do capital markets punish managerial myopia?
TRANSCRIPT
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Do Capital Markets Punish Managerial Myopia?
Jamie Y. Tong
University of Western Australia
Feida (Frank) Zhang
Murdoch University
2015 April
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Do Capital Markets Punish Managerial Myopia?
Abstract
The extant literature provides conflicting arguments and mixed results on whether capital
markets punish managerial myopia. Using managers’ cutting R&D to meet short-term
earnings goals as a research setting, this study reveals that capital markets actually penalize
managerial myopia, especially for firms with high investor sophistication. Our results are
consistent with Jensen’s (1988) contention that the security market is not shortsighted.
Additionally, we document that compensation, especially cash compensation, could be one of
the reasons why managers behave myopically.
JEL classifications: G32; M41
Keywords: Managerial Myopia; Capital Market; Investor Sophistication; CEO Compensation.
Data availability: The data are available from the public sources identified in the paper.
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I. Introduction
Defined as managers’ desire to achieve a high current stock price by inflating current
earnings at the expense of long-term cash flows or earnings (Stein 1989; Bhojraj and Libby
2005), managerial myopia is believed to be a first-order problem faced by modern firms
(Edmans 2009). Graham, Harvey, and Rajgopal (2005) survey and interview more than 400
executives, and document that 78% of executives would forgo a project with positive net
present value if the project would cause them to miss short-term earnings targets. Empirical
studies of managerial myopic behavior have focused mainly on R&D expenditure and the
evidence is consistent with managers myopically cutting investment in R&D to achieve
various income objectives (Dechow and Sloan 1991; Baber et al. 1991; Bange and De Bondt
1998; Roychowdhury 2006; Cooper and Selto 1991; Bens et al. 2002; Jacobs 1991; Asker et
al. 2011).
The origins of managerial myopia have been debated, and central to the debate is the
view that U.S. equity markets induce corporate managers to behave myopically (Jacobs 1991;
Porter 1992). The view arises from the belief that investors cannot see beyond current
earnings and will depress stock prices when there is any reduction in short-term earnings.
Because R&D investments are expensed under current Generally Accepted Accounting
Principles (GAAP), managers have incentives to avoid such investments in spite of the long-
term payoffs1. Essentially, managers underinvest in R&D to create the impression that the
firm’s current and future profitability is greater than it actually is, hoping this will boost
today’s share price (Stein 1989). Hence, managers are pressured into trading long-term
performance for short-term performance in order to meet stock market expectation, and
especially in order to secure impatient capital.
1 We acknowledge that managers might not always give up R&D projects for short-term benefits. On the one hand, capital
market rewards firms that meet/beat the earnings target. On the other hand, capital market also values R&D investment.
Hence, managers might have to make a trade-off between investing in long-term projects while missing earnings target and
forgoing long-term valuable projects to meet/beat earnings target.
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Prominent CEOs have expressed their concerns about the pressure from capital markets.
For example, Anne Mulcahy, former Chairperson and CEO of Xerox, stated that fixating on
short-term performance is one of “the most dysfunctional things” in the marketplace, and it
may hurt U.S. firms in the long run2. During Google’s IPO offering in 2004, management of
Google said it did not want to lose focus on its long-term goals and therefore declined to
provide frequent earnings guidance (Gigler et al. 2009). Such concerns are also shared by
regulators. An independent commission established by the U.S. chamber of Commerce
recommends discontinuing quarterly earnings guidance and believes reducing the pressures to
meet precise quarterly earnings target is an important first step in shifting the focus of the U.S.
capital markets away from quarterly results and toward the long-term performance of U.S.
companies (Cheng et al. 2007). Recent empirical studies on earnings guidance, analyst
coverage and takeover protection are supportive of the concerns from the industry and the
regulator (Hu et al. 2014; He and Tian 2013; Zhao et al. 2012).
However, whether capital markets are myopic has never been conclusively established
(Houston et al. 2010). If capital markets are shortsighted so that managers are pressured to
behave myopically, we would expect a positive stock price reaction to managers’ myopic
behaviors3. Managerial myopia is sacrificing long-term growth for the purpose of meeting
short-term goals (Porter 1992). This concept has three aspects: (1) there should be
underinvestment in long-term value creation projects; (2) the underinvestment should occur
with the objective of meeting short-term goals; and (3) such underinvestment must be sub-
optimal in the sense of impairing long-term growth and value creation. Based on different
measures/settings, prior empirical studies provide mixed results. Some scholars find that the
stock market reacts positively to announcements of R&D increases (Jarrell and Lehn 1985;
Woolridge 1988) even when such announcements occur in the face of an earnings
2 Information source: http://knowledge.wharton.upenn.edu/index.cfm?fa=viewArticle&id=1318&specialId=41. 3 In the long run, even shortsighted investors are likely to realize the unfavorable effects of managerial myopia.
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disappointment (Chan et al. 1990), suggesting that capital markets do reward R&D
investment immediately. While these studies document interesting and insightful results, they
only examine market response to R&D increase. We are still not clear about the stock market
reaction to myopia – cutting long-term projects (e.g., R&D) for short-term benefits. Using
U.K. data from 1989 to 2002, Osma and Young (2009) find that the sensitivity between one-
year return and earnings increases is lower if earnings increases are likely to come from
myopic R&D cut. What they focus is how managerial myopia influences earnings response
coefficients rather than how market reacts to managerial myopia. A more related paper is
Bhojraj, Hribar, Picconi, & McInnis (2009), in which the sample is divided into two groups.
The first group consists of firms that beat earnings target but have low earnings quality and
the second group consists of firms that miss earnings target but have high earnings quality.
Their results show that the first group of firms exhibits higher short-term stock price benefits
(measured by five-day returns surrounding the earnings announcement date) than the second
group. Further, such trend reverses over a three-year horizon, suggesting that managerial
myopia is punished by capital markets in the long term but not in the short run. Bhojraj et al.
(2009) use “low earnings quality but beat earnings target” to capture myopic behavior.
Particularly, firms are considered to be low in earnings quality if: (1) directional accruals are
above the median level of all firms in year t; and (2) change in R&D is below the median
level of all firms in year t; and (3) change in advertising is below the median level of all firms
in year t. However, the below-median change in R&D is not necessarily underinvestment4,
nor does it necessarily occur with the objective of meeting short-term earnings target because
the “meeting or beating” may come from discretional accruals or advertising decrease. Hence,
managerial myopia is not well captured in Bhojraj et al. (2009). Overall, prior studies provide
4 Because of the high variation across different industries, the median level of R&D change of all firms is
unlikely to be the optimal level of R&D change for each firm. Therefore, it’s questionable to use the median
level of all firms as the benchmark to judge whether one firm’s R&D change is underinvestment. For example,
if the median level of R&D change is 10%, then a firm will be classified as myopic even if it increases its R&D
investment by 8%.
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mixed findings.
In this paper we first sample firms for which earnings before R&D and taxes have
declined relative to the prior year, but have declined by an amount that can be reversed by a
reduction in R&D. By definition, all these firms are suspected of having myopic problems
because they have the incentive, as well as the ability, to cut R&D in order to meet earnings
targets. However, not all firms that have myopic problems will be involved in myopic
behaviors. Managers of these firms have two options – to cut or not to cut R&D. If they
choose to cut R&D, they are very likely to beat the target. If they choose not to cut R&D,
they have to miss the earnings target. Hence, if these firms do decrease R&D, then most
likely such cutting is for the objective of meeting earnings targets and therefore could be
regarded as myopic. We then focus on two subgroups of firms: (1) firms that cut R&D and
meet the previous year’s earnings (hereafter, myopic cutters) and (2) firms that do not cut
R&D, and thus miss the previous year’s earnings (hereafter, non-cutters). By the
classification, non-cutters are those who would have been able to meet/beat the earnings
target should they choose to decrease R&D. Myopic cutters, however, are those who would
have missed the earnings target should they maintain their prior R&D investment level. If the
market does not punish myopic R&D cutting, we should observe a better short-term stock
price performance for myopic cutters because these firms exhibit systematically higher
earnings surprises. If it turns out that myopic cutters show worse performance, then it
suggests that capital markets do punish myopic behavior because the only reason that myopic
cutters have higher earnings surprises than non-cutters is managerial myopia - cutting R&D
to meet earnings targets.5 We believe that our setting provides a cleaner setting to capture
managers’ myopic behavior.
5 This testing strategy makes it impossible to use zero as the earnings benchmark because earnings surprises (i.e., earnings
changes) will be incomparable for different firms. Although analyst forecast is also a popular benchmark, analysts may
change their forecasts from month to month. As a result, it is difficult for managers to make R&D cut decisions according to
analysts’ forecasts. Moreover, many analysts do not provide their forecasted R&D expenditure and thus make unavailable
the forecasted earnings before R&D. Therefore, we use earnings of the prior year rather than zero or analyst forecast as the
benchmark in this study.
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Applying an event study, we find that myopic cutters systematically underperform in a
five-day window surrounding the release of the earnings announcement. This indicates that
investors are able to see through the earnings manipulation by R&D cut and penalize such
myopic behavior. We also estimate ordinary least squares (OLS) regression to control the
factors that could influence five-day returns. The results show that myopic cutters still have
significantly lower five-day returns after controlling variables identified in prior studies.
If capital markets are efficient and do discount managerial myopia, we expect the
discount to be higher for firms with more sophisticated investors because these investors are
better able to “see through” managers’ myopic R&D cutting behavior and less likely to fixate
on earnings. Therefore, we further examine whether investor sophistication affects the market
reaction to the managerial myopia. We expect that managers in firms with more sophisticated
investors are more likely to be punished by the market for cutting R&D to meet/beat short-
term earnings targets. Consistent with our prediction, we find that the phenomenon of capital
markets punishing managerial myopic behavior exists only in the sub-sample of firms with
high investor sophistication. This finding further supports that capital markets with
sophisticated investors would punish manager’s myopic R&D cutting.
A question remained unsolved, however, is why managers still choose to behave
myopically. We further explore the incentives of managerial myopia by examining CEO
compensation. We find that CEOs who cut R&D to meet the previous year’s earnings receive
significantly higher cash pay (the sum of salary and annual bonus) and total pay (the sum of
cash pay and noncash pay) after controlling other important determinants of CEO
compensation. Our results provide another piece of evidence supporting the theory of
incomplete contract6 (Hart and Moore 1988). Particularly, when drawing up a contract, it is
6 A complete contract is one where each party could specify their respective rights and duties for every possible
future state of the world. However, complete contract does not exist in reality either because the state of the
world is not observable by all parties or because the cost of processing and using the information is too high.
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impracticable for each party to specify all relevant contingencies due to the costly
information and contracting. Regarding compensation contracts, some situations (e.g.,
whether firms will have a small earnings decline) are unpredictable before incentive plans are
made. Hence, the parties are likely to end up with an incomplete compensation contract,
which becomes one of the reasons why CEO behaves myopically.
Our paper contributes to the literature in a number of ways. First, it has long been argued
that the market pressure causes managers to behave myopically. If this argument holds, we
should observe a positive market reaction to managers’ myopic behavior. However, previous
empirical literature provides mixed results. On the one hand, some studies (Jarrell and Lehn
1985; Woolridge 1988) find that stock market rewards R&D increase but they did not address
how the market reacts to R&D decrease. Osma and Young (2009) did examine myopic R&D
cutting but they only focus on earnings response coefficients and did not provide direct
evidence on how market responds to managerial myopia. On the other hand, Bhojraj et al.
(2009) find that capital market punishes managerial myopia only in the long term, suggesting
that market does pressure managers to be involved in myopic behavior. However the way
they measure myopic behavior is not able to well capture myopia7. Our study is expected to
contribute to this line of research by reconciling previous mixed findings because our setting
enables us to better capture managers’ myopic behaviors. Specifically, our results show
significantly lower mean and median values of five-day returns for myopic cutters,
suggesting that capital markets do penalize the myopic behavior of managers (e.g., cutting
R&D to meet the previous year’s earnings). Additionally, by constructing a specific setting
where managers’ cutting R&D investment is most likely for meeting earnings target, we
provide a better measure for managers’ myopic behaviors.
Second, manipulating real operations such as R&D investment is a popular way of
7 Please refer to page 5 for detailed analysis on prior studies.
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earnings management (Bhagat and Bolton 2008), but there are few studies examining the
economic consequences of real earnings management8. Our study extends this stream of
research by showing that the market attaches a lower value to firms engaging in myopic R&D
cutting in the short run.
Finally, our results suggest that CEO compensation might be one of the possible reasons
why CEOs behave myopically. In this aspect, our study provides a new explanation on
managerial myopia and could contribute to the literature on both managerial myopia and
CEO compensation.
The remainder of the paper is organized as follows. The next section reviews literature
and develops hypothesis. Section III describes the empirical analysis and reports the results.
Section IV examines the effects of investment sophistication. Section V extends the analysis
to explore why managers act myopically. Section VI examines the effect of managerial
myopia on future performance. Section VI concludes.
II. Literature and Hypothesis
A large amount of literature provides evidence of managerial myopia with respect to
R&D spending. It is documented that R&D spending is significantly lower when the
spending jeopardizes managerial ability to report positive/increased earnings in the current
period (Baber et al. 1991; Roychowdhury 2006), or when CEOs are in the final years of their
administrative tenure (Dechow and Sloan 1991). Further, Bens et al. (2002) find that firms
experiencing employee stock option exercises divert resources away from real investment
projects to finance share repurchases resulting from the exercises.
Among all the factors that have been linked to managerial myopia, the transient nature
of capital market has received the most attention. It is argued that the pressure from the
capital market motivates managers to meet the Wall Street expectations even if doing so
8 One exception is Gunny (2010), who finds that firms use real earnings management to attain current-period
benefits that allow them to perform better in the future or signal to outsiders.
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would require costly changes in real activities, such as myopically cutting R&D or capital
expenditures.
Prior empirical studies generally find that managers engage in more/less myopic
behaviors in response to increased/decreased capital market pressure. Specifically, managers
are more likely to cut R&D investment to avoid an earnings decline when they foresee a
stock issuance (Cohen and Zarowin 2010; Bhojraj and Libby 2005), when institutional
investors have high portfolio turnover and engage in momentum trading (Bushee 1998) or
when there exists a threat from the takeover market. Similarly, He and Tian (2013) show that
firms with greater analyst coverage generate fewer patents and patents with lower impact,
suggesting that financial analysts as one key ingredient of the public equity market exert
pressure on managers to meet short-term goals and therefore impede firms’ investment in
long-term innovative projects. Recently, Hu et al. (2014) find that after earnings guidance
cessation, managers have less pressure to manage reported earnings to meet guidance
numbers and accordingly can focus on actions that secure long-term values.
Another stream of research investigates the capital market reaction to managers’ R&D
investment decision. The results from this stream are mixed. Several prior studies
documented that returns are positively associated with the announcement of R&D projects
(Jarrell & Lehn 1985; Woolridge 1988; Chan et al. 1990), which suggests that the markets do
reward management decisions that are consistent with long-term value creation. If capital
markets are not short-sighted, we should also observe a significant negative returns following
myopic R&D cut. Nevertheless, Bhojraj et al. (2009) find that stock market reaction is
positive to beaters even though they exhibit low earnings quality (measured by below-median
change in R&D, below-median change in advertising and above-median change in directional
accrual), indicating that capital markets do not punish managers’ myopic behaviors
immediately. However, the below-median change in R&D does not necessarily represent
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underinvestment. More importantly, because Bhojraj et al. (2009) combines R&D,
advertising and discretional accruals in their measurement, the below-median change in R&D
is not necessarily a sacrifice to achieve a short-term earnings target and therefore might not
reflect managerial myopia. Taken together, although prior studies examined the market
reaction to change in R&D investments, they did not directly investigate market response to
managerial myopia (i.e. sacrificing long-term growth for the purpose of meeting short-term
goals).
We argue that capital markets could see through managerial myopia and thus penalize it
accordingly. Despite widespread allegations of stock market “short termism,” research
persuasively supports a view that capital markets consider R&D investments as significant
value-increasing activities (Cheng 2004). Further, market participants would search for
methods to mitigate potential wasteful reduction of long-term profitable investment
(Chhaochharia and Grinstein 2007). For example, managers are less likely to cut R&D to
reverse an earnings decline when institutional ownership is high. Specifically, institutional
ownership serves to reduce pressures on managers for myopic investment behavior if
institutional investors have low turnover and momentum trading (Bushee 1998). Osma (2008)
provides evidence suggesting that independent directors have sufficient technical knowledge
to identify opportunistic reductions in R&D, and to efficiently constrain myopic R&D
spending. In addition, the presence of auditors and other experts who estimate R&D project
values could lessen the information asymmetry that generates mispricing and therefore
suppress managerial myopia (Chhaochharia and Grinstein 2007). Managers could also
credibly reveal their belief that the firm is undervalued by initiating stock repurchases or by
accepting compensation contingent on project outcome (Meulbroek et al. 1990). In line with
this logic, we expect that:
H1: Capital markets react negatively to the behavior of cutting R&D to meet
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earnings target.
The discussion leading up to H1 assumes that the investors are capable of searching for
and analyzing information. However, as prior studies indicate, investor sophistication varies
across firms (Callen et al. 2005; Bartov et al. 2000). Although the threshold of investor
sophistication for an efficient capital market is unknown, we could empirically test whether
the extent of investor sophistication matters. Sophisticated investors are more likely to see
through the myopic R&D cut behavior than unsophisticated investors. Therefore, it is more
likely that myopic R&D cut is punished when investors are sophisticated. If the investors’
reactions to managerial myopia vary across firms in a manner consistent with the effects of
investor sophistication, it will increase the reliability of our empirical results. Following prior
studies, we construct the second hypothesis as follows:
H2. The phenomena that capital markets react negatively to the behavior of cutting
R&D to meet earnings target exists mainly in firms with sophisticated investors.
III. Empirical Analysis
A. Sample and Measures
Our sample consists of firm-year observations drawn from the Compustat Database from
1972 to 2014. The earliest year is set at 1972 because prior to that year relatively few firms
on Compustat reported information on R&D outlays (Kothari et al. 2002). All price and
returns data are from the Center for Research in Security Prices (CRSP). To ensure that
micro-cap or penny stocks did not bias our results, we dropped firms with assets less than $10
million or the share price less than $1. Utilities and banks are also omitted from our sample,
because their financial statements tend to be different from those of other types of firms. As
mentioned, we include in our sample only firms that have both incentive and ability to cut
R&D to meet short-term earnings. Thus, firms are excluded unless their earnings before R&D
and taxes have declined relative to the prior year, but by an amount that can be reversed by a
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20%9 reduction in R&D. Specifically, we compute EBTRD (Earnings before Tax and R&D)
and exclude firms that do not satisfy the inequality “–0.2*(RDt-1)<=(EBTRDt-EBTRD t-1)<0”.
Furthermore, we exclude firms that cut R&D and missed the previous year’s earnings10
.
Finally, observations were deleted if either: (1) Compustat reports missing values for sales,
total assets, book value of equity, or market value of equity; or (2) data needed to compute
the variables are missing. The sample-selection criteria yield a total of 3,061 observations,
with 939 myopic cutters and 2,122 non-cutters. To assuage the effects of outliers, we
winsorize all variables except dummy variables at the 1st and 99th percentile values.
We classify firms into categories based on whether they cut R&D and meet the previous
year’s earnings. We construct a dummy variable CutRD, which is 1 if managers cut R&D at
year t (i.e., R&D expenditure is lower relative to the prior year) and earnings at year t are not
less than that of year t-1. CutRD equals 0 if managers do not cut R&D at year t. Thus, CutRD
in this paper is the measure of managerial myopia.
Testing our hypotheses also requires the measure of market reaction. To increase the
robustness of our study, we use three different measures to capture market reaction: raw
return, market-adjusted abnormal return, and size-adjusted abnormal return. All the returns
are calculated using a five-day window that begins two days before the earnings
announcement and ends two days after the earnings announcement. Five-day (adjusted)
returns are calculated as the (adjusted) cumulative return in the five-day window. The market-
adjusted (size-adjusted) return is calculated using daily CRSP returns, and is adjusted by
subtracting the cumulative market return (market return of firms in the same CRSP size
decile) over the same period.
B. Descriptive Statistics
9 When the ratio of the distance from the earnings goal relative to the prior year’s R&D ((EBTRDt-1-EBTRD t)/RDt-1) is
higher than 20%, the probability of R&D cut is low (13.47%). This indicates that, if the ratio is high, it will be difficult for
managers to cut R&D to meet earnings targets. To ensure that managers have both the incentive and the ability to cut R&D
to meet the previous year’s earnings, we require the ratio to be less than or equal to 20%. We also apply 10%, 15%, or 25%
as the thresholds and get qualitatively similar results. 10 If we include these firms in the sample and treat them as myopic cutters, the results remain unchanged.
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Panel A of Table I provides descriptive statistics separately for myopic cutters and non-
cutters. For the mean values, the myopic cutters have significantly lower R&D change (△
RD), firm size (SIZE), earnings (Earnings), previous year’s earnings (Lag_Earnings), and
distance (Distance) as well as significantly higher earnings surprises (Surprise), book-to-
market ratio (BM), and Momentum. For the median values, all the variables (except book-to-
market ratio) show significant difference between myopic cutters and non-cutters.
Panel B of Table I provides distribution of observations for myopic cutters and non-
cutters across industries (two-digit SIC code). Most observations (around 70% of myopic
cutters and 72% of non-cutters) clustered on five industries: Chemical and Allied Products
(SIC: 26), Industrial Machinery & Equipment (SIC: 35), Electronic & Other Electronic (SIC:
36), Instruments & Related Products (SIC: 38), and Business Service (SIC: 73).
C. Research Design
We first examine how investors react to myopic R&D cut by comparing abnormal
returns of myopic cutters and non-cutters. Because myopic cutters have significantly higher
earnings surprise than non-cutters (See Table I: p-values of mean/median difference for
Surprise is lower than 0.000), the market reaction of myopic cutters should be more positive
than that of non-cutters. Therefore, if we find that the market reaction to myopic cutters is
more negative than that towards non-cutters, then it indicates that the investors punish the
behavior of myopic R&D cut. Our research design provides a conservative way to detect the
punishment of managerial myopia by investors.
To mitigate the concern that there might be many other factors affecting the abnormal
returns, we further apply the regression method to examine the influences of R&D cut on the
market reaction. Following Larker, Ormazabal, and Taylor (2011), we test our research
question by estimating the following regression.
RETi,t=α0+ α1CutRDi,t+ α2ERDSurprisei,t+ α3ΔRDi,t+ α4SIZEi,t + α5BMi,t
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+α6Momentumi,t+ Firm and Year Fixed Effects+ ε (1)
where:
i = Index of firm i;
t Index of year t;
RET = Five-day (adjusted) returns are calculated as the (adjusted)
cumulative return beginning two days before the earnings
announcement and ending two days after the earnings
announcement. The market-adjusted (size-adjusted) abnormal
return is calculated using daily CRSP returns, and is adjusted by
subtracting the cumulative market return (market return of firms in
the same CRSP size decile) over the same period;
CutRD = A dummy variable that equals 1 if firms cut R&D and meet the
previous year’s earnings, and 0 if firms do not cut R&D and fail to
meet the previous year’s earnings;
ERDSurprise = Earnings before R&D of year t minus earnings before R&D of year
t-1;
ΔRD = R&D of year t minus R&D of year t-1;
SIZE = The natural logarithm of market value at the end of year t;
BM = Book value divided by market value;
Momentum = Market-adjusted return over the prior six months.
D. Empirical Results
Empirical results on Hypothesis 1 are reported in Table II. Panel A of Table II presents
both the mean and median values of five-day returns11
surrounding the release of earnings
announcement for myopic cutters and non-cutters, respectively. It shows that the mean and
median values of raw return, size-adjusted return, and market-adjusted return are all
significantly negative for myopic cutters. In contrast, for non-cutters, only the mean and
median of size-adjusted return are significantly negative. More importantly, column (5) of
Panel A reveals significantly lower mean values of five-day returns for myopic cutters (t-
statistics of -3.917, -3.787, and -3.684). The values of the mean differences of the five-day
returns between the two subgroups are around 1%, which is economically significant.
Similarly, the median values of five-day returns are also significantly lower for myopic
cutters (z-statistics of -6.883, -11.271, and -9.080). Because both the mean and median values
of earnings surprises of myopic cutters are significantly higher (0.269 vs. -0.321 for mean
11 We also try three-day stock returns, and the (untabulated) results are qualitatively similar.
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values and 0.120 vs. -0.200 for median values, shown in Table I), our results indicate that
capital markets do penalize the myopic behavior of managers (e.g., cutting R&D to meet the
previous year’s earnings).
The more informative results are shown in Panel B of Table II, which presents the
coefficient estimates for equation (1). In all three columns, the coefficients on CutRD are
significantly negative (The coefficients on CutRD are -0.009, -0.009 and -0.008 respectively,
all significant at 5% level), indicating that myopic cutters generally have lower abnormal
returns than non-cutters. Our results further support the view that capital markets punish
managerial myopia.
The results for the control variables are generally consistent with prior literature. The
positive coefficients on ERDSuprise are consistent with prior studies on earnings response
coefficients (ERCs). The coefficients on △RD are significant and positive in Column (1) and
(2). The positive coefficients on SIZE and BM are consistent with Larker et al. (2011).
IV. Effects of Investor Sophistication on Market Reaction to Managerial Myopia
Prior studies find that the investor sophistication is not homogenous in capital markets. If
investors of a firm are naive, they would be less likely to see through managers’ myopic
behavior and to punish accordingly. Following previous literature, we use the percentage of
shares held by institutions12
(Inst_Percent) to proxy for investor sophistication (e.g., Bartov et
al. 2000; Callen et al. 2005). Consistent with the extant literature, institutional investors
comprise banks, insurance companies, and investment companies, including their managers,
independent advisors, and others. The institutional holding data are from 13-f filings to the
SEC, provided by CDA Spectrum database. Our sample for testing hypothesis 2 consists of
2,146 firm-year observations covering the years from 1972 to 2014.
12 We use the number of institutions holdings shares as an alternative proxy for investor sophistication, and the empirical
results remain qualitatively unchanged (untabulated). Moreover, we also use total asset size and analyst following as proxies
for investor sophistication. The alternative measures of investor sophistication do not change our empirical results
qualitatively (untabulated).
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Table III reports descriptive statistics for variables used in the investor sophistication
tests. It shows that myopic cutters have significantly lower raw return, market-adjusted
return, and size-adjusted return, which is consistent with our hypothesis. Myopic cutters also
have lower mean values of change in R&D, firm size, earnings, previous year’s earnings
(Lag_Earnings), and distance (Distance) while significantly higher earnings surprises
(Surprise), book-to-market ratio (BM), and momentum. For the median values, all the
variables (except book-to-market ratio) show significant difference between myopic cutters
and non-cutters.
We conduct sub-sample regressions to examine the effects of investor sophistication on
the market reaction to managerial myopia. Specifically, we divide our sample into high-IS
and low-IS sub-samples. The high-IS (low-IS) sub-sample consists of firm-years for which
Inst_Percent is higher (lower) than the median in year t, thus representing the observations
for which investors are more (less) sophisticated. By estimating the equation (1) in the
strong/weak investor sophistication subgroups, respectively, we expect that our results hold in
the high-IS sample. Because the threshold of investor sophistication ensuring the ability to
see through the managerial behavior is unknown, we make no prediction on the results in
low-IS sample.
The sub-sample regression results are presented in Table IV. It shows that the
coefficients on CutRD are all significantly negative in high-IS sub-sample (t-statistics of -
3.166, -3.438, and -2.748, respectively), while insignificant in low-IS sub-sample (t-statistics
of -0.151, -0.342, and 0.174, respectively). The differences of coefficients on CutRD are
statistically significant (all at the 5% level or better), suggesting that the negative impact of
myopic behavior on stock returns is pronounced only when investors are sophisticated. Our
second hypothesis is supported.
V. Extension: Managerial Incentive to Behave Myopically
18
The above findings suggest that the market is efficient, and thus might not pressure
managers to cut R&D in a way that is myopic. A question that has remained unanswered,
however, is what causes managers to act myopically. In this section we further examine the
possible reasons for managerial myopic R&D cut.
Earnings is an important determinant of top executive compensation, because earnings-
based performance measures help to shield executives from fluctuations that are beyond their
control (Donaldson and Preston 1995). Missing an earnings benchmark may become a signal
of poor performance that induces the compensation committee to penalize managers.
Consistent with this argument, Matsunaga and Park (2001) find that CEOs’ annual bonuses
are positively related to the likelihood of meeting a quarterly earnings benchmark. Further,
meeting the earnings benchmark could constitute a public signal that allows the compensation
committee to justify higher compensation levels to shareholders and, thereby, to overcome
political constraints on CEO compensation (Porter and Kramer 2006). Thus, by meeting
earnings targets, managers are able to increase their personal wealth in the form of cash pay.
In view of this, we expect compensation to be a potential reason of managerial myopia13
.
In addition to cash pay, we also examine whether managerial myopia affects managers’
total pay and noncash pay. Total pay is the sum of cash pay and noncash pay, and thus is a
more comprehensive measure of CEO compensation. Noncash pay is the sum of the value of
equity grants during the year, the fringe benefits, and other long-term incentive plans, with
stock options valued at the end of the fiscal year using the Black-Scholes 1973 model
adjusted for dividends. Previous literature indicates that noncash pay is an important
component of CEO incentives (Murphy 1999).
We test our prediction by estimating the following model:
ΔPayi,t=β0+ β1CutRDi,t+ β2ΔROAi,t+ β3ΔRDi,t + β4RETi,t+ β5SIZEi,t-1+ β6Qi,t-1
13 We are not suggesting that corporate boards are myopic or they support managerial myopia. There are always some
contingencies that are not predictable and thus could not be factored into the compensation contract. The incompleteness of
the contract therefore becomes one reason why some managers eventually choose to behave myopically.
19
+ β7LEVi,t-1 + β8Ownershipi,t-1+ β9Tenurei,t + β10Eindexi,t-1 + β11Board Sizei,t-1
+ β12Board Indpendencei,t-1+ β13CEO Duality,t-1 + Firm and Year Fixed Effects
+ ε (2)
where:
i = Index of firm i;
j = Index of year t;
ΔPay = The change of CEO’s total pay, the change of CEO’s cash
pay, or the change of CEO’s noncash pay. The change of
CEO pay (total pay, cash pay, or noncash pay) is calculated as
the logarithm of pay (total pay, cash pay, or noncash pay) in
year t minus the logarithm of CEO pay (total pay, cash pay,
or noncash pay) in year t-1;
Cash Pay = CEO cash pay, including salary and annual bonuses;
Noncash Pay = CEO noncash pay, including value of equity grants during the
year, fringe benefits, and other long-term incentive plans;
Total Pay = The sum of cash pay and noncash pay;
ΔROA = ROAi,t -ROAi,t-1;
ΔRD = R&Di,t –R&Di,t-1;
ROA = Net income divided by total assets;
RET = Annual stock return;
SIZE = Logarithm of total assets;
Q = The book value of assets minus the book value of equity, plus
the market value of equity, scaled by the book value of assets;
LEV = Total debts divided by total assets;
Ownership = CEO ownership as a percentage of shares outstanding;
Tenure = Number of years as the CEO of the firm.
Eindex = an index based on six antitakeover provisions: staggered
board, poison pills, the supermajority requirement for
mergers, limits to the shareholder bylaw amendments, limits
to the charter amendments, and golden parachutes;
Board Size = the natural log of number of directors on board;
Board Independence = percentage of independent directors on board;
CEO Duality = a dummy variable which equals 1 if CEO and Chairman of
the board are the same person, 0 otherwise
The model and control variables in (2) are based on previous literature (Sloan 1993;
Cheng 2004; Cheng and Indjejikian 2009). The dependent variable Δpayi,t could be the
change of CEO cash pay, the change of CEO total pay, or the change of noncash pay. We use
20
a natural logarithmic transformation to control for skewness in CEO compensation14
. If
managers who cut R&D to meet the previous year’s earnings receive more compensation,
then β1 would be significantly positive.
We control ΔROA and Return because accounting and stock performance measures have
positive effects on CEO compensation (Baber et al. 1996; Lambert and Larcker 1987). We
also control other variables that have been identified by previous literature as being
associated with CEO compensation (Murphy 1999; Hartzell and Starks 2003; Cheng and
Indjejikian 2009). These include firm-level characteristics such as ΔRD, size, Tobin’s Q,
leverage, market-to-book ratio, CEO characteristics such as equity ownership and CEO
tenure, and corporate governance variables such as Eindex, board size, board independence,
and CEO duality.
We test our prediction on a sample of 324 firm years from 1993–200815
that have both
incentive and ability to cut R&D in order to meet the previous year’s earnings. We obtain the
annual compensation information from the Execucomp database. Since we are analyzing the
change in compensation, we require that a firm has two consecutive years of compensation
data. For this reason, as well, we restrict our sample to those executives who have been CEOs
for both of the consecutive years. The other sample selection criteria are the same as above.
The results are shown in Table VI. Columns (1), (2), and (3) report the results of total
pay, cash pay and noncash pay, respectively. In Columns (1) and (2), the coefficients on
CutRD are positive and significant (p-value less than 0.05), indicating that managers
generally receive benefits by acting myopically. In Column (3), the coefficient on CutRD is
positive but insignificant. Overall, our results indicate that compensation, especially cash
compensation, might be one of the possible reasons for managerial myopia.
14 The results remain unchanged when this transformation is not applied. 15 The earliest year is 1993 because Execucomp provides executive compensation data since 1992 and we need
to calculate the change of compensation using one-year-lag data. The ending year is 2008 due to the data
availability of Eindex (downloaded from Lucian Bebchuk's home page:
http://www.law.harvard.edu/faculty/bebchuk/data.shtml).
21
VI. Additional Analysis: Managerial Myopia and Future Performance
Managers’ myopic behavior is expected to do harm to firm performance in a long run.
Therefore, we further test whether myopic cutters experience lower future performance than
non-cutters. In particular, we rerun model (1) by replacing dependent variables with future
performance, which is captured by future stock market performance (measured by one-year
stock return after earnings announcement) and future financial performance (measured by
return on assets and return on equity at the end of next fiscal year).
The results are shown in Table VII. In all three columns, the coefficients on CutRD are
significantly negative (-0.173, -0.054 and -0.159 respectively, all significant at 5% level),
indicating that myopic cutters generally have lower future performance than non-cutters. Our
results further support the view that cutting R&D for short-term earnings target is
unfavorable to firms’ future performance.
VII. Conclusions
Many academics and practitioners believe that myopia is a first-order problem faced by
the modern firm (Edmans 2009), and it is concern for the stock price that leads to myopia.
Using managers’ R&D cutting to meet short-term earnings targets as a setting, our study
examines whether the market actually discounts managerial myopia.
The study is based on a sample of U.S. firms with declined pre-tax, pre-R&D earnings
relative to the prior year—but earnings that have declined by an amount that can be reversed
by cutting 20% of the previous year’s R&D. We investigate whether the market reacts
negatively to managerial myopia around the earnings announcement date, and whether it
attaches a lower value to firms that cut R&D myopically. Using five-day returns surrounding
the announcement of earnings as an indicator of market reaction, we provide empirical
evidence suggesting that the market is not myopic, and that it does penalize firms if managers
engage in myopic R&D cutting for the purpose of short-term goals. Our further tests indicate
22
that the negative market reaction to managerial myopia only exists in firms where investors
are more sophisticated.
Given that capital markets could “see through” managerial myopia, we further explore
why managers still choose to behave myopically. We conduct regressions to examine the
effects of managerial myopia on CEO cash pay, noncash pay, and total pay. Our results
suggest that CEOs who cut R&D myopically receive significantly higher total pay and cash
pay, indicating that the incomplete compensation contract is one of the reasons for managerial
myopia.
Although researchers argue that it cannot be true that the market is myopic, empirical
evidences are limited. Our study helps to reconcile previous findings and contributes to the
literature on managerial myopia. Further, this study extends the literature on the economic
consequences of real earnings management by indicating that the market discounts real
earnings management behaviors.
23
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26
Appendix 1: Variables Definitions
Variables Definitions
Raw Return calculated as the cumulative return beginning two days before the earnings announcement
and ending two days after the earnings announcement
Market-adjusted
Return
calculated as the cumulative market-adjusted abnormal return beginning two days before
the earnings announcement and ending two days after the earnings announcement. The
market-adjusted abnormal return is calculated using daily CRSP returns and adjusted by
subtracting the cumulative market return over the same period
Size-adjusted Return calculated as the cumulative size-adjusted abnormal return beginning two days before the
earnings announcement and ending two days after the earnings announcement. The size-
adjusted abnormal return is calculated using daily CRSP returns and adjusted by
subtracting the cumulative average return of firms in the same CRSP size decile over the
same period
Surprise measured as Earnings minus Lag_Earnings
△RD R&D of year t minus R&D of year t-1
SIZE the natural logarithm of market value at the end of fiscal year
BM book value divided by market value at the end of fiscal year
Momentum the market-adjusted return over the prior six months
Earnings earnings per share before extraordinary item of a given fiscal year
Lag_Earnings earnings per share before extraordinary item of the prior fiscal year
Distance change of earnings before R&D divided by R&D at the beginning of the fiscal year
CutRD a dummy variable that equals 1 if firms cut R&D to meet the previous year’s earnings, and
0 if firms do not cut R&D and fail to meet the previous year’s earnings.
ERDSurprise earnings before R&D of a given year minus earnings before R&D of the prior fiscal year
Inst_Percent percentage of ownership by institutional investors
Total Pay the sum of cash pay and noncash pay
Cash Pay the sum of salary and annual bonus
Noncash Pay the sum of the value of equity grants during the year, fringe benefits, and other long-term
incentive plans, with stock options valued at the end of the fiscal year using Black-Scholes
1973 model adjusted for dividends
ROA net income divided by total assets
RET annual stock return for the fiscal year
Q the book value of assets minus the book value of equity, plus the market value of equity,
scaled by the book value of assets at the end of fiscal year.
LEV total debts divided by total assets
Ownership the CEO ownership as a percentage of shares outstanding
Tenure the number of years as CEO of the firm
Eindex an index based on six antitakeover provisions: staggered board, poison pills, the
supermajority requirement for mergers, limits to the shareholder bylaw amendments, limits
to the charter amendments, and golden parachutes
Board Size the natural log of number of directors on board
Board Independence percentage of independent directors on board
CEO Duality a dummy variable which equals 1 if CEO and Chairman of the board are the same person,
0 otherwise.
Future Return the one-year stock return from the earnings announcement date
Future ROA return on assets of next fiscal year end
Future ROE return on equity of next fiscal year end
27
Table I Descriptive Statistics for Myopic Cutters and Non-Cutters
Panel A of this table presents the summary statistics for the two subgroups: firms that cut R&D to meet the previous year’s earnings (myopic
cutters), and firms that do not cut R&D and fail to meet the previous year’s earnings (non-cutters). The sample includes firm-years for which
earnings before R&D and taxes have declined relative to the prior year, but by an amount that can be reversed by a 20% reduction in R&D. The t-
statistics (z-statistics) of the mean (median) differences are provided. Surprise is earnings surprise, measured as Earnings minus Lag_Earnings. △RD is R&D of year t minus R&D of year t-1. RD is research and development expenditure divided by total asset. SIZE is the natural logarithm of
market value at the end of fiscal year. BM is book value divided by market value at the end of fiscal year. Momentum is the market-adjusted return
over the prior six months. Earnings (Lag_Earnings) is earnings per share before extraordinary item of a given fiscal year (the prior fiscal year).
Distance is change of earnings before R&D divided by R&D at the beginning of the fiscal year. Panel B of this table presents the distribution of
observations for myopic cutters and non-cutters across industries labeled by two-digit SIC codes. *, **, and *** denote significance at the 10%, 5%,
and 1% level, respectively, two-tailed.
Panel A: Summary Statistics of Myopic Cutters and Non-Cutters
Myopic Cutters (N=939) Non-Cutters (N=2,122) Difference
Variable
(1)
Mean
(2)
Median
(3)
Std Dev.
(4)
Mean
(5)
Median
(6)
Std Dev.
(1)-(4)
p-value
(2)-(5)
p-value
Surprise 0.269 0.120 0.446 -0.321 -0.200 0.494 <0.000*** <0.000***
△RD -0.142 -0.025 0.367 0.143 0.023 0.421 <0.000*** <0.000***
SIZE 5.126 4.849 2.083 5.623 5.403 1.995 <0.000*** <0.000***
BM 0.672 0.504 0.589 0.606 0.486 0.482 0.001*** 0.498
Momemtum 0.019 -0.047 0.411 -0.058 -0.092 0.328 <0.000*** <0.000***
Earnings 0.338 0.030 1.655 0.612 0.310 1.686 <0.000*** <0.000***
Lag_Earnings 0.069 -0.080 1.785 0.922 0.590 1.812 <0.000*** <0.000***
Distance 0.083 0.076 0.056 0.095 0.092 0.058 <0.000*** <0.000***
28
Panel B: Distribution of Observations across Industries
SIC Industry N(Myopic cutters) N(Non-Cutters)
1 Agricultural Production 2 9 7 Agricultural Services 1 1
12 Coal Mining 0 1
13 Oil and Gas Extraction 2 7
14 Nonmetallic Minerals, Ex. Fules 0 2
16 Heavy Construction, Ex. Building 3 2
20 Food and Kindred Products 6 21
22 Textile Mill Products 1 10
23 Apparel & Other Textile Products 0 2
24 Lumber and Wood Products 1 0
25 Furniture and Fixtures 2 12
26 Paper and Allied Products 8 17
27 Printing and Publishing 1 1
28 Chemical and Allied Products 239 535
29 Petroleum and Coal Products 4 12
30 Rubber & Misc. Plastics Products 7 17
31 Leather and Leather Products 0 2
32 Stone, Clay, and Glass Products 2 5
33 Primary Metal Industries 4 8
34 Fabricated Metal Products 9 19
35 Industrial Machinery & Equipment 102 297
36 Electronic & Other Electronic Equipment 152 372
37 Transportation Equipment 39 73
38 Instruments & Related Products 160 320
39 Misc. Manufacturing Industries 14 24
44 Water Transportation 1 0
45 Transportation by Air 0 1
47 Transportation Services 0 1
48 Communications 9 21
49 Electrics, Gas, and Sanitary Services 1 2
50 Wholesale Trade – Durable Goods 6 5
51 Wholesale Trade – Nondurable Goods 4 1
54 Food Stores 1 0
59 Miscellaneous Retail 4 3
73 Business Services 120 286
78 Motion Pictures 2 0
79 Amusement & Recreation Services 0 1
80 Health Services 7 6
82 Educational Services 0 3
87 Engineering & Management Services 15 15
99 Nonclassifiable Establishments 10 8
Total 939 2,122
29
Table II Market Reaction to Managerial Myopia
Panel A presents the five-day returns surrounding the earnings announcement for myopic cutters and non-cutters. The raw return is calculated using
daily CRSP returns. The market-adjusted (size-adjusted) abnormal return is calculated using daily CRSP returns and adjusted by subtracting the
cumulative market return (market return of firms in the same CRSP size decile) over the same period. Five-day (adjusted) returns are calculated as
the (adjusted) cumulative return beginning two days before the earnings announcement and ending two days after the earnings announcement. T-
statistics or z-statistics are presented in the brackets.
Panel B presents results of regression five-day returns surrounding the earnings announcement on myopic R&D cut and control variables. CutRD is
a dummy variable that equals 1 if firms cut R&D and meet the previous year’s earnings, and 0 if firms do not cut R&D and fail to meet the previous
year’s earnings. ERDSurprise is earnings before R&D of a given year minus earnings before R&D of the prior fiscal year. △RD is R&D of year t
minus R&D of year t-1. SIZE is the natural logarithm of market value. BM is book value divided by market value. Momentum is the market-adjusted
return over the prior six months. All variables except dummy variables are winsorized at the 1st and 99th percentile values. Heteroskedastic-robust
and cluster-adjusted t-statistics appear in brackets. *, **, and *** denote significance at the 10%, 5%, and 1% level, respectively, two-tailed.
Panel A. Event Day Returns
Types of Return
Myopic Cutters
N=939
Non-Cutters
N=2,122 Difference
(1)
Mean
(2)
Median
(3)
Mean
(4)
Median
(5)=(1)-(3)
(t-statistics)
(6)=(2)-(4)
(z-statistics)
Raw Return -0.012*** -0.006*** 0.002 -0.000 -0.013*** -0.006***
[-4.023] [-3.280] [0.943] [-0.834] [-3.917] [-6.833]
Market-adjusted Abnormal Return -0.014*** -0.011*** -0.002 -0.004 -0.012*** -0.007***
[-5.104] [-3.892] [-1.011] [-1.102] [-3.787] [-11.271]
Size-adjusted Abnormal Return -0.016*** -0.012*** -0.004** -0.005** -0.012*** -0.007***
[-5.738] [-4.518] [-2.240] [-2.325] [-3.684] [-9.080]
30
Panel B. Cross-Sectional Variation in Event Day Returns
(1) (2) (3)
Variable Raw Return Market-adjusted Abnormal Return Size-adjusted Abnormal Return
CutRD -0.009** -0.009** -0.008**
[-2.314] [-2.400] [-2.138] ERDSurprise 0.012*** 0.009*** 0.009***
[3.200] [2.645] [2.588]
△RD 0.006** 0.005* 0.004
[2.294] [1.671] [1.420] SIZE 0.005*** 0.005*** 0.005***
[4.806] [5.084] [5.484] BM 0.012*** 0.011** 0.010**
[2.851] [2.430] [2.373] Momentum 0.005 0.005 0.006
[0.889] [1.009] [1.132] Constant -0.050*** -0.054*** -0.053***
[-3.036] [-3.679] [-3.731] Firm Fixed Effects Yes Yes Yes
Year Fixed Effects Yes Yes Yes
Observations 3,061 3,061 3,061
Adjusted R2 0.021 0.018 0.015
31
Table III Summary Statistics for Investor Sophistication Test
This table presents the descriptive statistics of the variables used in investor sophistication test. The sample consists of 2,146 firm-year
observations covering the years from 1972 to 2014. All variables are defined in Appendix 1. *, **, and *** denote significance at the 10%, 5%,
and 1% level, respectively, two-tailed.
Myopic Cutters (N=644) Non-Cutters (N=1,502) Difference
Variable
(1)
Mean
(2)
Median
(3)
Std Dev.
(4)
Mean
(5)
Median
(6)
Std Dev.
(1)-(4)
p-value
(2)-(5)
p-value
Raw Return -0.002 -0.006 0.091 0.009 0.000 0.094 0.024** 0.032**
Market-adjusted Return -0.005 -0.009 0.090 0.006 -0.001 0.089 0.014** 0.025**
Size-adjusted Return -0.007 -0.009 0.089 0.004 -0.002 0.088 0.038** 0.024**
Surprise 0.377 0.110 1.484 -0.316 -0.200 0.823 <0.000*** <0.000***
△RD -0.234 -0.027 0.638 0.163 0.024 0.494 <0.000*** <0.000***
SIZE 5.374 5.045 2.142 5.734 5.502 2.003 <0.000*** <0.000***
BM 0.612 0.486 0.468 0.560 0.478 0.397 0.009*** 0.222
Momemtum 0.026 -0.026 0.374 -0.056 -0.090 0.314 <0.000*** <0.000***
Earnings 0.351 0.100 1.786 0.560 0.360 1.733 <0.011** <0.000***
Lag_Earnings -0.027 0.000 2.444 0.876 0.610 1.981 <0.000*** <0.000***
Distance 0.080 0.071 0.055 0.095 0.091 0.058 <0.000*** <0.000***
32
Table IV Effects of Investor Sophistication on Market Reaction to Managerial Myopia
This table presents the results of investor sophistication test. The dependent variables are Raw Return, Market-adjusted Abnormal Return and Size-
adjusted Abnormal Return in column (1), (2) and (3), respectively. We divide the full sample into High-IS sub-sample (firms with high investor
sophistication) and Low-IS subsample (firms with low investor sophistication). A firm is identified as being with high (low) investor sophistication
if its Inst_Percent is higher (lower) than the sample median. Inst_Percent is the percentage of ownership by institutional investors. All variables are
defined in Appendix 1. All variables except dummy variables are winsorized at the 1st and 99th percentile values. Heteroskedastic-robust and
cluster-adjusted t-statistics appear in brackets. *, **, *** denote two-tailed statistical significance at the 10%, 5%, and 1% levels, respectively.
(1)
Raw Return
(2)
Market-adjusted Abnormal Return
(2)
Size-adjusted Abnormal Return
High IS Low IS High IS Low IS High IS Low IS
CutRD -0.021*** -0.001 -0.022*** -0.002 -0.018*** 0.001
[-3.166] [-0.151] [-3.438] [-0.342] [-2.748] [0.174]
ERDSurprise 0.011* 0.000 0.009 -0.003 0.010* -0.003
[1.918] [0.031] [1.571] [-0.700] [1.698] [-0.763]
△RD -0.005 -0.001 -0.006 -0.002 -0.006 -0.001
[-0.818] [-0.454] [-0.985] [-0.735] [-1.062] [-0.525]
SIZE 0.001 0.004* 0.002 0.003 0.003 0.002
[0.545] [1.953] [0.888] [1.473] [1.179] [1.211]
BM 0.014* 0.027** 0.014* 0.027** 0.014* 0.022*
[1.730] [2.016] [1.660] [2.253] [1.666] [1.784]
Momentum 0.014 0.004 0.013 0.006 0.013 0.005
[1.327] [0.338] [1.221] [0.604] [1.255] [0.510]
Constant -0.015 -0.020 -0.021 -0.016 -0.028 -0.012
[-0.794] [-0.491] [-1.077] [-0.396] [-1.444] [-0.314]
Firm Fixed Effects Yes Yes Yes Yes Yes Yes
Year Fixed Effects Yes Yes Yes Yes Yes Yes
Observations 1,073 1,073 1,073 1,073 1,073 1,073
Adjusted R2 0.002 0.019 0.006 0.015 0.004 0.007
Difference of coefficients
on CutRD
High IS – Low IS
p=0.028**
High IS – Low IS
p=0.025**
High IS – Low IS
p=0.026**
33
Table V Summary Statistics for CEO Compensation Test This table presents the descriptive statistics of the variables used in CEO compensation test. The sample consists of 324 firm-year observations. △Total pay, △Cash Pay, and △Noncash Pay are the change of CEO’s total pay, cash pay, and noncash pay. Cash pay is the sum of salary and annual bonus. Noncash pay is the
sum of the value of equity grants during the year, fringe benefits, and other long-term incentive plans, with stock options valued at the end of the fiscal year using
Black-Scholes 1973 model adjusted for dividends. Total pay is the sum of cash pay and noncash pay. △ROA is change of ROA. ROA is calculated as net income
divided by total assets. △RD is R&D of a given year minus R&D of the prior fiscal year. RET is annual stock return. SIZE is the logarithm of total assets. Q is
calculated as the book value of assets minus the book value of equity, plus the market value of equity, scaled by the book value of assets at year t. LEV is total
debts divided by total assets. Ownership is the CEO ownership as a percentage of shares outstanding. Tenure is the number of years as CEO of the firm. Eindex is
an index based on six antitakeover provisions: staggered board, poison pills, the supermajority requirement for mergers, limits to the shareholder bylaw
amendments, limits to the charter amendments, and golden parachutes. Board size is the natural log of number of directors on board. Board independence is
percentage of independent directors on board. CEO duality is a dummy variable which equals 1 if CEO and Chairman of the board are the same person, 0
otherwise. *, **, and *** denote significance at the 10%, 5%, and 1% level, respectively, two-tailed. Myopic Cutters (N=87) Non-Cutters (N=237) Difference
Variable
(1)
Mean
(2)
Median
(3)
Std Dev.
(4)
Mean
(5)
Median
(6)
Std Dev.
(1)-(4)
p-value
(2)-(5)
p-value
△Total Pay 0.223 0.032 0.526 -0.094 -0.056 0.594 <0.000*** <0.000***
△Cash Pay 0.149 0.090 0.382 -0.125 0.000 0.388 <0.000*** <0.000***
△Noncash Pay 0.074 -0.026 0.638 0.031 0.002 0.653 <0.000*** <0.000***
△ROA 0.000 0.003 0.079 -0.041 -0.027 0.060 <0.000*** <0.000***
△RD -0.171 -0.018 0.565 0.068 0.006 0.433 <0.000*** <0.000***
RET 0.144 0.039 0.521 0.102 0.032 0.468 0.342 0.265
SIZE 7.150 6.658 1.701 7.161 7.021 1.520 0.959 0.449
Q 2.079 1.831 0.914 2.248 1.893 1.157 0.263 0.162
LEV 0.176 0.151 0.166 0.170 0.168 0.152 0.772 0.449
Ownership 0.016 0.003 0.047 0.018 0.003 0.043 0.802 0.893
Tenure 8.185 6.000 6.602 8.804 6.000 7.375 0.526 0.745
Eindex 2.569 3.000 1.299 2.442 3.000 1.303 0.466 0.836
Board Size 1.955 2.079 0.367 1.932 1.946 0.371 0.647 0.211
Board Independence 0.725 0.769 0.164 0.705 0.750 0.170 0.382 0.253
CEO Duality 0.554 1.000 0.501 0.659 1.000 0.475 0.102 0.920
34
Table VI CEO Compensation and Managerial Myopia
This table presents the results of CEO compensation test. The dependent variables are the change
of CEO’s total pay, cash pay, and noncash pay for column (1), column (2) and column (3),
respectively. All variables are defined in Appendix 1. All variables except dummy variables are
winsorized at the 1st and 99th percentile values. Heteroskedastic-robust and cluster-adjusted t-
statistics appear in brackets. *, **, *** denote two-tailed statistical significance at the 10%, 5%,
and 1% levels, respectively.
(1) (2) (3)
Variable △Total Pay △Cash Pay △Noncash Pay
CutRD 0.631** 0.370** 0.261
[2.122] [2.086] [0.728]
△ROAi,t -0.188 -0.135 -0.053
[-0.137] [-0.198] [-0.033]
△RDi,t 0.000 -0.000 0.000
[1.089] [-0.576] [0.985]
RETi,t 0.200 0.062 0.138
[0.729] [0.397] [0.446]
SIZEi,t-1 -0.025 -0.154 0.130
[-0.059] [-0.742] [0.279]
Qi,t-1 0.178 -0.052 0.230
[1.165] [-0.706] [1.286]
LEVi,t-1 0.447 0.261 0.186
[0.351] [0.558] [0.147]
Ownershipi,t-1 -5.990 -8.038 2.048
[-0.643] [-1.318] [0.195]
Tenurei,t -0.022 0.004 -0.026
[-0.883] [0.278] [-1.134]
Eindexi,t-1 -0.129 0.083 -0.211
[-0.882] [0.864] [-1.189]
Board Sizei,t-1 0.122 -0.011 0.133
[0.125] [-0.026] [0.134]
Board Independencei,t-1 0.428 0.284 0.144
[0.226] [0.363] [0.075]
CEO Dualityi,t-1 0.300 -0.032 0.332
[0.686] [-0.162] [0.727]
Constant -0.614 0.750 -1.364
[-0.203] [0.566] [-0.422]
Firm Fixed Effects Yes Yes Yes
Year Fixed Effects Yes Yes Yes
Observations 324 324 324
Adjusted R2 0.097 0.331 0.088
35
Table VII Future Performance and Managerial Myopia
This table presents results on regressing future stock market and financial performance on myopic
R&D cut and control variables. Future Return is the one-year stock return from the earnings
announcement date. Future ROA is return on assets of next fiscal year end. Future ROE is return on
equity of next fiscal year end. All variables are defined in Appendix 1. All variables except dummy
variables are winsorized at the 1st and 99th percentile values. Heteroskedastic-robust and cluster-
adjusted t-statistics appear in brackets. *, **, *** denote two-tailed statistical significance at the
10%, 5%, and 1% levels, respectively.
(1) (2) (3)
Variable Future Return Future ROA Future ROE
CutRD -0.173** -0.054*** -0.159**
[-2.530] [-2.586] [-2.015]
ERDSurprise 0.000 -0.000 0.000
[0.074] [-0.055] [0.225]
△RD -0.001 -0.000* -0.000
[-1.427] [-1.925] [-1.437]
SIZE -0.212*** 0.038** 0.088
[-3.335] [2.309] [1.579]
BM 0.105 -0.001 0.054
[0.747] [-0.043] [0.647]
Momentum -0.015 0.069** 0.205**
[-0.139] [2.286] [2.088]
Constant -0.122 -0.192** -0.557**
[-0.302] [-2.511] [-2.274]
Firm Fixed Effects Yes Yes Yes
Year Fixed Effects Yes Yes Yes
Observations 2,896 2,896 2,896
Adjusted R2 0.170 0.609 0.405