statistics

Constant Intercept 0.2601 5.680** 0.2740 6.017**

�i,t Beta �0.0104 �0.573 �0.0009 �0.505

Ln(M)i,t Size �0.0192 �3.333** �0.0185 �3.213**

Ln(B/M)i,t Book-to-market 0.0276 1.933 0.0251 1.724

Ln(LEV)i,t Leverage 0.0491 2.634* 0.0430 2.227*

(E(�) /P)i,t E /P 0.2878 1.435 0.2017 1.033

(E/P dummy)i,t Negative E dummy �0.0109 �0.322 �0.0083 �1.517

Qi,t Q-score 0.2873 3.834**

*, ** Denotes significance at the 0.05 and 0.01 levels, respectively, using a two-tailed test.a Regression variables are as follows:

Ri,t�1 � annual return in Year �1 after the Q scoring; the year begins three months after fiscal year-end;

�i,t � Capital Asset Pricing Model beta;Mi,t � market value;

(B/M)i,t � book-to-market ratio;(LEV)i,t � leverage, calculated as book value of total assets to book value of equity;

(E(�) /P)i,t � earnings-to-price ratio, positive earnings only;(E /P dummy)i,t � negative earnings dummy, 1 if earnings are negative, 0 otherwise; and

Qi,t � Q score in year t.We estimated regression coefficients for each month of the sample period, where firms in the estimation for eachmonth are those whose fiscal years ended three months earlier. Coefficients reported are mean regression estimatesover 240 monthly cross-sectional regressions from 1976 to1995.

operations (divided by average total assets), the estimated coefficient on accruals was�0.302, with a t-statistic of �3.46, and that on the Q-score was 0.260, with a t-statistic of3.12. We therefore conclude that the effect of the Q-score is incremental to that of accrualsrelative to total assets.

Analysis of SubscoresWe repeated the return tests substituting Q-scores calculated from C subscores: (1)

inventory accounting, (2) R&D, and (3) advertising. Substituting each subscore in separateregressions yielded results quite similar to those for the composite score. The mean, size-adjusted return for the zero-net-investment strategy in Year �1 for the R&D subscore was7.33 percent (significance level 0.000), 5.22 percent (0.000) for the inventory subscore, and4.24 percent (0.000) for the advertising subscore. However, when we estimated regressionsof the form in Table 7, but substituted the set of all three Q subscores for the overall Q-score in a single regression, the coefficient on the inventory subscore was not statistically


significant. This result suggests that much of the information about future returns in theinventory subscore is captured by the R&D and advertising subscores.

Control for Growth in Net Operating AssetsWe also estimated regressions similar to those in Table 7 including both the Q-score

and growth in net operating assets as explanatory variables to see whether the result mightbe due to growth in net operating assets (which affects the QA score). The coefficient onthe growth variable was �0.066 (with a t-statistic of �2.42) and that on the Q-score was0.218 (with a t-statistic of 2.70). Thus, Q continues to play an incremental role in fore-casting stock returns, after controlling for growth in net operating assets.

Analysis of QA and QB

We also repeated the tests basing investment positions on either QA or QB. We wereconcerned that QB, a levels measure of the conservative accounting reserve (rather than achange variable like QA) might be correlated with risk factors or with other factors thatpredict returns. For example, reserves (and thus QB) depend on the accumulated amount ofR&D investment relative to net operating assets and Chan et al. (2000) indicate that themarket undervalues R&D. However, the zero-net-investment strategy using QA returned asize-adjusted return in Year �1 of 8.19 percent (significance level 0.000), and 1.41 percent(0.160) in Year �2, and the QB-based strategy earned 5.69 percent (0.000) in Year �1 and2.37 percent (0.047) in Year �2.

VII. CONCLUSIONCommentators sometimes claim that the practice of conservatism in accounting pro-

duces higher-quality earnings. Conservatism yields lower earnings, they say, and so primafacie these conservative earnings are of higher quality. However, conservative accountingwith investment growth depresses earnings and accounting rates of return, and creates un-recorded reserves. Firms slowing investment release these reserves, creating earnings andhigher rates of return. If the change in investment is temporary, then the effects on earningsand rates of return are temporary, leading to lower-quality—or less sustainable—earnings.

Using a constructed index of accounting conservatism and a constructed index of thequality of earnings, this study identifies poor-quality earnings that result from changes ininvestment coupled with conservative accounting. The quality index forecasts how futurecore return on net operating assets is likely to change from current levels, so our indexprovides an analysis tool to identify low-quality earnings.

The study also shows that quality scores incrementally predict future stock returns,before transaction costs, over the 1976–1995 period, after controlling for measures com-monly conjectured as risk proxies, and after controlling for growth in net operating assetsand for accruals. These results suggest that the stock market did not penetrate the qualityof earnings of firms with conservative accounting during the sample period. Accordingly,with the caveat that risk may not have been adequately controlled, the study indicates thata quality analysis of the form in the paper would have been rewarded with abnormal returnsin the stock market during the sample period.

REFERENCESBaber, W., P. Fairfield, and J. Haggard. 1991. The effect of concern about reported income on dis-

cretionary spending decisions. The Accounting Review 66 (October): 818–829.Basu, S. 1997. The conservatism principle and the asymmetric timeliness of earnings. Journal of

Accounting and Economics 24: 3–37.


Beaver, W., and E. Engel. 1996. Discretionary behavior with respect to allowances for loan lossesand the behavior of security prices. Journal of Accounting and Economics 22: 177–206.

———, and S. Ryan. 2000. Biases and lags in book value and their effects on the ability of the book-to-market ratio to predict book return on equity. Journal of Accounting Research 38 (Spring):127–148.

Bernard, V., J. Thomas, and J. Wahlen. 1997. Accounting-based stock price anomalies: Separatingmarket inefficiencies and risk. Contemporary Accounting Research (Summer): 89–136.

Bublitz, B., and M. Ettredge. 1989. The information in discretionary outlays: Advertising, research,and development. The Accounting Review 64 (January): 108–124.

Chan, L., J. Lakonishok, and T. Sougiannis. 2000. The stock market valuation of research and de-velopment expenditures. Journal of Finance 56 (December): 2431–2451.

Fairfield, P., J. Whisenant, and T. Yohn. 2001. Accrued earnings and growth: Implications for earningspersistence and market mispricing. Working paper, Georgetown University.

———, and T. Yohn. 2001. Using asset turnover and profit margin to forecast changes in profitability.Review of Accounting Studies 6 (December): 371–385.

Fama, E., and J. Macbeth. 1973. Risk, return and equilibrium—Empirical tests. Journal of PoliticalEconomy 81 (May/June): 607–636.

———, and K. French. 1992. The cross-section of expected stock returns. Journal of Finance 47:427–465.

Feltham, J., and J. Ohlson. 1995. Valuation and clean surplus accounting for operating and financialactivities. Contemporary Accounting Research 11 (2): 689–731.

Freeman, R., and S. Tse. 1989. The multiperiod information content of accounting earnings: Confir-mations and contradictions of previous earnings reports. Journal of Accounting Research 27(Supplement): 49–79.

Gjesdal, F. 1999. A steady state growth valuation model: A note on accounting and valuation. Workingpaper, Norwegian School of Economics, Bergen, Norway.

Greenball, M. 1969. Appraising alternative methods of accounting for accelerated tax depreciation:A relative accuracy approach. Journal of Accounting Research 7: 262–289.

Hall, B. 1993. Industrial research during the 1980s: Did the rate of return fall? Brookings Papers onEconomic Activity, Microeconomics: 289–344.

Healy, P. 1985. The effect of bonus schemes on accounting decisions. Journal of Accounting andEconomics 7: 85–107.

———, and J. Wahlen. 1999. A review of the earnings management literature and its implicationsfor standard setting. Accounting Horizons 13 (December): 365–383.

Jones, J. 1991. Earnings management during import relief investigations. Journal of Accounting Re-search 29 (Autumn): 193–288.

Lee, C., J. Myers, and B. Swaminathan. 1999. What is the intrinsic value of the Dow? Journal ofFinance 54: 1693–1741.

Lev, B., and T. Sougiannis. 1996. The capitalization, amortization, and value-relevance of R&D.Journal of Accounting and Economics 21: 107–138.

Moehrle, S. 2002. Do firms use restructuring charge reversals to meet earnings targets? The AccountingReview 77 (April): 397–414.

Mulford, C., and E. Comiskey. 1996. Financial Warnings. New York, NY: John Wiley & Sons.Nissim, D., and S. Penman. 2001. Ratio analysis and equity valuation: From research to practice.

Review of Accounting Studies 6: 109–154.Ou, J., and S. Penman.1989. Financial statement analysis and the prediction of stock returns. Journal

of Accounting and Economics 14: 295–329.Penman, S., and T. Sougiannis. 1998. A comparison of dividend, cash flow, and earnings approaches

to equity valuation. Contemporary Accounting Research 15: 343–383.———. 2001. Financial Statement Analysis and Security Valuation. New York, NY: McGraw-Hill.Perry, S., and R. Grinaker. 1994. Earnings expectations and discretionary research and development

spending. Accounting Horizons (December): 43–51.


Sloan, R. 1996. Do stock prices fully reflect information in accruals and cash flows about futureearnings? The Accounting Review 71: 289–315.

Teoh, S., T. Wong, and G. Rao. 1998. Are accruals during initial public offerings opportunistic?Review of Accounting Studies 3: 175–208.

Zhang, X. 2000. Conservative accounting and equity valuation. Journal of Accounting and Economics29: 125–149.

———. 2001. Accounting conservatism and the analysis of line items in earnings forecasting andvaluation. Working Paper, University of California, Berkeley.

Documents

PENMANN_ZHANG Accounting Conservatism, Quality Earning & Stock Return