Proceedings of 24th International Business Research Conference
12 - 13 December 2013, Planet Hollywood, Las Vegas, USA, ISBN: 978-1-922069-37-5
Credit Rating, Post-Earnings-Announcement-Drift, and Arbitrage
from Transient Institutions
Guanming He1
This study establishes a robust link between credit rating and post-earningsannouncement-drift (PEAD). I find strong evidence that PEAD is much stronger
for firms with lower credit ratings. This is consistent with the notion that investors
are apt to underreact to earnings news of low credit rating firms which are
characterized by high uncertainty of asset fundamentals in the long run. The
credit rating effect on PEAD is unexplained by traditional information uncertainty
proxies such as earnings volatility, cash flow volatility, accrual quality, firm age,
idiosyncratic volatility, and analyst forecast dispersion. Further, I investigate
whether transient institutions exploit the differential of PEAD among different
rated firms in their arbitrage trades. My results reveal that transient institutions
tend to focus their arbitrage on low credit rating firms which are featured as
abundant in arbitrage profits and that ex ante transaction cost impedes the
transient institutions from exploiting the PEAD in the low rated firms. This
evidence is taken to be suggestive of underreaction story rather than arbitrage
risk story to explain why PEAD anomaly concentrates in low credit rating firms.
Keywords: Credit rating, Post-earnings-announcement-drift, Transient institutions, Arbitrage
JEL Classifications: M41 G24 G14
1. Introduction
Existent literature documents that market anomalies concentrate in high credit risk stocks. For
instance, Avramov et al. (2009a) shows that the puzzling negative cross-sectional relation
between dispersion in analyst earnings forecasts and future stock returns is explained by credit
rating that measures a firm’s credit risk. Avramov et al. (2007) find that price momentum
prevails only among firms with low credit ratings and is nonexistent among firms with high
credit ratings. Collectively, these findings suggest that credit rating is an important predictor of
market anomalies and that low credit rating firms provide a crucial source of arbitrage profits
from trading on the anomalies. Given the strong implication of credit rating for market anomaly,
the first objective of this study is to explore the role of credit rating in post-earningsannouncement-drift (hereafter, PEAD) that is yet another most robust anomaly well
documented in the asset pricing literature.
PEAD refers to the tendency of stocks to continue to earn positive (negative) average
abnormal returns after positive (negative) earnings surprises. Beginning with the early work by
Ball and Brown (1968), the finance and accounting literature (e.g., Bernard and Thomas 1989,
1990; Ball and Bartov 1996) documents that investors tend to underreact to earnings
1
Guanming He , Corresponding author. University of Warwick, United Kingdom. Tel.: +44-7570184501.
Email: guanming.he@wbs.ac.uk.
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Proceedings of 24th International Business Research Conference
12 - 13 December 2013, Planet Hollywood, Las Vegas, USA, ISBN: 978-1-922069-37-5
information at earnings announcements, followed by a continuous drift in the same direction as
earnings surprises. As such, firms that report high standardized unexpected earnings
(hereafter, SUE) subsequently outperform firms that report low SUE. This is known as PEAD
anomaly.
Extensive research (e.g., Odders-White and Ready 2006) documents the ability of credit
rating to capture uncertainty about the value of not only debt but also equity of a firm.
Theoretically, firm equity can be regarded as a call option on a firm with a strike price equal to
the face value of debt (e.g., Core and Schrand 1999). Default occurs when firm value falls
below the strike. Accordingly, default risk, which is embodied by credit rating, captures
uncertainty about a firm’s future earnings, growth rates and the cost of equity capital --- the
ingredients used in asset valuation (Merton 1974; Avramov et al. 2009a). Further, Standard &
Poor (2009) states that “Credit rating is meant to be forward-looking and their time horizon
extends as far as is analytically foreseeable2”, suggesting that credit rating is forward-looking
in discriminating risk and uncertainty of asset components of a firm in the long run. In view of
this, I posit that investors tend to delay their reaction to earnings news of low credit rating firms
until more additional relevant information become available and/or the precision of the
information increases following earnings announcements, resulting in stronger PEAD for low
rated firms.
I find strong evidence that PEAD is more salient for firms with lower credit ratings. In
particular, a univariate portfolio analysis reveals that a zero investment portfolio that longs the
highest SUE stocks and shorts the lowest SUE stocks yields larger payoffs (hereafter, PEAD
payoffs or earnings momentum payoffs) among lower credit rating group. This payoff
differential across credit rating groups cannot be explained by a battery of traditional
information uncertainty 3 proxies such as firm age, earnings volatility, accruals quality, cash
flow volatility, idiosyncratic volatility, and analyst forecast dispersion, as evidenced by the
results from the independent double sorts by credit rating and each of the information
uncertainty variables. Note that these variables primarily capture the earnings-preannouncement information uncertainty which could be largely resolved in a short run
(Shivakumar 2007). In contrast, credit rating captures the uncertainty that is forward-looking on
a long horizon. Presumably, investors should be concerned more about the forward-looking
uncertainty of asset fundamentals in a long run rather than in a short run when they use the
current earnings news to predict future earnings and price the firm equity. Hence, it is likely
that investors delay response to earnings news of firms featured by low credit rating rather
than by the high short-term information uncertainty. Consistent with this notion, my multivariate
regression analysis also reveals that the credit rating effect on PEAD is both independent of
and much stronger than the effect of those traditional information uncertainty proxies. In
particular, most information uncertainty variables lose their predictive power for PEAD whereas
the effect of credit rating remains highly significant. The results hold for PEAD associated with
2
Altman and Rijken (2004) quantify the impact of the long-term default horizon and show that, in contrast to one-year
default prediction models, credit rating agencies place less weight on short-term indicators of credit quality and focus on the
long term ones in assigning credit ratings to firms.
3
Following Zhang (2006), I define information uncertainty as ambiguity with respect to the implications of new information
for a firm’s value.
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Proceedings of 24th International Business Research Conference
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either seasonal random walk-based earnings surprises (hereafters, RW-based PEAD) or
analyst forecast-based earnings surprises (hereafters, AF-based PEAD) over different drift
windows and are robust to controlling for an array of other PEAD determinants (e.g.,
transaction cost) documented in the literature. These results also persist after adjusting the
PEAD for systematic risks.
Firms with lower credit ratings tend to have higher idiosyncratic risk. If arbitrageurs are not
well informed or not able to well diversify their investment portfolios, the credit risk might
restrain rather than stimulate the arbitrages on PEAD. This alternatively explains why the
PEAD is concentrated in high credit risk firms. Thus, I next examine whether transient
institutions exploit the significance of earnings momentum payoffs among low credit rating
firms and engage in the arbitrages accordingly. I focus on the transient institutions’ arbitrages
for two reasons: First, transient institutions make the majority of all trades in the US stock
market (e.g., Ke and Ramalingegowda 2005). Second, transient institutions are characterized
by high turnover and high portfolio diversification with short trading horizons and fragmented
investments in a large number of companies. Prior research (e.g., Gompers and Metrick 2001;
Yan and Zhang 2009) shows that transient institutions are sophisticated and informed
investors who trade actively to exploit their information advantage. They aim at maximizing the
short-term profits and tend to arbitrage anomalies in the financial market place (e.g., Ke and
Ramalingegowda 2005). Further, given prior evidence that transient institutions arbitrage
PEAD anomaly, it is also important to further investigate how capable transient institutions are
to exploit the PEAD payoff differential among different firms to enlarge their arbitrage profits. In
this way, we could obtain further insights into the role of transient institutions in contributing to
capital market efficiency.
The drift returns remain significant after adjusting for systematic risk and transaction cost,
suggesting that the PEAD affords an arbitrage opportunity for arbitrageurs to obtain real profits
(e.g., Bernard and Thomas 1989; Ke and Ramalingegowda 2005; Chung and Hrazdil 2011).
To the extent that credit rating is a potent determinant of PEAD payoffs, transient institutions
who wish to pursue high arbitrage profits should be particularly sensitive to a firm’s credit rating
information. If transient institutions are sophisticated in collecting and processing information,
they would be able to identify the significance of PEAD payoffs among low rated firms. Given
the high PEAD payoffs, high idiosyncratic risk from low credit rating firms should not impede a
transient institution from exploiting the PEAD so long as the transient institution is capable of
diversifying away the idiosyncratic risk through the portfolio diversification or the possession of
private information about firms. In this scenario, I expect that transient institutions tend to trade
more aggressively to exploit PEAD in firms with lower credit ratings. Results confirm my
expectation, suggesting that the transient institutions are not only sophisticated in information
processing but also well capable of diversifying idiosyncratic risk from firms with low credit
ratings. However, PEAD yet existent and concentrated in low credit rating firms points to the
fact that the transient institutions still do not fully exploit the PEAD among the low rated firms.
My further analysis reveals that ex ante transaction cost4 hinders the transient institutions from
exploiting the PEAD in the low rated firms.
4
Ex ante transaction cost is defined as the transaction cost that traders expect to incur for trading stocks in the stock market.
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Proceedings of 24th International Business Research Conference
12 - 13 December 2013, Planet Hollywood, Las Vegas, USA, ISBN: 978-1-922069-37-5
This study contributes to the existent literature in several aspects. First, this study
conducts the first exploration about the theoretical and empirical link between credit rating and
PEAD anomaly. I hypothesize that investors are apt to underreact to earnings news released
by low credit rating firms that are featured as having high future uncertainty of asset
fundamentals in the long run. My empirical results are consistent with the testable hypothesis,
that is, either seasonal-random-walk-based PEAD or analyst-based PEAD is more salient for
firms with lower credit ratings. This evidence lends support to the notion that investors’
underreaction to earnings news could be the outcome of investors’ rational behaviors. In this
way, this study complements the rationality explanation for financial anomalies in the literature
(e.g., Morris 1996; Lewellen and Shanken 2002; Dontoh et al. 2003; Francis et al. 2007), but is
contrasted with the irrationality story for the investors’ underreaction in some prior PEAD
studies (e.g., Bartov et al. 2000; Battalio and Mendenhall 2005).
Second, Avramov et al. (2007) find a strong and robust association between credit rating
and price momentum. However, prior studies (e.g., Chordia and Shivakumar 2006) also find
that price momentum is entirely subsumed and driven by earnings momentum (i.e., PEAD) and
hence merely a manifestation of the earnings momentum. Building on this strand of literature,
my study further establishes a strong predictable link between credit rating and earnings
momentum, and therein contributes to more understanding of the profitability of momentum
strategies. By showing that the credit rating effect on PEAD is unexplained by varied proxies
for information uncertainty and other drift-related variables, I corroborate that credit rating is a
potent predictor of PEAD anomaly.
Third, prior literature (e.g., Ke and Petroni 2004; Ke and Ramalingegowda 2005; Yan and
Zhang 2009) documents that transient institutions are sophisticated and trade actively to
exploit RW-based PEAD. However, there is no prior evidence on how these transient
institutions exploit the PEAD to enlarge arbitrage profits. This study sheds light on this issue
and finds that transient institutions tend to focus their arbitrage on PEAD among low rated
firms that are featured as abundant in arbitrage profits, and that this arbitrage strategy applies
not only to RW-based PEAD but also to AF-based PEAD.
Fourth, asset pricing literature (e.g., Francis et al. 2007; Arena et al. 2008; McLean 2010)
well documents that market anomalies concentrates among high idiosyncratic risk firms. There
are two explanations for this evidence. First, investors are prone to underreact to a larger
extent to firm-specific information featured by higher idiosyncratic risk. Second, idiosyncratic
risk represents limits of arbitrage for arbitrageurs, thereby allowing anomalies to persist and
remain conspicuous for stocks with high idiosyncratic risk. To the best of my knowledge, this
paper is the first to disentangle these two explanations. Specifically, by providing evidence that
credit risk represents a stimulus to rather than a limit of arbitrage for transient institutions, I rule
out the limit of arbitrage story (in terms of idiosyncratic risk) as an alternative explanation for
why stronger PEAD prevails among lower rated firms. Rather, I attribute the stronger PEAD
primarily to investors’ greater underreaction to earnings news released by lower rated firms5.
5
My first objective of exploring the role of credit ratings in PEAD is conceptually different from Avramov et al. (2013) in
that the objective of their paper is to empirically explore the potential implications of financial distress for asset pricing
anomalies. Furthermore, Avramov et al. do not tell why the anomalies are stronger for financial distress firms. They do not
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Proceedings of 24th International Business Research Conference
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The remainder of the paper proceeds as follows. Section 2 conducts literature review and
develops the testable hypothesis. Section 3 describes the data collection and sample selection
procedures. Section 4 presents the research methodologies to test the hypothesis. Section 5
discusses the results and Section 6 concludes.
2. Literature Review and Hypothesis Development
2.1. Credit rating and Pead
The literature has explored varied explanations for PEAD, and specifically, for why market
participants are slow in reacting to the earnings information. Irrational explanations as to investors’
underreaction to earnings news (e.g., Bartov et al. 2000; Battalio and Mendenhall 2005) pinpoints the
investors’ unsophistication in their information processing and suggests that the trading by naive
individual investors drives the PEAD. However, one limitation of these studies is that the quality of the
proxies used to capture investor sophistication, such as trade size and institutional ownership, is
questionable (Richardson et al. 2010). Using actual daily signed trading made by individual investors,
Hirshleifer et al. (2008) present direct contrary evidence that PEAD is not caused by the trading
activities of individual investors.
Another main strand of explanations for PEAD pertains to investors’ reliance on private information
more than public information. For instance, Daniel et al. (1998) present a model in which investors
overweight the value of their private signals and underweight the information content of important public
information such as earnings announcements. Hong and Stein (1999) suggest that market participants
might require additional private information to convert the news at earnings announcements into a
judgment about future earnings. More recent studies such as Liang (2003), Vega (2006), and Francis et
al. (2007) also find that the PEAD of a given firm is related to the amount of private information
available about that firm. In this vein, individual investors not driving PEAD as evidenced by Hirshleifer
et al. (2008) is probably because the individuals have to resort to (and thereby react promptly to)
important public information such as earnings news given their lack of private information. Despite
these evidence and explanations for PEAD, few studies in the PEAD literature show under what
circumstances investors are prone to underreact to the earnings news. This study extends this strand of
literature by showing whether the investors’ delayed reactions are subject to a firm’s credit risk proxied
by credit rating.
Prior studies (e.g., Merton 1974; Avramov et al. 2009a) argue that credit rating captures the
uncertainty about a firm’s future earnings, growth rates and the cost of equity capital --- the ingredients
used for asset valuation. Consistent with this assertion, Avramov et al. (2009a) find that credit rating
even subsumes the information in analyst forecast dispersion in capturing the uncertainty about future
earnings and future stock returns. Further, credit rating aims to discriminate relatively long-term credit
risk and long-term uncertainty about a firm’s asset components in future. This is distinctive from firm
age, earnings volatility, accruals quality, cash flow volatility, return volatility, idiosyncratic volatility, and
analyst forecast dispersion, which capture a firm’s historical information uncertainty and/or information
uncertainty to be resolved on a short horizon. Compared to these traditional information uncertainty
variables, credit rating is a long-term indicator of future uncertainty about a firm’s fundamentals (e.g.,
consider information uncertainty that may alternatively explain the effect of financial distress on the anomalies. Nor do they
exclude limit of arbitrage (in terms of a firm’s idiosyncratic risk) story as an alternative explanation for why the anomalies
concentrate among the financial distress firms.
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future earnings) at the earnings announcements. Accordingly, current earnings released by lower credit
rating firms have weaker implications for future earnings in the long run.
Rational explanations for financial anomaly (e.g., Morris 1996; Lewellen and Shanken 2002;
Francis et al. 2007) focus on investors’ rational processing of incomplete and imprecise information. In
particular, investors place less weight on signal that is of lower precision or lower quality and therein
rationally defer investing in stocks until more relevant information becomes available and/or the
precision of information increases in future. In this regard, the extent of investors’ underreaction at
earnings announcements depends on the precision and quality of the earnings signal in terms of its
implication for future earnings. As a lower credit rating firm is characterized by its weaker implication of
current earnings about future earnings, it’s more difficult for investors to interpret its earnings news and
form expectations about its future earnings in a timely manner. As a consequence, investors’ delayed
trading on earnings news of lower rated firms becomes more prominent. Accordingly, I expect that the
PEAD is stronger among firms with lower credit ratings.
Chordia and Shivakumar (2006) find that a zero investment portfolio that is long on stocks with
high SUE and short on stocks with low SUE are significantly related to business cycle proxied by
growth in GDP, industrial production, consumption, labor income, inflation and T-bill returns. Moreover,
Avramov and Chordia (2006) show that an optimizing investor who appreciates the business cycle is
able to successfully load on the momentum strategy during different phases of the economy. Since
credit risk varies over business cycle (e.g., Nickell et al. 2000; Bangia et al. 2002; Amato and Furfine
2004), it is natural to ask whether the earnings momentum payoffs are related to credit rating of a firm.
In this study, I provide a new and unexplored dimension in understanding the profitability of PEAD
trading strategy. To the extent that more prominent PEAD is generated by high credit risk firms,
earnings momentum profitability should be higher for firms with lower credit ratings.
In a similar vein, Francis et al. (2007) examines the effect of information uncertainty on PEAD.
They focus on information uncertainty before the earnings announcements and define information
uncertainty as the degree to which historical accruals map into cash flows. In contrast, I focus on future
uncertainty of asset valuation in a long run, which is captured by a firm’s credit rating. Moreover,
Francis et al.’s measure of information uncertainty by the standard deviation of residuals in the Dechow
and Dichev (2002) model is subject to two limitations. First, their measure directly equates poor
accounting quality with high information uncertainty. However, a firm that consistently overestimates
accruals has poor accounting quality but does not necessarily have high information uncertainty as
captured by the high accruals volatility. Second, firms could use accruals to report losses in a timely
fashion. Ball and Shivakumar (2006) show that timely loss recognition role of accruals implies a
nonlinearity in the mapping of accruals into cash flows. A failure to account for the nonlinearity in
Francis et al. (2007)’s study could result in greater accrual volatility stemming from timely loss
recognition rather than high information uncertainty (Shivakumar 2007). Last but not the least, Francis
et al. (2007) do not exclude limit of arbitrage (in terms of a firm’s idiosyncratic risk) story as an
alternative explanation for why PEAD is more pronounced for firms with high information uncertainty.
My study does so by further showing (in Section 2.2, 4.2, and 5.2) that credit risk stimulates rather than
restrains the arbitrage from transient institutions who are well informed and capable of diversifying
idiosyncratic risk in the low rated firms.
2.2. Credit rating and transient institutions’ arbitrage on Pead
Institutions can be classified into transient, dedicated and quasi-indexing institutions based on their
investment horizon and portfolio turnover. Transient institutions possess short trading horizons and
fragmented investments in a large amount of firms, and hence are featured by high turnover, high
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investment portfolio diversification and high frequency of arbitrage activities. In contrast, dedicated
institutions have a long-term strategy of holding large stakes in a fewer firms, and thus exhibit low
turnover and low diversification. Quasi-indexing institutions have a passive long-term buy-and-hold
strategy in a broad set of firms, and hence exhibit low turnover and high diversification.
Most prior studies (e.g., Nofsinger and Sias 1999; Gompers and Metrick 2001; Ke and
Ramalingegowda 2005) that examined the effect of institutional trading on asset prices and returns
suggest that institutional investors are sophisticated and informed. Suppose that some institutional
investors have superior information and can regularly identify mispricing of stocks, these institutional
investors should trade frequently to exploit their informational advantage and skill (e.g., Wermers 2000).
In contrast, institutional investors in possession of limited information would trade more cautiously.
Therefore, transient institutions (i.e., those actively trade to maximize short-term profits) should be more
sophisticated and better informed than dedicated and quasi-indexing institutions (i.e., those that have
long-term investment horizon). Consistent with this proposition, Yan and Zhang (2009) find that
transient institutions’ trading predicts future stock returns and future earnings surprises whereas
dedicated or quasi-indexing institutional trading does not forecast returns and earnings news. Ke and
Petroni (2004) find that transient institutions can predict a break in a string of consecutive quarterly
earnings increases but dedicated and quasi-indexing institutions cannot. Ke and Ramalingegowda
(2005) provide evidence that transient institutions, but not dedicated and quasi-indexing institutions,
exploit the PEAD.
Provided that transient institutions are sophisticated in collecting and processing information, they
should be able to identify that the earnings momentum payoffs concentrate among low credit rating
firms. Though low credit ratings are associated with high idiosyncratic risk for a firm, one can well
manage unsystematic risk at a low cost by holding a diversified portfolio or possessing private
information about a firm (e.g., Markowitz 1959; Hughes et al. 2007). Given the high PEAD payoffs, the
high idiosyncratic risk from low credit rating firms should not hinder a transient institution from exploiting
their PEAD so long as the transient institution is capable of diversifying away the idiosyncratic risk. Or
rather, if a transient institution is capable of diversifying idiosyncratic risk, the marginal benefits with
respect to an increase in credit risk should surpass the marginal costs for the transient institution. In this
scenario, I expect that transient institutions who wish to maximize their short-term arbitrage profits
would trade more aggressively to exploit PEAD among firms with lower credit ratings. On the contrary, if
transient institutions are not sophisticated enough to identify the significance of PEAD payoffs among
low rated firms or they are not capable of diversifying the idiosyncratic risk, we would not observe that
transient institutions trade more intensively to exploit PEAD in low rated firms.
In addition, prior studies (e.g., Bhushan 1994; Ke and Ramalingegowda 2005) provide evidence
that high ex ante transaction cost decreases the arbitrage activities of institutional investors and thereby
exacerbates the magnitude of PEAD. To the extent that transient institutions trade more intensively to
exploit PEAD in firms that have low credit ratings, I would further explore whether the ex ante
transaction cost would mitigate the intensity of transient institutions’ arbitrage on PEAD for the low rated
firms.
Last, given a long-term focus by dedicated and quasi-indexing institutions, they serve more of a
monitoring role for a company (Chen et al. 2007), care more about long-term firm performance, and are
less likely to engage in arbitrages as transient institutions do. Therefore, there should be no mitigating
effect of dedicated or quasi-indexing institutions’ trading on the magnitude of PEAD. Rather, the
dedicated and quasi-indexing institutions act more as normal investors who tend to underreact to
earnings news released by a firm with high credit risk.
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3. Data
3.1. Data source
The empirical analyses are based on data gathered from four sources: I/B/E/S, CRSP, Compustat,
and CDA Spectrum. Stock returns are obtained from all NYSE, AMEX, and NASDAQ stocks listed in
the Center for Research in Security Prices (CRSP) database. Firm credit ratings are extracted from the
Standard and Poor’s Long-Term Domestic Issuer credit ratings reported by Compustat from the second
quarter of 1985. So my sample period starts from July, 1985. Since institutions’ trading classification
data are only available for the period before 2010, my sample period ends in December, 2009. Table 1
shows the full sample distribution of credit ratings at the firm-quarter level from 1985-2009. The majority
of observations in the rated firm population fall within the credit rating level from BB- to BBB+, with BBB
level observations accounting for the highest percentage (10.28%). I collect institutional ownership data
on the CDA Spectrum while the institutions’ trading classification (transient, dedicated and quasiindexing) is obtained from Brian Bushee. I focus on the effect of transient institutions’ trading on PEAD
since prior studies (e.g., Ke and Ramalingegowda 2005) provide evidence that transient institutions
exploit PEAD whereas dedicated or quasi-indexing institutions do not. I require that firms have
necessary data to construct variables of interest in all my empirical analyses.
3.2. Measurement of earnings surprises
While the majority of prior studies focus on the drift associated with seasonal random walk-based
earnings surprises, recent studies (e.g., Livnat and Mendenhall 2006; Doyle et al. 2006) document a
drift associated with analyst forecast-based earnings surprises that appear not only distinct from but
even larger than the drift associated with random walk-based earnings surprises. So I focus on PEAD
associated with both the seasonal random walk-based and the analyst forecast-based earnings
surprises, namely, RW-based and AF-based PEAD, respectively. Following Bernard and Thomas
(1989), I define the random walk-based earnings surprise as current quarter earnings less earnings four
quarters ago, standardized by the standard deviation of earnings changes in the prior ten quarters. I
standardize the earnings surprise by the standard deviation rather than stock price, market
capitalization, or sales because these variables may in themselves proxy for size or expected returns
which would bias the results towards capturing cross-sectional variance in the expected returns
associated with these variables (Chordia and Shivakumar 2006). To compute the seasonal random
walk-based earnings surprise, I require firms to have earnings before extraordinary items and
discontinued operations for the same quarter in the prior year and to have at least 14 quarters of the
prior quarterly earnings.
Following Livnat and Mendenhall (2006), I define analyst forecast-based earnings surprise as the
unsplit-adjusted I/B/E/S actual EPS minus the most recent I/B/E/S median consensus analyst forecast,
deflated by stock price at the end of the fiscal quarter. Accordingly, I require firms to have both analyst
forecasts and actual earnings for the fiscal quarter in the I/B/E/S database. I also require that the
analyst forecasts be within one quarter before earnings announcement date to ensure that stale
forecasts are excluded in my analyses.
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4. Research Methodology
4.1. Credit rating and Pead
4.1.1. Portfolio analysis
I rank firms into deciles based on SUE for each fiscal quarter 6 . The PEAD trading strategy
involves a hedge portfolio which takes the long (short) position in the decile portfolios of firms with the
most positive (negative) SUE. Firms enter the portfolios on the second trading day following each
earnings announcement, and the portfolios are held for 60 trading days thereafter. Since prior studies
(e.g., Bernard and Thomas 1990) show that a significant portion of PEAD concentrates in a three-day
window surrounding the announcements of the next three quarterly earnings, I also have the long (short)
position held until one trading day after the earnings announcement date for quarter t+1, quarter t+2
and quarter t+3 (i.e., [2, Et+1+1], [2, Et+2+1], and [2, Et+3+1] relative to the earnings announcement date
for quarter t), respectively. Accordingly, I require firms to have stock returns data from the CRSP daily
stock file during the corresponding drift windows.
Following prior research on earnings-related anomalies (e.g., Dechow et al. 2008;
Baladrishnan et al. 2009), I use size-adjusted buy-and-hold abnormal returns to measure a
firm’s drift returns7. The expected returns for firm i on trading day t are defined as the equallyweighted returns for all firms in firm i’s size-matched deciles on day t, where size is measured
as market capitalization at the beginning of the most recent calendar quarter. The return to the
most positive (negative) SUE decile portfolio is then calculated as an equally-weighted
average of drift return of the corresponding firms in that SUE decile portfolio. Accordingly,
payoffs from the PEAD trading strategy are based on the portfolio returns from the strategy
implemented at the earnings announcements and held over the corresponding drift windows.
To investigate whether PEAD anomaly differs across different credit rating firms, I
implement the PEAD strategies by conditioning on both credit rating and SUE. I consider 10
SUE portfolios and 3 credit rating (Rating) groups. Specifically, the Rating-SUE portfolios are
formed on a sequential basis, sorting first on Rating and then on SUE. For each quarter, the
high (low) credit rating group contains the A- to AAA rated (the SD to CCC+ rated) firms based
on their Standard & Poor’s ratings for that particular quarter. The middle credit rating group
incorporates firms that have ratings from B- to BBB+. It’s expected that the profitability from the
PEAD trading strategy of being long (short) the highest (lowest) SUE decile portfolio is the
highest among the low credit rating group.
Further, I implement the PEAD trading strategy over subsamples of different rated firms.
Particularly, I start with the entire sample of rated firms and then sequentially exclude firms
with lower credit ratings. This analysis reveals the subsample of rated firms that drive the
6
To be consistent with Collins and Hribar (2000), I also use the cut-off values that define the deciles of SUE for period t-1 to
sort the SUE for period t into deciles. The results are qualitatively the same.
7
Inferences are unchanged if I follow prior research (e.g., Ogneva and Subramanyam 2007; Baladrishnan et al. 2009) to
estimate the expected returns based on Carhart’s (1997) 4-factor model. Nor would my inferences change if I employ the
calendar-time portfolio approach (e.g., Fama 1998; Mitchell and Stafford 2000) to assess the magnitude and statistical
significance of the PEAD.
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earnings momentum payoffs. I report the average payoffs from the PEAD trading strategy in
each subsample which is expected to gradually decline as I progressively drop the worse-rated
firms.
4.1.2. Regression analysis
I specify the following pooled OLS regression model to test the credit rating effect on PEAD:
(1)
Drift  0  1DSUE  2 DSUE * Rating  3 Rating   4Controls  5Controls * DSUE  
Drift is the size-adjusted buy-and-hold returns over the drift window and equals the compounded
raw returns minus the compounded equally-weighted returns of the same CRSP size decile and the
same CRSP exchange index (NYSE/AMEX/NASDAQ) that a firm belongs to8. Consistent with prior
studies, I rank RW-based and AF-based SUE into ten deciles indexed from 0 to 9 by quarters, and
divide the index by 9 to get DSUE which ranges from 0 to 1. As such, the coefficient on DSUE can be
readily interpreted as the excess returns one can earn over the drift window under a zero investment
portfolio strategy that takes a long position in the highest SUE decile (DSUE = 1) and a short position in
the lowest SUE decile (DSUE = 0). Given that PEAD persists for three quarters ahead following
earnings announcements, I focus on a drift over four different windows as I did in the portfolio analysis.
A positive coefficient on DSUE would suggest that investors do not fully incorporate the implications of
current earnings for future earnings at earnings announcement dates, thereby consistent with the
existence of PEAD documented in the prior research. Rating is coded 0 for firms that have ratings
ranging from SD/D to CCC+, 0.5 for firms with ratings from B- to BBB+, and 1 for firms with ratings from
A- to AAA 9 . I expect the coefficient on the interaction term between DSUE and Rating, 2, to be
negative and statistically significant to support the hypothesis that PEAD for lower rated firms is
stronger and that their earnings momentum payoffs are larger.
Since prior literature (e.g., Jiang et al. 2005; Zhang 2006) provides evidence that momentum
payoffs are higher in firms that have higher information uncertainty, PEAD should be stronger for firms
with higher information uncertainty. Hence, I control for a battery of information uncertainty proxies used
in the prior studies: Firm age (Firmage), return volatility (Returnvol), earnings volatility (Earningsvol),
cash flow volatility (Cashvol), accruals quality (AQ), idiosyncratic volatility (IdioVol), and analyst forecast
dispersion (Anadis) (Jiang et al. 2005; Zhang 2006; Francis 2007, among others)10. Stronger PEAD is
expected for firms with smaller age, higher return volatility, higher earnings volatility, higher cash flow
volatility, lower accruals quality, higher idiosyncratic volatility, or higher analyst forecast dispersion.
I also control for other factors that potentially affect the magnitude of PEAD. Following Mendenhall
(2004), I control for transaction cost, proxied by trading volume (Tradingvol) and share price (Price)11,
since PEAD is stronger when transaction cost is higher. I include firm size (Size) as prior studies (e.g.,
Foster et al. 1984) show that PEAD is more pronounced for smaller firms. Prior studies (e.g., Bartov et
8
I obtain almost identical results when I compute the PEAD in two alternative ways: (1) size-adjusted cumulative abnormal
returns over the drift window; (2) buy-and-hold abnormal returns over the drift window, where daily abnormal return is
calculated as the raw daily return minus the daily return on the quintile/decile portfolio of firms with approximately the same
size and book-to-market ratio as of the most recent fiscal quarter end.
9
The results are robust to using a dichotomized credit rating variable which is based on the classification of credit ratings into
investment grade and speculative grade categories.
10
Inferences on the impact of credit rating on PEAD remain unchanged if I include only one of these information uncertainty
proxies as the control in the regression analyses.
11
Use of bid-ask spread (Korajczyk and Sadka 2004; Hanna and Ready 2005) as an alternative measure of ex ante
transaction costs does not alter the inferences I drawn from the regression results.
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al. 2000) find that high institutional ownership mitigates PEAD, suggesting that firms whose investors
are more sophisticated are likely to suffer less from mispricing. Hence, I control for the percentage of
shares held by institutional investors (Insti) and expect it to be inversely related to PEAD. Following
Merndenhall (2002) and Zhang (2008), I further control for investor sophistication measured by analyst
following (Anacov). Since the probability that analysts uncover and publicize information about a firm’s
future earnings increases with the magnitude of analyst coverage, I expect that the analyst coverage
helps mitigate the PEAD as institutional holdings do. I control for the nature of earnings news
(Badnews) because negative earnings surprise is less persistent and hence it is more difficult to assess
its implication for future earnings (Hong et al. 2000). I include the fourth quarter earnings
announcement (4THQTR) since it provides more information to analysts and investors than does an
interim announcement and thus might be associated with lesser extent of PEAD (Cornell and
Landsman 1989). I also include an indicator variable for the post-Regulation Fair Disclosure period (FD)
to control for the effect of change in information environment due to the enforcement of Regulation FD.
All the control variables are defined in the appendix. Last, I include the industry-fixed effects in the
regression model. Standard errors are clustered two-way by firm and year-quarter to correct for both
cross-sectional and time-series correlation.
Following prior studies (e.g., Bartov 2000; Zhang 2008), I interact all the control variables with
DSUE. The interactions allow the slope of the SUE-return relation to vary with each control variable and
hence enable us to assess how these control variables affect the magnitude of the drift. Each
continuous control variable is ranked into deciles within each fiscal quarter and coded from 0 to 1. The
coding allows the coefficient on the interaction term to be interpreted as the additional spread in
abnormal returns, between the highest- and lowest-SUE decile portfolio, for firms with largest versus
smallest decile rank of the control variables (Mendenhall 2004). Also, the decile ranking of the control
variables address potential problem of outliers and nonlinearity in the earnings-returns relation (e.g.,
Bernard and Thomas 1990; Bartov et al. 2000).
4.2. Do transient institutions focus their arbitrage on PEAD in low rated firms?
To assess the effect of credit rating on transient institutions’ arbitrage intensity, I estimate the
following firm-fixed effect regression model of the quarterly changes in transient institutional ownership
for firm i in calendar quarter t´:
3
3
q 0
q 0
Transienti ,t´   0  1   q DSUEi ,t  q   2 Rating i ,t   3   q DSUEi ,t  q * Rating i ,t
3
 4 ExtTradi ,t   5   q DSUEi ,t  q * ExtTrad i ,t   6 BM i ,t   7 Sizei ,t   8 PWi ,t 
(2)
q 0
 9 FDi ,t  10 RET 1i ,t  11 ( RET 2  3)i ,t  12 ( RET 4  6)i ,t
3
(13   q DSUEi ,t  q * ExtTradi ,t * Rating i ,t )   i ,t
q 0
Transient is the change in transient institutional ownership as a percentage of the outstanding
shares. Institutional holdings are reported by CDA Spectrum Database only at the end of calendar
quarters. Thus, Transienti,t´ is measured for firm i over a calendar quarter from the end of calendar
quarter t´-1 to the end of calendar quarter t´, within which the earnings announcement date for fiscal
quarter t falls. SUEi,t is the standardized unexpected earnings surprise of firm i for fiscal quarter t.
DSUEi,t-q is the decile ranking of SUE of firm i for fiscal quarter t-q. PWi,t is the mean portfolio weight of
firm i in the portfolio of a transient institution for fiscal quarter t. PW measures the extent to which stock
investments of transient institutional investors are allocated to a given firm. I include PW because
transient institutions whose stock holdings are heavily weighted towards a given firm are more likely to
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sell shares in the subsequent quarter for portfolio diversifications. I control for firm size (Size) and bookto-market effect (BM) since they are systematically related to institutional ownership (Gompers and
Metrick 2001). I include RET1, RET2-3, and RET4-6 to control for the price momentum effect since
prior research (e.g., Griffin et al. 2003) finds that institutional investors tend to be momentum traders. If
transient institutions follow a momentum (contrarian) trading strategy, the coefficients on RET1, RET2-3,
and RET4-6 would be significantly positive (negative). I also include an indicator for the post-Regulation
Fair Disclosure period (FD) to control for the impact of Regulation FD on transient institutions’ trading
behaviors (Ke et al. 2008). All the control variables are defined in the appendix. Last, I include industry,
year, and quarter fixed effects in the regression model as there might be systematic variation in
institutional ownership across industries, over years, and over the four calendar quarters.
Prior studies (e.g., Bernard and Thomas 1990) document that a disproportionately large portion of
the abnormal returns from the zero-investment RW-based PEAD trading strategy, which are cumulated
from the day after earnings announcement for quarter t through the earnings announcement for quarter
t+3, are realized before the earnings announcement date for quarter t+1. Therefore, if transient
institutional investors exploit RW-based PEAD, they would well initial their arbitrage positions before
earnings announcement for quarter t+1. Accordingly, transient institutional ownership changes around
earnings announcement in quarter t´ should be positively related to the SUE for quarter t, and the
coefficient on DSUEt should be significantly positive. Meanwhile, since the direction of abnormal returns
associated with RW-based SUE reverses at earnings announcements for quarter t+4, transient
institutional investors should unwind their arbitrage positions no later than quarter t+3 and hence one or
more of the coefficients on lagged DSUEs (i.e., DSUEt-1 to t-3) should be negative. Consistent with Ke
and Ramalingegowda (2005), I do not expect all the coefficients on lagged DSUEs to be negative since
transient institutions could choose to liquidate their arbitrage positions in any one of the following three
quarters. Rather, I expect that the sum of the coefficients on lagged DSUEs is negative.
Prior literature (e.g., Livnat et al. 2006; Doyle et al. 2006) documents that the pattern of the AFbased drift is different from that of RW-based drift around the four subsequent quarterly earnings
announcements. As for AF-based PEAD, there exist positive autocorrelations of the abnormal returns
around the earnings announcements for quarter t, t+1, t+2, and t+3 but no reversal of abnormal returns
around the earnings announcement for quarter t+4. This suggests that analysts do not rely on a
seasonal random walk model to form earnings expectations and hence their earnings forecasts are free
from a bias from assuming that earnings follow a seasonal random walk pattern. Further, prior studies
(e.g., Livnat et al. 2006) show that AF-based drift concentrates around two subsequent quarterly
earnings announcements. In this respect, if transient institutions wish to exploit the AF-based PEAD,
they should well establish their arbitrage positions before the quarter t+2. Accordingly, I expect that
transient institutional ownership changes (Transienti,t´) are positively correlated with DSUEt or DSUEt-1
(i.e., the SUE for quarter t and quarter t+1, respectively), or both. Meanwhile, transient institutions might
find it optimal to unwind their arbitrage positions in quarter t+2 or quarter t+3 in order to shift their
capital to more profitable investments. Hence, transient institutional ownership changes (Transienti,t´)
would be negatively related to DSUEt-2 or DSUEt-3 (i.e., SUE for quarter t+2 and quarter t+3), or both.
To further support the rationale underlying the PEAD arbitrage strategy, I replicate two key
findings in the PEAD literature (results not tabulated): (1) the autocorrelation between DSUE in quarter
t+4 and DSUEs in quarter t+3 to t; and (2) the association between the market reactions to SUE in
quarter t+4 and DSUEs in quarter t+3 to t, with the coefficient estimates of DSUEt+3 to t denoting the
hedge returns around the subsequent four earnings announcements. A pooled cross-sectional and
time-series regression is used for the replication during the sample period of 1985-2009. Consistent
with Bernard and Thomas (1990, panel B of table 1) and Ball and Bartov (1996, table 1), I find positive
and declining first-, second-, and third-order autocorrelations and a negative fourth-order
autocorrelation for the RW-based DSUEs (i.e., {0.412, 0.276, 0.161, -0.109}). Also, consistent with
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Bernard and Thomas (1990, panel B of table 2) and Ball and Bartov (1996, table 2), I find positive and
significant RW-based hedge returns around the first two subsequent earnings announcements (0.007
and 0.004, respectively), an almost zero hedge return around the third subsequent earnings
announcement (-0.0005), and a significantly negative hedge return around the fourth earnings
announcement (-0.006). The autocorrelations for AF-based DSUEs are positive and declining through
four lags (i.e.,{0.342, 0.226, 0.166, 0.153}), which is very akin to that documented by Abarbanell and
Bernard (1992, panel B of table 1) and Livnat et al. (2006, table 7). The pattern of hedge returns for AFbased DSUEs over the subsequent four quarterly earnings announcements is {0.004, 0.002, -0.0002, 0.006}, with the first two and the fourth associations being significantly different from zero. The positive
hedge returns surrounding the first two subsequent earnings announcements are consistent with those
of Abarbanell and Bernard (1992, figure 1) and Livnat et al. (2006, table 7), while the negative hedge
return around the fourth subsequent earnings announcement is qualitatively similar to that observed in
Abarbanell and Bernard (1992, figure 1).
I first do a regression analysis on the main effect of DSUEs on transient institutions’ trading,
leaving out the moderating effect of the interaction terms in model (2). The results (not tabulated)
indicate that the coefficients on DSUE(t to t-3) are all highly significant in the predicted sign, demonstrating
that transient institutions trade actively to exploit RW-based and AF-based PEAD. In model (2), I allow
the regression coefficients on DSUEs to differ for high and low credit rating (Rating) firms. If transient
institutions trade more intensively to exploit PEAD among firms with lower credit ratings, I expect the
coefficients on Rating*DSUEs to be in the opposite sign to the coefficients on DSUEs and statistically
significant. In particular, in the case that transient institution investors exploit RW-based PEAD more
intensively for lower credit rating firms, the coefficient for Rating*DSUEt would be negative and
statistically significant, while one or more of the coefficients (or the sum of the coefficients) for
Rating*DSUE(t-1 to t-3) would be significantly positive. In the case that transient institutions exploit AFbased PEAD more aggressively for lower rated firms, one or the sum of the coefficients on
Rating*DSUEt and Rating*DSUE t-1 (on Rating*DSUEt-2 and Rating*DSUEt-3) would be significantly
negative (positive). Following Ke and Ramalingegowda (2005), I also control for ex ante transaction
cost (ExtTrad) and its interaction term with DSUEs. If transient institutions trade more intensively to
exploit PEAD among firms with lower ex ante transaction cost, the coefficients for ExtTrad*DSUEs
would also be in the opposite sign to the coefficients for DSUEs and statistically significant.
I further employ regression model (2) to test how credit rating and ex ante transaction cost jointly
affect transient institutions’ arbitrage intensity. The prior thesis of the test is to figure out whether ex
ante transaction cost impedes transient institutions from exploiting PEAD among low credit rating firms
only, leaving high credit rating firms out of account. To this end, based on the extant regression model
(2), I add up a three-way interaction among DSUE, Rating, and ExtTrad. With controls for both
DSUE*Rating* ExtTrad and DSUE*Rating, the coefficient on DSUE (on DSUE+DSUE*ExtTrad) denotes
the transient institutions’ arbitrage on PEAD for the low rated firms that have the low (high) ex ante
transaction cost. If ex ante transaction cost mitigates the intensity of the transient institutions’ arbitrage
on RW-based PEAD among the low credit rating firms, the coefficient on DSUEt*ExtTrad would be
significantly negative, while one or more of the coefficients (or the sum of the coefficients) on DSUEt1*ExtTrad, DSUEt-2* ExtTrad, and DSUEt-3*ExtTrad would be significantly positive. Similarly, if ex ante
transaction cost attenuates the extent of the transient institutions’ arbitrage on AF-based PEAD among
the low rated firms, at least one or the sum of the coefficients on DSUEt*ExtTrad and DSUEt-1*ExtTrad
(on DSUEt-2 *ExtTrad and DSUEt-3*ExtTrad) would be significantly negative (positive).
In addition, the three-way interaction term, DSUE*Rating*ExtTrad, tells how the impact of credit
rating on the transient institutions’ arbitrage differs between high and low ex ante transaction cost firms.
If the credit rating effect on the transient institutions’ arbitrage is stronger for firms with lower ex ante
transaction cost, DSUE*Rating*ExtTrad would be statistically significant in the opposite sign to DSUE*
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Rating.
Following prior studies (e.g., Bhushan 1994; Stoll 2000; Mendenhall 2004), I use the average of
daily dollar trading volume (i.e., the product of the CRSP daily closing price and the CRSP daily shares
traded, averaged over days from the beginning of the fiscal quarter to date -21 relative to the earnings
announcement date) as the proxy for the ex ante transaction cost of stocks trading12. High trading
volume corresponds to low ex ante transaction cost (e.g., Mendenhall 2004). I rank the trading volume
into deciles and convert it into [0, 1] with 0 (1) denoting the highest (lowest) trading volume. I then use
the converted score as the measure of ex ante transaction cost with 1 (0) representing the highest
(lowest) ex ante transaction cost.
Following Korajczyk and Sadka (2004) and Hanna and Ready (2005), I also employ bid-ask
spread, which is estimated using relative effective spreads or relative quoted spreads equally-weighted
over three months prior to the earnings announcements, as the proxy for ex ante transaction cost. My
results for model (2) are insensitive to this alternative specification. Though the effective bid-ask
spreads and the quoted spreads are direct estimates of transaction cost, the literature (e.g., Keim and
Madhavan 1998; Lesmond et al. 2004) emphasizes that the spreads underestimate the true transaction
cost for arbitrageurs due to the omission of relevant transaction cost such as opportunity cost and price
impact which can instead be captured by dollar trading volume (TradingVol). Another proxy for ex ante
transaction cost could be the limited dependent variable (LDV) measure, which is developed by
Lesmond et al. (1999) and based on the transaction cost implied by the investors’ trading behaviors.
This measure incorporates all costs that are reflected in the trading and assumes that all impediments
to efficient market reaction are due to transaction cost. However, there are other behavioral or
institutional factors that lead to an underreaction to market level news as well. Hence, the LDV measure
likely overestimates the ex ante transaction cost. Still, I also employ the LDV estimates of transaction
cost for the robustness check and obtain almost identical results.
5. Empirical Results
5.1. The impact of credit rating on PEAD
5.1.1. Results for portfolio analysis
Table 2 reports the results for payoffs from trading on PEAD in different credit rating groups. The
payoff to the PEAD trading strategy becomes larger as we move from the high to the low credit rating
group. Focusing on the RW-based PEAD over the window of [2, 61] relative to earnings announcement
dates, for example, the average payoff to the D10-D1 trading strategy for the high and medium credit
rating groups is 0.99% and 0.35%, respectively. In contrast, the payoff is much greater and
economically significant at 15.66% for the low credit rating group. The RW-based earnings momentum
payoffs over other drift window, [2, Et+1+1], [2, Et+2+1], and [2, Et+3+1], are also highest (lowest) for low
(high) credit rating group. So too are the payoffs from trading on the AF-based PEAD. These results
suggest that the profits of implementing the PEAD trading strategy stem primarily from firms with low
credit ratings.
12
I do not use the share price before earnings announcements (Price) as the proxy for a firm’s ex ante transaction cost
because RET1, RET2-3, and RET4-6, the control variable for the price momentum effects on transient institutions’ trading in
model (2), would mitigate the power of Price in capturing the ex ante transaction cost.
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The earnings momentum payoff differential across different credit rating groups might be
explained by information uncertainty proxies such as return volatility, accrual quality, cash flow volatility,
earnings volatility, firm age, idiosyncratic volatility, and analyst forecast dispersion. To address this
possibility, I assess the robustness of earnings momentum profitability across different credit rating
dimensions based on 3*3 portfolios which are sorted independently by credit rating and each of the
information uncertainty variables. The results (not tabulated for parsimony) indicate that earnings
momentum payoff still increases monotonically with the decrease in credit rating across all the groups
sorted by each of the information uncertainty variables. However, not all the credit rating groups exhibit
the pattern of a monotonic increase in PEAD payoffs with an increase in the magnitude of the
information uncertainty variables. Therefore, it is credit rating not those information uncertainty
variables that provides the divergent earnings momentum returns.
To ensure that the profits of implementing the PEAD trading strategy among low credit rating
firms does not merely compensate for exposure to systematic risk, I further use calendar-time portfolio
approach to risk-adjust the raw earnings momentum payoffs. The advantage of the calendar-time
portfolio approach is that the cross-sectional correlations of the abnormal returns are automatically
accounted for in the portfolio variance at each point in calendar time (Fama 1998; Mitchell and Stafford
2000). Sadka (2006) finds that the unexpected systematic variation of the variable component of
liquidity is priced within the context of PEAD portfolio returns. Hence, I incorporate the Sadka variable
liquidity factor, along with the Carhart’s four factors, in my portfolio regression. Consistent with prior
research (e.g., Mitchell and Stafford 2000; Francis et al. 2007), I use monthly returns to construct the
hedge portfolio returns which involves taking the long (short) position in the highest (lowest) SUE decile
portfolio. Firms enter the portfolio on the first day of the month following the earnings announcement,
and are held for nine months which normally covers three subsequent quarterly earnings
announcements13. Accordingly, the monthly return to the hedge portfolio is measured as the difference
between the equally-weighted average monthly return of the highest SUE decile portfolio and that of the
lowest SUE decile portfolio. The hedge portfolio monthly returns net of risk-free rate are then regressed
on the Sadka’s (2006) five factors (i.e., excess market returns, size, book-to-market, price momentum,
and variable liquidity factors) for each credit rating group, whereby alpha from the regression would
represent the average monthly earnings momentum payoffs that are adjusted for the systematic risk.
Table 3 reports the results for alphas from the Sadka-based calendar-time portfolio regressions for
each credit rating group. The alpha for the monthly RW-based earnings momentum payoffs amounts to
2.97%, 0.15%, and -0.25% (translated into an annualized return of 42.10%, 1.82%, and -2.92%) for the
low, medium, and high credit rating group, respectively, and are all statistically significant. The
annualized profits from implementing RW-based PEAD trading strategy are economically significant for
the low credit rating group but not for the high and medium credit rating group. The alpha for the
monthly AF-based earnings momentum payoffs is statistically and economically significant in positive
magnitude for the low credit rating group (t-stat.=1.82) but not for the high credit rating group (tstat.=1.21). Moving from low credit rating to high credit rating group, both the RW-based and the AFbased alphas decline in magnitude. Overall, these results suggest that higher earnings momentum
payoff for a lower credit rating firm does not represent compensation for systematic risk.
Table 4 reports the average payoffs from the PEAD trading strategy in each subsample as I
progressively drop the lower-rated firms. It also provides the percentage of the total number of firms
included in each subsample. Overall, we can observe a monotonic and significant decrease in the
average payoffs from the PEAD trading strategy as the lower rated firms are sequentially dropped out
13
Inferences are unchanged if I choose a three-month (six-month) portfolio holding period that normally covers one (two)
subsequent quarterly earnings announcements.
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of the full sample. For instance, the average payoff to the trading strategy for RW-based PEAD over the
window of [2, 61] totals 1.06% (t-stat.=2.07) for the full sample but drastically decreases to 0.35% (tstat.=0.66) when only firms with ratings from AAA to A- remain in the sample. Remarkably, the sample
that contains firms rated from AAA to BB- accounts for 79.24% but only has an average of 0.53%
payoff compared to the 1.06% payoff for the whole sample. These findings further demonstrate that
PEAD payoffs concentrate among firms with low credit ratings.
The univariate results needs to be interpreted with caution as they do not control for transaction
cost, firm size, and trading volume, etc., which also affect the magnitude of PEAD. Hence, I turn to the
multivariate analysis to account for these factors.
5.1.2. Results for regression analysis
Table 5 presents the regression results for the credit rating effect on PEAD. The coefficient on the
interaction term, DSUE*Rating, is negative and statistically significant, consistent with the hypothesis
that PEAD is more pronounced for firms with lower credit ratings. The sum of the coefficients on DSUE
and DSUE*Rating (i.e., 1+2) is positive and statistically significant, indicating that the drift still exists
among high credit rating firms though it is smaller in magnitude than the drift for firms with low credit
ratings. Further, the coefficients on the interaction of DSUE with all the information uncertainty proxies
(i.e., firm age, analyst forecast dispersion, earnings volatility, accruals quality, return volatility,
idiosyncratic volatility, and cash flow volatility) are not statistically significant at the conventional level in
their expected sign, whereas the coefficient on DSUE*Rating remains negative and significant. The
results qualitatively hold not only for RW-based PEAD but also for AF-based PEAD over the drift
window of [2, 61], [2, Et+1+1], [2, Et+2+1], and [2, Et+3+1], respectively. This suggests that the information
uncertainty purged out of credit rating effect does not distinguish among more and less profitable PEAD.
Hence, credit rating is a more primitive variable than those information uncertainty proxies in predicting
the PEAD. In addition, the coefficients on Rating for all the PEAD models are all positive and
statistically significant at the 1% level, which reconcile the prior evidence that stock returns are
negatively related to credit risk (Dichev 1998; Campbell et al. 2008; Avramov 2009b).
To assess the sensitivity of my findings to alternative risk adjustments, I also compute daily
expected returns based on Carhart’s (1997) 4-factor model to obtain the buy-and-hold abnormal returns
for the regression analyses. Along the lines of prior research (e.g., Ogneva and Subramanyam 2007;
Balakrishnan et al. 2010), I estimate the expected value of Carhart’s (1997) model using a 40-tradingday hold-out period which starts 55 trading days prior to the earnings announcement date. Following
Shumway and Warther (1999), if a firm is delisted due to poor performance during the return window,
the delisting return is assumed to be -35% if the firm is traded on NYSE/AMEX, and -55% if the firm is
traded on NASDAQ. My results are robust to this alternative risk adjustment on the drift returns.
5.2. Transient institutions’ trading behavior around earnings announcements
Column 1a of Table 6 shows the regression results for the impact of credit rating on the intensity of
transient institutions’ arbitrage on RW-based PEAD. As expected, DSUEt*Rating (DSUEt-1*Rating) has
a significantly negative (positive) coefficient which is in the opposite sign to the coefficients on DSUEt
(DSUEt-1). The overall coefficient for DSUEt-1*Rating+DSUEt-2*Rating+DSUEt-3*Rating is also in the
predicted positive sign and significant at the 1% level (F-stat.=11.81). These results evidence that
transient institutions trade less intensively to exploit RW-based PEAD for higher rated firms. The
coefficient on DSUEt*ExtTrad is significantly negative (t-stat.=-2.23), while the coefficients on DSUEt-1*
ExtTrad and DSUEt-2*ExtTrad are significantly positive (t-stat.=2.27 and 3.96, respectively). Hence,
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there is also evidence that transient institutions trade less aggressively to exploit RW-based PEAD
when ex ante transaction cost is higher.
Column 1b reports the joint effect of credit rating and ex ante transaction cost on transient
institutions’ trading for RW-based PEAD. Recall that with control for the three-way interaction term,
DSUEt to t-3 denotes the transient institutions’ ownership changes in response to SUE in quarter t to t-3
for low credit rating and low transaction cost firms only. As expected, the coefficient on DSUEt is
significantly positive, while DSUEt-1, DSUEt-2, and DSUEt-3 all have a significantly negative coefficient.
These results indicate that for firms with low credit ratings and low transaction cost, transient
institutional investors tend to take initial positions in earnings announcement quarter and then unwind
their positions in the subsequent three quarters, thereby substantiating that transient institutions
actively exploit RW-based PEAD among low transaction cost and low rated firms. Further, transient
institutions appear to exploit the PEAD less intensively among firms with lower ex ante transaction
costs, which is evidenced by the significantly negative (positive) coefficient on DSUEt*ExtTrad (DSUEt2*ExtTrad). DSUEt* Rating*ExtTrad has a significantly positive coefficient, while the sum of the
coefficients on DSUE(t-1 to t-3) *Rating*ExtTrad is significantly negative (F-stat.=-27.83). This indicates
that the credit rating effect on the transient institutions’ arbitrage on RW-based PEAD, as evidenced in
Column 1a, is less pronounced when ex ante transaction cost is higher.
Column 2a reports the results for the impact of credit rating on transient institutions’ arbitrages on
AF-based PEAD. The coefficients on DSUEt*Rating and DSUEt*Rating are significantly negative (t-stat.
=-5.15 and -2.24, respectively), while DSUEt-2*Rating and DSUEt-3 *Rating have a significantly positive
coefficient (t-stat.=2.48 and 2.10, respectively). Hence, transient institutions appear to exploit AF-based
PEAD far less aggressively in firms with higher credit ratings. There is also evidence to suggest that
high ex ante transaction cost mitigates the intensity of the transient institutions’ arbitrage on AF-based
PEAD. In particular, the coefficients for DSUEt*ExtTrad and DSUEt-1*ExtTrad are significantly negative
(t-stat.= -5.21 and -3.45, respectively), while DSUEt-2*ExtTrad and DSUEt-3*ExtTrad take on a
significantly positive coefficient (t-stat.=4.30 and 5.73, respectively).
Column 2b presents the results for the joint impact of credit rating and ex ante transaction cost on
transient institutions’ trading for AF-based PEAD. DSUEt and DSUEt-1 take on a significantly positive
coefficient (t-stat.=7.28 and 2.93, respectively), indicating that transient institutions initiate and enhance
their arbitrage positions in low transaction cost and low credit rating firms over quarter t and t+1. The
coefficients on DSUEt-2 and DSUEt-3 are significantly negative (t-stat.=-4.86 and -5.59, respectively),
indicating that transient institutions liquidate their arbitrage positions in low transaction cost and low
rated firms in quarter t+2 and t+3. Together, these results suggest that transient institutions tend to
exploit AF-based PEAD among firms with low credit ratings and low ex ante transaction cost. As
indicated by the significantly negative (positive) coefficient for DSUEt-1*ExtTrad (for DSUEt-2 to t3*ExtTrad), transient institutions appear to exploit the PEAD less aggressively among firms with lower
ex ante transaction costs. The sum of the coefficients for DSUEt*Rating*ExtTrad and DSUEt1*Rating*ExtTrad (for DSUEt-2 *Rating*ExtTrad and DSUEt-3*Rating*ExtTrad) is significantly (positive)
negative (F-stat.=3.16 (-7.71)), suggesting that the impact of credit rating on the transient institutions’
arbitrage on AF-based PEAD becomes less salient when ex ante transaction cost is higher.
Consistent with prior studies, transient institutional investors are price momentum traders as
evidenced by the significantly positive coefficients on RET1, RET2-3, and RET4-614. The magnitude of
14
The significantly positive coefficient on past returns (i.e., RET1, RET2-3, or RET4-6) alone is not sufficient to prove that
transient institutions exploit price momentum. This is because such a finding is also consistent with transient institutions
merely following a momentum trading strategy of buying (selling) shares that have high (low) past returns. Proof of the
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Proceedings of 24th International Business Research Conference
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the coefficient for RET1 is not statistically larger than that of the coefficient for DSUEt, indicating that
the transient institutions’ price momentum trades in a quarter is not larger than the PEAD-driven trades.
Thus, different from Ke and Ramalingegowda (2005), I find no evidence that the price momentum effect
dominates the PEAD arbitrage effect in the transient institutions’ trading. This is not surprising because
price momentum is primarily subsumed by PEAD but not vice versa (Chordia and Shivakumar 2006).
FD has a negative and significant coefficient, suggesting that transient institutions’ trading becomes
less active in the post FD regulation period. This may be because transient institutions are not entitled
to access a firm’s private information any more after the enforcement of FD regulation. In addition, I rerun regression model (2) where the dependent variable is replaced with dedicated or quasi-indexing
institutions’ ownership changes. Consistent with Ke and Ramalingegowda (2005), I find no evidence
that dedicated or quasi-indexing institutions trade to exploit the PEAD.
Lastly, transient institutions can rely on their private information along with other sources of public
information in addition to the past SUEs to infer future earnings surprises for their current trading
decisions. As such, the changes in transient institutions’ ownership could incorporate the information
about future earnings. To account for this possibility, I include future earnings surprises, DSUEt+1,
DSUEt+2, and DSUEt+3, in the regression model (2). In results not tabulated, the coefficients on DSUEt+2
and DSUEt+3 are both significantly positive while DSUEt+1 has an insignificantly positive coefficient. This
indicates that the transient institutions’ trading decisions incorporate the information about future
earnings surprises for quarter t+2 and quarter t+3, which is consistent with transient institutional
investors being informed. More importantly, the coefficients on the current and lagged DSUEs (i.e.,
DSUEt to t-3) are all qualitatively the same as those reported in Table 6. Therefore, I refute the possibility
that transient institutions’ trading behaviors as evidenced in Table 6 is a result of their inference about
future earnings based on other channels of information other than past earnings reports.
In sum, the results indicate that the transient institutions tend to focus their arbitrage on PEAD
among low credit rating firms which are characterized as abundant in arbitrage profits. Hence, we can
infer that transient institutions are not only sophisticated in information processing but also capable of
exploiting PEAD for low credit rating firms in spite of their high idiosyncratic risk. An increase in PEADbased arbitrage profits realized by the transient institutions in turn accelerates the speed that stock
price reflects the implications of current earnings for future earnings, thereby contributing to high market
efficiency. However, PEAD yet existent and concentrated in low credit rating firms also implies that the
transient institutions still do not fully exploit the PEAD for low credit rating firms. My further analysis
reveals that ex ante transaction cost impedes transient institutions from exploiting the PEAD in the low
rated firms, which reconciles the prior evidence (e.g., Ng et al. 2008) that transaction cost explains the
existence and persistence of PEAD. This helps explain why the PEAD payoffs that concentrate in low
rated firms are not arbitraged away.
Though firms with lower credit ratings are subject to higher idiosyncratic risk, I find no evidence
that the high idiosyncratic risk impedes transient institutions from exploiting PEAD among the low rated
firms. Hence, I refute the limit of arbitrage (in terms of idiosyncratic risk) story as an alternative
explanation for why PEAD prevails among low credit rating firms 15. Rather, I attribute the stronger
PEAD to investors’ greater underreaction to firms with lower credit ratings.
exploitation of price momentum also entails the evidence that arbitrageurs would liquidate their arbitrage positions before the
end of price momentum. So while it is interesting to compare the extent that transient institutions exploit PEAD to that they
exploit price momentum, I will leave this issue as an avenue for future research because of the unavailability of transient
institutions’ daily trading data.
15
There is no prior theoretical or empirical evidence that transaction cost is necessarily associated with credit rating.
According to Williamson (1985), the determinants of transaction cost incorporates frequency, specificity, uncertainty, limited
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6．Conclusion
This study conducts a theoretical and empirical exploration of how credit rating affects PEAD
anomaly. The primary thesis is that the PEAD is associated with credit risk and future uncertainty of
asset valuation faced by investors when they interpret earnings news. Based on all firms rated by
Standard and Poor’s over the July 1985 to December 2009 period, I find that the PEAD is concentrated
among firms with low credit ratings, suggesting that investors are more apt to underreact to earnings
news released by higher credit risk firms. This evidence implies that the underreaction to earnings news
could be the outcome of investors’ rational behavior. As such, this study complements the rationality
explanation for market anomalies in the literature which focuses on investors’ processing of incomplete
and uncertain information structures (e.g., Morris 1996; Lewellen and Shanken 2002; Dontoh et al.
2003; Francis et al. 2007), but is contrasted with the irrationality explanation in some prior PEAD
studies (e.g., Bartov et al. 2000; Battalio and Mendenhall 2005). My evidence contributes to the
ongoing debate on whether PEAD is a result of investors’ rational or irrational behaviors. By further
corroborating that the effect of credit rating on PEAD is not subsumed by the effect of information
uncertainty proxies (i.e., earnings volatility, cash flow volatility, return volatility, accrual quality, firm age,
idiosyncratic volatility, and analyst forecast dispersion), transaction cost as well as other drift
determinants, this study also demonstrate that credit rating is a potent predictor of PEAD anomaly.
Given that earnings momentum profitability varies among different types of firms, a follow-up issue
I address is how capable transient institutions are to exploit the differential of PEAD among different
rated firms in their arbitrage trades. I find strong evidence that transient institutions trade more
intensively to exploit both RW-based and AF-based PEAD for lower rated firms which are featured by
higher arbitrage profits. This implies that transient institutions are not only sophisticated enough to
identify the significance of PEAD among low rated firms but also well capable of diversifying their
idiosyncratic risk. In this respect, I eliminate the limit of arbitrage (in terms of idiosyncratic risk) story as
an alternative explanation for why PEAD concentrates among firms with low credit ratings. Despite the
evidence that transient institutions exploit the PEAD more intensively in firms with lower credit ratings,
the existence of PEAD yet concentrated among low credit rating firms points to the fact that the
transient institutions still do not fully exploit the PEAD for the low rated firms. My further analysis
reveals that ex ante transaction cost inhibits the transient institutions from exploiting the PEAD among
the low rated firms. This helps explain why the PEAD caused by low credit rating firms is not arbitraged
away. Given that other financial anomalies such as price momentum, analyst forecast dispersion effect
also concentrate in firms with low credit ratings (Avramov et al. 2007; Avramov et al. 2009a), future
research may further examine whether transient institutions would trade more intensely to exploit these
financial anomalies among lower credit rating firms to maximize their arbitrage profits. In this way, we
can obtain more insights into the extent to which transient institutions’ arbitrage of market anomalies
improves the efficiency of stock price.
rationality, and opportunistic behavior, all of which are difficult to be quantifiably identified. This may explain why, to my
knowledge, there is no prior empirical research on the determinants of ex ante transaction cost. Nor can I empirically conduct
a sound multivariate test on how credit rating is related to transaction cost. Thus, I leave it as an avenue for future research.
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Table 1: Distribution of credit ratings
S&P Ratings
Frequency
Percentage (%)
AAA
AA+
AA
AAA+
A
ABBB+
BBB
BBBBB+
BB
BBB+
B
BCCC+
CCC
CCCCC
C
D or SD
Total
3119
1504
5590
6702
10778
15182
13027
14691
17662
13193
8337
10884
14083
17571
9173
4578
2004
1178
522
441
10
1644
171873
1.81
0.88
3.25
3.90
6.27
8.83
7.58
8.55
10.28
7.68
4.85
6.33
8.19
10.22
5.34
2.66
1.17
0.69
0.30
0.26
0.01
0.96
100
Cumulative percentage
(%)
1.81
2.69
5.94
9.84
16.11
24.95
32.53
41.07
51.35
59.02
63.88
70.21
78.40
88.63
93.96
96.63
97.79
98.48
98.78
99.04
99.04
100
100
This table shows the full sample distribution of credit ratings at the firm-quarter level from 1985 to 2009. The firm credit
ratings are the long-term issuer credit ratings complied by Standard & Poor’s (S&P) and reported on Compustat. The
ratings range from AAA (highest rating) to D (lowest rating --- Debt in payment default). These ratings reflect credit
rating agencies’ expectations of the creditworthiness of firms with respect to their senior long-term debt obligations. The
sample period ranges from 1985 to 2009 with sample of 171873 firm-quarter observations.
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Table 2: Portfolio Analysis: Pead Across Credit Rating Groups
Analyst (AF)-based PEAD
Rating groups
Seasoned random walk (RW)-based PEAD
Portfolio
CCC+ --- C&D
D10-D1
D1
D10
BBB+ --- B-
D10-D1
D1
D10
AAA --- A-
D10-D1
D1
D10
AAA --- C&D
D10-D1
D1
D10
[2, 61]
0.1566
(2.65)***
-0.1576
(-4.15)***
-0.0010
(-0.02)
0.0099
(1.54)
0.0037
(0.69)
0.0136
(3.83)***
0.0035
(0.66)
-0.0041
(-0.94)
-0.0006
(-0.21)
0.0106
(2.07)**
-0.0014
(-0.33)
0.0092
(3.42)***
[2, Et+1+1]
0.1472
(2.40)**
-0.1617
(-3.97)***
-0.0145
(-0.34)
0.0160
(2.43)**
-0.0010
(-0.18)
0.0150
(4.11)***
0.0038
(0.69)
-0.0022
(-0.48)
0.0016
(0.52)
0.0155
(2.93)***
-0.0048
(-1.07)
0.0106
(3.85)***
[2, Et+2+1]
0.1694
(1.72)*
-0.1576
(-2.32)***
0.0117
(0.17)
0.0144
(1.46)
0.0043
(0.52)***
0.0187
(3.53)***
0.0118
(0.15)
0.0063
(0.95)
0.0051
(1.16)
0.0135
(1.71)*
0.0011
(0.16)
0.0146
(3.65)***
[2, Et+3+1]
0.1823
(1.09)
-0.0461
(-0.43)
0.1362
(1.06)
0.0129
(0.97)
0.0119
(1.05)
0.0249
(3.55)***
0.0002
(0.02)
0.0043
(0.49)
0.0045
(0.80)
0.0112
(1.03)
0.0092
(0.98)
0.0204
(3.76)***
[2, 61]
0.0987
(1.93)*
-0.0589
(-1.99)**
0.0398
(0.93)
0.0441
(5.44)***
-0.0172
(-2.70)***
0.0269
(5.37)***
0.0238
(1.21)
-0.0073
(-0.43)
0.0165
(1.54)
0.0467
(5.97)***
-0.0199
(-3.24)***
0.0268
(5.55)***
[2, Et+1+1]
0.0927
(1.73)*
-0.0609
(-2.01)**
0.0318
(0.69)
0.0407
(4.58)***
-0.0170
(-2.38)**
0.0237
(4.49)***
0.0243
(1.17)
-0.0112
(-0.58)
0.0131
(1.21)
0.0433
(5.09)***
-0.0199
(-2.92)***
0.0234
(4.60)***
[2, Et+2+1]
0.2360
(2.26)**
-0.0553
(-1.03)
0.1806
(2.02)**
0.0357
(2.43)**
-0.0014
(-0.12)
0.0343
(3.87)***
0.0213
(0.50)
0.0169
(0.42)
-0.0044
(-0.28)
0.0417
(2.93)***
-0.0046
(-0.41)
0.0371
(4.28)***
[2, Et+3+1]
0.2644
(1.89)*
-0.0167
(-0.21)
0.2476
(2.04)**
0.0221
(1.08)
0.0291
(-1.72)*
0.0511
(4.45)***
0.0405
(3.41)***
0.0205
(0.45)
-0.0200
(-1.09)
0.0279
(1.41)
0.0257
(1.58)
0.0536
(4.76)***
26
Proceedings of 24th International Business Research Conference
12 - 13 December 2013, Planet Hollywood, Las Vegas, USA, ISBN: 978-1-922069-37-5
This table reports size-adjusted PEAD for the following window: [2, 61], [2, Et+1+1], [2, Et+2+1], and [2, Et+3+1], where day zero is the earnings announcement date for
quarter q while Et+1, Et+2, and Et+3 are the earnings announcement date for quarter q+1, q+2, and q+3, respectively. Sample period ranges from 1985 to 2009. N
represents the number of observations in each Rating-SUE portfolio. Firms are grouped by credit ratings. In panel A, within each credit rating group, firms are sorted
into deciles based on current quarter earnings less earnings four quarters ago, scaled by standard deviation of earnings changes in the prior ten quarters. In Panel B,
within each credit rating group, firms are sorted into deciles based on the actual EPS minus the median consensus analyst forecast of EPS 90 days before earnings
announcement date, scaled by the stock price at the end of the fiscal quarter. Buy-and-hold abnormal return of a firm equals the compounded raw returns minus the
compounded benchmark return of the same CRSP size deciles and the same CRSP exchange index that the firm belongs to. Buy-and-hold abnormal returns for the
decile portfolios are computed by equally-weighting the buy-and-hold abnormal returns of all firms in that decile rank. The PEAD trading strategy involves buying the
winner portfolio (D10) and selling the loser portfolio (D1) and holding the positions for the entire drift window. D10-D1 corresponds to average payoffs from the PEAD
trading strategy during the holding period of both winner and loser portfolios. T-statistics are in parentheses.
***, **, * denote significance at 1%, 5%, 10% level (two-tailed tests), respectively.
27
Proceedings of 24th International Business Research Conference
12 - 13 December 2013, Planet Hollywood, Las Vegas, USA, ISBN: 978-1-922069-37-5
Table 3: Risk-Adjusted Earnings Momentum Payoffs across Credit Rating Groups
Rating group
CCC+ --- C&D
BBB+ --- BAAA --- A-
Rating group
CCC+ --- C&D
BBB+ --- BAAA --- A-
Alphas from Sadka-based calendar-time portfolio regressions
RW-based PEAD payoffs (monthly)
0.0297
(2.91)***
0.0015
(1.71)*
-0.0025
(-4.07)***
AF-based PEAD payoffs (monthly)
0.0101
(1.82)*
0.0029
(3.48)***
0.0022
(1.21)
Annualized returns translated from the Sadka-based Alphas
RW-based PEAD payoffs (annual)
42.095%
1.815%
-2.924%
AF-based PEAD payoffs (annual)
12.830%
3.536%
2.648%
The table reports alphas from the calendar-time portfolio regressions based on Carhart model augmented by the Sadka
permanent-variable liquidity factor (2006). For each calendar quarter, firms are ranked into decile portfolios based on SUE.
Firms enter the portfolio on the first day of the month following each earnings announcement and are held for ninth months.
The PEAD trading strategy involves taking the long (short) position in the highest (lowest) SUE decile portfolio. Accordingly,
monthly returns to the hedge portfolios are calculated as the difference between the equally-weighted average monthly
returns of the highest SUE decile portfolio and that of the lowest SUE decile portfolio. The hedge portfolio monthly return net
of risk-free rate are then regressed on the five Sadka-based monthly risk factors (i.e., excess market returns, size, book-tomarket, price-momentum, and permanent-variable liquidity factors) for each credit rating group, whereby alpha from the
regression represents the average monthly earnings momentum payoffs that are adjusted for the systematic risk. The
coefficients on the systematic risk factors are omitted for brevity.
28
Proceedings of 24th International Business Research Conference
12 - 13 December 2013, Planet Hollywood, Las Vegas, USA, ISBN: 978-1-922069-37-5
Table 4: Earnings Momentum Payoffs over Diminishing Subsamples
Panel A: Seasonal random walk (RW)-based PEAD
Stock
sample
[2, 61]
PEAD
Payoffs
AAA-SD
0.0106
(2.07)**
AAA-B0.0072
(1.41)
AAA-BB0.0053
(1.22)
AAA-BBB- 0.0078
(1.88)
AAA-A0.0035
(0.66)
[2, Et+1+1]
% of
firms
100
98.15
79.24
51.50
23.17
PEAD
Payoffs
0.0155
(2.93)***
0.0123
(2.33)**
0.0082
(1.74)*
0.0093
(2.05)**
0.0038
(0.69)
[2, Et+2+1]
% of firms PEAD
Payoffs
100
0.0135
(1.71)*
98.15
0.0099
(1.26)
79.24
0.0090
(1.34)
51.50
0.0118
(1.82)*
23.18
0.0118
(0.15)
[2, Et+3+1]
% of firms PEAD
Payoffs
100
0.0112
(1.03)
98.19
0.0083
(0.78)
79.36
0.0107
(1.24)
51.69
0.0201
(2.32)**
23.30
0.0002
(0.02)
% of firms
100
98.24
79.48
51.89
23.43
Panel B: Analyst forecast (AF)-based PEAD
Stock
sample
[2, 61]
PEAD
% of firms
Payoffs
AAA-SD
0.0467
100
(5.97)***
AAA-B0.0433
98.98
(5.60)***
AAA-BB0.0460
83.11
(5.36)***
AAA-BBB- 0.0359
55.25
(3.11)***
AAA-A0.0238
24.68
(1.21)
[2, Et+1+1]
PEAD
% of firms
Payoffs
0.0433
100
(5.09)***
0.0400
98.99
(4.65)***
0.0462
83.17
(4.97)***
0.0397
55.32
(3.21)***
0.0243
24.71
(1.17)
[2, Et+2+1]
PEAD
Payoffs
0.0417
(2.93)***
0.0329
(2.32)**
0.0464
(3.16)***
0.0359
(1.94)*
0.0213
(0.50)
[2, Et+3+1]
% of firms PEAD
Payoffs
100
0.0279
(1.41)
99.00
0.0183
(0.93)
83.26
0.0282
(1.53)
55.54
0.0241
(1.02)
24.85
0.0405
(0.82)
% of
firms
100
99.02
83.30
55.70
24.98
This table presents the payoffs of implementing the PEAD trading strategy over different rating subsamples for the
following drift window: [2, 61], [2, Et+1+1], [2, Et+2+1], and [2, Et+3+1], where day zero is the earnings announcement date of
quarter q while Et+1, Et+2, and Et+3 are the earnings announcement date of quarter q+1, q+2, and q+3, respectively. Sample
period ranges from 1985 to 2009. In panel A, firms are sorted into deciles based on current quarter earnings less earnings
four quarters ago, scaled by standard deviation of earnings changes in the prior ten quarters. In Panel B, firms are sorted
into deciles based on the actual EPS minus the median consensus analyst forecast of EPS 90 days before earnings
announcement date, scaled by the stock price at the end of the fiscal quarter. The buy-and-hold abnormal stock return of a
firm is calculated as the compounded raw returns minus the compounded benchmark return of the same CRSP size
deciles and the same CRSP exchange index that the firm belongs to. The portfolio buy-and-hold returns are computed by
weighting equally all firms in that decile ranking. The PEAD trading strategy involves buying the winner portfolio (D10) and
selling the loser portfolio (D1) and holding the positions for the entire drift window. The payoffs from implementing the
PEAD trading strategy corresponds to the equally-weighted average return of the worst performing decile portfolio less the
equally-weighted average return of the best performing decile portfolio over the holding period. Each subsample row in the
table represents a monotonically decreasing sample of firm-quarter observations, which is obtained by sequential
exclusion of observations with the lower credit ratings. Within each drift window, the first column shows the payoff from the
PEAD trading strategy while the second column provides the percentage of firms in each subsample. T-statistics are in
parentheses.
***, **, * denote significance at 1%, 5%, 10% level (two-tailed tests), respectively.
29
Proceedings of 24th International Business Research Conference
12 - 13 December 2013, Planet Hollywood, Las Vegas, USA, ISBN: 978-1-922069-37-5
Table 5: Multivariate Tests of the Impact of Credit Rating on PEAD
Seasonal random walk (RW)-based PEAD
Variable
Pred.
Sign
Intercept
?
DSUE
+
DSUE*Rating
-
Rating
+
Analyst forecast (AF)-based PEAD
[2, 61]
[2, Et+1+1]
[2, Et+2+1]
[2, Et+3+1]
[2, 61]
[2, Et+1+1]
[2, Et+2+1]
[2, Et+3+1]
Coeff.
(t-stat.)
Coeff.
(t-stat.)
Coeff.
(t-stat.)
Coeff.
(t-stat.)
Coeff.
(t-stat.)
Coeff.
(t-stat.)
Coeff.
(t-stat.)
Coeff.
(t-stat.)
-0.1264
(-4.63)***
0.1064
(4.43)***
-0.0445
(-3.11)***
0.0487
(4.32)***
-0.1200
(-4.48)***
0.1063
(4.67)***
-0.0587
(-3.92)***
0.0587
(5.39)***
0.1885
(-3.64)***
0.1575
(4.30)***
-0.1000
(-3.69)***
0.1009
(4.75)***
-0.2733
(-3.08)***
0.2110
(3.66)***
-0.1105
(-2.89)***
0.1266
(3.99)***
-0.0714
(2.85)***
0.0751
(2.46)**
-0.0556
(-2.37)**
0.0528
(3.32)**
-0.0708
(-2.63)***
0.0836
(2.50)**
-0.0547
(-2.07)**
0.0530
(3.12)***
-0.0657
(-1.20)
0.0945
(1.66)*
-0.1127
(-2.64)***
0.0933
(3.12)***
-0.1066
(-1.16)
0.1302
(2.73)***
-0.1673
(-2.37)**
0.1368
(2.80)***
0.0334
(3.21)***
-0.0107
(-0.64)
0.0190
(1.53)
0.0092
(0.50)
-0.0108
(-1.32)
0.0093
(0.85)
0.0088
(0.44)
0.0189
(0.69)
-0.0181
(-1.13)
-0.0011
(-0.05)
0.0264
(2.23)**
-0.0032
(-0.17)
0.0275
(2.14)**
-0.0012
(-0.06)
-0.0060
(-0.76)
0.0032
(0.30)
-0.0008
(-0.04)
0.0394
(1.39)
-0.0087
(-0.53)
-0.0317
(-1.24)
0.0642
(3.24)***
-0.0425
(-1.52)
0.0538
(2.23)**
0.0123
(0.37)
-0.0106
(-0.83)
0.0136
(0.84)
0.0114
(0.31)
0.0602
(1.30)
-0.0149
(-0.54)
-0.0436
(-1.02)
0.0909
(-3.42)***
-0.0637
(-1.91)*
0.0779
(2.54)**
0.0347
(0.83)
0.0024
(0.14)
0.0007
(0.03)
0.0317
(0.62)
0.0671
(1.04)
-0.0791
(-2.00)**
0.0139
(0.24)
0.0369
(3.07)***
-0.0187
(-0.91)
0.0256
(1.66)
-0.0100
(-0.44)
-0.0164
(-1.87)*
0.0246
(1.68)*
0.0109
(0.56)
0.0178
(0.69)
-0.0005
(-0.03)
-0.0342
(-1.09)
0.0333
(2.66)***
-0.0230
(-1.11)
0.0252
(1.32)
0.0010
(0.04)
-0.0138
(-1.39)
0.0220
(1.40)
-0.0001
(-0.00)
0.0363
(1.26)
0.0003
(0.01)
-0.0468
(-1.15)
0.0616
(2.60)***
-0.0438
(-1.15)
0.0563
(1.71)*
0.0010
(0.02)
-0.0048
(-0.31)
0.0032
(0.12)
0.0402
(1.04)
0.0059
(0.12)
0.0085
(0.29)
-0.0708
(-1.50)
0.0738
(2.54)**
-0.0438
(-0.97)
0.0870
(2.04)**
0.0021
(0.03)
0.0116
(0.58)
-0.0146
(-0.43)
0.0611
(1.23)
-0.0066
(-0.10)
-0.0344
(-0.93)
-0.0318
(-0.59)
[Info.
uncertainty
variables]
EarningsVol
+
DSUE*
EarningsVol
+
CashVol
+
SUE*CashVol
+
AQ
+
SUE*AQ
+
ReturnVol
+
SUE*ReturnVol
+
AnaDis
+
SUE*AnaDis
+
30
Proceedings of 24th International Business Research Conference
12 - 13 December 2013, Planet Hollywood, Las Vegas, USA, ISBN: 978-1-922069-37-5
Firmage
-
SUE*Firmage
-
IdioVol
+
SUE*IdioVol
+
-0.0032
(-0.37)
0.0058
(0.49)
0.0063
(0.34)
-0.0202
(-0.82)
-0.0068
(-0.76)
0.0107
(0.85)
0.0059
(0.27)
-0.0228
(-0.81)
-0.0047
(-0.29)
0.0014
(0.07)
0.0027
(0.08)
-0.0304
(-0.72)
0.0163
(0.74)
-0.0390
(-1.42)
0.0186
(0.40)
-0.0624
(-1.10)
0.0105
(1.23)
-0.0100
(-0.73)
-0.0073
(-0.36)
-0.0086
(-0.31)
0.0084
(0.82)
-0.0090
(-0.55)
0.0125
(0.56)
-0.0444
(-1.43)
0.0163
(0.89)
-0.0186
(-0.67)
-0.0119
(-0.32)
-0.0154
(-0.33)
0.0216
(0.82)
-0.0311
(-0.88)
0.0014
(0.03)
-0.0379
(-0.60)
0.0353
(1.09)
0.0220
(0.48)
0.0358
(2.33)**
-0.0637
(-3.20)***
-0.0386
(-1.17)
-0.0416
(-0.97)
-0.0700
(-1.78)*
0.0223
(0.44)
-0.0279
(-0.93)
0.0381
(1.20)
0.0640
(2.41)**
-0.0543
(-1.60)
-0.0058
(-0.39)
0.0319
(0.74)
0.0990
(2.15)**
-0.0340
(-0.56)
0.0247
(1.14)
-0.0558
(-1.96)**
-0.0882
(-1.90)*
-0.0024
(-0.04)
-0.0996
(-1.83)*
0.0149
(0.23)
-0.0332
(-0.79)
0.0299
(0.63)
0.0511
(1.37)
-0.0631
(-1.36)
-0.0111
(-0.54)
0.0559
(1.00)
0.0191
(0.89)
0.0074
(0.22)
0.0033
(0.41)
4.12E-5
(0.00)
-0.0070
(-0.36)
-0.0249
(-0.83)
-0.0475
(-2.06)**
0.0273
(0.78)
-0.0195
(-1.46)
0.0119
(0.75)
0.0663
(4.13)***
-0.0459
(-2.23)**
-0.0182
(1.74)*
0.0460
(1.95)*
0.0116
(0.47)
0.0297
(0.72)
0.0027
(0.31)
0.0036
(0.24)
-0.0188
(-0.97)
-0.0014
(-0.04)
-0.0351
(-1.63)
-0.0007
(-0.02)
-0.0085
(-0.54)
-0.0044
(-0.23)
0.0650
(3.85)***
-0.0465
(-2.26)**
-0.0242
(-2.11)**
0.0599
(2.28)**
0.0080
(0.23)
0.0492
(0.91)
0.0109
(0.69)
-0.0271
(-1.11)
-0.0464
(-1.37)
0.0225
(0.45)
-0.0671
(-1.73)*
-0.0178
(-0.33)
-0.0269
(-1.07)
0.0048
(0.15)
0.0524
(2.19)**
-0.0351
(-1.37)
-0.0763
(-4.05)***
0.1611
(3.63)***
0.0344
(0.81)
0.0289
(0.45)
0.0132
(0.56)
-0.0403
(-1.12)
-0.0922
(-1.97)**
0.0940
(1.45)
-0.0857
(-1.52)
-0.0671
(-0.89)
-0.0353
(-1.01)
-0.0124
(-0.31)
0.0339
(1.04)
-0.0329
(-0.90)
-0.1097
(-3.97)***
0.1995
(3.07)***
[Other control
variables]
AnaCov
?
SUE*AnaCov
-
Insti
?
SUE*Insti
-
TradingVol
?
SUE*TradingVol
-
Size
?
SUE*Size
-
Price
?
SUE*Price
+
THQTR
?
SUE*THQTR
-
Badnews
?
SUE*Badnews
+
0.0341
(1.81)*
-0.0168
(-0.63)
0.0197
(2.29)**
-0.0328
(-2.97)***
-0.0348
(-1.85)*
0.0044
(0.18)
-0.0104
(-0.47)
-0.0289
(-1.07)
-0.0034
(-0.18)
-0.0033
(-0.15)
0.0674
(4.19)***
-0.0440
(-2.10)**
0.0043
(0.46)
-0.0019
(-0.10)
0.0230
(1.20)
0.0099
(0.36)
0.0230
(2.81)***
-0.0362
(-3.44)***
-0.0213
(-1.05)
-0.0126
*
(-0.47)
-0.0188
(-0.83)
-0.0190
(-0.65)
-0.0109
(-0.57)
0.0128
(0.62)
0.0638
(3.84)***
-0.0431
(-2.04)**
-7.93E-7
(-0.00)
0.0095
(0.48)
31
Proceedings of 24th International Business Research Conference
12 - 13 December 2013, Planet Hollywood, Las Vegas, USA, ISBN: 978-1-922069-37-5
FD
?
SUE*FD
?
Observations
2
Adj.R
0.0305
(2.38)**
-0.0141
(-0.96)
0.0324
(2.36)**
-0.0160
(-1.04)
0.0679
(2.97)***
-0.0453
(-1.89)*
0.1012
(3.45)***
-0.0711
(-2.19)**
0.0367
(2.97)***
-0.0240
(-1.64)
0.0436
(3.26)***
-0.0353
(-2.37)**
0.0629
(3.00)***
-0.0359
(-1.63)
0.0943
(3.16)***
-0.0656
(-1.89)*
52324
0.0168
52002
0.0145
51422
0.0153
50788
0.0198
48186
0.0195
41632
0.0162
41114
0.0155
40769
0.0177
This table reports the regression results for the tests of the effect of credit rating on seasonal random walk (RW)-based or analyst forecast (AF)-based post-earningsannouncement-drift after controlling for information uncertainty and other PEAD determinants. Sample period ranges from 1985 to 2009. The dependent variable is
the drift returns, which equal the compounded raw returns minus the compounded benchmark returns of the same CRSP size deciles and the same CRSP exchange
index that a firm belongs to. DSUE corresponds to the decile rank of the standardized unexpected earnings (SUE), and is coded from 0 to 1. The seasonal random
walk (RW)-based earnings surprise is calculated as current quarter earnings less earnings four quarters ago, scaled by standard deviation of earnings changes in the
prior ten quarters. The analyst (AF)-based earnings surprise is calculated as the unsplit-adjusted I/B/E/S actual EPS for quarter t minus the I/B/E/S median analyst
consensus forecast of EPS 90 days preceding the earnings announcement date, deflated by stock price at the end of the quarter reported by Compustat. Rating is
coded 0 for firms with ratings ranging from SD/D to CCC, 0.5 for firms with ratings ranging from B- to BBB+, 1 for firms rated from A- to AAA. Other control variables
are defined in the appendix. If the control variables are continuous, they are ranked into deciles within each fiscal quarter and coded from 0 to 1 as well. T-statistics in
parentheses are based on robust standard errors clustered two-way by both firm and year-quarter. Industry dummies are included but omitted from the table for
brevity.
***, **, * denote significance at 1%, 5%, 10% level (two-tailed tests), respectively.
32
Proceedings of 24th International Business Research Conference
12 - 13 December 2013, Planet Hollywood, Las Vegas, USA, ISBN: 978-1-922069-37-5
Table 6: Firm-Fixed Effect Tests of the Impact of Credit Rating on Transient Institutions’
Arbitrage on PEAD
Dependent variable = Transienti,t´
Variables
Pred.
Sign
DSUE t
+
DSUE t-1
-
DSUE t-2
-
DSUE t-3
-
Rating
?
DSUE t*Rating
-
DSUE t-1*Rating
+
DSUE t-2*Rating
+
DSUE t-3*Rating
+
ExtTrad
?
DSUE t*ExtTrad
-
DSUE t-1*ExtTrad
+
DSUE t-2* ExtTrad
+
DSUE t-3*ExtTrad
+
DSUE t*Rating*ExtTrad
+
DSUE t-1*Rating*ExtTrad
-
DSUE t-2*Rating*ExtTrad
-
DSUE t-3*Rating*ExtTrad
-
Size
?
BM
?
PW
-
FD
-
RET1
+
RW-based PEAD
(1a)
(1b)
0.0100
(4.99)***
-0.0062
(-2.90)***
-0.0099
(-4.63)***
-0.0073
(-3.65)***
-0.0003
(-0.22)
-0.0089
(-4.21)**
0.0038
(1.69)*
0.0032
(1.43)
0.0024
(1.13)
-0.0096
(-6.61)***
-0.0037
(-2.23)**
0.0040
(2.27)**
0.0069
(3.96)***
0.0027
(1.63)
-0.0007
(-3.60)***
-8.81E-5
(-1.24)
-0.0673
(-1.52)
-0.0036
(-2.51)**
0.0102
(8.55)***
0.0141
(4.74)***
-0.0066
(-2.05)**
-0.0155
(-4.76)***
-0.0064
(-2.14)**
-0.0001
(-0.04)
-0.0150
(-3.91)***
0.0045
(1.07)
0.0114
(2.70)***
0.0011
(0.29)
-0.0097
(-6.66)***
-0.0107
(-2.51)***
0.0048
(1.05)
0.0168
(3.66)***
0.0014
(0.34)
0.0112
(1.87)*
-0.0013
(-0.19)
-0.0154
(-2.32)**
0.0020
(0.33)
-0.0007
(-3.63)***
-0.0001
(-1.23)
-0.0725
(-1.62)
-0.0036
(-2.51)**
0.0102
(8.55)***
Pred.
Sign
+
+
?
+
+
?
+
+
AF-based PEAD
(2a)
(2b)
0.0180
(8.03)***
0.0120
(5.28)***
-0.0138
(-6.11)***
-0.0144
(-6.51)***
0.0030
(1.76)*
-0.0126
(-5.15)***
-0.0056
(-2.24)**
0.0062
(2.48)**
0.0051
(2.10)**
0.0062
(3.61)***
-0.0089
(-5.21)***
-0.0060
(-3.45)***
0.0075
(4.30)***
0.0098
(5.73)***
+
+
?
?
+
0.0009
(3.94)***
-0.0002
(-0.70)
-0.1914
(-3.20)
-0.0058
(-3.52)***
0.0123
(8.94)***
0.0250
(7.28)***
0.0104
(2.93)***
-0.0172
(-4.86)***
-0.0193
(-5.59)***
0.0035
(2.03)**
-0.0233
(-4.99)***
-0.0031
(-0.64)
0.0113
(2.32)**
0.0123
(2.62)***
0.0061
(3.59)***
-0.0199
(-4.46)***
-0.0033
(-0.72)
0.0130
(2.81)***
0.0176
(3.91)***
0.0180
(2.68)***
-0.0042
(-0.61)
-0.0090
(-1.28)
-0.0127
(-1.87)*
0.0009
(3.84)***
-0.0002
(-0.78)
-0.2059
(-3.41)***
-0.0058
(-3.52)***
0.0124
(8.98)***
(To be continued on next page)
33
Proceedings of 24th International Business Research Conference
12 - 13 December 2013, Planet Hollywood, Las Vegas, USA, ISBN: 978-1-922069-37-5
RET2-3
+
RET4-6
+
0.0331
(40.28)***
0.0100
(30.20)***
0.0331
(40.29)***
0.0100
(30.18)***
+
+
0.0378
(40.06)***
0.0131
(32.13)***
0.0378
(40.06)***
0.0131
(32.18)***
F-test:
H01: DSUE t-1* Rating +
DSUE t-2* Rating + DSUE t-3 * Rating
=0
+
0.0094
(11.81)***
H02: DSUE t-1* ExtTrad+ DSUE t-2*
ExtTrad + DSUE t-3* ExtTrad = 0
+
0.0136
(40.38)***
H03: DSUE t-1*Rating* ExtTrad +
DSUE t-2*Rating *ExtTrad + DSUE t-3*
Rating * ExtTrad = 0
-
0.0170
(12.51)***
-0.0187
(-27.83)***
H04: DSUE t* Rating +
DSUE t-1* Rating = 0
-
-0.0182
(-33.10)***
H05: DSUE t-2* Rating +
DSUE t-3 * Rating = 0
+
0.0113
(12.87)***
-
-0.0149
(-46.61)***
-0.0232
(-19.72)***
H07: DSUE t-2* ExtTrad
+ DSUE t-3* ExtTrad = 0
+
0.0173
(62.31)***
0.0306
(33.82)***
H08: DSUE t*Rating* ExtTrad + DSUE
t-1*Rating *ExtTrad = 0
+
0.0222
(3.16)*
H09: DSUEt-2*Rating*ExtTrad +
DSUEt-3*Rating*ExtTrad =0
-
-0.0217
(-7.71)***
H06: DSUE t* ExtTrad +
ExtTrad = 0
Observations
2
Adj. R
DSUE t-1*
83462
0.0423
83462
0.0599
65048
0.0597
65048
0.0599
This table reports the multivariate tests of transient institutions’ arbitrage on PEAD during a fiscal quarter for the sample period 1985-2009.
The firm-fixed effect regression model is used in the tests. The dependent variable is the change in transient institutional ownership as a
percentage of the outstanding shares over a calendar quarter t´, within which the earnings surprise (SUEt) announcement date for fiscal
quarter t falls. DSUEt corresponds to the decile rank of the SUE for fiscal quarter t, and is converted to [0, 1]. ExtTrad corresponds to the
decile rank of trading volume (Tradingvol), and is converted to [0, 1] with 0 (1) denoting the lowest (highest) ex ante transaction cost for a firm.
For RW-based PEAD, the earnings surprise is calculated as current quarter earnings less earnings four quarters ago, scaled by standard
deviation of earnings changes in the prior ten quarters. For AF-based PEAD, the earnings surprise is calculated as the actual EPS minus the
median consensus analyst forecast of EPS 90 days preceding the earnings announcement date, scaled by the stock price at the end of the
fiscal quarter. Rating is coded 0 for firms with ratings ranging from SD/D to CCC, 0.5 for firms with ratings ranging from B- to BBB+, 1 for
firms rated from A- to AAA. ExtTrad is measured by the average of dollar trading volume (i.e., the product of the closing price and the shares
traded) from the beginning of the fiscal quarter to date -21 relative to the earnings announcement date, ranked into deciles and converted
into [0, 1] with 0 (1) representing the lowest (highest) ex ante transaction cost. Other control variables are defined in the appendix. Year,
quarter, and industry dummies are omitted from the table for brevity.
***, **, * denote significance at 1%, 5%, 10% level (two-tailed tests), respectively.
34
Proceedings of 24th International Business Research Conference
12 - 13 December 2013, Planet Hollywood, Las Vegas, USA, ISBN: 978-1-922069-37-5
Appendix Summary of Variable Definition
Control Variable Definition
Anacov
Insti
EarningsVol
BM
THQTR
FD
Rating
AnaDis
Badnews
IdioVol
CashVol
Firmage
ReturnVol
AQ
RET1
RET2-3
RET4-6
TradingVol
(ExtTrad)
Size
PW
Price
The natural logarithm of 1 plus the number of analysts following a firm, based on
analyst estimates reported by I/B/E/S.
Institutional ownership as a percentage of the outstanding shares at the end of a fiscal
quarter.
The standard deviation of quarterly earnings over 12 quarters ending at the end of a
fiscal quarter.
Common equity divided by market value of the equity at the beginning of a fiscal
quarter.
1 if current quarter is the fourth quarter of a fiscal year for a firm, and 0 otherwise.
1 if transient institutions’ trading occur after October 2000, and 0 otherwise.
Credit rating levels from AAA to D/SD which are transformed into conventional
numerical scores that take value of 1 for firms with ratings from SD/D to CCC+, 2 for
firms with ratings from B- to BBB+, and 3 for firms with ratings from A- to AAA.
Standard deviation of analyst earnings forecasts during a fiscal quarter, divided by
stock price at the end of the fiscal quarter.
1 if a firm reports a negative earnings surprise for a fiscal quarter, and 0 otherwise.
The standard deviation of the residuals from the following regression model over the
past 52 weeks: ri,t=i+1irm,t+2irm,t+1+3irm,t+2+4irm,t-1+5irm,t-2+i,t, where ri,t is the weekly
return on firm i and rm,t is the value-weighted CRSP index return.
The standard deviation of cash flow from operations in the past five years (with a
minimum of three years).
The number of months since a firm’s IPO. If the IPO date is not available in Compustat,
the firm age variable represents the number of months since CRSP first reported return
data for the firm.
The standard deviation of daily market excess return over a year ending at the earnings
announcement for a fiscal quarter.
The standard deviation of the residuals of the regression of working capital accruals on
cash from operations in the current period, prior period and future period, as well as
PPE and revenue changes (See Francis et al. (2005)).
Size-adjusted buy-and-hold returns from 30 days to 3 days before the earnings
announcement date.
Size-adjusted buy-and-hold returns over the two months ending 30 days prior to
earnings announcement date.
Size-adjusted buy-and-hold returns over the three months ending three months prior to
earnings announcement date.
The average of dollar trading volume (i.e., the product of the closing price and the
shares traded, both of which can be obtained from CRSP) (in millions of U.S. dollars)
from the beginning of a fiscal quarter to date -21 relative to the earnings announcement
date.
The natural logarithm of 1 plus market value of common equity at the beginning of the
fiscal quarter.
The mean portfolio weight (in percentage) of a stock in the portfolio of a transient
institution during a fiscal quarter.
Average daily closing price within one week before the earnings announcement for the
fiscal quarter.
35