Distress Risk Puzzle and Analyst Forecast Optimism

Abstract
A general consensus in the literature is that financial analysts make optimistic forecasts: they tend
to underreact to negative but overreact to positive information. In this study, we invoke this idea toprovide an explanation for distress risk puzzle, the phenomenon that high distress risk firms deliver
anomalously low subsequent returns. We find that analysts underestimate the implication of thepoor performance of higher distress risk firms, and thus make EPS and sales forecasts that are
generally more optimistic than the forecasts for the lower distress risk firms. Because market
respond to the analyst forecasts, investors initially overvalue the high distress risk firms; later on,
when those firms report less than expected performance, analysts revise their forecasts downwards
and cause the high distress risk firms to earn low future returns composing of both immediateforecast-revision responses and post-forecast-revision price drifts. We further document that(quarter) earnings announcements convey substantial amount of information that roughly drive
more than 60% of the analyst forecast revisions and 30% of the revision-related market responses.

11. Introduction
Dichev (1998) document that firms with high distress risk tend to earn anomalously lower
returns. In this study, we try to provide an explanation for this anomaly (the distress risk puzzle)
from the perspective of analyst forecasts. The intuition is that financial analysts tend to underreact
to negative information but overreact to positive information (e.g. Easterwood and Nutt 1999), andhence make too optimistic forecasts, such as the earnings per share (EPS) and sales forecasts, forthe high distress risk firms. Since investors generally base analyst forecasts to form their expectation
about the firms (e.g. Fried and Givoly, 1982; O'Brien, 1988), it will become likely that investors
will be initially guided to overvalue the high distress risk firms. Later on, when more information
about the firms’ poor performance is observed (e.g. from the quarterly earnings reports or press
releases), analysts will revise their forecasts downwards that subsequently causes stock prices todrop. Another possible cause of lower returns earned by high distress risk firms is that althoughanalysts revise their forecasts downwards upon receiving of new information, the market does not
respond to the forecast revisions timely and fully, but takes few more months to finish the response.
This so-called post-forecast-revision price drifts as documented by the literature (e.g. Givoly and
Lakonishok 1980) thus suggests that high distress risk firms may earn lower returns, not only aroundthe forecast revision dates but also the period in-between. The purpose of this paper is to provide
empirical evidence for these two explanations for the distress risk puzzle.
We follow common practice to use O-Score, probability of default, and credit rating tomeasure distress risks of the firms (e.g. Dichev 1998; Vassalou and Xing 2004; Avramov et al.
2013). Prior studies generally focus on the EPS forecasts made by the analysts. We also examine
the analysts’ consensus sales forecasts from I/B/E/S because prior studies document that analysts
make less optimistic sales forecasts than the earnings forecasts (e.g. Cheng et al. 2019). We study
how analysts make the sales forecasts for the firms with different distress risk levels; and how therevisions of the two forecasts contribute to the low stock returns in the subsequent periods. EPS andsales forecast errors are defined as the (scaled) actual realizations minus the analyst forecasts for thevariables at t+1. We find significant negative relations between EPS (sales) forecast errors and the
distress risk measures, suggesting that analysts make over-optimistic EPS and sales forecasts forhigher distress risk firms.

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Presumably, analysts observe the (distress risk) information of firms since the disclosure of
the firms’ financial reports. Later on when more information is publicly available (e.g. the quarter
earnings announcement), analysts will revise their previous forecasts (forecast revision) for thefirms. In the current paper’s context, we hypothesize and find that analysts gradually revise their
EPS and sales forecasts for high distress risk firms downwards, when they receive more information
about the firms’ poor performance. The revised forecasts trigger market to respond downwards thatgives rise to the lower returns by the high distress risk firms. We also observe abnormally low stock
returns for higher distress risk firms in the periods between forecast revision dates, consistent withthe post-forecast-revision price drifts phenomenon. Overall, the empirical evidences suggest thatboth EPS and sales forecast revisions and the post-forecast-revision price drifts contribute to thelower stock returns earned by high distress risk firms.
Our paper contributes to the analyst forecast and mispricing literature. First, althoughevidence indicates that analysts underreact to the negative information of firms, it is not clear if thisrelation exists in firms with high distress risk. This study will first provide such evidence. Second,
comparing to prior studies that focus mainly on earnings forecasts, we consider also the sales
forecasts. This helps us to understand how analysts behave when they analyze firms in extreme
conditions, and hence the potential source of forecast bias for the distressed firms. Third, we show
empirically how the distress risk puzzle is contributed by the market’s immediate- and postforecast-revision responses. Finally, prior studies explore factors that explain the cross-sectionalvariations in forecast revisions and the subsequent market responses (e.g. Clement and Tse, 2003;
Gleason and Lee, 2003); however, little is known about how firms’ distress status will affect forecast
revisions and the associated price drifts. We fill this void by providing evidences for whether firms
with high distress risk will incur more pronounced downward forecast revisions, revision priceresponses, and post-forecast-revision price drifts.
The remainder of this paper proceeds as follows. Section 2 reviews prior research related toour topic and present the main hypotheses. Section 3 describes the sample data and definitions of
the major variables. Section 4 provides the empirical results and section 5 concludes.

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2. Literature Review and Hypotheses Development
Why are high distress risk firms not rewarded by higher stock returns? To answer thisquestion, a number of studies attempt to provide the so-called risk-based explanations. For example,
Garlappi et al. (2008) and Garlappi and Yan (2011) propose that general hump-shaped relations
between default probability and equity returns can be obtained when shareholders can extract morevalue from renegotiation and when there exists shareholder recovery upon financial distress. In otherwords, the distress risk puzzle is just a manifestation of the ‘low risk-low return’ phenomenon. In asimilar vein, George and Hwang (2010) suggest that firms with high distress cost will optimallychoose low leverage to achieve a lower probability of financial distress; but because distress costs
heighten a firm’s exposure to systematic risk, expected returns are negatively related to the distressprobability. Using the Distance-to-Default to measure distress risk, Vassalou and Xings (2004)
show that default risk is systematic risk that small caps and firms with high book-to-market ratios
earn higher returns when they are with high distress risk. However, they find no significant
difference in returns between stocks with high and low default risk in other cases. Indeed, Da andGao (2010) claim that the abnormally high returns on stocks with high distress risk documented by
Vassalou and Xing (2004) are driven by short-term return reversals arising from a liquidity shock
triggered by a clientele change.
The risk-based explanations for the negative relation between stock returns and distress risk
seem to be setting-specific and incomplete. Another line of research suggests that it is the market
imperfection that causes the anomaly. For example, empirical evidence by Campbell et al. (2008)shows that firms with high distress risk are no less risky, as they have much higher return volatilities,
market betas and loadings on value and size risk factors than firms with low distress risk.
Furthermore, as they use broader measures for distress probability (logit models based on
accounting and market variables, and the Distance-to-Default), their results do not seem sensitive
to how distress risk is measured. Griffin and Lemmno (2002) show that the low average returns offirms with high distress risk are driven by the poor performance of firms with low book-to-market
ratios. Most importantly, they document return reversals around earnings announcement dates,suggesting that firms with high distress risk are mispriced by investors, who are subsequently
disappointed by the poor performance of the firms. A recent paper by Kim (2013) provides evidence
4and claims that the distress risk anomaly is a manifestation of investors’ underpricing (overpricing)of cash flows (accrual) component of earnings, i.e., the accrual anomaly (Sloan, 1996).
Being important participants of financial markets, financial analysts interpret and convey
firms’ disclosed information to investors (Livnat and Zhang 2012). Their earnings forecasts are
believed to be and commonly-used as proxies for the market’s expectations about the firms’ future
earnings (e.g. Fried and Givoly, 1982; O'Brien, 1988); and also stock prices response to analyst
forecast revisions (e.g. Givoly and Lakonishok 1980; Imhoff and Lobo 1984; Stickel 1991). Eltonet al. (1981) provide further evidence that analyst revisions are more value-relevant than reported
earnings per share. Clement and Tse (2003) find that investors react stronger to forecast revisions
released earlier in the year.
However, professional as financial analysts are, their forecasts are well-known to be overoptimistic for different reasons. For instance, analysts may have incentives issuing optimistic
forecasts to please the firm’s management (e.g. Das et al. 1998; Francis and Philbrick 1993; Dugar
and Nathan 1995; Dechow et al. 2000; Lim 2001; Cowen et al. 2006; Bradshaw et al. 2006). From
psychological point of view, analyst optimism may also be driven by e.g. self-selection bias (e.g.
McNichols and O’Brien 1997), firm and analyst characteristics (e.g. Teoh et al. 1998; Bradshaw et
al 2001; Drake and Myers 2011; Cheng et al. 2019). Some studies suggest that analysts misinterpret
information systematically and make biased forecasts (e.g. DeBondt and Thaler 1990; Abarbanell
1991; Abarbanell and Bernard 1992). Related to our study, and Hwang et al. (1996) and Easterwood
and Nutt (1999) provide evidence that poor firm performance correlates with excess forecast
optimism.
Following the mispricing argument, our research aims to provide an explanation for the
distress risk puzzle from the perspective of analyst forecast optimism. Motivated by the stylized factthat analysts tend to underreact to negative news, we generate the first hypothesis:
H1: Analysts make over-optimistic one-year-ahead forecasts for higher distress risk firms –Forecast errors are negatively related to the distress risk levels of the firm.
Previous studies also document that over-optimistic analyst forecasts relate to subsequentpoor firm performance after, e.g., initial public offerings (e.g. Rajan and Servaes 1997; Chahine2004), equity offerings (e.g. Dechow et al. 2000; Teoh and Wong 2002; Paleari et al. 2007; Lin et5al 2013), corporate external financing activities (Bradshaw et al. 2006). Hughes et al. (2008)
document that the predictable component of abnormal returns is significantly associated with futureforecast errors and suggest that pricing anomalies are not merely an artifact of inadequately
controlled risk. The intuition is that after making the initial forecasts, analysts revise their forecasts(forecast revision) downwards when more information is publicly available. Later on, stock prices
drop. We hypothesize that similar argument will apply to distressed firms. That is, the revised
downward forecasts trigger market to respond negatively that gives rise to the lower returns byhigher distress risk firms.
H2: Analysts revise their one-year-ahead forecasts downwards for higher distress risk firms –Forecast revisions are negatively related to the distress risk levels of the firm.
H3: Market respond downwards around the forecast revision dates for higher distress risk firms –The 3-day cumulative abnormal returns (CAR) around forecast revision dates are positively related
to the forecast revisions.
Although market reacts to the forecast revisions, the immediate responses are well-known
to be incomplete–Prices continue to drift in the same direction for certain period of time after therevisions (e.g. Givoly and Lakonishok 1980; Stickel 1991; Elgers et al. 2001; Gleason and Lee 2003;
Hui and Yeung 2013). Givoly and Lakonishok (1980) is among the earliest studies that document
this so-called post-forecast-revision price drifts phenomenon. Stickel (1991) shows that firms
having recent upward revisions in consensus forecasts earn higher abnormal returns for a period of
up to 12 months than firms having recent downward revised consensus forecasts. In their crosssectional analysis, Gleason and Lee (2003) provide evidence that post-forecast-revision price drifts
are more pronounced for revisions with higher innovation, for revisions made by celebrity analysts,
and for firms with lower analyst coverage. In this paper, we hypothesize that (downwards) postforecast-revision price drift is another channel that contributes to the lower returns earned by highdistress risk firms. The intuition is that as follows. Even if analysts become aware that they haveunderreacted the distress risk information of the firms and make corrections in the new forecasts,
investors may still not fully and timely respond to these distress-risk-related corrections by the
analysts. (Analysts are supposed more professional market participant than ordinary investors; yetanalysts still underreact to the distress risk information of the firms.) If this is the case, the postforecast-revision price drifts should be explainable by the distress risk levels of the firms.
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H4: Low abnormal stock returns for high distress risk firms persist between forecast revision dates– The cumulative abnormal returns between forecast revision dates (price drifts) are negatively
related to the distress risk levels of the firms.
To sum up, the four hypotheses are logically-connected to help explain the distress risk
puzzle. First, analysts make over-optimistic forecasts for firms with higher distress risk (possibly
because analysts underreact the bad (distressed) status of the firms). Then analysts revise theirforecasts more downwards for the higher distress risk firms to drive down the stock prices (the low
abnormal returns around forecast revision dates). However, market respond to the revisions
incompletely and prices continue to drift downwards after the forecast revisions.

3. Data and Variable Definition
We obtain from I/B/E/S the consensus (median) one-year-ahead forecasts and actual values
for annual (FPI = 1) sales (the “SAL” variable) and EPS for non-financial firms (SIC codes are<6000 or >6999) from 1997 to 2016 (a total of 20 years). Financial and monthly stock returns data
are from Compustat and CRSP respectively. To study the effects of distress risk on analyst
forecasting behaviors, analyses are performed on 10 evenly-sized groups of firms ranked according
to their distress risk levels at the beginning of each fiscal year such that firms in group 1 (10) havethe lowest (highest) distress risk levels. All variables are winsorized at the top/bottom 1 percent
level.
3.1 Distress Risk Measures
Sample firms are ranked into ten groups at the beginning of each fiscal year according totheir distress risk (DR) measures constructed by their last year’s financial or market-related data.
Three commonly-used metrics are used to measure the DR levels, namely, O-SCORE (Ohlson1980), probability of default (PD) (e.g. Merton 1974; Crosbie and Bohn 2003; Vassalou and Xing
2004; Bharath and Shumway 2008), and credit ratings (RATING) (e.g. Vazza et al. 2005; Avramov2013). By convention, the larger the O-SCORE, PD, and RATING, the higher the distress risk levelsof the firms.

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O-SCORE is widely used in the literature and we consider it as our benchmark DR measureso that our results are comparable to the findings of earlier studies (e.g. Dichev 1998). However,since O-Score is calculated solely by historical accounting data that may not perfectly predict future
corporate failure, we use also the probability of default and firms’ credit ratings (from theCompustat) as the secondary and tertiary measures for firms’ distress risk.1 Because the latter two
measures involve market information and judgements by the market professionals for the firms,
using them together with O-Score act as robustness check for our results. Appendix provides thedetails of how to construct the three DR measures.
3.2 Forecast Errors and Forecast Revisions
We obtain from I/B/E/S one-year-ahead (FY1) consensus (median) forecasts and actual
values for annual (FPI = 1) sales (the “SAL” variable) and earnings per share (EPS, adjusted forstock splits and dividends). Assume that it is at the beginning of fiscal year t. We define the oneyear-ahead EPS and sales forecast errors as the actual (realized) minus the forecasted values:
FE_EPS(j, t, m) = [Actual EPS(j, t) – consensus EPS forecast(j, t, m)]/Price(j, m-2d)
FE_S(j, t, m) = [Actual sales(j, t) – consensus sales forecast(j, t, m)]/Actual sales(j, t)
where (j, t, m) indicate firm j, fiscal year t and mth fiscal month. Consensus forecast with subscript(j, t, m) thus refers to the forecasted value for the end of fiscal year t, announced at the m th monthsof fiscal year t.
Analysts revise their forecasts when they observe more information. We define the monthby-month revisions of the one-year-ahead consensus EPS and sales forecasts as:
FR_EPS(j, t, m) = [Consensus forecast for EPS(j, t, m) – consensus EPS forecast (j, t, m-1)]/Price(j, m-2d)
FR_S(j, t, m) = [Consensus forecast for sales(j, t, m) – consensus sales forecast (j, t, m-1)]/Actual sales(j, t)

Another criticism of using O-SCORE as distress risk measure is that O-SCORE is highly correlated with the firm’s
accruals (Kim 2013). In other words, the distress risk puzzle may be just a manifesto of the accrual anomaly (Sloan
1996). This motives our study using also probability of default and credit rating as extra measures of distress risk toprovide evidence that the distress risk puzzle is not simply equivalent to the accrual.
18Again, (j, t, m) indicate firm j, fiscal year t and mth fiscal month. Revision with subscript (j,t, m) thus refers to the change in forecasted value for the end of fiscal year t, announced at the m thmonths of fiscal year t. Following common practice, EPS forecast errors (revisions) are scaled byprice(j, m-2d), stock price two days before the current forecast announcement (revision) date (month).
For sales forecast errors and revisions, we use total sales at the end of fiscal year t as the deflators.
3.3 Revision Returns and Post-Forecast-Revision Price Drifts
Stock returns obtained from CRSP are size-adjusted to measure the market responds to aforecast revision and post-forecast-revision price drifts. In each year stocks are ranked into 10
groups based on their market caps. Abnormal returns are then equal to the stock returns minus thevalue-weighted returns of the size group (portfolio) the stock belongs to. To measure the market
response to a forecast revision, we use CAR(-1, 1), 3-day cumulative abnormal returns center on
the forecast revision (announcement) date. Post-forecast-revision price drifts between two
consecutive forecasts are measured by CAR(2, -2), cumulative abnormal returns from two days after
one forecast to two days before the next one. Cumulative (buy-and-hold) abnormal returns is+𝑡calculated by standard formula: CAR(-t, t) = ∏+𝑡𝑘=−𝑡(1 + 𝑟𝑘 ) − ∏𝑘=−𝑡(1 + 𝐵𝑘 ), where rk and Bk are thedaily stock and size portfolio returns kth days after the forecast revision date.

4. Empirical Results
4.1 Bivariate Analysis
For a single firm within any fiscal (sample) year, CRET is defined as the 12-month
cumulative (buy-and-hold) returns of the firm starting from the 4th month after the last fiscal year
end. Within the same fiscal year, AFE_EPS and AFE_S are the averages of all monthly EPS andsales forecast errors; AFR_EPS and AFR_S are the averages of all monthly EPS and sales forecast
revisions; ACAR(-1, 1) and ACAR(2, -2) are the averages of all 3-day revision returns and postforecast-revision price drifts between two consecutive forecasts. (To ease the notation, firm andfiscal year subscripts are not included for the variables.) Table 1 Panel A-C report their medianvalues (by pooling all firm-years belonging to the same distress risk group) for the ten distress risk(DR) groups ranked by O-SCORE, PD and RATING respectively: osgrp, pdgrp and ratgrp 1 (10)9

represent lowest (highest) distress firms: the median O-SCORE from -5.71 to 4.48, median PD from0 to 28.15%, and median RATING from 5 to 16.
<< Insert Table 1 here>>
For all three DR measures, CRET show generally decreasing trends along the corresponding
DR groups (except for the very low risk groups): from 6.02% to -16.73% for osgrp; from 9.23% to-3.72% for pdgrp; and from 10.64% to -8.51% for ratgrp.2 In other words, firms with higher distressrisk earn lower stock returns, consistent with the distress risk puzzled as documented by priorliterature (e.g. Dichev 1998).
Turning to the two forecast errors and forecast revisions, again obvious decreasing trends
are observed for all ten DR groups sorted by the three DR measures. For osgrp, AFE_EPS (AFE_S)
decrease from -0.07% (-0.86%) to -0.58% (-20.96%); for pdgrp, from 0.05% (0.34%) to -2.90% (6.60%); and for ratgrp, from 0.02% (0.64%) to -2.40% (-6.52%). For AFR_EPS (AFR_S), values
decrease from -0.02% (-0.04%) to -0.12% (-0.19%) for osgrp; from 0.00% (0.00%) to -0.40% (1.21%) for pdgrp; and from -0.00% (0.00%) to -0.47% (-1.20%) for ratgrp. Differences between
median values of the lowest (1) and highest (10) DR groups are all significant by the two way
Wilcoxon test. Overall, the results support Hypothesis 1 and 2 that analysts make both overoptimistic EPS and sales forecasts for higher distress risk firms; and revise their forecasts more
downwards for higher distress risk firms.
Note that AFE_EPS (AFE_S) and AFR_EPS (AFR_S) are on an annual basis in the senses
that they are constructed by aggregating the monthly forecast errors and forecast revisions. To
provide more evidence that analysts revises their forecasts downwards throughout the fiscal year
when more information is received, especially for higher distress risk firms, we can look at thetrends by monthly FE_EPS(j, t, m), FE_EPS(j, t, m), FE_EPS(j, t, m), FE_EPS(j, t, m) within the fiscal year.
Because there are EPS, sales, forecast errors, forecast revisions, and 3×10 DR groups combinations,
to not sidetracking the readers, we put the results into Appendix C and just highlight the main
observations in the text. It is shown that both EPS and sales forecast errors generally increase frommost negative to least negative values (i.e., increasing trends) from the 1st month (M1) to the last2

We also measure the size-adjusted CRET that show similar decreasing trends along the three sets of DR groups: from
-10.91% to -26.57% for osgrp, from -11.24% to -15.65% for pdgrp, and from -7.96% to -24.81% for ratgrp.

10month (M10) of the fiscal year for the 3×10 DR groups.3 Furthermore, the increasing trends (forecast
errors) are more pronounced (negative) for high distress risk firms low distress risk firms, supporting
Hypothesis 1 that analysts tend to make more optimistic forecasts for higher distress risk firms. For
the EPS and sales forecast revisions, results show that they are obviously more negative for thehigher distress risk firms than the lower distress risk firms, i.e., analysts revise more downwards
their forecasts for higher distress risk firms (Hypothesis 2). However, clear trend patterns along the
fiscal months are not observed for different distress groups. We take it as reasonable because themagnitude of revisions depends on how much incremental information analysts receive when they
make the revisions. There is no theory suggesting that analysts will receive more incremental
information when it is closer to the forecast end dates (fiscal year end).
Finally, look at the averages of all 3-day abnormal returns and post-forecast-revision pricedrifts within the same fiscal year, ACAR(-1, 1) and ACAR(2, -2). For osgrp, ACAR(-1, 1) decrease
from -0.16% to –0.22%; for pdgrp, from -0.05% to -0.31%; for ratgrp, -0.01% to -0.35%. Decreasing
trends are generally observed, except for the DR groups sorted by O-SCORE and RATING. But
still, ACAR(-1, 1) are monotonic decreasing starting from ~DR groups 3-5. For the average pricedrifts, decreasing trends are far more evident, no exception for the three sets of DR groups. For
osgrp, ACAR(2, -2) decrease from -0.94% to -2.13%; for pdgrp, from -0.62% to -1.85%; for ratgrp,-0.72% to -1.86%. The findings that 3-day abnormal returns (center on the forecast revision date)are generally more negative for higher distress risk firms are supportive to Hypothesis 3 that
downwards revisions of analyst forecasts contribute to the abnormally low returns earned by highdistress risk firms. Whereas price drifts are more negative for higher distress risk firms, this indicatesthat market responses to forecast revisions are incomplete especially for firms with higher distressrisk (Hypothesis 4).
Table 2 reports the rank correlations among major variables that provide extra evidence
supporting the four hypotheses. First, EPS and sales forecast errors and forecast revisions arenegatively and significantly correlated with all three DR measures. Second, EPS and sales forecasts
errors and revisions are positively and significantly correlated with the 12-month cumulative(abnormal) returns, 3-day abnormal returns and post-forecast-revision price drifts. Finally, DR
measures are also negatively correlated with the 12-month cumulative (abnormal) returns, 3-day
3

We do not consider the forecast errors and revisions announced in the 11th month and 12th fiscal month because thenumber of observations in these two months normally decrease to less than 10% the observations of other fiscal months.

11

abnormal returns and post-forecast-revision. To further investigate the explicit contribution of
forecast revisions on the low returns of higher distress risk firms, we resort to multiple regression
analysis in the next section.
<< Insert Table 2 here>>
4.2 Multiple Regression Analysis
Results in the previous section suggest that analysts make over-optimistic EPS and sales
forecasts for higher distress risk firm; but later on revise their forecast downwards (Hypotheses 1
and 2). The downwards forecast revisions in turn drive the low abnormal returns of the higher
distress risk firms (Hypotheses 3 and 4). In this section, we perform formal regression analysis forthe four hypothesis. In the first sets of analysis, averages of all EPS (sales) forecast errors andforecast revisions within the same fiscal year, i.e. AFE_EPS (AFE_S) and AFR_EPS (AFR_S), areregressed on the DR measures, plus the control variables:
AFE_X = α + β*DR_I + γ*CTRL+ Err(FER)

AFR_X = α + β*DR_I + γ*CTRL+ Err(FRR)

where X = {EPS, S} specifies whether it is the EPS or sales model; DR_I = {O-SCORE_I, PD_I,
RATING_I} are the distress risk indicators equal to the firms’ belonging DR groups (by the three
DR measures) scaled by 10. For convenience, equations with the corresponding DR_I are labeled
as the O-SCORE, PD or RATING model; CTRL are the standard control variables including firmsize, book-to-market, accruals, dispersion of analyst EPS forecasts, analyst coverage, momentum,
trading volume. (See Appendix for their formal definitions). Err is the error terms.
The purposes of the forecast error regressions (FER) and forecast revision regressions (FRR)
are to provide evidence that forecast errors and forecast revisions are bigger and stronger for firms
with higher distress risk. Thus the variable of interest is the distress risk indicators, i.e. DR_I, and it
is expected that β < 0, i.e., forecast errors and forecast revisions are more negative for higher distressrisk firms.
Table 3 reports the pooled yearly regression results: Panel A shows the results for (FER) for
the three DR models, and Panel B the results for (FRR) for the three DR models. For (FER), β are
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negative for all three DR indicators (namely, O-SCORE_I, PD_I, and RATING_I) for both EPS andsales models. Furthermore, except for O-SCORE_I in EPS model, PD_I and RATING_I in the EPS
and sales models are all significant, consistent with Hypothesis 1 that forecast errors are negatively
related to the distress risk levels of the firms. Similar results are observed for (FRR). That is, allthree DR indicators negatively and significantly explain the EPS and sales forecast revisions, except
for the O-SCORE_I in the EPS model. In other words, analysts revise their EPS and sales forecasts
more downwards for firms with higher distress risks, as predicted by Hypothesis 2.
<< Insert Table 3 here>>
Why O-SCORE_I is insignificant in the EPS model? We investigate this question by
excluding firm size in the models (because coefficients of firm size are all significant in the three
EPS models, and firm size is commonly considered as a proxy for distress risk, e.g., Fama andFrench 1992). Un-tabulated results show that t-values of all three DR indicators in the “no-size”
EPS models largely increase, suggesting that O-SCORE and size may share considerable amount of
distress risk information to explain EPS forecast errors while PD and RATING embed extra
information not pertaining to firm size. Another observation is that firm size has little effect toexplain sales forecast errors, suggesting that size has quite different implications to the two forecasts(errors). We leave further investigation to future research.
In the second sets of analysis, averages of all 3-day cumulative abnormal returns within thesame fiscal year, i.e. ACAR(-1, 1), are regressed on the EPS and sales forecast revisions, plus thecontrol variables:
ACAR(-1, 1) = α + γ*AFR_EPS + δ*AFR_S + CTRL+ Err(CARR)

where CTRL are the same control variables as before. Err is the error terms.
The purpose of (CARR) is to provide evidence for Hypothesis 3 that EPS and/or sales
forecast revisions drive the 3-day abnormal center on the forecast revision dates, i.e. it is expectedthat γ, δ > 0. Table 4 column (1) reports the pool regression results for (CARR). The coefficients of
AFR_EPS, i.e. γ, is positively significant. For the sales forecast revisions, δ is positive but notsignificant. The results suggest that it is mainly the EPS forecast revisions that drive the 3-day
cumulative abnormal returns.
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<< Insert Table 4 here>>
We need to caution readers that Hypotheses 3 alone is not sufficient to explain the distressrisk puzzle because market’s immediate responses (e.g. the 3-day CAR) to forecast revisions is welldocumented phenomena in the literature (see the Introduction and Literature Review Sections). That
is why we also need Hypotheses 1 and 2 as starters to complete the argument for explaining the
distress risk puzzle. First, analysts make over-optimistic forecasts for firms with higher distress risk
(possibly because analysts underreact the bad (distressed) status of the firms). Later on the analysts
revise their forecasts more downwards for the higher distress risk firms. The more pronounced
downward forecast revisions then induce more negative market responses (the 3-day abnormal
returns) of the firms.
To provide more supports for the above argument. We conduct an extra test for whether
investors’ over-pricings of the higher distress risk firms are (or partially) due to analysts’
underreaction to the distress risk information of the firms. Specifically, we investigate how forecast
revisions and distress risk information as confounding variables affect the 3-day abnormal returns
by including the distress risk indicators, DR_I = {O-SCORE_I, PD_I, RATING_I}, to (CARR).
The idea is that if the lower returns of the higher distress risk firms are (or partially) due to thestronger distress-risk-related forecast revisions for the firms, including the distress risk indicators in
the regressions should weaken the explanatory powers (significance, in terms of t-values) of the
EPS and sales forecast revisions, i.e. AFR_EPS and AFR_S.4 For completeness, we also include theinteraction terms of distress risk indicators and forecast revisions (namely, DR_I×AFR_EPS and
DR_I×AFR_EPS) as extra control variables to detect whether the forecast revisions have differential
effects on the stock returns with respective to different distress risk levels. The modified model is
as follows:
ACAR(-1, 1) = α + β*DR_I + γ*AFR_EPS + δ*AFR_S + ε*DR_I×AFR_EPS + ζ*DR_I×AFR_S +CTRL+ Err

(CARR’)

4An alternate argument is that if investors do not fully consider the distress risk information of the firms when pricing
the stocks, the distress risk information in the forecast revisions should explain the 3-day abnormal returns. Teoh andWong (2002) invoke similar argument for explaining whether analysts misinterpret the information of accruals when
pricing IPO stocks. Teoh and Wong (2002) use predicted forecast errors (by the issue-year accruals) to explain the
future returns of the IPO firms.

14

Table 4 columns (2), (3) and (4) report the pool regression results for (CARR’) for the three
DR models. The coefficients of DR_I, i.e. β, are negatively significant in all except for the OSCORE model. Most importantly, the significances of AFR_EPS and AFR_S in all three DR models
reduce substantially. Now, AFR_EPS is only barely significant in the O-SCORE model, and
AFR_EPS is only barely significant in the RATING model. In other words, the EPS and sales
forecast revisions are indeed reflecting the distress risk information of firms to affect their 3-day
abnormal stock returns. For the two interaction terms, only the RATING indicator and sales forecast
revisions interaction, i.e. RATING_I×AFR_S, is significant in the RATING model. All otherinteraction terms are insignificant. The result suggests that it is not the differential sensitivities offorecast revisions to distress risk levels that explain the immediate low abnormal returns; rather it is
the larger magnitudes of the downward forecast revisions that drive the low abnormal returns of thehigher distress risks firms.
In the last sets of analysis, averages of all post-forecast-revision price drifts within the same
fiscal year, i.e. ACAR(2, -2), are regressed on the DR measures, EPS and sales forecast revisions,
their interaction terms with the distress risk indicators, plus the other control variables:
ACAR(2, -2) = α + β*DR_I + γ*AFR_EPS + δ*AFR_S + ε*DR_I×AFR_EPS + ζ*DR_I×AFR_S +ACAR(-1, 1) + CTRL + Err(DRR)

where DR_I = {O-SCORE_I, PD_I, RATING_I} are the distress risk indicators equal to the firms’
belonging DR groups (by the three DR measures) scaled by 10. For convenience, equation with thecorresponding DR_I are labeled as the O-SCORE, PD or RATING model; CTRL are the same
control variables as before. Err is the error terms. We follow Gleason and Lee (2003) to also include
the 3-day cumulative abnormal returns, i.e. ACAR(-1, 1), as an extra control variable for the
information content of the forecast revisions.
The purpose of the drift regression (DRR) is to provide evidence for Hypothesis 4 that postforecast-revision price drifts are more pronounced for higher distress risk firms, i.e. it is expectedthat β > 0. The intuition is that even if analysts become aware that they have underreacted the distressrisk information of the firms and make corrections in the new forecasts, investors may still not fully
and timely respond to these distress-risk-related revisions by the analysts. In other words, the postforecast-revision price drifts should still be explainable by the distress risk levels of the firms after
controlling for the revision contents by including the EPS and sales forecast revisions and ACAR(151, 1).5 For completeness, we also include the interaction terms between distress risk levels andforecast revisions to the regressions.
Table 5 reports the pool regression results for (DRR) for the three models. DR_I are
negatively significant for all three DR models, as predicted by Hypothesis 4. For the two forecast
revisions, it is only the sales forecast revisions, i.e. AFR_S, that reduce their explanatory powers (interms of t-values) in the three DR models; for the EPS forecast revisions, i.e. AFR_EPS, theirsignificances remain (even slightly increase) in the three models, consistent with the findings of theliterature (e.g. Gleason and Lee 2003). Overall, evidences suggest that market underreact to the
distress risk information embedded in both EPS and sales forecast revisions.
Turning to the interaction terms, the (distress risk indicators)×(sales forecast revisions)terms, i.e. DR_I×AFR_S, are all positive (but only significant in the O-SCORE model). That is,sales forecast revisions have (slightly) stronger effect on the price drifts by higher distress risk firms,
suggesting that market had underreacted to the sales forecast revisions more for the higher distressrisk firms, and as a result their subsequent price drifts are more pronounced. In contrast to the sales
forecast revisions, the (distress risk indicators)×(EPS forecast revisions) terms, i.e.
DR_I×AFR_EPS, are negative for all three models, indicating that EPS forecast revisions haveactually weaker effects on the price drifts by higher distress risk firms. Or equivalently, market had
already responded to the EPS forecast revisions sufficiently for the higher distress risk firms thanfor the lower distress firms, and thus the drifts for the higher distress firms become relatively
weaker.6<< Insert Table 5 here>>
4.3 Further Analysis
In this section we investigate the effect of earnings information on the returns behaviors of
the firms. The idea is that analysts revises their forecast when they receive more information about
the firms, including surely the earnings information (e.g. from quarter earnings announcements) and5

By including both revisions as the control variables, we can see whether sales forecast revisions also contribute to
the price drifts, as well as the EPS forecast revisions. To the best of our knowledge, the post-sales-forecast-revision
price drifts are not studied in the literature before.
6
If the 3-day abnormal returns (the immediate responses) and the post-forecast-revision price drifts are somewhatbalance on both sides. The non-trivial interaction terms in the DRR can explain why the interaction terms in the CARR
are insignificant.

16other information. We want to know how much portion of the (low) abnormal returns of the (highdistress risk) firms are contributed by the earnings information. Specifically, we exclude all 1st
analyst forecasts (revisions) after the quarter earnings announcements and then calculate the sum of
all EPS and sales forecast revisions (i.e. SFR_EPS and SFR_S), 3-day abnormal returns aroundrevisions, i.e. SCAR(-1, 1), and post-forecast-revision price drifts, i.e. SCAR(2, -2) within the same
fiscal years.
Table 6 Panel A-C report their median values (by pooling all firm-years belonging to thesame distress risk group) for the ten distress risk (DR) groups ranked by O-SCORE, PD andRATING respectively: osgrp, pdgrp and ratgrp 1 (10) represent lowest (highest) distress firms. First
look at the EPS and sales forecast revisions, comparing before and after excluding the 1 st forecast
revisions after the quarter earnings announcements, SFR_EPS and SFR_S drop substantially by anaverage percentages of about 70% and 60% for the three sets of DR groups. The drop percentages
are distinctly bigger for higher distress risk firms than lower distress risk firms; but SFR_EPS and
SFR_S (excluding the 1st forecast revisions after earnings announcements) still show clear
decreasing trends along the DR groups, i.e. larger EPS and sales forecast revisions for higher distressfirms. Turning to the 3-day abnormal returns and price drifts, comparing before and after excluding
the 1st revisions after the quarter earnings announcements, SCAR(-1, 1) and SCAR(2, -2) drop by
an average percentages of about 35% and 30% for the three sets of DR groups. The drop percentages
are, however, more or less the same within each set of DR groups. As before, SCAR(-1, 1) andSCAR(2, -2) are more negative for higher distress risk firms, after excluding the 1st forecast
revisions after earnings announcements, and clear decreasing trends remain. Overall, the results
indicate that (quarter) earnings announcements convey important information that non-triviallyaffect the analyst forecast revisions and the subsequent market responses.
<< Insert Table 6 here>>

5. Conclusion
This study provides an explanation for the high distress risk but low stock returns puzzle
from the analysts forecast perspective. We use O-SCORE, probability of default, and credit rating
as proxy for distress risk, and examine the analyst earnings per share (EPS) and sales forecasts for17the sample firms. Generally consistent results are obtained when firms are ranked by the three
independent distress risk measures. This provides extra evidence for the debate that the distress risk
puzzle may be merely special case results due to the use of a particular measure such as the OSCORE (e.g. Kim 2013).
Four logically-linked hypotheses are proposed that lay out the whole argument for
explaining the distress risk puzzle. First, analysts make over-optimistic forecasts for firms withhigher distress risk. Then the analysts revise their forecasts more downwards for the higher distressrisk firms to drive down the stock prices. Finally, market respond to the forecast revisions
incompletely and prices continue to drift downwards after the forecast revisions.
Empirical evidences are generally supportive for the four hypotheses, namely, we observed
significant negative relations between (EPS and sales) forecast errors (revisions) and distress risk
levels of the firms. The within-fiscal-year averages of 3-day abnormal returns (CAR) are positively
related to the EPS forecast revisions. Finally, the within-fiscal-year averages of post-forecastrevision price drifts are more pronounced for higher distress risks firms. The 3-day immediatemarket responses and the post-forecast-revision price drifts constitute the low abnormal returns ofhigher distress risk firms.
Our paper contributes to the analyst forecast and mispricing literature. We provide evidencesthat analysts underreact to the distress risk status of firms, and that analyst EPS and sales forecasts
are more biased for the higher distress risk firms. We show that both EPS and sales forecast revisions
explain the 3-day abnormal returns around forecast revisions and the post-forecast-revision pricedrifts; and most importantly distress risk information is a determinant of the cross-sectionalvariations in the post-forecast-revision price drifts associated with analyst (EPS and sales) forecast
revisions. Finally, we document that (quarter) earnings announcements convey substantial amountof information that roughly drive more than 60% of the analyst forecast revisions and 30% of therevision-related market responses.

18

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22

Table 1: Bivariate Analysis
Panel A: Sample firms are ranked by the O-SCORE into ten DR groups (osgrp) each year
osgrp

N

O-ScoreAFE_EPS

AFE_S

AFR_EPS

AFR_S

CRET

ACAR(-1,1)

ACAR(2,-2)

1
2
3
4
5
6
7
8
9
10
(10-1)

5427
5410
5428
5423
5424
5442
5504
5623
5815
6288

-5.71
-3.92
-2.97
-2.31
-1.75
-1.21
-0.67
0.01
1.10
4.48

-0.07%-0.08%-0.11%-0.16%-0.13%-0.13%-0.20%-0.39%-0.68%-0.58%-0.51%***-0.86%-0.89%-0.65%-0.75%-1.03%-1.48%-1.98%-3.10%-5.38%-20.96%-20.09%***-0.02%-0.03%-0.03%-0.03%-0.03%-0.04%-0.04%-0.07%-0.13%-0.12%-0.09%***-0.04%-0.04%-0.05%-0.06%-0.08%-0.09%-0.17%-0.24%-0.33%-0.19%-0.15%***

6.02%
7.64%
7.70%
7.46%
7.90%
8.19%
7.42%
4.50%
-0.14%-16.73%-22.75%***-0.16%-0.16%-0.12%-0.10%-0.08%-0.10%-0.10%-0.13%-0.16%-0.22%-0.06%***-0.94%-0.92%-0.83%-0.94%-0.84%-0.97%-0.88%-1.04%-1.20%-2.13%-1.19%***

Panel B: Sample firms are ranked by the PD into ten DR groups (pdgrp) each year
pdgrp

N

PD

AFE_EPS

AFE_S

AFR_EPS

AFR_S

CRET

ACAR(-1,1)

ACAR(2,-2)

1
2
3
4
5
6
7
8
9
10

4320
4303
4312
4337
4359
4382
4396
4439
4503
4613

0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.02%
0.85%
28.15%

0.05%
0.02%
0.00%
-0.09%-0.19%-0.30%-0.58%-0.76%-1.37%-2.90%

0.34%-0.30%-0.50%-0.91%-1.60%-1.76%-3.11%-3.97%-5.34%-6.60%

0.00%
0.00%-0.01%-0.02%-0.04%-0.06%-0.10%-0.15%-0.23%-0.40%

0.00%-0.01%-0.02%-0.05%-0.10%-0.13%-0.25%-0.38%-0.61%-1.21%

9.23%
9.08%
9.96%
7.84%
7.32%
6.79%
3.09%
1.35%
0.03%
-3.72%-0.05%-0.05%-0.08%-0.08%-0.11%-0.12%-0.19%-0.19%-0.20%-0.31%-0.62%-0.75%-0.85%-0.83%-0.98%-1.01%-1.11%-1.33%-1.50%-1.85%-2.95%***-6.94%***-0.40%***-1.21%***-12.96%***-0.27%***-1.23%***CRET

ACAR(-1,1)

ACAR(2,-2)(10-1)

Panel C: Sample firms are ranked by the RATING into ten DR groups (ratgrp) each year
RATING
ratgrpN
AFE_EPS
AFE_S
AFR_EPS
AFR_S
1
2
3
4
5
6
7
8
9
10
(10-1)

2116
1955
2087
1390
1419
1769
2262
1385
1398
1056

5.00
7.00
9.00
10.00
11.00
12.00
13.00
14.00
15.00
16.00

0.02%
0.03%
0.02%
-0.04%-0.06%-0.11%-0.21%-0.34%-1.09%-2.40%

0.64%
0.66%
0.24%
-0.24%-0.12%-0.56%-1.35%-2.26%-4.35%-6.52%

0.00%
0.00%-0.01%-0.02%-0.02%-0.03%-0.05%-0.07%-0.20%-0.47%

0.00%
0.01%-0.02%-0.02%-0.05%-0.04%-0.19%-0.25%-0.71%-1.20%

10.64%
11.77%
13.51%
8.50%
10.18%
9.42%
8.53%
4.48%
1.77%
-8.51%-0.01%
0.03%-0.01%-0.08%-0.05%-0.07%-0.08%-0.16%-0.25%-0.35%-0.72%-0.64%-0.64%-0.80%-0.86%-0.80%-0.94%-0.85%-1.34%-1.86%-2.41%***-7.16%***-0.46%***-1.20%***-19.16%***-0.34%***-1.14%***

This table presents the median values of CRET, defined as the 12-month cumulative (buy-and-hold) returns of the firm starting fromthe 4th month after the last fiscal year end; AFE_EPS and AFE_S, the averages of all monthly EPS and sales forecast errors within thesame fiscal year; AFR_EPS and AFR_S, the averages of all monthly EPS and sales forecast revisions within the same fiscal year; andACAR(-1, 1) and ACAR(2, -2), the averages of all 3-day revision returns and post-forecast-revision price drifts between two
consecutive forecasts within the same fiscal year. Appendix A describes the detailed definitions of the variables. Median values arecalculated by pooling all firm-years belonging to the same distress risk group, where the ten distress risk (DR) groups are ranked by
O-SCORE, PD and RATING respectively: osgrp, pdgrp and ratgrp 1 (10) represent lowest (highest) distress firms. Results for thethree sets of DR groups are reported in Panel A, B and C respectively. (10-1) report the differences between the median values of thehighest (10) and lowest (1) DR groups. Significance are tested by the two way Wilcoxon test. *** Significant at the 1% level; **Significant at the 5% level; * Significant at the 10% level.

23

Table 2: Rank Correlations Among Major Variables
O-SCOREPD
RATING
AFE_EPS
AFE_S
AFR_EPS
AFR_S
CRET
ACAR(-1,1)
ACAR(2,-2)

O-SCOREPD

RATING

AFE_EPS

AFE_S

AFR_EPS

AFR_S

CRET

ACAR(-1, 1)

0.39
0.48
-0.09
-0.14
-0.13
-0.18
-0.02
-0.02
-0.03

0.52
-0.22
-0.23
-0.36
-0.31
-0.01
-0.07
-0.13

-0.17
-0.20
-0.22
-0.21
-0.07
-0.07
-0.04

0.34
0.64
0.27
0.02
0.11
0.32

0.33
0.52
0.02
0.06
0.15

0.42
0.02
0.11
0.28

0.01
0.03
0.12

0.00
-0.03

-0.01

This table presents the Spearman rank correlations among major variables: O-SCORE, PD and RATING, the three DR
measures; CRET, defined as the 12-month cumulative (buy-and-hold) returns of the firm starting from the 4th month after thelast fiscal year end; AFE_EPS and AFE_S, the averages of all monthly EPS and sales forecast errors within the same fiscal
year; AFR_EPS and AFR_S, the averages of all monthly EPS and sales forecast revisions within the same fiscal year; ACAR(1, 1) and ACAR(2, -2), the averages of all 3-day cumulative (buy-and-hold) abnormal returns and post-forecast-revision pricedrifts between two consecutive forecasts within the same fiscal year. Appendix A describes the detailed definitions of thevariables. Correlations are calculated by pooling all firm-years data. All correlations (except those bolded) are significant at
the 1% level.

24

Table 3: Forecast Error Regression and Forecast Revision Regressions
Panel A: EPS and sales forecast error regressions for the three DR models
Variables .
O-SCORE_I

EPS models

RATING_I

TACC
DISP
MOMEN
ES
L_VOL
L_ANA
Year fixed effectN
R2

Sales models

0.007***(7.225)-0.007(-1.016)0.001*(1.789)0.031***(18.165)
0.121***(4.620)-0.004***(-3.761)-0.004***(-3.110)
Yes
41084
0.412

.

-0.110***(-7.716)-0.027***(-11.974)

PD_I

SIZE

.

-0.002(-0.859)

0.005***(4.143)-0.005(-0.597)
0.001(1.584)0.031***(15.715)
0.097***(3.295)-0.001(-0.693)-0.005***(-3.133)
Yes
33664
0.441

-0.045***(-4.332)-0.043***(-5.047)
0.006***(3.051)-0.029***(-2.659)
0.001(1.338)0.030***(8.023)0.223***(3.355)-0.004(-1.626)-0.011***(-4.417)
Yes
14576
0.372

-0.006(-1.074)-0.002(-0.045)-0.001(-0.816)
0.051***(9.591)0.214***(2.768)-0.019***(-4.062)0.035***(4.315)
Yes
37069
0.471

-0.001(-0.199)
0.022(0.507)-0.002(-1.044)0.046***(7.841)0.163**(1.961)-0.014***(-2.726)
0.031***(3.303)
Yes
30256
0.513

-0.099***(-3.234)
0.002(0.438)-0.036(-0.825)-0.000(-0.354)0.030***(4.941)0.194*(1.740)-0.018***(-2.863)
0.008(0.798)
Yes
13565
0.275

Panel B: EPS and sales forecast revision regressions for the three DR models
Variables .
O-SCORE_I

EPS models

RATING_I

TACC
DISP
MOMEN
ES
L_VOL
L_ANA
Year fixed effectN
R2

Sales models

0.001***(11.452)-0.002***(-4.533)
0.000***(3.238)0.002***(12.079)
0.027***(12.505)-0.001***(-9.779)-0.001***(-6.729)
Yes
41222
0.375

.

-0.008***(-6.556)-0.004***(-18.533)

PD_I

SIZE

.

-0.000(-1.235)0.001***(5.407)-0.002***(-3.678)0.000***(2.703)0.002***(9.357)0.025***(10.116)-0.000***(-3.490)-0.001***(-5.406)
Yes
33790
0.398

-0.015***(-12.151)-0.007***(-6.918)0.001***(3.523)-0.004***(-3.107)0.000**(2.501)0.001***(2.847)0.041***(6.837)-0.001***(-4.451)-0.002***(-7.430)
Yes
14626
0.358

0.006***(14.823)
0.003(0.884)-0.000(-0.550)-0.001(-0.799)0.070***(6.065)-0.005***(-12.543)-0.006***(-8.095)
Yes
37320
0.353

0.005***(10.287)
0.005(1.262)-0.000(-1.042)-0.004**(-2.289)
0.066***(4.938)-0.004***(-7.139)-0.006***(-7.121)
Yes
30354
0.369

-0.038***(-6.523)0.004***(3.901)-0.008(-0.981)-0.000(-0.380)-0.013***(-3.849)0.150***(3.945)-0.005***(-5.228)-0.010***(-6.331)
Yes
13556
0.355

25

Table 3 (Continue)
This table presents the pooled yearly regression results for the three DR models. Panel A reports the results
for the forecast error regressions (FER), where averages of all EPS (sales) forecast errors within a fiscal
year, i.e. AFE_EPS (AFE_S), are regressed on the DR measures plus the control variables; Panel B reports
the results for the forecast revision regressions (FRR), where averages of all EPS (sales) forecast revisions
within a fiscal year, i.e. AFR_EPS (AFR_S), are regressed on the DR measures plus the control variables.
Control variables include firm size, book-to-market, accruals, dispersion of analyst EPS forecasts, analyst
coverage, momentum, trading volume. Appendix A describes the detailed definitions of the variables. ***Significant at the 1% level; ** Significant at the 5% level; * Significant at the 10% level.

26

Table 4: 3-Day Cumulative Abnormal Returns Regressions
Variables
AFR_EPS
AFR_S
SIZE
TACC
MOMEN
ES
L_ANA
DISP
L_VOL

(1)
0.105***(2)
0.146*

(3)
0.120

(4)
0.098

(3.027)0.008(1.360)-0.000***(-2.588)0.001(0.734)0.004***(12.514)-0.005(-1.377)-0.001**(-2.005)-0.000***(-2.796)-0.000**(-2.056)(1.859)-0.012(-0.764)-0.000***(-2.961)
0.001(0.538)0.004***(12.804)-0.006(-1.606)-0.001*(-1.935)-0.000***(-2.657)-0.000**(-2.023)-0.001(-1.387)-0.054(-0.445)0.027(1.191)(1.239)-0.019(-1.110)-0.001***(-2.684)-0.000(-0.135)0.004***(11.814)-0.006(-1.588)-0.000(-0.654)-0.000***(-2.621)-0.000(-1.241)(0.889)-0.022*(-1.829)-0.000(-0.328)-0.002(-0.733)
0.004***(8.728)-0.006(-0.601)-0.001***(-3.237)-0.000**(-2.418)-0.000*(-1.708)

O-SCORE_I
O-SCORE_I×AFR_EPS
O-SCORE_I×AFR_S
PD_I-0.002***(-3.154)-0.035(-0.270)
0.031(1.493)

PD_I ×AFR_EPS
PD_I ×AFR_S
RATING_I
RATING_I ×AFR_EPS
RATING_I ×AFR_S
Year fixed effect
N
R2

Yes
37320
0.249

Yes
37320
0.249

Yes
30354
0.274

-0.003***(-2.655)-0.049(-0.324)0.052**(2.486)
Yes
13556
0.228

This table presents the pooled yearly regression results for the 3-day cumulative abnormal returns regressions.
Column (1) reports the results for (CARR), where averages of all 3-day cumulative abnormal returns within
the same fiscal year, i.e. ACAR(-1, 1), are regressed on the EPS and sales forecast revisions plus the control
variables; Columns (2)-(4) report the results for (CARR’) for the three DR models, where distress risk
indicators, i.e. DR_I = {O-SCORE_I, PD_I, RATING_I}, and their interaction terms with the the EPS andsales forecast revisions (namely, DR_I×AFR_EPS and DR_I×AFR_EPS) are included as extra control
variables. Control variables include firm size, book-to-market, accruals, dispersion of analyst EPS forecasts,
analyst coverage, momentum, trading volume. Appendix A describes the detailed definitions of the variables.
*** Significant at the 1% level; ** Significant at the 5% level; * Significant at the 10% level.

27

Table 5: Post-Forecast-Revision Price Drifts Regressions
Variables.
AFR_EPS
AFR_S
SIZE
TACC
MOMEN
ES
L_ANA
DISP
L_VOL
O-SCORE_I
O-SCORE_I×AFR_EPS
O-SCORE_I×AFR_S

O-SCORE model
1.792***(4.723)-0.134**(-2.113)-0.007***(-11.359)-0.006(-1.439)0.043***(36.045)-0.019(-1.229)-0.001(-1.271)0.000(0.759)-0.000(-0.451)-0.006***(-3.167)-1.612**(-2.459)0.288***(3.023)

PD_I

PD model1.193**(2.462)0.038(0.603)-0.009***(-11.819)-0.004(-0.940)0.042***(32.742)-0.018(-1.067)-0.001(-1.143)
0.000(0.158)0.000(0.373)-0.007***(-4.107)-0.738(-1.004)
0.045(0.507)

PD_I ×AFR_EPS
PD_I ×AFR_S
RATING_I
RATING_I ×AFR_EPS
RATING_I ×AFR_S
ACAR(-1, 1)
Year fixed effectN
R2

RATING model
1.618***(5.394)-0.009(-0.314)-0.007***(-7.130)-0.008(-1.403)0.040***(25.145)-0.011(-0.376)-0.001(-0.874)0.000**(2.217)0.001(1.487)-0.465***(-9.236)
Yes
37313
0.435

-0.435***(-8.640)
Yes
30351
0.463

-0.022***(-6.224)-1.103***(-2.712)0.075(1.377)-0.425***(-6.197)
Yes
13556
0.495

This table presents the pooled yearly regression results for the drift regression (DRR) for the three DRmodels, where averages of all post-forecast-revision price drifts within the same fiscal year, i.e. ACAR(2,-2), are regressed on the distress risk indicators i.e. DR_I = {O-SCORE_I, PD_I, RATING_I}, EPS andsales forecast revisions, their interaction terms, plus the other control variables. Control variables include
firm size, book-to-market, accruals, dispersion of analyst EPS forecasts, analyst coverage, momentum,
trading volume. 3-day cumulative abnormal returns, i.e. ACAR(-1, 1), is included to control for
information content of the forecast revisions. Appendix A describes the detailed definitions of thevariables. *** Significant at the 1% level; ** Significant at the 5% level; * Significant at the 10% level.

28

Table 6: The Effects of Earnings Information on Forecast Revisions and Abnormal Returns
BeforeAfter

After /
BeforeBeforeAfter

After /
BeforeBeforeAfter

Panel A: Sample firms are ranked by the O-SCORE into ten DR groups (osgrp) each year
SCAR
osgrp SFR _EPS SFR_EPS
SFR_S SFR_S(-1, 1)
1-0.23%-0.06%
25.09% -0.35% -0.15% 43.44% -1.56%
2-0.24%-0.07%
27.21% -0.33% -0.13% 40.76% -1.48%
3-0.29%-0.09%
30.66% -0.44% -0.18% 41.06% -1.14%
4-0.34%-0.12%
36.09% -0.50% -0.19% 38.55% -0.94%
5-0.30%-0.10%
32.07% -0.67% -0.19% 28.88% -0.82%
6-0.36%-0.12%
33.01% -0.71% -0.32% 44.74% -0.92%
7-0.40%-0.12%
31.08% -1.39% -0.56% 40.60% -0.99%
8-0.70%-0.25%
36.19% -2.02% -0.83% 40.97% -1.15%
9-1.19%-0.45%
37.98% -2.86% -1.17% 40.94% -1.38%
10-0.98%-0.28%
28.75% -1.42% -0.51% 35.64% -1.78%
Mean
31.81%
39.56%

SCAR(-1, 1)-0.95%-0.85%-0.78%-0.74%-0.51%-0.35%-0.58%-0.68%-1.06%-1.42%

Panel B: Sample firms are ranked by the PD into ten DR groups (pdgrp) each year
SCAR
pdgrp SFR _EPS SFR_EPS
SFR_S SFR_S(-1, 1)
1
0.00%
0.01%
n.a.
0.00%
0.02%
n.a.
-0.45%
2-0.03%
0.00%
0.00%-0.07%
0.00%
5.68% -0.50%
3-0.09%-0.01%
14.94% -0.15% -0.07% 44.30% -0.77%
4-0.20%-0.06%
28.00% -0.42% -0.15% 35.00% -0.75%
5-0.43%-0.16%
36.60% -0.78% -0.39% 50.54% -1.07%
6-0.58%-0.23%
39.22% -1.12% -0.40% 36.05% -1.12%
7-1.02%-0.39%
38.35% -2.09% -0.84% 39.93% -1.72%
8-1.43%-0.61%
42.39% -3.31% -1.42% 42.96% -1.68%
9-2.21%-0.95%
42.83% -5.36% -2.05% 38.34% -1.71%
10-3.75%-1.89%
50.54% -10.62% -4.78% 45.04% -2.73%
Mean
32.54%
37.54%

SCAR(-1, 1)-0.34%-0.35%-0.42%-0.57%-0.50%-0.63%-1.13%-1.21%-1.11%-1.91%

Panel C: Sample firms are ranked by the RATING into ten DR groups (ratgrp) each year
SCAR
ratgrp SFR _EPS SFR_EPS
SFR_S SFR_S(-1, 1)n.a.
1-0.03%
0.00%
1.18%
0.00%
0.17%
-0.11%
2-0.05%
0.00%
4.61%
0.07%
0.09% 122.53% 0.33%
3-0.11%-0.04%
33.72% -0.13% -0.04% 32.90% -0.10%
4-0.23%-0.08%
33.78% -0.14% -0.05% 35.42% -0.84%
5-0.22%-0.07%
32.14% -0.40% -0.09% 21.66% -0.54%
6-0.34%-0.10%
28.90% -0.36% -0.18% 51.22% -0.67%
7-0.50%-0.20%
39.07% -1.61% -0.84% 52.48% -0.77%
8-0.72%-0.31%
43.75% -2.34% -1.06% 45.17% -1.49%
9-1.85%-0.88%
47.66% -6.24% -3.04% 48.82% -2.23%
10-4.33%-2.58%
59.57% -10.84% -5.37% 49.51% -2.99%
51.08%
Mean
32.44%

SCAR(-1, 1)-0.16%
0.05%-0.20%-0.67%-0.16%-0.25%-0.76%-1.18%-1.19%-1.73%

After /
Before61.13%
57.60%
68.06%
78.87%
61.77%
38.40%
58.76%
59.45%
76.68%
79.55%
64.03%

74.80%
69.53%
54.27%
76.09%
46.39%
55.92%
65.71%
72.00%
64.64%
70.20%
64.96%

144.61%13.86%
209.32%79.28%
29.89%
37.47%
98.04%
79.12%
53.27%
57.81%
80.27%

BeforeAfter

SCAR(2, -2)-8.68%-8.52%-7.67%-8.58%-7.86%-9.06%-8.03%-9.35%-9.87%

SCAR(2, -2)-6.21%-5.88%-5.80%-6.40%-5.69%-5.97%-6.06%-6.05%-7.00%-15.58%-10.62%SCAR(2, -2)-5.91%-7.06%-7.84%-7.85%-8.95%-9.10%

SCAR(2, -2)-4.01%-4.84%-5.53%-5.78%-6.30%-6.47%-7.59%-8.59%-8.99%-10.05%-11.50%-12.75%-14.94%SCAR(2, -2)-6.91%-6.13%-6.22%-7.70%-7.80%-7.59%-8.68%-7.83%-11.59%-16.30%-10.51%SCAR(2, -2)-5.15%-4.12%-4.72%-5.45%-5.80%-4.50%-6.16%-5.15%-7.51%-13.09%After /
Before71.55%
68.95%
75.62%
74.59%
72.43%
65.93%
75.39%
64.66%
70.91%
68.17%
70.82%

67.75%
68.61%
70.53%
73.61%
70.41%
71.10%
75.52%
74.64%
70.57%
70.36%
71.31%

74.56%
67.27%
75.80%
70.83%
74.38%
59.27%
70.98%
65.85%
64.80%
80.27%
70.40%

This table presents the median values of SFR_EPS and SFR_S, the sum of all monthly EPS and sales forecast revisions within thesame fiscal year; and SCAR(-1, 1) and SCAR(2, -2), the sum of all 3-day revision returns and post-forecast-revision price drifts
between two consecutive forecasts within the same fiscal year, before and after excluding the 1st forecast revisions after (quarter)
earnings announcements. Appendix A describes the detailed definitions of the variables. Median values are calculated by pooling all
firm-years belonging to the same distress risk group, where the ten distress risk (DR) groups are ranked by O-SCORE, PD andRATING respectively: osgrp, pdgrp and ratgrp 1 (10) represent lowest (highest) distress firms. Results for the three sets of DR groups
are reported in Panel A, B and C respectively.

29

Appendix A. Definitions of the Major Variables
Definitions

O-SCORE

Calculated by using the coefficients from Model 1 in Ohlson (1980).

PD

The market value of the firm minus the face value of the firm’s debt and thendivided by the volatility of the firm using Merton DD model.

RATINGThe numeric rating is 1 = AAA, 2 = AA+, … If the rating is below B-, numericrating is set to 17. Ratings are from Compustat.

O-SCORE_I

Calculated by ranked O-SCORE and then scaled by 10.

PD_I

Calculated by ranked probability of default and then scaled by 10.

RATING_I

Calculated by ranked credit ratings and then scaled by 10.

FE_EPS(j, t, m)Actual earnings per share at the end of current fiscal year t minus consensus(median) earnings per share forecast for firm j announced at the mth month of thecurrent fiscal year t scaled by stock price 2 days before the current forecast
announcement date.

AFE_EPS(j, t)

Average of all EPS forecast errors for firm j within the same fiscal year t.

SFE_EPS(j, t)

Sum of all EPS forecast errors for firm j within the same fiscal year t.

FE_S(j, t, m)Actual sales at the end of current fiscal year t minus consensus (median) sales
forecast for firm j announced at the mth month of the current fiscal year t scaledby the actual sales at the end of the last fiscal year.

AFE_S(j, t)

Average of all sales forecast errors for firm j within the same fiscal year t.

SFE_S(j, t)

Sum of all sales forecast errors for firm j within the same fiscal year t.

FR_EPS(j, t, m)

Consensus (median) earnings per share forecast for firm j announced at the mthmonth of the current fiscal year t minus consensus (median) earnings per share
forecast for firm j announced at the m-1th month of the current fiscal year t scaledby the stock price 2 days before the current forecast announcement date.

AFR_EPS(j, t)

Average of all EPS forecast revisions for firm j within the same fiscal year t.

SFR_EPS(j, t)

Sum of all EPS forecast revisions for firm j within the same fiscal year t.

FR_S(j, t, m)

Consensus (median) sales forecast for firm j announced at the mth month of thecurrent fiscal year t minus consensus (median) sales forecast for firm j announced
at the m-1th month of the current fiscal year t scaled by the actual sales at the endof the last fiscal year.

AFR_S(j, t)

Average of all sales forecast revisions for firm j within the same fiscal year t.

SFR_S(j, t)

Sum of all sales forecast revisions for firm j within the same fiscal year t.

A
b
n
o
r
m
a
l
R
e
t
u
r
n
s

Forecast Revisions

Forecast Errors

DR Measures

Variables30

Control Variables

CAR(-1, 1)

3-day cumulative (buy-and-hold) abnormal returns centered on the forecast
announcement date. Abnormal returns are size-adjusted by subtracting the stock
returns value-weighted returns of the size group the stock belongs to.

ACAR(-1, 1)

Average of all 3-day cumulative (buy-and-hold) abnormal returns within the same
fiscal year.

SCAR(-1, 1)

Sum of all 3-day cumulative (buy-and-hold) abnormal returns within the same
fiscal year.

CAR(2, -2)

Cumulative (buy-and-hold) abnormal returns from 2 days after one forecast to 2
days before the next one. Abnormal returns are size-adjusted by subtracting thestock returns value-weighted returns of the size group the stock belongs to.

ACAR(2, -2)

Average of all post-forecast-revision price drifts between two consecutive
forecasts within the same fiscal year.

SCAR(2, -2)

Sum of all post-forecast-revision price drifts between two consecutive forecasts
within the same fiscal year.

CRET

12-month buy-and-hold returns starting from the 4th month after the beginning of
the current fiscal year.

SIZE

Natural logarithm of the year end market value of equity.

BTM

Book-to-market ratio at the beginning of the current fiscal year.

DISP

Standard deviation of analyst EPS forecasts over the seven-month horizon before
the last fiscal year end deflated by the price at the previous fiscal-year end andmultiplied by 100. See e.g. Barron et al. (1998); Kim and Zhang (2017)

ES

Changes in earnings per share deflated by last year's stock price. See e.g. Langand Lundholm (1996).

L_VOL

Natural log of the sum of the monthly trading volume over the 12-month period
before the earliest earnings forecast included in the sample. See e.g. (Hayes,
1998).

L_ANA

Natural logarithm of the number of analysts following a firm. See e.g. Lang andLundholm (1996).

MOMEN

Market-adjusted returns over the six months prior to the beginning of the current
fiscal year. See e.g. So (2013)

TACC

Change in noncash current assets minus the change in current liabilities, excluding
the current portion of long-term debt and taxes payable, minus depreciation
expense, scaled by total assets. See e.g. Kim (2013).

31

Appendix B. Distress Risk Measures
In this Appendix, we highlight the methods to calculate the three distress risk measures usedin this paper. For further technical details, please refer to the reference papers being cited.
B1. O-SCORE
Similar to Dichev (1998), O-SCORE for firm j at the end of fiscal year t is calculated by
Equation A1 below with the coefficients from the Model 1 in Ohlson (1980). For simplicity, firm
and time subscripts in are skipped.
O-SCORE(t) = – 1.32 – 0.407*log(total assets/GNP price – level index) + 6.03*(total liabilities/total
assets) – 1.43*(working capital/total assets) + 0.076*(current liabilities/current assets) – 1.72*(1 iftotal liabilities > total assets; else 0) – 2.37*(net income/total assets) – 1.83*(funds fromoperations/total liabilities) + 0.285*(1 if net loss for the last two years; else 0) – 0.521*(net income(t)– net income(t-1)]/[|net income(t)| + |net income(t-1)|](A1)

B2. Probability of Default
The probability of default (PD) for every firm each year is calculated in the spirit of the
Merton (1974) model. When the market value of a firms’ total assets (hereafter, firm value) is greaterthan the face value of the firm’s debt outstanding, equity holders find it profitable to continue
owning the firm. Otherwise, the equity holders declares bankruptcy. PD thus refers to the probability
that the firm goes into bankruptcy within certain period of time.
Firm value, V (> 0), is assumed following geometric Brownian motion:𝑑𝑉 = µ𝑉𝑑𝑡 + 𝜎𝑣 𝑉𝑑𝑊,
where µ is the expected return (continuously compounded) on V, 𝜎𝑣 is the volatility of V and dW isthe standard Wiener process. The firm has an outstanding pure discount bond with face value F
maturing in T years. Thus, the equity of the firm is a European call option on the underlying valueof the firm with strike price and time-to-maturity equal F and T. By the Black-Scholes-Merton
formula (Black and Scholes 1973), the value of the equity today, E, is given by
E = V Ɲ(d1) − e−rT F Ɲ(d2),

(B1)
32

where E is the market value of the firm’s equity, r is the instantaneous risk-free rate, Ɲ(·) is the
cumulative standard normal distribution function, d1 =𝑉𝐹ln( )+(𝑟+0.5𝜎𝑣2 )𝑇𝜎𝑣 √𝑇and d2 = d1 − 𝜎𝑣 √𝑇.

Furthermore, by the Ito’s lemma:𝑉 𝜕𝐸𝑉𝜎𝐸 = (𝐸) 𝜕𝑉 𝜎𝑉 = (𝐸)Ɲ(𝑑1 )𝜎𝑉 ,(B2)

where 𝜎𝐸 is the volatilities of the equity. Equation B2 is to translate the volatility of equity to thevolatility of total assets because the market value of total assets and its volatility are unobservable.
Following Bharath and Shumway (2008), Equations (B1) and (B2) are solved by iterative
procedure for V’s and 𝜎𝑣 ’s every day in the previous year. (Specially, the initial value of 𝜎𝑣 is set
to 𝜎𝐸 [E/(E + F)], where 𝜎𝐸 , E and F are observable parameters. We then calculate the implied log
return on assets each day and use the returns series to generate new estimates of 𝜎𝑣 and µ. The
procedure is repeated until the calculated 𝜎𝑉 converges, i.e., the absolute difference in adjacent 𝜎𝑣 ‘s−3is less than 10 ). Then the distance to default (DD) is calculated asDD =𝑉𝐹ln( )−(µ−0.5𝜎𝑣2 )𝑇𝜎𝑣 √𝑇, and thecorresponding (implied) probability of default within one year (assuming T = 1) is:
𝑃𝐷 = Ɲ(−𝐷𝐷)
B.3 Credit Rating
The third proxy for distress risk is credit rating (RATING) that evaluate the credit risk of a
firm as debtor. The higher its credit rating, the higher the chance the firm will be able to pay backits outstanding debts (i.e., lower distress risk). Credit ratings of sample firms are obtained from
Compustat, and following Avramov et al. (2013), we assign numeric value to each symbolic ratings
as AAA = 1, AA+ = 2, etc. If the rating is below B–, the value is set to 17. The higher number of
RATING, the higher the financial risk.

33

Appendix C. Monthly EPS and Sales Forecast Errors and Forecast Revisions
Panel A: Sample firms are ranked by the O-SCORE into ten DR groups (osgrp) each year
osgrp
1
2
3
4
5
6
7
8
9
10

M1
-0.22%-0.24%-0.30%-0.41%-0.37%-0.44%-0.58%-1.17%-2.00%-1.54%

M2-0.15%-0.15%-0.20%-0.29%-0.25%-0.28%-0.39%-0.83%-1.53%-1.21%

M3-0.10%-0.10%-0.14%-0.22%-0.18%-0.18%-0.27%-0.59%-0.93%-0.82%

M4-0.08%-0.08%-0.11%-0.17%-0.14%-0.15%-0.24%-0.53%-0.85%-0.69%

FE_EPS
M5-0.04%-0.07%-0.09%-0.14%-0.10%-0.12%-0.17%-0.41%-0.63%-0.42%

M6-0.03%-0.06%-0.08%-0.12%-0.09%-0.10%-0.14%-0.35%-0.55%-0.38%

M7
0.00%-0.02%-0.04%-0.09%-0.06%-0.07%-0.10%-0.27%-0.44%-0.33%

M8
0.00%
0.00%
0.00%
-0.04%-0.02%-0.03%-0.05%-0.14%-0.27%-0.18%

M9
0.02%
0.00%
0.00%
0.00%
0.00%
0.00%
-0.04%-0.11%-0.17%-0.08%

M10
0.03%
0.02%
0.02%
0.00%
0.00%
0.00%
0.00%
-0.03%-0.10%
0.00%

osgrp
1
2
3
4
5
6
7
8
9
10

M1
-5.25%-4.79%-3.78%-4.21%-4.70%-5.69%-9.33%

M2-4.80%-4.66%-3.45%-3.84%-4.89%-5.11%-8.51%

M3-3.88%-4.35%-3.36%-3.36%-4.30%-4.44%-7.46%

M4-3.17%-3.39%-2.67%-2.84%-3.63%-3.57%-5.89%

FE_S
M5-2.99%-3.21%-2.49%-2.89%-3.00%-3.01%-5.24%

M6-2.73%-2.87%-2.54%-2.81%-3.05%-2.81%-4.52%

M7-2.19%-2.43%-2.01%-2.30%-2.61%-2.47%-3.83%

M8-2.02%-2.21%-1.73%-2.22%-2.18%-2.36%-3.50%

M9-1.75%-1.89%-1.50%-2.14%-1.84%-2.08%-2.87%

M10-1.08%-1.16%-1.06%-1.37%-1.17%-1.47%-2.20%-15.45%-30.57%-94.00%-13.59%-26.39%-78.34%-11.28%-21.80%-60.58%-9.71%-17.38%-48.52%-8.36%-14.88%-37.18%-7.55%-13.68%-32.74%-6.42%-11.76%-28.15%-5.73%-10.01%-26.23%-5.03%-9.61%-24.20%-3.93%-7.82%-18.85%

osgrp
1
2
3
4
5
6
7
8
9
10

M1
-0.03%-0.03%-0.04%-0.05%-0.06%-0.07%-0.07%-0.11%-0.21%-0.23%

M2-0.03%-0.04%-0.06%-0.06%-0.07%-0.09%-0.09%-0.15%-0.28%-0.35%

M3-0.04%-0.04%-0.06%-0.07%-0.07%-0.08%-0.10%-0.19%-0.40%-0.43%

M4-0.02%-0.03%-0.03%-0.04%-0.04%-0.05%-0.05%-0.11%-0.16%-0.25%

FR_EPS
M5
0.03%
0.04%
0.04%
0.03%
0.04%
0.03%
0.03%
-0.04%-0.09%-0.12%

M6-0.03%-0.03%-0.02%-0.03%-0.03%-0.03%-0.04%-0.06%-0.08%-0.14%

M7-0.03%-0.04%-0.04%-0.04%-0.04%-0.05%-0.05%-0.07%-0.10%-0.08%

M8
0.02%
0.02%
0.01%
0.01%
0.02%
0.03%
0.03%
-0.05%-0.08%-0.07%

M9-0.04%-0.04%-0.04%-0.05%-0.04%-0.05%-0.04%-0.07%-0.11%-0.13%

M10-0.04%-0.05%-0.05%-0.06%-0.06%-0.07%-0.07%-0.10%-0.12%-0.07%

osgrp
1
2
3
4
5
6
7
8
9
10

M1
0.01%
-0.01%-0.03%-0.04%-0.05%-0.09%-0.11%-0.16%-0.26%-0.55%

M2
0.02%
0.02%
-0.03%-0.04%-0.08%-0.12%-0.16%-0.26%-0.36%-0.66%

M3
0.01%-0.02%-0.03%-0.04%-0.08%-0.11%-0.15%-0.25%-0.38%-0.75%

M4
0.02%
0.01%
0.00%
-0.03%-0.04%-0.07%-0.11%-0.17%-0.24%-0.47%

FR_S
M5
0.05%
0.04%
0.05%
0.04%
0.03%
0.03%
-0.06%-0.12%-0.18%-0.34%

M6
0.01%
0.01%
-0.01%-0.01%-0.04%-0.04%-0.06%-0.09%-0.13%-0.24%

M7
0.01%-0.02%-0.02%-0.03%-0.05%-0.06%-0.08%-0.12%-0.16%-0.31%

M8
0.04%
0.04%
0.03%
0.03%
-0.03%-0.04%-0.10%-0.13%-0.18%-0.42%

M9-0.01%-0.02%-0.03%-0.04%-0.05%-0.05%-0.07%-0.11%-0.14%-0.26%

M10-0.01%-0.03%-0.04%-0.05%-0.07%-0.09%-0.12%-0.14%-0.18%-0.36%

34

Panel B: Sample firms are ranked by the PB into ten DR groups (pdgrp) each year
pdgrp
1
2
3
4
5
6
7
8
9
10

M1
0.05%
0.00%
-0.08%-0.23%-0.52%-0.71%-1.27%-1.80%-2.75%-5.26%

M2
0.06%
0.00%
-0.02%-0.14%-0.38%-0.56%-0.99%-1.45%-2.24%-4.65%

M3
0.06%
0.02%
0.00%
-0.09%-0.29%-0.39%-0.76%-1.12%-1.88%-3.92%

M4
0.06%
0.03%
0.00%
-0.09%-0.23%-0.35%-0.64%-0.93%-1.61%-3.37%

pdgrp
1
2
3
4
5
6
7
8
9
10

M1
-2.76%-3.29%-4.48%-7.44%

M2-2.78%-2.85%-4.01%-7.04%-12.29%-10.82%-19.85%-20.17%-27.08%-32.40%-10.50%-10.05%-16.22%-17.40%-23.70%-26.86%M3-2.31%-2.92%-3.68%-6.07%-9.07%-9.45%

M4-1.45%-2.13%-3.24%-5.09%-7.17%-7.50%-13.28%-14.52%-18.41%-21.16%

pdgrp
1
2
3
4
5
6
7
8
9
10

M1
0.01%
-0.01%-0.03%-0.04%-0.05%-0.09%-0.11%-0.16%-0.26%-0.55%

M2
0.02%
0.02%
-0.03%-0.04%-0.08%-0.12%-0.16%-0.26%-0.36%-0.66%

pdgrp
1
2
3
4
5
6
7
8
9
10

M1
-0.02%-0.03%-0.08%-0.10%-0.17%-0.13%-0.26%-0.39%-0.55%-0.81%

M2
0.12%
0.00%
-0.04%-0.06%-0.22%-0.23%-0.52%-0.76%-1.21%-1.92%

FE_EPS
M5
0.04%
0.02%
0.00%
-0.07%-0.18%-0.30%-0.52%-0.67%-1.20%-2.62%

M6
0.04%
0.02%
0.00%
-0.06%-0.15%-0.26%-0.48%-0.62%-1.11%-2.45%

M7
0.04%
0.03%
0.00%
-0.03%-0.11%-0.21%-0.39%-0.51%-0.95%-2.20%

M8
0.04%
0.02%
0.00%
0.00%
-0.05%-0.12%-0.20%-0.29%-0.60%-1.43%

M9
0.04%
0.03%
0.00%
0.00%
-0.02%-0.10%-0.17%-0.21%-0.51%-1.24%

M10
0.04%
0.04%
0.03%
0.02%
0.00%
-0.04%-0.09%-0.10%-0.38%-0.91%-10.97%-12.02%-14.82%-17.20%

FE_S
M5-1.27%-2.44%-3.34%-4.78%-6.39%-6.48%-9.16%-9.98%

M6-1.11%-2.27%-3.20%-4.45%-5.68%-6.20%-8.17%-9.36%

M7-0.80%-1.89%-2.89%-3.72%-4.87%-5.47%-6.81%-8.08%-11.70%-13.49%-10.65%-12.16%-9.27%-10.32%

M3
0.01%-0.02%-0.03%-0.04%-0.08%-0.11%-0.15%-0.25%-0.38%-0.75%

M4
0.02%
0.01%
0.00%
-0.03%-0.04%-0.07%-0.11%-0.17%-0.24%-0.47%

FR_EPS
M5
0.05%
0.04%
0.05%
0.04%
0.03%
0.03%
-0.06%-0.12%-0.18%-0.34%

M6
0.01%
0.01%
-0.01%-0.01%-0.04%-0.04%-0.06%-0.09%-0.13%-0.24%

M7
0.01%-0.02%-0.02%-0.03%-0.05%-0.06%-0.08%-0.12%-0.16%-0.31%

M8
0.04%
0.04%
0.03%
0.03%
-0.03%-0.04%-0.10%-0.13%-0.18%-0.42%

M9-0.01%-0.02%-0.03%-0.04%-0.05%-0.05%-0.07%-0.11%-0.14%-0.26%

M10-0.01%-0.03%-0.04%-0.05%-0.07%-0.09%-0.12%-0.14%-0.18%-0.36%

M3
0.35%
0.03%
-0.01%-0.19%-0.41%-0.48%-1.04%-1.56%-2.47%-3.58%

M4
0.25%-0.08%-0.21%-0.40%-0.73%-0.85%-1.63%-2.24%-3.00%-5.13%

FR_S
M5
0.55%
0.16%
0.06%
-0.39%-0.88%-1.11%-2.11%-3.42%-4.33%-6.86%

M6
0.63%
0.08%
0.05%
-0.42%-1.05%-1.21%-2.37%-3.45%-4.58%-7.33%

M7
0.56%
0.10%
0.03%
-0.51%-1.21%-1.32%-2.76%-3.88%-5.16%-7.61%

M8
0.72%
0.08%
-0.01%-0.68%-1.42%-1.72%-3.61%-5.11%-6.26%-9.36%

M9
0.67%
0.06%
-0.14%-0.70%-1.59%-2.01%-3.79%-5.31%-7.22%-10.06%M10
0.70%-0.12%-0.26%-0.82%-1.71%-2.28%-4.14%-5.57%-7.68%-10.36%

M8-0.90%-1.83%-2.84%-3.56%-4.78%-5.13%-6.04%-7.47%-8.02%-8.39%

M9-0.97%-1.62%-2.49%-2.89%-4.41%-4.79%-5.44%-6.57%-7.45%-7.53%

M10-0.49%-0.96%-1.60%-2.18%-3.20%-3.64%-4.01%-4.84%-5.98%-6.18%

35

Panel C: Sample firms are ranked by the RATING into ten DR groups (ratgrp) each year
FE_EPS
ratgrp
M1
M2
M3
M4
M5
M6
M7
M8
1
0.00%
0.02%
0.02%
0.03%
0.01%
0.01%
0.02%
0.02%
2
0.00%
0.04%
0.05%
0.04%
0.03%
0.03%
0.04%
0.03%
3
-0.07%
0.00%
0.02%
0.03%
0.02%
0.03%
0.03%
0.03%
4
-0.17% -0.08% -0.04% -0.02% -0.02%
0.00%
0.00%
0.00%
5
-0.20% -0.12% -0.06% -0.06% -0.08% -0.07% -0.05% -0.02%
6-0.38% -0.21% -0.13% -0.11% -0.08% -0.05% -0.04% -0.03%
7-0.48% -0.37% -0.22% -0.18% -0.20% -0.20% -0.17% -0.11%
8-1.02% -0.84% -0.54% -0.48% -0.39% -0.36% -0.26% -0.19%
9-2.33% -1.93% -1.49% -1.30% -1.04% -1.00% -0.83% -0.55%
10-5.19% -4.67% -3.71% -3.17% -2.51% -2.10% -1.93% -1.42%

M9
0.02%
0.03%
0.03%
0.00%
0.00%
0.00%
-0.09%-0.15%-0.41%-1.12%

M10
0.02%
0.04%
0.05%
0.04%
0.02%
0.00%
-0.03%-0.05%-0.29%-0.86%

M9
1.03%
0.29%
0.59%
-0.68%-0.71%-1.16%-1.40%-3.09%-4.29%-8.91%

M10
1.24%
0.56%
0.68%
-0.33%-0.42%-1.13%-0.91%-2.44%-3.22%-7.13%-11.13%-25.54%

M2
1.78%
0.54%
0.27%
-1.86%-1.64%-2.13%-4.06%-5.68%-9.76%

M3
1.65%
0.54%
-0.05%-1.62%-1.63%-2.23%-3.81%-5.43%-8.70%

M4
1.77%
1.11%
-0.04%-0.92%-1.58%-1.82%-3.15%-4.28%-7.51%

FE_S
M5
1.46%
0.77%
-0.21%-0.85%-1.47%-1.77%-2.47%-4.68%-6.28%

M6
1.46%
0.69%
0.00%
-0.66%-1.35%-1.67%-2.36%-4.40%-6.40%

M7
1.47%
0.61%
0.03%
-0.37%-0.96%-1.35%-2.13%-3.93%-5.28%-21.70%-18.72%-15.34%-13.59%-12.45%-11.29%ratgrp
1
2
3
4
5
6
7
8
9
10

M1
-0.02%-0.03%-0.04%-0.05%-0.04%-0.05%-0.06%-0.17%-0.20%-0.38%

M2-0.03%-0.04%-0.05%-0.07%-0.06%-0.07%-0.10%-0.12%-0.28%-0.50%

M3-0.01%-0.02%-0.03%-0.04%-0.04%-0.06%-0.11%-0.16%-0.31%-0.93%

M4-0.01%-0.01%-0.02%-0.03%-0.02%-0.03%-0.05%-0.08%-0.14%-0.32%

FR_EPS
M5
0.03%
0.04%
0.04%
0.04%
0.06%
0.06%
0.06%
0.03%
-0.17%-0.29%

M6-0.01%
0.01%-0.02%-0.01%-0.01%-0.02%-0.03%-0.05%-0.10%-0.15%

M7-0.01%-0.02%-0.03%-0.04%-0.03%-0.04%-0.05%-0.08%-0.15%-0.16%

M8
0.02%
0.04%
0.03%
0.03%
0.02%
0.04%
0.03%
0.04%
-0.20%-0.34%

M9-0.02%-0.02%-0.03%-0.03%-0.04%-0.04%-0.04%-0.08%-0.12%-0.22%

M10-0.02%-0.02%-0.03%-0.04%-0.05%-0.06%-0.09%-0.10%-0.17%-0.28%

ratgrp
1
2
3
4
5
6
7
8
9
10

M1
-0.04%-0.06%-0.10%-0.11%-0.10%-0.08%-0.13%-0.18%-0.25%-0.89%

M2-0.10%-0.05%-0.10%-0.12%-0.11%-0.21%-0.29%-0.26%-0.64%-1.55%

M3
0.05%
0.02%
-0.04%-0.07%-0.02%-0.30%-0.56%-0.68%-1.89%-3.09%

M4
0.05%
0.00%
-0.08%-0.25%-0.16%-0.42%-0.90%-1.00%-2.50%-3.84%

FR_S
M5
0.34%
0.39%
0.04%
-0.05%
0.19%-0.22%-0.73%-1.32%-3.31%-4.86%

M6
0.34%
0.42%
-0.06%-0.01%
0.23%-0.19%-0.77%-1.39%-3.50%-5.52%

M7
0.18%
0.30%
-0.05%-0.08%
0.05%-0.20%-0.74%-1.46%-4.37%-6.29%

M8
0.51%
0.48%
-0.04%-0.37%
0.06%-0.52%-1.29%-2.29%-5.45%-7.78%

M9
0.50%
0.52%
-0.07%-0.49%
0.06%-0.52%-1.38%-2.27%-5.65%-8.55%

M10
0.39%
0.45%
-0.16%-0.42%-0.04%-0.37%-1.60%-2.67%-5.78%-8.88%

ratgrp
1
2
3
4
5
6
7
8
9
10

M1
1.59%
0.29%
-0.60%-1.60%-1.51%-2.32%-4.20%-6.52%

M8
0.96%
0.35%
0.33%
-0.73%-0.97%-1.56%-1.71%-3.82%-4.91%-9.00%

This Appendix presents the median values of FE_EPS and FE_S, and FR_EPS and FR_S, the (pooled) monthly EPS
and sales forecast errors and revisions within the sample periods. Median values of the 11th fiscal month (M11) and 12th
fiscal month (M12) are not reported because the number of observations in these two months are too few. Results forthe three sets of DR groups ranked by O-SCORE, PD and RATING are reported in Panel A, B and C respectively.

36