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SMU Classification: Restricted
Stock-Based Compensation, Financial Analysts,
and Equity Overvaluation
Presented by
Partha Mohanram University of Toronto
The views and opinions expressed in this working paper are those of the author(s) and not necessarily those of the School of Accountancy, Singapore Management University.
Stock-based Compensation, Financial Analysts, and Equity Overvaluation1
Partha Mohanram
Rotman School of Management
University of Toronto
Brian White
McCombs School of Business
University of Texas at Austin
Wuyang Zhao
McCombs School of Business
University of Texas at Austin
October 27, 2019
ABSTRACT:
Stock-based compensation (SBC) reduces the value of shareholder equity, ceteris paribus,
and is a significant and growing expense for many firms. Despite its valuation implications and its
growing importance, anecdotal evidence suggests that market participants ignore SBC in
valuation. We first find that firms with higher SBC exhibit both higher valuation ratios and lower
returns, suggesting overvaluation. In particular, such pattern becomes stronger for firms with larger
analyst coverage, implying that the sell-side optimism is an important driver of the overvaluation.
We then examine how financial analysts treat SBC in their valuation models. We find that analysts
exclude SBC in their street earnings forecasts, and provide more optimistically-biased target prices
for firms with higher SBC. A hand-collected sample of analyst reports indicates that analysts who
ignore SBC in valuation derive optimistically-biased price targets, whereas analysts who treat SBC
as an expense are unbiased on average. Together, our evidence indicates that market participants’
failure to account for stock-based compensation as an expense leads to the overvaluation of equity.
Keywords: stock-based compensation, financial analysts, overvaluation, non-GAAP, free cash
flow, discounted cash flow (DCF)
We thank Aswath Damodaran and Ross Jennings, an anonymous reviewer, and seminar participants at University of
Texas at Austin for their valuable comments. We appreciate the able research assistance provided by Yiying Chen,
Yibo Wang, and especially Pallavi Ram. Partha Mohanram acknowledges financial support from the Social Sciences
and Humanities Research Council (SSHRC) of Canada.
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Stock-based Compensation, Financial Analysts, and Equity Overvaluation
Stock-based compensation (henceforth SBC) is a significant and growing expense for
many firms. From fiscal year 2006 to 2018, mean (median) SBC as a percentage of operating
expense has increased almost monotonically year over year from 2.6% to 3.8% (0.8% to 1.4%) in
the universe of firms on Compustat & CRSP. Despite its importance, however, many firms and
market participants appear to ignore the expense associated with SBC. For example, Barth et al.
(2012) show that most firms exclude SBC expense in their non-GAAP earnings. Black et al. (2018)
find that SBC-related exclusions in non-GAAP EPS more than doubled from $0.31 per share in
2009 to $0.64 per share in 2014. Furthermore, anecdotal evidence indicates that very few analysts
consider the expense associated with options in their valuation models, suggesting that analysts
mimic firm managers’ practice of ignoring SBC (Mauboussin 2006; Lundholm and Sloan 2017,
238). In this paper, we investigate whether SBC leads to overvaluation in equity markets, and the
role played by analysts in the relationship between SBC and valuation.
Excluding SBC expense is likely to lead to overvaluation, regardless of whether analysts
use earnings-based or cash flow-based valuation models. In a recent opinion piece, Damodaran
(2019) warns that SBC can lead to overvaluation as firms are often valued based on multiples of
adjusted earnings, which in turn are frequently calculated by adding back the expenses associated
with SBC. Even when analysts use discounted cash flow (DCF) models, SBC can lead to
overvaluation if the expense associated with SBC is added back as a non-cash expense, along with
other non-cash expenses such as depreciation, in the calculation of free cash flows. As the DCF
model is based on cash flows and not accrual-based income, it adds back to earnings non-cash
expenses like depreciation, but subtracts out forecasted capital expenditures, which are the cash-
based analog to depreciation expense. However, unlike depreciation, there is no commonly
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accepted cash flow analog to SBC, so there is no corresponding subtraction as there is for capital
expenditures. But is stock based compensation truly free? The answer is clearly no. Firms will
either have to spend cash in repurchasing shares from the open market to reduce the impact of
dilution, or dilute the ownership of existing shareholders. As a result, if analysts do not account
for SBC appropriately in their DCF models—either by treating it as akin to a cash expense or,
arguably more difficult, accounting for cumulative and expected future dilution effects of issuing
more shares—DCF-based valuations will also be inflated (Guay et al. 2003, Damodaran 2005,
Bhojraj 2019).
Motivated by the divergence between the economic reality of SBC as an expense and
market participants’ apparent tendency to ignore SBC, we examine whether and how SBC leads
to overvaluation in this paper. First, we test whether the extent of SBC has an impact on firm’s
valuation ratios and future returns and whether analysts play a role in the process. Next, we
examine how SBC affects analyst behavior by testing how SBC affects exclusions in analysts’
street earnings forecasts and the well-documented optimism in their target price forecasts. Finally,
to show direct evidence that ignoring SBC leads to overvaluation, we hand-collect a sample of 585
analyst reports and focus specifically on whether the inclusion or exclusion of SBC by financial
analysts has an impact on their valuation estimates.
To answer our first research question, regarding whether higher SBC firms tend to be
overvalued, we look at the impact of SBC on four common valuation ratios: historical and forward
price-to-earnings or P/E; price-to-sales or P/S, in order to include loss firms; and price-to-intrinsic
value or P/V, which has been used in the literature as a measure of overvaluation (see Frankel and
Lee 1998; Badertscher 2011; Li and Mohanram 2019). We find that as SBC intensity (defined as
the ratio of SBC over sales) increases from the bottom to the top quintile within an industry, the
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average forward P/E ratio increases monotonically from 19.58 to 27.12. We find similar trends for
the average historical P/E ratio, P/S ratio, and P/V ratio. However, we also find that firms with
higher SBC intensity have significantly higher growth, as measured by past sales growth, expected
EPS growth, R&D intensity, suggesting that the relationship with higher valuation ratios is
endogenous to the expected growth.
We deal with the endogeneity in two ways. First, we carry out several sets of change
analyses, focusing on the impact of year-over-year changes in SBC intensity on the change in the
valuation ratios. In our baseline change analyses, we find that increases in SBC intensity are
associated with increases in valuation ratios, and more strikingly, decreases in SBC intensity are
associated with decreases in valuation ratios. When we first sort on the lagged SBC intensity or
lagged valuation ratios, we still see significant differences in change-in-valuation ratios between
firms that increase versus decrease SBC intensity; moreover, the differences increase with the
lagged SBC intensity or lagged valuation ratios. Second, we regress the valuation ratios on their
expected determinants and see whether we find an incremental relationship with SBC intensity.
After controlling for the effects of risk and growth, we continue to find a strong positive
relationship between SBC intensity and each of our four valuation measures. More strikingly,
when we replace all variables with their year-over-year change values, we continue to find a
positive relation between the change in SBC intensity and changes of all valuation ratios except
P/V. Taken together, various specifications provide the same inference: SBC is associated with
higher market valuation.
To confirm that the higher valuation we document is indeed overvaluation, we examine
ex-post returns over one year. We find that firms in the highest quintile of SBC have the lowest
ex-post returns, which are about 5% lower than those of firms in the bottom quintile. We continue
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to find this pattern after controlling for common risk-factors using calendar-time portfolio
approach and monthly Fama-MacBeth regressions, suggesting that differences in risk do not
explain this result. As with other market anomalies, comparing equal-weighted with value-
weighted results suggests that the market’s overvaluation of high SBC firms is concentrated in
smaller firms (e.g., Fama and French 2008; Hou, Xue and Zhang 2019).
What exactly is the market missing when it overvalues high SBC firms? To provide
evidence on this question, we examine two possible channels through which SBC can affect value:
future share buybacks and future dilution effects of issuing new shares. Over one-, three-, and five-
year windows, we find that both buybacks and dilutions increase monotonically from the bottom
to the top within-industry quintile of SBC intensity, suggesting that the market ignores both cash
and dilution effects of SBC.
We next explore the role played by financial analysts in the relation between SBC and
valuation. Financial analysts’ treatment of SBC is potentially important for valuation, as analysts
are arguably the most important information intermediary, whose research is followed closely by
the market and has a profound impact on asset pricing (see two recent comprehensive reviews by
Kothari et al. 2016 and Bradshaw et al. 2017). Using regression analyses in both levels and
changes, we find that the positive associations between SBC intensity and all valuation metrics
except P/S exist only for firms with analyst coverage, and become stronger as coverage increases.
These results suggest that the sell-side analysts are an important driver of the market’s
overvaluation of firms with high SBC.
Next, we explore how SBC affects analysts’ equity valuation. We find evidence that, on
average, analysts exclude SBC in their street earnings forecasts, and consequently provide more
optimistic (and ex post more biased) target prices for firms with high SBC intensity. The mean
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difference between individual analysts’ street and GAAP earnings forecasts scaled by price is 0.7%
for firms in the bottom quintile of SBC intensity; the difference increases monotonically and
almost doubles to 1.3% for firms in the top quintile. As analysts primarily use street earnings as a
basis for valuation (Gu and Chen 2004), their exclusion of SBC in such forecasts could contribute
to optimism in their valuation estimates and target prices. Indeed, we find that individual analysts’
target prices are 4.4 percentage points more optimistic for firms in the top quintile of SBC intensity
than for firms in the bottom quintile (17.8% vs. 13.4% higher than the current price). Such
optimism is not justified by the subsequent realized returns: the target prices of firms in the top
quintile of SBC intensity are also on average 4.4 percentage points more optimistically biased than
firms in the bottom quintile (mean target price forecast error of -11.4% vs. -6.9%).
The analyses up to this point are based on how we conjecture analysts treat SBC in their
valuation estimates. To provide more direct evidence on how analysts actually use these numbers,
we analyze a sample of 585 sell-side equity analyst reports for firms with high levels of SBC, and
which explicitly report a DCF valuation. We focus on DCF not only because it is the most popular
formal valuation model, but because such reports typically provide detailed information about how
the analysts treat SBC in valuation. We read these reports in detail and codify whether analysts
include or exclude SBC in their calculation of free cash flows for the DCF model. Consistent with
anecdotal evidence in Mauboussin (2006), a vast majority of analysts (356 reports) ignore SBC as
an expense in their valuation models, either by starting their calculation of free cash flows from
cash flow from operations, which excludes SBC, or by explicitly adding back SBC in their
calculation. However, we also find a significant minority (148 reports) of analysts who do treat
SBC as an expense, while the remaining 81 reports are unclear in this regard.
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We compare the implied returns associated with the DCF-derived target prices, and the ex-
post target price bias, for analysts who exclude SBC with those who include it. We find that the
subgroup that excludes SBC has much more optimistic target prices, with target price implied
returns of 19% that are significantly over-optimistic by 8.8%. Conversely, the subgroup that
includes SBC as an expense has lower target prices that are far less likely to be biased, with target
price implied returns of 12.7%, which underestimate actual returns by 2.2% (insignificant). These
inferences remain qualitatively the same if we focus only on firm-years with at least one report
with SBC inclusion and one report with SBC exclusion, or if we match each SBC inclusion report
with another exclusion report for the same firm issued at approximately the same time.
This paper contributes to the literature on SBC by examining how the market (as
represented by analysts) treats SBC and the ensuing valuation consequences. In particular, our
results provide strong evidence that the market tends to ignore SBC as an expense and that this
tendency results in the overvaluation of high SBC firms. Thus, our findings suggest that the market
fails to fully understand the valuation implications of SBC, which creates potential arbitrage
opportunities for informed traders, as Bhojraj (2019) conjectures. Importantly, our focus on the
market’s treatment of SBC distinguishes our paper from a separate stream of research that
examines the impact of executive compensation on firm performance (e.g., Murphy 1999; Core,
Guay and Larcker 2003). For example, Cooper, Gulen and Rau (2016) find that excess CEO
compensation is associated with lower future operating performance and stock returns, and
increased analyst forecast errors. In contrast to this literature, we study a different construct (SBC
granted to all employees) that affects valuation via a different mechanism (market participants’
tendency to ignore SBC).
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We also contribute to the literature on financial analysts in two ways. First, our evidence
linking analysts’ treatment of SBC with the overvaluation of firms with high SBC adds to the
growing literature on how analysts create market frictions (e.g., Engelberg et al. 2019; Guo et al.
2019). Second, we add to the target price literature by identifying a specific driver of analysts’
target price optimism: ignoring SBC as an expense. Given the well-documented evidence of
optimistic bias embedded in analysts’ target prices (see Bradshaw et al. 2013 for US evidence,
Bradshaw et al. 2019 for international evidence), it is remarkable that the minority of analysts in
our hand-collected sample who include SBC as an expense exhibit no optimistic bias in their target
prices.
Our study should also be of interest to practitioners and regulators. For practitioners, and
especially financial analysts, our results indicate that excluding SBC in deriving DCF-based
valuations contributes to their optimistic bias in valuation. Thus, our results support the
suggestions of Damodaran (2005; 2019) and Lundholm and Sloan (2017, 238), who suggest that
SBC should be subtracted as an expense in the calculation of free cash flows for DCF valuation,
essentially treating SBC as a cash expense. For regulators, who have taken an interest in the
proliferation of non-GAAP reporting, our paper identifies one potential consequence of misleading
non-GAAP reporting: overvaluation. As Bhojraj (2019) advocates, our results suggest that it would
be helpful for regulators to nudge managers to recognize SBC as an expense. This is particularly
relevant for cash flow metrics, as Adame et al. (2019) find that investors have become more
responsive to free cash flow surprises over time.
The rest of the paper is organized as follows. Section I tests the relationship between SBC
and valuation. Section II tests the impact of SBC on exclusions in analysts’ earnings forecasts and
target prices. Section III presents results from our hand-collected sample. Section IV concludes.
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I. STOCK BASED COMPENSATION AND VALUATION
In this section, we answer the question of whether SBC affects valuation. We start by
documenting a baseline association between SBC intensity and four common valuation ratios.
Then, we use various methods to address the possible endogeneity between high valuation ratios
and expected growth. Next, we examine the relation between SBC intensity and future returns to
confirm whether high valuation is indeed overvaluation. Finally, we test the mechanism by which
SBC imposes future costs on firms by examining whether firms with high SBC either see higher
dilution of stock ownership or expend resources in preventing dilution.
A. Research design: SBC and valuation metrics
We measure our main variable of interest, SBC intensity (SBCINT), as the ratio of stock-
based compensation over sales (STKCO/REVT) at the fiscal year end. We consider four valuation
ratios used in the prior literature, each of which is calculated as the ratio of price over a certain
base for value. Following Li and Mohanram (2014; 2019), we use the closing price at the end of
June to calculate all valuation ratios.
Our first ratio is forward price-to-earnings ratio (P/E1), where P is the closing price at the
end of June, and E1 is the forecasted EPS in year t+1 based on the cross-sectional forecasting
model. Earnings forecasts are based on the Residual Income forecasting model in Li and
Mohanram (2014), as explained in detail in Appendix A. We use cross-sectional forecasts rather
than analyst forecasts to maximize sample size and to avoid the well-documented bias in analyst
forecasts, which is important in this paper because such a bias is likely to be associated with SBC.
We also use a historical price-to-earnings ratio (P/E0), defined as the market cap at the end of June
divided by the latest available earnings (June-end price * SHROUT/IB). To ensure the availability
of annual financial information when portfolios are formed at the end of June, we follow prior
9
literature (e.g., Li and Mohanram 2014) and use EPS for fiscal years ending between April of year
t-1 and March of year t.
One key drawback of P/E ratios is that they are undefined for loss firms. To include loss
firms, we use the price-to-sales or P/S ratio, defined as the market cap at the end of June divided
by the latest available sales (June-end price * SHROUT/REVT). Akin to P/E0, we use sales from
fiscal years ending between April of year t-1 and March of year t. Finally, we consider the price-
to-intrinsic value or P/V ratio, which is widely used in the prior literature to measure overvaluation
(e.g., Frankel and Lee 1998, Badertscher 2011; Li and Mohanram 2019). To derive the intrinsic
value V, we first use the cross-sectional forecasting model to generate earnings forecasts for years
t+1 to t+5. We then calculate the intrinsic value of stocks based on the residual income model, as
detailed in Appendix A.
B. Sample construction
Table I presents a summary of our sample selection procedure. We begin with the universe
of 58,412 firm-year observations by 8,371 unique firms of the U.S. companies in the Computat &
CRSP intersection from fiscal year 2006 to 2017.1 The final sample varies for different valuation
ratios, ranging from 44,107 firm-year observations by 6,366 unique firms for P/S ratio, to 29,507
firm-year observations by 4,913 unique firms for historical P/E ratio. All continuous variables
except those related to future returns are winsorized at the 1% and 99% level.
C. Relationship between SBC and valuation ratios: Quintile analyses
Table II, Panel A reports means and medians of SBCINT, P/E1, P/E0, P/S, and P/V across
SBCINT quintiles, where quintiles are formed annually within industry (i.e. by Fama-French 49
1 Our sample starts in 2006 because SFAS 123-R came to effect in 2005, after which all public firms were required to
recognize SBC expense in the income statement. It ends in 2017 for tests in Section II because we need the closing
price in June at least three months after the fiscal year ends to calculate all four ratios.
10
industry and fiscal year).2 We find considerable variation in SBCINT: the bottom quintile has a
mean (median) of 0.3% (0.1%) but the top quintile has a mean (median) of 51.9% (4.0%). More
importantly, SBCINT exhibits strong positive associations with all valuation metrics. Moving
from the bottom to the top SBCINT quintile, mean P/E1 increases monotonically from 19.58 to
27.12, mean P/E0 from 22.12 to 31.10, mean P/S from 1.67 to 9.05, and mean P/V from 1.43 to
2.64. In all cases, the differences between extreme quintiles are highly significant statistically, with
t statistics all larger than 20. The medians show similar, strictly monotonic patterns.
One may argue that the strong positive associations between SBCINT and valuation
metrics are hardly surprising, because firms with good growth opportunities are more likely to pay
employees with SBC and such firms also enjoy higher valuation. For example, Frye (2004) finds
a significantly positive association between Tobin’s q and the percentage of SBC in total employee
compensation. Indeed, Panel B shows that sales growth (SGR, defined as the year-over-year
change in the sales divided by sales in the prior year), expected EPS growth (EPSGR, defined as
the annual growth from EPS1 to EPS5 derived from the cross-sectional forecasting model,
excluding firms with negative EPS1), and R&D intensity (RDINT, defined as the ratio of R&D
expenditure over sales) increase largely monotonically and significantly as SBCINT moves from
the bottom to top quintile. For example, mean SGR increases from 6.5% to 15.0%, mean EPSGR
from 12.1% to 12.6%, and mean RDINT from 2.1% to 19.5% from the bottom to the top quintile
of SBCINT, with similar patterns for medians. Thus, in examining the relationship between SBC
and valuation, we need to address the endogeneity related to expected growth opportunities.
2 As the number of observations in each industry-year cell is not necessarily divisible by 5 without a remainder, each
quintile has a slightly different number of observations. Note that we form quintiles within industry in order to control
for confounding industry-specific factors such as competition, regulation, and growth that affect both SBC and
valuation. Further, analysts usually cover firms in the same industries.
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D. Change analyses
Our first remedy for the endogeneity with growth is to conduct change analyses by
examining the relationship between the change in SBCINT and changes in valuation ratios. This
approach can be effective if the portion of SBCINT correlated with growth does not change over
a short period of time and can be eliminated by taking the year-over-year difference in SBCINT.
Panel A of Table III presents the results. We use the prefix “Δ” to indicate a change variable
that is calculated as the year-over-year change of the original variable (i.e., first difference). We
sort ΔSBCINT into quintiles within each industry-year, and examine the means and medians of
changes in valuation ratios. We find that ΔSBCINT also exhibits considerable variation: the mean
(median) is -9.5% (-0.7%) in the bottom quintile and 6.1% (0.6%) in the top quintile. More
importantly, changes in all our valuation ratios increase largely monotonically as ΔSBCINT
increases, and valuation ratios always change in the same direction with ΔSBCINT. Moving from
the bottom to the top quintile of ΔSBCINT, mean ΔP/E1 increases from -2.25 to 0.29, mean ΔP/E0
from -3.24 to 0.79, mean ΔP/S from -1.84 to 0.72, and mean ΔP/V from -0.19 to 0.07, with all
increases being highly significant (t-stats > 6). Medians show similar, largely monotonic patterns.
One criticism of the above change analyses is that it is not a ceteris paribus analysis—
firms with increasing SBC intensity might be very different from firms with declining SBC
intensity. To control for this, we carry out two additional change analyses by first sorting firms
based on either their lagged SBC intensity or lagged valuation ratios, and then examining the
difference in valuation ratio changes between firms with SBCINT increases vs. decreases.
In Panel B of Table III, we first sort all observations into quintiles based on lagged SBCINT
and then further sort them based on whether SBCINT increases or decreases. We find that after
controlling for lagged SBC intensity, the difference in lagged valuation ratios is generally
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insignificant. However, in most SBCINT quintiles across most valuation ratios (except ΔP/E0 and
ΔP/S in the bottom quintile), we find significant difference between the change in ratios based on
whether SBC intensity increases or decreases. Moreover, the results are weak for the low lagged
SBCINT quintile but become much stronger as the level of lagged SBCINT increases.
In Panel C of Table III, we first sort all observations into quintiles based on lagged
valuation ratios and then further sort them based on whether SBCINT increases or decreases. We
see strong mean-reversion of valuation ratios – firms with lower (higher) valuation in the prior
year tend to see an increase (decrease) in valuation ratios. Importantly, we find that controlling for
lagged valuation ratios is effective – the difference in lagged valuation ratios between firms with
increased versus decreased SBCINT is generally insignificant within each lagged ratio quintile.
However, we find significant differences between the change in valuation ratios between these two
groups (i.e., SBCINT increase vs. decrease). In all valuation quintiles, the SBC increase group
experiences a much greater increase (or much smaller decline) in valuation ratios compared to the
SBC decrease group. Overall, our three sets of change analyses show that increases in SBC
intensity are associated with increases in valuation ratios and vice versa.
E. Regression analyses
Our second remedy for the endogeneity with growth is to explicitly control for proxies of
growth opportunities in regression analyses. We model valuation ratios as a function of risk,
growth, as well as our variable of interest, SBCINT. We expect valuation ratios to be negatively
associated with risk and positively associated with growth. We estimate the following model:
Valuation Ratioi,t = α1*SBCINTi,t + Controlsi,t + industryi * yeart (1)
We use four proxies for risk: market cap (LMCAP), the market beta (β), earnings variance
(σ(EARN)), and financial leverage (LEV). Note that LMCAP is an inverse measure of risk—large
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firms are less risky and all else being equal, should have higher valuation ratios. We use three
proxies for expected growth: expected EPS growth (EPSGR), sales growth (SGR), and R&D
intensity (RDINT). All variables are defined in detail in Appendix A. We also include fixed effects
for the interaction of Fama-French 49 industry and fiscal year, and cluster standard errors by firm
and year to account for potential serial correlations in these two dimensions.
Table IV, Panel A reports summary statistics for all variables, including mean, median,
various percentiles, and standard deviation. Panel B presents the regression results. We find that
the coefficient on SBCINT is significantly positive at the 1% level across all valuation ratios. For
instance, the coefficient on SBCINT in the regression for the forward P/E ratio (P/E1) is 137.70.
In terms of economic significance, an increase in SBCINT from 0.003 in the first quartile to 0.014
in the third quartile is associated with an increase in P/E1 of 1.51 (137.7*(0.014-0.003)), which is
8.7% of the median P/E1 ratio (17.38). With respect to the control variables, we find that the signs
are mostly consistent with expectations. Specifically, LMCAP is positive (large firms are less
risky), is negative, σ(EARN) is negative except for P/S, LEV is negative or insignificant except
for P/E0, SGR is positive, EPSGR is positive or insignificant, and RDINT is positive throughout.
As a final step to address endogeneity, we run our regression analyses in changes and
estimate the following model:
ΔValuation Ratioi,t = α1*ΔSBCINTi,t + ΔControlsi,t + Valuation Ratioi,t-1
+ industryi * yeart (2)
We use the prefix “Δ” to indicate a change variable that is calculated as the year-over-year
change of the original variable. In Equation (2), we regress the changes in valuation ratios on the
change in SBC intensity and changes in all the control variables from Equation (1). We also include
the respective lagged valuation ratio (i.e., lagged dependent variable) as an additional control, as
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our earlier portfolio analyses suggests that valuation ratios tend to mean-revert. As in Equation
(1), we include fixed effects for the interaction of Fama-French 49 industry and fiscal year and
cluster standard errors by firm and year. Panel C presents the regression results for the change
analysis. We find that the coefficient on SBCINT is significantly positive at the 10% or better
level for all valuation ratios except for P/V. This provides strong evidence that the change is SBC
intensity is associated with changes in valuation ratios.
F. Future returns
Our results thus far indicate that SBC is associated with higher valuation. However, higher
valuation in itself does not necessarily imply overvaluation. In this section, we investigate future
returns to determine if SBC is indeed associated with overvaluation.
We first examine the pattern of future annual returns in the one-year period after financial
information is available. We examine compounded annual returns for the twelve-month period
starting on July 1st of the year after fiscal year end, imposing at least a 3-month lag on accounting
information for public availability when forming portfolios at the end of June. We use two return
measures—raw returns (RET1) and market-adjusted returns (RET1M).
The results are presented in Panel A of Table V. The first set of columns presents the results
for mean equally weighted returns. We first compute the mean annual returns by year and then
average the mean returns across the twelve years in our sample from 2006 to 2017. As we move
from the bottom quintile of SBCINT to the top quintile, we find a significant decline in returns.
For instance, firms in bottom decile of SBCINT earn mean market-adjusted returns of 1.52%,
while firms in the top decile earn -3.47%. The difference of -4.99% is highly significant (t-stat = -
3.73). The next set of columns present the results for value-weighted returns. We find that the
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return differences become insignificant, suggesting that the weak returns for high SBCINT firms
are probably driven by smaller firms.
The final set of columns present the mean risk characteristics across the quintiles. High
SBCINT firms tend to have higher . In addition, they appear to be significantly smaller and have
lower B/M ratios (i.e. more likely to be growth firms). To confirm that risk factors are not driving
our results, we run Fama-French calendar-time portfolio regressions using equal-weighted
monthly returns. We include the risk-factors in the 5-factor Fama-French model (Rm-Rf, SMB,
HML, RMW and CMA and also include the momentum factor (UMD). The results are presented
in Panel B of Table V. As the results indicate, firms in the bottom SBCINT quintile earn the highest
excess returns with an of 0.222, while firms in the top SBCINT quintile earn the lowest excess
returns with an of -0.209. The difference in excess returns between the highest and lowest
SBCINT quintiles is -0.431 (t-stat=-3.90). Annualized, this is equivalent to a return difference of
-5.29%. To summarize, our return results confirm that high SBCINT is associated not just with
high valuation, but overvaluation.
As an alternative approach, we run Fama-MacBeth style regressions of future 12-month
compound stock returns (RETm+1, m+12) on SBCINT while controlling for firm characteristics
known to predict future returns: Size (log of market cap or LMCAP), B/M (log of book-to-market
or LBM), short-term momentum (prior month return or RETm), and long term momentum
(compounded prior year return skipping the prior month or RETm-11,m-1). In addition to the raw
SBCINT variable, we also include a transformed z-score of SBCINT with zero mean and unit
variance (SBCINT less industry-year mean scaled by industry-year standard deviation of
SBCINT). We report results based on equal-weighted and value-weighted regressions, where
16
weights are the market cap at the month-end. To mitigate the impact of influential outliers, we
winsorize all variables other than future returns.
We run 136 monthly regressions (September 2006 to December 2017). The summarized
results are presented in Table V, Panel C. SBCINT has a significantly negative coefficient for both
equal-weighted (Coeff. = -0.043; t = -4.49) and value-weighted (Coeff. = -0.047; t = -2.49). When
we use the transformed z-score of SBCINT, the average coefficient is significantly negative for
equal-weighted (Coeff. = -0.029; t = -7.85) but not for value-weighted (Coeff. =-0.001; t = 0.14).
G. The cost of issuing SBC: Evidence from stock buybacks and share dilutions
Finally, we test the mechanism by which SBC imposes future costs on firms by examining
whether firms with high SBC either see higher dilution of stock ownership and/or expend resources
in preventing dilution. Specifically, we examine whether firms with high SBCINT spend more
cash to buy back shares or witness more dilutions in the next several years, or both.
Following prior literature on stock buybacks (e.g., Banyi et al. 2008; Bonaime et al. 2019),
we measure buybacks of each year as the purchase of common and preferred stock minus the
change in the value of preferred stock. To highlight the impact on firms’ cash flows, we scale
buybacks by the net operating cash flow of that fiscal year. We measure dilutions of each year as
the percentage increase of split-adjusted shares outstanding at the fiscal year end relative to that in
the previous year. As SBC usually has a vesting period of several years, we examine the cumulative
buybacks and dilutions in the next one, three, and five years. As we require that firms have data to
calculate the buybacks over the subsequent five years, the sample size is smaller.
Table VI, Panel A presents the results on stock buybacks. SBCINT exhibits strong positive
associations with future stock buybacks. Moving from the bottom to the top SBCINT quintile,
future buybacks as a percentage of operating cash flow increases monotonically from 11.6% to
17
28.2% if we focus on the immediate next year, 34% to 74.8% if we focus on the next three years,
and 61.9% to 129.6% if we extend our focus to the next five years. In all cases, the differences
between extreme quintiles are highly significant statistically (t statistics > 14). The results suggest
that firms are using buybacks to reverse the dilutive effect of SBC (Kahle 2002; Bens et al. 2003).
For firms that either do not have enough cash or choose not to repurchase shares, we should
observe dilution after SBC-related shares vest. Table VI, Panel B presents the results on stock
buybacks. We find strong positive associations with future share dilutions, although the relation is
not monotonic. Moving from the bottom to the top SBCINT quintile, future share dilutions
increase from 4.8% to 8.0% for one year, 21.7% to 36.1% for three years, and 57.0% to 105.4%
for five years. In all cases, the differences between extreme quintiles are highly significant
statistically (t statistics > 8). Taken together, the results in this table show that issuing SBC is
costly in terms of either spending cash in buying-back shares, or allowing share based being
diluted, or both. These results also corroborate findings in Table V that firms with high SBCINT
tend to earn lower abnormal returns by identifying two specific channels.
II. STOCK BASED COMPENSATION AND ANALYST BEHAVIOR
The prior section shows evidence consistent with equity markets overvaluing firms with
high SBC. In this section, we focus on the role of sell side analysts. We first examine the
association between sell side analysts and the overvaluation documented in the prior section. We
then analyze how analysts treat SBC and how this might influence their valuation.
A. The role of financial analysts
We focus on financial analysts because they are arguably the most important information
intermediary in capital markets, with their research having a profound impact on price discovery
and market efficiency (see Kothari et al. 2016). Their research outputs are closely followed by the
18
investing community (e.g., Mikhail et al. 2007), including major institutional investors such as
mutual funds (e.g., Brown et al. 2015). As a result, the market price tends to reflect the sell-side’s
overall optimism, which has been well-documented in the literature.3 Indeed, Engelberg et al.
(2019) and Guo et al. (2019) both find that analysts tend to recommend buys for stocks that are
classified as overvalued by various anomaly variables, suggesting that they contribute to
mispricing to the extent that investors follow their recommendations. If this is the case in the
context of SBC, we would expect that the pattern we observe in prior subsections is stronger
(weaker) for firms with more (less/no) analyst following.
We focus on regression analyses to test this prediction, for the purposes of both brevity and
controlling for factors that are correlated with both valuation and analyst coverage. Specifically,
we create a variable of analyst coverage (LnAnalyst), defined as the log of one plus the number of
analysts in IBES who provide EPS forecasts for a firm in the fiscal year. We then include this
variable and its interaction with SBCINT or ΔSBCINT in Equation (1) or (2), respectively. If
analysts contribute to the high valuation of firms with high SBC, we would expect the coefficients
of the interaction terms to be positive.
Table VII, Panel A reports the results of levels regressions. The coefficient of the
interaction term (SBCINT×LnAnalyst) is significantly positive at the 1% level for all valuation
metrics except P/S, indicating that the association between valuation and SBCINT is stronger for
firms with more analyst coverage. In other words, these results suggest that the sell-side
community is one channel through which firms with high SBC enjoy high valuation. Moreover,
we note that the main effect is insignificant in three out of four columns (except P/S), suggesting
3 The prior literature has documented various incentives of analysts contributing to the observed optimism in their
research outputs, such as generating more trading (e.g., Jackson 2005), and building management relationships for
future banking business (e.g., Lin and McNichols 1998) and access of information (e.g., Chen and Matsumoto 2006)).
19
that analysts are not merely one channel, but a primary channel through which SBC leads to
overvaluation. Table VII, Panel B reports the results of changes regressions. The coefficient of the
interaction term (ΔSBCINT×LnAnalyst) is significantly positive at the 5% level for ΔP/E1,
positive but insignificant for ΔP/E0 and ΔP/V, and negative but insignificant for ΔP/S. Thus, these
results broadly support our conjecture that the sell-side plays a vital role in the overvaluation of
firms with high SBC.
B. SBC and exclusions in analysts’ street earnings forecasts
To examine how analysts treat SBC in equity valuation, we start with their forecasts of
street earnings, the primary purpose of which is valuation (see First Call documentation as quoted
by Gu and Chen 2004). We conjecture that one reason SBC is associated with overvaluation is that
analysts ignore SBC in estimating value. If this is the case, we should find they tend to exclude
SBC in their street earnings forecasts. In other words, we should expect that the difference between
their street earnings forecasts and GAAP earnings forecasts for the same fiscal period would
increase with SBC intensity of the forecasted firm.
To test the above prediction, we use analysts’ forecasts of both “street” earnings and GAAP
earnings available in IBES Detail History – Detail File with Actuals (EPS and Non-EPS for US),
as in Bratten et al. (2018). Specifically, we start with 1,045,030 unique observations for the fiscal
years ended from 2006 to 2018, where analysts provide both street and GAAP forecasts of annual
EPS for a given firm on the same day. We merge this with Compustat data for stock-based
compensation with CRSP for price information, requiring stock price, measured three days prior
to the forecast date, to be at least $1 (see Bradshaw et al. 2018). Our final sample consists of
874,561 unique forecasts on 6,135 unique firms by 8,874 unique analysts.
20
We measure exclusions in analysts’ street EPS forecasts (Street Exclusion) as street EPS
forecasts minus GAAP EPS forecasts scaled by the closing price three trading days prior to the
forecast date. In addition, we calculate forecast error for street EPS forecasts (Street_Error) and
GAAP EPS forecasts (GAAP_Error) as the respective actual value minus forecast scaled by the
closing price three trading days prior to the forecast date. Finally, we calculate the difference in
forecast error (Error_Diff) as GAAP EPS error minus street EPS error.
Table VIII Panel A presents the results at the individual forecast level. We sort all forecasts
for the same fiscal year in each Fama-French 49 industry into quintiles based on the SBC intensity.
We find that analysts’ street earnings exclusions increase monotonically as we move from the
bottom to the top quintile of the SBC intensity. The magnitude of this increase is also substantial:
it almost doubles from 0.7% in the bottom quintile to 1.3% in the top quintile. The differences in
means between the top and bottom quintiles are highly significant (t = 43.11).4
Turning to forecast errors, we find that GAAP EPS forecast errors are consistently much
larger than street EPS errors across five different SBCINT quintiles. This is consistent with the
findings in Bradshaw et al. (2018), because GAAP earnings include various transitory items that
are difficult for analysts to forecast. We also note that analysts’ street EPS forecasts are more
optimistically biased for the top quintile of SBC intensity than for the bottom quintile. However,
the spread between top and bottom quintile for analysts’ GAAP EPS forecasts are much more
substantial. If we take the difference between these two forecast errors, we find that the mean
difference in the top quintile is much larger than that in the bottom quintile (-0.025 vs. -0.034).
This result is consistent with analysts’ ignoring SBC, because ignoring SBC would contribute to
4 Note that we do not tabulate medians in this table as most medians are zero for all variables across five SBCINT
quintiles. Nevertheless, Wilcoxon sign-rank tests suggest that the differences in medians between top vs. bottom
quintiles are also highly significant for all variables in both panels.
21
larger errors in forecasting GAAP EPS due to the fact that GAAP EPS considers SBC as an
expense while street EPS does not necessarily consider it as an expense.5
Overall, this section provides evidence that analysts tend to exclude SBC in their street
earnings forecasts and that doing so is detrimental to their ability to forecast (particularly GAAP)
earnings. In the next section, we examine the consequence of ignoring SBC in street-earnings
forecasts on analysts’ price targets.
C. SBC and analysts’ target price forecasts
Given that analysts tend to ignore SBC in their street earnings forecasts, we next examine
whether this contributes to the inflated valuation of firms with high SBC. That is, does the
treatment of SBC by analysts contribute to the well-documented optimism in their target prices
(Bradshaw et al. 2013; Bradshaw et al. 2019)? We predict that analysts are more optimistic
regarding firms with high SBCINT than firms with low SBCINT, and the ex post forecast errors
would be consequently larger for firms with high SBCINT.
We use the Detail History Price Target database from IBES to test the above predictions.
We start with 1,176,588 unique firm-analyst-date target price forecasts with a horizon of 12
months from 2006 to 2017. We end our sample in 2017 because we need to examine one-year-
ahead returns. We merge with CRSP for price and return data, and Compustat for SBC, and require
stock price to be at least $1 three trading days before the forecast date, leaving 992,890 forecasts.
Finally, as in Bradshaw et al. (2013), we keep only those forecasts with TP/P ratio lower than 4
and higher than the 1st percentile of the distribution (i.e., 0.2643 in our sample). Our final sample
5 The “actual” street earnings are determined by forecast aggregators such as IBES by adjusting for the GAAP items
excluded by the majority of analysts (Bratten et al. 2018). In other words, if SBC for a certain firm is excluded by the
majority of analysts in their street earnings forecasts, it would also be excluded in the “actual” street earnings as
reported in IBES. In such cases, excluding SBC in street earnings would not lead to higher forecast errors in street
earnings.
22
consists of 948,078 forecasts, from 10,300 unique analysts on 6,240 unique firms. Again following
Bradshaw et al. (2013), we measure the target price optimism (TP_IMPRET) by its implied return
calculated as TP over the closing price three days prior to the forecast date, minus one. We measure
the ex post forecast error (TP_FERR) as realized price in 12 months minus TP scaled by the closing
price three trading days prior to the forecast date.
Table VIII panel B presents the results at the individual forecast level. As before, we form
quintiles based on SBC intensity within a given year and within each Fama French 49 industry.
We find that analyst optimism increases monotonically with SBC intensity. Specifically, for the
firms in the bottom (top) quintile of SBC intensity, analysts forecast stock price increases of 13.4%
(17.8%) in the next 12 months on average. The difference between top and bottom quintile is
highly significant in both mean and median. Further, by benchmarking against realized returns, we
learn that analysts’ forecast optimism is unjustified: while all quintiles exhibit substantial
optimistic bias, the bias in the top quintile (-11.4%) is much larger in magnitude than that in all
other quintiles (-6.9% in the bottom quintile) and the difference between top and bottom quintile
is highly significant in both mean and median.6
III. HOW DO ANALYSTS ACTUALLY TREAT STOCK BASED COMPENSATION?
EVIDENCE FROM A HAND-COLLECTED SAMPLE
We present evidence in Section I that high SBC is associated with overvaluation. In Section
II, we show that analysts’ tendency to ignore SBC as an expense is one channel through which the
market tends to overvalue high SBC firms. However, our analysis thus far is aggregate and indirect.
What we cannot say based on results thus far is the following: analysts exclude SBC in their
calculation of earnings and valuation models, and in doing so, overvalue firms. Is it the case that
6 To show that our results are not driven by a subset of firms or analysts, we replicate Table VII using consensus
forecasts. Our inferences in both panels remain the same.
23
analysts who treat SBC as an expense have more moderate valuations while analysts who ignore
SBC are more likely to have higher valuations? To provide direct evidence, in this section we look
at exactly how individual analysts treat SBC, and how this affects their valuations.
We analyze how analysts’ treatment of SBC affects their valuation by examining a sample
of analyst reports for firms with high levels of SBC. We hand-collect detailed information on how
analysts’ treatment of SBC affects valuation by analyzing the inputs to their discounted cash flow
(DCF) models as disclosed in their reports. We focus on DCF-based reports for two reasons. First,
DCF is the most popular formal valuation method in the sell-side community (e.g., Demirakos,
Strong, and Walker 2004; Hand et al. 2017). Second, analysts who provide DCF-based valuations
are more likely to have detailed disaggregated forecasts that allow us to accurately infer whether
they treat SBC as an expense or not.
A. Data collection
As no readily available database includes the specific inputs that analysts use in their
valuation models, we manually collect this information from analyst reports. Moreover, different
brokers (and even different analysts who work for the same broker) utilize different report formats,
making it difficult to automate the data collection process. To make the data collection
manageable, we focus on a subsample where we would expect to see an effect of SBC on valuation,
should one exist: namely, firms with substantial SBC. Excluding SBC will have greater
consequences and analysts are more likely to explicitly disclose how they treat SBC when it is a
substantial expense.
To identify our sample, we apply the following filters: (1) Firm-years in the intersection of
CRSP and Compustat from 2005 to 2016; (2) total assets (AT), sales (REVT), and market cap all
larger than one billion USD at the end of the fiscal year; (3) SBC intensity higher than 5%; and (4)
24
SBC larger than 10 million USD. We have 345 firm-year from 115 firms after applying these
filters. We then download analyst reports from ThomsonOne, and we are able to find reports for
88 out of the 115 firms (289 out of 345 firm-years). We follow Hand et al. (2017) in identifying
analyst reports that include DCF valuation.7 We call those analyst reports DCF reports. We hired
research associates to identify each analyst’s first DCF report after annual earnings announcements
and collect (1) report information (the names of broker, lead analyst, recommendation, and target
price from the front page), and (2) DCF information (the starting point of FCF calculation, whether
SBC is directly involved in the calculation, and the target price derived from the DCF model).
In our data collection, two variables enable us to infer whether analysts treat SBC as an
expense in their FCF calculations: FCF starting point and SBC involvement. For example, if one
analyst starts from cash from operations (CFO) and does not subtract SBC in the process, then this
analyst does not treat SBC as an expense because SBC is added back during the reconciliation of
net income and CFO. If another analyst starts from net income, but adds back SBC in the
calculation of FCF, then this analyst also ignores SBC expense. Discovering how analysts treat
SBC in DCF turns out to be challenging because many analysts do not present their FCF
calculation explicitly or transparently. We provide a few examples in Appendix B. To ensure
accuracy, each report is double-checked by at least one co-author, and some ambiguous ones by
two.8
7 Following Hand et al. (2017), we use the following filters to identify DCF reports: (1) Date: Custom, 01/01/05 to
12/31/2017; (2) Keyword(s): DCF or “discounted cash flow*” in Table of Contents; (3) Report Type: Company; (4) Geography: United States; (5) Contributor: Non-broker research excluded. 8 We find that most analysts use the same method for all firms at a given point of time (they might change methods
across time possibly due to learning). However, there are a few analysts who use one method for some firms while
use other methods for other firms at roughly the same time. To avoid miscoding, as the final step of data collection,
we check those inconsistencies at the analyst level. Indeed, there are a few such cases with within-analyst/time across-
firm inconsistencies—we are unsure whether this is because of unintentional errors, strategic biases, or both.
25
B. Sample description
For the 289 firm-years by 88 firms with at least one DCF report in our sample period, 86
firm-years by 12 firms do not have any DCF reports in the 12 months after the earnings
announcements. For the remaining 203 firm-years by 76 firms, we identify 585 DCF reports –
each of which is the analyst’s first DCF report after the annual earnings announcement. These
reports are produced by 211 unique lead analysts from 59 brokers. We cannot discern the FCF
calculation in 81 reports, usually because of very limited information on DCF valuation (e.g., some
reports only report the aggregated net present value of future FCFs).
We describe our sample distribution in Table IX. Panel A shows that analysts use various
methods in calculating FCF in their DCF valuation models. The starting point for the FCF
calculation includes CFO, NI, OI, NOPAT (adjusted or not), EBIT (adjusted or not), and EBITDA
(adjusted or not). We provide a few examples in Appendix B. This diversity echoes the lack of
consensus from popular financial statement analysis and valuation textbooks on FCF calculation.
For example, Wahlen, Baginski, and Bradshaw (2016, 912) calculate free cash flow to the firm
(i.e., FCFF) as CFO minus CAPEX (with some adjustments for tax and operating cash), while
Easton et al. (2017, 13-6) calculate it as NOPAT minus increase in net operating assets. These two
approaches treat SBC differently: SBC is ignored in the first approach while it is recognized as an
expense in the second approach. In other words, the first approach would yield higher FCFF than
the second, even with the same set of financial statements.9
We classify reports based on whether SBC is included or excluded as an expense, based on
the starting point for calculating FCF and whether SBC is involved in the calculation. None of the
289 reports starting from CFO subtract SBC in the calculation, echoing the finding in Adame et
9 Wahlen et al. (2016) avoid inflating DCF valuation by skipping the line of SBC when they forecast cash flow
statements based on the information in the forecasted balance sheets and income statements.
26
al. (2018) that the most common definition of FCF by management is CFO minus CAPEX. Of the
215 reports that start from earnings-based metrics, 67 exclude SBC as an expense by explicitly
adding it back, while the remaining 148 reports include SBC as an expense. Thus, SBC is included
as an expense in 148 reports, excluded in 356 reports, and unclear in 81 reports we read, suggesting
that most analysts ignore SBC in their DCF valuations.
Panel B shows the trend in SBC treatment across time. In the earlier half of the sample
period (2005 – 2011), 38 out of 142 or 26.7% of all reports treat SBC as an expense. In the latter
half (2012 – 2018), 110 out of 443 or 24.8% of all reports treat SBC as an expense. There is hence
no evidence of “learning” across time, at least in the aggregate. Panel C shows that there is
considerable variation across brokerages in SBC treatment. The brokers most likely to treat SBC
as an expense are Macquarie, Morgan Stanley, and Wells Fargo, with more than half of their
reports treating SBC as an expense. Other major brokers, including Cantor Fitzgerald, Deutsche
Bank, Jefferies, Piper Jaffray, and UBS consider SBC as an expense in fewer than 10% of their
reports.
C. Impact of SBC treatment on analysts’ DCF valuations
To examine whether including or excluding SBC as an expense has a direct and explicit
impact on DCF valuation, we compare the target prices from reports that treat SBC as an expense
with those that do not. Similar to the prior section, we define target price implied return
(TP_IMPRET) and ex-post forecast error (TP_FERR), using the specific date of each report.
Panel A of Table X presents our main results. For the 145 reports that include SBC as an
expense, the mean (median) implied return is 12.7% (12.6%); while for the 325 reports that exclude
SBC expense, the mean (median) implied return is 19.0% (17.3%).10 The difference in mean
10 We lose 34 observations because of missing target price or daily return data in CRSP.
27
(median) between those two groups is significant at the 5% (10%) level. It is interesting to note
that these numbers are a little higher, but quite close to what we report in Table VIII Panel A on
the top quintile (mean = 17.8%; median = 15.1%). As we focus here on those firms with high SBC
intensity, this is consistent with our inference in Table VIII that higher SBC intensity is associated
with higher implied returns.
Also interesting, the target prices for reports with SBC inclusion do not exhibit optimistic
bias on average—the mean (median) is actually 2.2% (2.2%) lower than the realized price 12
months later, but the difference is insignificant statistically. Given that analysts’ target prices are
optimistically biased (see Bradshaw et al. 2013 for US evidence, Bradshaw et al. 2019 for
international evidence, or our results in Table VIII), it is remarkable that analysts who consider
SBC as an expense produce target prices that are, on average, unbiased. This is consistent with our
conjecture that ignoring SBC contributes to overvaluation, whereas treating SBC as an expense
reduces optimistic bias. By contrast, the target prices from reports that do not treat SBC as an
expense exhibit significant optimistic bias: the mean (median) bias is 8.8% (5.9%), which is
significant at the 1% level. The mean and median difference between reports that include versus
exclude SBC expense are also significant at the 5% level.11
Overall, our results show that reports that treat SBC as an expense are significantly less
optimistic in their target price forecasts, and less biased relative to realized returns. Importantly,
forecast error on average for these reports is insignificant, diverging from the common finding in
the literature that analysts are very optimistically biased in their target price forecasts. These results
11 We note that this bias is smaller than the bias we document in the prior section using the IBES sample. A probable
reason is that these reports all explicitly include a formal DCF analysis, a requirement that likely eliminates most
lower quality, more optimistically biased reports.
28
provide empirical support to Damodaran’s view (2005; 2019) that SBC should be considered as
an expense in the calculation of free cash flows even though it is a non-cash item.
To ensure that our results in Panel A are not driven by differences in covered firms with
SBC inclusion vs. exclusions, in Panel B of Table X we only use reports in 71 firm-years with at
least one SBC inclusion report and one SBC exclusion report. We average across all reports for a
given firm in a given year in each category (i.e., SBC inclusion or exclusion). The mean (median)
difference in implied returns is -3.5% (-4.0%), which is insignificantly different from zero.
However, the mean (median) of the difference in forecast errors is 9.2% (4.5%), which is
significantly different from zero at the 5% level.
We ignore the timing difference of all reports in Panel B as long as they are issued in the
same firm-year. However, market conditions might change dramatically within one year, which
would affect analysts’ forecast returns and errors.12 To address this issue, we deal with the timing
of the reports in Panel C of Table X. Specifically, we match each SBC-inclusion report with one
SBC-exclusion report on the same firm and year, and with the closest report date. To eliminate
large gap between two reports, we require the matched exclusion report to be issued within 30
days before or after the inclusion report. 49 pairs of matched reports survive and all inferences
remain the same. The mean (median) of the difference in implied returns is -6.7% (-13.5%), which
is significant at the 10% (5%) level. For forecast errors, the mean (median) of the difference is
5.9% (7.4%), which is insignificant at the conventional level (significant at the 5% level). Overall,
the within firm-year comparison in Panels B and C provide strong evidence that ignoring SBC
leads to more optimistically biased valuations.
12 Indeed, we note that the mean actual one-year-ahead return for SBC inclusion reports is 0.133 (i.e., 0.123 + 0.010)
while that for exclusion reports is 0.076 (i.e., 0.157 – 0.081). Realized return is the sum of implied return and forecast
error, as implied by the calculation of these two variables in Appendix A.
29
IV. CONCLUSIONS
In this paper, we show that firms with greater stock-based compensation (SBC) tend to be
overvalued in equity markets. Further evidence suggests that such overvaluation occurs because
market participants, particularly financial analysts, ignore the valuation effects of SBC. These
findings are important because SBC makes up an important and growing part of employee
compensation for many firms, raising the possibility of widespread overvaluation if market
participants continue to ignore or discount the effects of SBC in valuation.
Regarding the association between SBC and valuation, we find that common valuation
ratios—forward P/E, historical P/E, P/S and P/V—exhibit significant increases from the bottom to
the top quintile of SBC intensity, with the increases being monotonic across quintiles in almost
every case. These results are unlikely to be explained by an endogenous relationship between SBC
and growth opportunities, as we find similar results using change analyses, operationalized in
several different ways. Further, in regression analyses, we find an incremental, positive result of
SBC intensity on valuation ratios after controlling for risk and growth, using both levels and
changes specifications. Moreover, consistent with higher valuation ratios for higher SBC firms
being overvaluations, returns results indicate lower returns as SBC increases. As to the channel
through which SBC affects firm value, we find that both buybacks and dilutions increase with SBC
intensity, suggesting that the market ignores both cash and dilution effects of SBC.
We also find that analysts play an important role in the market’s overvaluation of high SBC
firms. First, the positive associations that we document between SBC intensity and valuation
metrics exist only for firms with analyst coverage, and become stronger as coverage increases,
implying financial analysts are an important driver of the market’s overvaluation of high SBC
firms. Second, we find large sample evidence that analysts tend not to treat SBC as an expense,
30
as exclusions from analysts’ street earnings forecasts increase monotonically and almost double
from the bottom quintile of SBC intensity to the top quintile. Consistent with these exclusions
biasing analysts’ valuation estimates, we find that analysts’ target prices are significantly higher
and ex post more optimistically biased for firms in the top quintile of SBC intensity compared to
the bottom quintile.
Finally, we hand collect a sample of analyst reports for high SBC firms to provide more
direct evidence that ignoring SBC leads to overvaluation. The key result from this analysis is that
analysts who fail to treat SBC as an expense in their DCF models (and who represent a majority
of analysts in our sample) provide optimistically-biased target prices. In stark contrast, analysts
who treat SBC as an expense provide target prices that are unbiased on average. This latter result
is particularly remarkable, given the extensively-documented optimistic bias in analysts’ target
prices.
To summarize, our results provide strong evidence that the market overvalues high SBC
firm, in large part because analysts tend to ignore the valuation effects of SBC. These results also
suggest opportunities for future research. For example, an unanswered question is why analysts
(and, presumably, other market participants) tend to ignore SBC in valuation. That is, do analysts
who ignore SBC in formal valuation models (e.g., DCF models) do so knowingly, perhaps in order
to justify optimistically-biased forecasts, or unknowingly, because they either underestimate or
fail to understand the valuation effects of SBC? Future research could address this and other
important questions related to the valuation effects of SBC.
31
References
Aboody, D., Barth, M. E., & Kasznik, R. (2004). SFAS No. 123 stock-based compensation
expense and equity market values. The Accounting Review, 79(2), 251-275.
Adame, K., Koski, J., & McVay, S. (2018). Free Cash Flow Disclosure in Earnings
Announcements. University of Washington working paper. Available at SSRN 3134922.
Adame, K., Koski, J., & McVay, S. (2019). Why Are Investors Paying More Attention to Free
Cash Flows? University of Washington working paper.
Badertscher, B. A. (2011). Overvaluation and the choice of alternative earnings management
mechanisms. The Accounting Review, 86(5), 1491-1518.
Banyi, M. L., Dyl, E. A., & Kahle, K. M. (2008). Errors in estimating share repurchases. Journal
of Corporate Finance, 14(4), 460-474.
Barth, M. E., Gow, I. D., & Taylor, D. J. (2012). Why do pro forma and street earnings not reflect
changes in GAAP? Evidence from SFAS 123R. Review of Accounting Studies, 17(3), 526-
562.
Bell, T. B., Landsman, W. R., Miller, B. L., & Yeh, S. (2002). The valuation implications of
employee stock option accounting for profitable computer software firms. The accounting
review, 77(4), 971-996.
Bens, D. A., Nagar, V., Skinner, D. J., & Wong, M. F. (2003). Employee stock options, EPS
dilution, and stock repurchases. Journal of Accounting and Economics, 36(1-3), 51-90.
Bhojraj, S. (2019). Stock compensation expense, cash flows, and inflated valuations. Cornell
University working paper.
Black, D. E., Christensen, T. E., Ciesielski, J. T., & Whipple, B. C. (2018). Non‐GAAP reporting:
Evidence from academia and current practice. Journal of Business Finance &
Accounting, 45(3-4), 259-294.
Black, E. L., Christensen, T. E., Kiosse, P. V., & Steffen, T. D. (2019). The Influence of Manager-
Analyst Interactions on Street Earnings: Evidence from Conference Calls and Excluded
Analysts. University of Georgia working paper. Available at SSRN 2992618.
Bodie, Z., Kaplan, R. S., & Merton, R. C. (2003). For the last time: Stock options are an
expense. Harvard Business Review, 81(3), 62-71.
Bonaime, A. A., Kahle, K. M., Moore, D., & Nemani, A. (2019). The Evolution of Employee
Compensation, Dilution, and Payout Policy. University of Arizona working paper.
Bradshaw, M. T., Brown, L. D., & Huang, K. (2013). Do sell-side analysts exhibit differential
target price forecasting ability? Review of Accounting Studies, 18(4), 930-955.
Bradshaw, M. T., Christensen, T. E., Gee, K. H., & Whipple, B. C. (2018). Analysts’ GAAP
earnings forecasts and their implications for accounting research. Journal of Accounting
and Economics, 66(1), 46-66.
32
Bradshaw, M., Ertimur, Y., & O’Brien, P. (2017). Financial analysts and their contribution to well-
functioning capital markets. Foundations and Trends® in Accounting, 11(3), 119-191.
Bradshaw, M. T., Huang, A. G., & Tan, H. (2019). The Effects of Analyst‐Country Institutions on
Biased Research: Evidence from Target Prices. Journal of Accounting Research, 57(1), 85-
120.
Bradshaw, M. T., & Sloan, R. G. (2002). GAAP versus the street: An empirical assessment of two
alternative definitions of earnings. Journal of Accounting Research, 40(1), 41-66.
Bratten, B., Larocque, S., & Yohn, T. L. (2018). Filling in the GAAPs in individual analysts’ street
earnings forecasts. University of Notre Dame working paper. Available at SSRN 3074701.
Brown, L. D., Call, A. C., Clement, M. B., & Sharp, N. Y. (2015). Inside the “black box” of sell‐
side financial analysts. Journal of Accounting Research, 53(1), 1-47.
Chen, S., & Matsumoto, D. A. (2006). Favorable versus unfavorable recommendations: The
impact on analyst access to management‐provided information. Journal of Accounting
Research, 44(4), 657-689.
Cooper, M. J., Gulen, H., & Rau, P. R. (2016). Performance for pay? The relation between CEO
incentive compensation and future stock price performance. University of Utah working
paper.
Core, J. E., Guay, W. R., & Larcker, D. F. (2003). Executive Equity Compensation and Incentives:
A Survey. Economic Policy Review, 9(1).
Christensen, T. E., Merkley, K. J., Tucker, J. W., & Venkataraman, S. (2011). Do managers use
earnings guidance to influence street earnings exclusions? Review of Accounting
Studies, 16(3), 501-527.
Damodaran, A. (2005). Employee stock options (ESOPs) and restricted stock: valuation effects
and consequences. NYU working paper. Available at SSRN 841504.
Damodaran, A. (2019). Why investors should be wary of stock-based compensation. Financial
Times. June 10, 2019.
Davis, J. L., Fama, E. F., & French, K. R. (2000). Characteristics, covariances, and average returns:
1929 to 1997. The Journal of Finance, 55(1), 389-406.
Demirakos, E. G., Strong, N. C., & Walker, M. (2004). What valuation models do analysts use?
Accounting horizons, 18(4), 221-240.
Easton, P. D., McAnally, M. L., Sommers, G. A., & Zhang, X. J. (2017). Financial statement
analysis & valuation. Cambridge Business Publishers. 5e.
Engelberg, J., McLean, R. D., & Pontiff, J. (2019). Analysts and anomalies. Journal of Accounting
and Economics. Forthcoming.
Fama, E. F., & French, K. R. (2008). Dissecting anomalies. The Journal of Finance, 63(4), 1653-
1678.
Frankel, R., & Lee, C. M. (1998). Accounting valuation, market expectation, and cross-sectional
stock returns. Journal of Accounting and Economics, 25(3), 283-319.
33
Frye, M. B. (2004). Equity‐based compensation for employees: firm performance and
determinants. Journal of Financial Research, 27(1), 31-54.
Gu, Z., & Chen, T. (2004). Analysts’ treatment of nonrecurring items in street earnings. Journal
of Accounting and Economics, 38, 129-170.
Guo, L., Li, F. W., & Wei, K. C. (2019). Security Analysts and Capital Market Anomalies. Journal
of Financial Economics. Forthcoming.
Guay, W., Kothari, S. P., & Sloan, R. (2003). Accounting for employee stock options. American
Economic Review, 93(2), 405-409.
Hand, J. R., Coyne, J. G., Green, J. R., & Zhang, X. F. (2017). The use of residual income valuation
methods by US sell-side equity analysts. Journal of Financial Reporting, 2(1), 1-29.
Hou, K., Xue, C., & Zhang, L. (2019) Replicating Anomalies. The Review of Financial Studies.
Forthcoming.
Jackson, A. R. (2005). Trade generation, reputation, and sell‐side analysts. The Journal of
Finance, 60(2), 673-717.
Kahle, K. M. (2002). When a buyback isn’ta buyback: Open market repurchases and employee
options. Journal of Financial Economics, 63(2), 235-261.
Kothari, S. P., So, E., & Verdi, R. (2016). Analysts’ forecasts and asset pricing: A survey. Annual
Review of Financial Economics, 8, 197-219.
Li, H. (2002). Employee stock options, residual income valuation and stock price reaction to SFAS
123 footnote disclosures. Santa Clara University working paper.
Li, K. K., & Mohanram, P. (2014). Evaluating cross-sectional forecasting models for implied cost
of capital. Review of Accounting Studies, 19(3), 1152-1185.
Li, K., & Mohanram, P. (2019). Fundamental analysis: Combining the search for quality with the
search for value. Contemporary Accounting Research. Forthcoming.
Lin, H. W., & McNichols, M. F. (1998). Underwriting relationships, analysts' earnings forecasts
and investment recommendations. Journal of Accounting and Economics, 25(1), 101-127.
Lundholm, R. J., & Sloan, R. G. (2017). Equity valuation and analysis with eVal. McGraw-Hill
Irwin. 4e.
Mauboussin, M. J. (2006). Common errors in DCF models. Available at
https://www3.nd.edu/~scorwin/fin70610/Common%20DCF%20Errors_LeggMason.pdf.
Mikhail, M. B., Walther, B. R., & Willis, R. H. (2007). When security analysts talk, who
listens? The Accounting Review, 82(5), 1227-1253.
Murphy, K. J. (1999). Executive compensation. Handbook of labor economics, 3, 2485-2563.
Wahlen, J., Baginski, S., & Bradshaw, M. (2016). Financial reporting, financial statement
analysis and valuation. 8e. Nelson Education. 8e.
34
APPENDIX A
Variable Definitions
Variable Definition
The market Beta, derived from by regressing monthly returns (RET) from CRSP on the value-weighted index (VWRETD) using five years of data prior to current fiscal
year end, ensuring at least 24 months of data are available.
σ(EARN) The standard deviation of income before extraordinary items (IBQ) scaled by sales (SALEQ) over the past eight quarters, using information from quarterly Compustat.
Buybacks The sum of the purchase of common and preferred stock (PRSTKC) minus the
change in the value of preferred stock (PSTK), scaled by net operating cash flow
(OANCF). We set Buybacks as missing if OANCF is zero or negative. We sum up the values of this variable across years to calculate the accumulative buybacks.
Dilutions The percentage change in the split-adjusted number of shares outstanding (CSHO *
AJEX) of a future year benchmarked against that in the current year.
EPSGR The annualized growth rate in EPS between EPS1 and EPS5, where EPS1 and EPS5
are one-year-ahead and five-year-ahead EPS forecasts from the cross-sectional
forecasting models. We exclude firm-year observations with negative EPS1.
GAAP_Error GAAP EPS forecast error, defined as the actual GPS (i.e., GAAP EPS) in IBES minus the related GPS forecast, divided by the closing price three trading days prior
to the forecast date.
LEV The ratio of total debt (DLC+DLTT) to total assets (AT) measured at the fiscal year end.
LMCAP The log of market capitalization (MCAP) measured at the end of June in all tests
except in the Fama-MacBeth regressions, where it is measured at the end of month m.
LBM The log of book-to-market ratio (B/M). We follow Davis et al. (2000) in calculating
book value. Specifically, book value is the stockholders’ equity (CEQ), plus balance
sheet deferred taxes and investment tax credit if available (TXDITC), minus the book value of preferred stock, for which we measure with redemption (PSTKRV),
liquidation (PSTKL), or par value (PSTK) of preferred stock (in that order). We use
the market capitalization at the end of June to measure the market value.
LnAnalyst The log of one plus the number of analysts who provide at least one EPS forecasts
for the firm in the fiscal year.
P/E0 Historical price-to-earnings ratio, defined as the market cap at the end of June divided by the latest available earnings (June-end price * SHROUT/IB). To make
sure the availability of annual financial information when portfolios are formed in
the end of June, we follow prior literature such as Li and Mohanram (2014) and use
35
the EPS of fiscal years ending from April of year t-1 to March of year t. If P/E0 > 100, it is set to 100.
ΔP/E0 The year-over-year change (i.e., first difference) in P/E0 as measured at the end of
June.
P/E1 Forward price-to-earnings ratio, defined as the price at the end of June divided by year t+1 earnings forecast. Earnings forecasts are based on the Residual Income
forecasting model in Li and Mohanram (2014). Specifically, we estimate a cross-
sectional forecasting model 𝐸𝑖,𝑡+1 = 𝛽0 + 𝛽1𝑁𝑒𝑔𝐸𝑖,𝑡 + 𝛽2𝐸𝑖,𝑡 + 𝛽3𝑁𝑒𝑔𝐸𝑖,𝑡 ∗𝐸𝑖,𝑡 + 𝛽4𝐵𝑖,𝑡 + 𝛽5𝑇𝐴𝐶𝐶𝑖,𝑡 + 𝜀𝑖,𝑡+𝜋 to generate earnings forecasts for year t+1 using
the previous 10 years of data, where E = (IB-SPI*(1-TXT/PI))/CSHO and TXT/PI is set zero if missing, NegE is an indicator for negative earnings, B = CEQ/CSHO;
TACC is the sum of the change in WC, the change in NCO, and the change in FIN,
divided by the number of shares outstanding. Specifically, WC = (ACT – CHE) – (LCT – DLC); NCO = (AT – ACT – IVAO) – (LT – LCT – DLTT); FIN = (IVST
+ IVAO) – (DLTT + DLC + PSTK). If P/E1 > 100, it is set to 100.
ΔP/E1 The year-over-year change (i.e., first difference) in P/E1 as measured at the end of June.
P/S Price-to-sales ratio, defined as the market cap at the end of June divided by the latest
available sales (June-end price * SHROUT/REVT)The same to P/E0, we use the
sales of fiscal years ending from April of year t-1 to March of year t. If P/S > 50, it is set to 50.
ΔP/S The year-over-year change (i.e., first difference) in P/S as measured at the end of
June.
P/V Price-to-value ratio, defined as the price at the end of June divided by the estimated
intrinsic value of the stock. To derive the intrinsic value, we first use the cross-
sectional forecasting model described above (i.e., the definition of P/E1) to generate earnings forecasts for years t+1 to t+5. Then we calculate the intrinsic value of
stocks using residual income model based on the following assumptions: (1) cost of
capital is risk free rate plus 5%, (2) future book values are estimated by assuming a
constant payout based on current payout (DVC/IB if IB>0, or DVC/(AT*0.06) if IB < =0). (3) terminal value is calculated after year 5, based on the assumption that
abnormal earnings grow at 3% in perpetuity, (4) If P/V > 10, then P/V is set to 10.
ΔP/V The year-over-year change (i.e., first difference) in P/V as measured at the end of June.
RDINT The ratio of R&D expenditure over sales (XRD/REVT) as measured at the fiscal
year end.
RET1 Annual raw returns compounded from monthly returns for the 12-month period starting July 1st of the year after fiscal year end.
RET1M Annual market-adjusted returns, calculated as RET1 minus the compounded market
return (VWRETD) over the same period.
36
RETm The raw return in the prior month. This variable is only used in the Fama-MacBeth regression in Table V.
RETm-11, m-1 The compounded prior year return skipping the prior month return. This variable is
only used in the Fama-MacBeth regression in Table V.
RETm+1, m+12 The compounded future 12-month return starting from the next month. This variable is only used in the Fama-MacBeth regression in Table V.
SBCINT The ratio of stock-based compensation over sales (STKCO/REVT) measured at the
fiscal year end.
ΔSBCINT The year-over-year change (i.e., first difference) in SBCINT as measured at the
fiscal year end.
SBC Inclusion Those analyst reports in which analysts consider SBC as expense in their free cash
flow calculation, such as starting from EBIT or EBITDA without adding SBC back.
SBC Exclusion Those analyst reports in which analysts do not consider SBC as expense in their free
cash flow calculation, such as calculating it as cash flow from operations minus
capex, or starting from EBIT or EBITDA but adding SBC back.
SGR The year-over-year change in the sales divided by sales in the prior year (REVTt –
REVTt-1)/REVTt-1 as measured at the fiscal year end.
Street Exclusion Analysts’ Street earnings exclusions, measured as analysts’ upcoming annual EPS forecasts minus the same analysts’ upcoming annual GPS forecasts on the same
date, divided by the closing price three trading days prior to the forecast date.
Street_Error Street EPS forecast error, defined as the actual EPS (i.e., the street EPS in IBES)
minus the related EPS forecast, divided by the closing price three trading days prior to the forecast date.
TP_IMPRET The return implied by price targets, defined as price target forecast divided by the
closing price three trading days prior to the forecast date then minus one. All prices are split-adjusted.
TP_FERR Price target forecast error, defined as the realized price 12 months after forecast date
minus price target, divided by the closing price three trading days prior to the forecast date. All prices are split-adjusted.
z(SBCINT) The Z-score of SBC intensity within each industry-year, constructed as SBCINT
minus the mean SBCINT in the same industry (Fama-French 49) -year, scaled by
the standard deviation of SBCINT in the industry-year. This variable is only used in the Fama-MacBeth regression in Table V.
37
Appendix B: Examples of FCF calculations in analyst reports
Example 1: Starting from Cash from Operating Activities
This is a Cowen & Co report on Zayo Group Holding on August 25, 2016 by Colby
Synesael. The calculation formula is listed in the cash flow statement.
Example 2: Starting from an adjusted earnings metric that excludes SBC
This is a Jefferies report on Google on January 22, 2009 by Youssef H. Squali. The DCF
valuation table indicates that the FCF calculation starts from EBITDA.
However, from a standalone table of Income statement forecasts, we know that the
(adjusted) EBITDA excludes SBC by adding it back.
38
Example 3: Starting from an adjusted earnings metric that includes SBC
This is a Morgan Stanley report on Intuitive Surgical on July 6, 2015 by David Lewis. The
FCF calculation starts from (adjusted) NOPAT and then subtracts SBC, indicating that this is a
report that treats SBC as expense.
Example 4: SBC and “non-cash charges”
Many analysts start from earnings metrics and add back “non-cash charges” without
specifying whether such non-cash charges include/are SBC (many of them are not).
This is a Credit Suisse report on LinkedIn on February 7, 2014 by Stephen Lu adds back
“Other non-cash charges.”
Only on a separate page of income statement forecast we can see that the other non-cash
charges are exactly equal to SBCs.
39
TABLE I
Sample Selection
Criterion Firm-
Years
Unique
firms
Observations between 2006-2017 with COMPUSTAT
GVKEY and assets (AT) > 0, sales (REVT) > 0, and non-
missing income (IB) and shares outstanding (CSHO).
58,412 8,371
Availability of price data (PRC) in CRSP so that P/S ratio
can be estimated (Baseline Sample)
44,107 6,366
Availability of data to generate cross-sectional forecasts
and estimate P/V ratio
37,313 5,669
Availability of data to generate cross-sectional forecasts
with EPS1 > 0 to calculate forward P/E ratio
31,561 5,028
Subset with EPS0 > 0 to estimate historical P/E ratio 29,507 4,913
This table presents our sample selection process for the main tests on the impact of stock-based compensation on
overvaluation. We present the resulting number of firm-year observations and the corresponding number of unique firms after applying the criterion in the first column.
40
TABLE II
Stock-based compensation and valuation metrics
Panel A: Valuation ratios by quintiles of SBC intensity
SBCINT Means Medians
Quintile
N SBCINT P/E1 P/E0 P/S P/V SBCINT P/E1 P/E0 P/S P/V
Bottom 8,575 0.3% 19.58 22.12 1.67 1.43 0.1% 15.52 16.25 0.82 1.02
2 8,973 0.9% 20.87 22.41 1.90 1.62 0.4% 16.92 17.22 1.08 1.16
3 8,922 2.3% 22.22 24.32 2.67 1.89 0.7% 17.87 18.19 1.41 1.29
4 8,940 6.4% 23.83 25.90 4.24 2.11 1.3% 18.84 19.08 1.73 1.38
Top 8,697 51.9% 27.12 31.10 9.05 2.64 4.0% 20.77 21.61 2.80 1.56
Top - Bottom 51.6% 7.54 8.98 7.38 1.21 3.9% 5.25 5.36 1.98 0.53
(t-stat)[z-stat] (39.6) (21.3) (20.3) (44.6) (33.5) [110] [24.9] [22.8] [62.9] [34.1]
Panel B: Growth metrics by quintiles of SBC intensity
SBCINT Quintile Means Medians
N SGR EPSGR RDINT SGR EPSGR RDINT
Bottom 8,575 6.5% 12.1% 2.1% 3.2% 10.3% 0.0%
2 8,973 9.7% 11.1% 4.6% 4.7% 9.4% 0.0%
3 8,922 11.5% 10.8% 8.4% 5.2% 9.3% 0.0%
4 8,940 11.8% 11.3% 11.3% 5.4% 9.7% 0.0%
Top 8,697 15.0% 12.6% 19.5% 6.3% 10.6% 0.0%
Top - Bottom 8.6% 0.5% 17.4% 3.1% 0.3% 0.0%
(t-stat)[z-stat] (11.7) (3.13) (28.7) [8.3] [3.1] [32.3]
This table presents the association between SBC intensity and valuation metrics. Panel A tabulates the mean and
median valuation ratios, and Panel B tabulates the mean and median growth metrics, both by quintiles of stock-based
compensation intensity (SBCINT). The quintiles of SBC Intensity are generated within each year-industry (i.e., fiscal
year and Fama-French 49 industry). The valuation ratios are the forward P/E ratio (P/E1), historical P/E ratio (P/E0),
price-to-sales ratio (P/S), and price-to-value ratio (P/V). The growth metrics are annual sales growth (SGR), future
growth in EPS (EPSGR), and R&D intensity (RDINT). The differences in variable means and medians between top
and bottom quintile and the related t-statistics and z-statistics are reported at the bottom of each panel. T-statistics are
calculated based on a pooled estimate of standard error. Z-statistics are based on the Wilcoxon sign-rank test. All
variables are defined in detail in Appendix A.
41
TABLE III
Stock-based compensation and valuation metrics: Change analyses
Panel A: Changes in valuation ratios by quintiles of changes in SBC intensity
Means Medians
ΔSBCINT Quintile N ΔSBCINT ΔP/E1 ΔP/E0 ΔP/S ΔP/V ΔSBCINT ΔP/E1 ΔP/E0 ΔP/S ΔP/V
Bottom 7,285 -9.5% -2.25 -3.24 -1.84 -0.19 -0.7% -0.96 -1.33 -0.10 -0.05
2 7,601 -0.5% -1.12 -1.49 -0.23 -0.07 -0.1% -0.41 -0.50 -0.01 -0.02
3 7,640 0.0% -0.09 -0.27 0.01 0.00 0.0% 0.07 -0.03 0.01 0.00
4 7,602 0.4% 0.43 0.65 0.07 0.05 0.1% 0.32 0.17 0.03 0.03
Top 7,402 6.1% 0.29 0.79 0.72 0.07 0.6% 0.38 0.19 0.03 0.03
Top – Bottom 15.5% 2.54 4.03 2.56 0.27 1.3% 1.34 1.52 0.13 0.08
(t-stat)[z-stat] (43.7) (6.56) (7.84) (22.4) (7.30) [105] [9.01] [9.05] [21.2] [9.48]
42
Panel B: Changes in mean valuation ratios based on SBC intensity increase/decrease, by quintiles of SBC intensity
Prior SBC
quintile ΔSBCINT N
Prior
P/E1 ΔP/E1 N
Prior
P/E0 ΔP/E0 N
Prior
P/S ΔP/S N
Prior
P/V ΔP/V
Bottom <=0 2,520 18.92 -0.42 2,307 20.82 -0.04 3,444 1.74 0.03 3,051 1.28 0.00
>0 2,796 19.17 0.61 2,490 21.29 0.42 3,838 1.43 0.05 3,345 1.37 0.05
Diff
0.26 1.03
0.48 0.47
-0.31 0.02
0.09 0.06
t-stat
0.63 2.41
0.93 0.88
-4.42 0.33
2.93 1.54
2 <=0 2,212 20.32 -1.14 1,983 21.41 -1.30 3,057 1.84 -0.11 2,659 1.48 -0.04
>0 3,371 20.33 0.17 3,012 20.95 0.24 4,579 1.83 0.28 4,038 1.56 0.03
Diff
0.02 1.31
-0.46 1.54
-0.01 0.40
0.08 0.07
t-stat
0.04 3.13
-0.98 3.02
-0.20 6.88
2.19 1.91
3 <=0 2,331 21.99 -1.31 2,110 23.94 -2.24 3,384 2.36 -0.29 2,835 1.77 -0.13
>0 3,014 21.10 0.96 2,759 22.46 0.82 4,209 2.61 0.51 3,632 1.72 0.08
Diff
-0.89 2.27
-1.48 3.06
0.25 0.80
-0.06 0.20
t-stat
-2.10 5.28
-2.74 5.47
2.53 9.81
-1.33 5.10
4 <=0 2,546 23.91 -2.24 2,305 25.40 -2.66 3,915 3.87 -0.85 3,145 1.90 -0.14
>0 2,422 23.03 0.46 2,232 23.97 1.36 3,699 4.10 0.33 3,071 1.95 0.07
Diff
-0.89 2.70
-1.43 4.02
0.23 1.18
0.05 0.21
t-stat
-1.82 6.20
-2.43 6.87
1.25 10.31
0.94 5.12
Top <=0 2,155 26.35 -3.09 1,932 29.99 -4.69 4,559 9.01 -2.39 3,176 2.45 -0.26
>0 1,403 26.92 0.92 1,302 30.03 1.66 2,846 8.10 0.49 2,069 2.44 0.11
0.57 4.01 0.04 6.35 -0.91 2.87 0.00 0.37
0.82 6.68 0.04 8.23 -2.67 16.41 -0.05 6.71
43
Panel C: Changes in mean valuation ratios based on SBC intensity increase/decrease, by quintiles of valuation ratios
Prior Valuation
quintile ΔSBCINT N
Prior
P/E1 ΔP/E1 N Prior P/E0 ΔP/E0 N Prior P/S ΔP/S N Prior P/V ΔP/V
Bottom <=0 2,297 9.25 4.47 2,005 9.25 5.28 3,652 0.54 0.08 2,920 0.60 0.34
>0 2,439 9.30 5.87 2,270 9.29 7.04 3,631 0.54 0.36 3,076 0.63 0.49
Diff
0.06 1.40
0.04 1.76
0.00 0.28
0.03 0.15
t-stat
0.53 3.87
0.30 3.78
-0.02 5.52
3.50 4.80
2 <=0 2,364 14.47 2.02 2,094 14.61 2.47 3,612 1.20 0.09 3,012 1.00 0.16
>0 2,709 14.45 3.20 2,497 14.46 3.55 4,003 1.22 0.46 3,295 1.00 0.30
Diff
-0.02 1.18
-0.15 1.08
0.02 0.37
-0.01 0.14
t-stat
-0.19 3.86
-1.23 2.95
0.66 6.21
-0.72 5.21
3 <=0 2,289 18.69 0.46 2,116 18.85 1.05 3,603 2.13 -0.13 2,948 1.38 0.05
>0 2,769 18.41 1.99 2,488 18.61 2.72 4,009 2.12 0.67 3,363 1.33 0.18
Diff
-0.28 1.53
-0.24 1.68
-0.01 0.80
-0.05 0.13
t-stat
-1.76 4.74
-1.39 4.28
-0.10 10.05
-3.35 4.72
4 <=0 2,423 24.58 -2.06 2,288 25.54 -1.76 3,553 3.99 -0.72 2,973 1.89 -0.07
>0 2,649 23.76 0.20 2,298 24.67 0.77 3,982 3.46 0.47 3,325 1.89 0.03
Diff
-0.82 2.25
-0.87 2.53
-0.53 1.20
0.00 0.09
t-stat
-3.26 6.26
-3.07 5.42
-3.84 11.17
0.15 2.93
Top <=0 2,391 43.40 -12.61 2,134 51.56 -17.12 3,939 12.10 -3.31 3,013 4.03 -1.04
>0 2,440 42.44 -8.80 2,242 49.30 -10.77 3,546 9.56 -0.43 3,096 3.93 -0.70
-0.96 3.81 -2.27 6.35 -2.54 2.88 -0.10 0.34
-1.42 5.68 -2.80 7.48 -7.34 16.10 -1.35 5.20
This table presents the association between changes in SBC intensity (SBCINT) and changes in valuation metrics. The valuation ratio changes presented are the
change in forward P/E ratio (P/E1), change in historical P/E ratio (P/E0), change in price-to-sales ratio (P/S), and change in price-to-value ratio (P/V). Panel
A tabulates the mean and median changes in valuation ratios by quintiles of change in stock-based compensation intensity (SBCINT). The valuation ratio changes
presented are the change in forward P/E ratio (P/E1), change in historical P/E ratio (P/E0), change in price-to-sales ratio (P/S), and change in price-to-value
ratio (P/V). Panel B partitions the sample into quintiles based on the prior year’s SBC intensity and further partitions each quintile into two groups based on
whether SBC intensity increases or not. Panel B partitions the sample into quintiles based on the prior year’s valuation ratio and further partitions each quintile into
two groups based on whether SBC intensity increases or not. Panels B and C present means of prior year valuation ratios (P/E1, P/E0, P/S and P/V) and changes
in valuation ratios (P/E1, P/E0, P/S and P/V), and compare differences with each quintile of the means and changes in the ratios for the two subsamples based
on whether SBCINT > 0 or not. The differences in variable means and medians between top and bottom quintile and the related t-statistics and z-statistics are
reported at the bottom of each panel. T-statistics are calculated based on a pooled estimate of standard error. Z-statistics are based on the Wilcoxon sign-rank test.
For Panel A (Panel B), quintiles of SBCINT (SBCINT) are generated within each year-industry (i.e., fiscal year and Fama-French 49 industry). All variables are
defined in detail in Appendix A.
44
TABLE IV
Stock-based compensation and valuation metrics: regression analyses
Panel A: Summary statistics of variables used in the regression
Stats N Mean p1 p10 p25 Median p75 p90 p99 σ
P/E1 25,784 21.69 2.524 8.078 12.02 17.38 25.08 37.99 100.0 16.91
P/E0 24,102 23.99 2.574 8.183 12.28 17.88 26.62 45.70 100.0 20.56
P/S 25,784 2.075 0.080 0.341 0.676 1.372 2.666 4.464 11.160 2.438
P/V 24,699 1.352 0.178 0.502 0.729 1.090 1.640 2.428 5.627 1.044
SBCINT 25,784 0.013 0.000 0.001 0.003 0.006 0.014 0.031 0.094 0.021
LMCAP 25,784 6.831 2.597 4.123 5.414 6.842 8.199 9.495 11.340 2.004
25,784 1.136 -0.075 0.380 0.675 1.061 1.494 1.971 3.191 0.654
σ(EARN) 25,784 0.143 0.003 0.010 0.018 0.034 0.074 0.190 1.232 1.321
LEV 25,784 0.203 0.000 0.000 0.041 0.160 0.313 0.462 0.849 0.192
SGR 25,784 0.097 -0.340 -0.093 -0.014 0.058 0.151 0.298 1.078 0.259
EPSGR 25,784 0.111 -0.030 0.023 0.053 0.093 0.150 0.229 0.411 0.085
RDINT 25,784 0.028 0.000 0.000 0.000 0.000 0.018 0.100 0.293 0.067
Panel B: Levels regressions
DV = P/E1 P/E0 P/S P/V
SBCINT 137.70*** 142.18*** 35.52*** 9.56***
(5.54) (5.10) (6.27) (4.87)
LMCAP 3.34*** 3.63*** 0.26*** 0.18***
(13.92) (18.60) (9.60) (12.62)
-1.63*** -1.79*** -0.32*** -0.10***
(-4.81) (-5.63) (-7.43) (-4.67)
σ(EARN) -0.69*** -0.67** 0.22** -0.05** (-3.44) (-2.78) (2.47) (-2.72)
LEV 1.85 3.12** -1.32*** -0.13
(1.41) (2.73) (-8.53) (-1.44)
SGR 5.42*** 6.22*** 0.50** 0.38***
(6.31) (6.09) (2.57) (6.47)
EPSGR 106.90*** 217.61*** -0.21 0.34
(16.04) (28.93) (-0.52) (1.75)
RDINT 6.63* 9.81* 3.07* 0.70**
(1.98) (2.02) (1.98) (2.46)
FF49 × Year FE Y Y Y Y
Observations 25,619 23,944 25,619 24,550
Adjusted R2 0.432 0.558 0.427 0.384
45
Panel C: Changes regressions
DV = P/E1 P/E0 P/S P/V
SBCINT 31.48* 40.29** 6.20* 1.26
(1.93) (2.95) (1.98) (0.93)
LMCAP 16.50*** 18.35*** 1.61*** 1.06***
(18.44) (26.41) (9.39) (12.25)
0.46*** 0.22 -0.02 0.02
(5.92) (0.59) (-0.35) (1.18)
σ(EARN) 0.50 0.02 0.12 -0.04
(1.54) (0.06) (0.93) (-1.12)
LEV 9.17*** 6.29** 0.09 0.65***
(7.75) (2.56) (0.53) (5.48)
ΔSGR 0.32 0.38 -0.65*** -0.04
(0.60) (0.57) (-5.03) (-1.38)
EPSGR 143.54*** 246.98*** 0.71*** 2.86***
(14.48) (21.97) (5.98) (7.05)
RDINT 31.10*** 55.25*** 4.01 1.92***
(5.10) (3.99) (1.76) (3.29)
Prior P/E1 -0.23***
(-6.36)
Prior P/E0 -0.20***
(-7.10)
Prior P/S -0.15**
(-3.02)
Prior P/V -0.22**
(-2.70)
FF49 × Year FE Yes Yes Yes Yes
Observations 20,002 18,104 20,002 19,049
Adjusted R2 0.676 0.739 0.437 0.581
This table uses regression analyses to assess the relation between SBC intensity and valuation metrics. Panel A
presents the summary statistics for the variables used in the regression analyses and Panel B reports the regression
results. Specifically, Panel A tabulates, the number of observations, mean, 1st percentile, 10th percentile, 25th percentile,
median, 75th percentile, 90th percentile, 99th percentile, and standard deviation. Panel B reports regression results when
the dependent variable is forward P/E ratio (P/E1), historical P/E ratio (P/E0), price-to-sales ratio (P/S), and price-to-
value ratio (P/V) and independent variables are risk and growth factors. Panel C reports regression results when the
dependent variable is change in forward P/E ratio (P/E1), historical P/E ratio (P/E0), price-to-sales ratio (P/S), and
price-to-value ratio (P/V) and independent variables are changes in risk and growth factors, as well as the
corresponding prior valuation ratio. All variables are defined in Appendix A. t-statistics (in parentheses) are two-way
clustered by firm and by year. * p < 0.1, ** p < 0.05, *** p < 0.01.
46
TABLE V
Stock-based compensation and future returns
Panel A: Mean future returns by quintiles of SBC intensity
Equal-weighted Value-weighted Risk Characteristics
Quintile
RET1 RET1M RET1 RET1M MCAP B/M
Bottom 8.40% 1.52% 7.85% 0.83% 1.11 6,089 0.803
2 8.22% 1.31% 8.81% 1.77% 1.20 5,754 0.675
3 8.96% 2.04% 9.29% 2.35% 1.24 5,705 0.604
4 7.52% 0.63% 7.60% 0.68% 1.26 4,087 0.572
Top 3.33% -3.47% 7.03% 0.09% 1.32 3,480 0.552
Top - Bottom -5.07% -4.99% -0.82% -0.73% 0.22 -2,608 -0.250
(t-stat) (-3.78) (-3.73) (-0.43) (-0.39) (6.35) (-13.19) (-9.76)
Panel B: Factor regressions for monthly returns by quintiles of SBC intensity
Quintile Rm-Rf SMB HML UMD RMW CMA Adj. R2
Bottom
0.222
(1.93)
0.885
(29.10)
0.508
(9.90)
-0.019
(-0.34)
-0.171
(-6.43)
-0.162
(-2.02)
0.097
(1.04)
93.8%
2
0.142
(1.48)
0.967
(38.24)
0.675
(15.80)
0.030
(0.62)
-0.205
(-9.23)
-0.136
(-2.05)
0.014
(0.18)
96.6%
3
0.170 (1.97)
0.993 (43.47)
0.677 (17.55)
-0.011 (-0.25)
-0.185 (-9.23)
-0.217 (-3.60)
-0.015 (-0.22)
97.3%
4
0.057 (0.68)
0.985 (44.33)
0.740 (19.70)
0.000 (0.01)
-0.190 (-9.74)
-0.236 (-4.03)
-0.128 (-1.86)
97.6%
Top
-0.209 (-1.48)
0.944 (25.35)
0.755 (12.01)
-0.194 (-2.74)
-0.276 (-8.46)
-0.521 (-5.31)
-0.077 (-0.67)
93.7%
Top - Bottom -0.431
(-3.90)
0.059
(2.03)
0.247
(5.00)
-0.174
(-3.14)
-0.105
(-4.09)
-0.359
(-4.66)
-0.174
(-1.93)
51.5%
47
Panel C: Fama-MacBeth monthly regressions results
DV = RETm+1, m+12 Equal-weighted Equal-weighted Value-weighted Value-weighted
Intercept 0.051 0.044 0.089 0.090
(1.15) (0.99) (1.54) (1.56)
SBCINT -0.043*** -0.047**
(-4.49) (-2.49)
z(SBCINT) -0.029*** 0.001
(-7.85) (0.14)
LMCAP 0.005** 0.005** -0.004 -0.004
(2.49) (2.59) (-0.96) (-0.95)
LBM 0.014 0.015* -0.026*** -0.024***
(1.60) (1.68) (-3.52) (-3.27)
RETm 0.015 0.013 0.037 0.030
(0.38) (0.35) (0.66) (0.55)
RETm-11, m-1 -0.031 -0.031 -0.044 -0.048 (-0.68) (-0.69) (-1.09) (-1.22)
Avg. Adjusted R2 2.0% 1.9% 7.3% 8.1%
months 136 136 136 136
Observations 456,206 456,206 456,206 456,206
This table presents the association between SBC intensity and future returns. Panel A tabulates the mean and medians
of future returns and by quintiles of stock-based compensation intensity (SBCINT). The quintiles of SBC Intensity
are generated within each year-industry (i.e., fiscal year and Fama-French 49 industry). Returns are annual returns
compounded from monthly returns for the 12-month period starting July 1st of the year after fiscal year end. RET1 is
raw returns, RET1M is market-adjusted returns i.e. RET1 minus the compounded market return (VWRETD) over the
same period. is systematic risk. MCAP is the market cap in million USD. B/M is the book-to-market ratio. t-statistics
are calculated based on a pooled estimate of standard error. Panel B presents calendar-time portfolio regressions using
equal-weighted monthly returns for the 12 months starting July 1st after the fiscal year end, with portfolios formed by
SBCINT quintiles. Portfolio returns are regressed on the market (Rm-Rf), size (SMB), book-to-market (HML),
momentum (UMD), profitability (RMW) and investment (CMA) factors. The regressions have 138 monthly
observations from July 2007 to Dec 2018. The final regression uses a hedge portfolio long in the lowest SBC firms
and short in the highest SBC firms. Panel C reports the average results of monthly Fama-MacBeth regressions of
future 12-month returns on SBCINT while controlling for known predictors of future returns, including the log of
market cap (LMCAP), the log of book-to-market ratio (LBM), prior month return (RETm), and the compounded prior
year return skipping the prior month return (RETm-11, m-1). In addition to using the raw value of SBCINT, we also include a transformed z-score of SBCINT (z(SBCINT)) as SBCINT minus the industry-year mean scaled by the
standard deviation of SBCINT in that industry-year. We present both results based on equal-weighted and value-
weighted regressions (i.e., the market cap at each month-end as the weight). Figures in parentheses are t-statistics.
48
TABLE VI
The cost of stock-based compensation: evidence from buybacks and dilutions
Panel A: Stock-based compensation and future buybacks
SBCINT
Quintile
N
Buybacks
Next One Year Next Three Years Next Five Years
Bottom 4,227 11.6% 34.0% 61.9%
2 4,572 18.7% 55.3% 97.6%
3 4,405 21.3% 60.8% 106.1%
4 4,463 26.2% 73.5% 127.8%
Top 4,315 28.2% 74.8% 129.6%
Top – Bottom 16.6% 40.8% 67.7%
(t-stat) (14.02) (15.04) (14.63)
Panel B: Stock-based compensation and future dilutions
SBCINT
Quintile
N
Dilutions
Next One Year Next Three Years Next Five Years
Bottom 4,227 4.8% 21.7% 57.0%
2 4,572 2.9% 11.3% 25.3%
3 4,405 3.2% 12.6% 28.6%
4 4,463 4.0% 17.3% 44.0%
Top 4,315 8.0% 36.1% 105.4%
Top – Bottom 3.2% 14.4% 48.4%
(t-stat) (8.46) (9.09) (9.21)
This table presents the association between SBC intensity and the cost of issuing SBC, as captured by future stock
buybacks and share dilutions. Panel A tabulates the means of future buybacks by quintiles of stock-based
compensation intensity (SBCINT) the future buybacks in the next one year, in the three years, and the next five years.
The quintiles of SBC Intensity are generated within each year-industry (i.e., fiscal year and Fama-French 49 industry).
We measure the buybacks each year as the sum of the purchase of common and preferred stock (PRSTKC) minus the change in the value of preferred stock (PSTK), scaled by net operating cash flow (OANCF). We set the value of
buybacks as missing if it is negative or zero. We sum up the values of this variable across years to calculate the
accumulative buybacks in the next three or five years. Panel B tabulates the means of future share dilutions in the next
one year, three years, and five years, by quintiles of stock-based compensation intensity (SBCINT). We measure the
dilutions in the next t years as the percentage change in the split-adjusted number of shares outstanding (CSHO *
AJEX) of in the fiscal end of year t benchmarked against that in the fiscal end of year 0. Figures in parentheses are t-
statistics.
49
TABLE VII
The role of analyst coverage in the relation between SBC and valuation metrics
Panel A: Levels regressions
DV = P/E1 P/E0 P/S P/V
SBCINT -38.62 43.57 32.91*** -3.41
(-1.39) (1.39) (3.29) (-1.64)
SBCINT×LnAnalyst 95.08*** 54.77*** 1.82 6.98***
(7.68) (4.65) (0.57) (6.61)
LnAnalyst -2.36*** -1.89*** -0.27*** -0.15***
(-5.95) (-5.65) (-5.24) (-5.65)
LMCAP 3.92*** 4.19*** 0.37*** 0.22***
(13.61) (18.46) (9.54) (11.20)
-1.47*** -1.62*** -0.28*** -0.09***
(-4.47) (-5.11) (-6.63) (-4.49)
σ(EARN) -0.50*** -0.57** 0.22** -0.03**
(-3.75) (-2.71) (2.51) (-2.65)
LEV 2.01 3.22** -1.28*** -0.13
(1.55) (2.84) (-8.50) (-1.43)
SGS 5.10*** 6.09*** 0.52** 0.35***
(6.68) (6.22) (2.71) (7.22)
EPSGR 107.18*** 218.00*** -0.13 0.34*
(16.21) (29.15) (-0.34) (1.90)
RDINT 0.80 6.52 3.08* 0.26
(0.25) (1.34) (1.96) (1.03)
FF49 × Year FE Y Y Y Y
Observations 25,619 23,944 25,619 24,550
Adjusted R2 0.447 0.563 0.432 0.406
50
Panel B: Changes regressions
DV = P/E1 P/E0 P/S P/V
SBCINT -11.20 6.94 6.54* 0.38
(-0.44) (0.21) (2.03) (0.15)
SBCINT×LnAnalyst 21.04** 16.72 -0.14 0.46
(2.28) (1.10) (-0.07) (0.59)
LnAnalyst 0.48*** -0.01 0.03** 0.05***
(3.91) (-0.12) (2.64) (7.07)
LMCAP 16.47*** 18.35*** 1.61*** 1.06***
(18.25) (26.38) (9.37) (12.11)
0.45*** 0.22 -0.02 0.02
(4.68) (0.58) (-0.37) (0.93)
σ(EARN) 0.46 0.04 0.12 -0.05
(1.38) (0.14) (0.90) (-1.34)
LEV 8.88*** 6.29** 0.07 0.61***
(7.39) (2.53) (0.42) (5.12)
ΔSGR 0.33 0.38 -0.64*** -0.03
(0.62) (0.56) (-5.02) (-1.19)
EPSGR 143.04*** 246.97*** 0.71*** 2.82***
(14.42) (21.94) (5.86) (6.93)
RDINT 30.69*** 54.87*** 3.98 1.84***
(5.03) (4.00) (1.77) (3.27)
Prior P/E1 -0.24***
(-6.49)
Prior P/E0 -0.20***
(-7.07)
Prior P/S -0.15**
(-3.06)
Prior P/V -0.24**
(-2.91)
FF49 × Year FE Yes Yes Yes Yes
Observations 20,002 18,104 20,002 19,049
Adjusted R2 0.677 0.739 0.438 0.586
This table assesses the role of analyst coverage in the relation between SBC intensity and valuation metrics. Panel A
reports regression results when the dependent variable is forward P/E ratio (P/E1), historical P/E ratio (P/E0), price-
to-sales ratio (P/S), and price-to-value ratio (P/V). Panel B reports regression results when the dependent variable is
change in forward P/E ratio (P/E1), historical P/E ratio (P/E0), price-to-sales ratio (P/S), and price-to-value ratio (P/V).
In Panel A (B), we include all control variables as in Panel B (C) of Table IV, as well as LnAnalyst and its interaction
with SBCINT (ΔSBCINT). All variables are defined in Appendix A. t-statistics (in parentheses) are two-way clustered
by firm and by year. * p < 0.1, ** p < 0.05, *** p < 0.01
51
Table VIII
Stock-based compensation and analyst behavior
Panel A: Stock-based compensation and exclusions in analysts’ street earnings forecasts
SBCINT Quintile N Street Exclusion Street_Error GAAP_Error Error_Diff
Mean Mean Mean Mean
Bottom 173,703 0.007 -0.015 -0.047 -0.025 2 176,418 0.008 -0.011 -0.047 -0.031
3 175,155 0.008 -0.012 -0.044 -0.027
4 175,070 0.010 -0.010 -0.040 -0.026 Top 174,201 0.013 -0.019 -0.064 -0.034
Top – Bottom
(t-stat)
0.006
(43.11)
-0.004
(-6.01)
-0.017
(-12.50)
-0.010
(15.02)
Panel B: Stock-based compensation and analysts’ price targets
SBCINT Quintile N TP_IMPRET TP_FERR
Mean Median Mean Median
Bottom 189,679 0.134 0.120 -0.069 -0.063
2 190,029 0.134 0.124 -0.052 -0.045 3 189,200 0.138 0.128 -0.063 -0.054
4 189,651 0.148 0.132 -0.060 -0.051
Top 189,519 0.178 0.151 -0.114 -0.107
Top – Bottom (t-stat)[z-stat]
0.044 (41.50)
0.031 [47.30]
-0.044 (-24.49)
-0.044 [-27.34]
This table presents the relation between stock-based compensation and analyst behavior using IBES data. Panel A
reports the association between SBC intensity and the difference between analysts’ street vs. GAAP earnings forecasts.
It tabulates, by quintiles of stock-based compensation intensity, the number of observations in each quintile, the mean
of analysts’ exclusion of street earnings forecasts benchmarking against their GAAP earnings forecasts (Street
Exclusion), the mean of street earnings forecasted errors (Street_Error), and the mean of GAAP earnings forecasted
errors (GAAP_Error). Panel B presents the association between SBC intensity and optimism in analysts’ price targets.
It tabulates, by quintiles of stock-based compensation intensity, the number of observations in each quintile, the mean
and median of forecasted returns implied by analysts’ target prices (TP_IMPRET), and the mean and median of
forecasted errors based on prices one year later (TP_FERR). The quintiles of SBC Intensity are generated within each
year-industry (i.e., fiscal year and Fama-French 49 industry). The differences in variable means between top and
bottom quintile and the related t-stats in parentheses are reported at the bottom of each panel). T-statistics are
calculated based on a pooled estimate of standard error. We do not report medians in this table as the medians are
mostly zero for all variables across SBCINT quintiles. All variables are defined in Appendix A.
52
TABLE IX
Analysts’ treatment of stock-based compensation in DCF valuation
Panel A: Starting points of free cash flow (FCF) calculations and SBC treatment
Starting point Add back SBC None
CFO or earnings metrics that exclude SBC as expenses 0 289
Earnings metrics that do not exclude SBC as expenses 67 148
SBC Inclusion (i.e., Treat SBC as an expense) 148
SBC Exclusion (i.e., Ignore SBC expense) 67 + 289 = 356
Unclear 81
Total 585
Panel B: Distribution of analysts’ treatment of SBC– by year of analyst report
Year SBC Inclusion
SBC Exclusion
Unclear
Total
N %
N %
N %
N
2005 0 0.0% 1 50.0% 1 50.0% 2
2006 5 83.3% 1 16.7% 0 0.0% 6
2007 8 36.4% 11 50.0% 3 13.6% 22
2008 9 28.1% 17 53.1% 6 18.8% 32
2009 1 5.9% 16 94.1% 0 0.0% 17
2010 4 13.3% 24 80.0% 2 6.7% 30
2011 11 33.3% 17 51.5% 5 15.2% 33
2012 6 23.1% 18 69.2% 2 7.7% 26
2013 20 40.0% 21 42.0% 9 18.0% 50
2014 14 21.5% 39 60.0% 12 18.5% 65
2015 24 26.1% 54 58.7% 14 15.2% 92
2016 22 23.9% 58 63.0% 12 13.0% 92
2017 23 20.9% 74 67.3% 13 11.8% 110
2018 1 12.5% 5 62.5% 2 25.0% 8
Total 148 25.3% 356 60.9% 81 13.8% 585
53
Panel C: Distribution of analysts’ treatment of SBC in DCF valuation – by broker
Broker Name SBC Inclusion
SBC Exclusion
Unclear
Total
N %
N %
N %
N
BMO Capital 2 18.2% 8 72.7% 1 9.1% 11
Brean Capital 5 45.5% 5 45.5% 1 9.1% 11
Canaccord Genuity 8 47.1% 7 41.2% 2 11.8% 17
Cantor Fitzgerald 1 4.8% 20 95.2% 0 0.0% 21
Cowen & Co 8 20.0% 30 75.0% 2 5.0% 40
Credit Suisse 6 15.4% 30 76.9% 3 7.7% 39
Deutsche Bank 1 5.6% 15 83.3% 2 11.1% 18
J P Morgan 15 29.4% 32 62.7% 4 7.8% 51
Jefferies 5 7.6% 56 84.8% 5 7.6% 66
Macquarie 9 69.2% 4 30.8% 0 0.0% 13
Morgan Stanley 36 50.0% 29 40.3% 7 9.7% 72
Piper Jaffray 1 6.3% 14 87.5% 1 6.3% 16
RBC Capital 3 27.3% 0 0.0% 8 72.7% 11
Suntrust Robinson 0 0.0% 8 72.7% 3 27.3% 11
UBS 2 6.7% 18 60.0% 10 33.3% 30
Wells Fargo 6 60.0% 3 30.0% 1 10.0% 10
Others 40 27.0% 77 52.0% 31 20.9% 148
Total 148 25.3% 356 60.9% 81 13.8% 585
This table describes the distribution of analysts’ treatment of stock-based compensation in DCF valuation, based on
the our manually-collected sample of analyst reports. Panel A classifies all reports based on two dimensions: the
starting point of FCF calculation, and whether SBC is explicitly involved in the calculation. Panel B tabulates, by year
the number and percentage of reports that include SBC as expenses in DCF (SBC Inclusion), the number and
percentage of reports that exclude SBC as expenses in DCF (SBC Exclusion), the number and percentage of reports
that are unclear in SBC treatment (Unclear), and the total number of reports. Panel C tabulates, by broker who have 10 or more reports in our sample, the number and the percentage of reports that include SBC as expenses in DCF
(SBC Inclusion), the number and the percentage of reports that exclude SBC as expenses in DCF (SBC Exclusion),
the number and the percentage of reports that are unclear in SBC treatment (Unclear), and the total number of reports.
54
TABLE X
Analysts’ treatment of stock-based compensation and price targets forecasts
Panel A: Report level analyses using target prices from DCF valuation
Type of reports N TP_IMPRET TP_FERR
Mean Median Mean Median
SBC Inclusion (I) 145 0.127*** 0.126*** 0.022 0.022
SBC Exclusion (II) 325 0.190*** 0.173*** -0.088*** -0.059***
Test: (I) - (II) = 0
(t-stat)[z-stat]
-0.063***
(-2.57)
-0.047*
[-1.70]
0.110**
(2.19)
0.081**
[2.09]
Panel B: Firm-year level analyses only using firm-years with reports including and excluding
SBC in FCF calculations
Type of reports N TP_IMPRET TP_FERR
Mean Median Mean Median
SBC Inclusion (I) 71 0.123*** 0.128*** 0.010 0.006
SBC Exclusion (II) 71 0.157*** 0.168*** -0.081 -0.039
Difference (I - II) (t-stat)[z-stat]
-0.035 (-1.29)
-0.040 [-1.48]
0.092** (2.14)
0.045** [2.26]
Panel C: Matching each inclusion report with an exclusion report issued within 30 days
before or after on the same firm, fiscal year, and with the closest report date
Type of reports N TP_IMPRET TP_FERR
Mean Median Mean Median
SBC Inclusion (I) 49 0.111*** 0.128*** 0.023 0.073
SBC Exclusion (II) 49 0.178*** 0.263*** -0.036 -0.001
Difference (I - II)
(t-stat)[z-stat]
-0.067*
(-1.88)
-0.135**
[-2.14]
0.059
(1.60)
0.074**
[2.22]
This table provides summary statistics of price targets based on the hand-collected analyst reports. In all four Panels,
we tabulate, by types of reports, the number of reports in each type, the mean and median of PT Implied Return (TP_IMPRET), and the mean and median of PT Forecasted Error (TP_FERR). Panel A uses the price targets derived
from DCF models. Panel B focuses on reports from firm-years with at least one SBC Inclusion report and one SBC
Exclusion report, and collapses the sample into the firm-year level by averaging across all reports in each category in
the same firm-year. Panel C matches each SBC Inclusion report with one Exclusion report on the same firm and fiscal
year, and with the closest report date. We also require that the matched Exclusion report be issued within 30 days
before or after the Inclusion report. The differences in variable means and medians between reports that include vs.
exclude SBC and the related t-stats in parentheses and z-stats in brackets are reported at the bottom of each panel. All
variables are defined in Appendix A. * p < 0.1, ** p < 0.05, *** p < 0.01