ID:JJF00280

Title:On the relationship between earnings quality and the degree of
information asymmetry:
Evidence from Japan

Author:Ahamed Roshan Ajward
Hitoshi Takehara

Pages:

76-98

Is Part Of:

Japan Journal of FinanceVol. 31 / No. 1
Jun. 2011

Publisher

Japan Finance Association

ISSN:

2186-3792

経営財務研究第 31 巻第 1 号■大会特集論文

On the Relationship between Earnings Quality and the Degree ofInformation Asymmetry: Evidence from Japan ＊Ahamed Roshan AJWARD（Waseda University）

Hitoshi TAKEHARA（Waseda University）

ABSTRACTThis research empirically investigates the relation between earnings quality and the degreeof information asymmetry of listed ﬁrms, as earnings quality being one of the important aspects
of ﬁnancial reporting function of a ﬁrm and information asymmetry a key concern in ﬁnancial
markets. The findings indicate a negative relation between earnings quality and the degree of
information asymmetry when earnings quality is proxied by using CFO Modified Jones Model(Kasznik, 1999), without controlling for alternative portfolio styles (however, not significant).
Contrary to our predictions, however, we observe a positive relation between them when earnings
quality is measured using Dechow and Dichev (2002) Model, which is explained as due to thestock market fluctuations during the sample period. Additionally, we also establish a positiveand signiﬁcant relation between earnings quality and liquidity conﬁrming our prediction in that
respect. Furthermore, the hedge portfolio test reveals that the degree of information asymmetry is
weakly related with stock returns in a systematic way, without statistical signiﬁcance.
Key Words：Earnings quality, information asymmetry, liquidity, market microstructure, efﬁcient market
hypothesis.

１

Introduction

Bhattacharya et al. (2010) indicate that one of the key roles of accounting information is toprovide information for efﬁcient capital allocation in ﬁnancial markets. They state that due to thisfact, the determinants and consequences of quality of accounting information is of major interestfor investors, corporate managers and accounting standard setters. In more speciﬁc terms, as one of＊76

The authors are grateful to an anonymous referee and Linyu Zhai, the managing editor of thejournal. We also thank to Takashi Ebihara, Hiroshi Kojima, Keiichi Kubota, Yoshio Masutani,
Katsuhiko Muramiya, Eiko Tsujiyama for their helpful comments on this paper.

経営財務研究第 31 巻第 1 号

the major constituents of accounting information, they indicate that poor earnings quality should
be given much research attention in determining the costs and consequences entailed. Derivingfrom the conclusions of previous studies such as Glosten and Milgrom (1985), Diamond (1985) andDiamond and Verrecchia (1991), as well as empirical evidence from Sloan (1996), they indicate thatpoor quality of earnings could lead to an increase in asymmetry of information in ﬁnancial markets.
Further, they indicate that even though a limited number of recent research papers investigate thenegative consequences of poor earnings quality, the evidence so far is controversial and incomplete.
A survey of literature on contemporary extant research on this dimension in the Japanese context
is also seen as limited and inconclusive. Hence, this study explicitly takes into consideration theimpact of earnings quality on the degree of information asymmetry for the ﬁrms listed in Tokyo
Stock Exchange.
This study distinguishes itself from extant research in many dimensions. First and mostimportantly, the usage of a more precise proxy to measure the degree of information asymmetry
is seen as a key distinguishing aspect. An investigation of contemporary studies reveals thatdifferent proxies have been used for the measurement of information asymmetry. Most often
used measures are spreads based measures and probability of private informed based trades, socalled PIN variable. We use a precise adjusted version of the PIN variable to measure the degreeof information asymmetry, which makes this study distinct. Further, in order to measure earnings
quality, we use two well-recognized earnings quality proxies that are based on accounting accruals,i.e., CFO Modified Jones Model (Kasznik, 1999) and Dechow and Dichev – hereafter referred as
D&D Model – (2002) based earnings quality proxies1.
In terms of ﬁndings of the study, we observe a negative relation between earnings quality and thedegree of information asymmetry, when earnings quality is proxied by abnormal accruals estimatedusing CFO Modiﬁed Jones Model, without controlling for alternative portfolio styles, i.e., small vs.
large and value vs. growth. The observation, however, is not statistically signiﬁcant. On the other
hand, we ﬁnd an unexpected positive relation between the earnings quality measure based on the
D&D Model and the degree of information asymmetry, which is assumed to be caused by stock
market ﬂuctuations during the sampling period. Ancillary to the above main objective and related
findings, in terms of earnings quality (measured by using both accruals based earnings quality
proxies indicated above) and liquidity, we find a statistically highly significant positive relation
as we had predicted. Finally, we also performed a hedge portfolio test to investigate the relation
between the degree of information asymmetry with stock returns when controlled for earnings
quality. While controlling for earnings quality by using the accruals based earnings quality proxies
alternatively and a trading strategy based on the degree of information asymmetry, we ﬁnd that1

Section 2.2 discusses the appropriateness of the usage of accrual based earnings quality proxies
over other earnings quality proxies in the context of this research.

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経営財務研究第 31 巻第 1 号

information asymmetry of the firms is weakly related with stock returns in a systematic way,although they are not statistically signiﬁcant.
The findings we obtained in this study are expected to be useful in understanding the role ofearnings quality information (i.e., as earnings being a key element in accounting information) on
the efficiency of capital markets. As indicated above, Bhattacharya et al. (2010) elaborates that
quality of ﬁnancial information plays a key role in proper capital allocation via ﬁnancial markets.
Therefore, in terms of policy implications, the findings of this study suggest that the qualityof earnings needs to be improved for a positive effect on capital markets; and we believe that
accounting standard setting could play a key role in achieving this objective.
The remainder of this paper is structured as follows. Section 2 elaborates the research outline
while deﬁning the key concepts and discussing previous research. The hypotheses proposed under
this study are indicated in Section 3. Section 4 indicates the measures utilized under this study
with a discussion on their appropriateness. Section 5 explains the main ﬁndings, while Section 6
explains brieﬂy the ﬁndings on the hedge portfolio testing. Finally, Section 7 concludes the paper.

2

Research Outline

2.1

Earnings quality and information asymmetry

Financial Accounting Standards Board (FASB, 1980) indicates the quality of accounting
information from the viewpoint of decision usefulness to the users. Dechow et al. (2010) view
quality of earnings as a function of a firm s fundamental performance and define earnings
quality as: Higher quality earnings provide more information about the features of a firm s
ﬁnancial performance that are relevant to a speciﬁc decision made by a speciﬁc decision-maker(Dechow et al., 2010, p.344). This definition emphasizes that higher quality of earnings provide
more information on the features of the ﬁnancial performance of a ﬁrm related to a particulardecision context.
While indicating that the above deﬁnition is of a conceptual nature, they elaborate that they are
not in a position to provide a single deﬁnition on what earnings quality is as quality depends onthe context of the decision making. They further explain that the fundamental performanceof a firm is not observable. Based on this discussion, it is apparent that empirical researchers
have used different proxies to deﬁne and measure quality of earnings depending on the decisioncontext the researcher is interested. After reviewing over 300 empirical studies, they summarize
that extant empirical researchers have used different earnings quality proxies such as accounting
accruals, earnings persistence, smoothness, timeliness, loss avoidance, investor responsiveness, aswell as external indicators such as re-statement and SEC enforcement releases, to proxy earnings
quality. They indicate that these represent different dimensions (i.e., features) of the fundamental
performance of the ﬁrm depending on the decision context. In this research, we are interested in
the decision context related to the (information efficiency of) capital markets. Since, conceptualand empirical research indicates that accounting accruals do have value relevance and contributes
78

経営財務研究第 31 巻第 1 号

to information asymmetry in the context of capital markets; we use accounting accruals based
proxies to measure earnings quality in this research (Sloan, 1996; Bhattacharya et al., 2010).
Accordingly, earnings quality is connoted by higher quality of accounting accruals, which provide
more information on the ﬁnancial performance of the ﬁrm to make informed decisions by investors(decision context). Thereby, under this study, we use two well-accepted proxies to measure theaccruals quality to represent quality of earnings, i.e., CFO Modiﬁed Jones Model (Kasznik, 1999)
and D&D Model (2002) based proxies2.
In comparison, the concept of information asymmetry has been defined concretely, denotingthat market participants have unequal sets of information (Lu et al., 2010)3. In other words,
information asymmetry indicates that certain investors are more informed than other investors
are, in making value judgments pertaining to the ﬁrm4. Literature indicates that the separation ofownership and control within publicly listed ﬁrms create such asymmetry (Percy, 2000). The next
sub-section discusses the literature pertaining to the relation between accruals based earnings
quality and information asymmetry, which sets the basis of this research.

2.2

Previous studies on earnings quality and information asymmetry

Kubota et al. (2010) indicate that there are two views on how information asymmetry inﬂuences
the behavior of capital markets; one is the rational view, which indicates that information
asymmetry does exist in capital markets (Easley and O Hara, 2004), and the next is the irrationalview, which propagates investors fixations (Shleifer, 2000). This research is based on the first
postulate that information asymmetry does exist and it inﬂuences the behavior and constituenciesof the capital markets.
Having discussed a deﬁnition for information asymmetry in Section 2.1 and the postulate thataffirms information asymmetry does, in fact, exist in the preceding paragraph; we now moveinto our primary focus of our paper, i.e., the relation between earnings quality and the degree of
information asymmetry. Extant research studies as Glosten and Milgrom (1985), Diamond (1985),
and Diamond and Verrecchia (1991) indicate that higher quality disclosures lead to a reduction in
information asymmetry. Furthermore, Diamond (1985) indicates that the incentives for acquiring
private information by investors are reduced when entities disclose information publicly. In otherwords, we could discern that higher disclosure quality reduces the incentive to search private
information. On the other hand, Diamond and Verrecchia (1991) indicate that revealing public
information to reduce information asymmetry can reduce the cost of capital of entities by attracting

2
3
4

Section 4.3 elaborates these accruals based earnings quality proxies in more detail.
The interested reader is directed towards extant literature: Jensen and Meckling (1976), Ross (1977),
Myers and Majluf (1984) for additional enlightenment.
Section 4.2 indicates an appropriate proxy measure – Adjusted PIN – that complies with thisdeﬁnition to measure information asymmetry.

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経営財務研究第 31 巻第 1 号

higher demand from large investors due to increased liquidity of its securities. This ﬁnding implies
that disclosure of public information can increase the liquidity of respective firm s securities aswell. It should be noted that, these research studies, however, relate to general public disclosureof information (as against particularly on earnings quality), and now we shall focus on literature
dealing directly with earnings quality and information asymmetry.
Bhattacharya et al. (2010) indicate that although earnings remain an important public
disclosure, extant research does not provide a reasonable basis for inferring a reliable relation
between earnings quality and information asymmetry. They note different reasons in arriving at
this conclusion such as usage of inadequate or improper proxies to portray core measures, usage ofsmaller sample sizes and subset of ﬁrms. Consequently, Bhattacharya et al. (2010) infer the relation
between accruals based earnings quality and information asymmetry based on ﬁndings of Sloan(1996). Accordingly, they argue that investors less informed may not be able to process information
contained in earnings compared to their more sophisticated counterparts – thereby creating an
information asymmetry – since findings of Sloan (1996) indicate that marginal investors fail toincorporate fully the mean reverting of the accruals of ﬁrms having high accruals.
Hence, we could infer the relation between (accruals based) earnings quality and information
asymmetry as high accruals leads to a poor quality of earnings and thereby higher degree of
information asymmetry, which establishes a negative relation between (accruals based) earnings
quality and the degree of information asymmetry. On the other hand, in the same token, it could
be inferred from the conclusions of Diamond and Verrecchia (1991), that the increased quality ofearnings is expected to increase the liquidity of the securities as well (recent empirical evidence
can be observed in Bhattacharya et al., 2008), which establishes a positive relationship between
earnings quality and liquidity. These two inferences establish the basis of the main hypothesis: H1and one of the related ancillary hypotheses: H2 elaborated in the Section 3 that are tested under
this study5.
Having discussed above the extant literature relating to the association between earnings quality
and information asymmetry, liquidity, respectively, we shall now focus on the relation between
information asymmetry and stock returns6. Kim and Verrecchia (1991, 1994), indicate that there is

5

6

80

The main objective of this research study is to investigate the association between earnings quality
and the degree of information asymmetry. However, since the research is based on the context
of capital markets, ancillary to this main objective, we also investigate the related association
between liquidity – as one of the key issues of capital markets that goes hand in hand with theconcept of information asymmetry – and earnings quality. Further, we investigate the related
association between the degree of information asymmetry and stock returns.
As noted in footnote 5, we investigate this additional relation at this juncture due to the need tolink this research in the context of capital markets. Naturally, investors may need to explore therelated relation between the degree of information asymmetry and stock returns, in the context ofa capital market.

経営財務研究第 31 巻第 1 号

a possibility that certain investors have a better processing ability of earnings related informationand may have a superior ability to forecast the information in the future earnings announcement
leading to an increase of information asymmetry at the time of such announcement periods than
non-announcement periods. This infers that investors of such private information can obtainabnormal returns and this establishes a positive relation between information asymmetry (inducedby earnings information) and abnormal stock returns. On the other hand, Lambert et al. (2007)
demonstrates that quality of accounting information directly and indirectly affects a ﬁrm s cost ofcapital by utilizing a model that is consistent with Capital Asset Pricing Model (CAPM). Insteadof using expected returns, they recast the CAPM into expected values and covariances of future
expected cash ﬂows. They indicate that this adjustment provides a better framework that does
not depend on historical observations of returns, which they see as inferior. They demonstrate
analytically that higher quality of accounting information influences the assessed covariancewith other ﬁrms, and this effect lowers the ﬁrm s cost of capital closer to the risk-free rate. This
demonstration as well as evidence of Kim and Verrecchia (1991, 1994) discussed earlier assert thata positive relation can be established between information asymmetry and stock returns, and sets
the basis for the ﬁnal ancillary hypothesis: H3, which is indicated in Section 3.
While, Section 3 summarizes all the hypotheses developed through the current discussion, next
sub-section explains the testing methodology adopted by us in this study.

2.3

Testing methodology

In terms of the testing methodology, we identify, number of positive differences of our study
with the study of Bhattacharya et al. (2010) and other local studies. First, we use a more precise
indicator to measure the degree of information asymmetry (Adjusted PIN) instead of using
controversial spread-based measures7. On the other hand, as noted in Section 1, we also use more
appropriate measures to proxy earnings quality by utilizing both CFO Modiﬁed Jones Model based
measure: abnormal accruals (ABNAC) and the D&D Model based measure: variability of short-term
total accruals (D&D) as well8. The ABNAC measures the abnormal component of accruals while the
D&D measure captures the overall variability in short-term accruals. While we use more precise
and appropriate proxies in this study, we also expect to set a precedence of this nature of studies
in the Japanese context. We believe that all these factors distinguish this study from local andinternational extant research.

7

Section 4.2 elaborates on the measures on information asymmetry. We use the proxy: Adjusted
PIN to measure the degree of information asymmetry instead of imprecise PIN or spread-based
measures. Higher Adjusted PIN values (which proxies the degree of information asymmetry)
indicates a higher degree of information asymmetry.
8 Section 4.3 discusses accruals based earnings quality proxies: ABNAC and D&D. Higher ABNAC or
D&D signify lower quality of accruals based earnings quality (footnote 9).

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経営財務研究第 31 巻第 1 号

Based on the preceding discussions, Section 3 develops the related hypotheses tested under thisstudy.

３

Hypotheses Development

In pursuant to the discussion in Section 2.2, under this section, we indicate our main research
hypothesis and ancillary hypotheses that are investigated under this study.

3.1

Main hypothesis

As discussed in Section 2.2, we expect that earnings quality be negatively related with the degreeof information asymmetry. It was noted in Section 2.3 that an increase in either of the alternative
accruals based earnings quality proxies: ABNAC or D&D indicate lower earnings quality9. Further,
it was noted that an increase in the proxy: Adjusted PIN, which denotes the degree of information
asymmetry, depicts increased degree of information asymmetry. Therefore we hypothesize that, an
increase in ABNAC or D&D variable (which proxy accruals based earnings quality) is accompanied
by an increase in the Adjusted PIN variable (which proxy the degree of information asymmetry)and vice versa, which can be denoted as follows:
H1: Higher quality earnings (lower ABNAC/D&D) lead to a lower degree of information
asymmetry (lower degree of Adjusted PIN).

3.2

Ancillary hypotheses

Ancillary to the main objective of this study that is predicted under H1 above, we venture to test
two related ancillary hypotheses that are related to the context of this research10.
We noted in Section 2.2 that we could expect a positive relation between earnings quality andliquidity, and thereby predict an empirically positive relation between these two under this study.
In other words, we expect that a decrease in either D&D or ABNAC variable, which represents
an increment in earnings quality (footnote 9), will be accompanied by an increase in liquidity (lowPSOS), and vice versa11. Thereby, the denotation of this hypothesis can be indicated as follows:
H2: Higher quality earnings (lower ABNAC/D&D) lead to higher liquidity (lower PSOS).

It should be noted that higher ABNAC or D&D represents lower earnings quality as increases inthese variables depict an increase in discretionary accruals (in case of ABNAC) or increase involatility of short-term accruals leading to higher estimations errors (in case of D&D) and hence,lower earnings quality. See Section 4.3 for further details.
10 Footnote 5 and 6.
11 In Section 4.2, the variable used to proxy liquidity, i.e., probability of symmetric order-ﬂow shocks:
PSOS measures illiquidity. Therefore, high PSOS indicates low liquidity.

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経営財務研究第 31 巻第 1 号

Further, the discussion in Section 2.2 supports a positive relation between the degree of
information asymmetry and stock returns. Accordingly, we predict that Adjusted PIN, whichmeasures the degree of information asymmetry, is positively associated with stock returns. In otherwords, more speciﬁcally, in our hedge portfolio test (Section 6), we expect a statistically signiﬁcanthigher return spread for the ﬁrst quintile ranked portfolio (which represents the lowest earnings
quality ranked portfolio, i.e., ABNAC1 and D&D1in Panel A and B of Table 5) compared to thelowest quintile ranked portfolio, which represents the highest earnings quality ranked portfolio (i.e.,
ABNAC5 and D&D5 in Panel A and B of Table 5). The expected empirical relation can be indicatedas follows:
H3: Higher degree of information asymmetry (low ABNAC/D&D) leads to higher stock returns.
The related empirical predictions of above hypotheses can be depicted in Figure 1 below.
Figure 1. Summary of predictions
In the ﬁgure plus (+) signs indicate positive relations while a minus (‒) sign connotes negative relation between theconcepts related to the empirical predictions investigated under this paper.

Information
Asymmetry

(–)

(+)

Controlling for Earnings Quality
Earnings Quality

Stock Return

Liquidity(+)

In the subsequent Section 4, we discuss the measures utilized under this study to investigate theempirical relations hypothesized under this section.

４

Measure of Earnings Quality, Information Asymmetry and Liquidity

4.1

Current and total accruals

In ﬁnancial accounting, the relation among earnings, cash ﬂows, and accruals is depicted using
equation as follows:
Earningst = Cash Flowst ± Accrualst

Further, accruals are decomposed into two main constituencies: current accruals and non-current
accruals as indicated below:

Total Accrualst = Current Accrualst + Noncurrent Accrualst

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経営財務研究第 31 巻第 1 号

This distinction and thereby the deﬁnitions elaborated next are required to estimate the accruals
based earnings quality proxies (Section 4.3) utilized under this research. The deﬁnitions of current
and total accruals are indicated below (Ebihara et al., 2010).
Current accruals (CACC) are deﬁned as follows:
CACCt = ΔCurrent assets – ΔCash & deposits – ΔShort-term investment securities – ΔShortterm loans receivable – (ΔCurrent liabilities – ΔShort-term loans payable – ΔCommercial
papers – ΔCurrent portion of the long-term loans payable – ΔCurrent portion of the bonds andconvertible bonds)
On the other hand, total accruals (ACC) are deﬁned as follows:
ACCt = CACCt – ΔProvision for retirement benefits – ΔProvision for directors’ retirement
beneﬁts – ΔOther long-term provisions – Depreciation for the period[where Δ denotes the change in a given accounting ﬁgures from year t–1 to year t.]
Both the current accruals (CACC) and total accruals (ACC) are deﬂated by the simple average ofthe book values of total assets. The next sections discuss the key measures used to proxy the core
concepts of this research study.

4.2

Degree of information asymmetry and liquidity

An examination of related contemporary studies reveals that different proxies have been used forthe measurement of information asymmetry such as spreads based measures (Welker, 1995; Healyet al., 1999) and probability of private informed based trades, so-called PIN variable (Brown and
Hillegeist, 2007).
Ertimur (2007) indicates that PIN variable originally developed by Easley et al. (2002) is
gradually being used in accounting literature over bid-ask spreads as a measure of information
asymmetry among investors. Further, she indicates that PIN measure provides a distinct advantage
for studies of this nature as it could be used to examine separately the relation between disclosure
quality with both the frequency of information events and relative intensity of informed trading(which comply with the deﬁnition of information asymmetry indicated in Section 2.1). Furthermore,
research has indicated that the alternative spread-based measures lead to interpretative difﬁculties(see Lee et al., 1994; Heﬂin et al., 2005).
Duarte and Young (2009), however, provide evidence that the PIN measure comprises of twoelements, i.e., it proxies not only for the degree of information asymmetry but also illiquidity(PSOS: probability of symmetric order-ﬂow shocks), which is unrelated to the degree of information
asymmetry of a ﬁrm. Hence, extant studies that use PIN as a proxy for information asymmetry is
seen quite challengeable. In this study, we use the adjusted version of PIN – referred as Adjusted
PIN in this paper – that precisely measures only the degree of information asymmetry, whichmakes the study unique both in local as well as international contexts.
Therefore, based on the discussion above, the adjusted version of the probability of private
information based trades, the so-called PIN variable, – Adjusted PIN – introduced by Easley et al.
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経営財務研究第 31 巻第 1 号(2002) and further decomposed by Duarte and Young (2009) has been used to as a measure of theinformation asymmetry.
Intuitively, PIN is the ratio between expected informed order ﬂow and expected total order ﬂow.
In other words, it is the unconditional probability of a randomly selected trade originates from an
informed trader, and is deﬁned as follows:

PIN =

αμ
αμ + ε b + ε s(1)

In equation (1), Į denotes a probability that a private information event will occur in a given day,ȝ denotes the order arrival rate based on private information, İb is the rate of arrival of uninformed
buy orders and İs is the arrival rate of uninformed sell orders.
As mentioned earlier, however, Duarte and Young (2009) extended the sequential trade market
microstructure model by Easley et al. (2002) and decomposes the PIN value into two components
representing information asymmetry (Adjusted PIN) and probability of symmetric order-flow
shocks (PSOS), where PSOS represents illiquidity, which is unrelated to the degree of information
asymmetry. The motivation of such extension was that the original PIN estimation model isgrounded on mainly on abnormal trading based on private information, where Duarte and Young(2009) argue that such trading might be inﬂuenced due to public information based liquidity shocks
as well, and thus, the extended model was developed. Hence, it is noted that Adjusted PIN is a more
purified measure that more precisely measures information asymmetry. Thus, we are motivatedusing this more precise measure in our study. Adjusted PIN and PSOS variables are deﬁned as:

Adjusted PIN =PSOS =α ((1 − δ ) • μ b + δ • μ s )α ((1 − δ ) • μb + δ • μ s ) + (∆ b + ∆ s ) θ + ε b + ε s(∆ b + ∆ s ) θα ((1 − δ ) •μ b + δ • μs ) + (∆ b + ∆ s ) θ + ε b + ε s

(2)

(3)

In equations (2) and (3), ș denotes the probability of symmetric order ﬂow shocks, Δb and ΔS
denote uninformed investors additional buy and sell order arrival rates when symmetric orderﬂow shock event occurs, respectively, and δ denotes the fraction of bad news in private information.
In this study, we estimate both Adjusted PIN and PSOS variables annually by using the one-yeartick-by-tick quote and transactions data starting from July 1st of year t-1 and ends in June 30th ofyear t.
Based on the above discussion, it should be noted that an increase in the Adjusted PIN variableindicates an increase in the degree of information asymmetry. On the other hand, an increase in the
PSOS variable signiﬁes a decrease in liquidity (as PSOS measures illiquidity).

4.3

Earnings quality measures

This study utilizes two well-accepted alternative measures to proxy earnings quality, which are
discussed in this section. First, we use the CFO Modiﬁed Jones Model proposed by Kasznik (1999)85

経営財務研究第 31 巻第 1 号

to decompose total accruals into abnormal accruals (denoted as ABNAC) and normal accruals, anduse abnormal accruals as the ﬁrst proxy to denote measure the quality of earnings. This measure(ABNAC) is noted as an ideal measure to capture earnings management of the managers of theﬁrms within accruals based earnings quality proxies (Dechow et al., 2010; Jones, 1991). It should
be noted that an increase in ABNAC indicates an increase in discretionary accruals, and thereby adecrease in earnings quality.
Furthermore, among accruals based earnings quality measures, it is noted in the literature(Francis et al., 2004; Aboody et al., 2005; Francis et al., 2005; Dhaliwal et al., 2006) that the model
proposed by Dechow and Dichev (2002) – which had been denoted as the D&D Model in Section 1– is also being deployed under this study. Dechow and Dichev (2002) deﬁne (short-term) accruals
as the magnitude of the estimation errors and indicate that higher the (short-term) accruals, thelesser the quality of earnings due to the possibility of higher estimation errors. Accordingly, theaccruals based earnings quality measures used in this study deals with both with the total accruals
as well as abnormal accruals adding to the reliability of the conclusions reached under this study.
Next, we discuss the speciﬁcations of these two models and derivation of the proxy measures.
As discussed above, abnormal accruals are derived using the CFO Modiﬁed Jones Model (Kasznik,
1999), which decomposes total accruals into normal and abnormal components. The estimation isdone employing the following cross-sectional regression model:
ACC j , p = α p + β 1, p ∆ADJREV j , p + β 2, p PPE j , p + β 3, p ∆CFO j , p + ε j , p(4)where ΔADJREV is the difference in changes in sales and accounts receivable, PPE is property,
plant and equipment – measured at net book value – and ΔCFO is the change in cash flow
operations. Subscripts j and p represents the ﬁrm and industry it belongs, respectively.
We used our original 24 industry classiﬁcation, which is based on the 33 industry classiﬁcation
by Tokyo Stock Exchange, which excludes the financial firms. The fitted values from the OLS
regression are the normal accruals (NAC), and the abnormal accruals (ABNAC) are deﬁned as thedifference between total accruals (ACC: Section 4.1) and normal accruals (NAC).
Then, the second measure of earnings quality we use is the one introduced by Dechow andDichev (2002). They deﬁne the earnings quality as volatility of residuals in the following time-series
regression model.
CACC j ,t = α j + β 1, j CFO j ,t −2 + β 2, j CFO j ,t −1 + β 3, j CFO j ,t
+ β 4 j CFO j ,t +1 + β 5, j CFO j ,t +2 + ε j ,t(5)where CACC is the current accruals, and CFO is the cash-flows from operations. Subscript t
denotes the period.
According to Dechow and Dichev (2002), the residual term obtained by regressing equation (5)is, by deﬁnition, the difference between the amount accrued and the amount realized. Hence, theysuggest the standard deviation of the residual term as the measure of accruals quality and theearnings quality. This study uses this D&D Model based measure (referred as D&D in this paper) of
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経営財務研究第 31 巻第 1 号

accrual based earnings quality as a point of enhancing the robustness of our ﬁndings. The standard
deviation of the residual (i.e., D&D) is calculated using a 5-year moving average basis that takes
into consideration ﬁnancial data for 1991 to 2010 and estimated for the period 1997 to 200812.

５

Accounting Accruals and Information Asymmetry, Liquidity

5.1

Data and descriptive statistics

Nikkei NEEDS database published by Nihon KeizaiShinbun Inc. is the primarily data source foraccounting variables. The monthly return data of individual firms was obtained through Nikkei
PortfolioMaster Database provided by Nikkei Media Marketing Co., Ltd. The pooled sample forthe period 1997–2008 consists of 17,429 ﬁrm-year observations in the sample for the analysis of
CFO Modiﬁed Jones Model and 13,534 observations for the sample used for the D&D Model. Table
1 depicts the industry classiﬁcation of the ﬁrms for both groups of samples, which is based on theTokyo Stock Exchange industry classiﬁcation. It should be noted that our sample is restricted to
non-ﬁnancial ﬁrms as ﬁnancial ﬁrms are quite different from both representation and substance.
Table 2 indicates the descriptive statistics pertaining to both sample groups, i.e., samples used for
CFO Modiﬁed Jones Model and the D&D Model earnings quality proxy estimations. The variables
represented are: earnings before extraordinary items (EBEI), cash flows from operations (CFO),
total accruals (ACC), current accruals (CACC), adjusted PIN (Adjusted PIN) that represents thedegree of information asymmetry, probability of symmetric order-ﬂow shocks (PSOS) that measures
illiquidity, abnormal accruals (ABNAC) estimated using CFO Modified Jones Model, earnings
quality measure based on D&D Model (D&D), ﬁrm size measured as natural logarithm of market
value of equity in million Japanese Yen (lnMV), book-to-price ratio in percent (BPR), respectively.
In this study, we use the market value of equity at the end of June.
In the descriptive statistics (Table 2), it is observed that, for both sample groups (i.e., sample for
CFO Modiﬁed Jones Model sample and the sample for D&D Model), the standard deviation (SD)of earnings (EBEI) is lower than that of cash-flows from operations (CFO), while the standard
deviation (SD) of total accruals (ACC) and current accruals (CACC) are also observed to be higher.
While observing a similar pattern in these variables, Kubota et al. (2010) indicate that managersof the sample ﬁrms may have engaged in earnings management to smooth earnings through somekind of income smoothening scheme. This characteristic is noted as a justification of usage ofaccruals based proxies (i.e., ABNAC and D&D) to measure earnings quality in this study.

12

The sample period 2000 to 2009 allows actual operating cash ﬂows to be used, while we estimatedcash ﬂows using unconsolidated individual ﬁnancial statements for the period 1991 to 1999 as
Japanese ﬁrms began to disclose consolidated cash-ﬂow statements only after 1999.

87

経営財務研究第 31 巻第 1 号

Table 1. Industry Classiﬁcation of the Firm-Years of the Sample#Firms and %Firms are the number and the percentages of ﬁrm-years, respectively of the sample. The ﬁrst two columns
in both the right-hand and left-hand sides of the table represent the sample ﬁrm-years utilized in the analysis related to
the CFO Modiﬁed Jones Model and the adjacent two columns to them relate to the sample ﬁrm-years used for the D&D
Model estimations, respectively. The 33 industry categorization of Tokyo Stock Exchange classiﬁcation has been used as
the industry classiﬁcation. The total number of ﬁrm-years of the ﬁrst sample is 17,429 and 13,534 for the next sample
respectively for the period 1997 ‒ 2008. All ﬁrms selected under both samples are having March 31 as its ﬁscal year end.

# Firms % Firms # Firms % Firms

Industry# Firms % Firms # Firms % Firms

Industry

Fishery & Agri.

63

0.361

57

0.421

Electric Appliances

1,762

10.110

1,347

9.953

Mining

54

0.310

48

0.355

Transportation Equip.

792

4.544

715

5.283

1,102

6.323

924

6.827

Precision Instruments

331

1.899

278

2.054

Foods

899

5.158

793

5.859

Other Products

725

4.160

523

3.864

Textiles & Apparels

582

3.339

506

3.739

Electric Power & Gas

261

1.498

241

1.781

Pulp & Paper

145

0.832

121

0.894

Land Transportation

471

2.702

427

3.155

Chemicals

1,359

7.797

1,131

8.357

Marine Transportation

156

0.895

156

1.153

Pharmaceutical

362

2.077

291

2.150

Air Transportation

36

0.207

36

0.266

Oil & Coal Products

89

0.511

60

0.443

Warehousing

283

1.624

236

1.744

Rubber Products

204

1.170

182

1.345

Communication

172

0.987

48

0.355

Glass & Ceramics

380

2.180

325

2.401

Wholesale Trade

1,564

8.974

1,161

8.578

Iron & Steel

472

2.708

417

3.081

Retail Trade

1,067

6.122

586

4.330

Nonferrous Metals

274

1.572

247

1.825

Other Financing Business

201

1.153

126

0.931

Metal Products

471

2.702

379

2.800

Real Estate

331

1.899

205

1.515

1,478

8.480

1,261

9.317

Services

1,343

7.706

707

5.224

Construction

Machinery

Table 2. Descriptive Statistics
In the table, statistics have been indicated for both CFO Modiﬁed Jones Model based sample (see column denoted as CFO
Jones) and D&D Model based sample (in column denoted as D&D).
EBEI: earnings before extraordinary items; CFO: cash ﬂow from operations; ACC: total accruals; CACC: current accruals;
Adjusted PIN: adjusted PIN that indicates the degree of information asymmetry; PSOS: probability of symmetric order-ﬂow
shock which measures illiquidity; ABNAC: abnormal accruals based on CFO Modiﬁed Jones Model; D&D: earnings quality
measure based on the D&D Model; lnMV: natural logarithm of the market value of equity at the end of June measured inmillion yen; BPR: book-to-price ratios as of the end of June; The variables EBEI, CFO, ACC and CACC are all divided by thesimple average of book values of total assets calculated based on book value at the beginning and ending of the ﬁscal year.
The number of observations under each sample is the same as in Table 1.

EBEI

Mean
CFO
Jones
0.027

0.026

SD
CFO
D&D
Jones
0.034
0.034

CFO

0.053

0.051

0.054

0.058

0.025

0.023

0.052

0.050

0.082

ACC

-0.029-0.028

0.057

0.068

-0.055-0.055-0.030-0.029-0.004

0.023

CACC

0.005

0.005

0.051

0.064

-0.015-0.016

0.003

0.003

0.023

18.801

Adjusted PIN

15.04415.116

5.851

5.966

11.237

11.19614.83514.90418.64830.099PSOS

25.71325.320

9.767

9.645

19.481

19.20224.57124.22030.567

0.024

ABNAC

0.000018

D&D

D&D

0.063
0.019

1st Quartile
CFO
D&D
Jones
0.011
0.010

-0.021

Median
CFO
D&D
Jones
0.023
0.023

0.001

-0.003

0.020
0.011

0.005

0.030

3rd Quartile
CFO
D&D
Jones
0.041
0.080

0.024

lnMV

10.54410.621

1.580

1.535

9.419

9.520

10.33910.423

11.547

11.604

BPR

97.118100.92069.90470.83952.75157.212

83.69987.463125.771128.88588

経営財務研究第 31 巻第 1 号

Table 3. Correlation Matrix
The table indicates the Pearson and Spearmanʼs rank correlations, which are based on the sample for CFO Modiﬁed
Jones Model. The lower left-hand diagonal section depicts the Pearsonʼs correlation matrix, while the upper right-handsection shows the Spearmanʼs rank correlations. The number of observations of the CFO Modiﬁed Jones sample
is the same as in Table 1. EBEI: earnings before extraordinary items; CFO: cash ﬂow from operations; ACC: total accruals; CACC: current accruals; Adjusted PIN: adjusted PIN that measures the degree of information asymmetry; PSOS:
probability of symmetric order-ﬂow shock that represents illiquidity; ABNAC: abnormal accruals based on CFO
Modiﬁed Jones Model; D&D: earnings quality measure based on the D&D model.
EBEI
EBEI
p-value

CFO
0.506
0.000

ACC
0.029

CACC
0.142

Adjusted PIN-0.178

PSOS
0.059

ABNAC
0.031

0.002

0.000

0.000

0.000

0.001

CFO

0.511

-0.5938-0.380-0.119-0.014-0.289p-value

0.000

0.0000

0.000

0.000

0.128

0.000

ACC

0.055

-0.539

0.772

0.007

0.045

0.663

p-value

0.000

0.000

0.000

0.441

0.000

0.000

CACC

0.137

-0.3670.843-0.036

0.000

0.540

p-value

0.000

0.000

0.000

0.000

0.980

0.000

Adjusted PIN-0.131-0.0950.007-0.019

0.019

0.003

p-value

0.000

0.000

0.477

0.044

0.048

0.738

PSOS

0.026

-0.0200.024-0.0090.000

p-value

0.006

0.030

0.012

0.321

0.983

ABNAC

0.068

-0.224

0.753

0.672

0.003

0.013

p-value

0.000

0.000

0.000

0.000

0.766

0.112

0.038
0.000

Table 3 indicates the Pearson correlation (see lower-left triangular part) and Spearman s
rank correlation (see upper-right triangular part) among the selected key variables on the sample
related to the CFO Modiﬁed Jones Model. It is noted under both types of correlations, the variable
that measures the degree of information asymmetry: Adjusted PIN (Adjusted PIN) has a weak andinsigniﬁcant positive relation with the CFO Modiﬁed Jones Model based earnings quality variable:
abnormal accruals (ABNAC). On the other hand, Probability of Symmetric Order-flow Shocks(PSOS) which proxy illiquidity has a weakly positive, but signiﬁcant (Spearman s rank correlation
is signiﬁcant at 1 percent) relationship with the abnormal accruals (ABNAC). Under the Pearson
correlation, however, the significance level is above 5 percent between the PSOS variable and
ABNAC.

5.2

Results of Regression Analysis

This section explains the ﬁndings we arrived through the regression analysis. In order to test themain hypothesis (H1) and the ancillary hypothesis (H2), which were indicated in Section 3 of thisstudy, we conduct a pooling regression analysis. Table 4 indicates the univariate and multivariate
estimates for regression analysis performed on CFO Modiﬁed Jones Model based earnings quality
measure (ABNAC) and the D&D based earnings quality measure (D&D) with measures forboth the degree of information asymmetry (Adjusted PIN) and illiquidity (PSOS) measures on a
separate basis. The natural logarithm of market value of equity at the end of June (lnMV) and the89

経営財務研究第 31 巻第 1 号

Table 4. Regressing Earnings Quality measures (and) with measures for
Information Asymmetry () and Illiquidity ()
Deﬁnitions of the variables are same as in table-note of Table 2. In both sub-sections of the Panel A and Panel B of thetable, the ﬁrst two lines in each indicate the univariate regression estimates, and the subsequent two lines indicate themultivariate regression estimates for each of the earnings quality measures.
Panel A: Dependent Variable: Adjusted PIN
Intercept ABNAC
D&D
lnMV
Coef.
15.0440.223

BPR

R-squared
0.000
0.049

p-val

0.000

0.765

Coef.

24.361-0.025-0.858

0.000

p-val

0.000

0.973

0.000

0.700

Coef.
p-val
Coef.
p-val

15.3060.000
25.3540.000-9.3370.000-11.4140.000

Panel B: Dependent Variable: PSOS
Intercept ABNAC
D&D
Coef.
25.713
1.978
p-val

0.000

0.112

Coef.
p-val

47.662
0.000

1.831
0.127

Coef.

25.05712.956

p-val

0.000

0.000

Coef.
p-val

46.950
0.000

7.643
0.006

0.002
-0.9030.000-0.002
0.048

0.048

lnMV

BPR

R-squared
0.000-1.8210.000-0.025
0.000

0.073
0.002

-1.8070.000-0.022
0.000

0.067

book-to-price ratio (BPR) have been used as control variables. The multivariate regression modeland thereby the control variables are chosen appropriately after careful examination of related
literature that relates to the context of this research (Fama and French, 1992; Easley et al., 2002;
Kubota and Takehara, 2009; Bhattacharya et al., 2009; Akins et al., 2011)13.
According to the results of univariate and multivariate regression analyses shown in Table 4, it
is noted that (see panel A) there is a positive relation between abnormal accruals (ABNAC) anddegree of information asymmetry (Adjusted PIN) on a univariate basis. Therefore, the predicted
negative relation between the earnings quality (proxy: ABNAC) and the degree of information
asymmetry expected under the hypothesis H1 is observed, which indicates that the higher thequality of earnings, the lower the degree of information asymmetry. The relation, however, is notstatistically signiﬁcant. Further, we observe a negative relation between ABNAC and Adjusted PIN
variables after controlling for the portfolio style using the ﬁrm size (lnMV) and book-to-price (BPR)

1390

The conclusions derived from the multivariate regression analyses under this section did notchange even after exploring the addition of additional control variables (results not tabulated).

経営財務研究第 31 巻第 1 号

ratio. This ﬁnding is contrary to our prediction. The relation is not signiﬁcant, however.
On the other hand, the D&D Model based earnings quality measure (D&D) and the degree of
information asymmetry (Adjusted PIN) has a negative relation against our prediction, both under
univariate and multivariate analyses. The negative relation may be explained as follows. As
explained in Section 4.2, the variable representing information asymmetry in this study is Adjusted
PIN, and it is deﬁned as the ratio of informed traders’ order-ﬂow based on their private information
to the total order flow. After observing the stock market behavior during the sample period, we
assume that the total order ﬂows increased at a higher proportion than the private information
based order ﬂows, which reduces the Adjusted PIN value (as the nominator – order ﬂow based on
private information – is quite stable and the denominator – total order ﬂow– increases). On theother hand, increasing stock market activity may be influenced by (among other factors) higher
earnings levels of ﬁrms. Such escalation in earnings may signify increase in the related volatility ofaccruals that leads to the increase in the accruals based D&D measure. Hence, by taking into these
two scenarios into consideration, we could observe the negative relation between Adjusted PIN and
D&D measures as indicated in Table 4 – Panel A.
In Table 4 (Panel B), it is also noted that there is a signiﬁcantly positive relation between themeasures for D&D based earnings quality measure (D&D) and illiquidity measure (PSOS) bothunder univariate and multivariate bases. Abnormal accruals (ABNAC) also have a positive (butstatistically insigniﬁcant) relation with PSOS. In other words, this relation indicates that higher
level of earnings quality leads to a higher degree of liquidity. This conﬁrms our hypothesis: H214.

６

Information Asymmetry and Stock Returns

The hedge portfolio test examines the extent of economic signiﬁcance of accrual mispricing by
estimating stock returns for one year forward to portfolios ranked by accruals and the degree of
information asymmetry (hypothesis: H3), as well as to portfolios ranked by accruals and liquidity

14

We observe low R-squared values in Table 4 under the multivariate regression analyses. As
observed in the introduction of this study, we are unable to observe studies that had used theproxy measure: Adjusted PIN to measure precisely the degree of information asymmetry andinvestigate its relation with quality of earnings. This makes us unable to compare directly our
results with local or international extant research. However, Muramiya et al. (2008), who use the
PIN to measure the degree of information asymmetry, obtain similar low R-squared values intheir regression analysis performed between PIN and abnormal accruals. We ﬁnd that the reason
for this being the distinct nature of Japanese data. In the study of Jayaraman (2008), whichuses U.S. data, a rather stronger U-shaped curve-linear relation between the PIN measure andtheir volatility-based earnings quality measure is depicted. By performing the same analyses of
Jayaraman (2008) on Japanese data, we observe the same conclusions that we had obtained under
Section 5.2 of this research, which is attributed to the absence of such a curve-linearity.

91

経営財務研究第 31 巻第 1 号

as an additional analysis15. Therefore, to examine these, we conduct hedge portfolio tests, andthe related ﬁndings are reported in Table 5 and Table 6. In both tables, we compute the average
spread (see Ave. Spr. columns) between the highest and lowest quintile portfolios to establish theempirical relations, while controlling for the level of earnings quality.
We constructed two-way ranked portfolios based on the earnings quality measures: ABNAC(abnormal accruals based on CFO Modified Jones Model) and D&D (earnings quality measure
based on D&D Model) alternatively, with the degree of information asymmetry (Adjusted PIN)
and illiquidity (proxied by PSOS), respectively. Further, in the construction two-way ranked
portfolios, we ﬁrst grouped the ﬁrms into quintiles depending on the magnitude of the earnings
quality measure (i.e., for ABNAC and D&D, respectively) for each year. In the second stage of theportfolio construction, under each of the above earnings quality measures based quintile portfolios,
we formed again ﬁve groups of portfolios based on the degree of information asymmetry (Adjusted
PIN) and liquidity measure (proxied by the illiquidity variable: PSOS), respectively16.
Since, generally firms that have a fiscal year ending in March announce, their earnings
information by mid-May (which is a requirement of the Tokyo Stock Exchange) and discloses theirﬁnancial statements at early June (at the latest) to facilitate holding annual general meetings at
the end of June, we formed the ranked portfolios at the end of June. The portfolio returns werecomputed for a 12-months period by equally weighting return series for each stock assigned to aparticular portfolio group. The spreads related to portfolio returns were computed based on highest
and lowest portfolios17.
According to the hypothesis H3, we predict that higher degree of information asymmetry willlead to higher stock returns. To investigate this hypothesis, in Table 5, we investigate the relation
between Adjusted PIN (proxy for the degree of information asymmetry) and stock returns while
controlling for the level of two earnings quality by using two earnings quality proxies: ABNAC (PanelA) and D&D (Panel B).
The monthly average return spreads (see Ave. Spr. column) in Table 5 indicate that we areunable to observe a statistically significant relationship between high Adjusted PIN and low
Adjusted PIN portfolios with stock returns.
As an additional analysis, on the other hand, we also could reasonably expect a higher stock
return when liquidity increases, while controlling for the level of earnings quality (Bhattacharya

15

We extend our hedge portfolio test to investigate the relation between liquidity and stock returns(while controlling for earnings quality) as an additional analysis (for the same reason cited infootnote 5 and 6). The results of this additional analysis are reported in Table 6.
16 Our sample consists of ﬁrms having March 31, as the ﬁscal year end.
17 That has been decided based on their respective earnings quality measure and the degree of
information asymmetry/ liquidity of the two-way portfolio.

92

経営財務研究第 31 巻第 1 号

Table 5. Average Monthly Return for portfolios formed on Abnormal Accruals and then Adjusted PIN
Deﬁnitions of the variables are same as in table-note of Table 2. Panel A depicts the average monthly returns for thetwo-way ranked portfolios constructed based on earnings quality proxy: ABNAC (abnormal accruals based on CFO
Modiﬁed Jones Model) and Adjusted PIN (degree of information asymmetry), while Panel B indicates average monthly
returns for the two-way ranked portfolio constructed based on earnings quality proxy: D&D (earnings quality measure
based on D&D Model) and Adjusted PIN (degree of information asymmetry). The number “1” at the end of each denotationof two-way portfolio heading indicates the highest ranking portfolio while number “5” indicates the lowest ranking of therespective portfolios (e.g. ABNAC1 represents the highest quintile of ABNAC based ranking, while AdjPIN1 indicates thehighest quintile of Adjusted PIN based ranking, in the respective portfolios).
Panel A: Abnormal accruals and Adjusted PIN ranked 25 portfolios
AdjPIN1

AdjPIN2

AdjPIN3

AdjPIN4

AdjPIN5

Ave. Spr.

p-value

ABNAC1

0.080

0.193

-0.088

0.142

0.013

0.067

0.933

ABNAC2

0.119

0.226

0.327

-0.0770.295-0.1750.815

ABNAC3

0.140

0.393

0.327

0.077

0.389

-0.2480.731

ABNAC4

0.302

0.311

0.418

0.148

0.429

-0.1270.864

ABNAC5

0.286

0.326

0.038

0.290

0.380

-0.094

0.908

Ave. Spr.

-0.206-0.133-0.126-0.148-0.367p-value

0.796

0.872

0.876

0.858

0.654

Panel B: D&D and Adjusted PIN ranked 25 portfolios
AdjPIN1

AdjPIN2

AdjPIN3

AdjPIN4

AdjPIN5

Ave. Spr.

p-value

D&D1

0.464

0.590

0.235

0.142

0.380

0.084

0.920

D&D2

0.364

0.653

0.326

0.037

0.376

-0.012

0.988

D&D3

0.296

0.504

0.409

0.047

0.172

0.124

0.868

D&D4

0.067

0.089

0.331

0.083

0.270

-0.203

0.782

D&D5

0.092

0.301

0.086

0.097

0.420

-0.3280.645

Ave. Spr.

0.372

0.289

0.150

0.045

-0.041p-value

0.633

0.725

0.842

0.955

0.958

et al., 2008). In other words, we could expect a negative relation between PSOS (which proxyilliquidity) and stock returns. More speciﬁcally stated, a higher return spread that is statisticallysigniﬁcant is expected for the ﬁrst quintile of ranked portfolio (which represents the lowest ranked
portfolio of earnings quality, i.e., ABNAC1 and D&D1 in Panel A and B of Table 6) compared to thelowest quintile of ranked portfolio that represents the highest quality of earnings ranked portfolio(i.e., ABNAC5 and D&D5 in Panel A and B of Table 6). Accordingly, in Table 6, we investigate
whether higher liquidity of respective firm’s stocks lead to higher stock returns. We use PSOS
as the measure of illiquidity and both ABNAC (Panel A) and D&D (Panel B) earnings quality
measures to construct the two-way hedge portfolios18.

18

It should be noted that since PSOS variable assesses illiquidity, in Table 6, the highest quintileof the PSOS based portfolio (i.e., PSOS1) that consists of the highest PSOS values represents thelowest liquidity (i.e., the highest illiquidity).

93

経営財務研究第 31 巻第 1 号

Table 6. Average Monthly Return for portfolios formed on Abnormal Accruals and then PSOS
Variables carry the same deﬁnitions as indicated in the table-note of Table 2. The average monthly returns for thetwo-way ranked portfolios based on earnings quality proxy: ABNAC (abnormal accruals based on CFO Modiﬁed Jones
Model) and PSOS (illiquidity) variables are depicted in Panel A, while in Panel B the average monthly returns for thetwo-way ranked portfolio constructed using earnings quality proxy: D&D (earnings quality measure based on D&D
Model) and PSOS (illiquidity). At the end of each denotation of two-way portfolio heading, number “1” indicates thehighest ranking portfolio while number “5” indicates the lowest ranking of the respective portfolios (e.g. ABNAC1 represents the highest quintile of ABNAC based ranking, while PSOS1 indicates the highest [i.e. most illiquid] quintile of
PSOS based ranking, in the respective portfolios).
Panel A: Abnormal accruals and PSOS ranked 25 portfolios
PSOS1
PSOS2
PSOS3
PSOS4

PSOS5

Ave. Spr.

ABNAC1-0.105

0.344

0.519

0.437

0.147

-0.251p-value
0.776

ABNAC2

0.170

0.154

0.344

0.346

0.625

-0.4550.559

ABNAC3

0.306

0.043

0.509

0.094

0.361

-0.0550.941

ABNAC4

0.179

0.388

0.116

0.027

0.186

-0.007

0.993

ABNAC50.044-0.151

0.027

0.257

0.188

-0.144

0.865

Ave. Spr.

-0.148

0.495

0.492

0.180

-0.041p-value

0.874

0.542

0.521

0.816

0.958

Panel B: D&D and PSOS ranked 25 portfolios
p-value

PSOS1

PSOS2

PSOS3

PSOS4

PSOS5

Ave. Spr.

D&D1

0.046

0.119

0.292

0.218

0.305

-0.259

0.720

D&D2

-0.017

0.137

0.242

0.136

0.351

-0.368

0.624

D&D3

0.252

0.402

0.385

0.202

0.180

0.072

0.925

D&D4

0.229

0.245

0.517

0.282

0.551

-0.322

0.696

D&D5

0.500

0.260

0.277

0.354

0.408

0.092

0.922

Ave. Spr.

-0.454-0.1420.016-0.136-0.103p-value

0.613

0.855

0.983

0.860

0.892

In Table 6, the average spreads (see Ave. Spr. column) indicate that we are not able to observe
the expected positive relation between liquidity and stock returns on a statistically significant
basis. We observe, for most of the cases there exist a weak but the expected positive relation
between liquidity and stock return, however.
Although we are not able to establish the hypothesis on a statistically signiﬁcant basis, it should
be noted that for some cases in Table 5, there exist a weak positive relation between Adjusted PIN(degree of information asymmetry) and stock returns, however. On the other hand, for most of thecases in Table 6, we observe a weak negative relation between PSOS (illiquidity) and stock returns,
which may not be attributed to mere chance.
There is, however, prior extant literature, which has established accrual anomaly in U.S. as wellas in Japan. In the U.S. context, Liu and Qi (2006) ﬁnd that by taking a long position in the lowest
accrual decile and a short position in the highest accrual decile earn a return of 13.3 percent which
they claim even higher than Sloan s (1996) 10.4 percent for a similar trading strategy. During oursampling period (1997 to 2008), however, return spreads from abnormal accruals trading strategy
are not statistically signiﬁcant in most cases. In addition, return spreads from D&D measure based
trading strategy are not significant. Hence, there is a possibility that we may not examine the94

経営財務研究第 31 巻第 1 号

hypothesis: H3 (as well as the additional analysis reported above) by applying the hedge portfolio
test, which had been commonly used in extant empirical accounting studies.
Accordingly, as a future research direction we expect to investigate these two hypotheses using
some other alternative methods (for an instance, using as one of the methods proposed in Brennan
et al. [1998]). Furthermore, we expect to estimate Adjusted PIN and PSOS variables on a quarterly
basis and examine the relation between the degree of information asymmetry with stock returns in
our future research.
Sections 5 and 6 above elaborated the ﬁndings we had obtained under this research study, andthe next section concludes the study while indicating future research directions.

７

Conclusion and Future Research

The main objective of this study was to investigate the empirical relation between earnings
quality and the degree of information asymmetry pertaining to listed ﬁrms in the First and Second
Sections of Tokyo Stock Exchange. Furthermore, due to this research positioned in a capital market
context, ancillary and related to the above main objective, we also investigated the relation between
earnings quality and liquidity, as well as the relation between earnings quality and stock returns ofthese listed ﬁrms.
We used the Adjusted PIN value proposed by Duarte and Young (2009), which is a well-reﬁned
measure to estimate the degree of information asymmetry. Further, the probability of symmetricorder-ﬂow shocks (PSOS), the counterpart measure for illiquidity was used to measure liquidity. We
used both CFO Modiﬁed Jones Model (Kasznik, 1999) and measure derived from the D&D Model(Dechow and Dichev, 2002) to estimate accruals based earnings quality proxies. We believe thatthese models capture the related concepts far better than the vaguer proxies used in contemporary
studies, which distinguishes this study from extant studies.
In terms of the relation between earnings quality and the degree of information asymmetry,
we observed a negative relation between them, when earnings quality was proxied by abnormal
accruals (ABNAC) estimated using CFO Modified Jones Model, on a univariate basis. The
observation, however, is not statistically significant. On the other hand, we find an unexpected
positive relation between the D&D Model based earnings quality measure (D&D) and the degreeof information asymmetry (Adjusted PIN), which is assumed to be caused by stock market
ﬂuctuations. In our ancillary analysis, in terms of earnings quality measured by using both ABNAC
and D&D, and liquidity proxied by PSOS, we ﬁnd a positive (for both ABNAC and D&D proxies,
respectively) and statistically signiﬁcant (for D&D) relation as we had predicted.
In addition to establishing above empirical relations, we also performed a hedge portfolio test toinvestigate the relation between information asymmetry with stock returns when controlled forearnings quality. While controlling for earnings quality (by using ABNAC and D&D alternatively)
and a trading strategy based on the degree of information asymmetry, we find that information
asymmetry of the ﬁrms is weakly related with stock returns in a systematic way, although they are95

経営財務研究第 31 巻第 1 号

not statistically signiﬁcant.
The findings of this research are expected to be useful in understanding the role of earnings
quality information (i.e., as earnings being a key element in accounting information) on the
efﬁciency and effectiveness of capital markets. Further, in terms of policy implications, the ﬁndingsof this study suggest that the quality of earnings needs to be improved for a positive effect on
capital markets (i.e., as far as reducing information asymmetry); and we believe that accounting
standard setting could play a key role in achieving this objective by enhancing the financial
reporting quality. In terms of future research directions, we suggest the usage of alternative proxies
for earnings quality (we had used primarily accruals based proxies in this study) as well as toinvestigate the impact of additional and alternative portfolio styles in investigating the predictedempirical relations19. Furthermore, we also suggest expanding the sample scope beyond the Firstand Second sections of Tokyo Stock Exchange by including other categories of listed ﬁrms as well asother Japanese stock exchanges.
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