Product Market Competition and Overreaction to Intra-Industry
Information Transfers*
NORIO KITAGAWA†
Graduate School of Business Administration,
KOBE UNIVERSITY

ABSTRACTThis study investigates the effect of product market competition on overreaction
to intra-industry information transfers. Prior studies show that the stock market
overreacts to information transfers resulting from the earnings announcements ofearly peer firms; however, few studies show how overreaction to informationtransfers occurs. This study reveals that investors overreact more to informationtransfers in higher product market competition. It also finds that in competitiveenvironments, investors overreact to negative transfers more than they do topositive transfers. The results imply that investors do not fully understand thedifferential persistence between industry-wide and firm-specific news released by
the industry, which is consistent with the functional fixation hypothesis.
Additional analyses also reveal that overreaction to information transfers is lessobserved when firms are less competitive: the announcement firms are bellwether
firms or firms in industries with high sales growth. The results are robust whenan alternative measure of product market competition is used. This study has
academic and practical implications, showing that product market competition
can lead to stock market mispricing in the context of information transfers.

* The author acknowledges the insightful comments received from Nobuhiro Asano, Masahiro Enomoto, Yumi Inamura,
Takuya Iwasaki, Hironori Kawase, Fumihiko Kimura, Toshio Moriwaki, Katsuhiko Muramiya, Koji Ota, Shota
Otomasa, Akinobu Shuto, Tatsushi Yamamoto, and participants at positive accounting theory workshop (PATW). I
also gratefully acknowledge insightful comments from Keishi Fujiyama (co-editor) and two anonymous reviewers. Allerrors are the responsibility of the author. The author also acknowledges the financial support received for this study
in the form of the Grant-in-Aid for Scientific Research (15K17161 and 18K01910) provided by Japan’s Ministry of
Education, Culture, Sports, Science and Technology.
†
Address: Graduate School of Business Administration, Kobe University, 2-1, Rokkodaicho, Nadaku, Kobe, Hyogo, 6578501 Japan. E-mail: kitagawa@b.kobe-u.ac.jp
Received April 12, 2021; accepted October 4, 2021; available online December 28, 2021
DOI: 10.11640/tjar.11.2021.01
Copyright©2021 Research Institute for Economics and Business Administration – Kobe University.

The Japanese Accounting Review, 11 (2021), 1-32

2

JEL Classification: M41
Keywords: Information Transfers; Mispricing; Functional Fixation Hypothesis; ProductMarket Competition; Japan

1. Introduction
Earnings announcements contain information that can be useful not only to investors in firms
making announcements, but also to investors in other firms in the same industry. Prior studies
report that a firm’s stock price reacts to the earnings announcements of industry peers (Firth 1976;
Foster 1981; Freeman and Tse 1992), a phenomenon often referred to as “intra-industry
information transfers.” This study investigates the effect of product market competition on
overreaction to intra-industry information transfers.
Previous studies focus on market efficiency in the context of information transfer and examine
whether the stock prices of non-announcing peers in the same industry (late announcers) fully
incorporate the earnings news of early or first announcing peers (early or first announcers). Thomas
and Zhang (2008) find that U.S. investors overreact to the earnings news of early announcers. They
also find that the positive correlation among early peers’ earnings news causes an overreaction basedon the representativeness heuristic (Tversky and Kahneman 1974). Cheng and Eshleman (2014)
investigate market overreaction based on the moderated confidence hypothesis (Griffin andTversky 1992; Bloomfield et al. 2000). However, no other studies have explored how overreaction
to information transfers occurs.
Extending these studies, I predict that market overreaction to intra-industry informationtransfers is related to product market competition. In a competitive environment, peers’ earnings
announcements can convey contrary signals; as studies on intra-industry information transfers
argue (Schipper 1990; Gleason et al. 2008), both positive and negative transfers occur. Positivetransfers refer to the market’s positive (negative) reaction to peer firms’ good (bad) news. Thesetransfers are driven primarily by industry-wide news. Negative transfers refer to the market’s
negative (positive) reaction to peer firms’ good (bad) news. These transfers are driven mainly by thefirm’s competitive moves. Although positive transfers are dominant on average (Firth 1976; Foster
1981; Freeman and Tse 1992; Gleason et al. 2008), competition sometimes causes negative transfers(Lang and Lundholm 1996; Kim et al. 2008; Koo et al. 2017). Thus, product market competition
can make it more difficult for investors to extract and interpret their peers’ earnings announcements,
which can be associated with market mispricing in information transfers.
More specifically, I predict that product market competition causes market overreactions to
intra-industry information transfers in two ways. In the first scenario, competition causes negativetransfers and investors overreact to them. Prior literature shows that investors do not fully
understand the lower persistence of firm-specific news and overreact to firm-specific news morethan they do to industry-wide news (Hui et al. 2016). The suggestion that investors do notrecognize differences in the persistence of earnings components and fixate on earnings is known as
the functional fixation hypothesis (Sloan 1996). Thus, the evidence provided in the literature
supports the functional fixation hypothesis. If this is the case, investors will overreact to negativetransfers more than positive transfers, as negative transfers are caused by firm-specific news. In thesecond scenario, competition increases the dominance of competitive shifting news in the peer

Kitagawa: Product Market Competition and Overreaction to Intra-Industry Information Transfers

3

firm’s earnings announcement, but as investors do not fully recognize the increase in competitive
shifting news, they wrongly identify the news as industry-wide news. Some studies argue that firmspecific news is less transparent and more difficult to detect than industry-wide news (Ayers andFreeman 1997; Elgers et al. 2008). Therefore, it is possible that in competitive environments,
investors overlook a part of the competitive shifting news and overreact to positive transfers. Sinceboth scenarios predict that product market competition causes the market to overreact to intraindustry information transfers, I first examine the relationship between product market
competition and market overreactions to information transfers. Subsequently, I investigate which
scenario is more compelling or, in other words, which type of transfer (positive or negative) drives
market overreaction.
To address this issue, I focus on Japanese firms’ earnings announcements because of several
advantages: Japanese firms pay closer attention to their industry peers and consider their behaviors.
For example, Abegglen and Stalk (1985) and Asaba (2002) argue that, compared to U.S. firms,
Japanese firms tend to behave homogeneously or simultaneously because they want to avoid fallingbehind their industry peers. Similarly, Cooper (1995) indicates that since Japanese firms are usuallylean enterprises, meaning they can quickly imitate the successful behavior of a certain firm. Thesefindings have two implications. First, peer firms’ accounting information is more relevant because
Japanese firms’ behaviors are more closely related to their industry peers. This implies that intraindustry information transfer is more important in Japan. Second, in Japan, competitive advantages
are short-lived, considering that Japanese firms prefer to rapidly catch up with their peers. Thisimplies that the persistence of earnings derived from competitive shifting is lower and investors are
more likely to overreact to negative transfers when the functional fixation hypothesis is supported.
Therefore, the Japanese setting provides a good environment for testing my hypothesis.
Using a sample of 22,106 firm-year observations covering 2001 to 2013, the empirical analysis
confirms this prediction. The results show a pronounced market overreaction to the firstannouncer’s earnings news when firms face high product market competition. Furthermore, I findthat, in competitive environments, the market overreacts more to negative transfers than to positivetransfers. With additional analyses, I further confirm that the market overreaction to intra-industry
information transfers is less pronounced when firms are less competitive, that is, when negativetransfers are less dominant. Specifically, I find that investors react less to bellwether firms’ earnings
announcements than they do to other firms’ earnings announcements. I also find that overreaction
does not occur in industries with high sales growth. In the main analyses, I use the HerfindahlHirschman Index (HHI) as a proxy for product market competition. However, the results are robust
when I use a composite measure of product market competition calculated using the HHI, productsubstitutability, market size, and entry costs.
This study contributes to the literature in several ways. First, the study contributes to theliterature on information transfers by showing a new determinant of market overreaction to
information transfers. Prior literature indicates the existence of positive and negative transfers (Kimet al. 2008; Koo et al. 2017). Extending these studies, I show that investors react more to negativetransfers caused by product market competition. Second, the study contributes to the literature on
product market competition. Many studies focus on both the positive and negative aspects ofproduct market competition (e.g. Karuna 2007; Li 2010; Markarian and Santalo 2014). This study
provides evidence that product market competition can lead to stock market mispricing, which isconsistent with the negative aspect of competition. Finally, this study contributes to the extant
research into the market mispricing of industry-wide and firm-specific earnings components. Prior

4

The Japanese Accounting Review, 11 (2021), 1-32

studies show that firm-specific earnings news is less persistent than industry-wide earnings news,
and investors do not fully understand the different levels of persistence of such news (Hui and
Yeung 2013; Hui et al. 2016). I examine this issue in the context of information transfers andprovide evidence that investors react more to firm-specific news released by peer firms, which isconsistent with the results in the existing literature.
The remainder of this paper is organized as follows. In Section 2, I review the extant literatureand develop the main hypotheses. Section 3 outlines the study’s research design. Section 4 describes
the sample selection process and presents the descriptive statistics. Section 5 reports the main
results and Section 6 provides the results of the additional analyses. Section 7 summarizes the main
findings of this study and concludes the paper.

2. Literature Review and Hypotheses Development
2.1. Overreaction to Information Transfers
The prior literature provides evidence of information transfers in various contexts (e.g. Firth1976; Foster 1981; Baginski 1987; Freeman and Tse 1992; Lang and Stulz 1992; Ali et al. 1994;
Docking et al. 1997; Gleason et al. 2008; Kim et al. 2008; Pandit et al. 2011; Wang 2014; Koo et
al. 2017; Brochet et al. 2018).1 Recent studies have examined the existence and the determinantsof market anomalies associated with information transfers. Thomas and Zhang (2008) provide
evidence that investors in late announcers overreact to early announcers’ earnings news and thatprice corrections occur when the late announcers report their earnings. 2 Further, Thomas andZhang (2008) find that overreaction to information transfers is pronounced when the early
announcers’ earnings surprises are positively correlated with one another. This implies that investors
inflate their expectations following a series of either positive or negative news released by their earlypeers, which is consistent with the representativeness heuristic bias discussed in behavioral financeby Tversky and Kahneman (1974).
Cheng and Eshleman (2014) investigate investors’ overreaction to information transfers alonga supply chain and find that a supplier’s investors overreact to customer earnings announcements.
Furthermore, Cheng and Eshleman (2014) find that overreaction declines with the strength of
economic ties, measured by the supplier firms’ sales to the customer as a percentage of the firm’s
total sales (or the cost of goods sold). Ramalingegowda et al. (2012) focus on information transfer
through shareholdings and find that a firm’s stock price overreacts to its blockholder’s earnings
announcements and that the blockholder’s stock price underreacts to the firm’s earnings
announcements. They conclude that these results are consistent with the moderated confidence
hypothesis, positing that investors do not fully gauge the reliability of the signal and overreact(underreact) to unreliable (reliable) news (Griffin and Tversky 1992; Bloomfield et al. 2000).

2.2. Hypotheses Development
This study investigates how overreaction to information transfer occurs. Specifically, I
1Some studies also focus on information transfers in Japan. For example, Otogawa and Yamaji (2006) find that there areinformation transfers within Japanese financial and capital corporate groups (kinyuu and shihon keiretsu). Okumura(2013) finds that information transfers are caused by earnings restatements in Japan.
2
Kitagawa (2014) focuses on Japanese firms and finds that the late announcers’ investors overreact to the first announcer’s
earnings news. In contrast to these studies, Ramnath (2002) and Easton et al. (2010) show that investors underreact toearly peers’ earnings announcements.

Kitagawa: Product Market Competition and Overreaction to Intra-Industry Information Transfers

5hypothesize that product market competition causes the market to overreact to intra-industry
information transfers by focusing on the existence of two types of transfer. Previous studies haveargued that there are positive and negative information transfers (Schipper 1990; Gleason et al.
2008). Positive transfers occur when the first announcer’s earnings convey cash flow implications
that affect the entire industry, such as changes in consumer demand, industry-wide economic trends,
and growth opportunities (Firth 1976; Foster 1981; Freeman and Tse 1992). Thus, positivetransfers mean that the first announcer’s good (bad) news will be a positive (negative) signal about
the late announcers. On the other hand, negative transfers occur when the first announcers attain
good (bad) news by taking market share away from (giving market share to) late announcers(Schipper 1990; Lang and Lundholm 1996; Kim et al. 2008; Koo et al. 2017). Thus, negativetransfers mean that the first announcer’s good (bad) news is a negative (positive) signal about thelate announcers.
While positive transfers are dominant in general (Firth 1976; Foster 1981; Freeman and Tse1992), some research shows that competition causes negative transfers because it initiatescompetitive shifts among peers (Kim et al. 2008; Koo et al. 2017). For example, Kim et al. (2008)
observe negative (positive) transfers when the announcer and information receiver are defined asrival (non-rival) firms in the same industry. Koo et al. (2017) find that news releases attributed toa firm’s competitive moves trigger negative information transfers.
Extending previous studies, I focus on whether investors react indifferently to positive andnegative transfers. Although this is an open question, based on the functional fixation hypothesis,
I predict that investors overreact more to negative transfers. The functional fixation hypothesis
posits that investors do not fully understand the different levels of persistence of earnings
components (Sloan 1996; Hui and Yeung 2013; Hui et al. 2016). Sloan (1996) investigates thenature of the information contained in the accrual and cash flow components of earnings and findsthat the accrual component is less persistent than the cash flow component and investors overreact
to the accrual component. In a recent study, Hui et al. (2016) find that firm-specific earnings news
is less persistent than industry-wide earnings news and that investors overreact to firm-specificearnings news. These studies imply that investors tend to overreact to information contained in theearnings component that is less persistent. As mentioned before, studies regarding informationtransfers argue that firm-specific news causes negative transfers, whereas industry-wide news causes
positive transfers. Thus, given Hui et al.’s (2016) findings that investors overreact more to theinformation contained in firm-specific earnings, investors will overreact more to negative transfers
than to positive transfers. Therefore, I predict that investors will overreact more to informationtransfers in competitive environments, where negative transfers are more pronounced. The first
hypothesis is as follows:
Hypothesis 1: Market overreaction to intra-industry information transfers is more
pronounced in highly competitive environments.
However, even if Hypothesis 1 is supported, there can be an alternative scenario. In this case,competition increases the dominance of competitive shifting news in peer firms’ earnings
announcements, but investors do not interpret it correctly and misidentify competitive shiftingnews as industry-wide news. Previous studies argue that firm-specific news is less timely and its
valuation implications are less transparent (Ayers and Freeman 1997; Elgers et al. 2008), suggestingThe Japanese Accounting Review, 11 (2021), 1-32

6

the difficulty of correctly detecting competitive shifting news. As mentioned above, positivetransfers are dominant in general (Firth 1976; Foster 1981; Freeman and Tse 1992; Gleason et al.
2008), suggesting that peers’ earnings announcements typically convey industry-wide news.
Therefore, it is possible that investors overlook parts of the competitive shifting news whencompetition intensifies and misinterpret competitive shifting news as industry-wide news. If thisis the case, then investors will overreact to positive transfers in competitive environments.
This scenario also leads to the prediction that investors overreact to information transfers incompetitive environments. However, as the first hypothesis assumes that investors overreact tonegative transfers in competitive environments, this prediction is inconsistent with the scenario in
Hypothesis 1. Therefore, to confirm that the scenario in Hypothesis 1 is valid, I compare market
overreactions to positive and negative transfers in competitive environments. If Hypothesis 1 isvalid, then negative information transfers, rather than positive transfers, drive overreactions incompetitive environments. Thus, Hypothesis 2 is developed:
Hypothesis 2: Negative transfers, rather than positive transfers, drive the market overreaction
to intra-industry information transfers in competitive environments.

3. Research Design
3.1. Proxy for Product Market Competition
As the standard measure of product market competition, I use the HHI, which is a commonlyused measure for empirical work on industrial organizations and is based on grounded theory (e.g.
Tirole 1988). 3 The index is computed as the sum of the squared market shares, as shown inequation (1).
𝐻𝐻𝐼 = ∑

𝑆𝑎𝑙𝑒𝑠 ,∑

𝑆𝑎𝑙𝑒𝑠 ,

× −1

(1)

where Salesi,j is the sales revenue of firm i in industry j computed from the NEEDS-Financial
QUEST database. Nj is the number of firms in industry j. I compute the HHI for each year andindustry using all firms in the NEEDS-Financial QUEST universe. I exclude firm-year observations
for which sales data are missing. The firm-year observations are classified according to industry
using the two-digit Nikkei Middle Industry Classification Code (Nikkei gyousyu chu-bunrui). The
HHI is multiplied by minus one, so a small (large) negative value indicates higher (lower) productmarket competition.

3.2. Regression Models
3.2.1. Regression Model to Test Hypothesis 1
Extending Thomas and Zhang’s (2008) work, I develop the following model to test Hypothesis
1:

3

The literature on empirical accounting also typically adopts HHI as a proxy for product market competition (e.g.
Markarian and Santalo 2014). As a robustness check, I adopt the composite measure as an alternative proxy forcompetition. See Section 6.2.

Kitagawa: Product Market Competition and Overreaction to Intra-Industry Information Transfers

Ann. Returnt = β0 + β1 Responset + β2 Response × HHIDt + β3 HHIDt+ ∑ Control Variables +∑ Year Dummies+ ∑ Industry Dummies +εt

7(2)

The dependent variable in Model 2 (Ann. Returnt) represents the late announcer’s three-day
market-adjusted abnormal returns around their earnings announcement date in year t. The threeday period covers [−1, +1], where day 0 corresponds to the earnings announcement date. The
market-adjusted abnormal return is measured as the raw return minus the value-weighted market
return.4
For the independent variables, I focus on the coefficient of Response and the interaction termof Response and HHID. Response represents the late announcer’s three-day market-adjusted
abnormal returns in response to the first announcer’s earnings announcements in year t. If pricecorrections in response to information transfers are observed around the late announcer’s earnings
announcement date, the coefficient of Response is expected to be significantly negative. HHID is adummy variable equal to 1 if HHI is in the fourth quartile and 0 otherwise. Thus, HHID takes avalue of 1 (0) if firms face higher (lower) product market competition. Therefore, if the reaction to
information transfers becomes stronger when firms face higher product market competition, whichis consistent with Hypothesis 1, the coefficient of Response × HHID in Model 2 should besignificantly negative.
Following prior studies (Thomas and Zhang 2008; Cheng and Eshleman 2014), Model 2
includes several control variables. To control for news from the first announcer, I include Peer’s Ann.
Return, defined as the first announcer’s three-day market-adjusted abnormal return around theirearnings announcement date in year t. Ann. Returnt-1 is the late announcer’s three-day marketadjusted abnormal return around their earnings announcement date in year t-1. This variable isincluded to control for underreaction to prior earnings news. Moreover, following Fama and French(1993), I control for the risk factors of firm size (logMV) and book-to-market ratio (logBM). In
addition, one-month buy-and-hold returns (Return 1mo.) and six-month buy-and-hold returns(Return 6mo.) are included to control for the stock return reversal effect ( Jegadeesh 1990) andmomentum effect ( Jegadeesh and Titman 1993; Chan et al. 1996), respectively.
Furthermore, I add four types of control variables that prior studies do not include (Cheng andZhang 2008; Cheng and Eshleman 2014). First, I add the time lag between the first and lateannouncers’ earnings announcements (Ann. Lag) because Givoly and Palmon (1982) find that early
announcing firms tend to have higher announcement returns. Second, I add the late announcers’
ROA (ROA) and the change in ROA (ΔROA) to control their performance,5 since late announcers’
announcement returns (Ann. Return) should reflect the idiosyncratic news contained in ROA andΔROA. Third, some studies ( Jiang et al. 2005; Zhang 2006) find that uncertainty affects the market
response to earnings news. Thus, I add the volatility of ROA (Std. ROA), stock returns (Std. Return),
4

The value-weighted market return is calculated based on the market value of the equity of all the listed firms, except
for institutional firms, preferred securities, Japanese real estate investment trusts ( J-REITs), and special investment
corporations. The market values of the equity are calculated based on the stock price at the end of the previous operatingday. As a robustness check, I use equal-weighted market returns and find that the main conclusion does not change.
5
Although existing literature (Thomas and Zhang 2008; Cheng and Eshleman 2014) controls total accruals (ACC), mymodel does not include it because ACC is included in ROA. As a robustness check, I regress the model that includes
ACC and find that the conclusion does not change.

The Japanese Accounting Review, 11 (2021), 1-32

8

FIGURE 1. TIMETABLE OF MAIN VARIABLES
The end of fiscal year t(End of March)
Other control variables

At least five days

Earnings announcement
date of first announcer

Stock price reaction of
first announcer

Earnings announcement
date of late announcer

Peer’s Ann. Returnt
Information transfers

Stock price reaction oflate announcer

Responset

Ann. Returnt
Price correction to
information transfers

and industry return volatility (Std. Industry Return) to control for firm- and industry-level
uncertainty.
Fourth, I consider the effects of the Japanese ownership structure. Studies argue that crossshareholdings and stable shareholdings are unique features in the Japanese equity market (Hoshi
et al. 1990; Aoki and Patrick 1995; Douthett and Jung 2001; Shuto and Kitagawa 2011; Shuto andIwasaki 2014). Moreover, such an ownership structure affects the market response to earnings news
(Fang and Jung 2001; Fang and Wong 2002). Therefore, I control for the proportion of stableshareholdings (Stable Own.) and cross-shareholdings among firms (Cross Own.). 6 , 7 Finally, I
include year and industry dummies to control for fixed year and industry effects.
The expected signs of the coefficients of Ann Returnt-1, logBM, Return 6mo., ROA, and ΔROA
are positive, and those of logMV, Return 1mo., Ann. Lag, Std. ROA, Std. Return, and Std. Industry
Return are negative. I do not predict the specific signs of the coefficients of Cross Own. and Stable
Own. The time graph and detailed definitions of the variables are shown in Figure 1 and theappendix, respectively.

6

Stable shareholders include financial institutions, trust banks, and other financial institutions (e.g. brokerage companies
and securities financing companies). They also include parent companies and exclude domestic companies that are
defined as cross-shareholders. Cross-shareholders include cross-shareholding domestic companies listed on theJapanese stock markets at the end of the fiscal year.
7
In the Japanese equity market, foreign ownership has increased in recent years. Therefore, I include the proportion offoreign ownership (Foreign Own.) into the model (2) as a robustness check. The result shows that my main conclusion
does not change after controlling for foreign ownership.

Kitagawa: Product Market Competition and Overreaction to Intra-Industry Information Transfers

93.2.2. Regression Model to Test Hypothesis 2
To test Hypothesis 2, I develop Model 3 and estimate for the high-competition subsample(HHID = 1). All variables, except for Neg. Transfer and Weak Transfer, are defined in the previous
subsection.
Ann. Returnt = β0 + β1 Responset + β2 Response × Neg. Transfert+ β3 Response × Weak Transfert + β4 Neg. Transfert+ β5 Weak Transfert + ∑ Control Variables+ ∑ Year Dummies + ∑ Industry Dummies +εt(3)Here, Neg. Transfer is a dummy variable equal to 1 if Response × Peer’s Ann. Return is in thefirst quartile (this quartile contains firms with large negative information transfers) and 0 otherwise.
Weak Transfer is a dummy variable equal to 1 if Response × Peer’s Ann. Return is in the second or
third quartiles and 0 otherwise.
As defined earlier, Response is the late announcer’s stock response to the first peer’s earnings
announcement and Peer’s Ann. Return is the first peer’s announcement return. Therefore, the same
sign for Response and Peer’s Ann. Return (i.e. Response × Peer’s Ann. Return is positive) means thatthe late announcer’s investors react positively (negatively) to the first peer’s good (bad) news, whichis consistent with the occurrence of positive transfers (Foster 1981; Thomas and Zhang 2008).
However, different signs for Response and Peer’s Ann. Return (i.e. Response × Peer’s Ann. Return isnegative) mean that the late announcer’s investors react negatively (positively) to the first peer’s
good (bad) news, consistent with the occurrence of negative transfers (Foster 1981; Thomas andZhang 2008). However, the sample includes firms with Peer’s Ann. Return × Response of almost zero.
These firms can be noise when comparing the market overreaction to positive and negative transfers.
Thus, I divide the sample into three groups: a positive transfer sample (firms whose Response ×
Peer’s Ann. Return is in the fourth quartile), a weak transfer sample (firms whose Response × Peer’sAnn. Return is in the second or third quartiles), and a negative transfer sample (firms whose Response
× Peer’s Ann. Return is in the first quartile). Subsequently, I developed a negative transfer sample
dummy (Neg. Transfer) and weak transfer sample dummy (Weak Transfer).
I focus on the coefficient of the interaction term of Response and Neg. Transfer. If the market
reaction to intra-industry information transfers in competitive environments is driven by negative
rather than positive transfers, which is consistent with Hypothesis 2, the coefficient of Response ×
Neg. Transfer will be significantly negative. I do not predict the sign of the coefficient of Response ×
Weak Transfer.

4. Sample and Descriptive Statistics
4.1. Sample Selection
I obtained financial statement data from the NEEDS-Financial QUEST database of Nikkei,
Inc.8 and stock price data from the Nikkei Portfolio Master database of Financial Data Solutions,
Inc. The data on stable shareholdings and cross-shareholdings are obtained from the Data Packageof Cross-Shareholding and Stable Shareholding database of the NLI Research Institute. My initial

8

If the firm does not report consolidated financial statements, I used parent-only financial statement data.

The Japanese Accounting Review, 11 (2021), 1-32

10

TABLE 1. SAMPLE SELECTION CRITERIA
Criteria

Obs.

Japanese listed firms from January 2001 to December 2013 excluding financial institutions

58,494

Less: Fiscal year does not end in March(18,539)

Less: Firms that changed fiscal year end during the calculation of variables(255)

Less: Firms that correspond to first announcers(574)

Less: First announcer’s earnings announcement date does not precede firm’s earnings
announcement date by at least five calendar days

(1,183)

Less: The necessary data for calculating first announcer’s variables (Peer’s Ann. Return) is notavailable(269)

Less: The necessary data for calculating other variables (Ann. Return, Response, Ann. Returnt-1,
logMV, logBM, Return 1mo., Return 6mo., ROA, ΔROA, Std. ROA, Std. Return, Std. Industry
Return, Cross Own., Stable Own.) are not available(15,568)

Final sample

22,106

Notes: Table 1 provides details of the sample selection criteria. Financial statement data wereobtained from the NEEDS-Financial QUEST database of Nikkei. I used consolidated financial
statements. If consolidated financial statements were not available, I used parent-only financial
statements. Stock price data were obtained from the Nikkei Portfolio Master database of Financial
Data Solutions, Inc. I obtained data on cross-shareholdings and stable shareholdings from theData Package of Cross-Shareholding and Stable Shareholding database of the NLI ResearchInstitute.

sample consists of 58,494 ﬁrm-year observations covering 2001 to 2013. 9 The initial sample
focuses on annual earnings announcements and does not include financial institutions, such as
banks, securities companies, insurance companies, and credit and leasing firms. A total of 18,539
firm-year observations were deleted from the initial sample because this study focuses on firms withfiscal year-ends in March to ensure that a firm and its peers report results for the same fiscal year.
Further, I deleted 255 firm-year observations for firms that changed their accounting periods
during the analysis period. In addition, I deleted 574 firm-year observations because theycorresponded to the first announcer. I imposed the requirement that the first announcer’s earnings
announcement date must precede the late announcer’s earnings announcement date by at least fivecalendar days to avoid an overlap between Ann. Return and Response. Consequently, 1,183 firmyear observations were deleted. After deleting the observations with data missing on the firstannouncer (Peer’s Ann. Return) and other variables (Ann. Return, Response, Ann. Returnt-1, logMV,
logBM, Return 1mo., Return 6mo., Ann. Lag, ROA, ΔROA, Std. ROA, Std. Return, and Std. Industry
Return), the final sample consisted of 22,106 ﬁrm-year observations. The sample selection
procedure is shown in Table 1.

4.2. Descriptive Statistics
Table 2 summarizes the descriptive statistics. For each year, I winsorized all sequential variables
9

The NEEDS-Financial QUEST database includes data on earnings announcements from 2000. I include lagged Ann.
Return as a control variable, so my sample period starts in 2001.

Kitagawa: Product Market Competition and Overreaction to Intra-Industry Information Transfers

11

TABLE 2. DESCRIPTIVE STATISTICS
MeanMedian

Max

Min

SD

Skewness

KurtosisAnn. Return

0.006

0.002

0.328

-0.229

0.065

0.747

6.196

Obs.
22,106

Response

0.000-0.0030.227-0.132

0.040

1.016

7.393

22,106Peer’s Ann. Return

0.017

0.005

0.293

-0.175

0.066

1.111

5.829

22,106

Ann. Returnt-1

0.009

0.005

0.335

-0.221

0.063

0.615

5.853

22,106

logMV

9.919

9.727

14.745

5.712

1.691

0.471

2.893

22,106

logBM

0.058

0.116

1.860

-2.5890.691-0.5183.553

22,106Return 1mo.

0.055

0.031

1.109

-0.363

0.157

1.618

8.837

22,106Return 6mo.

0.059

0.012

2.798

-0.805

0.385

1.663

9.528

22,106

Ann. Lag

4.308

4.543

5.252

1.609

0.760

-2.4507.537

22,106

ROA

0.015

0.018

0.204

-0.4240.053-2.25415.042

22,106

ΔROA

0.000

0.001

0.347

-0.3330.050-0.15312.511

22,106

Std. ROA

0.030

0.018

0.303

0.002

0.038

3.702

20.633

22,106

Std. Return

0.103

0.088

0.472

0.021

0.060

2.053

9.378

22,106

Std. Industry Return

0.055

0.053

0.127

0.018

0.018

0.618

3.747

22,106

Cross Own.

0.096

0.077

0.381

0.000

0.089

0.944

3.290

22,106

Stable Own.

0.146

0.067

0.700

0.000

0.182

1.491

4.034

22,106

Notes: Table 2 presents the descriptive statistics. All sequential variables are winsorized at 1% and
99% of the respective distributions. Please see the definition of variables in the Appendix.

at 1% and 99% of their respective distributions to mitigate the impact of outliers. Table 2 shows
the mean value of Peer’s Ann. Return is greater than that of Ann. Return, suggesting that the firstannouncer’s earnings return tends to be higher than that of late announcers. Furthermore, the meanvalue of Response is almost 0. These results are consistent with the findings of prior studies, such asThomas and Zhang (2008).
Table 3 reports the descriptive statistics for the HHI (HHI) by industry and shows that service,
construction, and retail trade industries are in relatively higher competition. The shipbuilding andrepairing, mining, and rubber product industries are in lower competition. These tendencies are
consistent with prior studies in Japan (e.g. Iwasaki et al. 2014).
Table 4 presents the Pearson and Spearman rank-order correlation matrices for the regression
variables. Response is significantly and positively associated with Peer’s Ann. Return, which isconsistent with prior studies’ reports that positive transfers are dominant (e.g. Firth 1976; Foster
1981; Freeman and Tse 1992). Moreover, Response is significantly and negatively associated with
Ann. Return, suggesting that the late announcer’s investors misprice from the first announcer’s news
and that price corrections occur when the late announcers report their earnings; this is consistent
with the findings of Thomas and Zhang (2008).1010

I also compute the variance inflation factor (VIF) to check for multicollinearity and I find that all the mean values of
VIF are below 4. As the standard VIF value for multicollinearity detection is 10, I conclude that there is no
multicollinearity in the regression models.

The Japanese Accounting Review, 11 (2021), 1-32

12

TABLE 3. DESCRIPTIVE STATISTICS FOR PRODUCT MARKET COMPETITION (HHI) BY
INDUSTRY
Code

Industry group name

Mean

Median

SD

Obs.

1

Foods

3

Textile Products-0.051-0.0500.011

967-0.102-0.108

0.014

590

5

Pulp and Paper-0.142-0.145

0.010

182

7

Chemicals-0.029-0.030

0.002

1,637

9

Drugs

-0.063-0.068

0.011

461

11

Petroleum-0.211-0.207

0.017

50

13

Rubber Products-0.340-0.347

0.025

150

15

Stone, Clay and Glass Products-0.092-0.092

0.007

512

17

Iron and Steel-0.125-0.124

0.006

576

19

Nonferrous Metal and Metal Products-0.054-0.052

0.010

1,155

21

Machinery-0.036-0.035

0.003

1,974

23

Electric and Electronic Equipment-0.049-0.048

0.002

2,302

25

Shipbuilding and Repairing-0.452-0.469

0.034

55

27

Motor Vehicles and Auto Parts-0.133-0.133

0.006

615

29

Transportation Equipment-0.129-0.133

0.021

120

31

Precision Equipment-0.083-0.088

0.013

406

33

Other Manufacturing-0.061-0.061

0.004

649

35

Fish and Marine Products-0.275-0.274

0.010

42

37

Mining-0.325-0.297

0.138

35

41

Construction-0.022-0.022

0.003

1,648

43

Wholesale Trade-0.053-0.052

0.004

2,304

45

Retail Trade-0.027-0.028

0.003

787

53

Real Estate-0.044-0.044

0.009

547

55

Railroad Transportation-0.073-0.074

0.001

299

57

Trucking-0.126-0.127

0.007

285

59

Sea Transportation-0.265-0.271

0.013

107

61

Air Transportation-0.324-0.310

0.057

31

63

Warehousing and Harbor Transportation-0.069-0.076

0.021

390

65

Communication Services-0.158-0.148

0.018

223

67

Utilities: Electric-0.165-0.165

0.002

51

69

Utilities: Gas-0.257-0.256

0.004

49

71

Services-0.011-0.011

0.001

2,907

Notes: Table 3 presents the descriptive statistics for the HHI (HHI) by industry. In this study, HHI
is defined as the sales-based HHI times minus one. The definition of each industry is based on
the Nikkei Middle Industry Classification (33 industries). The code in the table refers to the twodigit Nikkei Middle Industry Classification Code.

0.01

-0.01
-0.01
-0.01
0.02
0.02

-0.02

0.10

-0.03

0.07

-0.01

-0.14

-0.01

-0.05

0.00

-0.03

-0.02

0.01

0.03

0.02

(3) Peer’s Ann. Return(4) Ann. Returnt-1(5) logMV(6) logBM(7) Return 1mo.

(8) Return 6mo.

(9) Ann. Lag(10) ROA(11) ΔROA(12) Std. ROA(13) Std. Return(14) Std. Industry Return(15) Cross Own.

(16) Stable Own.

-0.02

-0.04

0.10

0.02

0.03

0.02

0.03

-0.02

0.08

0.00

-0.01

0.00

-0.02

0.07

-0.02

(3)

0.02

0.02

0.00

0.05

-0.04

0.12

0.06

0.00

-0.03

0.02

0.02

-0.02

-0.01

0.01

0.09

(4)

-0.06

0.02

-0.04

-0.14

-0.19

0.04

0.28

-0.22

0.14

-0.03

-0.40

-0.01

0.00

0.04

-0.01

(5)

-0.01

0.23

0.01

-0.21

-0.23

-0.06

-0.17

0.08

-0.27

-0.01

-0.44

0.01

-0.02

0.01

0.06

(6)

0.00

0.00

0.24

0.26

0.04

0.00

-0.07

0.00

0.37

0.01

0.00

0.02

0.01

0.00

0.00

(7)

-0.02

0.01

0.08

0.14

-0.01

0.25

0.23

-0.01

0.32

-0.23

0.17

-0.02

0.09

0.02

-0.12

(8)

-0.22

0.10

-0.05

0.04

0.04

0.01

-0.07

-0.06

0.05

0.03

-0.26

0.02

-0.09

-0.07

0.02

(9)

0.01

-0.02

-0.08

-0.20

-0.34

0.47

-0.12

0.25

-0.08

-0.33

0.31

0.05

0.01

0.02

-0.07

(10)

0.00

0.02

-0.04

0.02

-0.02

0.37

-0.02

0.32

0.02

-0.10

0.06

0.15

0.04

0.04

0.00

(11)

-0.04

-0.24

0.16

0.40

0.01

-0.12

0.03

-0.05

0.03

-0.14

-0.18

-0.05

0.05

0.01

-0.04

(12)

-0.01

-0.16

0.32

0.39

0.01

-0.12

0.06

-0.04

0.18

-0.13

-0.08

-0.01

0.02

0.00

-0.03

(13)

0.01

-0.09

0.36

0.23

-0.02

-0.05

0.00

0.02

0.22

0.02

-0.05

-0.02

0.08

0.02

0.00

(14)

-0.24

-0.11

-0.17

-0.27

0.02

-0.11

0.01

0.05

0.04

0.24

0.06

0.03

-0.03

0.01

0.04

(15)

Notes: Table 4 presents the Pearson and Spearman correlations. The top (bottom) diagonal in the table shows the Spearman (Pearson)
correlations. All sequential variables are winsorized at 1% and 99% of the respective distributions. Bold indicates statistical significanceat a less than 0.01 level of significance using a two-tailed t-test. Please see the definition of variables in the Appendix.

0.03

-0.01

0.04

0.02

0.01

0.02

0.00

0.07

-0.02

-0.02

(2)

(2) Response(1) Ann. Return(1)

TABLE 4. PEARSON AND SPEARMAN CORRELATIONS

-0.01

-0.03

-0.03

-0.08

0.01

-0.07

-0.16

-0.02

0.03

0.05

0.02

0.02

-0.02

0.04

0.03

(16)
Kitagawa: Product Market Competition and Overreaction to Intra-Industry Information Transfers
13

14

The Japanese Accounting Review, 11 (2021), 1-32

5. Empirical Results
5.1. Portfolio Test
Table 5 presents the results of the portfolio test. In Panel A of Table 5, the firms are divided
into 10 decile portfolios based on Peer’s Ann. Return for each year, and the mean value of Responseis shown. In the pooled sample, the mean value of Response tends to be larger, as Peer’s Ann. Return
increased from D1 to D10. In addition, the three-day D10−D1 hedge portfolio return is 0.007,
which is significantly different from 0.
Subsequently, I divide the firms into four groups based on the HHI quartiles by year andfurther sort each group into 10 portfolios based on Peer’s Ann. Return by year. In all HHI quartile
samples, the three-day D10−D1 hedge portfolio returns are significantly positive.11 However, thehedge portfolio returns are likely to be lower in the third and fourth HHI quartile samples, whichcontain firms facing greater product market competition. This could be because the positive andnegative information transfers offset each other.
Panel B of Table 5 summarizes the proportion of positive and negative information transfers
for the full sample and each HHI quartile subsample. Here, I classify firms into a positive (negative)
transfer group if the sign of Response × Peer’s Ann. Return is positive (negative). In the pooled sample,
the proportion of positive transfers was slightly higher than that of negative transfers. More
importantly, I find that the proportion of negative (positive) transfers increases (decreases) as thelevel of the HHI increases. The proportions in the lowest and highest HHI quartiles were
statistically different. This implies that negative transfers will become more pronounced as productmarket competition increases.
In Panel C of Table 5, I divide the firms into 10 decile portfolios based on the Response foreach year and show the mean value of Ann. Return. In the pooled sample, the mean value of Ann.
Return tended to decrease as the Response increased from D1 to D10. In addition, the three-day
D10−D1 hedge portfolio return is -0.006 and significantly different from 0. These results suggestthat Japanese investors typically overreact to information transfers, which is similar to the findingsof Thomas and Zhang (2008).
Furthermore, I divide the firms into four groups based on the HHI quartiles by year and further
sort each group into 10 portfolios based on the Response by year. In the third and fourth HHI
quartiles (these quartiles contain firms facing higher competition), the mean value of Ann. Return
decreases as the Response increases from D1 to D10 and the three-day D10−D1 hedge portfolio
return is significantly different from 0. In contrast, in the other quartiles, the three-day D10−D1
hedge portfolio returns were not significantly different from 0. This result suggests that an
overreaction to information transfers is observed when firms face higher product market
competition, which supports Hypothesis 1.

11

Note that Peer’s Ann. Return and HHI are not measured by firm-year level but by industry-year level—the observationsof each of the deciles differ. Therefore, the pooled D10–D1 is not necessarily equal to the average of D10–D1 in the
HHI quartiles.

Kitagawa: Product Market Competition and Overreaction to Intra-Industry Information Transfers

15

TABLE 5. PORTFOLIO TEST BY THE LEVEL OF PRODUCT MARKET COMPETITION
Panel A: The Mean Response by Quartile Based on HHI and by Decile Based on Peer’s Ann. Return
Level of product market competition (HHI)
PooledD1 (Lowest)
D2
D3
D4
D5
D6
D7
D8
D9
D10 (Highest)
D10 – D1
t-stat.
Obs.

-0.002-0.007-0.0040.002
0.000-0.001
0.003
0.002
0.000
0.005
0.007***(5.599)22,1061 (Lowest)-0.0030.002-0.002-0.0030.001-0.001
0.008
0.001
0.008
0.007
0.010***(4.498)
6,941

2

3

-0.008-0.011-0.001-0.016
0.000
0.003
0.006
-0.002-0.001
0.002
0.009***(4.063)5,306-0.001-0.004
0.001
0.010
0.006
0.008
-0.0030.001-0.0050.005
0.006**(2.377)5,611

4 (Highest)
0.005-0.0340.000-0.006-0.004-0.006-0.011
0.000
0.010
0.013
0.007**(2.325)
4,248

Panel B: Proportion of Positive and Negative Transfers by Quartile Based on HHI
Level of product market competition (HHI)
PooledPositive transfers
51.87%
Obs.
11,466
Negative transfers
48.13%
Obs.
10,640χ2-stat. (Lowest = Highest)

1 (Lowest)

2

54.07%
54.54%
3,753
2,894
45.93%
45.46%
3,188
2,412
71.040 (Pr. = 0.000)

34 (Highest)

50.04%
2,808
49.96%
2,803

47.34%
2,011
52.66%
2,237

Panel C: The Mean Ann. Return by Quartile Based on HHI and by Decile Based on Response
Level of product market competition (HHI)
D1 (Lowest)
D2
D3
D4
D5
D6
D7
D8
D9
D10 (Highest)
D10 – D1
t-stat.
Obs.

Pooled
0.007
0.007
0.009
0.005
0.007
0.007
0.008
0.008
0.005
0.001
-0.006**(-2.313)
22,1061 (Lowest)
0.005
0.007
0.007
0.005
0.007
0.010
0.009
0.006
0.006
0.003
-0.003(-0.591)
6,941

2
0.005
0.006
0.009
0.006
0.012
0.010
0.004
0.008
0.002
0.003
-0.003(-0.647)
5,306

3
0.011
0.010
0.007
0.004
0.010
0.010
0.007
0.008
0.008
0.004
-0.007*(-1.651)
5,611

4 (Highest)
0.006
0.002
0.006
0.007
0.002
0.002
0.006
0.006
0.004
-0.006-0.012**(-2.002)
4,248

Notes: Table 5 of Panel A describes the mean Response by quartile based on HHI and by decile
based on Peer’s Ann. Return. Panel B shows the proportion of positive information transfers (i.e.

The Japanese Accounting Review, 11 (2021), 1-32

16

Response × Peer’s Ann. Return is positive) and negative information transfers (i.e. Response × Peer’sAnn. Return is negative) according to the level of HHI. Panel C shows the mean Ann. Return by
quartile based on HHI and by decile based on Response. For each year, I divide the sample into
four groups based on HHI; each group is further sorted into 10 portfolios based on Peer’s Ann.
Return (Response). All sequential variables are winsorized at 1% and 99% of the respective
distributions. *** and ** indicate the value is significantly different from 0 at the 1% and 5% levels,
respectively, using a two-tailed t-test. Please see the definition of variables in the Appendix.

5.2. Regression Analysis
Table 6 presents the regression results for Model 2. To mitigate the cross-sectional and timeseries dependence problems of the panel data, I use standard errors clustered at the firm and year
levels, as proposed by Petersen (2009). First, I divide the sample into two subsamples based on
HHID and estimate the regression model (2) (without Response × HHID and HHID) for eachsubsample. The result shows that the coefficient of Response is negative and significant only at the
10% level in the low competition sample (HHID = 0). On the other hand, in the high-competition
sample (HHID = 1), the coefficient of Response is significantly negative at the 5% level and morenegative than in the low competition sample.
Then, I estimate Model 2 for the full sample. The results show that the coefficient of theinteraction term of Response and HHID is significantly negative at the 5% level. This suggests that
overreaction to the first peer’s earnings announcement is significantly higher in highly competitiveenvironments, which is consistent with Hypothesis 1.
As for the control variables, the coefficients of Ann. Returnt-1 and ΔROA were found to besignificantly positive in the pooled sample and both subsamples. The coefficient of logBM issignificantly positive in the pooled and low-competition samples. The coefficients of Ann. Lag andStd. ROA are significantly negative in the pooled and high-competition sample. These results are
consistent with expectations.12
Table 7 provides the regression results for Model 3. As mentioned earlier, my main focus is on
the results for the high-competition sample. However, for comparison, the table also reports theresults for the low-competition sample. In the high-competition sample, the coefficient of Response× Neg. Transfer was significantly negative at the 5% level. The result of the F-test reveals that thesum of the coefficients of Response and Response × Neg. Transfer is significantly different from zero.
These results suggest that the market overreaction to information transfers in competitiveenvironments is driven by negative rather than positive transfers, which is consistent with
Hypothesis 2. On the other hand, both the coefficients of Response and Response × Weak Transfer
are not significant. The result of the F-test reveals that the sum of the coefficients of Response andResponse × Weak Transfer is not different from zero. This suggests that market overreaction to
information transfers in competitive environments is driven only by negative transfers. Interestingly,
in the low-competition sample, the coefficient of Response × Neg. Transfer is insignificant, although
the result of the F-test reveals that the sum of the coefficients of Response and Response × Neg.
Transfer is significantly different from zero. This result means that the market overreaction due to
12

The coefficient of Return 6mo. is significantly negative and that of Return 1mo. is not significant, which are inconsistent
with the predictions. One possible explanation is that Return 6mo. reflects a reversal effect because of the highcorrelation between Return 6mo. and Return 1mo. (see Table 4). In fact, the coefficient on Return 1mo. becomessignificantly negative when Return 6mo. is eliminated from the independent variable.

Kitagawa: Product Market Competition and Overreaction to Intra-Industry Information Transfers

17

TABLE 6. EFFECT OF PRODUCT MARKET COMPETITION ON OVERREACTION (HYPOTHESIS 1)

Intercept
Response

Pred. sign?
−

Response × HHID−

HHID
Peer’s Ann. Return
Ann. Returnt-1
logMV
logBM
Return 1mo.
Return 6mo.
Ann. Lag
ROA
ΔROA
Std. ROA
Std. ReturnStd. Industry Return
Cross Own.
Stable Own.
Year Dummies
Industry Dummies
Adj. R2
Obs.

?
+
+
−

+
−

+
−

+
+
−
−
−
?
?

(1) Low-competition
sample (HHID = 0)

(2) High-competition
sample (HHID = 1)

(3) Pooled sample

Coeff.
0.019**-0.034*

Coeff.
0.047***-0.071**t-stat.
(2.561)(-2.270)

Coeff.
0.024***-0.034*t-stat.
(3.399)(-1.799)(-2.307)(2.040)(-1.362)(1.302)(-1.041)(-3.014)(-2.240)(-0.113)(2.265)(-3.686)(-0.271)(0.405)(-1.030)(0.736)-0.050**
0.003
0.008
0.081***-0.0000.003**-0.001-0.019***-0.002*-0.044***0.068***-0.033*-0.019
0.043
0.006
0.001

(-1.983)(0.389)(0.582)(4.679)(-0.189)(2.489)(-0.285)(-3.909)(-1.838)(-2.800)(4.328)(-1.816)(-0.796)(0.592)(0.902)(0.381)0.018
0.087***
0.000
0.003***-0.001-0.018***-0.002-0.068***0.074***-0.010-0.0230.035
0.010-0.000t-stat.
(2.013)(-1.815)(0.943)(4.842)(0.182)(2.580)(-0.190)(-3.885)(-1.587)(-3.907)(4.339)(-0.556)(-1.128)(0.370)(1.259)(-0.111)

Yes
Yes
0.062
17,858-0.055**0.054**-0.001
0.003-0.008-0.019***-0.004**-0.0030.062**-0.072***-0.0100.092-0.018
0.004

Yes
Yes
0.066
4,248

Yes
Yes
0.061
22,106

Notes: Table 6 reports the regression results of Model 2 to test Hypothesis 1. All sequential
variables are winsorized at 1% and 99% of the respective distributions. The t-statistics inparentheses are corrected for heteroscedasticity and cross-sectional and time-series correlation
using a two-way cluster at the firm and year levels, as proposed by Petersen (2009). ***, **, and *indicate the value is significantly different from 0 at the 1%, 5%, and 10% levels, respectively, using
a two-tailed t-test. Please see the definition of variables in the Appendix.

negative transfers is more distinct in the high-competition sample. This is consistent with economic
theory, which states that competition lessens the persistence of firm-specific earnings (e.g. Waring
1996; Hui and Yeung 2013; Hui et al. 2016).13

13

As a robustness check, I divide the sample into two subsamples based on the median value of HHI and regress Ann.
Return on Response in each subsample. The empirical results show that the coefficient of Response in the above-mediansample (-0.056) is significantly negative (t = -2.406), although the significance level is lower than that in the fourth
quartile sample. In contrast, in the subsample with HHI below the median, the coefficient of Response (-0.037) is
insignificant (t = -1.596). These results are consistent with Hypothesis 1. Moreover, the regression results of Model 3
reveal that the coefficient of Response×Neg. Transfer (-0.110) is significantly negative (t = -2.750), which is consistent
with Hypothesis 2. These results suggest that my conclusion does not depend on the cut-off point of the HHI.

The Japanese Accounting Review, 11 (2021), 1-32

18

TABLE 7. EFFECT OF PRODUCT MARKET COMPETITION ON OVERREACTION TO POSITIVE
AND NEGATIVE INFORMATION TRANSFERS (HYPOTHESIS 2)(1) Low-competition sample(HHID = 0)

(2) High-competition sample(HHID = 1)

Pred. sign

Coeff.

t-stat.

Coeff.

t-stat.

?
−
−
?
?
?

0.020**-0.020-0.0410.003
0.000-0.0020.010
0.087***
0.000
0.003***-0.001-0.018***-0.002-0.068***0.074***-0.011-0.0240.030
0.010-0.000(2.226)(-0.834)(-1.069)(0.062)(0.139)(-1.498)(0.539)(4.850)(0.208)(2.581)(-0.267)(-3.898)(-1.558)(-3.880)(4.322)(-0.594)(-1.186)(0.311)(1.264)(-0.096)0.045**-0.009-0.139**
0.018
0.004
0.002
-0.067***0.055**-0.001
0.003-0.010-0.019***-0.004**-0.0040.063**-0.075***-0.0140.162-0.0180.004(2.344)(-0.246)(-2.454)(0.266)(0.864)(0.501)(-3.089)(2.077)(-1.280)(1.318)(-1.346)(-3.035)(-2.195)(-0.141)(2.402)(-4.222)(-0.367)(0.865)(-1.005)(0.747)

Intercept
Response
Response × Neg. Transfer
Response × Weak Transfer
Neg. Transfer
Weak Transfer
Peer’s Ann. Return
Ann. Returnt-1
logMV
logBM
Return 1mo.
Return 6mo.
Ann. Lag
ROA
ΔROA
Std. ROA
Std. ReturnStd. Industry Return
Cross Own.
Stable Own.
Year Dummies
Industry Dummies
Adj. R2
Obs.
Response + Response × Neg. Transfer
Response + Response × Weak Transfer

+
+
−

+
−

+
−

+
+
−
−
−
?
?

Yes
Yes
0.062
17,858
Coeff.
-0.061**-0.017

Yes
Yes
0.067
4,248
Prob. > F
(0.020)(0.696)

Coeff.
-0.148***0.009

Prob. > F
(0.009)(0.877)

Notes: Table 7 reports the regression results of Model 3. All sequential variables are winsorized at
1% and 99% of the respective distributions. The t-statistics in parentheses are corrected forheteroscedasticity and cross-sectional and time-series correlation using a two-way cluster at thefirm and year levels, as proposed by Petersen (2009). ***, **, and * indicate the value is significantly
different from 0 at the 1%, 5%, and 10% levels, respectively, using a two-tailed t-test. Please see
the definition of variables in the Appendix.

6. Additional Analysis
6.1. Overreactions to Information Transfers Where Firms Are Less Competitive
6.1.1. Overreactions Associated with Bellwether Firms’ Earnings Announcements
In this section, I conduct additional analysis to validate the main conclusions of this study. To
this end, I examine reactions to information transfers when the first and late announcers are lesscompetitive. If the main conclusion is supported, there should be less overreaction to information

Kitagawa: Product Market Competition and Overreaction to Intra-Industry Information Transfers

19transfers when firms are less competitive.
First, I examine overreactions to information transfers associated with bellwether firms’
earnings announcements. Bellwether firms’ earnings announcements are likely to convey more news
about macroeconomic conditions (Anilowski et al. 2007; Bonsall et al. 2013; Barth and So 2014).
That is, bellwether firms’ earnings news tends to reflect market-wide news rather than competitive
shifting news. Moreover, bellwether firms attract attention from peer firms’ investors as a leading
indicator of the market or industry (Hann et al. 2019). This implies that peer firms’ investors do
not regard bellwether firms as rivals who compete for market share. Therefore, I predict thatbellwether firms’ earnings announcements will cause positive transfers rather than negative transfers
and that there will be smaller overreactions to information transfers. Following Anilowski et al.
(2007) and Barth and So (2014), who define bellwether firms as large firms included in the S&P
500 index, for this study, I define bellwether firms as firms included in the Nikkei 500 index.14 I
develop the Nikkei 500 index dummy (NIKKEI500), which is equal to 1 if firms are included in
the Nikkei 500 index and 0 otherwise, and estimate Model 2 by using NIKKEI500 instead of
HHID.
Table 8 summarizes the empirical results. When the first announcers are not bellwether firms(NIKKEI500 = 0), the coefficient of Response is significantly negative. In contrast, when the firstannouncers are bellwether firms (NIKKEI500 = 1), the coefficient of Response is insignificant. These
results suggest that bellwether firms’ earnings capture more industry-wide effects and are lessaffected by competition; hence, overreaction to their announcements is lower, which is consistent
with the previous prediction. However, the interaction term of Response and NIKKEI500 is
insignificant, suggesting that the results in Table 8 are not robust.
6.1.2. The Effect of Industry Sales Growth on Overreactions to Information Transfers
In the second analysis, I examine the effect of industry sales growth on overreaction to
information transfers. In a high-growth industry, firms can achieve increased sales without wresting
market share away from industry peers because the entire market is also expanding. Therefore, firms
will be less competitive in a high-growth industry and there will be fewer market overreactions to
information transfers.
To investigate this issue, I developed a high-growth industry dummy (Industry Growth), whichis equal to 1 if the growth ratio of industry sales (= [industry aggregate sales for year t − industry
aggregate sales for year t-1]/industry aggregate sales for year t-1) is in the fourth quartile (large
positive) and 0 otherwise. Then, I estimated Model 2 using Industry Growth instead of HHID.
The results are summarized in Table 9. In a low-growth industry subsample (Industry Growth= 0), the coefficient of Response is significantly negative at the 1% level. In contrast, in the highgrowth industry subsample (Industry Growth = 1), the coefficient of Response is insignificant. Fromthe pooled regression analysis using the interaction term of Response and Industry Growth, I findthat the coefficient of Response × Industry Growth is significantly positive at the 1% level. The resultof the F-test reveals that the sum of the coefficients of Response and Response × Industry Growth issignificantly different from zero. These results suggest that market overreaction to information
14

The Nikkei 500 is comprised of 500 stocks selected from Japanese common stocks in the first section of the Tokyo
Stock Exchange (TSE). It excludes ETFs, REITs, preferred equity contribution securities, and tracking stocks otherthan common stocks. As a robustness check, I redefined “bellwether firms” as firms included in the Nikkei 225 index
and obtained similar results.

The Japanese Accounting Review, 11 (2021), 1-32

20

TABLE 8. OVERREACTION TO INFORMATION TRANSFERS ASSOCIATED WITH BELLWETHERFIRMS’ EARNINGS ANNOUNCEMENTS

Pred. sign
Intercept
Response
Response × NIKKEI500

NIKKEI500
Peer’s Ann. Return
Ann. Returnt-1
logMV
logBM
Return 1mo.
Return 6mo.
Ann. Lag
ROA
ΔROA
Std. ROA
Std. ReturnStd. Industry Return
Cross Own.
Stable Own.
Year Dummies
Industry Dummies
Adj. R2
Obs.

?
−
+
?
+
+
−
+
−
+
−
+
+
−
−
−
?
?

Response + Response × NIKKEI500(1) Non-bellwether
firms’ earnings
announcements(NIKKEI500 = 0)
Coeff.
t-stat.

(2) Bellwether
firms’ earnings
announcements(NIKKEI500 = 1)
Coeff.
t-stat.

(3) Pooled sample

0.026***-0.049**(3.471)(-2.234)

0.043***-0.037(3.528)(-0.571)

0.002
0.080***-0.0000.003**-0.001-0.019***-0.002**-0.041**
0.069***-0.037*-0.015
0.012
0.005
0.002

(0.130)(4.780)(-0.133)(2.315)(-0.330)(-4.032)(-2.014)(-2.351)(4.075)(-1.936)(-0.657)(0.138)(0.639)(0.648)

0.031
0.023
-0.004**0.006**
0.010-0.002-0.001-0.127***
0.088
0.005
-0.0510.384*
0.021-0.011(0.633)(0.761)(-2.255)(2.417)(0.572)(-0.267)(-0.330)(-2.969)(1.560)(0.083)(-1.612)(1.784)(1.059)(-1.127)0.024***(3.358)-0.048** (-2.177)
0.026(1.323)0.001(0.479)0.009(0.572)0.080***(4.647)-0.000(-0.179)
0.003**(2.539)-0.001(-0.276)-0.018*** (-3.921)-0.002*(-1.880)-0.044*** (-2.811)
0.068***(4.329)-0.034*(-1.830)-0.019(-0.798)
0.040(0.549)0.006(0.898)0.001(0.371)
Yes
Yes
0.061
22,106
Coeff.
Prob. > F
-0.022(0.758)

Yes
Yes
0.059
19,610

Yes
Yes
0.099
1,697

Coeff.

t-stat.

Notes: Table 8 reports overreaction to information transfers associated with bellwether firms’
earnings announcements. All sequential variables are winsorized at 1% and 99% of the respective
distributions. Bellwether firms are defined as firms included in the Nikkei 500. The dependent
variable is Ann. Return, which is the late announcer’s three-day market-adjusted abnormal returnaround the late announcer’s earnings announcement date in year t. The t-statistics in parentheses
are corrected for heteroscedasticity and cross-sectional and time-series correlation using a twoway cluster at the firm and year levels, as proposed by Petersen (2009). ***, **, and * indicate thatthe values are significantly different from 0 at the 1%, 5%, and 10% levels, respectively, using a
two-tailed t-test. Please see the definition of variables in the Appendix.

transfers is only observed in lower growth industries, which is consistent with the scenario thatinvestors overreact to information transfers in a highly competitive environment.

Kitagawa: Product Market Competition and Overreaction to Intra-Industry Information Transfers

21

TABLE 9. INDUSTRIAL GROWTH AND OVERREACTION TO INFORMATION TRANSFERS(1) Middle- and lowgrowth industry(Industry Growth = 0)

(2) High-growthindustry(Industry Growth = 1)

(3) Pooled sample

t-stat.

Coeff.

(1.283)(0.307)0.024***(3.465)-0.060*** (-3.483)
0.066***(2.642)0.000(0.334)0.008(0.528)0.080***(4.643)-0.000(-0.168)
0.003**(2.510)-0.001(-0.255)-0.018*** (-3.955)-0.002*(-1.874)-0.044*** (-2.772)
0.068***(4.331)-0.033*(-1.793)-0.019(-0.786)
0.042(0.605)0.006(0.868)0.001(0.355)
Yes
Yes
0.061
22,106
Coeff.
Prob.>F0.006(0.858)Pred. sign

Coeff.

t-stat.

Coeff.

Intercept
Response?
−

0.024**-0.060***(2.415)(-3.518)

0.027
0.010

Response × Industry Growth

+
?
+
+

0.015
0.087***-0.0000.003**
0.001-0.018***-0.002**-0.039*0.063***-0.035*-0.027
0.032
0.008
0.001

(0.864)(4.442)(-0.102)(2.321)(0.216)(-3.654)(-2.226)-0.007(-0.236)
0.053**(2.529)-0.001(-0.308)
0.001(0.753)-0.008(-0.822)-0.020*** (-4.368)-0.001(-0.507)-0.078*** (-3.063)
0.096***(3.337)-0.010(-0.219)0.010(0.315)0.289(1.412)-0.001(-0.114)0.001(0.198)
Yes
Yes
0.063
4,669

Industry Growth
Peer’s Ann. Return
Ann. Returnt-1
logMV
logBM
Return 1mo.
Return 6mo.
Ann. Lag
ROA
ΔROA
Std. ROA
Std. ReturnStd. Industry Return
Cross Own.
Stable Own.
Year Dummies
Industry Dummies
Adj. R2
Obs.

−

+
−

+
−

+
+
−
−
−
?
?

(-1.928)(3.940)(-1.781)(-1.060)(0.648)(0.985)(0.259)

Yes
Yes
0.064
17,437

Response + Response × Industry Growtht-stat.

Notes: Table 9 reports the effect of industry growth on overreaction to information transfers. All
sequential variables are winsorized at 1% and 99% of the respective distributions. I adopt a highgrowth industry dummy (Industry Growth) that is equal to 1 if the growth ratio of industry sales
is in the fourth quartile and 0 otherwise. The dependent variable is Ann. Return, which is the lateannouncer’s three-day market-adjusted abnormal return around the late announcer’s earnings
announcement date in year t. The t-statistics in parentheses are corrected for heteroscedasticity
and cross-sectional and time-series correlation using a two-way cluster at the firm and year levels,
as proposed by Petersen (2009). ***, **, and * indicate that the values are significantly different
from 0 at the 1%, 5%, and 10% levels, respectively, using a two-tailed t-test. Please see thedefinition of variables in the Appendix.

6.2. Alternate Measure of Product Market Competition
In this subsection, I re-examine the main analysis using an alternative measure of productmarket competition. Karuna (2007) argues that industry concentration alone could be a poor proxy22

The Japanese Accounting Review, 11 (2021), 1-32

for competition due to endogeneity and suggests that competition should be measured using
multidimensional variables. Thus, following Karuna (2007), I develop a composite measure of
product market competition (PMC). First, I compute the three competition measures: 1) productsubstitutability (DIFF) (= industry aggregate sales/industry aggregate operating costs),15 2) market
size (MKTSIZE) (= natural log of aggregate sales by industry), and 3) costs of entry (ENTCOST)(= natural log of the weighted average gross value of the cost of property, plant, and equipment forfirms in the industry). Subsequently, I reduce the HHI and the three competition measures given
above into a single index (PMC) using factor analysis.16
I develop the product market competition dummy (PMCD), which is equal to 1 if PMC is in
the fourth quartile (i.e. a highly competitive environment) and 0 otherwise. Using PMCD insteadof HHID, I re-estimate Models 2 and 3. If the result supports Hypothesis 1, then the coefficient of
Response × PMCD in Model 2 should be significantly negative. If the result supports Hypothesis 2,
then the coefficient of Response × Neg. Transfer in Model 3 should be significantly negative in thehigh-competition sample (PMCD = 1).
Table 10 presents the regression results for testing Hypothesis 1. In both subsamples, thecoefficient of Response is significantly negative, but the magnitude of the coefficient is higher in thehigh-competition sample (PMCD = 1). Furthermore, I estimate Model 2 using Response × PMCD
for the pooled sample. The coefficient of Response × PMCD was significantly negative. These results
suggest that the market overreacts to the first peer’s earnings announcement when firms face highproduct market competition.
Table 11 reports the regression results for testing Hypothesis 2. In the high-competition
sample, the coefficient of Response × Neg. Transfer is significantly negative at the 5% level. The
results of the F-test reveal that the sum of the coefficients of Response and Response × Neg. Transfer
is statistically different from zero. On the other hand, both the coefficient of Response and thecoefficient of Response × Weak Transfer are insignificant. These results suggest that in highly
competitive industries, only negative transfers drive market overreactions to information transfers.
Thus, the main results are robust when using an alternate proxy for competition.

6.3. Other Robustness Checks
I conducted further robustness checks. First, I examined the possibility that HHI does not
capture product market competition, but instead captures stock return volatility. To this end, I reestimate Model 2, which includes the interaction term of Response and HHI after controlling forthe interaction term of Response and Std. Return or Response and Std. Industry Return or both. As
defined earlier, Std. Return is the stock return volatility and Std. Industry Return is the industry
stock return volatility over the past 12 months. Table 12 summarizes the results of the regression
analysis. In each column, the coefficient of Response × HHID remained significantly negative after
controlling for Response × Std. Return, Response × Std. Industry Return or both. These results suggestthat HHI does not only capture stock return volatility.

15

Operating costs include the cost of goods sold, general and administrative expenses, depreciation, depletion, andamortization.
16
The untabulated result shows that a single factor loaded by four competition measures (HHI, DIFF, MKTSIZE, and
ENTCOST) justifies about 40% of the cumulative variance. Furthermore, principal component 1 has positive signs, as
expected. Thus, I conclude that the factor analysis provides useful composite measures to capture the extent of
product market competition.

Kitagawa: Product Market Competition and Overreaction to Intra-Industry Information Transfers

23

TABLE 10. TESTING HYPOTHESIS 1 USING AN ALTERNATIVE PRODUCT MARKETCOMPETITION MEASURE

Intercept
Response
Response × PMCD
PMCD
Peer’s Ann. Return
Ann. Returnt-1
logMV
logBM
Return 1mo.
Return 6mo.
Ann. Lag
ROA
ΔROA
Std. ROA
Std. ReturnStd. Industry Return
Cross Own.
Stable Own.
Year Dummies
Industry Dummies
Adj. R2
Obs.

(1) Low-competition
sample(PMCD = 0)

(2) High-competition
sample(PMCD = 1)

(3) Pooled sample

Pred. sign

Coeff.

t-stat.

Coeff.

t-stat.

Coeff.

?
−−

0.024***-0.040**(2.860)(-2.115)

0.057***-0.079**(4.907)(-2.019)0.011
0.081***-0.0000.003**-0.005-0.017***-0.002*-0.055***0.070***-0.050**-0.021
0.057
0.008
0.004

(0.585)(4.591)(-0.263)(2.221)(-1.113)(-3.818)(-1.811)(-3.096)(4.176)(-2.776)(-0.910)(0.730)(1.212)(1.156)0.011(0.826)0.074***(3.502)0.001(0.574)0.004(1.570)0.024**(2.386)-0.026***(-3.657)-0.002(-1.150)0.005(0.125)0.062**(2.059)0.031(0.660)-0.014(-0.404)-0.437**(-2.060)-0.002(-0.125)-0.010(-1.980)
Yes
Yes
0.081
4,893

0.024***(3.415)-0.039**(-2.188)-0.043**(-2.069)0.001(0.148)0.009(0.586)0.080***(4.675)-0.000(-0.178)0.003**(2.515)-0.001(-0.281)-0.018*** (-3.922)-0.002*(-1.857)-0.044*** (-2.802)
0.068***(4.329)-0.033*(-1.832)-0.019(-0.797)
0.043(0.592)0.006(0.891)0.001(0.375)
Yes
Yes
0.061
22,106

?
+
+
−

+
−

+
−

+
+
−
−
−
?
?

Yes
Yes
0.060
17,213t-stat.

Notes: Table 10 reports the regression results of Model 2 using an alternative product market
competition measure. All sequential variables are winsorized at 1% and 99% of the respective
distributions. The t-statistics in parentheses are corrected for heteroscedasticity and crosssectional and time-series correlation using a two-way cluster at the firm and year levels, as
proposed by Petersen (2009). ***, **, and * indicate the value is significantly different from 0 at the
1%, 5%, and 10% levels, respectively, using a two-tailed t-test. Please see the definition of variablesin the Appendix.

Second, I examine whether investors’ reactions to good news differ from their reactions to bad
news. I define a dummy variable (Bad News), equal to 1 if the first announcer reports bad news (i.e.
Peer’s Ann. Return is negative) and 0 otherwise (i.e. Peer’s Ann. Return is positive). Then, I reestimate Model 2, which includes the interaction term of Response and Bad News. If investors’
reactions to the first announcer’s negative news differ from the reactions to positive news, thecoefficient of Response × Bad News should be statistically significant. The results, which are nottabulated, show that the coefficient of Response × Bad News is positive (0.036) but not significant

The Japanese Accounting Review, 11 (2021), 1-32

24

TABLE 11. TESTING HYPOTHESIS 2 USING AN ALTERNATIVE PRODUCT MARKETCOMPETITION MEASURE(1) Low-competition sample(PMCD = 0)
Intercept
Response
Response × Neg. Transfer
Response × Weak Transfer
Neg. Transfer
Weak Transfer
Peer’s Ann. Return
Ann. Returnt-1
logMV
logBM
Return 1mo.
Return 6mo.
Ann. Lag
ROA
ΔROA
Std. ROA
Std. ReturnStd. Industry Return
Cross Own.
Stable Own.
Year Dummies
Industry Dummies
Adj. R2
Obs.
Response + Response × Neg. Transfer
Response + Response × Weak Transfer(2) High-competition sample(PMCD = 1)

Pred. sign

Coeff.

t-stat.

Coeff.

t-stat.

?
−
−
?
?
?

0.024***-0.027-0.0340.000
0.001-0.001
0.003
0.081***-0.0000.003**-0.006-0.018***-0.002*-0.055***0.070***-0.050***-0.022
0.060
0.008
0.004

(3.113)(-1.235)(-0.961)(0.003)(0.494)(-0.735)(0.163)(4.605)(-0.236)(2.247)(-1.168)(-3.825)(-1.793)(-3.074)(4.190)(-2.812)(-0.953)(0.766)(1.228)(1.158)0.058***-0.007-0.174**0.026-0.000-0.002-0.0020.074***
0.001
0.004
0.023**-0.026***-0.0030.008
0.064**
0.026-0.020-0.443**-0.002-0.010*(4.852)(-0.112)(-2.402)(0.299)(-0.059)(-0.505)(-0.119)(3.539)(0.630)(1.530)(2.372)(-3.739)(-1.146)(0.203)(2.155)(0.555)(-0.602)(-2.228)(-0.137)(-1.922)

+
+
−

+
−

+
−

+
+
−
−
−
?
?

Yes
Yes
0.060
17,213
Coeff.
-0.061**-0.027

Yes
Yes
0.084
4,893
Prob. > F
(0.025)(0.572)

Coeff.
-0.181***
0.019

Prob. > F
(0.002)(0.771)

Notes: Table 11 reports the regression results of Model 3. All sequential variables are winsorized
at 1% and 99% of the respective distributions. The t-statistics in parentheses are corrected forheteroscedasticity and cross-sectional and time-series correlation using a two-way cluster at thefirm and year levels, as proposed by Petersen (2009). ***, **, and * indicate the value is significantly
different from 0 at the 1%, 5%, and 10% levels, respectively, using a two-tailed t-test. Please see
the definition of variables in the Appendix.

(t = 1.514), suggesting that investor reactions to positive and negative news do not differ. Therefore,
I conclude that the sign of the first announcer’s news does not affect the results of this study.
Third, I investigate the effect of the first and late announcers’ earnings announcements on
overreaction to information transfers. When the late announcer’s earnings announcement date is

Kitagawa: Product Market Competition and Overreaction to Intra-Industry Information Transfers

25

TABLE 12. EFFECT OF PRODUCT MARKET COMPETITION ON OVERREACTIONCONTROLLING FOR UNCERTAINTY
(1)

(2)

(3)

Pred. sign

Coeff.

t-stat.

Coeff.

t-stat.

Coeff.

t-stat.

Intercept?

0.024***(3.385)0.024***(3.446)0.024***(3.428)

Response−-0.053**(-2.575)-0.096*(-1.904)-0.099*(-1.879)

Response × HHID−

-0.053**(-2.062)-0.050**(-2.005)-0.052**(-2.093)

HHID
Response × Std. Return

?
−

0.003
0.145

(0.386)(0.790)0.003(0.397)

0.003
0.071

(0.395)(0.431)

Response × Std. Industry Return

−

1.044

(1.266)

0.949(1.290)Peer’s Ann. Return
Ann. Returnt-1
logMV

+
+
−

0.008
0.081***-0.000(0.584)(4.669)(-0.187)

0.008
0.080***-0.000(0.558)(4.697)(-0.183)

0.008
0.081***-0.000(0.559)(4.685)(-0.182)

logBM
Return 1mo.
Return 6mo.
Ann. Lag
ROA
ΔROA
Std. ROA
Std. ReturnStd. Industry Return

+
−
+
−
+
+
−
−
−

0.003**-0.001-0.019***-0.002*-0.044***0.068***-0.033*-0.0190.041(2.521)(-0.269)(-3.893)(-1.838)(-2.781)(4.321)(-1.814)(-0.786)(0.570)0.003**-0.001-0.019***-0.002*-0.044***0.068***-0.033*-0.0190.037(2.489)(-0.296)(-3.906)(-1.838)(-2.799)(4.356)(-1.826)(-0.804)(0.525)0.003**-0.001-0.019***-0.002*-0.044***0.068***-0.033*-0.0190.037(2.507)(-0.288)(-3.895)(-1.839)(-2.788)(4.350)(-1.825)(-0.801)(0.520)

Cross Own.
Stable Own.
Year Dummies
Industry Dummies

?
?

0.006
0.001

(0.905)(0.392)

0.006
0.001

(0.911)(0.381)

0.006
0.001

(0.912)(0.388)

Yes
Yes

Yes
Yes

Yes
Yes

Adj. R2

0.061

0.061

0.061

Obs.

22,106

22,106

22,106

Notes: Table 12 reports the regression results of Models 2 and 3 controlling for uncertainty. All
sequential variables are winsorized at 1% and 99% of the respective distributions. The t-statistics
in parentheses are corrected for heteroscedasticity and cross-sectional and time-series correlation
using a two-way cluster at the firm and year levels, as proposed by Petersen (2009). ***, **, and *indicate the value is significantly different from 0 at the 1%, 5%, and 10% levels, respectively, using
a two-tailed t-test. Please see the definition of variables in the Appendix.

sufficiently distant from the first announcer’s date, the late announcer’s overreaction can be expected
to be adjusted by other firms’ announcements during the period. Thus, I include the interaction
term of Response and Ann.Lag (Response × Ann.Lag) in the model (2). Response and Ann.Lag aredefined in Section 3.2.1. Supposing that the timing of the late announcer’s earnings announcement
is related to the magnitude of the overreaction to information transfers, the coefficient of Response
× Ann.Lag will be statistically significant. The untabulated result shows that the coefficient of
Response × Ann.Lag is positive (0.024) and statistically insignificant (t = 1.133). Meanwhile, thecoefficient of Response × HHID is negative (-0.053) and remains significant (t = -2.006). These
results imply that the time lag between the first and late announcers’ earnings announcements is
not related to the overreaction to information transfers.

26

The Japanese Accounting Review, 11 (2021), 1-32

The final robustness check uses a sample that includes late announcers whose earnings
announcement dates are not five days apart from the first announcer’s earnings announcement date.
The number of observations increased from 22,106 to 23,179. The results, which are not tabulated,from the re-estimated Model 2 using all late announcers show that the coefficient of Response (0.072) is significantly negative (t = -2.079) only when product market competition is high (HHID= 1). Additionally, in Model 2, with the interaction term of Response and HHID, the coefficient of
Response × HHID remains negative (-0.056) and significant (t = -1.988). The regression results ofModel 3 reveal that the coefficient of Response × Neg. Transfer (-0.161) is significantly negative.
These results are consistent with Hypotheses 1 and 2.

7. Summary and Conclusion
This study hypothesizes and finds that overreactions to a first announcer’s earnings
announcement are more pronounced when firms face high product market competition. In addition,
I find that overreactions in competitive environments are driven by negative transfers and not by
positive transfers. The results imply that investors do not fully understand the differential
persistence between industry-wide and firm-specific news released by the industry, which isconsistent with the functional fixation hypothesis. Furthermore, additional analyses reveal that
overreactions to information transfers are observed less when firms are less competitive. Specifically,
when the announcement firms are bellwether firms or firms in industries with high sales growth,
an overreaction is not observed. The results are robust when I use an alternative measure of productmarket competition. The findings make academic and practical contributions, showing that productmarket competition can lead to stock market mispricing in the context of information transfers.
This study had some limitations for future research. First, I did not control for the effects ofsimultaneous events. Some studies have found that other events, including share repurchaseannouncements (Hertzel 1991; Erwin and Miller 1998; Massa et al. 2007), merger and acquisition
announcements (Eckbo 1983; Stillman 1983; Akhigbe and Madura 1999), stock split
announcements (Slovin et al. 1995; Tawatnuntachai and D’Mello 2002), and equity offering
announcements (Szewczyk 1992), cause information transfers. If these events occur concurrently
with earnings announcements, they can affect the results of this study. Second, although this study
focuses on the Japanese stock market, future research could examine whether the findings of thisstudy can be generalized to other countries’ stock markets.

REFERENCES
Abegglen, J. C., and G. Stalk. 1985. Kaisha, the Japanese Corporation. New York: Basic Books.
Akhigbe, A., and J. Madura. 1999. The industry effects regarding the probability of takeovers.
Financial Review 34 (3): 1–17.
Ali, A., J. Ronen, and S. H. Li. 1994. Discretionary disclosures in response to intra-industry
information transfers. Journal of Accounting, Auditing and Finance 9 (2): 265–282.
Anilowski, C., M. Feng, and D. J. Skinner. 2007. Does earnings guidance affect market returns?
The nature and information content of aggregate earnings guidance. Journal of Accounting andEconomics 44 (1): 36–63.

Kitagawa: Product Market Competition and Overreaction to Intra-Industry Information Transfers

27

Aoki, M., and H. Patrick. 1995. The Japanese Main Bank System: Its Relevance for Developing andTransforming Economies. Oxford University Press.
Asaba, S. 2002. Nihon Kigyo no Kyoso Genri: Doshitsuteki Kodo no Jissho Bunseki (Competitive
Principles of Japanese Firms: Empirical Analyses on Similar Behavior). Toyo Keizai Shinposha (in Japanese).
Ayers, B., and R. N. Freeman. 1997. Market assessment of industry and firm earnings information.
Journal of Accounting and Economics 24 (2): 205–218.
Baginski, S. P. 1987. Intraindustry information transfers associated with management forecasts ofearnings. Journal of Accounting Research 25 (2): 196–216.
Barth, M. E., and E. C. So. 2014. Non-diversifiable volatility risk and risk premiums at earnings
announcements. The Accounting Review 89 (5): 1579–1607.
Bloomfield, R., R. Libby, and M. W. Nelson. 2000. Underreactions, overreactions and moderated
confidence. Journal of Financial Markets 3 (2): 113–137.
Bonsall, S. B., Z. Bozanic, and P. E. Fischer. 2013. What do management earnings forecasts convey
about the macroeconomy? Journal of Accounting Research 51 (2): 225–266.
Brochet, F., K. Kolev, and A. Lerman. 2018. Information transfer and conference calls. Review of
Accounting Studies 23 (3): 907–957.
Chan, L. K. C., N. Jegadeesh, and J. Lakonishok. 1996. Momentum strategies. The Journal of
Finance 51 (5): 1681–1713.
Cheng, C. S. A., and J. D. Eshleman. 2014. Does the market overweight imprecise information?
Evidence from customer earnings announcements. Review of Accounting Studies 19 (3): 1125–
1151.
Cooper, R. 1995. When Lean Enterprises Collide. Boston, MA: Harvard Business School Press.
Docking, D. S., M. Hirschey, and E. Jones. 1997. Information and contagion effects of bank loanloss reserve announcements. Journal of Financial Economics 43 (2): 219–239.
Douthett, Jr, E. B., and K. Jung. 2001. Japanese corporate groupings (keiretsu) and theinformativeness of earnings. Journal of International Financial Management and Accounting 12(2): 133–159.
Easton, P., G. Gao, and P. Gao. 2010. Pre-earnings announcement drift. Available at :http://ssrn.com/abstract=1786697.
Eckbo, B. E. 1983. Horizontal mergers, collusion, and stockholder wealth. Journal of Financial
Economics 11 (1-4): 241–273.
Elgers, P. T., S. L. Porter, and L. U. Emily Xu. 2008. The timing of industry and firm earnings
information in security prices: A re-evaluation. Journal of Accounting and Economics 45 (1): 78–
93.
Erwin, G. R., and J. M. Miller. 1998. The intra‐industry effects of open market share repurchases:
Contagion or competitive? The Journal of Financial Research 21 (4): 389–406.
Fama, E. F., and K. R. French. 1993. Common risk factors in the returns on stocks and bonds.
Journal of Financial Economics 33 (1): 3–56.
Fan, J. P., and T. J. Wong. 2002. Corporate ownership structure and the informativeness ofaccounting earnings in East Asia. Journal of Accounting and Economics 33 (3): 401–425.

28

The Japanese Accounting Review, 11 (2021), 1-32

Firth, M. 1976. The impact of earnings announcements on the share price behaviour of similar typefirms. The Economic Journal 86 (342): 296–306.
Foster, G. 1981. Intra-industry information transfers associated with earnings releases. Journal of
Accounting and Economics 3 (3): 201–232.
Freeman, R., and S. Tse. 1992. An earnings prediction approach to examining intercompany
information transfers. Journal of Accounting and Economics 15 (4): 509–523.
Givoly, D., and D. Palmon. 1982. Timeliness of annual earnings announcements: Some empiricalevidence. The Accounting Review 57 (3): 486–508.
Gleason, C. A., N. T. Jenkins, and W. B. Johnson. 2008. The contagion effects of accounting
restatements. The Accounting Review 83 (1): 83–110.
Griffin, D., and A. Tversky. 1992. The weighing of evidence and the determinants of confidence.
Cognitive Psychology 24 (3): 411–435.
Hann, R. N., H. Kim, and Y. Zheng. 2019. Intra-industry information transfers: Evidence fromchanges in implied volatility around earnings announcements. Review of Accounting Studies 24(3): 927–971.
Hertzel, M. G. 1991. The effects of stock repurchases on rival firms. The Journal of Finance 46 (2):707–716.
Hoshi, T., A. Kashyap, and D. Scharfstein. 1990. The role of banks in reducing the costs of financial
distress in Japan. Journal of Financial Economics 27 (1): 67–88.
Hui, K. W., K. K. Nelson, P. E. Yeung. 2016. On the persistence and pricing of industry-wide andfirm-specific earnings, cash flows, and accruals. Journal of Accounting and Economics 61 (1): 185–
202.
Hui, K. W., and P. E. Yeung. 2013. Underreaction to industry-wide earnings and the post-forecast
revision drift. Journal of Accounting Research 51 (4): 701–737.
Iwasaki, T., N. Kitagawa, and A. Shuto. 2014. The effect of product market competition on
discretionary management forecasts. Proceedings In 2015 Midyear Meeting of theInternational Accounting Section of the American Accounting Association.
Jegadeesh, N. 1990. Evidence of predictable behavior of security returns. The Journal of Finance 45(3): 881–898.
Jegadeesh, N., and S. Titman. 1993. Returns to buying winners and selling losers: Implications forstock market efficiency. The Journal of Finance 48 (1): 65–91.
Jiang, G., C. M. C. Lee, and Y. Zhang. 2005. Information uncertainty and expected returns. Review
of Accounting Studies 10 (2): 185–221.
Karuna, C. 2007. Industry product market competition and managerial incentives. Journal of
Accounting and Economics 43 (2-3): 275–297.
Kim, Y., M. Lacina, and M. S. Park. 2008. Positive and negative information transfers frommanagement forecasts. Journal of Accounting Research 46 (4): 885–908.
Kitagawa, N. 2014. Accounting information transfers and market efficiency. Journal of Economics
and Business Administration 210 (6): 59–74 (in Japanese).
Koo, D. S., J. J. Wu, and P. E. Yeung. 2017. Earnings attribution and information transfers.
Contemporary Accounting Research 34 (3): 1547–1579.

Kitagawa: Product Market Competition and Overreaction to Intra-Industry Information Transfers

29

Lang, M., and R. Lundholm. 1996. The relation between security returns, firm earnings, andindustry earnings. Contemporary Accounting Research 13 (2): 607–629.
Lang, L. H. P., and R. Stulz. 1992. Contagion and competitive intra-industry effects of bankruptcy
announcements. Journal of Financial Economics 32 (1): 45–60.
Li, X. 2010. The impacts of product market competition on the quantity and quality of voluntary
disclosures. Review of Accounting Studies 15 (3): 663–711.
Markarian, G., and J. Santalo. 2014. Product market competition, information and earnings
management. Journal of Business Finance and Accounting 41 (5-6): 572–599.
Massa, M., Z. Rehman, and T. Vermaelen. 2007. Mimicking repurchases. Journal of Financial
Economics 84 (3): 624–666.
Okumura, M. 2013. Contagion effects of earnings restatements and reliability of accounting
information. Contemporary Disclosure Research 13: 137–151 (in Japanese).
Otogawa, K., and H. Yamaji. 2006. Corporate groups and stock pricing in Japan: Informationtransfer effects of earnings forecast revision. Kobe Economic and Business Review 51: 11–31.
Pandit, S., C. E. Wasley, and T. Zach. 2011. Information externalities along the supply chain: The
economic determinants of suppliers’ stock price reaction to their customers’ earnings
announcements. Contemporary Accounting Research 28 (4): 1304–1343.
Petersen, M. A. 2009. Estimating standard errors in finance panel data sets: Comparing approaches.
Review of Financial Studies 22 (1): 435–480.
Ramalingegowda, S., T. Shu, and E. Yeung. 2012. Moderated confidence and under- and overreactions
to related firms’ news. Available at : http://ssrn.com/abstract=1785340.
Ramnath, S. 2002. Investor and analyst reactions to earnings announcements of related firms: An
empirical analysis. Journal of Accounting Research 40 (5): 1351–1376.
Schipper, K. 1990. Information transfers. Accounting Horizons 4 (4): 97–107.
Shuto, A., and T. Iwasaki. 2014. Stable shareholdings, the decision horizon problem and earnings
smoothing. Journal of Business Finance and Accounting 41 (9-10): 1212–1242.
Shuto, A., and N. Kitagawa. 2011. The effect of managerial ownership on the cost of debt: Evidence
from Japan. Journal of Accounting, Auditing and Finance 26 (3): 590–620.
Sloan, R. G. 1996. Do stock prices fully reflect information in accruals and cash flows about future
earnings? The Accounting Review 71 (3): 289–316.
Slovin, M. B., M. E. Sushka, and S. R. Ferraro. 1995. A comparison of the information conveyed
by equity carve-outs, spin-offs, and asset sell-offs. Journal of Financial Economics 37 (1): 89–
104.
Stillman, R. 1983. Examining antitrust policy towards horizontal mergers. Journal of Financial
Economics 11 (1-4): 225–240.
Szewczyk, S. H. 1992. The intra‐industry transfer of information inferred from announcements ofcorporate security offerings. The Journal of Finance 47 (5): 1935–1945.
Tawatnuntachai, O., and R. D’Mello. 2002. Intra‐industry reactions to stock split announcements.
Journal of Financial Research 25 (1): 39–57.
Thomas, J., and F. Zhang. 2008. Overreaction to intra-industry information transfers? Journal of
Accounting Research 46 (4): 909–940.
Tirole, J. 1988. The Theory of Industrial Organization. Cambridge: The MIT Press.

30

The Japanese Accounting Review, 11 (2021), 1-32

Tversky, A., and D. Kahneman. 1974. Judgment under uncertainty: Heuristics and biases. Science
185 (4157): 1124–1131.
Wang, C. 2014. Accounting standards harmonization and financial statement comparability:
Evidence from transnational information transfer. Journal of Accounting Research 52 (4): 955–
992.
Waring, G. 1996. Industry differences in the persistence of firm-specific returns. The American
Economic Review 86 (5): 1253–1265.
Zhang, X. F. 2006. Information uncertainty and stock returns. The Journal of Finance 61 (1): 105–137.

Kitagawa: Product Market Competition and Overreaction to Intra-Industry Information Transfers

31

APPENDIX: DEFINITIONS OF VARIABLES
VariablesAnn. Return=Response=

Peer’s Ann. Return=

Ann. Returnt-1=

Return 1mo.

=

Return 6mo.

=

logMV

=

logBM

=

Definitions
Late announcer’s three-day market-adjusted abnormal return aroundthe late announcer’s earnings announcement date in year t. Marketadjusted abnormal return is calculated as raw return minus valueweighted return excluding financial institutions. Value-weighted
market return is calculated based on the market value of the equityof all the listed firms except for financial institutions, preferred
securities, Japanese real estate investment trusts ( J-REITs), andspecial investment corporations. The market values of equity arecalculated based on the stock price at the end of the previous
operating day. The three-day window is centered on the lateannouncer’s earnings announcement date for year t ([-1, +1]).
Late announcer’s three-day market-adjusted abnormal return aroundthe first announcer’s earnings announcement date in year t. Marketadjusted abnormal return is calculated in the same way as for Ann.
Return. The three-day window is centered on the first announcer’s
earnings announcement date in the industry for year t ([-1, +1]).
First announcer’s three-day market-adjusted abnormal return aroundthe first announcer’s earnings announcement date in year t. Marketadjusted abnormal return is calculated in the same way as for Ann.
Return. The three-day window is centered on the first announcer’s
earnings announcement date in the industry for year t ([-1, +1]). If
there are multiple first announcers for the industry in year t, Peer’sAnn. Return is calculated as an arithmetic mean of their Peer’s Ann.
Return.
Late announcer’s three-day market-adjusted abnormal return aroundthe late announcer’s earnings announcement date in year t-1.
Market-adjusted abnormal return is calculated in the same way as forAnn. Return. The three-day window is centered on the lateannouncer’s earnings announcement date for year t-1 ([-1, +1]).
One-month buy-and-hold stock return ending one week before thelate announcer’s earnings announcement date in year t.
Six-month buy-and-hold stock return ending one week before thelate announcer’s earnings announcement date in year t.
The late announcer’s natural logarithm of the market value of equity
in year t. Market value of equity equals the closing price times theshares outstanding at the end of the fiscal year t.
The late announcer’s natural logarithm of book-to-market ratio inyear t. Book-to-market ratio is defined as net assets divided by market
value of equity at the end of the fiscal year t.

The Japanese Accounting Review, 11 (2021), 1-32

32

Ann. Lag

=

ROA

=

ΔROA
Std. ROA==

Std. Return=Std. Industry Return=

Cross Own.

=

Stable Own.

=

HHID

=

Neg. Transfer=

Weak Transfer=

NIKKEI500=

Industry Growth

=

PMCD

=

The natural log of the difference between the first and lateannouncers’ earnings announcement dates in year t.
Late announcers’ ROA in year t. ROA is calculated as net income foryear t divided by total assets at the end of year t-1.
Late announcers’ change in ROA in year t.
The ROA volatility of late announcers measured over the past five
years.
The stock return volatility of late announcers measured over the past
12 months.
The industry stock return volatility of late announcers measured overthe past 12 months.
The fraction of late announcers’ cross-shareholdings among therelated firms at the end of year t. Cross-shareholders include crossshareholding domestic companies listed on the Japanese stock
markets at the end of year t.
The fraction of late announcers’ stable shareholdings at the end ofyear t. Stable shareholders include financial institutions, trust banks,and other financial institutions (e.g. brokerage companies andsecurities financing companies). They also include parent companies
and exclude domestic companies that are defined as crossshareholders.
The dummy variable, which is equal to 1 if HHI is in the fourth
quartile (this quartile contains the firms with the highest HHI ineach year) and 0 otherwise. HHI is defined as the sales-based HHItimes minus one.
The dummy variable, which is equal to 1 if Response×Peer’s Ann.
Return is in the first quartile (this quartile contains the firms withlarge negative information transfers) and 0 otherwise.
The dummy variable, which is equal to 1 if Response×Peer’s Ann.
Return is in the second or third quartiles and 0 otherwise.
The dummy variable, which is equal to 1 if firms are included in the
Nikkei 500 index and 0 otherwise.
The dummy variable, which is equal to 1 if the growth ratio ofindustry sales is in the fourth quartile and 0 otherwise.
The dummy variable, which is equal to 1 if PMC is in the fourth
quartile and 0 otherwise. PMC is the competition measure for year tusing factor analysis based on HHI, product substitutability (DIFF),
market size (MKTSIZE), and entry cost (ENTCOST).