Abnormal Accrual, Informed Trader, and Long-Term Stock
Return: Evidence from Japan∗
Katsuhiko Muramiya†, Kazuhisa Otogawa‡, and Tomomi Takada‡

Abstract: This study examines the association among abnormal accruals, long-term stock
returns, and probability of informed trading. Some analytical and empirical research for postearnings announcement drift provide evidence that a high arrival rate of informed traders helps
stock prices become more eﬃcient. We focus on the abnormal accrual anomaly, and investigate
these studies’ implications using data from the Tokyo Stock Exchange in Japan. Consistent
with these studies, we show that stocks with a high probability of informed trading exhibit lessabnormal accrual mispricing relative to stocks with a low probability of informed trading.

JEL Classiﬁcation: G15; M41

Keywords: Abnormal accruals, Market microstructure, High-frequency data, Informed trader.
Data Availability: All data are available from public sources.

∗

We gratefully acknowledge the valuable comments and suggestions of Hitoshi Takehara, Masahiko Shibamoto,Kazuyuki Suda, Akinobu Shuto, Woo-Jong Lee, Takashi Kamihigashi, and workshop participants at the 2008
American Accounting Association Annual Meeting in Anaheim and Kobe University. This research was partially
supported by the Ministry of Education, Science, Sports and Culture, Grant-in-Aid for Scientiﬁc Research (C),19539003, 2007–2008 (Muramiya).
†

Corresponding author. Research Institute for Economics and Business Administration, Kobe University.

Address 2-1, Rokkodai, Nada, Kobe, Hyogo 657-8501, Japan.
Tel. & Fax +81 78-803-7039; E-mail address muramiya@rieb.kobe-u.ac.jp (Muramiya).
‡

Graduate School of Business Administration, Kobe University

1

1

Introduction

This study examines the relationship among abnormal accruals, long-term stock return, andinformed trading activity. Sloan (1996) ﬁnds that market participants overweigh (underweigh)future earnings implications of current accruals (cash ﬂows), and therefore, stocks with relatively
high (low) accruals tend to have negative (positive) future abnormal returns. Using the Mishkintest to examine whether the market participants have a rational expectation, he concludes thatthe result is consistent with naı̈ve investor hypothesis, that market participants “ﬁxate” onearnings and fail to distinguish between the persistency of accrual and cash ﬂow components ofcurrent earnings.
Xie (2001) follows up Sloan (1996) by distinguishing between abnormal and normal components of total accruals. He contends that market participants particularly overestimate theabnormal portion of accruals stemming from managerial discretion, and concludes that the mispricing of total accruals that Sloan (1996) documents is due largely to abnormal accruals.
These studies raise one research question in our mind: do all market participants fail todistinguish the accrual and cash ﬂow components (abnormal accruals and normal accruals) ofcurrent earnings?
It is a rational assumption that at least two types of traders are in the stock market: uninformed traders and informed traders. The former trade stocks using only public information(e.g., earnings announcements) and are not good at analyzing the information. The latter tradestocks using not only public information, they also access private information sources and areskillful at analyzing the information. Because of the private information and analyzing skill,
informed traders are likely to be more sophisticated than uninformed traders at estimate theintrinsic value of stocks.
In particular, it is diﬃcult for uninformed traders who access only public information sources
and have limited analytical ability to distinguish between accruals and cash ﬂow or to derive
the abnormal component from total accruals. In contrast, informed traders easily distinguishbetween abnormal and normal components of total accruals, and correctly understand the future
earnings implications of these accruals using public and private information and their highanalytical skills. Therefore, it is expected that with actively informed trading, the price ofstocks is unlikely to diverge from intrinsic value, correctly reﬂecting properties of abnormal2

accruals over stocks with inactively informed trading.
Consistent with this prediction, some analytical research shows that more informed trading
helps market eﬃciency. For example, Callen et al. (2000) present the proposition that reducing
uninformed trading curbs deviations of market prices form an eﬃcient price. In addition, even if
a deviation exists, Easley and O’Hara (1992a) demonstrate that increasing the fraction of tradesfrom informed traders accelerates the convergence of stock price to an eﬃcient price, because thetrading activity reveals the information about the true value to the other market participants.
Furthermore, in keeping with these theoretical predictions, Vega (2006) focuses on an anomaly
related to accounting, post-earnings announcement drift, and investigates whether these theoretical predictions are valid. She ﬁnds that stocks with a high arrival rate of informed traders
experience low or insigniﬁcant drift.
These previous studies suggest that the more actively informed traders trade a stock, themore quickly the stock price converges on an eﬃcient price; hence, this leads to the disappearanceof the anomalies. We focus not on total accruals but on abnormal accruals that are more undetectable information for uninformed traders, and test whether informed trading activity aﬀects
the abnormal accrual mispricing. It is likely that more undetectable information for uninformed
traders leads to raise the power of our test, which veriﬁes the validity of the aforementioned
theoretical research.
Following Vega (2006), we use probability of informed trading (PIN) developed by Easley
et al. (1996) and Easley et al. (2002) in market microstructure research to capture the trading
activity of informed traders. We ﬁnd that the average size-BE/ME adjusted abnormal returns
from abnormal accrual based trading strategy for low PIN stocks is signiﬁcantly positive, 6.35
percent over 12 months, while those for high PIN is insigniﬁcant, 0.59 percent. This negative
relation between abnormal accrual mispricing and PIN is robust even after controlling for otherfactors known to inﬂuence the accrual anomaly, namely arbitrage risk and transaction costs (e.g.,
Mashruwala et al. 2006). We conclude that it is consistent with naı̈ve investor hypothesis, that
is, abnormal accrual mispricing is related negatively to traders’ ability to understand earnings
information properly.
Many researchers have been interested in the accrual anomaly, and still continue to debate
about the source. What drives the observe accrual anomaly? Prior research suggests that two
explanations exist on why the accruals-based trading strategy earns an abnormal return. First,

3

investors fail to understand accurately the persistence of accruals (Sloan 1996) or the componentof accruals (Xie 2001). This is called the mispricing explanation. In contrast, some researchers
explain that the accrual anomaly is due to risk factors. For example, Khan (2007) shows that a
considerable portion of the abnormal returns to the accrual-based trading strategy is explainedby risk in using extended Intertemporal Capital Asset Pricing Model (ICAPM). This is called
the risk explanation.
The accrual anomaly is observed in many countries (e.g., Pincus et al. 2007), so ascertaining
its cause is an important research issue throughout the world. Our results are consistent with themispricing explanation because if the risk explanation is true, then the accrual anomaly should
be observed regardless of informed trading activity. Our results can be interpreted as showingthat the trading from informed traders helps keep the price eﬃcient; thereby, stock prices reﬂect
recondite information such as abnormal accrual stemming from managerial discretion. Thus,
the anomaly for the stocks with actively informed trading, is likely to disappear quickly.
This paper is related most closely to two studies, Collins et al. (2003) and Balsam et al.
(2002). These studies show that the proportion of sophisticated investors’ participation, as
proxied by the percentage of common stock owned by institutions, is negatively associatedwith market mispricing. Collins et al. (2003) ﬁnd that stocks with high institutional ownershipexhibit less accrual mispricing relative to stocks with low institutional ownership. This researchis consistent with investor sophistication mitigating the accrual mispricing phenomenon.
Furthermore, Balsam et al. (2002) investigate how investors react to the abnormal accrualcomponents before and after the release of the full set of ﬁnancial statements in Form 10-Q,
which provides market participants with the data necessary to estimate the abnormal portionof total accruals. They ﬁnd that negative association between abnormal accruals and abnormal
returns prior to the release of Form 10-Q varies systematically with investor sophistication; i.e.,
for stocks with low institutional ownership, they do not observe negative correlation between
abnormal accruals and abnormal returns prior to the release of Form 10-Q, while they observe
such correlationship for stocks with high institutional ownership. Balsam et al. (2002) interpretthat sophisticated investors recognize accruals management and understand that abnormal accruals for future earnings precede the 10-Q ﬁling date, because they are able to access other,more private sources of information. Therefore, they conclude that high investor sophistication
quickly impounds abnormal portions of accruals into stock prices.

4

In sum, consistent with this paper, these researches suggest that sophisticated investors who
have more information (i.e., informed traders) than uninformed traders react correctly to thecomponents of earnings, even before disclosure of the data necessary to compute the normal andabnormal components of accruals. Moreover, even if traders’ reactions with regard to abnormal
accruals are insuﬃcient to impound into stock prices, informed traders should arbitrage theabnormal accruals quickly mispricing away.
Our study diﬀers in two respects from these prior researches that examine the association
between informed trading and the mispricing of accruals. First, we use PIN to capture thetrading activity of informed traders. Prior research (e.g., Collins et al. 2003; Balsam et al. 2002;
Ali et al. 2000) uses the percentage of outstanding common shares held by institutional owners.
This is an indirect noisy measure of informed trading activity because some institutional investors
trade the stocks actively, but others just hold them: more holding of stocks by sophisticatedinvestors might not mean more actively informed trading1 . On the other hand, PIN is a direct
measure of informed trading activity because PIN is estimated by using high-frequency data
based on direct individual trading. Therefore, prior research may have bias against the informed
trading measure; therefore, it was unlikely to examine whether informed trading aﬀects theaccrual anomaly in direct way.
Second, we examine whether informed trading aﬀects the relationship between abnormal
accrual and long-term stock returns. Contrary to Balsam et al. (2002), who focus on short-term
stock returns, we focus on long-window ones because the exact date when investors are provided
the data necessary to estimate the abnormal portion of total accruals cannot be speciﬁed in
the case of the Japanese stock market. More importantly, the purpose of this study is toreveal which explanation, market mispricing or risk, is a more plausible cause of the accrual
anomaly by examining the association between informed trading and the accrual anomaly. This
research design using long-window returns enables us to distinguish the source of the accrual
anomaly. Our result that informed trading negatively aﬀects the association between abnormal
1

Ali et al. (2000) use the participation of all institutional investors for the stocks as proxy for investor sophisti-

cated, while Collins et al. (2003) use the percentage of common shares held by transient institutions to eliminate
the institutions that are unlikely to trade on information utilizing Bushee (1998) three categories of institutionsclassiﬁed by trading behavior. The measurement of Collins et al. (2003) decreases the measurement error relative
to that of Ali et al. (2000). However, the measurement error is not fully eliminated even by the procedure ofCollins et al. (2003), because some transient institutions trades on information but others do not.

5

accruals and long-term stock returns, even after controlling for risk factors or other factors (e.g.,
arbitrage risk and transaction costs) known to aﬀect the long-term stock returns, suggests thatthe mispricing explanation is supported.
The paper proceeds as follows. Section 2 reviews prior research and develops our hypothesis.
In Section 3, we present data description and deﬁnitions of variables, and Section 4 reports
empirical tests for our hypothesis, using data from the Japanese stock market. Finally, Section
5 presents the summary and conclusions.

2

Prior Research and Hypothesis Development

Market microstructure research examining the interaction of information and stock prices has
become increasingly conspicuous. Most research in this area assumes a realistic portrayal that
there are uninformed traders and informed traders in the stock markets. Because of private
information, informed traders have an information advantage over uninformed traders about
intrinsic values of stocks. Some market microstructure research demonstrates that this information asymmetry aﬀects the deviation of stock prices from eﬃcient value and the speed of priceadjustments to converge to an eﬃcient price or full information value.
For example, Callen et al. (2000) show that the probability that stock prices deviate fromtheir eﬃcient price increases as noise trading by uninformed traders increases, because the noise
trading prevents market participants from estimating precisely the eﬃcient price, so convergence
may not obtain over the long term. Moreover, they demonstrate that when the prices go way
up or down from the intrinsic value, convergence to a stable equilibrium price is exponentiallyfast as noise trading by uninformed traders is eliminated.
In keeping with Callen et al. (2000), Easley and O’Hara (1992b) present the proposition that
increasing the fraction of trades from informed traders hastens the adjustment process, because
their trading activity reveals information about the true value to the market participants. Consequently, they claim that informed trading helps to accelerate the rate at which prices reﬂect
full information.
Furthermore, Easley and O’Hara (1992a) illustrate that price adjustment properties in various markets diﬀer only in the number of composition of traders by developing a simulationof their theoretical model. Because not all trades arise from informed traders but also from
uninformed traders, prices frequently move in the wrong direction. Informed traders proﬁt from6

trading if prices are not at full information levels, and they continue to trade until prices become eﬃcient (e.g., O’Hara 1995). Easley and O’Hara (1992a) ﬁnd that repeated trading by
informed traders eventually reveals those traders’ information, and prices eventually adjust to
full information levels theoretically. In addition, they show that the speed of adjustment depends on the composition of traders; markets with more informed traders have a higher rate ofprice convergence.
In sum, the result of these theoretical researches suggests that more activity of informed(uninformed) trading leads to the probability that the stock prices will deviate from eﬃcientvalue decreases (increases), and that informed (uninformed) trading causes prices to converge
faster (slower) even if stock prices are mispriced temporarily.
These theoretical predictions have implication for anomalies research. They imply thatstocks with more informed traders are not observed anomalies, or that the anomalies disappear
over a short term. Vega (2006) focuses on a well-known accounting anomaly; post-earnings
announcement drift (PEAD), and tests whether this argument is valid. Consistent with thisargument, she ﬁnds that stocks with a high arrival rate of informed traders experiences low
or insigniﬁcant PEAD. Vega (2006) concludes that the more information (both private andpublic) traders have about the true value of a stock, and the more they agree and trade on thisinformation, the smaller the abnormal return drift. In contrast, we focus on another accounting
anomaly, abnormal accrual anomaly, and examine whether a high arrival rate of informed traders
is negatively associated with the abnormal accrual mispricing.
Sloan (1996) ﬁnds that market participants overweigh (underweigh) future earnings implications of current accruals (cash ﬂows); therefore, stocks with relatively high (low) accruals tend
to have negative (positive) future abnormal returns. This phenomenon is referred to as accrual
anomaly. He concludes that market participants are not exactly rational for accrual informationand stock prices act “as if” they fail to correctly anticipate future implications of current accrual.
Bradshaw et al. (2001) show that even professional information intermediaries—ﬁnancial analysts or auditors—do not understand the future consequences associated with current accrual.
Their evidence reinforces the interpretation of Sloan (1996) that market participants do not
anticipate fully the future implications of current accrual. These researches suggest that market
participants, even professional information intermediaries, misunderstand the persistence of accrual and that the stock price diverges from the intrinsic value of reﬂected accrual information;7

therefore, the current accruals predict the future return in the process of converging to that
intrinsic value.
Xie (2001) reexamines the accrual anomaly to explore the cause, and ﬁnds that market
participants overestimate the persistence of abnormal components of the accrual estimated by
the Jones (1991) model. He concludes that market participants overprice the portion of abnormal
accruals stemming from managerial discretion. This research can be interpreted as indicatingthat accrual anomaly is due to misunderstanding the abnormal accruals component by naı̈ve
market participants. Therefore, Xie (2001) brings one research question to mind: is the market
mispricing with respect to abnormal accruals homogeneous among the stocks?
The composition of traders that are informed versus uninformed should diﬀer from one stock
to another, thus leading to diﬀerences in the percentage of naı̈ve uninformed traders. Informed
traders always are more sophisticated than uninformed traders about the true value of the stock.
Further, as Vega (2006) mentioned, informed traders are skillful at analyzing public information.
Therefore, informed traders are expected to understand the accrual implication, the part ofpublic information, for future earnings by utilizing not only superior analyzing skill but also
their private information. Informed traders are likely to detect accrual management throughmanagerial discretion even if earnings are inﬂated or deﬂated by using accruals; therefore, they
will unscramble the accrual implication properly. As a result, when informed trading behavior
is more active, stock prices will reﬂect more information processed by informed traders andbecome eﬃcient. In addition, the uninformed traders can infer accurately the true value of thestocks from the price reﬂecting informed traders’ beliefs.
Consequently, more active informed trading leads to less accrual mispricing due to managerial
discretion, since the traders have high ability to analyze the accrual information and their private
information. Alternatively, even if the accrual mispricing is temporary, informed traders trade
the stock until the mispricing disappears. Therefore, more active informed trading leads toquicker disappearance of the mispricing. In the context of the long-window abnormal accrual
mispricing demonstrated by Xie (2001), the degree of mispricing diﬀers among the stocks; stocks
with higher informed trading have less abnormal accrual mispricing over the long term. So, theability of abnormal accrual to predict long-term stock returns is expected to negatively relate
to the activity of informed trading. These arguments suggest the following hypothesis:8

H: Stocks with high activity of informed trading exhibit less long-term abnormal accrual mispricing relative to stocks with low activity of informed trading.

If our hypothesis is accepted, long-term returns for stocks with high informed trading activity
should be less sensitive to abnormal accruals information. There are two reasons for this prediction: First, these stocks are unlikely to diverge from eﬃcient value impounding current abnormal
accruals for future earnings implications. Second, as a result of informed trading arbitrage, stock
prices become eﬃcient in the short-term even if stock prices are mispriced temporarily.

3

Data Description and Deﬁnition of Variables

We start by explaining the Japanese reporting system. Figure 1 describes the timeline ofJapanese ﬁnancial reporting and an overview of the measurement in our analysis. As ﬁrms
having March ﬁscal year end account for around 75% of listed ﬁrms, then we explain ﬁscal year
end of March as an example.
Japanese ﬁrms are required by stock exchanges to announce an overview of some of theirﬁnancials (i.e., summary report), such as sales, earnings, dividends, and management earnings
forecasts of these items for the following year within 45 days after the end of the ﬁscal year.
However, this simple overview does not include information to estimate the abnormal portionof total accruals, so traders, especially uninformed traders who are unable to access private
information sources, cannot precisely estimate abnormal accrual at that point in time. Afterannouncing this summary report, ﬁrms also have to report audited ﬁnancial statements, such asbalance sheet, income statement, and cash ﬂow statement within three months of the ﬁscal year’s
end. Investors can access these statements on ﬁrms’ web sites or on the Electronic Disclosure
for Investors’ NETwork (EDINET) system, which is like the Electronic Data-Gathering, Analysis, and Retrieval (EDGAR) system in the U.S. All traders eventually can estimate abnormal
accruals precisely, using reported audited ﬁnancial statements.
We constrain our sample to ﬁscal year end of March, because the ﬁscal year end of mostJapanese ﬁrms is March. Further, in view of the Japanese ﬁnancial reporting system, buy-andhold return calculations begin at the end of June, three months after the ﬁscal year end. Thislag ensures to enable all traders to estimate the abnormal portion of total accruals based on
public information.
93.1

Abnormal accruals

To estimate abnormal accruals, we used the Jones (1991) model as follows2 :T Ai,t = α + β1 ∆Salesi,t + β2 P P Ei,t + εi,t ,(1)

where T Ai,t is total accruals, ∆Sales is the change of sales, P P E is equal to gross property,
plant, and equity in the period t. All the variables are scaled by the average of total assets. Total
accruals are obtained by subtracting CFO from net income using a cash ﬂow statement approach,which is consistent with Hribar and Collins (2002). Although income before extraordinary items
has been used to obtain total accruals in previous research examining U.S. ﬁrms, net income isused in this study. Since the deﬁnition of extraordinary items in Japan is diﬀerent from that in
the U.S., and we do not have unusual and/or infrequent items in income statements in Japan,
net income for Japanese ﬁrms has similar characteristics to income before extraordinary itemsin the U.S. Therefore, we estimate total accruals as the diﬀerence between net income and CFO.
Abnormal accruals are determined as the diﬀerence between total accruals and normal accruals,
with expected total accruals estimated from this equation. Normal accruals are estimated incross-section for each two-digit Nikkei Industrial Code and year combination, where the ﬁrm ofinterest is excluded in estimating the model. In short, we estimate normal accruals in out ofsample analysis.

3.2

Informed Trading Activity Measure

3.2.1

Probability of informed trading

We use probability of informed trading (PIN) to capture the trading activity of the informed
trader. Easley et al. (2002) (hereafter EHO) propose the structural model based on Glosten
and Milgrom (1985) and Easley and O’Hara (1987) to develop an estimation model of PIN. The
model consists of three players; market maker, uninformed trader, and informed trader. The
uninformed trader and informed trader are assumed to trade a stock with competitive and risk
neutral market markers. The market marker sets bid and ask prices, and revises the quotes

2

Our main results which are shows in section 4 are robust to using alternative estimation procedure of abnormal

accruals, such as modiﬁed Jones model (Dechow et al. 1995) and CFO Jones model (Kasznik 1999).

10

depending on trades that occur. Because the market maker is competitive and risk neutral,these prices are expected value of asset conditional on his information at the time of trade. On
any day, the uninformed trader buys or sells for reasons that are exogenous to the model. That
trading is determined by the independent Poisson process. On the other hand, the informed
trader buys or sells only on days for which information events have occurred. That trading is
also determined by the independent Poisson process. The informed trader is assumed to be risk
neutral and trades to maximize his expected proﬁt. If the informed trader observes a high signal
about an asset, then he will buy; conversely, he will sell if he observes a low signal. Therefore, theexcess buy or sell order (i.e., abnormal order ﬂow) is generated by the occurrence of information
event. The EHO model estimates the PIN measure based on the occurrence of information event
and abnormal order ﬂow.
The EHO model assumes that the information events occur only once per day, and theprobability that an information event will occur on a given day is α. These information eventsare low signal with δ or high signal with 1 − δ. Uninformed traders arrive the market regardlessof the occurrence of information events, and daily arrival rates are εb for buy orders and εs forsell orders. On the other hand, informed traders arrive at the market conditional on days forwhich information events will occur. That is, on low (high) signal event days, arrival rates are µfor sell (buy) orders from informed traders. Therefore, on days with an information event, buy
or sell arrival rates will be increased by µ depending on the signal content; arrival rates are εb
for buy orders and εs + µ for sell orders if the signal is low, and arrival rates are εb + µ for buy
orders and εs for sell orders if the signal is high.
All of these arrival processes are determined by an independent Poisson process and each
event type is assumed to be independent of each other. As the probability of a no event day, a
day with a low signal, and a day with a high signal, respectively, is given by 1 − α, αδ, α(1 − δ),so the likelihood of observing B buys and S sells on a certain day is:(µ + εb )B −εs (εs )S
e
B!
S!
B
S
(εb ) −(µ+εs ) (µ + εs )(εb )B −εs (εs )S+αδe−εbe+ (1 − α)e−εb
e
,
B!
S!
B!
S!

L(θ|B, S) = α(1 − δ)e−(µ+εb )(2)where (B, S) is the total number of buys and sells for the day and θ = (µ, εb , εs , α, δ) is theparameter vector. This function consists of the sum of three Poisson probabilities weighted by
the probability of each event type. The data set over multiple days allows us to estimate the11parameters. Because days are independent, the likelihood function for T days is a product of
the above likelihood over days,
L(θ|M ) =T∏

L(θ|Bi , Si ),(3)iwhere (Bi , Si ) is trading data for day i = 1 . . . , T and M = ((B1 , S1 ), . . . , (BT , ST )) is the data
set. In quarterly earnings announcement drift analysis, Vega (2006) set T equal to the 40 trading
days before an earnings announcement is released. In contrast, we analyze an annual earnings
announcement, then we set T equal to trading days during from July of year t−1 to June of year
t. We estimate the parameter vector by maximizing the likelihood function. The probability of
informed-based trading (PIN) is deﬁned as

P IN =α̂µ̂.
α̂µ̂ + εˆb + εˆs(4)High PIN means that the proportion of executed orders originating from an informed traderis high. In short, informed traders trade the stocks frequently.

3.2.2

Algorithm for classifying as buyer- or seller-initiated trade

We need the number of buyer- and seller-initiated trades each day to estimate PIN. Previous
research in analysis of PIN in the U.S. uses the Lee and Ready (1991) algorithm, i.e., thecombination of quote and tick test to classify trades as buys or sells (e.g., Easley et al. 2002;
Vega 2006; Duarte et al. 2008). On the other hand, we do not use the Lee and Ready (1991)
algorithm but only the quote test.
The mechanism for trading equities on Japanese stock market diﬀers from that in the U.S.
Trading on the NYSE and the AMEX is managed by exchange-designated specialists. The
specialists collect public limit orders (which are maintained in a private limit order book thatcannot be readily viewed by the public), match incoming buy and sell orders, and purchase andsell securities for their own account (Lehmann and Modest 1994). Contrary to the U.S. stock
market structure, the main Japanese market, the Tokyo Stock Exchange (TSE) is order-driven;
all orders except for opening and closing are executed based on price and time priority rules.
As a result of this structure, it is extremely improbable that executed price is diﬀerent from thelast quoted ask or bid price; therefore using the quote test is suﬃcient to classify trades as buys

12

or sells in the TSE. In fact, the percentage of unclassiﬁed trades to total trades in TSE using
the quote test is no more than 1 percent since 2001.

3.3

Abnormal return

Most prior research in the U.S. examines the accrual anomaly with the hedge portfolio return
test, estimating the Fama and French (1993) three-factor model and/or the Carhart (1997)
four-factor model alpha (e.g., Mashruwala et al. 2006; Xie 2001). However, we do not use
these methods to test hedge portfolio returns, but a characteristic-based performance measuredeveloped by Daniel et al. (1997) because the factor model is rejected in the TSE, while thecharacteristic model is not (Daniel et al. 2001).
Chan et al. (1991) reveal a signiﬁcant relationship between expected returns and two variables, market capitalization and book-to-market ratio, in the Japanese market. Therefore, wechoose these two characteristics to calculate the abnormal return of each stock. Although Daniel
et al. (2001) take the momentum eﬀect into account, we do not control that eﬀect, because previous research provides weak and insigniﬁcant evidence of momentum in Japan (e.g., Chui et al.
2000).
We calculate abnormal return using the following procedure. We begin by constructing
25 similar characteristic portfolios through a 5 × 5 sort on market capitalization and book-to
market quintiles using all non-ﬁnancial stocks. In August of each year t from 2001 to 2006, all
TSE stocks on the Nikkei Japanese daily stock return database, like the CRSP in the U.S., areranked on market capitalization, which is stock price times the number of shares outstanding
for a stock. These quintile breakpoints for market capitalization are used to allocate all TSE
and other stock exchanges stocks to ﬁve market capitalization quintiles. Similar, TSE quintile
breakpoints for book-to-market are used to allocate all TSE and other stock exchange stocks to
ﬁve book-to-market quintiles. Book-to-market is book value of equity for the ﬁscal year from
April of year t − 1 to March of year t divided by market capitalization at the end of March ofyear t. Negative book value stocks are excluded. We sort book-to-market at the end of August
to be sure that book value from April of year t − 1 to March of year t is known thoroughly bymarket participants.
We construct the 25 size-BE/ME portfolios as intersections of the ﬁve sizes and the ﬁve
BE/ME groups and calculate value-weighted returns on the portfolios from September of t to13August of t + 1. The abnormal return of a particular stock then is calculated by subtracting thevalue-weighted portfolio’s return from the stock’s return.

3.4

Other variables aﬀecting abnormal accruals anomaly

Mashruwala et al. (2006) ﬁnd that future abnormal returns to the accrual-based hedge portfolio
are higher in stocks with higher idiosyncratic volatility proxied for lack of a close substitute in
the U. S. markets3 . In addition, Mashruwala et al. (2006) show that the accruals-based hedge
portfolio yields higher future abnormal returns for stocks with higher transaction costs. They
conclude that two sources of barriers to arbitrage—lack of close substitutes and transaction
costs—prevent arbitrageurs from eliminating accrual mispricing. Therefore, we control for theseeﬀects in our research.

3.4.1

Lack of closed substitutes

High idiosyncratic volatility proxied for absence of a close substitute deters arbitrage activity
because it makes arbitrage activity riskier, i.e., higher arbitrage risk (e.g., Shleifer and Vishny1997). This argument suggests that high arbitrage risk (absence of close substitutes) createsbarriers to arbitrage away accrual mispricing. Following Mashruwala et al. (2006) and Mendenhall (2004), we employ the residual variance from a standard market model regression of the
returns of the weighted average market index over the 48 months ending June 30 of year t as aproxy for arbitrage risk.

3.4.2

Transaction costs

Mashruwala et al. (2006) ﬁnd that transaction costs also impose barriers to arbitrage away
accrual mispricing. We employ two variables to capture the transaction costs: trading volume
and frequency of zero daily returns. Numerous studies show that trading volume is negatively
associated with one cost of trading—bid-ask spread (see Callahan et al. 1997). In addition,
Lesmond et al. (1999) assert that zero return is positively associated with transaction costs;
stocks with high transaction costs have more zero returns than stocks with low transaction

3

Further, Pincus et al. (2007) provide the evidence that this phenomenon is generalizable to some othercountries including Japan.

14

costs. Thus, we employ these two variables as a proxy for transaction costs, and deﬁne trading
volume as average daily trading volume (closing share price times daily number of shares traded)on June of year t and the frequency of zero returns as those frequency of zero daily returns overthe period from July of year t − 1 to June of year t.
3.5

Data description

Our sample consists of all March ﬁscal year end non-ﬁnancial ﬁrms on the TSE during fromthe period 2001 to 2006, together with required data from the Nikkei NEEDS TICK database,Nikkei daily returns database, and Nikkei NEEDS ﬁnancial data ﬁles. We obtain trade andquote data from the Nikkei NEEDS TICK database, market price and volume data from Nikkei
daily returns database, and ﬁnancial data from Nikkei NEEDS ﬁnancial data ﬁles4 .
Following Brown et al. (2004), we eliminate observations with stocks with corner solutions
for α and δ, additionally using the following ﬁlters: (1) α < 0.02 | 0.98 < α and (2) δ <0.02 | 0.98 < δ, because these cases are unrealistic. Finally, the number of observations is 7,315
ﬁrm-year and ranges from 1,100 in year 2001 to 1,298 in year 2006.

4

Empirical Results

4.1

Descriptive statistics

Table 1 presents descriptive statistics for estimated parameters of PIN. The time-series patternsof the cross-sectional median of the parameters (α, the probability that an information event
occurs and δ, the probability that the information is a low signal) in Figure 2 (a), and threeparameters relating to traders composition (µ, the arrival rate of informed traders, and εb andεs , the arrival rate of uninformed buyers and sellers) in Figure 2 (b).
Similar to Easley et al. (2002), who estimate parameters by using the EHO model for NYSE
stocks from 1983 to 1998, the estimates of α and δ are stable across years, and so they do nothave a trend. On the other hand, the estimates of µ, εb , and εs have an upward trend. Thisevidence is dovetailed with the fact that the number of trades and trading volume increases
signiﬁcantly by disseminating online trades over this period.
4

Nikkei NEEDS TICK database, Nikkei daily returns database, and Nikkei NEEDS ﬁnancial data ﬁles inJapan correspond to the trade and quotes (TAQ) database, CRSP, and Compustat in the U.S., respectively.

15

The mean (median) of δ, 0.35 (0.33), suggests that the probability of bad news was generally
lower than that of good news. Most of our sample period, especially from 2002 to 2006, enjoyedan economic boom; thus, this evidence is in accord with our intuition.
Figure 2 (c) shows the time-series pattern of the cross-sectional distribution of PIN. Although
α is stable over time, PIN actually decreases year-by-year because uninformed buyers and sellers
in 2006 are ﬁve times and four times, respectively, while informed traders are two-and-a-halftimes higher than in 2001. Further, we also show that cross-sectional PIN distribution has
been becoming tighter during the sample period; thus, the cross-sectional diﬀerence of raw PIN
is becoming smaller, which leads to deteriorating power of the test that PIN aﬀects accrual
mispricing. Therefore, we do not report the result using raw PIN data, but the result of aportfolio test based on ranked PIN in our analyses.
Table 2 shows the descriptive statistics of overall variables with the exception of PIN. In
keeping with prior research in the U.S., mean T A is negative in Japan, reﬂecting depreciation
and amortization charges included in the calculation. On the other hand, contrary to the U.S.
market, mean BE/ME in the Japanese stock market is closed to 1. This suggests that averaged
stock’s market value is approximately equal to the liquidating value over our sample period.
Table 3 present the Spearman correlation matrix for the primary variables of interest. Consistent with prior research in the U.S. (e.g., Easley et al. 2002), P IN is negatively correlatedwith Size. This suggests that smaller stocks tend to have higher PIN. Additionally, P IN ishighly correlated with transaction cost measures, V ol and Zerof req. Then, in a multivariate
setting, we control for these variables. As Lesmond et al. (1999) report, Zerof req is negatively
correlated with Size and V ol in our sample. This means that zero returns are very common
for small stocks. The highly negative correlation between V ol and Zerof req implies that thesevariables are likely to capture the same aspect, namely transaction costs; low V ol and highZerof req indicate that transaction costs are high.
In addition, Abnac is signiﬁcantly correlated with Aret12 (ρ = −0.07) at the 0.01 level. Thissuggests that low (high) Abnac stocks tend to experience high (low) future returns, and that
there is a possibility observing the abnormal accrual anomaly in Japan. We further investigate
it by using a hedge portfolio return test in next section.

16

4.2

Replication in the Japanese stock market

Pincus et al. (2007) investigate whether accruals mispricing is observed in other non-U.S. countries, including Japan, by using Global Vantage Industrial/Commercial (GVIC) and Global
Vantage Issues (GVI) database over 1994–2002. They ﬁnd that accrual mispricing, but notabnormal accrual mispricing, is observed in Japan. However, Kubota et al. (2006) ﬁnd that
not only total accruals but also abnormal accruals can predict future return using the Japanesedatabase over 1980–2002, and then conclude that abnormal accrual mispricing is also observedin Japan and that the accrual anomaly is due largely to abnormal accruals by managerial discretion. Thus, there are mixed results for abnormal accrual mispricing in Japan, so we startinvestigating whether abnormal accrual mispricing occurs in Japan using our sample.
Table 4 reports the mean value of X-month buy-and-hold raw return (RretX) and sizeBE/ME adjusted X-month buy-and-hold abnormal return (AretX), and other selected variables
for each portfolio. We use the following procedure for constructing each portfolio: for each year,
we rank stocks by abnormal accruals and assign them to quintiles. The return accumulation
period begins on July 1 to ensure complete dissemination of information to estimate abnormal
accruals for all traders.
This table shows that over 3, 6, 9, and 12-month periods following portfolio formation, stocks
with small abnormal accruals generally have larger raw return than those of large abnormal
accruals. For example, the lowest abnormal accrual quintile earns, on average, a raw return over
12 months of 16.7 percent, while the highest abnormal accrual quintile earns an average raw
return of 11.2 percent. Similarly, we ﬁnd that stocks in the lowest abnormal accrual quintile earn,on average, abnormal annual return of 3.8 percent while those in the highest abnormal accrual
quintile earns of −1.6 percent. In other words, over the following year, the abnormal return tothis hedge portfolio, i.e., Q1−Q5, is 5.4 percent (p < 0.01). These ﬁndings suggest that as market
participants misunderstand current abnormal accruals implications for future earnings, stock
prices diverge from the intrinsic value of reﬂected abnormal accruals information. Therefore,
the current abnormal accruals can predict the future return in the process of converging to theintrinsic value of the stocks. This is contrary to Pincus et al. (2007). We demonstrate that asin the U.S. as demonstrated by Xie (2001), the abnormal accruals anomaly is also observed in
the Japanese stock market, at least for our sample period.

17In addition, we also ﬁnd that stocks with extreme abnormal accruals have attributes thatare undesirable to most traders, for example, high beta, small size, high arbitrage risk, or hightransaction cost (i.e., high frequency of zero returns), from Table 4. These ﬁndings are consistent
with the U.S. research (e.g., Mashruwala et al. 2006; Lev and Nissim 2006). Also in Japan, these
attributes may prevent the accrual information from being impounded into stock. Therefore,
we show the results controlling for these attributes later.

4.3

Hedge portfolio abnormal return tests

Our hypothesis predicts that future stock returns for high PIN are less sensitive to the abnormal
accrual information, but that there is a strong relationship between the abnormal accruals andfuture stock returns among low PIN stocks. For high PIN stocks, this is because private information about intrinsic value that correctly reﬂects the persistence of abnormal accruals alreadyhas been revealed to the market at the earnings announcements due to informed trading activity.
In addition, even if prices of high PIN stocks diverge from the intrinsic value at the earnings announcements, they become eﬃcient quickly as a result of trading from informed traders. Thus,
we should observe a negative relationship between the predictive ability of abnormal accruals
for long-term future returns and PIN.
To evaluate the eﬀect of informed trading activity on the predictive ability of abnormal
accruals for future returns, each annual sample is ranked based on informed trading activity
measure, PIN, and abnormal accruals. Low (High) PIN is deﬁned as stocks with PIN below(above) bottom (top) 20 percent of annual raw PIN across stocks. Likewise, Low (High) Abnac
is deﬁned as stocks with abnormal accruals below (above) bottom (top) 20 percent of annuallyabnormal accruals across stocks.
Panel A of Table 5 shows that the abnormal accrual mispricing of low PIN stocks is generally
higher than that of high PIN stocks as expected. The average hedge portfolio abnormal returnfor low PIN stocks is signiﬁcantly positive, 3.04 percent and 6.35 percent over 6- and 12-month
periods, respectively, while that for high PIN stocks is insigniﬁcant, 2.38 percent and 0.59 percent
over 6- and 12-month periods, respectively. This result is also demonstrable from Figure 3. Thisﬁgure illustrates the cumulative abnormal returns from a 12-month buy-and-hold strategy basedon abnormal accruals. To construct this graph, a long-position is taken in the bottom quintile
stocks based on abnormal accruals, and a short-position is taken in the top quintile stocks.
18

This graph reports the diﬀerence in cumulative buy-and-hold abnormal returns between thebottom and top quintiles at monthly intervals over the next one year. This ﬁgure shows that theabnormal accrual-based hedge returns for high PIN stocks are consistently smaller than thosefor low PIN stocks.
PIN is strongly negatively correlated with market capitalization, so the result of Panel A of
Table 5 and Figure 3 has possibilities that examine the relationship between abnormal mispricing and market capitalization. Thus, Panel B of Table 5 reports the relationships among thepredictive ability of abnormal accruals for future returns, PIN, and market capitalization. In
addition to sorting based on abnormal accruals and PIN, we further classiﬁed the stocks into
two groups, Large stocks and Small stocks, within each Abnac-P IN portfolio. Consistent with
Panel A of Table 5 and Figure 3, the 12-month hedge portfolio abnormal return is signiﬁcantly
positive for low PIN stocks but insigniﬁcant for high PIN stocks even after controlling for market
capitalization. The returns are 8.9 percent (p < 0.01) and 4.9 percent (p < 0.01) for Small/Low
PIN stocks and Large/Low PIN stocks, and −2.8 percent and 2.2 percent for Small/High PIN
stocks and Large/High PIN stocks. This suggests that the result of Panel A of Table 5 does not
reﬂect just the fact that market capitalization aﬀects the abnormal accruals mispricing.
These results demonstrate that among low PIN stocks, abnormal accruals information has
not been impounded into stocks prices soon after earnings announcements but is impounded into
stock prices over the long time, while among high PIN stocks, abnormal accruals information
have been impounded into stock prices before or soon after earnings announcements. Thus, weare able to support our hypothesis in univariate setting.
Figure 4 provides evidence on the stability of these results that abnormal accrual-based
hedge abnormal returns for high PIN are smaller than those for low PIN stocks. This ﬁgureplots the annual hedge portfolio abnormal return for each of the six ﬁscal years in our sample.
The result holds in of the ﬁve of six years, with 2001 the exception. Therefore, we interpret that
this phenomenon is not time-speciﬁc, but occur constantly.

4.4

Cross-sectional regression results

In the previous section, we used a univariate analysis and provided evidence supporting ourhypothesis that the abnormal accrual mispricing is cross-sectionally correlated with the informed
traders’ activity measure, PIN. Next, we use multivariate analysis and investigate whether PIN
19

plays an important role in impounding accrual information into stock prices. We start by
estimating the following standard asset pricing regression:

Aret12i,t = α0 +2006∑

ατ −2001 Y Dτ,i,t + β1 Abnaci,tτ =2002+β2 Betai,t + β3 Sizei,t + β4 BE/M Ei,t + εi,t ,(5)where Aret12 is the size-BE/ME adjusted return during the 12-month period beginning thefourth month after the end of the ﬁscal year. Y D is an annual indicator variable. Beta, Size,and BE/M E are included to control for the eﬀects that prior research have documented (e.g.,
Fama and French 1992; Chan et al. 1991). To mitigate the inﬂuence of outliers of abnormal
returns, we eliminate observations with the lowest and highest 1 percent of Aret12 each year.
In this regression, the coeﬃcient β1 measures the predictive ability of abnormal accruals for
future returns. If more active informed trading, indicated by high PIN, mitigates the abnormal
accrual mispricing, then the coeﬃcient β1 is much toward zero for high PIN stocks. Table 6
presents the regression results for the highest PIN quintile and other four quintiles (i.e., Q1
to Q4). The t-statistics in parentheses are based on White (1980) heteroscedasticity-consistent
standard errors. The coeﬃcient on Abnac is close to zero (β1 = −1.58) for the highest-PIN quintile but signiﬁcantly negative (β1 = −13.07) for the other quintiles at the 0.01 level (one-tailedtest), even after controlling for some factors known to be associated with future returns. Consistent with the earlier results, this result shows that the negative association between abnormal
accruals and subsequent annual returns is stronger for low PIN stocks.
Finally, we use Mashruwala et al. (2006)-type regression to control for several factors that are
known to aﬀect the mispricing; we regress size-BE/ME adjusted returns for stocks on abnormal
accruals, abnormal accruals interacted with proxy for informed trading activity, P IN , lack of
substitutes, Arbrisk, and transaction costs proxies, V ol and Zerof req. In addition, we control
for Beta, Size, and BE/M E that are known to aﬀect the stock returns in this equation. We
estimate the following regression:
Aret12i,t = α0 +

2006
∑

dec
ατ −2001 Y Dτ,i,t + β1 Abnacdec
i,t + β2 Abnaci,t ∗ DHP INi,tτ =2002
dec
dec
dec
dec
dec
+β3 Abnacdeci,t ∗ Arbriski,t + β4 Abnaci,t ∗ V oli,t + β5 Abnaci,t ∗ Zerof reqi,tdec
dec+β6 Betadec
i,t + β7 Sizei,t + β8 BE/M Ei,t + εi,t .

20

(6)

Following Mashruwala et al. (2006), we do use not raw data, but decile rank for independent
variables except for PIN. The superscript “dec” indicates the scaled annual decile rank (−0.5
to 0.5) for the respective variables. The ranking process is executed as follows. Every year,
we assign a decile-based rank to each variable from one to ten and transform this rank bysubtracting one and dividing by nine. Finally, we subtract 0.5 from each of these transformed
ranks such that the decile ranks range from −0.5 to 0.5. The coeﬃcient on Abnac using thiscoding means the returns to a hedge portfolio are long in the lowest decile and short in thehighest decile.
We do not transform to decile rank for PIN, and instead we use an indicator variable,
DHP IN , taking the value of one if stocks assign the highest-PIN quintile and zero otherwise.
There are two reasons why we use indicator variable for PIN. First, Easley and O’Hara (1992b)
and Easley and O’Hara (1992a) demonstrate nonlinear association between the speed of priceconvergence to eﬃcient value and informed trading activity. However, as we are not able to apriori specify the appropriate function form in real stock markets, we use an indicator variable
so that we should not lose the statistical power to test the hypothesis that abnormal accrual
mipricing is not pronounced among high PIN stocks. Second, as Figure 2 (c) shows, crosssectional PIN distribution is tight. Because of this tight distribution, cross-sectional diﬀerencesof PIN are less observable. Therefore, in order to isolate the eﬀect of extreme high PIN stocks
on abnormal accrual mispricing, we compare stocks with extremely high PIN with others to test
the hypothesis. This idea strengthens our statistical power.
Table 7 presents the result of estimating Eq. (6). Similar to estimating Eq. (5), we estimate
the Eq. (6) with sample after excluding observations of the lowest and highest 1 percent of
Aret12 each year. The ﬁrst column (1) shows the result when we regress the size-BE/ME
adjusted returns on Abnacdec . The coeﬃcient on the Abnacdec is −5.30 (t = −4.86). This meansthat the abnormal returns from an abnormal accrual-based trading strategy yields an annual
abnormal return of 5.3 percent.
The second column (2) shows that the interaction of Abnacdec and DHP IN is insigniﬁcant(coef. = 4.24, t = 1.22) but β1 + β2 , which measures the total impact of abnormal accruals on
abnormal stock returns for the highest-PIN quintile, is statistically insigniﬁcant (coef. = −1.96,
F = 0.55) at the conventional level. This result implies that the size-BE/ME adjusted returns
are not signiﬁcantly diﬀerent from zero for the highest-PIN quintile stocks and signiﬁcantly

21

negative for the others, and therefore abnormal accrual mispricing is not pronounced amonghigh PIN stocks. Consistent with our hypothesis, abnormal accruals for high PIN stocks havelower explanatory power for future returns. This result is robust to control for arbitrage risk
and transaction costs in column (3) and also other risk factors in column (4).
The column to the extreme right reports the Fama and MacBeth (1973) estimation result of
the regression. The coeﬃcients are averaged over the six years and the t-statistics are computedfrom the average and standard deviation of the estimated coeﬃcients, although the degree of
freedom is problematic. Consistent with the result in the U.S. markets reported by Mashruwala
et al. (2006), the coeﬃcient on the interaction term Abnacdec ×Arbrisk dec is signiﬁcantly negative(coef. = −5.61, t = −2.80). This result suggests that abnormal returns to the abnormal accrual
trading strategy increase in arbitrage risk (lack of close substitutes) also in Japan. On theother hand, contrary to Mashruwala et al. (2006), the interaction of Abnacdec and V oldec or
Zerof req dec , which are proxied for transaction costs, are not statistically insigniﬁcant in using
our sample. The other interaction term Abnacdec × DHP IN is signiﬁcantly positive at the
0.05 level or higher (one-tailed test), as expected. This result means that abnormal returns
from trading strategy based on abnormal accruals for high PIN stocks are lower than those forthe other stocks, that is, stock returns for high PIN are less sensitive to the abnormal accrual
information.
Overall, the results from the cross-sectional regression support our hypothesis that stocks
with more active informed trading exhibit less abnormal accrual mispricing relative to stocks
with less active informed trading. We interpret these results as the evidence that more active
informed trading decreases the probability that stock prices deviate from eﬃcient prices, reﬂecting abnormal accruals, and that the stock prices become eﬃcient in the short-term by active
informed trading utilizing both public and private information, even if the stock prices are temporarily mispriced. In sum, our results show that abnormal accruals mipricing is not pronounced
among high PIN stocks. Then, the association between abnormal accruals and long-term future
returns for high PIN stocks is lower than that for low PIN stocks. Consistent with analyticalresearch and empirical research for PEAD, a high PIN helps the market become more eﬃcienteven for abnormal accruals.

22

5Conclusion

Xie (2001) ﬁnds that market participants overprice the abnormal component of accruals, andthen stocks with high abnormal accruals tend to have low future returns. He interpreted thisresult as evidence that market participants do not see through managers’ attempts to manipulateearnings. Xie (2001) result leads to a question: do all market participants fail to assess thepersistence of abnormal accruals correctly?
We assume that informed traders correctly understand the properties of abnormal accrualsby accessing both public and private information sources and having higher analyzing skills forthe information than uninformed traders. We then investigate whether informed trading activity
aﬀects the abnormal accrual mispricing.
Using PIN measure to capture the informed trading activity, we ﬁnd that the mispricing ismore pronounced for stocks with relatively low PIN. This result is consistent with analyticalresearch (Callen et al. 2000; Easley and O’Hara 1992b) and empirical research for PEAD (Vega2006) that high PIN helps the market become more eﬃcient.
Mashruwala et al. (2006) ﬁnd that arbitrage risk and transaction costs impose barriers to
exploiting accrual mispricing, and conclude that even if smart investors see through the implication of accruals, they would ﬁnd it diﬃcult to eliminate such mispricing. Inconsistent withMashruwala et al. (2006), our result implies that if many smart informed traders trade thestock, its price can be impounded quickly even by recondite information such as abnormal accruals. Thus, this research suggests that informed trades play an important role in the speed
and eﬃciency of the price discovery process.
Our research has the following implications for accrual anomaly research. In the U.S., there
are mixed results for the cause of the anomaly, that is, the mispricing explanation (Sloan 1996;
Xie 2001) or the risk explanation (Khan 2007). We ﬁnd that the mispricing explanation is valid
for the accrual anomaly by examining the association between PIN and abnormal accruals.
Accrual anomaly is observed in many countries (Pincus et al. 2007). Our research design is able
to apply for accrual anomaly research in other countries in order to examine the cause of theanomaly.
In addition, our research has also important implications for regulators and policy planners
concerned with the stock markets. Our ﬁndings suggest that more active trading from traders23

who have much information makes the market more eﬃcient. Therefore, as Callen et al. (2000)
argued, by reducing uninformed noise trading through disclosure regulations, policy makers
could diminish wasteful trading exponentially, and thereby increase economic welfare.

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27

Obs.

Mean

SD

Q1

Median

Q3

α

7,315

0.27

0.12

0.17

0.26

0.36

δ

7,315

0.35

0.20

0.21

0.33

0.50

µ

7,315

70.6

71.3

20.0

47.4

99.1

εb

7,315

54.3

84.1

5.8

20.2

64.7

εs

7,315

60.7

91.3

7.9

25.6

73.8

P IN (%)

7,315

20.7

9.8

14.8

18.9

24.5

Table 1: Summary statistics for P IN
This table reports the summary statistics for estimated parameters of PIN. The parameters are estimated using the EHO
model given by the likelihood function in Eq. (2) and (3), for each year from 2001 to 2006. The parameters α and δ denotethe probability of an information event and that information is low signal, respectively. The arrival rate of informed tradersis µ, the arrival rate of uninformed buyers and sellers are εb and εs , respectively.

28

Obs.

Mean

SD

Q1

Median

Q3

TA

7,315

−0.04

0.06

−0.06

−0.04

−0.01

Abnac

7,315

0.00

0.08

−0.02

0.00

0.02

Aret3 (%)

7,256

0.5

16.0

−8.8

−0.8

7.7

Aret6 (%)

7,211

−0.1

26.8

−14.1

−2.0

10.2

Aret9 (%)

7,193

1.2

30.6

−16.1

−1.9

13.2

Aret12 (%)

7,150

1.3

37.3

−18.3

−3.4

13.6

Beta

7,315

0.98

0.64

0.56

0.91

1.33

Size

7,315

24.47

1.57

23.36

24.24

25.47

BE/M E

7,315

0.98

0.79

0.55

0.85

1.26

Arbrisk (×100)

7,315

1.2

0.3

0.6

1.0

1.7

V ol (million yen)

7,315

563

2,010

17

73

329

Zerof req

7,315

25

21

11

19

32

Table 2: Summary statistics for overall variables except for P IN
This table reports summary statistics for overall variables with the exception of PIN. T A is total accruals deﬁned as CFO
minus net income using a cash ﬂow statement approach, deﬂated by averaged total assets. Abnac is abnormal accruals
determined as the diﬀerence between total accruals and normal accruals, expected total accruals estimated from Jones(1991) model, equation (1). Normal accruals are estimated in cross-section for each two-digit Nikkei Industrial Code andyear combination, where the ﬁrm of interest is excluded in estimating the model (out-of-sample method). AretX is thesize-BE/ME adjusted X-month buy-and-hold return. Beta is CAPM beta measured using 48 monthly return observations
ending June 30 of year t. Size is market value of equity as of June 30 of year t. BE/M E is book-to-market ratio calculated
as the ratio of the year-end of book value of equity to the market value of equity on June-end of year t. Arbrisk is arbitrage
risk estimated as the residual variance from a standard market model regression of its returns on the returns of the weighted
average market index over the 48 months ending June 30 of year t. V ol is trading volume (proxy for transaction costs),
measured as daily average trading volume (closing share price times daily number of shares traded) on June of year t.
Zerof req is also proxy for transaction costs, denoting the frequency of zero daily return calculated as the frequency of zero
daily returns over the period July of year t − 1 to June of year t.

29

AbnacAret12

P IN

Beta

Size

BE/M E

Arbrisk

V ol

Abnac

1.00

Aret12

−0.07

1.00

P IN

0.06

−0.04

1.00

Beta

−0.04

0.03

−0.14

1.00

Size

−0.04

0.02

−0.57

−0.03

1.00

BE/M E

0.09

0.01

0.29

−0.22

−0.46

1.00

Arbrisk

−0.01

−0.05

0.15

0.45

−0.26

−0.15

1.00

V ol

−0.05

0.02

−0.63

0.19

0.85

−0.55

−0.04

1.00

0.03

0.01

0.49

−0.14

−0.65

0.37

0.07

−0.64

Zerof req

Zerof req

1.00

Table 3: Spearman correlation matrix
This table reports the spearman correlation matrix. The parameters are for PIN estimated by using the EHO model givenby the likelihood function in equation (2) and (3), and PIN are is calculated as Eq. (4). Other variables are as deﬁned inTable 2.

30

Low Abnac

High Abnac
Q5

T otalQ1 − Q5

Q1

Q2

Q3

Q4

Abnac

−0.09

−0.02

0.00

0.02

0.08

0.00

Rret3 (%)

−0.2

−0.1

−0.3

−0.9

−1.5

−0.6

1.2*

Rret6 (%)

2.4

3.9

1.8

1.3

−0.7

1.7

3.1**

Rret9 (%)

12.2

14.0

10.7

11.8

9.4

11.6

2.8*

Rret12 (%)

16.7

17.2

13.4

14.5

11.2

14.6

5.5**

Aret3 (%)

1.3

0.7

0.6

−0.2

0.1

0.5

1.2*

Aret6 (%)

1.1

1.6

−0.5

−0.8

−1.7

−0.1

2.8**

Aret9 (%)

2.7

3.3

0.0

0.5

−0.5

1.2

3.2**

Aret12 (%)

3.8

3.7

0.3

0.4

−1.6

1.3

5.4**

P IN (%)

20.3

20.1

19.9

21.1

22.0

20.7

Beta

1.12

0.95

0.87

0.90

1.08

0.98

Size

24.35

24.66

24.70

24.48

24.16

24.47

BE/M E

0.87

0.95

0.99

1.06

1.02

0.98

Arbrisk (×100)

1.46

1.11

0.99

1.04

1.48

1.21

V ol (million yen)

662

621

580

458

494

563

Zerof req

25

24

24

25

26

25

Table 4: Mean values of selected characteristics for each portfolio
This table reports mean values of selected characteristics for each portfolio of stocks to quintiles based on the abnormal
accruals. We use the following procedure for constructing each portfolio: for each year, we rank stocks by abnormal accruals
and assign them to quintiles. The return accumulation period begins on July 1. Q1 − Q5 means the average size-BE/ME
adjusted abnormal return to hedge strategy, i.e., long in the lowest abnormal accrual quintile and short in the highest
quintile. RretX is the raw X-month buy-and-hold return. Other variables are as deﬁned in Table 2. ** (*) indicatessigniﬁcant diﬀerence at the 0.01 (0.05) level using one-tailed t-test.

31

32

0.03
1.70
2.32
4.19

−1.72
−1.34
−1.19
−2.16

High Abnac
High P IN
1.74
3 months
3.04∗ 6 months
3.50∗ 9 months
6.35∗∗ 12 months

Low−
High
1.96
2.34
2.47
0.82

Low Abnac
1.47
−0.04
1.72
0.24

High Abnac

4.28

Large/
Low P IN
12 months
4.93∗

8.89∗

Low−
High

Large/High P IN
12 months

Small/High P IN
12 months

1.01

0.38

Low Abnac

Table 5: Mean hedge portfolio abnormal returns

−0.65

−4.86

High Abnac− 1.14

3.14

High Abnac

2.15

−2.76

Low−
High

0.49
2.38
0.75
0.59

Low−
High

and P IN , we further sorted the stocks into two groups with in each Abnac-P IN portfolio. ** (*) indicates signiﬁcant diﬀerence at the 0.01 (0.05) level using one-tailed t-test.

for each three-dimension portfolio, Abnac, P IN , and market capitalization. We use the following procedure for constructing each portfolio; in addition to sorting based on Abnac

quintile and short in the highest quintile. Panel B of this table presents mean values of the size-BE/ME adjusted abnormal returns from abnormal accruals-based trading strategy

The return accumulation period begins on July 1. Low − High means the average size-BE/ME adjusted abnormal return to hedge strategy, i.e., long in the lowest abnormal accruals(top) 20% of annually raw P IN across stock. Low (High) Abnac is deﬁned as stocks with Abnac below (above) bottom (top) 20% of annually the abnormal accruals across stock.

procedure for constructing each portfolio; each annual sample is ranked based on P IN and abnormal accruals. Low (High) P IN is deﬁned as stocks with P IN below (above) bottom

Panel A of this table shows mean values of the size-BE/ME adjusted abnormal returns from abnormal accruals-based trading strategy for each portfolio. We use the following

4.02

Small/
Low P IN
12 months

Low Abnac

Panel B: Test of return to the abnormal accrual-based trading strategy after controlling for Size and PIN

Low P IN
3 months
6 months
9 months
12 months

Low Abnac

Panel A: Test of return to the abnormal accrual-based trading strategy after controlling for PIN

Q1 − Q4

Pred.
Intercept

Abnac

Beta

Size

BE/M E

adj. R2
obs.

?

−

Q5

−4.85

−9.50

−1.99

−37.59(−7.72)(−1.20)(−1.52)(−1.57)−13.07−13.17

−1.58

−2.11

(−2.45)(−2.47)(−0.17)(−0.22)

+

1.11

1.05

(1.67)(0.60)

0.08

1.34(0.28)(1.38)

1.53

2.62(1.79)(1.64)−+

2.30%
5, 6242.37%
1, 3780.62%
5, 6240.75%
1, 378

Table 6: Annual cross-sectional return regression
This table shows the estimation result of regression model Eq. (5) for the highest-PIN quintile and other four quintiles(i.e., Q1 to Q4). The t-statistics in parentheses are based on White (1980) heteroscedasticity-consistent standard errors.
The dependent variable, Aret12, is deﬁned as subtracting the value-weighted portfolio’s return from the stock’s return overa 12-month period. Abnac is abnormal accruals determined as the diﬀerence between total accruals and normal accruals,
with expected total accruals estimated from the Jones (1991) model. Normal accruals are estimated in cross-section foreach two-digit Nikkei Industrial Code and year combination, where the ﬁrm of interest is excluded in estimating the model(out-of-sample method). Beta is CAPM beta measured using 48 monthly return observations ending June 30 of year t.
Size is market value of equity as of June 30 of year t. BE/M E is book-to-market ratio calculated as the ratio of bookvalue of equity to the market value of equity on the June end of year t. The t-statistics in parentheses are based on White(1980) heteroscedasticity-consistent standard errors.

33

Pred.
Intercept?

Abnacdec−

Abnacdec ∗ DHP IN+

Abnacdec ∗ Arbrisk dec−

Abnacdec ∗ V oldec+

Abnacdec ∗ Zerof req dec−

DHP IN?

Betadec+

Sizedec−

BE/M E dec+

β1 + β2F -Stat.(β1 + β2 = 0)
Prob. Fadj. R2

0

(1)

(2)

(3)

−4.26
(−4.80)−5.30(−4.86)−4.19(−7.01)−6.20(−4.90)4.24(1.44)−4.12(−6.88)−5.95(−4.68)4.04(1.22)−4.52(−1.24)3.28(0.70)
4.32(0.94)−0.48(−0.52)−0.47(−0.51)

2.05%

−1.96
0.55
0.46
2.06%

(4)

−4.13
(−6.75)−5.95(−4.68)4.03(1.22)−4.62(−1.25)3.42(0.73)
4.49(0.97)−0.40(−0.39)2.72(2.34)−0.10(−0.07)1.05(0.75)
−1.91
−1.92
0.43
0.43
0.51
0.51
2.05%
2.10%

FM
0.19
(0.08)−5.83(−5.26)
3.77(2.24)−5.61(−2.80)1.97(0.52)
3.06(0.54)−0.93(−0.31)1.80(0.62)−0.86(−0.13)0.84(0.27)
−2.06

3.14%

Table 7: Cross-sectional regression of abnormal returns on abnormal accruals and P IN
This table shows the estimation result of regression model Eq. (6). The dependent variable, Aret12, is deﬁned as subtracting
the value-weighted portfolio’s return from the stock’s return over 12-month. DHP IN is an indicator variable taking thevalue of one if stocks are assigned to the highest-PIN quintile and zero otherwise. Arbrisk is arbitrage risk estimated as
the residual variance from a standard market model regression of its returns on the returns of the weighted average market
index over the 48 months ending June 30 of year t. V ol is trading volume (proxy for transaction costs), measured as daily
average trading volume (closing share price times daily number of shares traded) on June of year t. Zerof req is also proxy
for transaction costs, denoting the frequency of zero daily return calculated as the frequency of zero daily returns over theperiod July of year t − 1 to June of year t. Beta is CAPM beta measured using 48 monthly return observations ending
June 30 of year t. Size is market value of equity as of June 30 of year t. BE/M E is book-to-market ratio calculated as theratio of book value of equity to the market value of equity on the June end of year t. The t-statistics in parentheses except
for the column to the extreme right are based on White (1980) heteroscedasticity-consistent standard errors. The far rightcolumn reports the Fama and MacBeth (1973) estimation result. The superscript “dec” denotes the scaled annual decile
rank (−0.5 to 0.5) for the respective variable.

34

Figure 1: Timeline of Japanese ﬁnancial reporting and overview of the measurement of main
variables

35

0.4
0.3
0.2
alpha
delta

0.1
0
2001

2002

2003

2004

2005

2006

2002

2003

2004

2005

2006

2002

2003

2004

2005

2006

(a)

80
60

mu
epsilon_b
epsilon_s

40
20
0
2001

(b)

40%
30%
20%
10%
0%
2001

(c)
Figure 2: Time-series properties of estimated parameters and distribution of P IN
Figure (a) shows the median estimated parameters, α and δ, in the structural model given by the likelihood function
in equation (2) and (3) each year. Similarly, Figure (b) shows the median estimated parameters, µ, εb , and εs , eachyear. Figure (c) shows the 5th, 10th, 25th, 50th, 75th, 90th, and 95th percentiles each year in the sample period for the
cross-sectional distribution of P IN given by the function in equation (4).

36

Hedge Portfolio Abnormal Return (%)

7
6
5
4
3

Low PIN
High PIN

2
1
0
-1

+0

+1

+2

+3

+4

+5

+6

+7

+8

+9

+10

+11

+12

Month Following Portfolio Formation

Figure 3: Buy-and-hold returns from abnormal accrual-based trading strategy
This ﬁgure shows average buy-and-hold size-BE/ME adjusted abnormal returns for Low PIN quintile and High PIN quintile
produced by abnormal accrual-based trading strategy. To construct this graph, a long-position is taken in the bottom quintile
stocks based on abnormal accruals and a short-position is taken in the top quintile stocks. This graph reports the diﬀerencein cumulative buy-and-hold abnormal returns between the bottom and top quintiles at monthly intervals over the next one
year. Low (High) PIN quintile consists of stocks with PIN below (above) bottom (top) 20 percent of annual raw PIN acrossstock. The return accumulation period begins on July 1 each year.

37

Hedge Portfolio Return (%)

10
Low PIN
High PIN

5

0

2001

2002

2003

2004

2005

2006

-5

-10

Figure 4: Year-by-year 12-month abnormal returns from abnormal accrual-based trading strategy
This ﬁgure presents average size-BE/ME adjusted abnormal returns for Low PIN quintile and High PIN quintile by eachyear to a hedge portfolio taking a long position in the stock with the lowest quintile of abnormal accruals and short positionin the stock with the highest quintile of abnormal accruals. Low (High) PIN quintile consists stocks with PIN below (above)
bottom (top) 20 percent of annual raw PIN across stock. The return accumulation period begins on July 1 each year.

38