The Impact of Fair Disclosure Regulation on Analyst Forecasts
Evidence from Japan
Abstract: In September 2016, the Japan Securities Dealers Association introduced guidelines
prohibiting analysts from obtaining an earnings preview before earnings official announcement. I
analyze how analyst forecasts error and dispersion changed before and after the guidelines wereintroduced. The results show that analyst forecasts dispersion decreased post the introduction of theguidelines, which suggests that management was less likely to selectively disclose material information.
In addition, the results show that analyst forecasts error decreased post the introduction of theguidelines. This suggests that the positive effect of improving the accuracy of management forecasts
outweighs the negative effect of decreasing selective disclosure.
Keywords: fair disclosure regulation, analyst forecasts, management forecasts
1. Introduction
In September 2016, the Japan Securities Dealers Association (JSDA) introduced guidelines
prohibiting analysts from obtaining an earnings preview before earnings official announcement. This
paper investigates the impact of the guidelines on analyst forecasts dispersion and accuracy. There arethree reasons to focus on this issue. The first is that global investors have questioned the transparencyof Japanese stock markets. In Japan, it was a common practice for analysts to communicate material
information to a subset of investors before it was publicly announced (JSDA 2016). Analysts could
obtain information through a “preview interview” which allows access to management for the purposeof obtaining settled earnings information. It was difficult for global investors to accept “preview
interview”1 system since it was unique to Japan. As a result, a discussion about the appropriateness of
this practice began around 2013 (Okumura et al. 2017). The need for change was also prompted byseveral securities firms receiving administrative sanctions over the analysts’ management of
undisclosed material information2. The problem was that analysts transmitted material information to

The term “preview interview” means that analysts interview listed firms in order to obtain financial closing
information prior to the “silent period” during which listed firms were prohibited from communicating unpublishedmaterial information to investors or analysts until four weeks after their earnings announcement.
2
For an overview of these cases, refer to the following press release by the FSA. “Administrative Disposition against
Deutsche Securities Inc.” Available at https://www.fsa.go.jp/news/27/syouken/20151215-1.html, ”Administrative
Disposition against Credit Suisse Securities Inc.” Available at https://www.fsa.go.jp/news/27/syouken/201604251.html.
11

a subset of customers and induced them to buy and sell related shares. The Tokyo Stock Exchange
imposed a penalty of 60 million yen for infringing trading participant rules and the JSDA imposed apenalty of 30 million yen for violation of rules concerning analyst reporting 3. Whether such practices
have diminished following introduction in Japan of fair disclosure regulation is for global investors.
Second, there is growing interest in fair disclosure regulation 4 in Japan. As a consequence of
inappropriate use of information by securities firms, the JSDA introduced “Guidelines for Reportingand Information Transmission to Issuers by Analysts of Association Members” (hereafter“Guidelines”) in September 2016. Following this, in April 2018 the Financial Services Agency (FSA)
introduced an enforceable “Fair Disclosure Rule” (hereafter “FDR”)5. Because introduction of the
Guidelines is relatively recent in Japan, its economic impact has not been previously examined. This
paper focuses whether has the Guidelines changed analyst behavior. Specifically, it examines whether
the amount of selective disclosure has decreased and whether the earnings forecast environment haschanged post-guidelines.
Third, there is a lack of consistent results for prior studies that have investigated the impact offair disclosure regulation outside of Japan. The lack of consistent prior results has been attributed todifficulty related to controlling for other events that occurred simultaneously with the introduction of
the regulation. In addition, for United States (U.S.) studies on fair disclosure regulation, it is likely thatearnings forecasts practices have affected the results. Specifically, U. S. management forecasts are
For the penalties, please refer to the following news sources by Tokyo Stock Exchange and The Japan Securities
Dealers
Association.
“Disposition
againstDeutscheSecurities
Inc.”
Availableathttps://www.jpx.co.jp/news/1060/20160216-01.html, “Disposition and Recommendation for Association Member”
Available at http://www.jsda.or.jp/kyoukaiin/syobun/kyokaiin/files/20160216_PRESS_RELEASE2.pdf.
4 In this paper, “Fair Disclosure Regulation” means both “Fair Disclosure Rule” (FDR) enforced by Financial
Instruments and Exchange Act of the Financial Services Agency in 2018 and “Guidelines for Reporting andInformation Transmission to Issuers by Analysts of Association Members” enforced by The Japan Securities
Dealers Association in 2016. The same applies to “Regulation Fair Disclosure” (Regulation FD) enforced by SEC.
5 The “Guidelines for Reporting and Communicating Information to Issuers by Analysts of Association Members”
prohibits the following: interviewing listed firms on unpublished material information and transmitting theunpublished information by means other than analyst reports. “Fair Disclosure Rule” forced listed firms to disclosematerial information simultaneously and publicly once they released the information to a subset of investors or
analysts.
2
3

voluntarily and firms covered by three or more analysts account for about sixty percent or more of
NYSE listed firms. In contrast, for Japanese companies, management forecasts are effectively
mandated (Kato et al. 2009) and firms with three or more analysts are only about twenty percent of
TSE listed firms6. Though prior research suggests that analysts are influenced by management forecasts(Ota 2010), it may be difficult to properly examine the relationship between management forecasts andanalyst forecasts in the U.S. due to the voluntary management earnings forecast practice. Therefore,
Japan’s effective mandatory management forecast arrangements provide a better setting to examine theimpact of fair disclosure regulation.
The results of this paper indicate that analyst forecasts dispersion decreased post-Guidelines.
This suggests the amount of private information disclosed by managements to analysts decreases andthe dependence of analysts on management forecasts increased post-Guidelines. In addition, thefindings show that analyst forecast error declined post-Guidelines. Overall, the results suggest thepositive effects of increasing management forecasts accuracy outweigh the negative effects ofdecreasing the amount of private information post-Guidelines.
This paper is organized as follows. Related literature is reviewed in Section 2, and thedevelopment of hypotheses is provided in Section 3. Section 4 describes the research design andsamples. Section 5 presents and interprets the result of the main analysis. A concluding discussion ispresented in Section 6.
2. Prior Literature
Most of the prior research on FDR type regulation has been conducted in the U. S. since theregulation was introduced there in 2000. Securities and Exchange Commission (SEC) concern about
selective disclosure of material information by management to analysts lead to introduction of the U.S.
Regulation Fair Disclosure (hereafter “Regulation FD”) (SEC 2000). Prior U.S. research has analyzed
For each of NYSE listed firms and TSE listed firms, I compiled analyst coverage data as of 2018 obtained from
Capital IQ.
36whether selective disclosure by management declined post the introduction of Regulation FD. The
amount of selective disclosure by management before and after Regulation FD is determined by using
analyst forecast dispersion as information asymmetry among analysts is reflected analyst forecasts
dispersion (Barron et al. 1998). Pre-Regulation FD, some analysts were able to obtain private
information, while others were not. Therefore, if all analysts obtain the same information postRegulation FD, analyst forecast dispersion should be reduced.
Findings regarding the effect of Regulation FD on analyst forecast dispersion are mixed. Kross
and Suk (2012) find that forecasts dispersion declined in tests for a pre-Regulation FD period fromfourth quarter 1996 through third quarter 2000 and a post-Regulation FD period from fourth quarter2000 thorough third quarter 2004. This suggests that analysts are more reliant on general management
forecasts as selective disclosure by management decreased after Regulation FD was introduced. In
contrast, Agrawal et al. (2006) test a pre- and post-Regulation FD periods from first quarter 1995
through second quarter 2004 and find that forecasts dispersion increased post-Regulation FD. This
result indicates that analysts increase their forecast effort to compensate for the reduction of selective
disclosure by management.
Forecast accuracy may also be affected by the change in earnings forecasts environment foranalysts due to restrictions on selective disclosure by management. The more information sources
available to analysts and the higher the accuracy of information, the easier it is for them to accurately
forecast earnings. Analysts can use information obtained from management forecasts and otherinformation sources such as customers, suppliers or employees.
On one hand, prior studies suggest it is more difficult for analysts to forecast earnings accurately
when the amount of private information obtained from management decreases post-Regulation FD(Agrawal et al. 2006; Keskek et al. 2017). On the other hand, studies show that analyst forecasts
accuracy increased post-Regulation FD (Shane et al. 2001; Kross and Suk 2012). In addition, other4

studies do not find any change in analyst forecasts accuracy after introduction of Regulation FD (Heflin
et al. 2003; Bailey et al. 2003; Francis et al. 2006). These studies suggest that extra analyst effort offsets
forecasts error due to the reduction of selective disclosure. Overall, the extant literatures related to theintroduction of the U.S. Regulation does not provide a clear picture as to whether the amount of
selective disclosure by management decreased or whether the earnings forecasts environment foranalysts changed.
3. Hypothesis Development
Most of the prior research on fair disclosure type regulation has been conducted in the U.S., but
it has failed to provide consistent results regarding the effect of the regulation on analyst forecasts
dispersion and accuracy. There are three possible reasons for the prior mixed results. The first is thepossible confounding effect of other events occurring at the same time of the introduction of Regulation
FD (Koch et al. 2013). Francis et al. (2006) identify the following events during the post-Regulation
FD period (from 2000 through 2002) : the crash of the dot-com bubble, the U. S. economic recession,decimalization, and accounting scandals such as those involving WorldCom and Enron. These events
may influence the results of prior studies.
The second reason is possible selection bias related to U.S. managers with high ability having
an incentive to disclose management forecasts for the purpose of obtaining private profits. Moreover,
the accuracy of forecasts published by such managements are high (Trueman 1986; Baik et al. 2011).
In addition, analysts tend to revise their earnings forecasts based on management forecasts when theaccuracy is high (Ota 2010). That is, analysts are more likely to rely on higher accuracy management
forecasts. It may be difficult to properly examine the relationship between management forecasts andanalyst forecasts in the U.S. due to the voluntary management earnings forecast practice.
Third, the variables used in prior analysis are a potential problem. Prior research uses analyst
forecasts dispersion to determine if the level of selective disclosure was reduced in the post-Regulation5

FD period. However, analyst forecasts dispersion may reflect the dependence of analysts on public
information such as management forecasts. Managers in the U.S. have options to increase or decrease
the amount of disclosure post-Regulation FD. Along with this, it is expected that the reliance of analysts
on management forecasts could change before and after Regulation FD.
Analysts in Japan are likely to rely on management forecasts when they release earnings
forecasts (Ota 2007). Because there is little change in the dependence of analysts on management
forecasts it is possible to better assess whether the Guidelines affects analyst forecasts dispersion in theJapanese setting.
This paper first tests how analyst forecasts dispersion changed post-Guidelines. The introductionof Guidelines forces analysts to refrain from interviewing management, which is likely to make it
difficult for them to utilize unpublished material information in making their forecast. Analysts who
can no longer obtain private information become more reliant on public information. Barron et al.
(1998), in an analytical study, show that analyst forecasts dispersion is a proxy variable for the amountof private information used in forecasting earnings. They use the uncertainty of information
communicated by managements to analysts to account for forecasts dispersion. If the uncertainty ofprivate information is high, analysts tend to rely only on public information. Based on Barron et al.
(1998), analyst forecasts dispersion decreases if the quantity of selective disclosure declines andanalysts do not collect additional earnings information post-Guidelines 7.
In this paper, the focus is on analyst forecasts released around the announcement of management
earnings forecasts. Analysts’ forecasts released forty-five days before and after the announcement ofAnalytical research by Kotani (2017) suggests that the more analyst coverage, the more motivated analysts are to
become free riders post-FDR. That is, analysts may rely more on public information instead of collecting additional
information additionally. The Regulations enforce management to disclose material information publicly once they
communicate the information to a subset of analysts or investors. If an analyst obtains information from management
at high cost, other analysts can get the same information at no cost, which allows analysts to gain high reputationfrom capital markets while saving the cost of collecting information (Kotani 2017).
6
7

the latest management forecasts are collected 8 . The difference is then calculated between analyst
forecasts before management forecasts is disclosed and the revised forecast immediately after
management forecasts disclosure. Analysis is conducted of this difference before and after the
introduction of the Guidelines.
Analysts who were able to obtain unpublished material information in advance could incorporate
the information into their earnings forecasts pre-Guidelines. However, this information should not be
available post-Guidelines. Therefore, analyst forecasts dispersion before the announcement ofmanagement forecasts should increase post-Guidelines. In addition, the reliance of analysts on
management forecasts is likely to increase post-Guidelines (Kotani 2017). Analyst forecasts are likely
to converge on management forecasts post-Guidelines. For this reason, the difference between thestandard deviation of analyst forecasts released after management forecasts and that released before
should decrease post-Guidelines. Based on the above expectation, the following hypothesis is tested.
H1: Analysts’ forecast dispersion decreases post-Guidelines.
The next hypothesis related to how analyst forecasts accuracy changes before and after
Guidelines. Post-Guidelines, analysts are required to refrain from interviewing management in orderto obtain unpublished material information. Therefore, it may be difficult for analysts to accurately
forecasts earnings post-Guidelines. This should be associated with a decrease in accuracy of analyst
forecasts. However, analyst forecasts error may decrease if the accuracy of management forecasts
improves and the dependence of analysts on management forecasts is greater. Managers have little
incentive to disclose earnings forecasts accurately because they are able to control market expectations
through the private channels pre-Guidelines. However, it is difficult for managements to bias earnings

This analysis is based on Kross and Suk (2012). Besides, Nara and Noma (2013) suggest that though analysts arelikely to rely on management forecasts until two weeks after the announcement of management forecasts, they collectearnings information additionally four weeks after the release of management forecasts. That is, analyst forecasts
released at the latest point before the announcement of management forecasts are more likely to reflect analyst’s
analysis based on other than management forecasts.
78

forecasts because such information could lower the credibility of the subsequent disclosure (Hutton
and Stocken 2009). Therefore, managements have an incentive to publish accurate earnings forecasts
so that they maintain their reputation.
Heflin et al. (2012) find that in the U.S. management forecasts accuracy increased after the
introduction of Regulation FD and suggest that managers have an incentive to disclose earnings
forecasts conservatively to avoid negative earnings surprises. Ota (2010) shows that analysts tend torevise their earnings forecasts based on management forecasts when the accuracy is high. That is,analysts are more likely to rely on higher accuracy management forecasts. If the positive effects ofincreasing management forecasts accuracy outweigh the negative effect of decreasing the amount of
information analysts obtains, analyst forecasts error should decline post-Guidelines. Based on this
discussion, the second hypothesis regarding analyst forecast error is tested.
H2: Analysts’ forecasts error decreases post-Guidelines.
4. Research Design
4.1 Empirical Models
The test of H1 is based on the model developed by Kross and Suk (2012). Analysis of the amountof selective disclosure changes before and after Guidelines is conducted analyst forecasts dispersion
before and after the management forecasts announcement (see Equation 1 below). This analysis uses
management forecasts that were revised immediately before the earnings announcement (hereafter,
latest management forecasts). The model uses analyst forecasts that were published before and after
forty-five days of the release of the latest management forecasts (Figure 1). Managements do not haveenough information to forecast earnings at the beginning of the fiscal year. But latest management
forecasts reflect all of the information available to them. Hence, analysis is possible based on thechange in management earnings forecast behavior before and after Guidelines.
Management forecasts are obtained from Nikkei NEEDS Financial Quest 2.0, while analyst
8forecasts and financial data are obtained from Thomson Reuters I/B/E/S/. Note that t is the period when
the latest management forecasts were released.
𝛥𝑑𝑖𝑠𝑝𝑡 = 𝛽0 + 𝛽1 𝑓𝑑 + 𝛽2 𝑠𝑢𝑟𝑡 + 𝛽3 𝑐𝑎𝑟 + 𝛽4 𝑙𝑜𝑠𝑠𝑡 + 𝛽5 𝛥𝑟𝑜𝑎𝑡 + 𝛽6 𝛥𝑔𝑑𝑝𝑡 + 𝛽7 𝑟𝑑𝑡−1 + 𝛽8 𝑙𝑚𝑣𝑒𝑡+ 𝛽9 𝑚𝑓𝑒𝑡 + 𝛽10 𝑛𝑎𝑓𝑡 + 𝜀・・・(1)
ΔdisptAnalyst forecasts dispersion. |the standard deviation of analyst forecasts published at thefdearliest time in forty-five days after management forecasts t– the standard deviation ofanalyst forecasts released at the latest time in forty-five days before management
forecastst|/ share price at the beginning of fiscal year t.
Dummy variable on FD. One if latest management forecasts are released in fiscal year

surt

2017 or 2018, zero if they are released in fiscal year 2014 or 2015.
|acutual EPSt−analyst forecasts consensus EPS(median) released thirty days before
management forecastst |/ share price at the beginning of fiscal year t.
Three-day accumulated abnormal return of announcement of latest management
forecastst.

car
losst
Δroat
Δgdpt

Dummy variable that takes a value of one if the actual EPS is the red, and zerootherwise.
Percentage change in ROA (normal profits/total assets) relative to t-1.
Percentage change in GDP relative to t-1.

rdt-1
lmvet
mfet

R&D expendituret-1/ total assetst-1
Natural logarithm of market capitalizationt-1.
Latest management forecast error. |acutual EPS t−latest management forecasts EPSt/shareprice at the beginning of fiscal year t.

naft

Natural logarithm of analyst coveraget.
Insert Figure 1
The dependent variable Δdispt is analyst forecasts dispersion, which is calculated as follows: |thestandard deviation of analyst forecasts published at the earliest time in forty-five days after
management forecasts t– the standard deviation of analyst forecasts released at the latest time in fortyfive days before management forecastst|/ share price at the beginning of fiscal year t.
The independent variable is fd. H1 predicts that analyst forecasts dispersion after the release of9management forecasts decreases post-Guidelines. Therefore, a negative sign is expected for fd.
Hypothesis 2 is also tested using the model developed Kross and Suk (2012) (see Equation (2)below), which focuses on how analyst forecasts error changes before and after the Guidelines.
|𝑒𝑟𝑟𝑜𝑟𝑡 | = 𝛽0 + 𝛽1 𝑓𝑑 + 𝛽2 𝑠𝑢𝑟𝑡 + 𝛽3 𝑐𝑎𝑟 + 𝛽4 𝑙𝑜𝑠𝑠𝑡 + 𝛽5 𝑛𝑎𝑓𝑡 + 𝛽6 Δ𝑔𝑑𝑝𝑡 + 𝛽7 𝑟𝑑𝑡−1 + 𝛽8 𝑙𝑚𝑣𝑒𝑡+ 𝛽9 𝑚𝑓𝑒𝑡 + 𝛽10 𝑛𝑎𝑓𝑡 + 𝜀・・・(2)|errort|Analyst forecasts consensus error. |Analyst forecasts consensus EPS (median) released
within forty-five after the management forecastst−acutual EPSt|/share price at the
beginning of fiscal year t.

|errort| represents analyst forecasts consensus error for period t. |errort| is the absolute of thedifference between analyst forecasts consensus EPS (median) released within forty-five after theannouncement of management forecasts and actual EPS scaled by share price at the beginning of fiscal
year t. fd is variable of interest. Hypothesis 2 predicts management forecasts accuracy improves andreliance of analysts on management forecasts increases post-Guidelines, which results in lower analyst
forecasts error. A negative sign is predicted for fd.
The same control variables are used in both models. The variable Sur measures the surprise inanalysts’ expectations. It is calculated as the absolute value of the difference between analyst forecasts
consensus Earnings Per Share (EPS) (median) released thirty days before the release of management
forecasts and actual EPS scaled by share price at the beginning of fiscal year t. The more difficult it is
to forecast earnings, the greater the surprise component analysts’ expectations. Car is three-day
accumulated abnormal return before the announcement of latest management forecast. Loss is dummy
variable that takes a value of one if the actual EPS is the negative, and zero otherwise. It is difficult toforecast earnings when performance is weak and the reliance of analysts on management forecasts areless when their accuracy is low (Ota 2010). For the same reason, a control is included for the percentagechange in ROA (normal profits/total assets) relative to t-1. The variable Δgdp is included to control for10the percentage change in GDP relative to t-1. Management forecasts tend to be pessimistic (optimistic)
when the economy is rising (failing) (Ota 2006). It is predicted that a higher percentage change in GDP
relative to t-1 is more likely to bias management forecasts. I also include rd (R&D expendituret-1/ total
assetst-1). Since proprietary cost is high for firms with higher R&D expenditure (Wang 2007), such
firms have incentive to disclose biased earnings forecasts. Lmve is natural logarithm of market
capitalization. Jaggi (1980) suggests that management forecasts accuracy by large firms is higher thansmall firms. Mfe is management forecasts error. Ota (2010) suggests that the management forecasts
accuracy affects the reliance of analysts on management forecasts. Naf is natural logarithm of analyst
coverage. The Guidelines encourages analysts to collect information other than private information forevaluation of firm (JSDA 2016). It is expected that analysts with greater ability to collect earnings
information are highly evaluated post-Guidelines and less capable analysts may be excluded. It is
expected that analyst forecasts dispersion for firms which experience reduced analyst coverage is lessthan other firms.
White (1980) heteroscedasticity consistent t-statistics are calculated for both models. All
continuous variables are winsorized at the 1 st and 99th percentile in order to mitigate the influence ofoutliers.
4.2 Sample and Descriptive Statistics
The sample is collected before and after the implementation of the Guidelines. It includes a preGuidelines period from March 2014 through March 2015, and a post-Guidelines period from March2017 through March 2018. Administrative dispositions for several securities firms that were a trigger
for the enforcement of Guidelines occurred from December 2015 to February 2016 (see footnote 3). In
addition, Osaki (2017) reveals a decrease in “preview interviews” between October 2015 and March2016.
The sample is selected as follows (see Table 1): (1) firms listed with first section of TSE except
11

for them belonging to banks, securities, insurance and other financial businesses (based on Nikkei
industry middle classification), (2) firms those fiscal year ends March 31 st, (3) firms covered by morethan three analysts. Requirement (3) is included to test the effects of Guidelines, which forced analysts
to refrain from interviewing management for the purpose of obtaining unpublished material
information. Hence, the more analyst coverage, the greater the impact of Guidelines. The final sample
is 908 firm-years. Management forecasts are obtained from Nikkei NEEDS Financial Quest 2.0. andanalyst forecasts and financial data are obtained from Thomson Reuters I/B/E/S/.
Insert Table 1

Table 2 shows descriptive statistics. The mean of Δdispt which represents the difference between
standard deviation of analyst forecasts released before and after management forecasts is 0.001. This
indicates analyst forecast dispersion increases after the announcement of management forecasts. Since
analysts in Japan tend to revise their forecasts based on management forecasts (Nara and Noma 2013),
it is expected that analyst forecasts dispersion will decrease after management forecasts. However,some analysts incorporate unpublished material information into their forecasts at least pre-Guidelines.
Therefore, analyst forecasts dispersion after management forecasts may become relatively large.
Insert Table 2

Table 3 displays the Pearson and Spearman correlation of each variable in the test of H1and H2.
Δdispt is negatively related to fd (Pearson’s correlation coefficient=−0.085; Spearman’s correlation
coefficient=−0.066). This indicates analyst forecasts dispersion decreases post-Guidelines, which isconsistent with H1. The variable |errort| is negatively related to fd (Pearson’s correlation coefficient=−0.098; Spearman’s correlation coefficient=−0.129). This suggests analyst forecasts error declines
post-Guidelines, which is consistent with H2.
12Insert Table 3

5. Research Results
5.1 Univariate Analyses
Tables 4 and Table 5 report the results of univariate analyses. According to Table 4, Δdisp issmaller fd equals one, and the difference is statistically significant at the 1% level (difference is −0.001; t-value=2.576). This shows a decrease in the dispersion of analyst forecasts post-Guidelines,
which is consistent with H1. Table 4 also shows that |errort| is smaller when fd equals one and thedifference is statistically significant at the 1% level (difference is − 0.041; t-value=2.976). This
indicates analyst forecasts error declined post-Guidelines, which is consistent with H2. Results of tests
of differences in median value reported in Table 5 the same results.
Insert Table 4 and Table 5

5.2 Multivariate Analyses
Table 6 shows the result of multivariate analysis related to H1. As reported in Column (1) of Table
6, the coefficient for fd is negative and statistically significant at the 5% level (coefficient=−0.001; tvalue= − 2.01). This shoes the dispersion of analyst forecasts made after management forecasts
decreased post-Guidelines, consistent with H1. Table 7 shows the result for the test of H2. Columns(1) and (2) in Table 7 show a negative coefficient for fd, which is statistically significant at the 1% level(coefficient=−0.042[−0.035]; t-value=−3.37[−2.92]). This shows the error of analyst forecasts revised
after management forecasts declined post-Guidelines, which is consistent with H2.
Results for control variables in Table 6 show the coefficient of surt is positive and statisticallysignificant at the 1% level (coefficient=0.042; t-value=2.46). The coefficient for surt should be larger
when it is difficult to forecast earnings. Analysts who cover the firms with uncertainty of earnings may
13

have incentives to rely on private information at least pre-Guidelines. This could lead the increase inanalyst forecasts dispersion post-Guidelines. Columns (1) and (2) in Table 7 shows the coefficient ofsurt is positive and statistically significant at the 5% level (coefficient=0.786[0.706]; tvalue=2.24[1.91]). This result is consistent with more difficult earnings forecasting leading to larger
analyst forecasts error. Columns (1) and (2) in Table 7 show the coefficient for loss is positive andstatistically significant at the 10 (1) % level (coefficient=0.085[0.108]; t-value=1.79[2.85]). This showsthe error of analyst forecasts for loss-making firms is greater, which is consistent with Ota (2009) who
suggests that management forecasts for loss-making firms are biased upward and analysts do not see
through the bias. Columns (1) and (2) in Table 7 shows rd is negative and statistically significant at the
5% level (coefficient= − 0.852;t-value= − 2.40). A positive relationship is expected between R&D
expenditure and analyst forecasts error because firms with high R&D expenditure have highproprietary costs (Wang 2007). However, firms with a lot of proprietary information have incentives
to disclose the information through private channel, at least pre-Guidelines (Financial Instruments andExchange Law Study Group 2018). Hence, analysts previously revised their forecasts based on private
information, which may be reflected in the increase in analyst forecasts accuracy.
In summary, the multivariate results show following: (1) the amount of private information by
management decreased post-Guidelines, (2) the reliance of analysts on management forecasts increased
post-Guidelines, (3) the positive effects of increasing management forecasts accuracy outweigh thenegative effects of the reduction in the amount of private information.
Insert Table 6 and Table 7

6. Conclusions
The purpose of this paper is to investigate the impact of Guidelines on analyst’s earnings forecast
behavior. The results indicate that analyst forecasts dispersion decreased post-Guidelines. This suggests
the amount of private information disclosed by managements to analysts decreased and the dependence
14

of analysts on management forecasts increased post-Guidelines. In addition, the findings show thatanalyst forecasts error declined post-Guidelines. Overall, the results suggest the positive effects ofincreasing management forecasts accuracy outweigh the negative effects of decreasing the amount ofprivate information post-Guidelines.
The implications of results are two-fold. First, this paper shows the effectiveness of the Guidelines.
The Guidelines was introduced because global investors criticized the practice of management
communicating information to a subset of analysts or investors. Moreover, several securities firms
received administrative sanctions over the analyst’s management of undisclosed material information.
This paper provides evidence that the amount of private information disclosed by management
decreased post-Guidelines. However, this result is limited because it is not possible to directly observe
private information.
Second, this paper confirms the external validity of prior U.S. studies on fair disclosure regulation.
The analyses are conducted using the models developed by Kross and Suk (2012). However, Koch etal. (2013) in reviewing related research point out that prior research is affected by uncontrolled
confounding events. In addition, management earnings forecast practice in the U. S. makes it difficult
to interpret the results of prior studies as being attributable to disclosure regulation. This paper
addresses this problem by using Japanese management and analyst forecasts.
The results indicate analyst forecasts accuracy increased post-Guidelines. This result is interpreted
as indicating the positive effects of increasing management forecasts outweigh the negative effects ofdecreasing the amount of private information and increased reliance by analysts on management
forecasts post-Guidelines. However, it is possible other factors influence the change in analyst forecasts
accuracy before and after Guidelines. That is, if more analysts rely on high ability analysts when they
revise their earnings forecasts, then the accuracy of forecasts consensus increases. Whether Japanese
analysts become more reliant on high ability analysts post-Guidelines is an issue that can be addressed15

in future research.

16

Figure1 Timing of analyst forecasts

AF 1

latest MF← within 45 days →AF 2← within 45 days →Δdisp=(the standard deviation of AF2− the standard deviation of AF1)/ share price at the
begininng of fiscal year t※AF (MF):analyst forecasts (management forecasts)

Table 1 Sample Selection
Requirements（1）firms listed with the first section of TSE except forfirms belonging to bank, securities, insurance andother financial businesses (based on Nikkei
industry middle classification).
（2）firms those fiscal year ends March 31.
（3）firms covered with more than three analysts.
（4）firms each data required is available.

Firm-year
11,168

7,144
1,424
908

Table 2 Descriptive Statistics (N=908)
VariableΔdispt|errort|
fd
surt
car
losst
Δroat
Δgdpt
rdt-1
lmvet
mfet
naft

Mean
Std. Dev.
0.001
0.006
0.093
0.210
0.500
0.500
0.020
0.046
0.001
0.047
0.040
0.195
0.189
1.281
0.012
0.008
0.012
0.017
11.527
0.474
0.011
0.030
0.935
0.228

Min
-0.037
0.000
0
0.000
-0.1270-5.956-0.0020.000
10.423
0.000
0.477

Q1
0.000
0.004
0
0.003
-0.0250-0.0200.006
0.000
11.2040.002
0.778

Median
0.000
0.012
0.59
0.008
0.001
0
0.000
0.015
0.002
11.492
0.004
0.954

Q3
0.002
0.044
1
0.019
0.025
0
0.213
0.018
0.019
11.801
0.010
1.114

Max
0.046
1.265
1
0.466
0.193
1
12.0000.020
0.089
12.9980.370
1.398

I use same firms during each sample period (a pre-Guidelines period from March 2014 through March 2015
and a post-Guidelines period from March 2017 through March 2018). The number of data for fd=1(0) is 454
respectively. Though the median of fd output by stata 15 is 0.5, fd takes only a value of one or zero in theoriginal data.
17
9

Table 3 Correlation of H1, H2
Δdispt|errort|

surt

car

Δroat

losst

Δgdpt

rdt−1

lmvet

mfet

Δdispt
1
0.236
0.375
0.011
0.044
0.016
−0.0270.015−0.1450.078|errort|
0.184
1
0.526
−0.0210.202−0.0220.031
0.020−0.234
0.299
surt
0.283
0.347
1
0.067
0.294
0.012
−0.0250.061−0.2610.383
car−0.013−0.055−0.0031−0.0330.179−0.094−0.016−0.068
0.039
losst
0.139
0.254
0.525
−0.0401−0.101
0
0.003−0.116
0.078
Δroat
0.094
−0.084−0.1040.057−0.0271−0.2920.071−0.0280.057
Δgdpt−0.029−0.017−0.001−0.068−0.001−0.2301−0.004
0.057
0.023
rdt−1
−0.005−0.059−0.041−0.023−0.0290.059−0.007
1
0.004
0.026
lmvet
−0.111−0.133−0.19−0.070−0.129−0.061
0.119
0.088
1
−0.125
mfet
0.128
0.316
0.430
−0.0060.319−0.101
0.033−0.053−0.0981
naft−0.049−0.010−0.070−0.037−0.022
0.015
0.011
0.209
0.655
−0.027
The Pearson’s correlation coefficients are shown in lower left, and Spearman’s correlation coefficients are shown in upper right.

18

naft
−0.078−0.096−0.006−0.020−0.011
0.023
0.037
0.189
0.639
0.064
1

Table 4 Test of the Difference of Mean
fd=(0)
fd=(1)
Δdispt0.002
0.001|errort|
0.114
0.073

（1）−（0）

Table 5 Test of the Difference of Median
fd=(0)
fd=(1)
Δdispt0.00040.0002|errort|
0.016
0.009

（1）−（0）−0.001−0.041t-value
2.576***
2.976***−0.0002−0.007z-value
1.998**
3.876***

Table 6 Verification Results (H1)
Dep. Var.: Δdispt

Predict Sign

constant

?

(1)

(2)

0.009

[1.45]−0.002[−0.07]−0.001[−1.49]fd−−0.001[−2.01]**

surt

＋

0.042

[2.46]***

0.041[1.98]**

car−0.004[−1.02]−0.003[−0.72]

losst

−
＋

−0.001[−0.19]0.000[0.05]

Δroat

−

0.001

[1.01]

0.001[1.42]

Δgdpt

＋

0.017

[0.60]

0.022[0.69]

rdt−1

−

0.010

[0.72]−0.102[−1.01]lmvet−0.001[−1.45]0.000[0.07]

mfet

−
＋

0.014

[0.85]

0.005[0.35]

naft

−

0.001

[0.89]

0.002[0.41]

Industry

Yes

No

Fixed Effects

No

Firm

N

908

908

Adj. R2

13.47%

R2 within

11.02%Max VIF

3.59

2.01

***p<0.01, **p<0.05, *p<0.01. The values in parentheses represent t-values.

Table 7 Verification Results (H2)
Dep. Var.: |errort|

Predict Sign

(1)

constant?

0.341[2.12]**−0.058[−0.11]fd−−0.042[−3.37]***−0.035[−2.92]***

surt

＋

0.786

[2.24]**

0.706[1.91]**

car−−0.170[−1.26]0.017[0.16]

losst

＋

0.085

[1.79]*

0.108[2.85]***

19(2)

Δroat＋−0.002[−0.22]0.008[1.44]

Δgdpt

＋

0.435

[0.51]

0.666[1.49]

rdt−1−−0.852[−2.40]**3.145[1.77]*

lmvet−−0.028[−1.74]*0.009[0.19]

mfet

＋

0.465

[1.25]−0.944[−3.08]***

naft

−

0.004

[0.10]

0.017[0.29]

Industry

Yes

No

Fixed Effects

No

Firm

N

908

908

Adj. R2

37.82%

R2 within

15.42%Max VIF

3.59

2.01

***p<0.01, **p<0.05, *p<0.01. The values in parentheses represent t-values.

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