excess returns to r&d-intensive firms

Review of Accounting Studies, 7, 133–158, 2002©C 2002 Kluwer Academic Publishers. Manufactured in The Netherlands.

Excess Returns to R&D-Intensive Firms

DENNIS CHAMBERSUniversity of Illinois at Urbana-Champaign

ROSS JENNINGSUniversity of Texas at Austin

ROBERT B. THOMPSON IIAmerican University

Abstract. Recent studies indicate that both current R&D investment levels and current or recent changes in R&Dinvestment are positively associated with subsequent excess (risk-adjusted) stock returns. The tentative explanationoffered for these results is that shares of R&D-intensive firms are “mispriced” because investors fail to see throughearnings distortions caused by conservative accounting for R&D costs. However, an alternative explanation is thatconventional controls for risk do not completely capture the riskiness of R&D-intensive firms, causing measuredexcess returns for these firms to be biased upward. This study provides evidence useful for distinguishing betweenthe mispricing and risk explanations for R&D-related excess returns. Overall, our empirical results suggest thatthe positive association between R&D investment levels and excess returns is more likely to result from failure tocontrol adequately for risk than from mispricing. On the other hand, our results do not rule out the possibility ofa second source of excess returns that are due to mispricing and that are associated with changes in the level ofR&D investment.

Keywords: research and development, mispricing, market efficiency, conservative accounting

JEL Classification: M41, G14, O32

Previous research provides consistent evidence of a contemporaneous positive associationbetween firms’ research and development outlays and both share prices and returns. Thisevidence suggests that investors view R&D outlays as investments that are expected toproduce future benefits, and that they take such benefits into consideration when pricingshares. However, several recent studies also indicate that a strategy of investing in R&D-intensive firms earns excess (risk-adjusted) returns. Lev and Sougiannis (1996) and Chanet al. (2001) report a positive association between measures of the level of R&D investmentand subsequent excess returns, while Lev et al. (2000) and Penman and Zhang (2002) reporta positive association between current or recent changes in R&D investment and subsequentexcess returns. These studies tentatively conclude that investors are misled by conservativeaccounting for R&D costs, which tends to understate earnings when R&D investment isincreasing and to overstate earnings when R&D investment declines.

An alternative explanation for excess returns to R&D-intensive firms is that these returnsare compensation for risk-bearing. Under this explanation, either prior studies do not controlcompletely for “known” risk characteristics, or there is an additional risk characteristicassociated with R&D activities for which investors are being compensated. Following Famaand French (1992, 1993), previous studies control for systematic return behavior associated

134 CHAMBERS, JENNINGS AND THOMPSON

with firm size (market capitalization) and book-to-market ratio, both of which are thoughtto be indicators of equity risk. However, these controls are based on empirical results forlarge samples of firms and are unlikely to perfectly control for risk for all firms and in alltime periods.

In this study, we investigate whether the measured excess returns to R&D-intensive firmsreported in previous studies are more likely due to mispricing by investors or to compen-sation for bearing risk. The evidence we provide contributes both to the general debateover the informational efficiency of U.S. securities markets and to the related debate overthe appropriateness of current financial reporting policy for R&D activities. The excess re-turns reported in prior studies result from implementable trading strategies based on readilyavailable information, and thus would represent a clear example of market inefficiency ifthey are due to mispricing. At the same time, any inefficiency that exists may be relatedto current R&D accounting rules, which require preparers to expense R&D outlays imme-diately even though these outlays are intended to create economic assets. If the resultingfinancial statements mislead investors, the excess returns to R&D firms may be reduced oreliminated by alternative R&D accounting policies that better reflect the expected futurebenefits of R&D activities.

Our analysis, based on all NYSE-, ASE-, and NASDAQ-traded firms over the period1979–1998 that meet the minimal data requirements for the study, proceeds in three stages.First, we confirm the finding of Lev and Sougiannis (1996) and Chan et al. (2001) that thereis a positive association between level of R&D investment and subsequent excess returns.We conduct additional analysis to confirm that excess returns to R&D-intensive firms arestatistically significant, that they are not induced by our controls for risk, and that they arenot an artifact of our measure of the level of R&D investment.

The second stage of our analysis examines whether excess returns associated with the levelof R&D investment are consistent with compensation for risk-bearing. We find that positiveaverage excess returns to R&D-intensive firms persist for up to ten years when sampleobservations are aligned in “event” time, but vary greatly from year to year in calendartime. This behavior is consistent with the risk explanation. We also find that variation inanalysts’ forecasts of future earnings and variation in actual future earnings, both consideredindicators of overall firm risk, are much larger for R&D-intensive firms than for firms withrelatively low levels of R&D investment.

The third stage of our analysis investigates whether mispricing explains the positive as-sociation between levels of R&D investment and subsequent excess returns. Overall, ourevidence suggests that this is not the case. The mispricing scenario offered in Lev et al.(2000) and Penman and Zhang (2002) predicts that excess returns are negative (positive)in periods of growth (decline) in R&D investment because investors are misled by re-ported earnings numbers that are “too low” (“too high”). Consistent with this scenario,we observe a negative association between excess returns and contemporaneous changesin R&D investment. However, controlling for changes in R&D investment does not atten-uate the strong positive association between excess returns and R&D investment levels.We also find that analysts’ earnings forecasts are at least as optimistic for R&D-intensivefirms as for those with little or no R&D investment, and that excess returns for R&D-intensive firms followed by many analysts are of about the same magnitude as those forfirms followed by few or no analysts. Taken together, these results suggest that the positive

EXCESS RETURNS TO R&D-INTENSIVE FIRMS 135

association between future excess returns and current R&D investment levels is not ex-plained by mispricing.

In the next section of the paper we review relevant prior research. Section two describesour sample, specifies variables used in the study, and provides various descriptive statistics.Section three documents the relation between R&D intensity and excess returns reported inprior studies. Sections four and five present evidence related to the risk and mispricing ex-planations for excess returns to R&D-intensive firms, respectively. Section six summarizesour results and offers some concluding remarks.

1. Prior Research

Numerous studies in economics, finance, and accounting suggest that investors view R&Dexpenditures as investments that are expected to produce future benefits. One set of stud-ies, using a variety of methodologies, indicates that firms’ market values are positivelyrelated to R&D outlays (Ben-Zion, 1978; Hirschey and Weygandt, 1985; Megna andKlock, 1993; Shevlin, 1991; Sougiannis, 1994; and Hand, 2001). Other studies provideevidence that changes in market values are positively related both to innovations in R&Dexpenditures (Bublitz and Ettredge, 1989) and to announcements related to R&D activity(Woolridge, 1988; Chan et al., 1990; and Austin, 1993). Finally, several studies provideevidence that capitalizing and amortizing R&D costs on a pro forma basis results in sum-mary accounting measures that are more closely associated with security prices and securityreturns than those based on the current requirement to expense R&D costs when incurred(Loudder and Behn, 1995; Lev and Sougiannis, 1996; Monahan, 1999; and Chamberset al., 2002).

Despite this evidence that current share prices reflect expected benefits from R&D activi-ties, several recent studies suggest that prices may not fully reflect the information containedin past R&D expenditures. These studies fall into two groups. The first group reports a pos-itive association between current measures (based on public information) of the level ofR&D investment and future excess returns. Lev and Sougiannis (1996, 1999) report that ameasure of unrecognized R&D assets based on current and past R&D outlays is positivelycorrelated with security returns over the 12 months subsequent to release of current periodaccounting information. Similarly, Chan et al. (2001) provide evidence that firms with rel-atively high current R&D expenditures earn excess returns over the subsequent three years.The second group of studies reports a positive association between measures of current orrecent change in the level of R&D investment and future excess returns. Lev et al. (2000)find that future excess returns to R&D firms are positively related to several measures ofthe relative growth rate for R&D outlays in prior periods. Penman and Zhang (2002) reportthat firms whose ratios of unrecorded R&D assets to net operating assets have increased(decreased) in the current year earn positive (negative) excess returns in the following year.In measuring excess returns, all of these studies control for firm characteristics that arecommonly thought to be associated with risk.1

The last two studies can be viewed as providing a direct examination of whether investorsare misled by conservative accounting for R&D outlays. To the extent that R&D investmentscreate economic assets, the current “expense when incurred” accounting treatment for R&D


has the effect of understating earnings and related indicators of value when a firm increasesits R&D investment, and the reversing effect of overstating earnings and related indicators ofvalue when the firms R&D investment is decreased. Lev et al. (2000) and Penman and Zhang(2002) explicitly discuss this possibility and provide empirical results that suggest thatinvestors are misled by current R&D accounting. The first two studies, Lev and Sougiannis(1996) and Chan et al. (2001), can also be viewed from this perspective. However, theyexamine whether future excess returns are associated with the level of R&D investment,which is not necessarily related to the income statement bias of conservative accounting forR&D outlays.

While these studies can all be interpreted to indicate that mispricing accounts for theexcess returns to R&D-intensive firms, some of the evidence they present can be viewed asconsistent with the risk explanation. For example, the combined evidence from these studiessuggests that excess returns are associated with both the change in R&D investment, a directmeasure of the income statement bias of conservative accounting for R&D, and with thelevel of R&D investment, which is a measure of more general R&D activity. This suggeststhat either the level of R&D investment and change in R&D investment are highly correlated,and both reflect relevant bias in R&D accounting, or there are two separate sources of excessreturns to R&D firms, one associated with financial statement bias and the other with theextent of R&D activity. In addition, the R&D-related excess returns reported by Chan et al.(2001), Lev et al. (2000), and Penman and Zhang (2002) persist for three to five years andshow little sign of diminishing. This suggests that either these returns are due to risk, or theyare due to mispricing that is both substantial and very slow to reverse. Thus, prior researchdoes not provide clear evidence on whether excess returns to R&D-intensive firms are dueto mispricing exclusively, or whether the risk explanation can account for all or part of theexcess returns.

To address this issue, we focus on the positive association between the level of R&Dinvestment and subsequent excess returns documented by Lev and Sougiannis (1996) andChan et al. (2001). We first provide evidence on the extent to which this relation can beexplained as compensation for risk-bearing. We then control for potential excess returnsassociated with changes in R&D investment and investigate the extent to which excessreturns to the level of R&D investment can be explained by mispricing.2

2. Sample Selection and Variable Definitions

2.1. The Sample

This study is based on firms in the Compustat PST, full coverage, and merged researchannual databases for any year from 1979 to 1998. We identified 89,419 firm-years thatsatisfied a minimal set of Compustat data requirements.3 This “main” sample includes13,442 firms distributed across 73 two-digit (442 four-digit) SIC codes. As we explainbelow, some of our analyses are based on analysts’ earnings forecasts from the I/B/E/Sdatabase, and these data are not widely available prior to 1984. These analyses are conductedusing a “reduced” sample that consists of the 72,317 firm-years in our main sample fromthe period 1984–1998.


2.2. Descriptive Statistics

For purposes of subsequent analysis, we divide the firm-years in our main sample into twogroups—those with an unrecognized R&D asset in the current year (“R&D firms”) and thosewithout an unrecognized R&D asset in the current year (“non-R&D firms”). FollowingLev et al. (2000) and Chan et al. (2001), we estimate the net R&D asset (and relatedR&D amortization) that would have been reported if all R&D outlays were capitalized andamortized over five years, rather than expensed immediately. Thus, the pro forma R&Dasset at the end of year t (RDASSETt) and R&D amortization for year t (RDAMORTt) aregiven by

RDASSETt = RDEXPt + 0.8(RDEXPt−1) + 0.6(RDEXPt−2)

+ 0.4(RDEXPt−3) + 0.2(RDEXPt−4)

RDAMORTt = 0.2(RDEXPt−1 + RDEXPt−2 + RDEXPt−3 + RDEXPt−4 + RDEXPt−5)

where RDEXPt is the R&D outlay reported as expense for year t under current accountingrules.4

Table 1 reports the number of non-R&D and R&D observations for each year of our20-year study period. Over the 20 years as a whole, 35,841 firm-years, or about 40 percentof the sample, are classified as R&D observations. The proportion of R&D firms in thesample varies across years, but has no obvious trend. For R&D firms only, Table 1 alsoreports year-by-year averages and medians of the ratio of R&D expenditures to sales andof the ratio of the pro forma R&D asset to reported total assets. These ratios tend to risethroughout the study period, indicating that R&D activities have become more significantover the past two decades.

Table 2 presents additional descriptive statistics for the R&D firms in our main sample.The first two rows report selected percentiles of the distribution of reported R&D expense(RDEXP), scaled by current net income and current sales. These numbers indicate that R&Dexpenditures vary considerably across the sample, and are substantial in relation to earningsand sales for many firms in the sample. The third and fourth rows of the table report per-centiles of the distribution of pro forma R&D amortization (RDAMORT), again scaled bycurrent sales and current earnings. Scaled values of RDAMORT are generally smaller in mag-nitude than scaled values of RDEXP, suggesting that R&D outlays are growing through timefor most firms in the sample. The last two rows of the table, which summarize the distributionof R&D assets (RDASSET) scaled by reported common stockholders’ equity and by totalassets, suggest considerable variation across the sample in unrecognized R&D assets and in-dicate that such assets may be material for a large proportion of firms in the R&D subsample.

3. Relation between Excess Returns and R&D Activity

In this section we examine excess returns for groups of firms based on two alternativemeasures of R&D activity, the ratio of current period R&D assets to market value of equity(Lev and Sougiannis, 1996) and the ratio of current period R&D assets to current periodsales.


Table 1. Annual sample size and average and median of ratio of R&D expenditure to sales and ratio of pro formaR&D assets to total assets.

R&D Expenditures R&D AssetsSample Size to Sales∗ to Total Assets∗

Year Non-R&D R&D Avg. Median Avg. Median

1979 1,771 1,233 0.05 0.01 0.08 0.051980 1,871 1,270 0.07 0.02 0.08 0.051981 2,042 1,388 0.20 0.02 0.09 0.061982 2,109 1,427 0.14 0.03 0.12 0.081983 2,333 1,658 1.96 0.03 0.13 0.08

1984 2,377 1,721 0.43 0.04 0.14 0.091985 2,379 1,716 0.31 0.04 0.17 0.101986 2,530 1,801 0.39 0.04 0.18 0.111987 2,717 1,846 0.36 0.04 0.18 0.111988 2,626 1,813 0.44 0.04 0.21 0.11

1989 2,597 1,767 0.62 0.04 0.21 0.121990 2,598 1,751 0.59 0.04 0.23 0.121991 2,674 1,816 1.33 0.04 0.21 0.121992 2,800 1,958 2.30 0.05 0.21 0.131993 3,680 2,147 1.58 0.05 0.23 0.14

1994 3,957 2,255 2.74 0.05 0.26 0.131995 3,960 2,418 2.49 0.05 0.25 0.131996 4,128 2,731 1.83 0.06 0.25 0.141997 4,055 2,763 2.33 0.07 0.29 0.151998 374 362 1.10 0.06 0.29 0.15

Full sample—1979–1998

Total 53,578 35,841Avg. 2,679 1,792 1.06 0.04 0.19 0.11

Reduced sample—1984–1998

Total 43,452 28,865Avg. 2,897 1,924 1.26 0.05 0.22 0.12

∗For observations in the R&D subsample only.

3.1. Measuring Excess Returns

For each of the 89,419 observations in our main sample, we define the investment date as thefirst day of the fifth month following the firm’s fiscal year end, and calculate excess returnsfor each of the three consecutive twelve-month periods following that date. As in previousstudies that have reported excess returns to R&D-intensive firms, we measure excess returnsusing procedures described in Fama and French (1993). We construct 25 value-weighted,annually rebalanced portfolios that vary according to both market capitalization (Size) andbook-to-market ratio (BVE/MVE).5 Monthly returns to a given Size-BVE/MVE portfolioserve as benchmark “expected” returns for observations in our sample that are similar inmarket capitalization and book-to-market to firms in that portfolio. On the investment date,we match a given sample firm-year to its Size-BVE/MVE control portfolio by comparingcurrent levels of market capitalization and book-to-market for the observation to benchmark


Table 2. Distribution of reported R&D expense, pro forma R&D amortization, and Pro Forma R&D assets—R&Dsubsample, 1979–1998.

Percentile

Variable 10 25 50 75 90

R&D Expense/net income −0.92 −0.17 0.19 0.80 1.99

R&D Expense/sales 0.00 0.01 0.04 0.10 0.26

R&D Amortization/net income −0.53 −0.06 0.11 0.45 1.28

R&D Amortization/sales 0.00 0.01 0.02 0.05 0.14

R&D Assets/BV of owners’ equity 0.03 0.08 0.20 0.43 0.87

R&D Assets/total assets 0.01 0.04 0.11 0.24 0.45

The table reports selected percentiles of the indicated distributions for 35,841 firms-years in the R&D firmsubsample.

R&D Expense is reported R&D expense for the current year.R&D Amortization is the amortization that would have been reported if R&D expenditures were capitalized and

amortized over five years beginning in the year after the expenditures were made.R&D Assets is the asset that would have been reported if R&D expenditures were capitalized and amortized

over five years beginning in the year after the expenditures were made.

portfolio cutoffs for the preceding June 30.6 To insure that risk adjustments are current, weupdate this assignment on June 30 of each subsequent year. The measured annual excessreturn for sample observation i in year t following the investment date is computed as:

ERit =12∏

k=1

(1 + Rik) −12∏

k=1

(1 + Rpk)

where Rik is firm i’s raw (cum-dividend) return for month k, and Rpk is the month k returnto the relevant Size-BVE/MVE benchmark portfolio.7

3.2. Future Excess Returns and Measures of R&D-Intensity

Table 3 reports three years of average annual post-investment excess returns for non-R&Dfirm-year observations and for five roughly equal-sized groups of observations based onthe ratio of current period R&D assets to market value of equity (panel A) and on theratio of current period R&D assets to sales (panel B). The groups are only roughly equalin size because they are formed using quintile cutoffs calculated in the prior year. Thefirst column of each panel reports average excess returns for non-R&D observations.8 Forthis group, returns are no larger than half of one percent in any of the three years followinginvestment, suggesting that our procedures to control for risk have been reasonably effectivefor observations in this subsample.

The remaining columns in panel A indicate that excess returns are increasing in the ratioof reported R&D assets to market value of equity. For each of the three years followingthe investment date, average excess returns increase monotonically across the five groups.For the three years as a whole, the average annual excess return for group 5 is 6.9 percent,


Table 3. Average annual excess returns for firm-years grouped by measures of R&D activity and capitalexpenditures—1979–1998.

Year FollowingA. Groups Based on R&D Assets/MVE

Investment Date Non-R&D 1 (Low) 2 3 4 5 (High)

1 0.001 −0.018 −0.004 0.022 0.028 0.078(0.546) (−2.391) (−0.615) (2.840) (3.044) (7.145)

2 0.004 −0.027 −0.008 0.024 0.052 0.070(1.378) (−3.342) (−1.052) (3.188) (5.429) (6.241)

3 0.004 −0.012 0.014 0.023 0.046 0.060(1.335) (−1.424) (1.686) (2.715) (4.294) (5.423)

Avg. 0.003 −0.019 0.001 0.023 0.042 0.069

Year FollowingB. Groups Based on R&D Assets/Sales

Investment Date Non-R&D 1 (Low) 2 3 4 5 (High)

1 0.002 −0.003 0.007 0.024 0.058 0.025(0.604) (−0.416) (0.979) (3.185) (6.372) (2.276)

2 0.004 −0.001 0.012 0.031 0.038 0.036(1.414) (−0.197) (1.569) (3.880) (4.045) (3.092)

3 0.005 0.010 0.016 0.037 0.032 0.041(1.548) (1.207) (2.121) (4.178) (3.352) (3.287)

Avg. 0.003 0.002 0.011 0.031 0.043 0.034

Year FollowingC. Groups Based on Capital Expenditures/MVE

Investment Date CAPEX = 0 1 (Low) 2 3 4 5 (High)

1 −0.003 0.006 0.019 0.023 −0.001 −0.021(−0.273) (1.032) (3.810) (4.230) (−0.125) (−3.371)

2 −0.013 −0.004 0.022 0.007 0.010 0.007(−1.111) (−0.626) (3.705) (1.339) (1.841) (1.211)

3 −0.013 −0.007 0.013 0.017 0.017 0.012(−1.306) (−1.118) (2.184) (3.086) (2.828) (1.890)

Avg. −0.011 −0.002 0.018 0.016 0.009 0.000

R&D Assets is the asset that would have been reported if R&D expenditures were capitalized and amortized overfive years beginning in the year after the expenditures were made.

MVE is the market value of equity at the end of the fourth month following the fiscal year end.Groups are formed on the basis of each ratio using quintile cutoffs calculated in the prior year. Returns are

computed beginning on the “investment date,” which is the first day of the fifth month after fiscal year end. Excessreturns are computed for each observation, for each year subsequent to the investment date, and are averagedacross all firm-years in the same R&D group.

The annual excess return is the difference between the firm’s actual return for that year and the return on thefirm’s relevant “control” portfolio based on size and the ratio of book value of equity to market value of equity.

We construct control portfolios as described in Fama and French (1993). Each June 30 we assign all stocks inthe CRSP and COMPUSTAT data bases to one of five groups based on market value of equity (Size). We thenindependently assigned all stocks to one of five groups based on the ratio of their “adjusted” book value of equity tomarket value of equity (BVE/MVE), where book value of equity is “adjusted” to include our estimate of the firm’sR&D assets. In assigning firms to size groups, breakpoints are based on NYSE size quintiles. The intersection ofthese two grouping procedures yields 25 Size-BVE/MVE groups. For each Size-BVE/MVE group, we computedthe average value-weighted return for each month during that July–June fiscal year.


compared with −1.9 percent for group 1. Panel B reports a similar pattern, but with smallerexcess returns, for groups based on the ratio of current period R&D assets to sales. Thesereturns generally increase across the five groups for each of the three years, with the excep-tion of group five whose average returns are smaller than those for group four in both thefirst and second years after the investment date and on average across the three years.9

The t-statistics reported in Table 3 indicate that average excess returns for groups 3–5 aresignificantly positive at the one-percent level. However, to the extent that sample observa-tions are not independent, these t-statistics may be overstated. To address this possibility,we constructed an additional test of statistical significance that does not depend on distri-butional assumptions about the sample. For each Compustat year during our study period,we chose Nt firm-years at random from the full sample, where Nt is the average number offirm-years in that year’s five R&D groups. After combining these firm-years into a singlegroup, we then computed average excess returns for each of the three 12-month periodsfollowing the investment date. We repeated this process 1,000 times to generate an empiricaldistribution of excess returns to samples that have the same sample size and the same timedistribution as the R&D groups in Table 3, but which are otherwise formed solely on thebasis of chance. The 5th and 95th percentiles of this distribution are −0.3 and 2.2 percentfor the first year, −0.1 percent and 2.3 percent for the second year, and 0.0 percent and2.7 percent for the third year. Average excess returns reported in Table 3 that lie outside ofthese intervals can be viewed as unusual in the context of this distribution. By this criterion,all of the returns for groups 3–5 in panels A and B are significant, except for the third-yearexcess return for group 3 in panel A, which is marginally significant.

3.3. Sensitivity Analysis

In this section we examine two additional issues related to the excess returns reported inTable 3. First, we address the possibility that the results are induced by attempting to controlfor the Fama and French factors. To do this, we repeated the analysis using raw returns andfound that the mean three-year return difference between groups five and one based on theratio of R&D asset to market value of equity was even greater (9.4 percent compared with8.8 percent in panel A of Table 3). This suggests that the risk controls do not induce, butrather partly mitigate return differences between high and low R&D-intensity firms.

Second, we address the possibility that the excess returns reported in Table 3 may berelated more to the choice of deflator than to the level of R&D activity. This is suggested bythe fact that excess returns to R&D-intensive firms are larger when our grouping procedureis based on the ratio of R&D assets to market value of equity rather than on the ratio of R&Dassets to sales. To address this possibility, we repeated the analysis after grouping R&D firmsin the sample based on the ratio of current capital expenditures to market value of equity.This provides a check on whether deflating any measure of investment by market valueof equity induces a pattern of increasing measured excess returns. The results, reported inpanel C of Table 3, indicate that there is very little association between this ratio and excessreturns in future periods. For the averages across the three future periods, the excess returnsare low in the extreme groups and slightly positive in the middle groups. This suggests thatthe pattern of increasing excess returns reported in panel A is not simply due to the equityvalue deflator.


3.4. Summary

The results reported in this section provide convincing evidence of a positive associationbetween the level of R&D investment and post-investment excess stock returns, especiallywhen R&D-intensity is measured by the ratio of unrecorded R&D assets to market value ofequity (hereafter denoted as RDA). In the remainder of the study we examine whether thepattern of increasing excess returns to this measure of R&D-intensity is more consistentwith a risk explanation or a mispricing explanation.10

4. Evidence on the Risk Explanation for Excess Returns to R&D Firms

The excess returns to R&D firms reported in Table 3 and in prior studies are residual ratesof return that remain after controlling for size (market capitalization) and book-to-marketratio. These factors are known to be associated with average realized stock returns and areoften interpreted as proxies for “riskiness” (Fama and French, 1992, 1993). However, theyare unlikely to provide complete measures of risk for all firms and periods. This may beespecially the case for R&D-intensive firms. For example, theoretical work by Berk et al.(2000) suggests that firms with high levels of R&D investment may be more risky thanother firms, ceteris paribus. These authors argue that an investment in an R&D programis in essence a compound option to purchase a stream of operating cash flows, and henceshould be more risky, other things equal, than an investment in the operating cash flowsthemselves. Under this view, R&D-intensive firms may be differentially risky in a way thatis not contemplated in controls for size and book-to-market, and adjusting realized returnsfor those factors is unlikely to completely control for risk.

To investigate whether excess returns to R&D-intensive firms are likely to result from in-adequate control for risk, we examine (a) whether these returns persist over time, (b) whetherthey are more highly variable than excess returns to firms that are not R&D-intensive, and(c) whether measures of earnings uncertainty are correlated with R&D intensity after con-trolling for Fama and French risk factors.

4.1. The Duration of Excess Returns

If positive excess returns to R&D-intensive firms are compensation for risk, we would expectthese returns to persist indefinitely, provided that the risk characteristics of the sample firmsare not changing significantly over time. To investigate this, we divide all firm-years in thesample into non-R&D, low-R&D, and high-R&D groups on the basis of RDA, the ratio ofunrecognized R&D assets to market value of equity. Non-R&D firm-years are those with anRDA of zero in the current year; low-R&D (high-R&D) firms are those in RDA groups 1 and2 (3, 4 and 5) from Table 3. For each firm in each group, we calculate annual excess returnsfor each of the ten years following the investment date (the first day of the fifth monthfollowing fiscal year end), data permitting. The returns are aligned in “event time” andaveraged across available observations within groups for each of the ten years following theinvestment date.11 These averages should be indicative of expected returns to investmentsin firms of varying R&D intensity that are well-diversified both across firms and time.


Table 4. Average annual excess returns for high-R&D, low-R&D, and non-R&D firms over ten years followinginvestment date—1979–1998.

Year FollowingR&D Activity Based on RDA

Investment Date Non-R&D Low-R&D High-R&D

1 0.001 −0.011 0.0442 0.004 −0.018 0.0493 0.004 0.001 0.0434 0.006 0.007 0.0355 0.008 0.014 0.037

6 0.002 0.019 0.0417 −0.001 0.024 0.0438 −0.000 0.004 0.0359 −0.003 −0.004 0.03610 −0.003 −0.012 0.037

Avg. years 1–5 0.005 −0.001 0.042Avg. years 6–10 −0.001 0.006 0.038Avg. years 1–10 0.002 0.002 0.040

RDA is the R&D asset that would have been reported for the current year if R&D expenditures were capitalizedand amortized over five years beginning in the year after the expenditures were made, divided by market value ofequity at the end of the fourth month following the fiscal year end.

For each year following the investment date (t + 1 through t + 10), excess returns are averaged across allobservations for each group.

For a given investment year, the high-R&D (low-R&D) group includes observations in RDA groups 3, 4 and5 (RDA groups 1 and 2) in that year. See notes to Table 3 for description of grouping procedure. The non-R&Dgroup includes observations with RDA equal to zero.

Table 4 reports average annual excess returns for each group for each of the ten yearssubsequent to the investment date, as well as cross-year averages for years 1–5, 6–10, and1–10. The pattern of excess returns is remarkably consistent across the ten-year period foreach group. For non-R&D and low-R&D firms, average returns vary little from zero in anyof the ten years following investment. For high-R&D firms, in contrast, average returnsare no less than 3.5 percent in any year and are similar in magnitude across the ten years,averaging roughly 4 percent for the first five years, the second five years, and for the ten yearsoverall.

The persistence of positive excess returns to high-R&D firms for many years is consistentwith a risk explanation for these returns. In contrast, persistent excess returns seem lessconsistent with a mispricing explanation. Over the ten years as a whole, the cumulativeaverage excess return to high-R&D firms is about forty percent. If these returns arise frommispricing, the firms in the high-R&D group must have been undervalued on the investmentdate (on average) by at least forty percent, and correcting this undervaluation must haverequired at least ten years.

4.2. Cross-Year Variation in Excess Returns

Portfolio theory suggests that investors are compensated through higher expected returnsfor prospective variation in investment returns that cannot be “diversified away.” Thus, if


measured excess returns to R&D-intensive firms are actually compensation for bearingnondiversifiable risk, we would expect to observe greater variation over time in excessreturns to a well-diversified portfolio of R&D-intensive firms than to a well-diversifiedportfolio of firms with little or no R&D investment. We examine whether this is the caseby computing, for each Compustat data year in the sample, average excess returns for thefirst year following investment for non-R&D, low-R&D, and high-R&D firms. AverageCompustat data year excess returns for these groups provide a reasonable approximation ofcalendar-time returns to three equal-weighted portfolios that vary systematically in R&Dintensity, and which presumably include a sufficient number of firms to eliminate mostdiversifiable return variation.12

Table 5 reports year-by-year average excess returns, the average of these returns acrossyears, and the cross-year standard deviation of average excess returns for each of the threegroups. The average excess return is near zero for the non-R&D and low-R&D subsamples,and 4.6 percent for the high-R&D subsample. Moreover, the cross-year standard deviationof “portfolio” excess returns for high-R&D firms is more than two-and-a-half times largerthan that for either non-R&D firms or for low-R&D firms. This higher cross-year variation

Table 5. Year-by-year average annual excess returns for high-R&D, low-R&D, and non-R&D firms—1979–1997.

R&D Activity Based on RDA

Year Non-R&D Low-R&D High-R&D

1979 −0.013 0.040 0.0711980 0.006 −0.011 −0.0161981 −0.013 0.022 0.1441982 0.015 −0.033 0.0031983 0.045 −0.076 −0.036

1984 0.020 −0.004 0.0091985 −0.004 0.034 0.0341986 0.008 −0.011 −0.0081987 0.015 −0.017 −0.0491988 0.026 0.049 −0.011

1989 −0.013 0.035 −0.1301990 0.013 0.045 0.1231991 0.094 −0.059 0.0151992 0.015 −0.007 0.0381993 −0.048 −0.001 0.148

1994 −0.029 0.005 0.3151995 0.047 −0.053 −0.0161996 0.009 −0.045 −0.0771997 −0.025 0.016 0.059

Average excess return 0.009 −0.004 0.046Cross-year std. deviation 0.032 0.037 0.093

See note to Table 4 for definition of RDA and for measurement of excess returns.The table reports the average excess return for the first year following the investment date for each COMPUSTAT

data year in the study with available data (1979 through 1997).The high-R&D (low-R&D) group includes observations in RDA groups 3, 4 and 5 (RDA groups 1 and 2). See

notes to Table 3 for description of grouping procedure. The non-R&D group includes observations with RDAequal to zero.


for the high-R&D subsample is consistent with a risk explanation for this group’s highexcess returns.

4.3. Earnings Forecast Dispersion and Earnings Variability

Prior research suggests that both dispersion in analysts’ earnings forecasts and post-investment earnings variability can be viewed as indicators of risk.13 Thus, finding thatthese measures are higher for R&D-intensive firms than for other firms would be consis-tent with a risk explanation for excess returns to R&D-intensive firms. We investigate thispossibility using the reduced 1984–1998 sample for which analysts’ forecasts are available(see Table 1). We begin by dividing observations from these years into non-, low-, andhigh-R&D groups as discussed in the previous section. We then delete from each groupall observations with fewer than two one-year-ahead earnings forecasts available in theI/B/E/S database at fiscal year end. Each remaining observation in the high-R&D groupis then matched with randomly selected observations from the low- and non-R&D groupsthat (a) are from the same year, and (b) have the same Size-BVE/MVE control portfolioassignment. Observations are retained only if a successful three-way match occurs.

This procedure results in triplets of non-R&D, low-R&D, and high-R&D firms that differin level of R&D activity, but are distributed similarly over time and have similar distributionsof Fama-French risk attributes. There are 4,697 such triplets for which we have I/B/E/S datato calculate coefficients of variation of one-year-ahead earnings forecasts from the monthpreceding the fiscal year end. There are 3,839 triplets for which we have sufficient futureearnings data to calculate coefficients of variation based on up to five observations.14

The results of these analyses are reported in Table 6. Panel A indicates that analystsexhibit greater disagreement about year-ahead earnings for R&D-intensive firms than forothers. At each percentile, the coefficient of variation for high-R&D firms is greater thanthat for either low- or non-R&D firms. Based on the sign test, the median difference issignificant at the 0.1 percent level for both comparisons. Similarly, panel B indicates thatpost-investment reported earnings is more highly variable for high-R&D firms than for theother two groups, and median differences for these two comparisons are also significantat the 0.1 percent level. These results indicate that after controlling for Fama-French riskfactors, the earnings of high-R&D firms tend to be more uncertain than the earnings oflow- and non-R&D firms. To the extent that forecast variation and earnings uncertainty areindicators of risk, these results are consistent with a risk explanation for measured excessreturns to R&D-intensive firms.

4.4. Summary of Evidence on the Risk Explanation

The evidence presented in this section indicates that the pattern of increasing excess returnsto R&D-intensity reported in panel A of Table 3 is associated with risk characteristicsof R&D firms. Compared to firms with little or no R&D investment, R&D-intensive firms(a) earn excess returns that persist for up to ten years, (b) have future excess return variabilitythat cannot be diversified away within a portfolio of firms with substantial R&D investment,


Table 6. Coefficients of variation of analysts’ one-year-ahead earnings forecasts and future earnings for non-,low-, and high-R&D firms—1984–1998.

Percentile

10 25 50 75 90

A. Coefficient of variation of one-year-ahead forecasts

Non-R&D firms 0.017 0.030 0.056 0.118 0.243Low-R&D firms 0.019 0.036 0.071 0.132 0.251High-R&D firms 0.023 0.043 0.080 0.164 0.340

B. Coefficient of variation of earnings over subsequent five years

Non-R&D firms 0.117 0.213 0.417 1.017 3.010Low-R&D firms 0.149 0.246 0.470 1.086 3.201High-R&D firms 0.184 0.327 0.725 1.792 4.654

Panel A is based on matched samples of 4,697 high-R&D firm-years, 4,697 low-R&D firm-years, and 4,697non-R&D firm-years.

The high-R&D (low-R&D) group consists of matched observations in RDA groups 3, 4 and 5 (RDA groups 1and 2) for the current year (year t). See notes to Table 3 for description of grouping procedure. The non-R&Dgroup consists of matched observations with RDA equal to zero in the current year (year t).

Analysts’ forecast errors are measured as the difference between the average I/B/E/S forecast available at fiscalyear end and actual earnings for the forecasted period, divided by share price at the end of the fiscal year.

All included observations have at least two one-year-ahead forecasts available from I/B/E/S. To form matchedsamples, we began with high-R&D observations and matched them with both a low-R&D and a non-R&D ob-servation from the same year and with the same Size-BVE/MVE control portfolio assignment. Observations wereretained in the sample only if a successful three-way match was possible.

Panel B is based on matched samples of 3,839 high-R&D, 3,839 low-R&D, and 3,839 non-R&D firm-years,formed using a similar matching procedure, but subject to the availability of earnings data for the five yearsfollowing the current year.

and (c) have greater variability of analysts forecasts and future earnings. The next sectionof the paper provides evidence on the extent to which the excess returns are also consistentwith mispricing.

5. Evidence on the Mispricing Explanation for Excess Returns to R&D Firms

As discussed in Lev et al. (2000) and Penman and Zhang (2002), expensing R&D costswhen incurred can have perverse effects on accounting profitability measures when R&Dinvestment is expected to generate future benefits. In a period of growth (decline) in R&Dinvestment, accounting earnings will tend to be understated (overstated) relative to earningsthat would result from an appropriate policy of capitalization and amortization.15 If investorsfail to “see through” the effects of this conservative accounting, returns will be lower thanexpected on average for firms increasing R&D investment, and higher than expected onaverage for firms decreasing R&D investment.

In this section we conduct four separate analyses to determine the contribution, if any, ofthis form of mispricing to the positive association between level of R&D investment andsubsequent excess returns reported in Table 3. We first examine whether R&D firms earningthe largest (smallest) post-investment excess returns exhibit a decreasing (increasing) R&Dinvestment in the same period, as would be expected under the mispricing scenario outlined


above. Second, we directly examine the association between changes in unrecognized R&Dassets and post-investment excess returns. Third, we investigate whether analysts are overlyoptimistic (pessimistic) about the earnings of firms that are increasing (decreasing) theirR&D investment. Finally, we examine the extent to which the excess returns to R&D firmsdocumented in Table 3 vary with the extent of following by analysts.

5.1. Conservative Accounting and R&D-Intensity

If the pattern of excess returns reported in Table 3 is due to the form of mispricing describedabove, we expect that firms in RDA group 1 are generally increasing their R&D outlaysand that firms in RDA groups 3, 4, and 5 are generally decreasing their R&D outlays. Toaddress this possibility, we report in Table 7 the median ratio of the change in unrecordedR&D assets to market value of equity (�RDA) for each of the three years following theinvestment date. The results for all three years suggest that the relation between the level ofR&D assets (which is positively associated with post-investment excess returns in Table 3)and subsequent changes in R&D assets is U-shaped. The smallest changes are reported forgroups 1 and 5 and the largest changes are reported for groups 3 and 4. Thus, there is noevidence in Table 7 that the pattern of excess returns reported in Table 3 is due to largeincreases in unrecorded R&D assets for RDA group 1 and large decreases in R&D assetsfor RDA groups 3, 4, and 5.

5.2. Conservative Accounting and Excess Returns

Next, we examine the relation between post-investment excess returns and contemporaneouschanges in R&D investment by forming groups that differ in the potentially distorting effectof changes in R&D assets (�RDA) while controlling for the firm’s actual performance inthe same period.16 Our control for performance is based on adjusted return on net operatingassets (ARNOA), which measures return on investment without the distorting effects ofconservative accounting for R&D costs.17 Specifically, we first rank firms on the basis of

Table 7. Median �RDA for R&D firm-years grouped on RDA—1979–1998.

Year FollowingGroups Based on RDA

Investment Date 1 (Low) 2 3 4 5 (High)

1 0.004 0.017 0.024 0.025 0.0132 0.003 0.015 0.020 0.020 0.0073 0.002 0.012 0.016 0.015 0.006

The table reports median values for �RDA for each group based on RDA.�RDA is the change from beginning to end of the respective year in the R&D asset that would have been

recorded if firms were capitalizing and amortizing R&D outlays over five years divided by market value of equityat the beginning of the year.

RDA is the R&D asset for year t that would have been recorded if firms were capitalizing and amortizing R&Doutlays over five years divided by the firm’s market value of equity at the end of the fourth month following thefiscal year end.


ARNOA for fiscal year t +1, and based on this ranking divide the sample into groups of fiveobservations each. We then rank the observations within each of the resulting groups by�RDA for year t + 1, and combine observations according to these ranks. This procedureyields five groups that are very similar in terms of actual performance during the year,but which differ on the basis of the increase or decrease in R&D investment. Group 1consists of observations with relatively large increases in R&D investment during the year,and group 5 consists of observations with relatively small increases (or relatively largedecreases) in R&D investment. Under the mispricing explanation, we expect excess returnsto be increasing from group 1 to group 5.

Table 8 reports descriptive statistics and average excess returns for the five �RDA groups.The first column indicates that the median firm in group 1 increased its R&D investmentby 7.0 percent of market value of equity, while the median firm in group 5 decreased itsinvestment by 0.5 percent of market value of equity. The second column indicates that firmsthat increase their R&D investment are more R&D-intensive as measured by the ratio ofR&D assets to market value of equity (RDA). In particular, note that the level of RDA for

Table 8. Medians of selected variables for R&D firm-years grouped by �RDA—1979–1998.

�RDA Group �RDA RDA ARNOA RRNOA XRET

1 (Most positive) 0.070 0.190 0.079 0.037 0.048(5.046)

2 0.038 0.105 0.079 0.061 0.007(0.697)

3 0.016 0.070 0.079 0.075 −0.005(−0.611)

4 0.003 0.054 0.079 0.084 −0.001(−0.200)

5 (Most negative) −0.005 0.068 0.079 0.103 0.076(7.852)

The table reports median values for �RDA, RDA, ARNOA, and RRNOA, and the average value and (in parentheses)t-statistics for XRET, for each group based on �RDA.

�RDA groups are formed by first ranking on ARNOA within year and RDA quintile, and dividing the sampleinto groups of five observations. Within each five-observation group, firms are ranked on �RDA and assigned togroups one (highest) through five (lowest).

�RDA is the change from year t to year t + 1 in the R&D asset that would have been recorded if firms werecapitalizing and amortizing R&D outlays over five years divided by market value of equity at the end of the fourthmonth following fiscal year end.


ARNOA is the ratio of adjusted income for year t + 1 to adjusted net operating assets for year t + 1. Adjustedincome is reported income plus R&D expense minus R&D amortization that would have been recorded if firmswere capitalizing and amortizing R&D outlays over five years. Adjusted net operating asset are reported netoperating assets plus the R&D asset that would have been recorded if firms were capitalizing and amortizing R&Doutlays over five years.

RRNOA is the ratio of reported income for year t + 1 to reported net operating assets for year t .XRET is excess returns for the first year after the investment date, and is computed as the difference between

the firm’s actual return for that year and the return on the firm’s relevant “control” portfolio based on size and theratio of book value of equity to market value of equity (see note to Table 3 for more details).


group 1 (0.190) is nearly twice the level for group 2 (0.105), which itself is larger than thelevel for the other three groups.

The next two columns in Table 8 indicate that these five groups are equivalent at themedian in terms of adjusted earnings for year t + 1, but are very different in terms ofreported earnings for year t +1. The median firm in each group has an adjusted return on netoperating assets (ARNOA) of 7.9 percent, while the median reported return on net operatingassets (RRNOA) varies from 3.7 percent for group 1 to 10.3 percent for group 5. Thus, thegrouping procedure successfully captures R&D-related accounting-induced differences inperformance across the five groups. If investors are misled by this distortion, we expect thatexcess returns will increase as we go from �RDA group 1 to �RDA group 5.

The final column of Table 8 reports average year t + 1 excess returns for the five �RDAgroups. Excess returns for firms in �RDA groups 2 through 4 are all very small, as might beexpected under the mispricing scenario when accounting distortions are not extreme. Alsoconsistent with the mispricing scenario, the average excess return for group 5 observationsis large and positive (7.6 percent). Firms in group 5 are those who have reduced R&Dinvestment in the current year, and whose reported earnings is overstated as a consequence.Note also that the median RDA for observations in group 5 is relatively small. This suggeststhat the excess returns to group 5 do not have the same underlying cause as the excess returnsto high RDA groups we reported in Table 3. Finally, we observe that the average excess returnfor group 1 observations is also large and positive (4.8 percent). This is inconsistent with themispricing scenario, which predicts negative excess returns for this group. However, notethat this group has the largest ratio of R&D asset to market value of equity, which suggeststhat positive excess returns to the level of R&D assets may be obscuring the potential fornegative returns to the change in R&D assets.

To further investigate the relation between excess returns associated with the level of R&Dinvestment (reported in Table 3) and those associated with changes in R&D investment(reported in Table 8), we divide the observations in each �RDA group into five RDA groupsbased on the original RDA cutoffs that were used in constructing Table 3. Table 9 reportsaverage excess returns for each of these 25 groups for the first year following the investmentdate, as well as the excess return difference between groups five and one for both �RDA(final row) and RDA (final column).

The excess returns reported in the 25 cells of Table 9 provide evidence of two independentsources of excess returns. First, as the final row of the table indicates, for each RDA groupexcept the third, the average excess return for observations in �RDA group 5 is largerthan that for observations in �RDA group 1, with differences ranging from 2.8 percentto 8.5 percent. Thus, excess returns are larger (smaller) for firms that decrease (increase)R&D investment in the current year, after controlling for contemporaneous performance,regardless of the level of their R&D investment. This pattern is consistent with the mispricingscenario. Second, the final column of the table indicates that for each �RDA group, theaverage excess return for observations in RDA group 5 is larger than that for observationsin RDA group 1, with differences ranging from 1.7 percent to 19.9 percent. That is, firmswith substantial levels of R&D investment earn higher excess returns than those with littleR&D investment, regardless of whether they are currently increasing or decreasing theirinvestment. This pattern is not predicted by the mispricing explanation, but is consistentwith a risk explanation for measured excess returns.


Table 9. Average annual excess returns for R&D firm-years grouped on the basis of both RDA and �RDA—1979–1998.

Level of R&D Activity (RDA)

�RDA Group 1 (Low) 2 3 4 5 (High) (5 − 1)

1 (Most positive) 0.002 −0.015 0.057 0.030 0.081 0.079(0.036) (−0.585) (2.631) (1.796) (5.013)

2 −0.081 −0.028 0.002 0.007 0.118 0.199(−3.024) (−1.708) (0.124) (0.399) (3.564)

3 −0.025 −0.014 0.004 0.013 0.008 0.033(−1.525) (−1.120) (0.303) (0.527) (0.216)

4 −0.018 0.014 0.019 −0.017 −0.002 0.017(−1.493) (1.077) (1.148) (−0.872) (−0.065)

5 (Most negative) 0.029 0.039 0.049 0.107 0.166 0.137(2.305) (2.750) (1.971) (3.443) (5.944)

(5 − 1) 0.028 0.055 −0.008 0.077 0.085

The table reports the average value and (in parentheses) t-statistics for XRET, for each group based on �RDA andRDA.

The groups are formed by first ranking on ARNOA within years, and dividing the sample into groups of fiveobservations. Within each five-observation group, firms are ranked on �RDA and assigned to �RDA groups one(highest) through five (lowest). Those five �RDA groups are then crossed with the five RDA groups used in Table 3.

�RDA is the change from year t to year t + 1 in the R&D asset that would have been recorded if firms werecapitalizing and amortizing R&D outlays over five years divided by net operating assets.


ARNOA is the ratio of adjusted income for year t + 1 to adjusted net operating assets for year t + 1. Adjustedincome is reported income plus R&D expense minus R&D amortization that would have been recorded if firmswere capitalizing and amortizing R&D outlays over five years. Adjusted net operating asset are reported netoperating assets plus the R&D asset that would have been recorded if firms were capitalizing and amortizing R&Doutlays over five years.

XRET is excess returns for the first year after the investment date, and is computed as the difference betweenthe firm’s actual return for that year and the return on the firm’s relevant “control” portfolio based on size and theratio of book value of equity to market value of equity (see note to Table 3 for more details).

Apart from the direction of excess return differences for high and low �RDA firms, themispricing explanation also predicts negative excess returns for firms that increase theirR&D investments (e.g., �RDA groups 1 and 2), and positive excess returns for firms thatdecrease their R&D investment (e.g., �RDA group 5). In Table 9, negative returns for�RDA groups 1 and 2 are evident only when the level of R&D investment is low. For�RDA group 5, returns are positive at all levels of RDA, as predicted by the mispricingscenario, but at the same time are monotonically increasing in RDA. Taken together, thesepatterns also suggest two independent sources of excess returns in the sample data.

To provide additional evidence on whether excess returns to R&D firms are associatedboth with the level of R&D investment and changes in R&D investment, we estimate aregression of excess returns on changes in R&D investment and levels of R&D investmentthat captures the two-way classification in Table 9 while simultaneously controlling for


contemporaneous performance:

XRETt+1 = a0 + a1G�RDA,t+1 + a2GRDA,t + a3GARNOA,t+1 + ε (1)

−0.257 0.073 0.168 0.421

(−30.60) (6.94) (15.75) (40.39)

In this regression, XRETt+1 is the excess return for firm i for the first year following theinvestment date. G�RDA,t+1, a variable that takes on values ranging from zero to one, isdetermined by sorting sample observations (within years) from highest to lowest on �RDA,partitioning the sorted observations into five �RDA groups (labeled zero through four),and dividing the resulting group ranks by four.18 Analogous procedures were followedto determine values for GRDA,t and GARNOA,t+1. By construction, G�RDA is decreasing in�RDA, while GRDA and GARNOA are increasing in their underlying variables. This regressionis designed to isolate the association between �RDA and excess returns while controllingfor RDA, and vice versa. If the relation between XRET and the independent variables islinear, however, an estimated slope coefficient from the regression can be interpreted notsimply as a conditional correlation, but also as a measure of the excess return to a zero-investment portfolio that is weighted to exploit information in the associated independentvariable that is orthogonal to information contained in the other independent variables.19

Estimation results for regression (1) are reported above. The coefficient estimate forGARNOA is positive and highly significant, as would be expected if excess returns are stronglyinfluenced by current performance. The coefficient estimate for G�RDA of 0.073 (t = 6.94)indicates that after controlling for the level of R&D investment and for contemporaneousperformance, excess returns are associated with contemporaneous changes in the level ofR&D investment. Consistent with the mispricing scenario, the positive coefficient estimateindicates that excess returns would have been earned by buying (selling short) firms that aredecreasing (increasing) their R&D investments. The magnitude of the coefficient estimate isgenerally similar to the excess return differences reported in the last row of Table 9. Similarly,the coefficient estimate for GRDA of 0.168 (t = 15.75) indicates that after controlling forchanges in R&D investment and for contemporaneous performance, excess returns arepositively associated with the level of R&D investment reflected in RDA. The magnitudeof this coefficient estimate is generally consistent with excess return differences reportedin the final column of Table 9. Overall, these results suggest that measured excess returnsto R&D firms are influenced both by the level of R&D investment and by changes in R&Dinvestment.

We also estimated a second version of regression (1) that includes a ranked, standard-ized variable (GAFE) that captures contemporaneous analysts’ earnings forecast errors. Theunderlying forecast error is the difference between the consensus one-year-ahead forecastavailable from the I/B/E/S database at the end of the preceding fiscal year and actual earn-ings for the current year, scaled by price at the end of the fiscal year.20 GAFE is constructedto take on values from zero (most negative difference between actual earnings and forecast)to one (most positive difference between actual earnings and forecast). This variable shouldaccount for much of the mispricing reflected in G�RDA, or mispricing from any other source,and should also account for much of the performance reflected in GARNOA, and its inclusionshould reduce the contribution of these two variables to the regression. In contrast, the


inclusion of GAFE should have little effect on the coefficient estimate for the RDA groupvariable (GRDA) if that variable is not related to mispricing.

Because earnings forecasts are not widely available in the I/B/E/S database prior to1984, we restrict this analysis to firm-years in our sample from the period 1984–1998. Weestimated the regression both with and without the forecast error variable for all firms withavailable forecast data. For the regression that does not include the forecast error variable, theestimated coefficients for G�RDA (0.096, t = 6.14) and GRDA (0.139, t = 8.66) are similarto those reported above for the full sample. After including the forecast error variable, thecoefficient estimate for G�RDA falls to 0.008 (t = 0.51), while the estimated coefficient forGRDA (0.136, t = 9.47) remains about the same. This pattern of results suggests that theexcess returns associated with RDA are unlikely to be related to mispricing.21

5.3. Future Excess Returns and Analysts’ Forecasts

Under the mispricing scenario, conservative accounting for R&D investments causes an-alysts and investors to be surprised by lower-than-expected earnings when firms increasetheir investment in R&D, and to be surprised by higher-than-expected earnings when firmsdecrease their investment in R&D. Under these conditions, one should observe relativelyoptimistic earnings forecasts (negative forecast errors) in periods of positive �RDA andrelatively pessimistic forecasts (positive forecast errors) in periods of negative �RDA.22

Similarly, if the positive association between excess returns and the level of R&D invest-ment is due to mispricing, we should see earnings forecasts that are relatively pessimistic(positive forecast errors) for high-RDA firms.

To investigate this possibility, we estimate the following regression:

AFEt+1 = c0 + c1G�RDA,t+1 + c2GRDA,t + c3GARNOA,t+1 + ε (2)

0.091 0.036 −0.012 0.129

(38.04) (13.36) (−4.43) (50.37)

In regression (2), AFEt+1 is measured as the difference between actual year t+1 earnings andthe consensus I/B/E/S forecast at the beginning of year t +1, scaled by price at the beginningof year t + 1. The independent variables are the same as those in regression (1). If earningsforecast errors are relatively more positive for firms whose R&D investment is declining(�RDA group 5), as the mispricing scenario suggests, we would expect a positive coefficientestimate for G�RDA. Similarly, we would expect a positive coefficient for the performancemeasure GARNOA because firms with good performance are more likely to have pessimisticforecasts that underestimate their performance. Finally, a positive coefficient estimate forGRDA would suggest the possibility that RDA is also related to mispricing.

Estimation results for regression (2), based on firm-years with available forecast datafrom the period 1984–1998, are reported above. Consistent with the mispricing scenario,the estimated coefficient for G�RDA is positive and significant, indicating that analysts tend tounderestimate earnings in periods when R&D investment is declining and to overestimateearnings when R&D investment is increasing. In contrast, the small negative coefficientestimate for GRDA indicates that analysts tend slightly to overestimate earnings for firmswith high levels of R&D investment, and is not consistent with a mispricing explanation


for excess returns associated with RDA. Similarly, the coefficient estimate for GARNOA

is significantly positive, consistent with relatively pessimistic forecasts for firms whoseperformance in the current period was good.

5.4. Future Excess Returns and Investor Following

In this section we examine whether excess returns associated with the level of R&D invest-ment are more pronounced for firms that are not closely followed by analysts, as would beexpected if the excess returns are due to mispricing. To measure “extent of following byanalysts,” we use the number of one-year-ahead earnings forecasts reported in the I/B/E/Sdatabase in the last month of the firm’s fiscal year. Because earnings forecasts are notwidely available in the I/B/E/S database prior to 1984, we restrict this analysis to the 72,317firm-years in our main sample from the period 1984–1998.

We divide this subsample on the basis of RDA into high-R&D (groups 3–5 from Table 3),low-R&D (groups 1–2 from Table 3), and non-R&D firms. To capture following by analysts,we divide each of these subsamples into observations for which the number of analysts’ one-year-ahead forecasts reported in the I/B/E/S database at fiscal year end is (a) zero, (b) oneto five, and (c) greater than five. Sample sizes for the resulting groups and subgroups arereported in panel A of Table 10.

Table 10. Sample sizes and average annual returns over three years following investment date for high, low, andnon-R&D firms, for subsamples stratified by extent of following by analysts—1984–1998.

R&D Activity Based on RDA

Sample Non-R&D Low-R&D High-R&D

A. Sample sizes

Total 1984–1988 sample 43,452 11,438 17,427

Number of I/B/E/Sone-year-ahead forecasts

0 24,369 5,871 10,0571–5 9,555 2,519 3,961>5 9,528 3,048 3,409

B. Average 3-year excess returns

Total 1984–1998 sample 0.002 −0.008 0.053

Number of I/B/E/Sone-year-ahead forecasts

0 0.000 −0.027 0.0521–5 0.009 0.014 0.062>5 0.002 0.011 0.047

The table reports average annual excess returns over five years subsequent to the investment date. See the note toTable 4 for details of the measurement of excess returns.

The high-R&D (low-R&D) group consists of matched observations in RDA groups 3, 4 and 5 (RDA groups 1and 2) for the current year (year t). See notes to Table 3 for description of grouping procedure. The non-R&Dgroup consists of matched observations with RDA equal to zero in the current year (year t).

“Number of I/B/E/S One Year Ahead Forecasts” indicates the number of one-year-ahead earnings forecasts inthe I/B/E/S database in the last month of the previous fiscal year.


Panel B of Table 10 first reports average annual excess returns over the three yearsfollowing the investment date for all observations in the high-R&D, low-R&D, and non-R&D groups. Excess returns for the high-R&D observations are somewhat higher, and thosefor low- and non-R&D observations are about the same as, the average of years one throughthree reported in Table 4 for the longer period 1979–1998. The next three rows report3-year average annual excess returns for the high-, low-, and non-R&D observations aftersubdividing these observations according to extent of following by analysts. The mispricingscenario suggests that we should find the smallest excess returns for observations withthe greatest following by analysts and largest for observations with no analyst following.Contrary to this expectation, the excess returns are large and positive for all levels of analystfollowing and do not seem to vary systematically with analyst following.

5.5. Summary of Evidence on the Mispricing Explanation

This section of the paper examines whether the positive association between measuredexcess returns and the level of R&D investment can be explained by a simple mispricingscenario. Overall, we find no evidence that this is the case. We do find evidence of a patternof excess returns that is consistent with the mispricing scenario, but four separate analysesindicate that this pattern is independent of the positive association between excess returnsand level of R&D investment. First, we find that median increases in R&D investmentare no different for high-RDA firms than for low-RDA firms. Second, after controlling forcontemporaneous changes in the level of R&D investment, we find that high-RDA firmsearn substantially greater future excess returns than low-RDA firms. Third, after controllingfor contemporaneous changes in R&D investment, we find that analysts’ forecasts aremore optimistic for high-RDA firms than for low-RDA firms. Finally, we report that excessreturns for high-RDA firms are not related to the extent of analyst following. Each of theseresults is inconsistent with a mispricing explanation for the positive association betweenpost-investment excess returns and the level of R&D investment documented in this andprevious studies.

6. Summary and Conclusions

Several previous studies indicate that publicly available measures of both the level of firms’R&D investment and of current or recent growth in R&D investment are positively asso-ciated with excess (risk-adjusted) returns in subsequent years. This study examines twoalternative explanations for these findings. The first is that conventional controls for riskused in previous studies as the basis for measuring excess returns do not completely capturethe riskiness of R&D-intensive firms. Under this explanation, the pattern of excess returnsreported in previous studies actually reflects compensation for risk-bearing. The secondexplanation is that excess returns to R&D firms are due to “mispricing.” Under this expla-nation, R&D-intensive firms earn negative (positive) excess returns in periods of increasing(decreasing) R&D investment because investors fail to see through the earnings effects ofconservative accounting for R&D costs.


To provide evidence useful for distinguishing between these explanations, we conducta variety of analyses based on a large sample of firms from the period 1979–1998 thatincludes both R&D-intensive firms and firms with little or no R&D investment. We find thatthe positive association between level of R&D investment and subsequent excess returnspersists for at least ten years following investment, that excess returns are much morehighly variable through time for R&D intensive firms than for firms with little or no R&Dinvestment, and that both analysts’ forecasts of future earnings and actual future earningsare more highly variable for R&D-intensive firms than for others. In addition, although wefind a positive association between excess returns and contemporaneous changes in R&Dinvestment, as predicted by the mispricing scenario, our evidence suggests that this patternis independent of excess returns related to the level of R&D investment.

Overall, these findings indicate that the positive association between excess returns andR&D investment levels reported in previous studies is more likely to result from failureto control adequately for risk than from accounting-induced mispricing. If this is the case,the valuation consequences of R&D-related mispricing may be smaller than a “strictlymispricing” interpretation of earlier results would suggest. Our results also suggest thatthe Fama-French three-factor model, a common basis for measuring excess returns andfor estimating cost of capital, may substantially underestimate expected returns for R&D-intensive firms. Given the growing importance of R&D activities in the general economy,this is an important area for further research.

Acknowledgments

The authors are grateful for useful comments made by Augustine Duru, Leslie Hodder,workshop participants at New York University and the 2001 Review of Accounting StudiesConference, two anonymous reviewers, Xiao-Jun Zhang (the discussant) and Richard Sloan(the associate editor). We are grateful for the use of analyst forecast data provided by I/B/E/S.

Notes

1. Though the exact methods vary, each study controls for general market movements as well as systematic returnbehavior related to size and book-to-market ratio (Fama and French, 1992, 1993).

2. A third possible explanation is that investors erroneously overvalued R&D-intensive firms during the periodfor behavioral reasons unrelated to accounting. If this were the case, excess returns earned over the last twodecades would be expected to reverse in the future. However, this “R&D bubble” explanation can only beexamined in the context of a longer time series of data that extends into the future.

3. We included an observation in the sample at this stage if certain Compustat and CRSP data were available(month of fiscal year end, assets, sales, earnings, common dividends, common stockholders’ equity, marketvalue of equity at end of the fiscal year and at the end of the fourth month after fiscal year end, and at least somereturns during the year beginning with the fifth month following the fiscal year end), subject to the furtherrequirements that the current fiscal year was a full year and that the firm’s year end book value of equity waspositive. The last of these requirements, which eliminated 12,610 observations from the sample, is imposedbecause we compute excess returns using Fama and French (1992, 1993) “size” and “book-to-market” controlportfolios whose construction excludes negative book equity firms.

4. Compustat either reports the amount of R&D expense for the firm, or provides codes that indicate that R&Dexpense is either “missing” or “immaterial.” In the latter two cases, we set R&D expense to zero.


5. The controls to compute excess returns are implemented on a July–June fiscal year basis. To compute benchmarkportfolio returns for a given fiscal year, all stocks in the intersection of the CRSP and COMPUSTAT databasesare assigned to one of five groups on the basis of market value of equity at the prior calendar year end (Size),and are independently assigned to one of five groups on the basis of their ratio of book value of equity tomarket value of equity (BVE/MVE) at the firm’s accounting year end in the prior calendar year. Breakpointsfor determining Size and BVE/MVE group membership are quintiles of their respective distributions for NYSEfirms at June 30. These grouping procedures yield 25 Size-BVE/MVE groups. For each of the 25 groups, wecompute the value-weighted return for each month during the period July 1 though June 30. This procedurewas repeated for each annual period ending June 30.

6. In computing the book-to-market ratio used to match sample observations with control portfolios, “book value”is the sum of common stockholders’ equity at year end, deferred tax liability at year end, and net imputedR&D assets at year end. The first two terms of the sum follow Fama and French (1993); the final adjustmentwas also made by Chan et al. (2000). However, our results are not sensitive to inclusion of net R&D assets aspart of “book value.”

7. We computed returns for delisted firms as follows. During the delisting year we cumulate actual returns forthese firms up to the point of delisting (including the delisting return, if any), and then substitute the firm’scontrol portfolio return for the missing returns during the remainder of that year. Delisted firms were excludedfrom the computation of excess returns in the years after their delisting.

8. Average excess returns for non-R&D firms differ slightly from panel A to panel B, as 317 non-R&D observa-tions with zero sales were eliminated from the sample in constructing panel B.

9. Chan et al. (2000) conduct similar analyses based on current period R&D expenditures (rather than the R&Dasset) divided by market value of equity and sales, and find results that are very similar to those reported inTable 3. For the ratio of R&D expenditure to market value of equity, they report average annual returns overthree years of 5.4 percent for group 5 and −1.8 percent for group 1. We repeated our Table 3 analysis forthis ratio, and found average annual returns over three years of 7.0 percent for group 5 and −1.7 percent forgroup 1.

10. The fact that the denominator of the R&D-intensity measure that best captures the propensity of R&D firmsto earn excess returns is the market value of equity is not evidence in and of itself for either the mispricing orthe risk explanation. A low market value that increases this ratio could occur either as a result of stocks thatare mispriced and valued “too low,” or as a result of high risk in which future dividends are discounted usinga higher expected rate of return.

11. Because of data constraints, the annual excess returns are averaged across a declining number of sample years.The year 1 excess returns are based on all 20 years in the study period, from 1979–1998, while the year 10excess returns are averaged over the 11 years in the study period, 1979–1989, for which ten years of excessreturns are available.

12. For the high-R&D (low-R&D) groups the number of observations varies from 637 (440) to 1,577 (1,023)across the 19 years in the study period. For the non-R&D group the number of observations varies from 1,668to 3,603. The fact that the non-, low-, and high-R&D portfolios contain different numbers of firms in a givenCompustat year raises the issue of whether these portfolios are “large enough” to eliminate diversifiable risk.However, we find that cross-year standard deviations of portfolio returns are virtually the same as those inTable 5 when results are based on half of the sample selected at random.

13. For discussion of dispersion of analysts’ forecasts as an indicator of risk see Barron and Stuerke (1998), Brown(1998), and Han and Manry (2000). For discussion of variance of earnings as an indicator of risk see Beaver,Kettler and Scholes (1970).

14. Average excess returns to the high-, low-, and no-R&D firms in these subsamples are similar to those reportedin Table 4 for the full sample.

15. Note that growth (decline) in R&D investment, as measured by increases (decreases) in the magnitude ofunrecognized R&D assets, occurs when new R&D expenditures during a period exceed (are less than) R&Damortization. These are the same conditions that cause reported earnings based on immediate expensing to besmaller than (larger than) adjusted earnings based on capitalization and amortization.

16. �RDA is computed as the change in R&D asset from fiscal year t to fiscal year t + 1, while excess returns arecomputed for the interval from the fifth month of fiscal year t +1 through the fourth month of fiscal year t +2.While these two intervals are not exactly contemporaneous, the choice of return interval insures that the R&Dasset at the end of fiscal year t is known by the market prior to the the start of the return interval, and that theR&D asset at the end of fiscal year t + 1 is known by the market prior to the end of the return interval. This


approach differs from previous studies, which in general grouped firms on the basis of past R&D investmentbehavior, assuming that the past accounting distortions would be reversed in the future, and then examinedexcess returns over subsequent years, without specifying when the reversal was likely to occur. In contrast,we examine groups that differ in terms of the accounting distortions that occurred in the same year the excessreturns are measured.

17. ARNOA is the ratio of adjusted earnings for year t + 1 to adjusted net operating assets for year t . Adjustedearnings is reported earnings plus reported R&D expense (RDE) minus our computation of R&D amortization(RDAMORT), i.e., the earnings that firms would have reported if they had capitalized and amortized earningsover five years. Adjusted net operating assets is net operating assets as computed in Penman and Zhang (2001)and Nissam and Penman (2000) plus the R&D asset that firms would have reported if they had capitalizedR&D costs and amortized these costs over five years.

18. This grouping differs from that used in Tables 8 and 9 in that it is based only on �RDA.19. Both the design of this regression and the interpretation of the regression coefficients are based on Abarbanell

and Bushee (1998).20. “Actual” earnings was taken from the I/B/E/S database when available, which was the case for about 98 percent

of the sample observations, and from the Compustat database otherwise.21. The behavior of the coefficient estimate for GARNOA was also as expected. Without (with) the forecast error

group variable the coefficient for GARNOA was 0.346 (0.083), and the associated t-statistic was 23.11 (4.93).The coefficient estimate for GAFE , the forecast error variable, was 0.469 (t = 29.77).

22. Note that prior research suggests that analysts’ forecasts a year or more in advance of the earnings announcementtend to be optimistically biased on average. Thus, a forecast could be relatively more pessimistic simply bybeing less optimistic. See Richardson et al. (1999) for recent evidence on this issue.

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