Discussion Papers In Economics
And BusinessAccounting comparability and the accuracy of management earnings
forecasts in Japan

Wenjun Kuang

Discussion Paper 23-07

May 2023

Graduate School of Economics
Osaka University, Toyonaka, Osaka 560-0043, JAPANAccounting comparability and the accuracy of management earnings
forecasts in Japan*
Wenjun Kuang†
Abstract
This study examines the influence of accounting comparability on management earnings
forecast accuracy. Using the Japanese setting, where management earnings forecasts are
effectively mandated and accounting comparability could be more exogenous than in othercountries, I find a positive relationship between accounting comparability and management
earnings forecast accuracy. This indicates that comparable financial information could improve
the accuracy of management earnings forecasts. I also provide evidence that the positive
relationship between accounting comparability and management earnings forecast accuracy ismore pronounced when subject firms have higher institutional ownership and peer firms havehigher accounting (i.e., accrual) quality. My results suggest that accounting comparability
increases the disclosure quality of management earnings forecasts by enhancing investors’monitoring of managerial behaviours and facilitating managerial learning from peer firms.
Additional analyses show that the positive effect of accounting comparability on management
forecast accuracy becomes strengthened after the 2008 quarterly reporting regulation in Japan.
However, there is no evidence showing that International Financial Reporting Standards (IFRS)
adoption increases the positive effect of comparability. Overall, this study enriches the literature
on determinants of the accuracy of management earnings forecasts and highlights the positive
consequences of accounting comparability in the Japanese capital market.
Keywords: Accounting comparability; Management earnings forecast; Monitoring effect;
Learning effect
JEL: M41; M48*

This work was supported by JST SPRING, Grant number JPMJSP2138. I would like to express thanks toProfessor Atsushi Shiiba and Professor Katsuhiko Muramiya for their helpful comments and suggestions. I thankProfessor Shin-ichiro Ono and Professor Shota Otomasa for their outstanding research support. I am also gratefulfor the suggestions made by anonymous reviewers at the 2023 Annual Meeting of the American Accounting
Association.
†
Graduate School of Economics, Osaka University (Email: winniekwj@gmail.com)

1.Introduction
The credibility of management earnings forecasts has been a central issue for a long time (Kinget al., 1990; Kato et al., 2009). According to the prior literature, management earnings forecast
is one of the key disclosure mechanisms that may influence the market expectations of thefirm’s financial performance, the information asymmetry between insiders and investors, andmanagerial reputation for accurate and reliable reporting (Hirst et al., 2008; Preussner andAschauer, 2022). High-quality management earnings forecast plays an important role in thedevelopment of well-functioning capital markets. For example, it may affect the stock prices(Pownall et al., 1993; Baginski et al., 1993; Kato et al., 2009), decrease information asymmetry(Coller and Yohn, 1997), and influence analysts’ behaviours (Baginski and Hassell, 1990;
Chatalova et al., 2016). Although prior studies have investigated the effects of accounting
characteristics such as conservatism (e.g., Hui et al., 2009; Sun and Xu, 2012) and restatement(e.g., Muramiya and Takada, 2017) on management earnings forecasts, there is a lack of
research on whether accounting comparability, a unique cross-firm attribute of accounting
report quality, could affect managerial behaviours in predicting forward-looking estimates.
Given that comparability enhances the usefulness of financial statements for decision-making,I focus on examining the relationship between accounting comparability and management
earnings forecast accuracy in the Japanese setting, where management earnings forecasts are
effectively mandated and accounting comparability could be more exogenous than in othercountries.1
In this study, I argue that the accuracy of management earnings forecasts may increase withaccounting comparability because of the enhanced monitoring and learning effects. On one
hand, comparability can enable managers to learn from peer firms’ financial results.
Specifically, it can reduce the costs of information acquisition and enable managers to havemore available information about peer firms (De Franco et al., 2011). This can facilitate
managerial learning from the operation and strategies of comparable firms and allow managers
to better predict the impact of economic events in the future (Chen and Gong, 2019). By
reducing the uncertainty in judgments and evaluating firms’ relative performance moreaccurately, managers are more likely to predict correct forward-looking estimates (e.g., sales,
costs, and cash flows). On the other hand, comparability can enhance investors’ monitoring ofmanagerial behaviours. Due to the attenuated information asymmetry and improved
information environments, comparability can not only let external stakeholders better monitor
managers’ decision-making at lower monitoring costs, but also enable internal monitoring
agents like boards of directors to achieve a better understanding of firm performance anddiscipline managers from opportunistic reporting (Zhang et al., 2020). Therefore, managers infirms with higher comparability may be disincentivised towards opportunistic practices (Kimet al., 2016), and thus predict more reliable future estimates. In sum, I argue that the accuracyof management earnings forecasts will increase with the level of accounting comparability.
I focus on the Japanese setting because it is easier to verify the relationship between accounting
comparability and management earnings forecast accuracy in Japan. First, accounting
comparability could be more exogenous in Japan than in other countries. Specifically,
comparability between firms is considered largely fixed (especially in short-term periods) andexogenous to managerial discretion, which is the outcome of accounting standards andindustry-working rules (Chen and Gong, 2019). However, accounting reporting comparability
1Comparability can significantly increase the usefulness of accounting information because financial statement
users’ decisions often involve a choice between alternatives, which cannot be made rationally without comparative
information (Ahn et al., 2020).

1

is a function consisting of not only accounting standards, but also interpretation, auditing, andthe environment of regulation, litigation, and enforcement (Barth et al., 2012). Thus, accounting
comparability could be endogenously determined by managerial discretions. 2 For example,
Sohn (2016) claims that managers can change the level of the comparability of their financial
statements through various activities such as Mergers and Acquisitions (M&As) anddivestitures as the source of “creative accounting techniques”. According to prior studies(Odagiri and Hase, 1989; Jackson and Miyajima, 2008), M&As in Japan are not as popular as
in other countries such as the U.S. due to the Japanese management concept and labour practicestyle.3 Compared with American management, Japanese management prefers internal growth(e.g., the creation of subsidiaries) over external growth driven by M&As. Moreover, the lifetime
employment style in Japan makes managers resistant to hostile turnover attempts. Thereby,
Japanese firms tend to maintain stable operations and are less likely to be intruded on by M&As.
Besides, stable shareholdings (e.g., shareholdings owned by financial institutions) with longterm contracts and implicit arrangements dominate the Japanese equity market (Douthett andJung, 2001; Shuto and Kitagawa, 2011; Shuto and Iwasaki, 2014). Given that stable investors
aim to maintain long-term relationships with firms and do not intervene in management unlessa firm is found to be underperforming (Kaplan and Minton, 1994), managers in Japanese firms
are likely to keep a stable accounting function and fixed reporting style. As comparability
reflects the historical implementation in the process of identifying the optimal choice (e.g.,business models and production functions) with business characteristics (Sohn, 2016), which isbased on a firm’s accounting system selected and operated for a long time, managers rarely
exert discretions in changing their firms’ accounting comparability in stable operating andreporting environments. Therefore, I conjecture that the comparability among Japanese firms
is more likely to be exogenous to managerial discretion than in other countries.
Second, the disclosure system of management earnings forecasts is unique in Japan.
Specifically, management earnings forecast is effectively mandated for Japanese firms.4 Prior
studies have emphasized that managers in Japanese firms have stronger incentives to issue
accurate earnings forecasts because disclosure cannot signal the credibility of management(Kato et al., 2009; Ishida et al., 2021). Moreover, management earnings forecasts can be
regarded as an important performance target in compensation contracts (Suda and Hanaeda,
2008). Therefore, managers are more likely to issue accurate forecast numbers to avoid negative
economic consequences, establish a good forecast reputation, and increase personal
compensation. Additionally, the mandatory reporting system of earnings forecast in Japan
implies that using Japanese data could mitigate the self-selection problem, which is difficult tocontrol in prior studies using U.S. samples (e.g., Baik et al., 2011; Schabus, 2022). Besides,
Japanese management forecasts are mainly point-estimated forecasts (Kato et al., 2009), instead
2

Although accounting comparability is largely driven by exogenous determinants such as accounting standards,
accounting standards cannot completely eliminate subjectivity and diversity in the choice and application of
accounting methods (Chen and Gong, 2019).
3
Specifically, Odagiri and Hase (1989) show that only 7.5% of 899 manufacturing firms listed at the Tokyo Stock
Exchange (TSE) experienced M&A events during 1964–1984, while the percentage is five times higher at 38.4%of the 1,000 largest manufacturing firms in the U.S. during 1950–1972. Jackson and Miyajima (2008) indicate thatthe average annual deal volume of Japanese M&A relative to GDP is substantially lower than the other four
countries (the U.S., the U.K., France, and Germany) during 1991–2005, which occupies only 2.5% of GDP during
the boom period.
4
Although the disclosure of management forecasts is basically voluntary, the vast majority of listed firms in Japanare proactively reporting earnings forecasts. For example, Kato et al. (2009) find that 93.7% of listed firms present
their forecast estimates. Ishida et al. (2021) find that 93.5% of listed firms in their initial sample issue earnings
forecasts. Thus, the forecast disclosure system is regarded as statutorily enforceable and effectively mandated inJapan (Kato et al., 2009; Ishida et al., 2021).

2

of range-estimated forecasts in U.S. samples. This helps mitigate the measurement error
problem in calculating forecast accuracy. Considering the above reasons, I posit that it is better
to use the Japanese setting to verify the relationship between accounting comparability andmanagement earnings forecast accuracy.
I first hypothesize that there is a positive association between accounting comparability and theaccuracy of management earnings forecasts. According to prior studies, comparable financial
information can not only enhance investors’ monitoring of managerial behaviours but also
facilitate managerial learning from peer firms (Kim et al., 2016; Chircop et al., 2020). Therefore,
I predict that accounting comparability may increase the disclosure quality of management
earnings forecasts, as reflected in the higher accuracy of earnings forecasts. Furthermore, I inferthat the above positive relationship between accounting comparability and management
earnings forecast accuracy is influenced by firms’ monitoring and learning environments.
Specifically, I hypothesize that the positive association between comparability andmanagement forecast accuracy is stronger when subject firms have higher institutional
shareholdings and when peer firms have higher accounting (i.e., accrual) quality.
To test the above predictions, I follow prior studies (e.g., Kim et al., 2016; Zhang et al., 2020)
to use the method of De Franco et al. (2011) to construct the measure of accounting
comparability, which is the closeness of the accounting functions (how economic events arereflected in accounting numbers) between two firms in the same two-digit Nikkei industry.
Specifically, I generate three measures of accounting comparability: median comparability for
all firms in the same industry, average comparability for all firms in the same industry, andcomparability based on top-ten firms. Further, I follow Muramiya and Takada (2017) and Ishida
et al. (2021) to measure the accuracy of management earnings forecast as the absolute value of
initial management earnings forecast error scaled by the lagged market value of equity. Usinga sample of 17,318 firm-year observations from 2007 to 2019, I find that three measures ofaccounting comparability are negatively associated with the absolute value of initialmanagement earnings forecast error at the 1% level.
I further test whether the above relationship between accounting comparability andmanagement earnings forecast accuracy varies cross-sectionally. Specifically, to test the effectof shareholdings by institutional investors on the above relationship, I include the interactionsof three measures of accounting comparability with the institutional ownership in the model.
Similarly, to test the incremental effect of accounting quality on the positive relationshipbetween accounting comparability and management earnings forecast accuracy, I add theinteractions of accounting comparability measures with accounting quality to the model. The
results show that the positive relationship between accounting comparability and management
earnings forecast accuracy is stronger when subject firms have higher institutional ownershipand when peers firms exhibit higher accounting quality. My results suggest that accounting
comparability increases the disclosure quality of management earnings forecasts by enhancing
investors’ monitoring of managerial behaviours and facilitating managerial learning from peer
firms.
To test the robustness of the findings, this study follows previous literature (e.g., Kim et al.,2021; Kitagawa, 2021; Ishida et al., 2021) to generate two alternative proxies for management
earnings forecast accuracy (the magnitude of forecast revision and the absolute value of initial
forecast error deflated by lagged total assets) and two alternative proxies for accounting
comparability (comparability calculated based on the prices leading earnings model andcomparability calculated based on values of top-four firms). Overall, the results indicate that3the significant and positive relationship between accounting comparability and management
earnings forecast accuracy is robust to alternative measures of management forecast accuracy
and accounting comparability.
Further, I conduct additional analyses to examine the effect of the 2008 quarterly reporting
regulation and the 2010 IFRS adoption on the relationship between comparability andmanagement forecast accuracy. The results show that the positive relationship between
comparability and management forecast accuracy is strengthened after the enforcement of thequarterly reporting regulation. However, there is no evidence showing that IFRS adoption
increases the positive effect of accounting comparability.
This study contributes to the literature in several ways. First, this study contributes to theliterature that examines the determinants of management earnings forecast accuracy bysupplementing the influence of accounting comparability, which is a unique cross-firm attributeof accounting report quality. Related to this study, Chen and Gong (2019) examine therelationship between comparability and financial reporting quality. They find thatcomparability is positively associated with managerial forecast accuracy and precision,indicating that comparability improves the ability of managers to prepare higher-quality
financial statements and predict future firm performance. This study extends Chen and Gong
(2019) by considering two effects of accounting comparability—the learning and monitoring
effects—which increase the information quality used to forecast accurately and decrease themanagerial incentives to opportunistically report forecasts. 5 Moreover, this study proposes
several cross-sectional hypotheses on whether the positive relationship between accounting
comparability and management earnings forecast accuracy becomes stronger when the learningor monitoring effect gets enhanced. Second, this study enriches extant literature that examines
the benefits of accounting comparability. Particularly, this study provides evidence on theconsequence of comparability in the Japanese setting, where both learning and monitoring
effects of comparability are strong and thus easier to investigate. Specifically, non-transientinstitutional investors are more active in monitoring firms’ activities than transient institutional
investors (Bushee, 1998). Given that stable shareholdings owned by financial institutions
dominate the Japanese equity market (Shuto and Iwasaki, 2014), they are more likely to
discipline managers from opportunistic engagements. With sharper inferences aboutsimilarities and differences across firms that comparability provides, they could better monitor
managerial decision-making. Moreover, managers in Japan are more motivated to learn frompeer firms’ information. They carefully observe the activities of peer firms and regard theindustry-average performance as an important benchmark (Yamaguchi, 2022). Thus, managers
may have stronger incentives to investigate the comparable financial statement of peer firms
and learn from their strategies and operations. Third, this study adds to the growing literaturethat examines the consequences of IFRS adoption by providing evidence in Japan, where firms
adopt IFRS voluntarily. Particularly, on June 30, 2009, the Business Accounting Council (BAC)of Japan announced an interim report to allow the optional adoption of IFRS starting in 2010
(BAC, 2009). Given the increasing number of firms adopting IFRS in Japan, examining theeffects of IFRS adoption on the relationship between comparability and management forecast

5

Note that Chen and Gong (2019) find that the coefficient of comparability is only significant for restatement
firms classified as resulting from non-intentional actions. It is not significant for intentional firms, indicating thatcomparability is associated with a reduced likelihood of restatements resulting from non-intentional errors. This
study argues that intentional bias of earnings forecasts that are driven by managerial incentives to opportunisticallyreport forecasts will decrease as accounting comparability increases.

4

accuracy is very important for firms and investors. 6 Finally, this study makes a practicalcontribution to policy-makers. The results of this study show that comparability could improve
the accuracy of management earnings forecasts, indicating the usefulness of comparability inenhancing disclosure quality. The implications of this study suggest that it is important to havepolicies that increase accounting comparability to improve market efficiency and information
environments in the capital markets of Japan and other countries.
The remainder of this paper is structured as follows. Section 2 provides a literature review anddevelops the hypotheses. Section 3 describes the research design. Section 4 shows the empiricalresults for the baseline test and cross-sectional tests. Section 5 presents the results of robustness
checks and additional analyses. Section 6 concludes the study.
2.Literature Review and Hypothesis Development
2.1 Literature Review
The roles of accounting comparability: Monitoring effect and learning effectAccounting comparability is an important qualitative characteristic of accounting information.
The FASB defines comparability as the “qualitative characteristic that enables users to identify
and understand similarities in, and differences among, items” (FASB, 2018, pp. 5). According
to prior studies, there are two roles of accounting comparability: (1) the monitoring effect(Zhang et al. 2020; Kim et al., 2021); (2) the learning effect (Chircop et al., 2020; Chircop,2021).
First, comparability could serve as an effective governance tool in monitoring and governing
managerial behaviours. Specifically, comparability can lower the costs of acquiring informationand increase the quantity and quality of information (De Franco et al., 2011). Thus, information
becomes more transferrable within and among comparable firms, which will reduce themonitoring costs of internal and external monitoring agents (Zhang et al., 2020). As a result,investors are more able to detect managers’ misuse of corporate resources and monitor
managers’ behaviours such as investment decisions and the allocation of resources (Ahn et al.,2020). Therefore, accounting comparability could play an important role in mitigating agency
problems.
A growing body of empirical studies supports this view. For instance, Kim et al. (2016) findthat investors’ perceived level of expected crash risk is lower for firms with higher accounting
comparability, suggesting that comparability could disincline managers from bad news
withholding. Chen et al. (2018) find that acquirers have higher acquisition profitability whentarget firms’ financial statements are more comparable, showing that comparability of target
firms makes it easier for boards and outside shareholders’ to monitor managers’ M&A decisions.
More recently, Zhang et al. (2020) show that comparability is negatively associated with thedegree of inefficiency in labour investments. Kim et al. (2021) find that financial statementcomparability is positively associated with the marginal value of cash holdings.
Second, comparable accounting information of peer firms can facilitate the subject firms’
learning process (Chircop et al., 2020). According to the theories of observational learning and6

For instance, Gu (2021) finds that about 4.8% of Japanese listed firms chose to report financial statements basedon IFRS in 2015.

5

informational cascades, a firm would imitate other firms’ decisions, especially when other firms
in the industry appear to have greater expertise (“fashion leaders”) (Bikhchandani et al., 1998).
Greater accounting comparability will provide managers with more understandable andinformative information (Chircop et al., 2020). By gaining more insight into peer firms’
information, managers may infer the best choice in decision-making (Devenow and Welch,1996).
Consistent with this view, Chircop et al. (2020) show that subject firms with greater accounting
comparability exhibit higher innovative efficiency. They argue that comparability enhances theability to predict future cash flows from R&D investments with uncertain outcomes. Chircop
et al. (2021) find that comparability increases firm productivity, indicating that comparable
information enables subject firms to better identify and understand the productivity-enhancing
practices of peer firms. Most recently, Hong et al. (2023) find that comparability is negatively
associated with the tax distance between a focal firm’s tax avoidance and those of its industry
peers.
Determinants of the accuracy of management earnings forecasts
Accounting information to forecast
One important factor affecting managers’ forecasting quality is the availability of high-quality
information about firms’ inside operations and outside environments (Goodman et al., 2014).
Although managers may have much private information about their firms, it would be difficult
for them to estimate future prospects accurately without access to high-quality internal financial
data, for example, data on the cost structure, sales, investing activities, and fixed/intangible
assets (Muramiya and Takada, 2017). To make accurate predictions, they may attempt to collectand synthesize all public information that is useful for generating predictions (Schabus, 2022).
Specifically, they could utilize integrated internal accounting information as input to create
their forecasts, or they may acquire and process information about the industry and economywide prospects.
In line with the above theory, Feng et al. (2009) document that managers in firms withineffective internal controls issue less accurate forecasts due to erroneous internal management
reports when forming their guidance. Muramiya and Takada (2017) find that restatement firms
report less accurate management forecasts, showing that high-quality input data are essential
for managers to formulate accurate forecasts. Most recently, Chen et al. (2022) find that
corporate site visits increase management forecast accuracy, suggesting that corporate site visits
could serve as a communication channel through which managers learn from investors. Schabus(2022) shows that there is a positive relationship between firms’ direct and indirect board
connections and management earnings forecast accuracy since managers may benefit fromwell-connected directors in learning macroeconomic developments.
Managerial incentives
The fundamental intention of management earnings forecasts is to provide stakeholders withcredible and unbiased information about a company’s future performance, which could be
regarded as the corporate objective (Preussner and Aschauer, 2022). For example, managers
may issue reliable initial forecasts to reduce the degree of information asymmetry (Coller and
Yohn, 1997). However, managers may issue earnings forecasts for their own self-interest (Hirst6

et al., 2008). They are likely to have plenty of discretions in calculating the forecasts andmanage the forecasts purposely to satisfy personal incentives, which is consistent with the“opportunistic behaviours” view from the theoretical implications of positive accounting theory(Iwasaki et al., 2023). For instance, Aboody and Kasznik (2000) find that managers are more
likely to issue bad news forecasts if they receive options shortly before earnings announcements,
showing that managers manipulate investors’ expectations around stock option awards for
personal gains. Rogers and Stocken (2005) find that managers mispresent the forecasts toincrease benefits from insider tractions. Cheng and Lo (2006) find that managers exploit
disclosure policies of earnings forecasts for trading profits when litigation risk is sufficiently
low. More recently, Wang and Zhang (2022) suggest that managers are more likely to issue
soft-talk cash flow forecasts when their equity-based compensation is higher.
Consistent with the view that managers could bias forecasts strategically, a great number ofstudies show that the improvement of corporate governance and monitoring mechanisms may
mitigate their self-interests motivated behaviours in earnings forecasts. For example, Baginski
and Hassell (1997) find that managers produce more precise earnings forecasts for firms withgreater analyst following. Karamanou and Vafeas (2005) show that managers are more likely
to make or update earnings forecasts in firms with more effective board and audit committee
structures, and the forecast accuracy increases with better governance. Ajinkya et al. (2005)
find that firms with more outside directors and higher institutional ownership produce more
frequent and accurate forecasts.
Management forecast system in Japan
In Japan, the Financial Instruments and Exchange Act (the Act) requests listed firms to submit
the annual securities report (Yuka Shoken Houkokusho) within three months of the end of the
fiscal year. To supplement the lack of timeliness in statutory disclosure under the Act, listed
firms are required by Japanese stock exchanges to publish financial highlights (Kessan Tanshin,or “summary of financial statements”), which could be called the Timely Disclosure Rules(Kato et al., 2009). In addition, Japanese stock exchanges strongly encourage Japanese firms toproactively disclose future estimates relating to the company’s outlook for future performanceand financial situation (e.g., forecasts for sales, ordinary income, net income, and earnings per
share) together with actual financial results for the current year (The Kessan Tanshin
Guidelines). Besides, listed firms are required to revise their forecasts if there are changes inearnings estimates of 30% or more (the “Significance Rule” of the Act; Kato et al., 2009; Ishida
et al., 2021).
Although the disclosure of management forecasts is basically voluntary, the vast majority oflisted firms in Japan are proactively reporting earnings forecasts. For example, Kato et al. (2009)find that 93.7% of listed firms present their forecast estimates. Ishida et al. (2021) find that 93.5%
of listed firms in their initial sample issue earnings forecasts. Thus, the forecast disclosuresystem is regarded as statutorily enforceable and effectively mandated in Japan (Kato et al.,2009; Ishida et al., 2021). This unique feature of Japanese financial reporting implies that using
Japanese data could mitigate the self-selection problem, which is difficult to control in studies
focusing on U.S. samples (e.g., Baik et al., 2011; Schabus, 2022). In addition, using Japanesemanagement forecasts provides several other advantages. For example, as management
earnings forecasts in Japan are bundled with earnings announcements, management forecast
accuracy is less biased by timing issues (Nagata and Nguyen, 2017; Kitagawa, 2021). Besides,

7

Japanese management forecasts are mainly point-estimated forecasts (Kato et al., 2009), which
could mitigate the measurement error problem in calculating forecast accuracy.
2.2 Hypothesis Development
Accounting comparability and the accuracy of management earnings forecasts
Comparable financial reporting of peer firms enables managers to better predict future
prospects by facilitating the learning process. According to prior studies, a manager’s reportingand decision-making behaviours may be influenced by the financial information of peer firms(Chircop et al., 2020; Chircop, 2021). As comparability reduces the costs of informationacquisition and process, managers are likely to have more available and understandable
information from peer firms (De Franco et al., 2011). 7 For example, they may have more
information about industry-level demand and cost conditions (Durnev and Mangen, 2009).
They could also know how competitors perceive and react to market developments (Schabus,2022). Such information could enable managers to learn from the operation and strategies ofcomparable firms and have a better understanding of the economic event. This argument ismore pronounced in the Japanese setting. In Japan, managers are more likely to regard
information about peer firms as an important information source and are more motivated tolearn from such information. For example, Yamaguchi (2022) finds that Japanese firms havestrong incentives to achieve the benchmark of industry-average profitability (e.g., forecast
earnings).8 Comparability reduces the uncertainty in judgments and thus may help managers toevaluate the relative performance of their firms and predict the impact of economic events inthe future (Chen and Gong, 2019). 9 With increased quality and quantity of information,
managers are able to predict forward-looking estimates (e.g., sales, costs, and cash flows) moreaccurately. Even though they make inaccurate judgements at first, they could modify theiractions quickly based on the peer firms’ comparable information.
Accounting comparability can also lead to more reliable future estimates of subject firms by
mitigating the opportunistic reporting behaviours of managers. Based on prior studies, themonitoring and governance from internal and external stakeholders (e.g., boards of directors,
analysts, institutional investors, and auditors) could constrain the managerial opportunism infinancial reporting (Healy and Palepu, 2001; Archambeault et al., 2008). Moreover, themonitoring effectiveness is documented to be affected by the availability of firm-specificinformation with high quality (Chen et al., 2017; Zhang et al., 2020). Accounting comparability
enables outsiders to not only better understand the profitability of projects in a firm but also
make inferences about the firm’s relative performance from comparison with the disclosed
information of peer firms (Kim et al., 2021). Less informed investors are also likely to conduct
simple and standardized but still effective financial analyses (Kim et al., 2013). With attenuated7

Chircop et al. (2020) argue that managers of subject firms could benefit from multiple comparable informationof peer firms, including financial statements which contain aggregated data and information disclosed throughout
the year (e.g., earnings announcements and market updates).
8
Specifically, Yamaguchi (2022) shows that managers in Japanese firms tend to carefully keep an eye on theactivities of peer firms and treat the industry-average performance as an important benchmark. Such incentives
are stronger among Japanese firms due to the following two reasons: (1) Japanese firms that miss industry-average
profitability targets are seen as unsuccessful and lacking competitive advantages; (2) Japanese firms are afraid oflosing competitive advantages.
9
For instance, subject firms can learn from peer firms’ financial results to better predict future cash flows from
R&D investments with uncertain outcomes (Chirco et al., 2020). They could also better estimate the allowance for
doubtful accounts by considering economic and industry trends (Chen and Gong, 2019).

8

information asymmetry and improved information environments, external stakeholders canbetter monitor managers’ decision-making at lower monitoring costs. In particular, they can
detect opportunistic behaviours of managers even for undisclosed news. Thereby, managers infirms with higher comparability may be disincentivised towards opportunistic practices (Kimet al., 2016).
Moreover, managers with comparable reporting may have more incentives to issue accurate
earnings forecasts by considering the negative consequences of the damage done to theirreputation by publishing inaccurate forecasts. Suda and Hanaeda (2008) document that the
management earnings forecasts benchmark can be regarded as the most important earnings
benchmark in Japan. Given that Japanese managers are required to disclose forecasts
mandatorily, they are motivated to issue reliable forecasts to avoid negative economic
consequences and establish a good forecast reputation (Ishida et al., 2021). Since comparable
financial reporting enables internal monitoring agents like boards of directors to achieve a better
understanding of firm performance and make meaningful comparisons with industry peers(Zhang et al., 2020), they may encourage managers to present high-quality forecasts to enhance
the firm reputation and discipline managers from opportunistic reporting.
Thus, I propose the following hypothesis:
H1: There is a positive association between accounting comparability and the accuracy ofmanagement earnings forecasts.
However, the benefit of comparability in increasing managerial learning and investors’monitoring may be compromised if comparable peers misrepresent in financial reporting. Basedon management theory on organizational legitimacy (Suchman, 1995), firms will mimic otherfirms’ policies to keep relative performance with peers and defend against potential criticisms
or uncertainty (e.g., Deephouse 1996; Ordanini et al., 2008; De Franco et al., 2023). Sincecomparable firms usually face similar macroeconomic and competitive pressures, they may
regard the financial reporting of peer firms as an important reference and may make similar
incorrect decisions (Kim et al., 2016). Managers may interpret the misstated earnings as
industry trends and cost functions (Li, 2016). 10 Even though firms want to learn from thefinancial information of comparable firms, they may get inaccurate conclusions by referring topeers with reporting misconduct. As such, comparable reporting can become a bad benchmark
and magnify the distorted decisions of peer firms, which results in inaccurate expectations of
future earnings.11

10

Specifically, Li (2016) finds that the adverse impact of accounting misreporting on peer firms can be generalized
to a wide range of peer firms’ operational decisions, indicating the negative externality of financial misstatementson peer firms.
11
Another possible concern is that comparability may also result in the cost for managers due to the crowding-out
effect (Shen et al., 2022). More disclosure could squeeze out other types of information (e.g., private information
issued by analysts), which may make overall information set inferior to managers (Goldstein and Yang, 2019;
Chen and Lu, 2019). Thus, higher information availability engendered by comparability could squeeze out otherinformation, which may impede managerial learning from the market. Considering the management forecast
system in Japan, I argue that the information loss caused by the crowding-out effect of comparability would not
be significant. Specifically, analyst forecasts may play different roles in the capital markets in the U.S. and Japan(Sakawa et al., 2022). In Japan, it is practical for analysts to rely on management forecasts as a basis for their own
forecasts (Ota, 2011). Moreover, Japanese analysts have relatively less private information than U.S. analysts(Friesen and Weller, 2006). Therefore, reduced managerial learning due to the decreased private information by
analysts would not be significant in this study.

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The effect of institutional ownership on the AC-MF link
Prior studies emphasize the importance of institutional investors in reducing managerial
incentives in self-serving behaviours such as earnings management and financial fraud (Chunget al., 2002; Chhaochharia et al., 2012). Unlike individual investors, institutional investors are
sophisticated investors who typically fulfill a monitoring role in constraining managerial
opportunism (Bushee, 1998; Ramalingegowda and Yu, 2012). Moreover, they consistently
probe the firm for more specific, unbiased, and accurate information about future earnings(Ajinkya et al., 2005).12 Therefore, if a subject firm has more comparable information with peer
firms, it would be easier for institutional investors to infer the managerial reporting opportunismand detect bias in forward-looking estimates.
In particular, Bushee (1998) shows that the incentives of institutions to monitor managerial
myopia depend on their investment horizons. Non-transient institution owners with long-term
investments are more likely to decrease incentives for managerial opportunism than transient
owners. As pointed out in previous literature (Douthett and Jung, 2001; Shuto and Kitagawa,
2011; Shuto and Iwasaki, 2014), stable shareholdings by financial institutions which involve
long-term contracts and implicit arrangements dominate the Japanese equity market. Comparedwith short-term institutional investors, stable institutional investors are more likely to maintainlong-term relationships with firms and prevent managers from myopic engagements. They
would make decisions based on their perception of the firm’s future prospects and ability tofulfill obligations (Kreps et al., 1982; Raman and Shahrur, 2008; Dou et al., 2013). Sincecomparability enables them to make sharper inferences about similarities and differences acrossfirms, such informational advantages could be translated into superior monitoring incentives
and abilities. If a firm is found to perform badly, stable institutional shareholders will exert
interventions on management such as re-contracting and changing the CEO (Kaplan and
Minton, 1994). As a result, managers tend to have weaker incentives to present unreliable
forecasts and have stronger incentives to report accurate accounting information regarding
business prospects. 13 Therefore, I expect to observe a stronger relationship between
comparability and the accuracy of management earnings forecasts for firms with higher
institutional ownership:
H2: The positive association between comparability and management forecast accuracy isstronger in firms with higher institutional shareholdings.
The effect of accounting quality on the AC-MF link
Prior studies document that the quality of peer firms’ accounting estimates may affect thebenefits of accounting comparability gained by subject firms (Chircop et al.,2020; Chircop,2021). Although higher comparability may enable managers to acquire and process information
about firms’ operations and overall environments (Chen and Gong, 2019), managers cannot
make accurate forecasts without high-quality information (Goodman et al., 2014). Moreover,financial statements with higher reporting quality are more informative and could better
12

For example, Healy et al. (1999) document a positive relationship between institutional ownership anddisclosure rating. Ajinkya et al. (2005) find that firms with greater institutional ownership are more likely to issue
specific, accurate and less optimistically biased forecasts.
13
For instance, Shuto and Iwasaki (2014) find that firms with more stable shareholdings (e.g., stable shareholdings
by institutional investors) are more likely to conduct informative income smoothing.

10

perform the function of timely price adjustment (Biddle et al., 2009; Callen et al., 2013), which
could be more useful to company stakeholders. Subject firms could gain more benefits bylearning from the high-quality accounting information of peer firms. If two firms with highly
comparable accounting systems present low-quality earnings numbers, the potential benefits oflearning from peer firms’ financial information would become limited. In line with thisargument, Chircop (2021) finds that the positive relation between accounting comparability andfirm productivity is stronger when subject firms operate in industries characterized by higher
accounting quality. Chircop et al. (2020) find that the positive effect of accounting
comparability on innovative efficiency is more pronounced if peer firms have higher accounting
quality. They argue that accounting quality and comparability act as complements to jointly
determine the subject firm’s learning from peer firms’ financial statements.
Thus, I posit that the positive relation between accounting comparability and the accuracy ofmanagement earnings forecasts is stronger if peer firms exhibit higher accounting quality intheir financial statements:
H3: The positive association between comparability and management forecast accuracy isstronger if peer firms have higher accounting (i.e., accrual) quality.
3. Research Design
3.1 Measure of management earnings forecast accuracy
Following Muramiya and Takada (2017), Kitagawa (2021), and Ishida et al. (2021), I measure
the accuracy of management earnings forecast as the absolute value of initial management
earnings forecast error (Abs(MFE)), which is the absolute difference between realized earnings
and the initial management earnings forecast, scaled by the lagged market value of equity (i.e.
|actual earnings for year t – initial management earnings forecast for year t| / market value ofequity for year t – 1). Higher values of Abs(MFE) correspond to lower values of management
earnings forecast accuracy, indicating higher distances between earnings forecasts and realized
earnings.
3.2 Measures of accounting comparability
Following Kim et al. (2016) and Zhang et al. (2020), I use the method of De Franco et al. (2011)
to construct the measure of accounting comparability, which is the closeness of the accounting
functions (how economic events are reflected in accounting income) between two firms.
First, I run the following time-series regression using firm i’s quarterly earnings and stock
returns to measure firm i’s accounting function in period t:
𝐸𝑎𝑟𝑛𝑖𝑛𝑔𝑠!,# = 𝛼! + 𝛽! 𝑅𝑒𝑡𝑢𝑟𝑛!,# + 𝜀!,#(1)

where 𝐸𝑎𝑟𝑛𝑖𝑛𝑔𝑠!,# is the quarterly net income before extraordinary items scaled by the market
value of equity at the end of the previous quarter and 𝑅𝑒𝑡𝑢𝑟𝑛!,# is the quarterly stock returns
for firm i in quarter t. The estimated coefficients 𝛼! and 𝛽! represent the accounting function offirm i. Similarly, I estimate 𝛼$ and 𝛽$ that constitute firm j’s accounting function.

11

Then I compute expected earnings using the two estimated accounting functions for each firm.
Specifically, I predict firm i’s accounting income (expected earnings) using the estimatedaccounting functions of both firm i and firm j, assuming they had experienced the same
economic events (had the same return, 𝑅𝑒𝑡𝑢𝑟𝑛!,# ). I calculate:𝐸(𝐸𝑎𝑟𝑛𝑖𝑛𝑔𝑠)!,!,# = 𝛼4! + 𝛽5! 𝑅𝑒𝑡𝑢𝑟𝑛!,#
𝐸(𝐸𝑎𝑟𝑛𝑖𝑛𝑔𝑠)!,$,# = 𝛼4$ + 𝛽5$ 𝑅𝑒𝑡𝑢𝑟𝑛!,#

(2)
(3)

where 𝐸(𝐸𝑎𝑟𝑛𝑖𝑛𝑔𝑠)!,!,# is the expected earnings of firm i based on its own accounting function
and return in period t. 𝐸(𝐸𝑎𝑟𝑛𝑖𝑛𝑔𝑠)!,$,# is the expected earnings of firm j based on theaccounting function of firm j and firm i’s return in period t. I then compute accounting
comparability for each firm i - j pair as the negative value of the average absolute difference
between the expected earnings using the accounting functions of firm i and firm j for theprevious 16 quarters:
𝐶𝑜𝑚𝑝𝐴𝑐𝑐𝑡!,$,# = −#1?
|𝐸(𝐸𝑎𝑟𝑛𝑖𝑛𝑔𝑠)!,!,# − 𝐸(𝐸𝑎𝑟𝑛𝑖𝑛𝑔𝑠)!,$,# |16 #%&'(4)

Specifically, I estimate accounting comparability (𝐶𝑜𝑚𝑝𝐴𝑐𝑐𝑡!,$,# ) for all i - j pairs within thesame two-digit Nikkei industry group. Next, for each firm i, I calculate a firm-year measure ofaccounting comparability (AcctIndi,t) by averaging 𝐶𝑜𝑚𝑝𝐴𝑐𝑐𝑡!,$,# for all combinations of firmi and firm j in the same industry during year t. Then I rank 𝐶𝑜𝑚𝑝𝐴𝑐𝑐𝑡!,$,# for all i - j pairs fromthe highest to lowest. Following De Franco et al. (2011), Kim et al. (2016), Zhang et al. (2021),
and Hong et al. (2023), I estimate the mean value of 𝐶𝑜𝑚𝑝𝐴𝑐𝑐𝑡!,$,# of the ten firms j in the same
industry with the highest comparability to firm i in year t (Acct10i,t) and the median of
𝐶𝑜𝑚𝑝𝐴𝑐𝑐𝑡!,$,# for all combinations of firm i and firm j in the same industry (Acct_mediani,t).
In general, greater values of AcctIndi,t, Acct10i,t, and Acct_median i,t indicate higher accounting
comparability between firm i and its peer firms in the same industry group.
3.3 Control variables
I include a set of control variables that have been suggested by prior studies to affectmanagement earnings forecast accuracy (e.g., Ota, 2011; Muramiya and Takada, 2017; Ishida
et al., 2021; Kitagawa, 2021; Schabus, 2022). Specifically, I control for two dummy variables,
𝐿𝑜𝑠𝑠_𝐷!,#%& (equals to one if the firm reports losses and zero otherwise) and𝐼𝑛𝑐𝑟𝑒𝑎𝑠𝑒_𝐷!,#%& (equals to one if the firm’s net income has increased compared to the prior year,
and zero otherwise), which are used to control for firms’ earnings stream (Ishida et al., 2021).
I control for firm fundamentals including firm size (𝑆𝐼𝑍𝐸!,#%& ), profitability (𝑅𝑂𝐴!,#%& ), andleverage (𝐿𝐸𝑉!,#%& ) because these firm characteristics may induce managers to intentionally orunintentionally report inaccurate earnings forecasts (Muramiya and Takada, 2017). Earnings
volatility (𝐸𝑎𝑟𝑛_𝑣𝑜𝑙!,#%& ) is controlled for firm-specific earnings uncertainty because priorstudies find that firms facing greater operating or earnings uncertainty are likely to issue
relatively inaccurate earnings forecasts (Gong et al., 2009; Schabus, 2022). Considering theinfluence of firm growth on managerial forecast behaviours suggested by previous studies (Ota,
2011; Muramiya and Takada, 2017), I also control for firm growth (𝐺𝑟𝑜𝑤𝑡ℎ!,#%& ). Finally, I
follow Muramiya and Takada (2017) to control for the lagged management forecast accuracy

12

(𝐴𝑏𝑠(𝑀𝐹𝐸)!,#%& ) in the empirical model since prior studies find that management earnings
forecast errors are persistent (Kato et al., 2009; Gong et al., 2011). Please see Appendix A for
more detailed definitions of the above variables.
3.4 Research model
To determine the effects of accounting comparability on management earnings forecast
accuracy, I follow prior studies (Muramiya and Takada, 2017; Ishida et al., 2021; Kitagawa,
2021; Schabus, 2022) to use the following baseline ordinary least squares (OLS) model:𝐴𝑏𝑠(𝑀𝐹𝐸)!,# = 𝛽( + 𝛽& 𝐶𝑜𝑚𝑝𝐴𝑐𝑐𝑡!,#%& + 𝛽) 𝐿𝑜𝑠𝑠_𝐷!,#%& + 𝛽* 𝐼𝑛𝑐𝑟𝑒𝑎𝑠𝑒_𝐷!,#%&(5)+ 𝛽+ 𝑆𝐼𝑍𝐸!,#%& + 𝛽' 𝑅𝑂𝐴!,#%& + 𝛽, 𝐿𝐸𝑉!,#%& + 𝛽- 𝐸𝑎𝑟𝑛_𝑣𝑜𝑙!,#%&+ 𝛽. 𝐺𝑟𝑜𝑤𝑡ℎ!,#%& + 𝛽/ 𝐴𝑏𝑠(𝑀𝐹𝐸)!,#%& + ∑𝑌𝑒𝑎𝑟 + ∑𝐼𝑛𝑑𝑢𝑠𝑡𝑟𝑦 + 𝜀!,#where 𝐴𝑏𝑠(𝑀𝐹𝐸)!,# is the proxy for management earnings forecast accuracy, and
𝐶𝑜𝑚𝑝𝐴𝑐𝑐𝑡!,#%& is the lagged accounting comparability measure (AcctIndi,t-1, Acct10i,t-1, and
Acct_mediani,t-1). I follow Ishida et al. (2021) and Schabus (2022) to control for both year andindustry-fixed effects and cluster the standard errors at the firm and year levels (Petersen, 2009).
My variable of interest is 𝐶𝑜𝑚𝑝𝐴𝑐𝑐𝑡!,#%& . Since I hypothesise that higher comparability may
lead to more accurate management earnings forecasts, a negative coefficient on 𝐶𝑜𝑚𝑝𝐴𝑐𝑐𝑡!,#%&
supports my H1.
To test cross-sectional hypotheses from H2 to H3, I use the following models (6) and (7) byincluding the interactions of 𝐶𝑜𝑚𝑝𝐴𝑐𝑐𝑡!,#%& with institutional shareholdings (𝐼𝑛𝑠𝑡𝑂𝑤𝑛!,#%& )and accounting quality (𝐴𝑐𝑐𝑜𝑢𝑛𝑡𝑖𝑛𝑔 𝑞𝑢𝑎𝑙𝑖𝑡𝑦!,#%& ), respectively:𝐴𝑏𝑠(𝑀𝐹𝐸)!,# = 𝛽( + 𝛽& 𝐶𝑜𝑚𝑝𝐴𝑐𝑐𝑡!,#%& + 𝛽) 𝐶𝑜𝑚𝑝𝐴𝑐𝑐𝑡!,#%& ∗ 𝐼𝑛𝑠𝑡𝑂𝑤𝑛!,#%&+ 𝛽* 𝐼𝑛𝑠𝑡𝑂𝑤𝑛!,#%& + 𝛽+ 𝐿𝑜𝑠𝑠_𝐷!,#%& + 𝛽' 𝐼𝑛𝑐𝑟𝑒𝑎𝑠𝑒_𝐷!,#%& + 𝛽, 𝑆𝐼𝑍𝐸!,#%&(6)+ 𝛽- 𝑅𝑂𝐴!,#%& + 𝛽. 𝐿𝐸𝑉!,#%& + 𝛽/ 𝐸𝑎𝑟𝑛012 !,#%& + 𝛽&( 𝐺𝑟𝑜𝑤𝑡ℎ!,#%&+ 𝛽&& 𝐴𝑏𝑠(𝑀𝐹𝐸)!,#%& + 𝛽&) 𝐹𝑜𝑟𝑂𝑤𝑛!,#%& + 𝛽&* 𝑀𝑎𝑛𝑎𝑂𝑤𝑛!,#%& + ∑𝑌𝑒𝑎𝑟+ ∑𝐼𝑛𝑑𝑢𝑠𝑡𝑟𝑦 + 𝜀!,#where 𝐼𝑛𝑠𝑡𝑂𝑤𝑛!,#%& is the percentage of the shares owned by institutional investors for year t– 1, 𝐹𝑜𝑟𝑂𝑤𝑛!,#%& is the percentage of the shares owned by foreign investors for year t – 1, and𝑀𝑎𝑛𝑎𝑂𝑤𝑛!,#%& is the percentage of the shares owned by directors for year t – 1. Specifically,
I follow Nagel (2005) to calculate the standardized decile rank of 𝐼𝑛𝑠𝑡𝑂𝑤𝑛!,#%& . I first rank theownership of institutional investors into decile for each year and then standardize the decile toa range of 0 – 1.
𝐴𝑏𝑠(𝑀𝐹𝐸)!,# = 𝛽( + 𝛽& 𝐶𝑜𝑚𝑝𝐴𝑐𝑐𝑡!,#%& + 𝛽) 𝐶𝑜𝑚𝑝𝐴𝑐𝑐𝑡!,#%& ∗ 𝐴𝑐𝑐𝑜𝑢𝑛𝑡𝑖𝑛𝑔 𝑞𝑢𝑎𝑙𝑖𝑡𝑦!,#%&+ 𝛽* 𝐴𝑐𝑐𝑜𝑢𝑛𝑡𝑖𝑛𝑔 𝑞𝑢𝑎𝑙𝑖𝑡𝑦!,#%& + 𝛽+ 𝐿𝑜𝑠𝑠_𝐷!,#%& + 𝛽' 𝐼𝑛𝑐𝑟𝑒𝑎𝑠𝑒_𝐷!,#%&(7)+ 𝛽, 𝑆𝐼𝑍𝐸!,#%& + 𝛽- 𝑅𝑂𝐴!,#%& + 𝛽. 𝐿𝐸𝑉!,#%& + 𝛽/ 𝐸𝑎𝑟𝑛_𝑣𝑜𝑙!,#%&+ 𝛽&( 𝐺𝑟𝑜𝑤𝑡ℎ!,#%& + 𝛽&& 𝐴𝑏𝑠(𝑀𝐹𝐸)!,#%& + ∑𝑌𝑒𝑎𝑟 + ∑𝐼𝑛𝑑𝑢𝑠𝑡𝑟𝑦 + 𝜀!,#where 𝐴𝑐𝑐𝑜𝑢𝑛𝑡𝑖𝑛𝑔 𝑞𝑢𝑎𝑙𝑖𝑡𝑦!,#%& is the measure of a firm’s quality of accruals. Specifically, I
follow Francis et al. (2005) and Kim et al. (2021) to use the following two measures of
𝐴𝑐𝑐𝑜𝑢𝑛𝑡𝑖𝑛𝑔 𝑞𝑢𝑎𝑙𝑖𝑡𝑦!,#%& : 𝐴𝑄_𝐷𝐷!,#%& and 𝐴𝑄!,#%& . 𝐴𝑄_𝐷𝐷!,#%& and 𝐴𝑄!,#%& are computed
13

based on the standard deviation of firm-level residuals from the Dechow and Dichev’s (2002)
model and modified Dechow and Dichev’s (2002) model (augmented by McNichols (2002))
during the previous five years, respectively.14 Following Chircop et al. (2020), I then generate
𝐴𝑄_𝐷𝐷!,#%& and 𝐴𝑄!,#%& by multiplying the standard deviations by – 1 and standardizing themso that they have a mean of zero and a standard deviation of 1.15 A negative coefficient for theinteraction term (𝐶𝑜𝑚𝑝𝐴𝑐𝑐𝑡!,#%& ∗ 𝐴𝑐𝑐𝑜𝑢𝑛𝑡𝑖𝑛𝑔 𝑞𝑢𝑎𝑙𝑖𝑡𝑦!,#%& ) supports H3 that the positive
association between comparability and management forecast accuracy is stronger if peer firms
have higher accounting quality.
4. Empirical Results
4.1 Sample selection
Table 1 reports my sample selection procedure. The necessary data of management earnings
forecasts, financial statements, and stock data are obtained from the Nikkei NEEDS Financial
QUEST database. My initial sample starts with 47,498 firm-year observations from 2007 to
2019. 16 I first exclude 1,436 observations working in banking, securities, and insurance.
Following Ishida et al. (2021), I then drop 2,763 firm-year observations with the number ofmonths in a fiscal period not equal to 12 and observations that changed the accounting period
during the analysis period. Next, I remove 1,826 observations with missing values related to
management earnings forecast accuracy and 23,106 observations with missing values related
to accounting comparability. Finally, I delete 1,049 firm-year observations with missing datafor control variables. My final sample consists of 17,318 firm-year observations for the main
analysis.17 I then winsorize all continuous variables that lie in the upper or lower 1% of the
distribution to reduce the influence of outliers.
[Insert Table 1 about here]

Specifically, the Dechow and Dichev’s (2002) model is 𝑇𝐶𝐴!,# = 𝛽$ + 𝛽% 𝐶𝐹𝑂!,#&% + 𝛽' 𝐶𝐹𝑂!,# + 𝛽( 𝐶𝐹𝑂!,#)% +𝜀!,# and the modified Dechow and Dichev’s (2002) model is 𝑇𝐶𝐴!,# = 𝛽$ + 𝛽% 𝐶𝐹𝑂!,#&% + 𝛽' 𝐶𝐹𝑂!,# +𝛽( 𝐶𝐹𝑂!,#)% + 𝛽* ∆𝑅𝑒𝑣!,# + 𝛽+ 𝑃𝑃𝐸!,# + 𝜀!,# , where 𝑇𝐶𝐴!,# is total current accruals in year t. 𝐶𝐹𝑂!,#&% , 𝐶𝐹𝑂!,# , and𝐶𝐹𝑂!,#)% are operating cash flow (earnings before extraordinary items – total accruals (𝑇𝐴!,# )) for the current year,
prior year, and the future year, respectively. ∆𝑅𝑒𝑣!,# is change in revenue in year t, and 𝑃𝑃𝐸!,# is the gross valueof property, plant, and equipment (PPE) in year t. All variables are scaled by average total assets between year t –1 and year t. Following Enomoto et al. (2020), I compute 𝑇𝐶𝐴!,# as ((Δcurrent asset – Δcash – Δtrading securities– Δshort-term loans receivable) – (Δcurrent liability – Δshort-term loan payable – Δnote payable for PPE –Δaccrued amount payable for PPE)) and compute 𝑇𝐴!,# as ((Δcurrent asset – Δcash – Δtrading securities – Δshortterm loans receivable) － (Δcurrent liability – Δshort-term loan payable – Δnote payable for PPE – Δaccrued
amount payable for PPE) – Δlong-term allowance – depreciation).
15
Higher (lower) values of 𝐴𝑄_𝐷𝐷!,#&% and 𝐴𝑄!,#&% indicate higher (lower) accounting quality.
16
My sample period begins in 2007 due to the disclosure time of quarterly financial reports in Japan. Most firms
disclosed financial reports twice a year until 2002. Starting April 1, 2003, the TSE asks firms listed on its Firstand Second Sections to disclose quarterly summary reports. After this requirement, the TSE began to require firms
to disclose quarterly financial statements from the fiscal year ending in March 2004. Since 𝐶𝑜𝑚𝑝𝐴𝑐𝑐𝑡!,#&% isestimated on the 16 previous quarters of data, the earliest year with values of 𝐶𝑜𝑚𝑝𝐴𝑐𝑐𝑡!,#&% is 2007.
17
The sample for H2 is relatively smaller due to the missing values of the ownership structure. The sample for H3
is also smaller than that for the main analysis due to the calculation of accounting quality. Specifically, there is asample of 15,558 firm-year observations for 𝐴𝑄_𝐷𝐷!,#&% and a sample of 15,500 firm-year observations for
𝐴𝑄!,#&% , respectively.
14

14

4.2 Descriptive statistics
Table 2 reports descriptive statistics for variables. Specifically, Panel A shows summary
statistics for all samples. The mean and median values of the absolute value of management
earnings forecast errors (Abs(MFE) t) are 0.045 and 0.017, respectively, which are larger thanthose reported by previous studies using U.S. samples (e.g., Baik et al., 2011; Schabus, 2022).18
For three measures of accounting comparability (AcctInd t-1, Acct10 t-1, and Acct_median t-1), themean values are -0.018, -0.006, and -0.015, respectively, which are much larger than thosereported in previous studies using U.S. or international samples (except for Japan). 19 For
example, Kim et al. (2016) show that the measure of average 4 highest 𝐶𝑜𝑚𝑝𝐴𝑐𝑐𝑡!,#,$(COMPACCT4 in their study) has a mean of -0.513 and the measure of average 𝐶𝑜𝑚𝑝𝐴𝑐𝑐𝑡!,#,$(COMPACCTIND in their study) has a mean of -3.238. Neel (2017) shows that the mean valueof the median 𝐶𝑜𝑚𝑝𝐴𝑐𝑐𝑡!,#,$ (CompAcct in his study) is -0.149 for the pre-IFRS adoption period
and -0.095 for post-IFRS adoption period. The above results show that the accounting
comparability in Japanese firms is significantly higher than the comparability in other countries(e.g., U.S. and European IFRS firms), which is consistent with Mukai (2017).
Panel A also shows that the average ownership of institutional investors is 22.7% and theaverage ownership of foreign investors is 12.8%. The average percentage of the shares owned
by directors is 4.2%, which is much smaller than those of institutional and foreign investors.20
The table also indicates that the average value of AQ_DD t-1 is -0.023 and the mean of AQ t-1 is-0.022, which are consistent with Enomoto et al. (2020).21 Other control variables also exhibit
similar descriptive statistics to those reported by prior studies (e.g., Kitagawa, 2021; Ishida etal., 2021).
Panel B presents the results of comparison tests between two groups with high and low
accounting comparability. Specifically, I classify the sample into two subsamples (lowcomparability and high-comparability) based on the median Acct10 t-1 for each sample year.
Then I test the difference in variables between the two subsamples. As shown in Panel B, themean of Abs(MFE) t in the low-comparability group are significantly larger than that in thegroup of high comparability, which is consistent with my baseline hypothesis. Moreover, thehigh-comparability group has higher institutional ownership and higher levels of accounting
quality.
[Insert Table 2 about here]
Table 3 shows the Pearson correlation matrix of the variables. As shown in Table 3, threemeasures of accounting comparability (AcctInd t-1, Acct10t-1, and Acct_mediant-1) aresignificantly and positively correlated with each other. The correlations between three measuresof accounting and Abs(MFE) t are negative and statistically significant at the 1% level (with the18

Specifically, Baik et al. (2011) report a mean value of 1.703 for management forecast error (multiplied by 100)and a median value of 0.455. Schabus (2022) shows that the average management forecast accuracy measured by
the absolute value of forecast errors multiplied by -100 is -1.12, and the median is -0.42. The descriptive statistics
of Abs(MFE) t in Ishida et al. (2021) are also larger than those of Baik et al. (2011).
19
These results are consistent with the descriptive statistics reported by previous Japanese studies. For example,
Wakabayashi (2016) indicates that the average of the median 𝐶𝑜𝑚𝑝𝐴𝑐𝑐𝑡!,,,# is -0.021 for the sample period from2012 to 2015.
20
The results are consistent with previous studies. For example, Ishida et al. (2021) show that the averagemanagerial ownership is 3.7%. Zeitun and Goaied (2021) show that the average foreign ownership is 12.18%.
21
Enomoto et al. (2020) show that the mean (median) value of AQ in the post-2001 sample is –0.020 (–0.016).

15

coefficients of -0.264, -0.263, and -0.278, respectively), indicating that there is a positive
correlation between comparability and management earnings accuracy, as predicted in H1.
Besides, there is a significantly negative correlation between Abs(MFE) t and InstOwn t-1 as wellas two measures of accounting quality (AQ_DD t-1 and AQ t-1).
[Insert Table 3 about here]
4.3 Tests of Hypothesis 1
Table 4 presents the empirical results of model (5) to test Hypothesis 1. As shown in columns(1) to (3), three measures of accounting comparability (AcctInd t-1, Acct10 t-1, and Acct_mediant-1) are negatively associated with Abs(MFE) t that indicates the management forecast errors,with the coefficients of -0.810, -1.202, and -0.783, respectively. This finding is consistent with
Hypothesis 1 that accounting comparability increases the accuracy of management earnings
forecasts. For economic significance, the results indicate that one standard deviation increasein the AcctInd t-1 reduces the management forecast error by an estimate of 0.009 (0.010 and 0.009
for Acct10 t-1 and Acct_median t-1, respectively).22 Given that the mean value of Abs(MFE)t is 0.045(as reported in Table 2), the effect of comparability on management forecast accuracy is
economically significant. Therefore, H1 is supported based on the results in Table 4.
For the results of the control variables, the coefficients of Loss_D t-1 are significant and positive.
This indicates that firms that reported losses in the previous year are more likely to issue
forecasts with lower accuracy, which is consistent with previous studies (e.g., Kato et al., 2009;
Ishida et al., 2021). The coefficients of SIZE t-1 are significantly negative and the coefficients oflagged management forecast errors (Abs(MFE) t-1) are significantly positive, which are also
consistent with the results in prior studies (e.g., Muramiya and Takada, 2017; Kitagawa, 2021).
Overall, the results of the control variables are generally consistent with prior studies.
[Insert Table 4 about here]
4.4 Tests of Hypothesis 2
Table 5 reports the empirical results of model (6) to test the effect of institutional ownership on
the relationship between accounting comparability and management forecast accuracy. To
examine the prediction, I add institutional ownership (𝐼𝑛𝑠𝑡𝑂𝑤𝑛!,#%& ) and its interaction withthree measures of accounting comparability (AcctInd t-1, Acct10 t-1, and Acct_median t-1) to themodel. Considering the influence of shareholdings by other investors, I also control the foreign
ownership (𝐹𝑜𝑟𝑂𝑤𝑛!,#%& ) and managerial ownership (𝑀𝑎𝑛𝑎𝑂𝑤𝑛!,#%& ). Since institutional
ownership (𝐼𝑛𝑠𝑡𝑂𝑤𝑛!,#%& ) is highly correlated with firm size (SIZE t-1) (with a coefficient of
0.542 at the 1% level), I follow Kim et al. (2016) to add the interaction between SIZE t-1 andaccounting comparability (AcctInd t-1, Acct10 t-1, and Acct_median t-1) to the model to reduce
the potential bias suggested by Yzerbyt et al. (2004).

22

I obtain 0.009 by multiplying 0.011 (the standard deviation of AcctInd t-1 reported in Table 2) with 0.810 (thecoefficient on AcctInd t-1 in column (1)). Similarly, I obtain 0.010 by multiplying 0.008 (the standard deviation of
Acct10 t-1 reported in Table 2) with 1.202 (the coefficient on Acct10 t-1 in column (2)), and I obtain 0.009 bymultiplying 0.012 (the standard deviation of Acct_median t-1 reported in Table 2) with 0.783 (the coefficient on
Acct_median t-1 in column (3)).

16Columns (1) to (3) present the test results. The coefficients of institutional ownership(𝐼𝑛𝑠𝑡𝑂𝑤𝑛!,#%& ) are significantly negative, which is in line with previous studies (e.g., Ajinkya
et al., 2005; Ishida et al., 2021) that governance mechanisms such as institutional shareholdings
assist in monitoring managerial disclosure behaviours, as reflected in the management earnings
forecast accuracy. The coefficients of the interaction terms (AcctInd t-1 * InstOwn t-1, Acct10 t-1* InstOwn t-1, and Acct_median t-1 * InstOwn t-1) are -0.438, -0.492, and -0.450, respectively.
Moreover, the coefficients are statistically significant at less than the 5% level. These results
indicate that subject firms with larger shareholdings by institutional investors are more likely
to issue forecasts accurately if they have more comparable information with peer firms, whichis consistent with the prediction of H2.
Table 5 also shows that foreign ownership (𝐹𝑜𝑟𝑂𝑤𝑛!,#%& ) may decrease the accuracy ofmanagement forecasts, which is consistent with the view that foreign ownership strengthens
capital market pressure (Kitagawa, 2021). The coefficients of managerial ownership(𝑀𝑎𝑛𝑎𝑂𝑤𝑛!,#%& ) are insignificant, which is consistent with Ishida et al. (2021).
[Insert Table 5 about here]
4.5 Tests of Hypothesis 3
Table 6 reports the empirical results of model (7) to test the effect of accounting quality on therelationship between accounting comparability and management forecast accuracy.
Specifically, I include the interactions of three measures of accounting comparability (AcctInd
t-1, Acct10 t-1, and Acct_median t-1) with two measures of accounting quality (AQ_DD t-1 and AQt-1) in the model, respectively.
As shown in Table 6, the coefficients of AQ_DD t-1 and AQ t-1 are significantly negative in allcolumns, suggesting that higher accounting quality could decrease management earnings
forecast errors. The coefficients of the interaction terms AcctInd t-1 * AQ_DD t-1, Acct10 t-1 *AQ_DD t-1 and Acct_median t-1 * AQ_DD t-1 are negative and significant at 5%-10% in columns(1)-(3). Besides, the coefficients of the interaction terms AcctInd t-1 * AQ t-1, Acct10 t-1 * AQ t-1
and Acct_median t-1 * AQ t-1 are also significantly negative in columns (4)-(6). The above results
are in line with previous literature (e.g., Biddle et al., 2009; Chircop, 2021) that higher
accounting quality could reduce information asymmetry between firms and facilitate
managerial learning from peer firms, which leads to a positive incremental effect of accounting
quality on the relation between comparability and management forecast accuracy. Overall, theresults of Table 6 are consistent with H3 that the positive association between comparability
and management forecast accuracy is stronger if peer firms have higher accounting (i.e., accrual)
quality.
[Insert Table 6 about here]
5. Robustness Tests and Additional Analyses
5.1 Robustness tests
Alternative measures of management earnings forecast accuracy

17

To test the robustness, this study also generates two alternative proxies for management
earnings forecast accuracy: Abs(Revision_mag) t and Abs(MFE)2 t. Specifically, I follow
Kitagawa (2021) and Ishida et al. (2021) to generate the magnitude of forecast revision(Abs(Revision_mag) t), which is measured as the absolute value of management forecast
revisions in year t scaled by the lagged market value of equity (|initial management earnings
forecast for year t – the latest management earnings forecast for year t|/market value of equity
for year t – 1). I then follow Kitagawa (2021) to generate Abs(MFE)2 t which is the absolutevalue of MFE in year t deflated by the lagged total assets (|actual earnings for year t – initialmanagement earnings forecast for year t|/total assets for year t – 1). Similarly, I also control forthe lagged management forecast accuracy (Abs(Revision_mag) t-1 and Abs(MFE)2 t-1) in theempirical model.
Table 7 Panel A presents the results of the above robustness tests. In general, the coefficientsof three measures of accounting comparability (AcctInd t-1, Acct10 t-1, and Acct_median t-1) arenegative and significant at less than the 5% level, indicating that the effect of accounting
comparability on management earnings forecast accuracy is robust to alternative measures ofmanagement forecast accuracy.
Alternative measures of accounting comparability
I consider two alternative measures of accounting comparability: comparability calculated
based on the prices leading earnings model and comparability calculated based on values of
top-four firms. Following De Franco et al. (2011) and Kim et al. (2021), I use the prices leading
earnings model to estimate the accounting function of each firm. Specifically, I incorporate
lagged stock returns into equation (1) to re-estimate CompAcct. I then generate AcctInd_PLE t1, Acct10_PLE t-1, and Acct_median _PLE t-1 by following the same method used to calculate
the primary measures of comparability. My second alternative proxy for the comparability is themean value of CompAcct based on top-four firms. Following De Franco et al. (2011) and Kim
et al. (2016), I generate Acct4 t-1 which is the average of the four highest CompAcct values forfirm i in year t – 1. Similarly, I also generate Acct4_PLE t-1 by using the prices leading earnings
model.
Table 7 Panel B reports the results of regression on alternative measures of accounting
comparability. As shown in Panel B, the coefficients of alternative comparability measure 1(AcctInd_PLE t-1, Acct10_PLE t-1, and Acct_median_PLE t-1) and measure 2 (Acct4 t-1 andAcct4_PLE t-1) are significantly negative for all columns.
Change the sample period
According to the 2008 quarterly reporting regulation, Japanese firms are required to issue
quarterly financial reports after the fiscal year 2008. Considering the increasing number ofquarterly financial reports after the quarterly reporting regulation, I follow Wakabayashi (2016)
to use the sample period from 2012 to examine the relationship between comparability andmanagement forecast accuracy. As shown in Table 7 Panel C, AcctInd t-1, Acct10 t-1, and
Acct_median t-1 are significantly and negatively associated with comparability, showing that thepositive relationship between comparability and management forecast accuracy is robust todifferent sample periods. Overall, the results are generally identical to the main analysis
reported in Table 4.
[Insert Table 7 about here]18

5.2 Additional analyses: A further investigation of the accounting regulations
The effect of quarterly reporting regulation of Japanese Accounting Standards (2008)
In Japan, the new Japanese Accounting Standards (2008) requires firms to issue quarterly
financial reports since 2008, after the enactment of the 2007 Financial Instruments andExchange Act. Before the quarterly reporting regulation, there was no legal or risk penalty if
firms didn’t disclose full-scale quarterly financial statements (Kubota and Takehara, 2016).
Since quarterly financial statements become compulsory and are required to be published withauditors’ reviews since 2008, the disclosure frequency gets increased and more timely
information could be provided to investors, which may reduce agency conflicts (Koga andYamaguchi, 2023). For example, Kubota and Takehara (2016) find that Japanese firms that
disclose quarterly financial statements have lower information asymmetry. Given the enhanced
access to information, managers may be more likely to learn from peer firms and investors
could be more able to monitor managerial behaviours. In this case, the hypothesized positive
relationship between comparability and management forecast accuracy becomes strengthened.
To examine the effect of the quarterly reporting regulation on the relationship between
accounting comparability and management forecast accuracy, I first construct a dummy
variable of QRR that takes a value of 1 for the year after 2008, and a value of 0 before 2008. I
then add QRR and its interaction with comparability to the model. Since the sample period starts
from 2007, I set the year 2007 as the pre-QRR period and set the years 2008-2009 as the postQRR period. In addition, I use two samples. The first sample is the full sample of 2007-2009. I
then follow Neel (2017) to select firms that are present for the entire 3-year period as the second
sample (56 firms per year).23
Table 8 presents the results. Specifically, Panel A shows the descriptive statistics of two
samples. Panel B shows the regression results of two samples. As shown in columns (1)-(3),the coefficients of three interaction terms, AcctInd t-1 * QRR, Acct10 t-1 * QRR, and Acct_mediant-1 * QRR are significantly negative at less than the 5% level. Columns (4) and (6) also showthat the coefficients of the interactions of comparability with QRR are significant and negative
for samples with data during the entire 3-year period. These results indicate that the positive
relationship between comparability and management forecast accuracy is strengthened after theenforcement of the 2008 quarterly reporting regulation.
[Insert Table 8 about here]
The effect of IFRS adoption in Japan
According to the interim report issued by the BAC (BAC, 2009), Japanese-listed firms are
permitted to adopt IFRS voluntarily from the fiscal year ending in March 2010. Since 2010,
there are an increasing number of firms that adopt IFRS and the use of IFRS is growing

23

For both samples, I excluded firms that adopt IFRS or U.S. GAAP.

19

rapidly.24 Regarding the differences between IFRS and Japanese GAAP (J-GAAP), Gu (2021)
argues that J-GAAP could be regarded as the equivalent of the IFRS since the AccountingStandards Board of Japan and the International Accounting Standards Board launched a projectto achieve convergence between J-GAAP and IFRS in 2005. Although most of the majordifferences between two accounting standards have been eliminated, there still remain somedifferences (e.g., amortization of goodwill and R&D costs, the booking of impairment loss, etc.)(Sato and Takeda, 2017). In terms of the effects of IFRS adoption on accounting quality inJapan, prior studies find that IFRS adopters are likely to have less income smoothing and higher
conditional conservatism (Shimamoto and Takeda, 2020; Gu, 2021). However, Wakabayashi(2018) finds that after IFRS adoption, the comparability level of Japanese firms first decreased
to the lowest point in 2012 and then showed an upward trend.25 In general, the results on theconsequences of IFRS adoption in Japan are mixed.
In addition to IFRS and J-GAAP, Japanese firms can also choose to apply U.S. GAAP. For
example, Gu (2021) finds that about 4.8% of Japanese-listed firms applied IFRS in 2005, while
0.8% applied U.S. GAAP. Prior studies indicate mixed results on the difference in accounting
quality across U.S. GAAP and IFRS firms. Specifically, Lin et al. (2012) find that firms
adopting U.S. GAAP generally have better accounting quality than IFRS adopters. In contrast,
Leuz (2003) find that there is no evidence supporting claims that U.S. GAAP firms performbetter than IFRS firms in informational quality.
Based on previous studies, I conjecture that the effect of comparability on management forecast
accuracy may differ across firms that adopt IFRS and J-GAAP (or U.S. GAAP). To examine
the effect of IFRS adoption on the relationship between comparability and management forecast
accuracy for firms applying different accounting standards, I follow prior studies (Cameran etal., 2014; Gu, 2021; Amano, 2021) to construct the variable of IFRS, which is measured as 1 if
a firm adopted IFRS voluntarily and 0 otherwise. I then add IFRS and its interaction withcomparability to the model. To reduce the potential self-selection bias, I follow previous studies(Cameran et al., 2014; Sato and Takeda, 2017; Gu, 2021; Amano, 2021) to use the PSM method
to select the control group. To better examine the effect of IFRS adoption on the relationshipbetween comparability and the accuracy of management forecasts, I use two matched samples
as benchmarks: firms that adopt J-GAAP and firms that adopt U.S. GAAP. Specifically, I use
the one-to-one nearest neighbour matching method with no replacement to match eachobservation in the IFRS adoption group with a non-adopter (a J-GAAP firm and a U.S. GAAP
firm, respectively).26 Following Sato and Takeda (2017) and Gu (2021), I set the sample period
starting from 2010.
Table 9 reports the results of the effect of IFRS adoption in Japan. Panel A shows that comparedwith J-GAAP firms, IFRS adopters have high management forecast accuracy and high24

For example, the TSE announced that as of March 2023, there are 253 firms having adopted IFRS and 10 firms
planning
to
adopt
IFRS
in
the
near
future,
which
is
263
firms
in
total.
(https://www.jpx.co.jp/equities/improvements/ifrs/02.html)
25
Wakabayashi (2018) argues that the change from J-GAAP to IFRS did not lead to an improvement in thecomparability of Japanese firms at first due to the revision and abolition of accounting standards. As theconvergence progresses, comparability may show an upward tendency in recent years.
26
To conduct the PSM procedure, I follow previous studies (Cameran et al., 2014; Sato and Takeda, 2017; Gu,
2021; Amano, 2021) to build the following logit model to predict the likelihood of IFRS adoption:
𝐼𝐹𝑅𝑆!,# = 𝐿𝑜𝑔𝑖𝑡 (𝛽$ + 𝛽% 𝑆𝐼𝑍𝐸!,# + 𝛽' 𝐿𝐸𝑉!,# + 𝛽( 𝑅𝑂𝐴!,# + 𝛽* 𝐿𝑂𝑆𝑆!,# + 𝛽+ 𝐴𝑏𝑠_𝑂𝑃𝐶𝐹!,# + 𝛽- 𝐹𝑜𝑟𝑒𝑖𝑔𝑛!,#+ 𝛽. 𝑂𝑣𝑒𝑟𝑠𝑒𝑎𝑠_𝑟𝑎𝑡𝑖𝑜!,# + 𝛽/ 𝐼𝑛𝑣𝑒𝑠𝑡!,# + 𝛽0 𝐺𝑟𝑜𝑤𝑡ℎ2!,# + ∑𝑌𝑒𝑎𝑟 + ∑𝐼𝑛𝑑𝑢𝑠𝑡𝑟𝑦 + 𝜀!,# )Please see Appendix B for details.

20comparability. However, there is no significant difference in management forecast accuracy
and comparability between U.S. GAAP firms and IFRS firms. Panel B presents the regression
results. Columns (1)-(2) indicate that the coefficients of the interactions of comparability withIFRS are insignificant, while the coefficient of Acct_median t-1 * IFRS in column (3) is positiveand significant at the 10% level. The above results suggest that IFRS adoption doesn’t increase
the positive effect of comparability on management forecast accuracy across J-GAAP firms
and IFRS firms. Columns (4)-(6) report the test results for IFRS adopters and U.S. GAAP firms.
The coefficients of three interaction terms, AcctInd t-1 * IFRS, Acct10 t-1 * IFRS, and
Acct_median t-1 * IFRS are significantly negative at less than the 10% level. These results showthat the positive effect of comparability on management forecast accuracy is stronger for IFRS
adopters than for U.S. GAAP firms.27[Insert Table 9 about here]
The above results are consistent with the view that the effect of IFRS adoption depends on theenforcement pressures and institutional environments (Leuz, 2003; Tarca, 2004; Wakabayashi,2018). Compared with voluntary adoption, mandatory adoption can bring out more consistent
application and enforcement because a large number of firms are affected simultaneously(Barth et al., 2012). Thus, the effectiveness of IFRS adoption may differ across firms that adopt
IFRS voluntarily and mandatorily. For example, Horton et al. (2013) find that forecast accuracy
improves significantly for mandatory IFRS adopters, but the results are not robust for voluntary
IFRS adopters. They also find that IFRS adoption could improve forecast accuracy more if
there is a larger difference between IFRS earnings and local GAAP earnings. Li and Yang (2016)
expect IFRS adoption to have no impact on the forecast behaviour of Japanese firms because
management forecasts are fully mandated. Given the uniqueness of the Japanese accounting
and disclosure system, the effectiveness of IFRS adoption on the relationship between
comparability and management earnings forecasts may be affected by the Japanese-specificcontextual factors including organizational and cultural factors.28
6. Conclusion
This study examines the effect of accounting comparability on management earnings forecast
accuracy in Japan, where management earnings forecasts are effectively mandated andaccounting comparability could be more exogenous than in other countries. Comparability can
reduce the costs of information acquisition and enable managers to have more available
information about peer firms (De Franco et al., 2011). This could facilitate managerial learningfrom the financial information of comparable firms (Chircop et al., 2020), and thus managers
are more likely to predict correct forward-looking estimates. Besides, comparability canenhance investors’ monitoring of managerial behaviours due to the attenuated information
asymmetry and decreased monitoring costs (Kim et al., 2016; Zhang et al., 2020). As a result,managers may be disincentivised towards opportunism and thus provide more reliable27

Cooke (1993) shows that the earnings of Japanese firms under J-GAAP are likely to be more prudent than thosereported under U.S. GAAP. In addition, the number of firms that adopt U.S. GAAP is decreasing in recent years.
For example, Gu (2021) shows that the average growth rate of U.S. GAAP adopting firms is -8.5% from 2010 to2014. Given the difference between J-GAAP and U.S. GAAP as well as the convergence of J-GAAP and IFRS, it
could be more difficult for U.S. GAAP adopting firms to learn from peers or get monitored by investors than IFRS
adopting firms.
28
Tsunogaya et al. (2015) argue that the main reasons for the concern about the IFRS application in Japan are thecode law tradition, the triangular legal system, and the strict compliance with national legislation.

21

disclosures, as reflected in more accurate future estimates. Therefore, I predict that the accuracyof management earnings forecasts may increase with accounting comparability.
Following Kim et al. (2016) and Zhang et al. (2020), I use the method of De Franco et al. (2011)
to construct the measure of accounting comparability. The results indicate a positive
relationship between accounting comparability and management earnings forecast accuracy,
suggesting that comparable financial information could improve the accuracy of management
earnings forecasts. Moreover, the positive relationship between accounting comparability andmanagement earnings forecast accuracy is more pronounced when subject firms have higher
institutional ownership and peer firms have higher accounting (i.e., accrual) quality. Additional
analyses show that the positive effect of accounting comparability on management forecast
accuracy becomes strengthened after the 2008 quarterly reporting regulation in Japan. However,there is no evidence showing that IFRS adoption increases the positive effect of comparability.
Overall, this study enriches the literature on determinants of the accuracy of management
earnings forecasts and highlights the positive consequences of accounting comparability in theJapanese capital market.

22

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26

Table 1 Sample selection procedure
Criteria

Firm-years

Firm-years that listed on Japanese stock markets with financial data and price data 47,498
obtained from the database during 2007 – 2019
Less:
Banks, securities firms, insurance firms and other financial institutions(1,436)

Firms with the number of months in a fiscal period not equal to 12 and firmsthat changed accounting period during the analysis period(2,763)

Missing data for calculating dependent variables(1,826)

Missing data for calculating independent variables(23,106)

Missing data for control variables(1,049)Number of observations in the final sample for main analysis

17,318This table shows the sample selection procedures in this study. The initial sample is based on the Nikkei NEEDS
Financial QUEST during 2007– 2019. My final sample consists of 17,318 firm-year observations for the main
analysis.

27

Table 2 Descriptive statistics
Panel A: Summary statistics
Abs(MFE) t
AcctInd t-1
Acct10 t-1
Acct_median t-1
Loss_D t-1
Increase_D t-1
SIZE t-1
ROA t-1
LEV t-1
Earn_Vol t-1
Growth t-1
Abs(MFE) t-1
InstOwn t-1
ForOwn t-1
ManaOwn t-1
AQ_DD t-1
AQ t-1

N
17318
17318
17318
17318
17318
17318
17318
17318
17318
17318
17318
17318
16249
16249
16249
15558
15500

Mean
.045
-.018
-.006
-.015
.118
.592
10.437
.034
.480
.021
1.036
.046
.227
.128
.042
-.023
-.022

SD
.096
.011
.008
.012
.322
.492
1.724
.044
.196
.022
.080
.098
.124
.121
.086
.021
.020

Min
0
-.104
-.063
-.102
0
0
6.006
-.314
.049
.001
.787
0
0
0
0
-.206
-.191

p25
.006
-.021
-.007
-.017
0
0
9.169
.015
.329
.008
.993
.007
.132
.026
.002
-.027
-.026

Median
.017
-.015
-.003
-.011
0
1
10.229
.031
.480
.014
1.024
.018
.219
.090
.007
-.017
-.016

p75
.044
-.011
-.002
-.008
0
1
11.566
.052
.631
.025
1.063
.044
.320
.201
.036
-.011
-.010

Max
2.443
-.002
0
-.002
1
1
14.985
.266
1.042
.229
1.806
2.287
.533
.551
.709
-.003
-.003

Panel B: Summary statistics by groups

Abs(MFE) t
Loss_D t-1
Increase_D t-1
SIZE t-1
ROA t-1
LEV t-1
Earn_Vol t-1
Growth t-1
Abs(MFE) t-1
InstOwn t-1
ForOwn t-1
ManaOwn t-1
AQ_DD t-1
AQ t-1

Low Accounting Comparability
N
Mean
Median
8556
.063
.025
8556
.178
0
8556
.578
1
8556
10.025
9.794
8556
.024
.025
8556
.532
.544
8556
.025
.017
8556
1.030
1.019
8556
.069
.027
7983
.216
.209
7983
.104
.067
7983
.042
.007
7353
-.026
-.019
7306
-.025
-.018

High Accounting Comparability
N
Mean
Median
8762
.027
.012
8762
.059
0
8762
.605
1
8762
10.83810.694
8762
.044
.037
8762
.429
.422
8762
.017
.012
8762
1.041
1.030
8762
.024
.013
8266
.236
.228
8266
.150
.122
8266
.043
.007
8205
-.020
-.015
8194
-.019
-.015

T-Statistic
t-value
24.657***24.859***-3.683***-31.937***-30.655***
35.870***24.948***-8.877***31.170***-10.176***-24.598***-0.488-19.535***-18.817***

This table presents the descriptive statistics for the variables used in the main analysis. Panel A shows thesummary statistics for full samples. InstOwn t-1 is the original institutional ownership. AQ_DD t-1 and AQ t-1 are thevariables of accounting quality before standardization, respectively. Panel B presents the results of comparison
tests between two groups with high and low accounting comparability. All continuous variables are winsorized at
1 and 99 percentiles. Please see Appendix A for all variable definitions.

28

Table 3 Pearson correlation matrix
Variables(1) Abs(MFE)t(2) AcctIndt-1(3) Acct10 t-1(4) Acct_median t-1(5) Loss_D t-1(6) Increase_D t-1(7) SIZE t-1(8) ROA t-1(9) LEV t-1(10) Earn_vol t-1(11) Growth t-1(12) Abs(MFE) t-1(13) InstOwn t-1(14) ForOwn t-1(15) ManaOwn t-1(16) AQ_DD t-1(17) AQ t-1

(1)
1.000
-0.264***-0.263***-0.278***0.268***-0.107***-0.224***-0.239***0.165***
0.158***-0.026***
0.342***-0.093***-0.115***0.052***-0.170***-0.167***

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

(11)

(12)

(13)

(14)

(15)

(16)

(17)

1.000
0.833***
0.978***-0.290***-0.002
0.343***
0.287***-0.228***-0.399***0.134***-0.429***0.250***
0.190***-0.131***0.351***
0.341***

1.000
0.857***-0.261***0.018**
0.263***
0.295***-0.265***-0.292***0.126***-0.403***0.151***
0.177***-0.047***0.246***
0.239***

1.000-0.315***0.003
0.350***
0.322***-0.240***-0.401***0.133***-0.444***0.249***
0.202***-0.112***
0.336***
0.328***

1.000-0.279***-0.189***-0.526***
0.117***
0.275***-0.101***0.510***-0.119***-0.120***0.055***-0.173***-0.162***1.000
0.086***
0.260***-0.007-0.035***0.058***-0.144***0.007
0.057***
0.017**
0.025***
0.023***

1.000
0.281***-0.060***-0.167***0.144***-0.230***0.542***
0.731***-0.276***0.226***
0.219***

1.000-0.245***-0.120***0.281***-0.340***0.069***
0.285***
0.062***
0.103***
0.088***

1.000-0.052***-0.014*0.197***
0.098***-0.155***-0.085***-0.076***-0.099***1.000
0.179***
0.251***-0.237***-0.028***0.198***-0.529***-0.483***1.000-0.092***-0.035***0.153***
0.126***-0.091***-0.050***1.000-0.117***-0.130***0.054***-0.233***-0.228***1.000
0.390***-0.389***0.249***
0.259***

1.000-0.178***0.111***
0.104***

1.000-0.173***-0.178***1.000
0.945***

1.000

This table shows the Pearson correlation matrix of the variables. All continuous variables are winsorized at 1 and 99 percentiles. *, ** and *** refer to significance level at 10%, 5% and 1%,
respectively. Please see Appendix A for all variable definitions.

29

Table 4 Accounting comparability and management earnings forecast accuracy(1)
Abs(MFE) t
AcctInd t-1(2)
Abs(MFE) t-0.810***(-5.713)

Acct10 t-1-1.202***(-5.951)

Acct_median t-1
Loss_D t-1
Increase_D t-1
SIZE t-1
ROA t-1
LEVt-1
Earn_Vol t-1
Growth t-1
Abs(MFE) t-1
Constant
Year, Industry
Observations
Adj. R-squared(3)
Abs(MFE) t

0.019***(3.634)-0.004***(-2.798)-0.007***(-13.436)-0.120***(-4.628)0.034***(7.649)0.109**(2.120)0.038*(1.954)0.192***(6.656)0.022(1.152)Control
17,3180.208

0.019***(3.681)-0.004***(-2.763)-0.007***(-13.244)-0.120***(-4.595)
0.034***(7.557)0.122**(2.418)0.039**(2.037)0.190***(6.662)0.016(0.803)Control
17,3180.209-0.783***(-5.854)0.018***(3.546)-0.004***(-2.970)-0.007***(-13.262)-0.114***(-4.414)0.034***(7.603)0.100*(1.943)0.039**(2.017)0.191***(6.624)0.022(1.111)
Control
17,3180.209

The dependent variable in the three columns, Abs(MFE) t, is the measure of management earnings forecast accuracy, which is
calculated as the absolute value of initial MFE (earnings forecast error) in year t deflated by lagged market value of equity. Higher
values of Abs(MFE) correspond to lower values of management earnings forecast accuracy, indicating higher distances between
earnings forecasts and realized earnings. AcctInd t-1, Acct10 t-1, and Acct_median t-1 are three measurements of accounting
comparability. Greater values of AcctIndi,t, Acct10i,t, and Acct_median i,t indicate higher accounting comparability. The t-values inall columns are based on standard errors clustered by firm and year. *, ** and *** refer to significance level at 10%, 5% and 1%,
respectively. Please see Appendix A for other variable definitions.

30

Table 5 Accounting comparability and management earnings forecast accuracy:
the effect of institutional ownership(1)
Abs(MFE) t
AcctInd t-1 * InstOwn t-1(2)
Abs(MFE) t-0.438***(-2.579)

Acct10 t-1 * InstOwn t-1-0.492**(-1.988)

Acct_median t-1 * InstOwn t-1
AcctInd t-1 * SIZE t-1-0.450***(-2.642)0.346***(4.105)

Acct10 t-1 * SIZE t-1

0.381***(3.218)Acct_median t-1 * SIZE t-1
AcctInd t-1

0.344***(4.101)-3.909***(-4.734)

Acct10 t-1-4.564***(-4.002)

Acct_median t-1
InstOwn t-1
Loss_D t-1
Increase_D t-1
SIZE t-1
ROA t-1
LEVt-1
Earn_Vol t-1
Growth t-1
Abs(MFE) t-1
ForOwn t-1
ManaOwn t-1
Constant
Year, Industry
Observations
Adj. R-squared(3)
Abs(MFE) t-0.007***(-2.603)
0.016***(3.041)-0.003**(-2.416)-0.005***(-3.045)-0.147***(-5.293)0.039***(8.804)0.118**(2.107)0.023*(1.679)0.180***(6.283)0.074***(6.844)0.012(0.936)0.010(0.443)Control
16,2490.222-0.002*(-1.694)
0.015***(3.008)-0.003**(-2.336)-0.008***(-8.843)-0.150***(-5.341)0.038***(8.533)0.127**(2.283)0.023(1.630)0.182***(6.409)0.074***(6.953)0.012(0.970)0.048***(2.842)Control
16,2490.221-3.872***(-4.743)-0.006***(-2.613)0.015***(2.990)-0.003**(-2.544)-0.006***(-4.478)-0.144***(-5.201)
0.039***(8.741)0.111**(1.969)0.025*(1.774)0.179***(6.235)0.073***(6.810)0.012(0.986)0.021(1.018)Control
16,2490.223

Abs(MFE) t is the measure of management earnings forecast accuracy. Higher values of Abs(MFE) correspond tolower values of management earnings forecast accuracy. AcctInd t-1, Acct10 t-1, and Acct_median t-1 are three31

measurements of accounting comparability. Greater values of AcctIndi,t, Acct10i,t, and Acct_median i,t indicate
higher accounting comparability. InstOwn t-1 is the percentage of the shares owned by institutional investors at theend of year t – 1. ForOwn t-1 is the percentage of the shares owned by foreign investors at the end of year t – 1.
ManaOwn t-1 is the percentage of the shares owned by directors at the end of year t – 1. InstOwn t-1 are first ranked
into deciles for each year and then standardized into a range of 0 – 1. AcctInd t-1 * InstOwn t-1, Acct10t-1 * InstOwn
t-1, and Acct_median t-1 * InstOwn t-1 are the interactions of InstOwn t-1 and three measures of accounting
comparability (AcctInd t-1, Acct10 t-1, and Acct_median t-1), respectively. The t-values in all columns are based onstandard errors clustered by firm and year. *, ** and *** refer to significance level at 10%, 5% and 1%,
respectively. Please see Appendix A for other variable definitions.

32

Table 6 Accounting comparability and management earnings forecast accuracy:
the effect of accounting quality(1)
Abs(MFE) t
AcctInd t-1 * AQ_DD t-1(2)
Abs(MFE) t(3)
Abs(MFE) t(4)
Abs(MFE) t(5)
Abs(MFE) t-0.190**(-2.037)

Acct10 t-1 * AQ_DD t-1-0.258*(-1.765)

Acct_median t-1 * AQ_DD t-1-0.168*(-1.932)

AcctInd t-1 * AQ t-1-0.214**(-2.302)

Acct10 t-1 * AQ t-1-0.297**(-2.026)-0.195**(-2.250)

Acct_median t-1 * AQ t-1
AcctInd t-1-0.939***(-6.401)

Acct10 t-1-0.943***(-6.213)-1.356***(-6.072)-1.362***(-5.889)-0.901***(-6.501)-0.009***(-3.199)

Acct_median t-1
AQ_DD t-1

-0.010***(-3.144)-0.008***(-3.195)

AQ t-1
Loss_D t-1
Increase_D t-1
SIZE t-1
ROA t-1(6)
Abs(MFE) t

0.019***(3.858)-0.003**(-2.387)-0.006***(-12.989)-0.132***0.020***(3.961)-0.003**(-2.339)-0.006***(-12.850)-0.129***0.019***(3.767)-0.003**(-2.572)-0.006***(-12.837)-0.126***33-0.904***(-6.296)-0.011***(-3.379)
0.019***(3.831)-0.003**(-2.270)-0.006***(-12.633)-0.146***-0.008***(-3.394)0.020***(3.940)-0.003**(-2.239)-0.006***(-12.490)-0.141***-0.009***(-3.450)
0.019***(3.736)-0.003**(-2.446)-0.006***(-12.465)-0.140***LEVt-1
Earn_Vol t-1
Growth t-1
Abs(MFE) t-1
Constant
Year, Industry
Observations
Adj. R-squared(-4.776)
0.034***(7.233)-0.011(-0.162)0.019(0.922)0.164***(5.572)0.040*(1.918)Control
15,5580.210(-4.651)0.034***(7.108)-0.002(-0.025)0.021(1.044)0.163***(5.545)0.033(1.564)Control
15,5580.210(-4.558)
0.034***(7.189)-0.020(-0.308)
0.021(1.000)0.163***(5.529)0.039*(1.861)
Control
15,5580.210(-5.082)
0.033***(7.050)0.019(0.295)0.023(1.066)0.164***(5.515)0.035(1.571)
Control
15,5000.210(-4.911)
0.033***(6.927)0.028(0.443)0.025(1.177)0.162***(5.489)0.027(1.230)Control
15,5000.210(-4.886)
0.033***(7.007)0.011(0.170)0.025(1.133)0.162***(5.483)0.034(1.523)Control
15,5000.211

Abs(MFE) t is the measure of management earnings forecast accuracy. Higher values of Abs(MFE) correspond to lower values of management earnings forecast accuracy.
AcctInd t-1, Acct10 t-1, and Acct_median t-1 are three measurements of accounting comparability. Greater values of AcctIndi,t, Acct10i,t, and Acct_median i,t indicate higher
accounting comparability. AQ_DD t-1 is the measure of accounting quality generated based on the standard deviation of firm-level residuals from the following Dechow andDichev’s (2002) model during the previous five years. AQ t-1 is the measure of accounting quality generated based on the standard deviation of firm-level residuals from thefollowing modified Dechow and Dichev’s (2002) model (augmented by McNichols (2002)) during the previous five years. AcctInd t-1 * AQ_DD t-1, Acct10t-1 * AQ_DD t-1, and
Acct4t-1 * AQ_DD t-1are the interactions of AQ_DD t-1 and three measures of accounting comparability (AcctInd t-1, Acct10 t-1, and Acct_median t-1), respectively. AcctInd t-1 *AQ t-1, Acct10t-1 * AQ t-1, and Acct_median t-1 * AQ t-1 are the interactions of AQ t-1 and three measures of accounting comparability (AcctInd t-1, Acct10 t-1, and Acct_median t-1),
respectively. The t-values in all columns are based on standard errors clustered by firm and year. *, ** and *** refer to significance level at 10%, 5% and 1%, respectively.
Please see Appendix A for other variable definitions.

34

Table 7 Robustness tests
Panel A: Alternative measures of management earnings forecast accuracy
Alternative management forecast accuracy measure 1(1)(2)
Abs(Revision_mag) t Abs(Revision_mag) t
AcctInd t-1-0.623***(-4.885)

Acct10 t-1-0.940***(-5.280)

Acct_median t-1
Other Control Variables
Year, Industry
Observations
Adj. R-squared

Yes

Yes

-0.616***(-5.086)
Yes

Control
17,302
0.206

Control
17,302
0.206

Control
17,302
0.206

Alternative management forecast accuracy measure 2(1)(2)
Abs(MFE)2 t
Abs(MFE)2 t
AcctInd t-1-0.112**(-2.187)

Acct_median t-1

Year, Industry
Observations
Adj. R-squared

Yes

Yes

-0.125***(-3.550)
Yes

Control
17,3180.252

Control
17,3180.251

Control
17,3180.252

Panel B: Alternative measures of accounting comparability
Alternative accounting comparability measure 1(1)(2)
Abs(MFE) t
Abs(MFE) t
AcctInd_PLE t-1-1.092***(-6.351)

Acct_median _PLE t-1

Year, Industry(3)
Abs(MFE) t-0.832***(-6.222)

Acct10_PLE t-1

Other Control Variables(3)
Abs(MFE)2 t-0.127***(-3.382)

Acct10 t-1

Other Control Variables(3)
Abs(Revision_mag) t

Yes

Yes

-0.791***(-6.221)
Yes

Control

Control

Control

35

Observations
Adj. R-squared

16,0380.207

16,0380.207

Alternative accounting comparability measure 2(1)
Abs(MFE) t
Acct4 t-1

Year, Industry
Observations
Adj. R-squared(3)
Abs(MFE) t-1.462***(-5.757)

Acct4_PLE t-1
Other Control Variables

16,0380.207

Yes-1.272***(-6.389)
Yes

Control
17,3180.209

Control
16,0380.207

Panel C: Change the sample period(1)
Abs(MFE) t
AcctInd t-1(2)
Abs(MFE) t-0.844***(-6.279)

Acct10 t-1-1.118***(-5.739)

Acct_median t-1
Other Control Variables
Year, Industry
Observations
Adj. R-squared(3)
Abs(MFE) t

Yes

Yes

-0.824***(-6.525)
Yes

Control
12,9030.207

Control
12,9030.206

Control
12,9030.208

Abs(Revision_mag) t is the first alternative measure of management earnings forecast accuracy, which is measured
as the absolute value of management forecast revisions in year t scaled by lagged market value of equity.
Abs(MFE)2 t is the second alternative measure of management earnings forecast accuracy, which is the absolutevalue of initial MFE (earnings forecast error) in year t deflated by lagged total assets. Higher values ofAbs(Revision_mag) t and Abs(MFE)2 t indicate lower management earnings forecast accuracy. AcctInd_PLEt-1,
Acct10_PLE t-1, and Acct_median _PLE t-1 are alternative measures of accounting comparability that are calculated
based on the prices leading earnings model to estimate the accounting function. Acct4 t-1 and Acct4_PLE t-1 arealternative measures of accounting comparability that are calculated based on the mean value of CompAcct of topfour firms. The t-values in all columns are based on standard errors clustered by firm and year. *, ** and *** referto significance level at 10%, 5% and 1%, respectively. Please see Appendix A for other variable definitions.

36

Table 8 Accounting comparability and management earnings forecast accuracy:
the effect of the 2008 quarterly reporting regulation in Japan
Panel A Descriptive statistics
Summary statistics: full samples
Abs(MFE) t
AcctInd t-1
Acct10 t-1
Acct_median t-1
Loss_D t-1
Increase_D t-1
SIZE t-1
ROA t-1
LEV t-1
Earn_Vol t-1
Growth t-1
Abs(MFE) t-1

N
1738
1738
1738
1738
1738
1738
1738
1738
1738
1738
1738
1738

Mean
.079
-.011
-.005
-.009
.093
.517
10.775
.037
.488
.017
1.066
.029

SD
.177
.006
.005
.006
.290
.500
1.672
.039
.191
.018
.082
.065

Min
0
-.041
-.039
-.042
0
0
6.225
-.272
.055
.002
.867
0

p25
.008
-.012
-.006
-.010
0
0
9.520
.018
.342
.007
1.017
.004

Median
.028
-.009
-.003
-.007
0
1
10.623
.034
.501
.012
1.050
.011

p75
.076
-.008
-.002
-.006
0
1
11.903
.054
.633
.020
1.098
.027

Max
2.443
-.002
-.001
-.002
1
1
14.797
.235
.988
.214
1.806
.909

Min
0
-.039
-.039
-.037
0
0
6.604
-.190
.062
.002
.869

p25
.004
-.012
-.006
-.010
0
0
9.879
.029
.230
.009
1.014

Median
.014
-.009
-.003
-.007
0
1
10.920
.046
.400
.014
1.043

p75
.040
-.007
-.002
-.005
0
1
12.438
.069
.569
.026
1.100

Max
.542
-.002
-.001
-.002
1
1
14.447
.235
.988
.214
1.570

Summary statistics: samples with data during 2007-2009
Abs(MFE) t
AcctInd t-1
Acct10 t-1
Acct_median t-1
Loss_D t-1
Increase_D t-1
SIZE t-1
ROA t-1
LEV t-1
Earn_Vol t-1
Growth t-1

N
168
168
168
168
168
168
168
168
168
168
168

Mean
.044
-.011
-.005
-.009
.060
.565
11.166
.053
.403
.022
1.068

SD
.084
.006
.006
.006
.237
.497
1.713
.049
.183
.027
.100

37

Abs(MFE) t-1

168

.026

.069

0

.003

.010

.020

.542

Panel B Accounting comparability and management earnings forecast accuracy:
the effect of the 2008 quarterly reporting regulation in Japan(1)
Abs(MFE) t
AcctInd t-1 * QRR(2)Abs(MFE) t(3)
Abs(MFE) t-6.054***(-3.371)

Acct10 t-1 * QRR-5.746**(-2.393)
3.811**(2.207)3.480**(2.130)3.304*(1.914)

Acct_median t-1

Loss_D t-1
Increase_D t-1
SIZE t-1
ROA t-1
LEVt-1
Earn_Vol t-1-3.874**(-2.245)

3.406*(1.777)-0.012(-0.625)0.025(0.672)-0.014**(-2.352)-0.012***(-3.339)-0.247*(-1.832)0.044(1.470)0.343(1.044)(6)
Abs(MFE) t-3.056(-1.549)-5.109***(-3.124)

Acct10 t-1

QRR

(5)
Abs(MFE) t-3.985**(-2.435)

Acct_median t-1 * QRRAcctInd t-1(4)
Abs(MFE) t

3.429**(2.114)0.010(0.691)
0.025(0.671)-0.014**(-2.292)-0.012***(-3.374)-0.248*(-1.829)0.046(1.542)0.379(1.144)0.021(1.390)0.025(0.670)-0.013**(-2.230)-0.012***(-3.324)-0.242*(-1.796)0.047(1.634)0.387(1.228)38

0.015(0.875)0.156(1.643)-0.001(-0.043)-0.008(-1.315)0.051(0.241)
0.035(0.966)
0.557**(1.991)0.032**(2.181)0.159(1.652)0.001(0.094)-0.008(-1.313)0.059(0.266)0.036(0.972)0.547*(1.941)2.746*(1.678)0.026(1.614)0.150(1.553)-0.000(-0.014)-0.007(-1.292)
0.052(0.245)0.032(0.881)0.561*(1.882)

Growth t-1
Abs(MFE) t-1
Constant
Year, Industry
Observations
Adj. R-squared

0.239*(1.791)0.747***(2.760)-0.119(-0.974)
Control
1,738
0.248

0.236*(1.799)0.761***(2.782)-0.133(-1.091)
Control
1,738
0.247

0.237*(1.782)0.755***(2.775)-0.128(-1.040)
Control
1,738
0.247

0.003
(0.053)0.109(0.548)0.085(1.642)Control
168
0.474-0.001(-0.018)
0.127(0.637)0.080(1.524)Control
168
0.467

0.012
(0.207)0.103(0.496)0.064(1.316)
Control168
0.470

Abs(MFE) t is the measure of management earnings forecast accuracy. Higher values of Abs(MFE) correspond to lower values of management earnings forecast accuracy.
AcctInd t-1, Acct10 t-1, and Acct_median t-1 are three measurements of accounting comparability. Greater values of AcctIndi,t, Acct10i,t, and Acct_median i,t indicate higher
accounting comparability. QRR is a dummy variable that takes a value of 1 for the year after 2008, and a value of 0 before 2008. AcctInd t-1 * QRR, Acct10t-1 * QRR, and
Acct_mediant-1 * QRR are the interactions of QRR and three measures of accounting comparability (AcctInd t-1, Acct10 t-1, and Acct_mediant-1), respectively. The t-values in allcolumns are based on standard errors clustered by firm and year. *, ** and *** refer to significance level at 10%, 5% and 1%, respectively. Please see Appendix A for othervariable definitions.

39

Table 9 Accounting comparability and management earnings forecast accuracy:
the effect of IFRS adoption in Japan
Panel A Descriptive statistics
Summary statistics of PSM samples (IFRS adopters and non IFRS adopters (J-GAAP))IFRS Adopting Firms
Non-Adopting firms (J-GAAP)N
MeanMedianN
MeanMedianAbs(MFE) t
965
.025
.012
965
.031
.012
AcctInd t-1
965
-.016
-.014
965
-.017
-.015
Acct10 t-1
965
-.004
-.002
965
-.005
-.003
Acct_median t-1
965
-.012
-.010
965
-.013
-.011
Loss_D t-1
965
.104
0
965
.109
0
Increase_D t-1
965
.593
1
965
.639
1
SIZE t-1
965
12.54412.742965
12.446
12.546
ROA t-1
965
.046
.040
965
.044
.042
LEV t-1
965
.477
.484
965
.473
.480
Earn_Vol t-1
965
.023
.017
965
.022
.016
Growth t-1
965
1.055
1.040
965
1.040
1.030
Abs(MFE) t-1
965
.030
.013
965
.035
.014

T-Statistic
t-value-2.223**4.446***
3.249***
4.092***-0.369-2.107**
1.516
1.066
0.397
1.265
3.833***-1.689*

Summary statistics of PSM samples (IFRS adopters and non IFRS adopters (GAAP))IFRS Adopting Firms
Non-Adopting firms (GAAP)N
MeanMedianN
MeanMedianAbs(MFE) t
89
.032
.009
89
.033
.013
AcctInd t-1
89
-.016
-.015
89
-.017
-.016
Acct10 t-1
89
-.003
-.002
89
-.003
-.002
Acct_median t-1
89
-.011
-.010
89
-.013
-.011
Loss_D t-1
89
.045
0
89
.169
0
Increase_D t-1
89
.494
0
89
.584
1
SIZE t-1
89
13.87614.09189
13.891
14.122
ROA t-1
89
.045
.041
89
.032
.033

T-Statistic
t-value-0.083
1.066
0.168
1.645
-2.710***-1.201-0.122
1.728*

40

LEV t-1
Earn_Vol t-1
Growth t-1
Abs(MFE) t-1

89
89
89
89

.443
.024
1.040
.025

.473
.020
1.034
.011

89
89
89
89

.449
.026
1.022
.038

.393
.020
1.017
.014

-0.206-0.5162.129**-1.494

Panel B Accounting comparability and management earnings forecast accuracy:
the effect of IFRS adoption in Japan(1)
Abs(MFE) t
AcctInd t-1 * IFRS

(2)
Abs(MFE) t(3)
Abs(MFE) t

0.919(1.552)

Acct10 t-1 * IFRS

1.627
(1.624)-1.106*(-1.762)2.940*(1.659)7.500*(1.919)Acct_median t-1

Loss_D t-1
Increase_D t-1
SIZE t-1
ROA t-1-4.503**(-1.971)-1.418(-1.351)
0.012(1.285)0.008(1.161)-0.004(-1.305)-0.004***(-3.565)-0.015(-0.441)(6)
Abs(MFE) t-6.135*(-1.910)1.105*(1.856)

Acct10 t-1

IFRS

(5)
Abs(MFE) t-4.395*(-1.889)

Acct_median t-1 * IFRSAcctInd t-1(4)
Abs(MFE) t-1.253**(-2.041)0.011(1.523)0.008(1.108)-0.004(-1.405)-0.004***(-3.546)-0.007(-0.187)

0.004(0.797)0.008(1.201)-0.004(-1.354)-0.005***(-4.021)-0.023(-0.601)41-0.065*(-1.896)
0.032(1.023)-0.008(-1.154)-0.005(-1.158)-0.322**(-2.501)-0.010(-1.033)
0.033(1.038)-0.005(-0.671)-0.007(-1.443)-0.372**(-2.541)2.494*(1.656)-0.049**(-2.028)0.033(1.040)-0.009(-1.279)-0.006(-1.411)-0.299**(-2.391)

LEVt-1
Earn_Vol t-1
Growth t-1
Abs(MFE) t-1
Constant
Year, Industry
Observations
Adj. R-squared

0.026***(3.559)-0.069(-0.955)-0.035**(-2.230)
0.166**(2.355)0.092***(2.791)
Control
1,930
0.166

0.027***(3.577)-0.033(-0.447)-0.034**(-2.177)0.166**(2.362)0.106***(3.538)Control
1,930
0.167

0.025***(3.523)-0.084(-1.147)-0.033**(-2.068)
0.161**(2.300)0.092***(2.855)Control
1,930
0.170

0.082**(2.141)-0.161(-0.775)
0.015(0.159)0.293(1.395)0.058(0.434)Control
178
0.356

0.082**(2.255)-0.109(-0.495)
0.028(0.277)0.393(1.618)0.040(0.280)Control
178
0.350

0.081**(2.099)-0.195(-0.863)-0.013(-0.144)0.271(1.377)0.085(0.675)Control178
0.372

Abs(MFE) t is the measure of management earnings forecast accuracy. Higher values of Abs(MFE) correspond to lower values of management earnings forecast accuracy.
AcctInd t-1, Acct10 t-1, and Acct_median t-1 are three measurements of accounting comparability. Greater values of AcctIndi,t, Acct10i,t, and Acct_mediani,t indicate higher
accounting comparability. IFRS is a dummy variable measured as 1 if a firm adopted IFRS voluntarily and 0 otherwise. AcctInd t-1 * IFRS, Acct10t-1 * IFRS, and Acct_mediant1 * IFRS are the interactions of IFRS and three measures of accounting comparability (AcctInd t-1, Acct10 t-1, and Acct_mediant-1), respectively. The t-values in all columns are
based on standard errors clustered by firm and year. *, ** and *** refer to significance level at 10%, 5% and 1%, respectively. Please see Appendix A for other variable
definitions.

42

Appendix A
Variable

Definition of variable

Management Earnings Forecasts
Abs(MFE) t

The absolute value of initial MFE (earnings forecast error) in year t deflated by lagged
market value of equity (|actual earnings for year t – initial management earnings forecast
for year t|/market value of equity for year t – 1).

Accounting Comparability
CompAcct

Negative value of the absolute difference between the expected earnings using theestimated coefficients for the accounting functions of firms i and j, respectively. It is
calculated for each firm i − j pair for firms in the same two-digit Nikkei industry.

AcctInd t-1

The mean value of CompAcct for firm i for all firm i − j pairs in the same industry inyear t – 1.

Acct10 t-1

The average of the ten highest CompAcct values for firm i in year t – 1.

Acct_median t-1

The median value of CompAcct for firm i for all firm i − j pairs in the same industry inyear t – 1.

Control Variables
Abs(MFE) t-1

The absolute value of initial MFE in year t – 1 deflated by lagged market value of equity

Loss_D t-1

A dummy variable that equals to one if the firm reports net losses in year t – 1, andzero otherwise.

Increase_D t-1

A dummy variable that equals to one if the firm’s net income in year t – 1 has increased
compared to the prior year, and zero otherwise.

SIZE t-1

The log value of the market value of equity at the end of year t – 1.

ROA t-1

Net income before extraordinary items scaled by lagged total assets in year t – 1.

LEV t-1

Total liabilities divided by total assets at the end of year t – 1.

Earn_Vol t-1

The standard deviation of ROA over a 5-year period from year t – 5 to t – 1, where ROA
is measured as net income before extraordinary items scaled by lagged total assets.

Growth t-1

The average of sales growth over a 5-year period from years t – 5 to t – 1, where sales
growth is measured as the ratio of sales in year t scaled by sales in year t – 1.

Cross-sectional Test
InstOwn t-1

The percentage of the shares owned by institutional investors at the end of year t – 1,
ranked into deciles for each year and then standardized to a range of 0 – 1.

ForOwn t-1

The percentage of the shares owned by foreign investors at the end of year t – 1.

ManaOwn t-1

The percentage of the shares owned by directors at the end of year t – 1.

43

AQ_DD t-1

The standard deviation of firm-level residuals from the following Dechow and Dichev’s(2002) model during the previous five years:
𝑇𝐶𝐴!,$ = 𝛽% + 𝛽& 𝐶𝐹𝑂!,$'& + 𝛽( 𝐶𝐹𝑂!,$ + 𝛽) 𝐶𝐹𝑂!,$*& + 𝜀!,$The standard deviation is then multiplied by – 1 and standardized so that it has a meanof zero and a standard deviation of 1.

AQ t-1The standard deviation of firm-level residuals from the following modified Dechow andDichev’s (2002) model (augmented by McNichols (2002)) during the previous five
years:
𝑇𝐶𝐴!,# = 𝛽$ + 𝛽% 𝐶𝐹𝑂!,#&% + 𝛽' 𝐶𝐹𝑂!,# + 𝛽( 𝐶𝐹𝑂!,#)% + 𝛽* ∆𝑅𝑒𝑣!,# + 𝛽+ 𝑃𝑃𝐸!,# + 𝜀!,#The standard deviation is then multiplied by – 1 and standardized so that it has a meanof zero and a standard deviation of 1.
Robustness Test and Additional Analyses
Abs(Revision_mag) t

The absolute value of management forecast revisions in year t deflated by lagged market
value of equity (|initial management earnings forecast for year t – the latest management
earnings forecast for year t|/market value of equity for year t – 1).

Abs(MFE)2 t

The absolute value of initial MFE (earnings forecast error) in year t deflated by lagged
total assets (|actual earnings for year t – initial management earnings forecast for year t|/
total assets for year t – 1).

AcctInd_PLE t-1

An alternative measure of AcctInd that is calculated based on the prices leading earnings
model to estimate the accounting function.

Acct10_PLE t-1

An alternative measure of Acct10 that is calculated based on the prices leading earnings
model to estimate the accounting function.

Acct_median_PLE t-1

An alternative measure of Acct_median that is calculated based on the prices leading
earnings model to estimate the accounting function.

Acct4 t-1

The average of the four highest CompAcct values for firm i in year t – 1.

Acct4_PLE t-1

An alternative measure of Acct4 that is calculated based on the prices leading earnings
model to estimate the accounting function.

QRR

A dummy variable that takes a value of 1 for the year after 2008, and a value of 0 before
2008.

IFRS

A dummy variable measured as 1 if a firm adopted IFRS voluntarily and 0 otherwise.

44

Appendix B
1. Logit model for IFRS adoption
I follow previous studies (Cameran et al., 2014; Sato and Takeda, 2017; Gu, 2021; Amano,2021) to build the following logit model to predict the likelihood of IFRS adoption:
𝐼𝐹𝑅𝑆!,# = 𝐿𝑜𝑔𝑖𝑡 (𝛽( + 𝛽& 𝑆𝐼𝑍𝐸!,# + 𝛽) 𝐿𝐸𝑉!,# + 𝛽* 𝑅𝑂𝐴!,# + 𝛽+ 𝐿𝑂𝑆𝑆!,# + 𝛽' 𝐴𝑏𝑠_𝑂𝑃𝐶𝐹!,#+ 𝛽, 𝐹𝑜𝑟𝑂𝑤𝑛!,# + 𝛽- 𝑂𝑣𝑒𝑟𝑠𝑒𝑎𝑠_𝑟𝑎𝑡𝑖𝑜!,# + 𝛽. 𝐼𝑛𝑣𝑒𝑠𝑡!,# + 𝛽/ 𝐺𝑟𝑜𝑤𝑡ℎ2!,#+ ∑𝑌𝑒𝑎𝑟 + ∑𝐼𝑛𝑑𝑢𝑠𝑡𝑟𝑦 + 𝜀!,# )Where 𝐴𝑏𝑠_𝑂𝑃𝐶𝐹!,# is the absolute value of operating cash flow deflated by total assets in yeart – 1, 𝑂𝑣𝑒𝑟𝑠𝑒𝑎𝑠_𝑟𝑎𝑡𝑖𝑜!,# is the ratio of foreign sales to total sales in year t, 𝐼𝑛𝑣𝑒𝑠𝑡!,# is the total
investment cash flows scaled by total non-current assets, and 𝐺𝑟𝑜𝑤𝑡ℎ2!,# is the total assets
divided by lagged total assets.
Table A presents the results of the logit model. As shown in column (1), the coefficients of
𝑆𝐼𝑍𝐸!,# , 𝑅𝑂𝐴!,# , and 𝑂𝑣𝑒𝑟𝑠𝑒𝑎𝑠_𝑟𝑎𝑡𝑖𝑜!,# are significantly positive, suggesting that larger firms
with higher profitability and foreign sales ratio tend to adopt IFRS. However, the effect of
𝐹𝑜𝑟𝑂𝑤𝑛!,# on IFRS adoption is insignificant. These results are consistent with Amano (2021)
and Sato and Takeda (2017).
Table A Logit model for IFRS adoption

SIZE t
LEV t
ROA t
LOSS t
Abs_OPCF t
Foreign t
Overseas_ratio t
Invest t
Growth2 t
Constant
Year, Industry
Observations

(1)
IFRS
IFRS adopters and non IFRS
adopters (J-GAAP)

(2)
IFRS
IFRS adopters and non IFRS
adopters (U.S. GAAP)

0.984***(21.764)0.794***(3.192)-2.595**(-2.328)0.227(1.359)2.706***(3.317)-0.829(-1.605)
1.596***(8.974)-0.227(-0.604)
1.083***(3.144)-15.016***(-25.043)
Control
14,102-0.269*(-1.933)
2.597**(2.045)8.458(0.849)-0.665(-1.046)2.605(0.366)-7.478***(-3.832)-4.056***(-5.147)-1.185(-0.363)1.138(0.803)6.682**(2.508)Control
350

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(B1)

Pseudo. R-squared

0.326

0.231

The t-values in all columns are based on standard errors clustered by firm and year. *, ** and *** refer tosignificance level at 10%, 5% and 1%, respectively. Please see Appendix A for other variable definitions.

2. Balanced Property Test
Table B shows the results of the balanced property test of the matched samples. The results
indicate that there is no significant difference between IFRS adopters and non-adopters foreach variable used in the logit model, confirming that these matched pairs are well balanced.
Table B Balanced property test
Panel A Summary statistics of PSM samples (IFRS adopters and non IFRS adopters (J-GAAP))IFRS Adopting Firms
Non-Adopting firms (J-GAAP)N
MeanMedianN
MeanMedianSIZE t965
12.63312.857965
12.55
12.704
LEV t
965
.474
.478
965
.471
.474
ROA t
965
.049
.043
965
.048
.044
LOSS t
965
.076
0
965
.088
0
Abs_OPCF t
965
.091
.083
965
.090
.084
ForOwn t
965
.252
.251
965
.258
.248
Overseas_ratio t
965
.395
.441
965
.389
.408
Invest t
965
-.120
-.107
965
-.120
-.100
Growth2 t
965
1.071
1.046
965
1.073
1.054

T-Statistic
t-value-1.286-0.377-0.1030.996-0.1771.090-0.411
0.089
0.372

Panel B Summary statistics of PSM samples (IFRS adopters and non IFRS adopters (U.S. GAAP))IFRS Adopting Firms
Non-Adopting firms (U.S. GAAP)N
MeanMedianN
MeanMedianSIZE t89
13.96814.11989
13.95914.119
LEV t
89
.434
.461
89
.447
.384
ROA t
89
.044
.041
89
.040
.040
LOSS t
89
.112
0
89
.124
0
Abs_OPCF t
89
.079
.068
89
.084
.077
ForOwn t
89
.363
.354
89
.365
.382
Overseas_ratio t
89
.649
.687
89
.648
.700
Invest t
89
-.099
-.092
89
-.103
-.102
Growth2 t
89
1.035
1.024
89
1.041
1.046

T-Statistic
t-value
0.074-0.4330.566-0.231-0.7530.136-0.048-0.2750.525*, ** and *** refer to significance level at 10%, 5% and 1%, respectively. Please see Appendix A for othervariable definitions.

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