2013-38
Management Forecasts, Idiosyncratic Risk,
and Information Environment
Norio Kitagawa Shin’ya Okuda

Management Forecasts, Idiosyncratic Risk, and the Information
Environment1

First version: July 2013
Last update: December 2014

Norio Kitagawa
Graduate School of Business Administration, Kobe University
Phone and fax: +81-78-881-1212
kitagawa@b.kobe-u.ac.jp
Shin’ya Okuda*
Graduate School of Economics
Phone and fax: +81-52-872-5743
467-8501 Yamanohata 1, Muzuhocho, Mizuho, Nagoya, Aichi
s-okuda@econ.nagoya-cu.ac.jp

1

The authors appreciate Masahiro Enomoto, Koji Ota, Katsuhiko Muramiya, and the participants at the2013 Japanese Association for Research in Disclosure for helpful comments. The authors also
acknowledge the comments received from participants of the workshop at Tokyo Keizai University. The
authors have received financial support for the research, authorship, and publication of this article. Norio
Kitagawa and Shin’ya Okuda gratefully acknowledge the financial support of the Grant-in-Aid for
Scientific Research (numbers 21730370 and 23730454, respectively) provided by Japan’s Ministry of
Education, Culture, Sports, Science and Technology.
* Corresponding author.
1Management Forecasts, Idiosyncratic Risk, and the Information
Environment

Abstract
Management forecasts are an important source of information for the Japanese stock
market. In this paper, we use management forecast error as a proxy for disclosure quality toinvestigate the relationship between disclosure quality and idiosyncratic risk.We find thatmanagement forecast error is positively related to idiosyncratic risk, suggesting thathigh-quality public information reduces idiosyncratic risk. Furthermore, we present
evidence that management forecast error is less positively related to idiosyncratic risk inrelatively good information environments.

JEL classification: M41; G12; G14
Keywords: Management forecasts; Idiosyncratic risk; Information environment; Disclosure
quality; Japanese stock market

21. Introduction
Studies (e.g., Campbell et al. 2001; Morch et al. 2000) have identified an increase in thelevel of average stock return volatility. This paper uses management forecast error as aproxy for disclosure quality to investigate the relationship between disclosure quality andidiosyncratic risk. Japan’s stock exchanges ask firms to forecast the following year’s key
accounting figures. Although not all firms are required to provide these forecasts, mostlisted firms do.1 Ota (2010) suggests that management forecasts have higher correlation
with and incremental explanatory power for stock prices than realized income, indicating that management forecasts represent an important information source forJapanese stock markets.
This study contributes to the literature in two ways. First, we investigate therelationship between the quality of disclosed information and firm risk. Rajgopal and
Venkatachalam (2011) argue that good information reduces firm risk: the higher the qualityof accruals, as proposed by Dechow and Dichev (2002), the lower a firm’s idiosyncraticrisk. Okuda and Kitagawa (2011) investigate the relationship between five earnings quality
measures (e.g., accruals quality, earnings predictability, and earnings smoothness) andidiosyncratic risk during a period of accounting standard reform in Japan. They find that thehigher a firm’s quality of earnings, the lower its idiosyncratic risk, which is consistent withthe findings of Rajgopal and Venkatachalam (2011). Contrariwise, Hutton et al. (2009) findthat financial statement opacity measured by discretionary accruals is positively associated1

In Iwasaki et al. (2012), for example, 95.42% of listed companies covered during the sample period(1997–2009) reported management forecasts.
3

with stock return synchronicity because firms with high synchronicity have lessidiosyncratic information in their stock price. Datta et al. (2013) show that the relationshipbetween financial statement opacity and idiosyncratic risk is not found if they use theperformance-matched discretionary accruals developed by Kothari et al. (2005) and thetwo-way clustered standard error proposed by Petersen (2009). Therefore, the effect offinancial information transparency on idiosyncratic risk remains as an empirical question.
Unlike these studies, we consider management forecast accuracy as a proxy for thequality of the disclosed information and examine the relationship between management
forecast error and idiosyncratic risk. Muramiya (2005) finds that firms with lower
management earnings forecast accuracy have a higher cost of capital than do firms withhigher management earnings forecast accuracy. Ota (2011) suggests that Japanese analysts
are at least somewhat aware of the factors related to systematic bias in management
earnings forecasts. Therefore, management forecast error that is unexpected by investors
should have a larger impact on idiosyncratic risk. Using well-known determinants ofmanagement forecast bias, we decompose management forecasts into expected components,
which are explained by forecast biases, and unexpected components.
Furthermore, we examine the relationship between management forecasts andidiosyncratic risks after controlling for the determinants of management forecasts. Studies(e.g., Gotoh, 1997; Iwasaki et al., 2012; Kato et al., 2009; Ota, 2006) have found thatmanagers’ initial earnings forecasts for a fiscal year are systematically upward biased, andthey have analyzed the determinants of that management forecast bias. Asano (2007) findsthat firms manage their forecasts as well as their earnings. In order to control the4determinants of management forecasts, we regress management forecasts and regard theabsolute value of residuals as management forecast error. We show that our measures havemore explanatory power than do plain management forecast error.
Second, we examine how the effects of management forecast error differ according to
the quality of the information environment. Botosan (1997) finds that for firms in a poor
information environment, greater disclosure is associated with a lower cost of capital.
Aman (2011) finds an interactive effect between forecast credibility and media coverage of
earnings performance. This study uses firm size and analyst following as proxies for afirm’s information environment.
Our analyses indicate that management forecast error is positively related to
idiosyncratic risk, suggesting that high-quality disclosed information reduces idiosyncratic
risk, which is consistent with Rajgopal and Venkatachalam (2011). We further show thatmanagement forecast errors are less positively related to idiosyncratic risks for larger firms
and firms with analyst following, suggesting that management forecast accuracy is lessimportant for firms with good information environments.
The rest of this study proceeds as follows. In Section 2, we discuss the hypothesis
development; in Section 3, we discuss the research design; in Section 4, we describe thesample selection and descriptive statistics; and in Section 5, we present the results. The
final section concludes the study and suggests future research possibilities.

2. Hypothesis development
Theoretical support for a negative association between disclosure level and5idiosyncratic risk is found not only in the accounting literature but also in the financial
literature. For example, Diamond and Verrecchia (1991) show that improving disclosure
reduces stock market volatility. Easley and O’Hara (2004) employ a model indicating that afirm’s disclosure policy can influence its idiosyncratic risk.
In response to these studies, Rajgopal and Venkatachalam (2011) use the quality ofearnings as a proxy for the quality of disclosed information and find that it is negatively
associated with lower idiosyncratic risk. Okuda and Kitagawa (2011) also show that thehigher a Japanese firm’s quality of earnings, the lower its idiosyncratic risk.
In addition to financial reporting, management forecasts are also a major channel of
disclosed information. The Tokyo Stock Exchange and other Japanese stock exchanges ask
that firms forecast the following year’s key accounting figures. Although not all firms areforced to provide their forecasts, virtually all listed firms do. Management forecasts havethus attracted both practical and academic attention. Ota (2010) suggests that management
forecasts have the highest correlation with and incremental explanatory power for stock
prices.
These arguments lead to our first hypothesis:

Hypothesis I: Management forecast errors are positively correlated with idiosyncraticrisks.

Next, we turn to the interaction between the information environment and disclosed
information. Botosan (1997) finds that the association between the cost of equity capital6

and disclosure levels is less significant for firms that attract a greater number of analysts.
Moreover, Aman (2011) finds an interactive effect between forecast credibility and media
coverage of earnings performance, suggesting that the information environment affects theimpact of management forecasts on the stock market. Thus, we develop hypothesis II,
which predicts that management forecast errors are less positively correlated with
idiosyncratic return volatility when firms face a better information environment.
To examine hypothesis II, we adopt two common proxies for information environment.
The first measure is firm size. Research suggests that size proxies for the amount of priorinformation available about a firm (e.g., Atiase, 1985; Bhushan, 1989; Collins et al., 1987;
Freeman, 1987; Grant, 1980). For example, Atiase (1985) and Freeman (1987) have shownthat the relationship between management forecast accuracy and stock returns is weak inlarge firms. We thus assume that larger firms have a better information environment.
The second measure is analyst following. Because analysts play a significant role asintermediaries between firms and external parties, analyst following is commonly
considered as a proxy for the quality of the information environment (Atiase et al., 1988;
Bhushan, 1989; Collins et al., 1987; Freeman, 1987; Lobo & Mahmoud, 1989; O’Brien &Bhushan, 1990). More recently, Frankel and Li (2004) find that increased analyst following
is associated with reduced profitability of insider trades and reduced insider purchases. We
thus assume that information environment gets better when one or more analysts actively
track and publish opinions on the firms.
Using these two proxies,2 we examine testable hypotheses IIa and IIb.
2

Note that these two measures are not intended to be mutually exclusive. In fact, Collins et al. (1987)
7

Hypothesis II: Management forecast errors are less positively correlated with idiosyncratic
return volatility when firms face a better information environment.
Hypothesis IIa: Management forecast errors are less positively correlated with
idiosyncratic return volatility when firms are relatively large.
Hypothesis IIb: Management forecast errors are less positively correlated with
idiosyncratic return volatility when one or more analysts actively track and publish
opinions on the firms.

3. Research design
3.1 Idiosyncratic risk
First, we describe the procedure for measuring the two main variables, idiosyncraticrisk and management forecast error. Although some related literature (e.g., Foerster et al.,2010) uses the market model, we use the three-factor model in Fama and French (1993) tomeasure idiosyncratic return volatility. This measure is the same as that of Rajgopal and
Venkatachalam (2011). More specifically, we measure excess returns as the residual from a
regression of equation (1):

RET i , m  R f , m   i   RMRF ,i R M , m  R f , m    SMB ,i SMB m   HML ,i HML m   i , m(1)

and Bushman (1989) suggest that the number of analysts following a firm is positively related to thefirm’s market value.
8where RETi, m corresponds to the daily stock return for firm i in month m, Rf, m is therisk-free rate in month m,3 and (RM, m – Rf, m) is the value-weighted excess market returns inmonth m.4 SMBm is the size factor spread portfolio in month m, and HMLm is thebook-to-price ratio factor spread portfolio in month m.
We estimate equation (1) for each year using daily data covering from July 1 at year t
to June 30 at year t + 1. We define the idiosyncratic return volatility (RMSE) as the sample
standard deviation of the excess returns.5

3.2 Residual management forecast error
As previously mentioned, our study examines the association between management
forecast accuracy and idiosyncratic risk. Thus, we first calculate the total management
forecast error variable, defined as a sum of the sales forecast error, ordinary income (i.e.,earnings before extraordinary items, special items, and taxes) forecast error, and net income
forecast error. These forecast errors are defined as initial management forecasts of sales foryear t minus actual sales for year t divided by total assets for year t − 1, and divided by eacherror’s standard deviation in order to match the units.6

3
4

We define the risk-free rate as the government bond yield over ten years.
We define the market return as the rate of change in the Tokyo Stock Price Index (TOPIX).
5 Our measures for idiosyncratic risk are different from those of Aman (2011) in some regards. First,
Aman (2011) uses a return generation model in which the daily return for each firm is explained by the
daily market portfolio (the rate of change in the TOPIX) and the industry average return, whereas we use
the three-factor model in Fama and French (1993). Second, Aman (2011) calculates the idiosyncratic risk
as one minus R-squared in the return generation model and as log-transformed. In contrast, we calculate
idiosyncratic risk as the standard deviation of the residual in the three-factor model.
6 We also used another standardized management forecast error. First, the mean value was subtracted
from each management forecast error by year, and then the difference between the management forecast
error and the mean was divided by the standard deviation. However, our conclusions did not change.
9However, management forecasts are biased; several studies have identified thedeterminants of management forecast error (Iwasaki et al., 2012; Kato et al., 2009; Rogers& Stocken, 2005). For example, Ota (2006, 2011) shows that financial distress, firm growth,
firm size, and prior forecast errors are associated with bias in Japanese management
forecasts. In addition, Ota (2011) suggests that Japanese analysts are at least somewhat
aware of the factors related to systematic bias in management earnings forecasts. If
investors are aware of these systematic management forecast errors, the idiosyncratic risk
for the following year should be more strongly correlated with the unsystematic portion ofmanagement forecast errors (i.e., those forecast errors not explicable by the factors related
to systematic management forecast bias). Therefore, we first determine the unsystematic
portion of management forecast errors and then investigate its relationship with
idiosyncratic risk. We calculate the residual value by estimating equation (2) below andusing it as a proxy for unsystematic management forecast errors (hereafter referred to as“residual management forecast errors”).

MFEi ,t   0   1 MFEi ,t 1   2 RMSEi ,t   3 SIZE i ,t   4 CINC i ,t   5 CRATIOi ,t  6 LEVi ,t   7 LOSS i ,t   8 GROWTH i ,t   9 DIVi ,t  YD  (2)where the dependent variable is the composite measure of management forecast errors foryear t (MFEt). To capture the various effects of management forecast errors in a single
measure, we conduct a principal component analysis on three variables regarding
management forecast errors: (1) sales forecast errors for year t (MFE_SLSt), (2) ordinary

10

income forecast errors for year t (MFE_OIt), and (3) net income forecast errors for year t(MFE_NIt). Table 1 provides the results of the principal component analysis. Panel A showsthat the first principal components have eigenvalues greater than one and account for
approximately 65% of the total variance. Panel B reports the first components, all of whichhave positive signs, as expected. Thus, we define the first principal component as thecomposite measure of management forecast errors (MFE).

(Insert Table 1 about here)

We include some determinant factors of management forecast accuracy as independent
variables. In this regard, we mainly follow Ota (2006, 2011), which investigates thedeterminants of management forecast bias in Japanese listed firms. First, we include themanagement forecast error for the previous year (MFE t−1), as studies have shown evidenceof the persistence of management forecast error (e.g., Gong et al., 2009; Ota, 2006, 2011).
It is possible that our results could be explained by the endogenous relation that firms
with higher idiosyncratic risk have earnings that are more difficult to predict. In order tocope with this possibility, we include simultaneous idiosyncratic risk (RMSE). Consideringthat managers tend to release optimistic forecasts (e.g., Kato et al., 2009), we expect RMSE
to be positively related to management forecast errors. Several studies have found that
forecast behavior is associated with firm size (e.g., Baginski & Hassell, 1997; Bamber &Cheon, 1998; Choi & Ziebart, 2004). After hypothesizing that large firms are likely to issue
conservative earnings forecasts because they regard management forecasts as commitments11to stakeholders, Ota (2006) finds a negative relationship between firm size andmanagement forecast errors. Following these studies, we include firm size (SIZE),
calculated as the natural log of the market value at the end of year t.
Ota (2006) shows that firms issue prudential forecasts before seeking externalfinancing. Therefore, we include a capital increase dummy (CI) that takes one if firms
increase their contributed capital and zero otherwise.
The literature shows that managers of distressed firms are more likely to issue
optimistic earnings forecasts than are the managers of other firms (e.g., Frost, 1997; Ota,
2006; Rogers & Stocken, 2005). Thus, we include the current ratio (CRATIO) and financial
leverage (LEV) as independent variables. Because firms suffering losses are likely todisclose optimistic forecasts (e.g., Ota, 2006), we include a loss firm dummy (LOSS) as anindependent variable.
We also include sales growth (GROWTH) as an independent variable. High-growthfirms experience a relatively large negative stock price response to negative earnings
surprises (e.g., Skinner & Sloan, 2002) and are, therefore, more likely to engage in earnings
guidance to meet their expectations at the earnings announcement date (e.g., Choi &Ziebart, 2004; Matsumoto, 2002; Ota, 2006; Richardson et al., 2004;). We expect
GROWTH to be negatively related to MFE.
Finding that firms whose management dividend forecasts increase over current
dividends have a negative management forecast error, Ota (2006) posits that increased
dividend forecasts contain information about strong future firm performance beyond thatprovided by management earnings forecasts. Therefore, we include an increased dividend

12forecast dummy (DIV) with a value of one if a firm increases its management dividend
forecasts over current dividends and zero otherwise. Last, we include year dummies (YD) tocontrol year effects. Detailed definitions of the variables in estimation model (3) areprovided in Appendix A.
We estimate equation (2) with a dynamic panel data model. Specifically, ourestimation method is based on Arellano and Bond (1991), which developed a generalized
method of moments (GMM) estimator that treats the model as a system of equations, one
for each time period. The sample for this estimation model consists of 8,527 firm-year
observations covering 2000 to 2008.7 Each variable is winsorized at the 1st and 99th
percentiles by year. We then calculate the residual from equation (1). The absolute value ofthe residual corresponds to the unsystematic portion of management forecast error(ARMFE). We use the absolute value because both highly optimistic and pessimistic
management forecasts can be interpreted as firm-specific risks for investors. To check therobustness of our results, we apply the same procedure to three specific management
forecasts (i.e., sales forecasts, ordinary income forecasts, and net income forecasts) andcalculate the absolute value of the residual forecast error (ARMFE_SLS, ARMFE_OI, and
ARMFE_NI).8
Table 2 provides the estimation results of the residual management forecast error
determined using the dynamic panel data model. Coefficients on management forecast

7

We describe the sample selection criteria in more detail in Table 3.
We did not include operating income forecasts in our analysis due to data availability constraints. In
2007, the Tokyo Stock Exchange (TSE) began to require listed firms to provide operating income
forecasts because of their growing importance for investors. Therefore, no pre-2007 data were available.

813

errors for year t − 1 (MFEt−1, MFE_SLSt−1, MFE_OIt−1, and MFE_NIt−1) are significantly
positive, suggesting that management forecast errors have serial correlations. In addition,coefficients on the loss dummy (LOSS) are significantly positive, and coefficients on thefirm size (SIZE), sales growth (GROWTH), and change in dividends dummy (DIV) aresignificantly negative, which is consistent with prior studies.

(Insert Table 2 about here)

3.3 The relationship between residual management forecast error and idiosyncraticrisk
To test hypothesis I on the relationship between management forecast error andex-post idiosyncratic risk, we estimate equation (3) as follows:

RMSEi ,t   0   1 ARMFEi ,t 1   4 SIZEi ,t 1   6 ROAi ,t 1   7 GROWTH i ,t 1   8 LOSS i ,t 1
  9 LEVi ,t 1   10 INSTi ,t 1   11CROSS i ,t 1   12 FOREIGN i ,t 1  YD  (3)The dependent variable is RMSEt, which is defined as idiosyncratic return volatility basedon the three-factor model in Fama and French (1993) for fiscal year t.
In independent variables, the test variable is the measure of the residual management
forecast error (ARMFE), as described in Section 3.2. If hypothesis I is supported, thecoefficient of ARMFE will be positive. To check the robustness of our result, we test therelationship between idiosyncratic volatility and three specific management forecast errors

14

as well as the total management forecast error (ARMFE). Specifically, we examine therelationship between idiosyncratic volatility and the absolute value of the residual
management forecast error for (1) sales (ARMFE_SLS), (2) ordinary income (ARMFE_OI),
and (3) net income (ARMFE_NI).9 We also predict that the coefficients of ARMFE_SLS,
ARMFE_OI, and ARMFE_NI will be significantly positive.
We control for several variables affecting return volatility in the cross-section. Firm
size (SIZE) is expected to negatively relate to idiosyncratic volatility because small firms
experience higher return volatility (e.g., Pastor & Veronesi, 2003; Rajgopal &Venkatachalam, 2011). We define SIZE as the natural log of total assets. We control for firm
profitability, which is posited to relate negatively to return volatility (e.g., Wei & Zhang,2006). Thus, we use net income divided by total assets (ROA) and the loss dummy (LOSS)
as control variables. In addition, as high-growth firms experience higher stock return
volatility (e.g., Cao et al., 2006; Malkiel & Xu, 2003; Rajgopal & Venkatachalam, 2011),
we use the rate of sales changes as a proxy for firm growth. Because distressed firms
experience greater stock return volatility (e.g., Campbell et al., 2001; Rajgopal &Venkatachalam, 2011), we include the variables controlling financial distress, defined as
financial leverage (LEV) and measured by total liabilities divided by total assets.
In addition, the literature indicates that ownership structure influences idiosyncratic
volatility. For example, Brockman and Yan (2009) show that blockholders increase
idiosyncratic volatility because of their informational advantage. Sias (1996) and Malkiel
9

As mentioned, we do not examine the relationship between idiosyncratic return volatility and theabsolute value of the residual management forecast error for operating income because of data
availability constraints.
15

and Xu (2003) report that institutional ownership has a positive impact on future
volatility.10 Ferreira and Matos (2008) show that high foreign institutional ownership is
associated with high firm-level idiosyncratic variance because foreign investors prefer toinvest in high-risk firms.
To control for the effect of ownership structure, this study includes the following three
independent variables: institutional ownership (INST), cross-shareholdings (CROSS), andforeign ownership (FOREIGN). Because Japanese firms are interrelated through equity
ownership cross-holdings and generally rely on large commercial banks, such as a mainbank (Douthett & Jung, 2001; Shuto & Kitagawa, 2011), INST and CROSS are theimportant ownership variables in Japan.
At last, we include year dummies (YD) to control year effects. Appendix B provides
detailed definitions of the variables in model (3).

3.4 The effect of the information environment on the relationship between residual
management forecast errors and idiosyncratic risk
To test hypothesis IIa on the effect of firm size on the relationship between residual
management forecast errors and idiosyncratic risk, we estimate equation (4) below:

RMSEi ,t   0   1 ARMFEi ,t 1   2 ARMFE  SIZEq1i ,t 1   3 ARMFE  SIZEq 4 i ,t 1
  4 SIZEq1i ,t 1   5 SIZEq 4 i ,t 1   6 ROAi ,t 1   7 GROWTH i ,t 1   8 LOSS i ,t 1(4)

  9 LEVi ,t 1   10 INSTi ,t 1   11CROSS i ,t 1   12 FOREIGN i ,t 1  YD  
10On the other hand, Brandt et al. (2009) dispute the findings of Malkiel and Xu (2003), and report anegative relationship between institutional ownership and idiosyncratic volatility among low-priced
stocks.
16where SIZEq1 is an indicator variable set to one if the level of total assets is in the firstquartile, where that quartile contains the firms with the lowest total assets in each year, andzero otherwise. SIZEq4 is an indicator variable set to one if the level of total assets is in the
fourth quartile, where that quartile contains the firms with the highest total assets in eachyear, and zero otherwise. Other variables are defined earlier.
To test hypothesis IIa, we include the interaction term between ARMFE and thedummy variables based on the quartile of total assets in equation (4). The first (fourth)quartile of firm size, SIZEq1 (SIZEq4), indicates the poor (good) information environment.
We expect the coefficient of ARMFE × SIZEq4 to be negative (and the coefficient of
ARMFE × SIZEq1 to be positive), consistent with hypothesis IIa.
We estimate equation (5) to examine hypothesis IIb on the effect of analyst coverageon the relationship between residual management forecast errors and idiosyncratic risk.

RMSEi ,t   0   1 ARMFEi ,t 1   2 ARMFE  COVi ,t 1   3COVi ,t 1   4 SIZEi ,t 1   5 ROAi ,t 1
  6 GROWTH i ,t 1   7 LOSS i ,t 1   8 LEVi ,t 1   9 INSTi ,t 1   10 CROSS i ,t 1(5)

  11 FOREIGN i ,t 1  YD  

To test hypothesis IIb, we include the interaction term between ARMFE and thedummy variables based on the analyst coverage (COV) in equation (5). COV is an indicatorvariable set to one if one or more analysts actively track and publish opinions on a company
and its stock, and zero otherwise. If COV is equal to one, it indicates a good information
environment. We expect the coefficient of ARMFE × COV to be negative, consistent with17

hypothesis IIb.

4. Sample and descriptive statistics
4.1 Sample selection
The sample is selected based on the following criteria:(1) The firms are listed on Japanese stock exchanges from 2000 to 2008.
(2) The firms’ fiscal year ends in March.
(3) The firms are not banks, securities firms, insurance firms, or other financial
institutions.11(4) Management forecasts, financial statements, stock prices, and other data (such as
ownership structure) necessary for estimating our models are available.
We obtain our data on the consolidated financial statements from the Nikkei Financial
Data CD-ROM and DVD editions available from Nikkei Media Marketing. We obtain ourstock price data from the Portfolio Master of Financial Data Solutions. Data on theinstitutional factors in cross-shareholdings and stable shareholdings are collected from the
NLI Research Institute’s Data Package of Cross-Shareholding and Stable Shareholding.
Details on the sample selection criteria are provided in Table 3. The final sample comprises
7,457 firm-year observations.

(Insert Table 3 about here)

11

The industries of the sample firms are identified using the Nikkei medium industry classification code
(Nikkei gyousyu chu-bunrui).
18

4.2 Descriptive statistics
Table 4 presents the descriptive statistics for the variables used in this study. In orderto mitigate the effects of outliers, each sequential variable is winsorized at the 1st and 99th
percentiles by year. The mean (median) values of the residual management forecast error
and the residual forecast error for sales, ordinary income, and net income are 0.613, 0.415,
0.582, and 0.302 (0.432, 0.265, 0.427, and 0,227), respectively. The mean (median) valueof idiosyncratic risk is 2.073 (1.925), which is similar to that of prior studies.

(Insert Table 4 about here)

Figure 1 shows the mean and median absolute values of the residual management
forecast error (ARMFE) by year. Although ARMFE is relatively high in 2007 (the meanvalue is 0.876, and median value is 0.631), the overall ARMFE levels do not differ
dramatically across years. The mean values of ARMFE_SLS, ARMFE_OI, and ARMFE_NI
indicate similar tendencies with ARMFE.

(Insert Figure 1 about here)

Table 5 presents a correlation matrix for the variables used in the main analysis, with
Pearson (Spearman) correlations below (above) the diagonal. ARMFE, ARMFE_SLS,
ARMFE_OI, and ARMFE_NI correlate positively with each other. For the Spearman rank

19

correlation, ARMFE has a positive correlation with RMSE, as expected. We need notconsider the multicollinearity problem in our model because no extremely high correlation
among independent variables is observed.12(Insert Table 5 about here)

5. Main results
5.1 The results of testing hypothesis I
Table 6 shows the results of the multivariate regressions of model (3). The table showsthat ARMFE is significantly positive at below 0.1 levels, as expected. We also find that thecoefficients of the components of ARMFE, ARMFE_SLS, and ARMFE_NI are significantly
positive. These results indicate that less accurate management forecasts increase
idiosyncratic return volatility, supporting hypothesis I. However, the coefficient on
ARMFE_OI is insignificant, which is not consistent with hypothesis I. The overall results
provide weak evidence on the management forecasts’ accuracy and the idiosyncratic return
volatility.
The table also shows that almost all control variables have their expected signs and arestatistically significant at conventional levels, except for ROA, GROWTH, and CROSS.
12

In our regression analysis, we calculate the variance inflation factor (VIF) to verify whether amulticollinearity problem, signified by a high correlation among some of the independent variables,
exists. We find that the VIF values are all less than 3. Considering that the standard VIF value formulticollinearity detection is 10, we may conclude that there is no multicollinearity problem in ourmodels.
20

Institutional ownership, a distinctive ownership structure in Japan, has a negative effect on
idiosyncratic risk. In addition, the coefficient on foreign ownership becomes significantly
positive.

(Insert Table 6 about here)

5.2 The results of testing hypothesis II
Table 7 presents the results of the estimation of equation (4) found during the test of
hypothesis IIa. The coefficients on ARMFE, ARMFE_SLS, ARMFE_OI, and ARMFE_NI
are larger and t-values slightly larger than those of equation (3). In addition, coefficients on
ARMFE × SIZEq1, ARMFE_SLS × SIZEq1, ARMFE_OI × SIZEq1, and ARMFE_SLS ×
SIZEq1 are insignificant. These results suggest that management forecast accuracy is more
important for medium or smaller firms.
On the other hand, the coefficients on ARMFE × SIZEq4, ARMFE_SLS × SIZEq4,
ARMFE_OI × SIZEq4, and ARMFE_SLS × SIZEq4, which are our main concerns, aresignificantly negative at less than 0.01 levels. These results imply that the accuracy oflarger firms’ management forecasts has a less significant impact on idiosyncratic risk,
which is consistent with hypothesis IIa.

(Insert Table 7 about here)

Table 8 reports the results of the estimation of equation (5) from the test of hypothesis

21IIb. The table describes the effect of analyst coverage on the relationship between theabsolute value of residual management forecast error and idiosyncratic risk. The results arequite similar to those in Table 7. The coefficients on ARMFE, ARMFE_SLS, ARMFE_OI,
and ARMFE_NI are larger and t-values tend to be larger than those of equation (3),suggesting that management forecast accuracy is more important for firms with no analyst
following. In addition, the coefficients on ARMFE × COV, ARMFE_SLS × COV,
ARMFE_OI × COV, and ARMFE_NI × COV are all significantly negative. This suggeststhat firms with one or more analysts actively tracking and publishing opinions on themshow a lower correlation between idiosyncratic risk and the absolute value of residual
management forecast errors, which is consistent with hypothesis IIb.

(Insert Table 8 about here)Thus, our results support hypotheses IIa and IIb: in firms with a good information
environment, management forecast accuracy is less important to the evaluation of firms’
specific risk.

6. Conclusion
This paper has considered management forecast error as a proxy for disclosure quality
and has investigated the relationship between disclosure quality and idiosyncratic risk. Our
analyses show that management forecast error is positively related with idiosyncratic risk,
indicating that high-quality public information reduces that risk. Furthermore, our evidence

22demonstrates that management forecast error is less positively related with idiosyncraticrisk in firms with the highest total assets and firms with one or more analysts actively
tracking and publishing opinions on them. This indicates that management forecast error isless positively related with idiosyncratic risk in relatively good information environments.

23

References
Aman, H. (2011). Firm-specific volatility of stock returns, the credibility of management
forecasts, and media coverage: Evidence from Japanese firms. Japan and World
Economy, 23(1), 28–39.
Arellano, M., and Bond, S. (1991). Some tests of specification for panel data: Monte Carloevidence and an application to employment equations. Review of Economic Studies,
58(2), 277–297.
Asano, T. (2007). Expectation management and earnings management related to the
managements’ forecasts. The Journal of Business Analysis, 23, 33–42 (written in
Japanese).
Atiase, R. (1985). Predisclosure information, firm capitalization, and security pricebehavior around earnings announcements. Journal of Accounting Research, 23(1),21–36.
Baginski, S., & Hassell, J. (1997). Determinants of management forecast precision. The
Accounting Review, 72(2), 302–312.
Bamber, L. S., & Cheon, Y. S. (1998). Discretionary management earnings forecast
disclosures: Antecedents and outcomes associated with forecast venue and forecast
specificity choices. Journal of Accounting Research, 36(2), 167–190.
Botosan, C. A. (1997). Disclosure level and the cost of capital. The Accounting Review,72(3), 21–40.
Brandt, M. W., Brav, A., Graham, J. R., & Kumar, A. (2009). The idiosyncratic volatility
puzzle: Time trend or speculative episodes? Review of Financial Studies, 23(2),24863–899.
Brockman, P., & Yan, X. S. (2009). Block ownership and firm-specific information.
Journal of Banking and Finance, 33(2), 308–316.
Bushman, R. (1989). Collection of information about publicly traded firms: Theory andevidence. Journal of Accounting and Economics, 11(2–3), 183–206.
Campbell, J. Y., Lettau, M., Malkiel, B. G., & Xu, Y. (2001). Have individual stocks
become more volatile? An empirical exploration of idiosyncratic risk. The Journal of
Finance, 56(1), 1–43.
Cao, C., Simin, T., & Zhao, J. (2006). Can growth options explain the trend in idiosyncratic
risk? Review of Financial Studies, 21(6), 2599-2633.
Cho, M., Hah, Y. D., & Kim, O. (2011). Optimistic bias in management forecasts by
Japanese firms to avoid forecasts losses. The International Journal of Accounting,46(1), 79–101.
Choi, J., & Ziebart, D. (2004). Management earnings forecasts and the market's reaction topredicted bias in the forecast. Asia Pacific Journal of Accounting and Economics,
11(2), 167–192.
Collins, D. W., Kothari, S. P., & Rayburn, J. D. (1987). Firm size and the information
content of prices with respect to earnings. Journal of Accounting and Economics, 9(2),111–138.
Datta, S., Datta, M. I., & V. Singh. (2013). Opaque financial reports and R2: Revisited.
Review of Financial Studies, 23(1), 10-17.
Dechow, P. M., & Dichev, I. D. (2002). The quality of accruals and earnings: The role of25

accrual estimation errors. The Accounting Review, 77 (supplement), 35–59.
Diamond, D. & Verrecchia, R. (1991). Disclosure, liquidity, and the cost of capital. Journalof Finance, 46(4), 1325–1360.
Douthett, E. B., & Jung, K. (2001). Japanese corporate grouping (Keiretsu) and theinformativeness of earnings. Journal of International Financial Management andAccounting, 12(2), 133–159.
Easley, D., & O’Hara, M. (2004). Information and the cost of capital. Journal of Finance,69(4), 1553–1583.
Fama, E., & French, K. (1993). Common risk factors in the returns on stocks and bonds.
Journal of Financial Economics, 33(1), 3–56.
Ferreira, M., & Matos, P. (2008). The colors of investors’ money: The role of institutional
investors around the world. Journal of Financial Economics, 88(3), 499–533.
Foerster, S. R., Sapp, S. G., & Shi, Y. (2010). The impact of management earnings forecasts
on firm risk and firm value. Working paper, University of Western Ontario.
Frankel, R., & Li, X. (2004). Characteristics of a firm’s information environment and theinformation asymmetry between insiders and outsiders. Journal of Accounting andEconomics, 37(2), 229–259.
Freeman, R. N. (1987). The association between accounting earnings and security returns
for large and small firms. Journal of Accounting and Economics, 9(2), 195–228.
Frost, C., (1997). Disclosure policy choices of UK firms receiving modified audit reports.
Journal of Accounting and Economics, 23(2), 163–187.
Gong, G., Li, L. Y, & Wang, J. J. (2011). Serial correlation in management earnings forecast

26errors. Journal of Accounting Research, 49(3), 677–720.
Gotoh, M. (1997). Kaikei to Yosokujouhou. Tokyo: Chuokeizai-sha (in Japanese).
Grant, E. (1980). Market implication of differential interim information. Journal of
Accounting Research, 18(1), 255–268.
Hutton, A. P., Marcus, A. J., & Tehranian, H. (2009). Opaque financial reports, R2, andcrash risk. Journal of Financial Economics, 94(1), 67–86.
Iwasaki, T., Kitagawa, N., & Shuto, A. (2012). Managerial discretion over their initial
earnings forecasts. Working paper, Kobe University.
Jackson, A. B. (2011). Stock return volatility surrounding management earnings forecasts.
Working paper, the University of New South Wales.
Kato, K., Skinner, D. J., & Kunimura, K. (2009). Management forecasts in Japan: An
empirical study of forecasts that are effectively mandated. The Accounting Review,
84(5), 1575–1606.
Kothari, S. P., Leone, A. J., & Wasley, C. E. (2005). Performance matched discretionary
accrual measures. Journal of Accounting and Economics, 39(1), 163–197.
Lobo, G. J., & Mahmoud, A. A. W. (1989). Relationship between differential amounts ofprior information and security return variability. Journal of Accounting Research,
27(1), 116–134.
Matsumoto, D. A. (2002). Management’s incentives to avoid negative earnings surprises.
The Accounting Review, 77(3), 483–514.
Malkiel, B. G., & Xu, Y. (2003). Investigating the behavior of idiosyncratic volatility.
Journal of Business, 76(4), 613–644.

27

Muramiya, K. (2005). Management forecast accuracy and cost of equity capital. Security
Analysts Journal, 43(9), 1–20.
O’Brien, P. C., & Bushman, R. (1990). Analyst following and institutional ownership.
Journal of Accounting Research, 28 (Supplement), 55–76.
Okuda, S., & Kitagawa, N. (2011). Relationship between earnings quality and idiosyncraticrisk during period of accounting standard reform in Japan. Security Analysts Journal,
49(8), 91–100 (written in Japanese).
Ota, K. (2006). Determinants of bias in management earnings forecasts: Empirical evidence
from Japan. In G. N. Gregoriou & M. Gaber (Eds.), International Accounting:Standards, Regulations, and Financial Reporting. Burlington, MA: Elsevier Press
Ota, K. (2010). The value relevance of management forecasts and their impact on analysts’
forecasts: Empirical evidence from Japan. ABACUS, 46(1), 28–59.
Ota, K. (2011). Analysts’ awareness of systematic bias in management earnings forecasts.
Applied Financial Economics, 21(18), 1317–1330.
Pastor, L., & Veronesi, P. (2003). Stock valuation and learning about profitability. The
Journal of Finance, 58(5), 1749–1789.
Petersen, M. A. (2009). Estimating standard errors in finance panel data sets: Comparing
approaches. Review of Financial Studies, 22(1), 435–480.
Rajgopal, S., & Venkatachalam, M. (2011). Financial reporting quality and idiosyncratic
return volatility. Journal of Accounting and Economics, 51(1–2), 1–20.
Richardson, S., Teoh, S., & Wysocki, P. (2004). The walkdown to beatable analyst
forecasts: The role of equity issuance and insider trading incentives. Contemporary28

Accounting Research, 21(4), 885–924.
Rogers, J. L., & Stocken, P. C. (2005). Credibility of management forecasts. The
Accounting Review, 80(4), 1233–1260.
Shuto, A., & Kitagawa, N. (2011). The effect of managerial ownership on the cost of debt:
Evidence from Japan. Journal of Accounting Auditing and Finance, 26(3), 590–620.
Skinner, D., & Sloan, R. (2002). Earnings surprises, growth expectations, and stock returns
or don’t let an earnings torpedo sink your portfolio. Review of Accounting Studies,7(2–3), 289–312.
Sias, R. W. (1996). Volatility and the institutional investor. Financial Analysts Journal,
52(2), 13–20.
Wei, S., & Zhang, C. (2006). Why did individual stocks become more volatile? Journal ofBusiness, 79(1), 259–292.

29Appendix A
Definitions of variables in estimation model (2)Variables
MFE t

MFE_SLS t

MFE_OI t

MFE_NI t
RMSE t
SIZE t
CINC tCRATIO t
LEV t
LOSS tGROWTH t
DIV t

Measurement
Comp1 from principal component analysis of three variables regarding
management forecast errors: (1) management forecast error of sales, (2)management forecast error of ordinary income, and (3) management
forecast error of net income.
Management forecast error of sales (= [initial management forecasts ofsales for year t minus actual sales for year t] / total assets for year t − 1).
The management forecast error for sales for year t is divided by thestandard deviation of the error for year t.
Management forecast error of ordinary income (= [initial management
forecasts of ordinary income for year t minus actual ordinary income foryear t] / total assets for year t − 1). The management forecast error of
ordinary income for year t is divided by the standard deviation of theerror for year t.
Management forecast error of net income (= [initial management
forecasts of net income for year t minus actual net income for year t] /total assets for year t − 1). The management forecast error of net income
for year t is divided by the standard deviation of the error for year t.
Idiosyncratic return volatility based on the three-factor model in Fama
and French (1993) for fiscal year t.
Natural log of total assets at the end of year t.
Indicator variable with a value of one if a firm increases its contributed
capital and zero otherwise.
Current assets divided by current liabilities at the end of year t.
Total liabilities divided by total assets at the end of year t.
Indicator variable with a value of one if a firm reports a net loss and zerootherwise.
Sales growth at the end of year t.
Indicator variable with a value of one if a firm increases its management
dividend forecasts over the current dividends and zero otherwise.

30

Appendix B
Definitions of variables in estimation model (3)Variables
RMSE t

ARMFE t−1

ARMFE_SLS t−1
ARMFE_OI t−1
ARMFE_NI t−1
SIZE t−1
ROA t−1
GROWTH t−1
LOSS t−1
LEV t−1
INST t−1
CROSS t−1
FOREIGN t−1

Measurement
Idiosyncratic return volatility based on the three-factor model in Fama
and French (1993) for fiscal year t.
Absolute value of the residual management forecast errors for year t − 1.
Management forecast error is defined as Comp1 from principal
component analysis of three variables regarding management forecast
errors: (1) management forecast error of sales, (2) management forecast
error of ordinary income, and (3) management forecast error of net
income. Residual management forecast errors are defined as the residualof estimation model (2).
Absolute value of the residual management forecast errors for sales foryear t − 1. Residual management forecast errors are defined as theresidual of estimation model (2).
Absolute value of the residual management forecast errors for ordinary
income for year t − 1. Residual management forecast errors are definedas the residual of estimation model (2).
Absolute value of the residual management forecast errors for net
income for year t − 1. Residual management forecast errors are definedas the residual of estimation model (2).
Natural log of total assets at the end of year t.
Return on assets for fiscal year t − 1.
Sales growth for fiscal year t − 1.
Indicator variable with a value of one if the firm reports a net loss andzero otherwise.
Total liabilities divided by total assets at the end of fiscal year t − 1.
The percentage of institutional ownership at the end of fiscal year t − 1.
The percentage of cross-shareholdings at the end of fiscal year t − 1.
The percentage of foreign ownership at the end of fiscal year t − 1.

31

Figure 1
Residual management forecast error by year

Panel A: Mean value of absolute value of residual management forecast error

Panel B: Median value of absolute value of residual management forecast error

Figure 1 shows mean and median absolute value of residual management forecast error by year. The definitions of each variable are as
follows. ARMFE = absolute value of residual management forecast error for sum of the sales, ordinary income, and net income.
ARMFE_SLS = absolute value of residual management forecast error for sales. ARMFE_OI = absolute value of residual management
forecast error for ordinary income. ARMFE_NI = absolute value of residual management forecast error for net income. Each variable is
Winsorized at the 1st and 99th percentiles by year.

32

Table 1
Principal component analysis result
Panel A: Total variance explained

1.960

Difference ineigenvalue
1.359

Variance explained
(%)
0.654

Cumulative variance
(%)
0.654

Comp2

0.601

0.162

0.200

0.854

Comp3

0.439

0.146

1.000

Comp3

Unexplained

Component

Eigenvalue

Comp1

Panel B: Principal components (eigenvectors)
Variable

Comp1

Comp2

AMFE_SLS t

0.544

0.829

0.131

0.000

AMFE_OI

0.601-0.275-0.7510.000

AMFE_NI

0.586-0.487

0.648

0.000

Table 1 presents the principal component analysis of management forecast errors. MFE_SLS t = management forecast error of sales (=[initial management forecasts of sales for year t minus actual sales for year t] / total assets for year t − 1). The management forecast error
for sales for year t is divided by the standard deviation of the error for year t. MFE_OI t = management forecast error of ordinary income(= [initial management forecasts of ordinary income for year t minus the actual ordinary income for year t] / total assets for year t − 1).
The management forecast error of ordinary income for year t is divided by the standard deviation of the error for year t. MFE_NI t =management forecast error of net income (= [initial management forecasts of net income for year t minus the actual net income for year t]/ total assets for year t − 1).

Table 2
Regression for the residual forecast error

Expected
Constant

MFE t

MFE_SLS t

Coef.

z-valueCoef.

z-valueCoef.

z-value?

−3.678**−3.152−1.637*−1.930−4.772***−5.3730.147***

10.094*MFE t−1
MFE_SLSt−1

MFE_OI t

sign

＋
＋

0.205***

15.21533

MFE_NI t
Coef.
−0.721z-value−1.149

MFE_OI t−1＋

MFE_NI t−1
RMSE t＋＋−0.011−0.439−0.006

SIZE t−−0.308**−3.0740.169***

12.771−0.327−0.021−1.171−0.172**−2.366−0.438***−5.7470.152***
0.006−0.00312.3620.498−0.063CI t−*−0.038−0.995−0.032−1.134−0.033−1.159−0.002-0.090CRATIO t

＋

0.047

1.404

−0.010−0.4122.172−0.6020.048***

2.722

LEV t

＋

0.725**

2.299−0.145−0.6250.053**−0.1391.265***

7.578

LOSS t＋GROWTH t−

0.489***−1.430**15.071−15.3640.081***−1.499**3.370−21.8110.223***−0.750***9.353−10.927

0.422***−0.222***−4.508DIV t−−0.170**−3.631−0.136***−7.217−0.082***−6.041*24.783*−6.599−0.069**Year Dummies

*
Yes

*
Yes

Yes

Yes

Obs.

8,527

8,527

8,527

8,527

Table 2 reports the estimation results of the residual management forecast error found using the dynamic panel data model. The
definitions of each variable are as follows. MFE t = management forecast error, which is defined as Comp1 from principal component
analysis of three variables regarding management forecast errors. MFE_SLS t = management forecast error of sales (= [initialmanagement forecasts of sales for year t minus actual sales for year t] / total assets for year t − 1). The management forecast error forsales for year t is divided by the standard deviation of the error for year t. MFE_OI t = management forecast error of ordinary income (=[initial management forecasts of ordinary income for year t minus the actual ordinary income for year t] / total assets for year t −1). The
management forecast error of ordinary income for year t is divided by the standard deviation of the error for year t. MFE_NI t =management forecast error of net income (= [initial management forecasts of net income for year t minus the actual net income for year t]/ total assets for year t − 1). The management forecast error of net income for year t is divided by the standard deviation of the error foryear t. RMSE t = idiosyncratic return volatility based on the three-factor model in Fama and French (1993) for fiscal year t. SIZE t =natural log of total assets at the end of year t. CINC t = indicator variable with a value of one if the firm increases its contributed capitaland zero otherwise. CRATIO t = current assets divided by current liabilities at the end of year t. LEV t = total liabilities divided by total
assets at the end of year t. LOSS t = indicator variable with a value of one if the firm reports a net loss and zero otherwise. GROWTH t =sales growth at the end of year t. DIV t = indicator variable with a value of one if the firm increases its management dividend forecasts
over the current dividends and zero otherwise. All sequential variables are Winsorized at the 1st and 99th percentiles by year. The t-values
are calculated by the robust estimation of the fixed-effects panel data models, and ***, **, and * denote statistical significance at the 0.01,
0.05, and 0.1 levels, respectively, using a two-tailed t-test.

Table 3Sample selection criteria

34

The listed firms (other than banks, securities firms, insurance firms) from 2000 to

24,5662008
Less: The firms’ fiscal year does not end in March(7,584)

Less: Other financial institutions (Nikkei industry code #52)(441)

Less: Firms that changed their fiscal year end(1,029)Less: Missing data for calculation of idiosyncratic risk

(7,384)

Less: Missing data for calculation of management forecast error(116)

Less: Missing other data for estimation of model (2)

(515)
8,527

Less: Missing other data for estimation of models (3), (4), and (5)(1,070)

Final sample

7,457

Table 3 provides details on the sample selection criteria. We obtained the data relating to the consolidated financial statements from the
Nikkei Financial Data CD-ROM and DVD editions available from Nikkei Media Marketing. We obtained the stock price data from the
Nikkei Portfolio Master of Nikkei Media Marketing. The data regarding the institutional factors in cross-shareholdings and stableshareholdings were collected from the NLI Research Institute’s Data Package of Cross-Shareholding and Stable Shareholding.

Table 4
Descriptive statistics

RMSE

Mean
2.073

Median
1.925

Max
4.605

Min
0.782

35

SD
0.817

Skewness
1.047

Kurtosis
4.398

N
7,457

ARMFEARMFE_SLS
ARMFE_OI
ARMFE_NI
SIZE
ROA
GROWTH
LOSS
LEV
INST
CROSS
FOREIGN

0.613
0.415
0.582
0.302
11.804
0.017
0.035
0.178
0.566
0.325
0.235
0.101

0.432
0.265
0.427
0.227
11.620
0.017
0.025
0.000
0.574
0.320
0.193
0.070

3.429
2.793
3.157
1.745
14.701
0.111
0.469
1.000
0.956
0.593
0.673
0.395

0.007
0.006
0.010
0.005
9.527
−0.135−0.269
0.000
0.127
0.066
0.022
0.004

0.683
0.534
0.604
0.309
1.236
0.041
0.133
0.382
0.204
0.129
0.162
0.098

2.885
3.474
2.656
2.985
0.455
−1.8632.249
1.686−0.166
0.088
0.904
1.217

13.77517.896
12.58816.2212.840
14.97420.668
3.843
2.284
2.249
3.087
3.906

7,457
7,457
7,457
7,457
7,457
7,457
7,457
7,457
7,457
7,457
7,457
7,457

Table 4 presents the descriptive statistics for the variables used in this study. The definitions of each variable are as follows. RMSE =idiosyncratic return volatility based on the three-factor model in Fama and French (1993) for fiscal year t. ARMFE = absolute value of theresidual management forecast errors for year t − 1. Management forecast errors are defined as Comp1 from principal component analysisof three variables regarding management forecast errors. ARMFE_SLS = absolute value of residual management forecast error for sales
for year t − 1. ARMFE_OI = absolute value of residual management forecast error for ordinary income for year t − 1. ARMFE_NI =absolute value of residual management forecast error for net income for year t − 1. SIZE = natural log of total assets at the end of fiscal
year t − 1. ROA = return on assets for fiscal year t − 1. GROWTH = sales growth for fiscal year t − 1. LOSS = indicator variable with avalue of one if the firm reports a net loss and zero otherwise. LEV = total liabilities divided by total assets. INST = the percentage ofinstitutional ownership at the end of fiscal year t − 1. CROSS = the percentage of cross-shareholdings at the end of fiscal year t − 1.
FOREIGN = the percentage of foreign ownership at the end of fiscal year t − 1. IND = the percentage of individual ownership at the endof fiscal year t − 1. All sequential variables are Winsorized at the 1st and 99th percentiles by year.

36

Table 5
Correlation matrix(1)(1) RMSE t(2)0.119***(3)
0.087***(2) ARMFEt−1

0.115***(3) ARMFE_SLS t−1

0.093***

0.752***(4) ARMFE_OI t−1

0.103***

0.815***

0.557***(5) ARMFE_NI t−1

0.029*

0.557***

0.311***

0.411***−0.207***0.408***

0.388***

0.328***

0.121***

0.088***

0.095***

0.198***

0.262***

0.156***(6) SIZE t−1(7) ROA t−1(8) GROWTH t−1(9) LOSS t−1−0.259***−0.108***0.302***

0.620***(4)
0.079***−0.060***−0.046(5)
0.041***

(6)

(7)

(8)

−0.146***−0.289***−0.193***0.651***

0.348***

0.468***

0.143***

0.159***

0.132***

0.146***

0.242***

0.155***

0.015

0.127***

0.167***

0.154***
0.344***−0.101***-0.023−0.069***0.221***

0.087***

0.167***−0.086***−0.0070.189***−0.073***0.048***

0.434***(9)0.314***(10)0.220***−0.057***−0.022*−0.070***0.061***(11)−0.066***−0.022*−0.137***−0.032***−0.098***−0.052***−0.120***−0.010−0.081***−0.087***

0.061***

0.269***

0.043***−0.063***0.233***−0.662***−0.473***−0.020*−0.075***−0.285***−0.143***−0.030*−0.034***0.422***−0.024***0.239***−0.080***−0.607***−0.206***−0.216***−0.021***0.189***−0.018**−0.049***0.015*

0.012
0.036***

0.254***−0.096***−0.174***

0.493***(10) LEV t−1

0.216***

0.054******
0.120***

0.084***

0.114***

0.196***(11) INST t−1

0.021

0.062***

0.034

0.036*

0.041**

0.422***(12) CROSS t−1

0.134***(13) FOREIGN t−1−0.095***−0.126***
0.258***−0.071***0.212***−0.128***0.199***−0.100***0.188***−0.021***0.365***

0.059***−0.018***0.111***−0.024***0.065***(12)
0.088***

0.240***−0.216***(13)−0.170***0.190***
0.205***
0.083***
0.090***
0.472***
0.438***
0.270***−0.040***0.022−0.234***0.059***

0.056***−0.322***−0.513***0.080***

0.230***−0.373***

Table 5 presents the correlation matrix for the variables used in the main analysis, with Pearson (Spearman) correlations below (above) the diagonal. The definitions of each variable are as follows. RMSE =idiosyncratic return volatility based on the three-factor model in Fama and French (1993) for fiscal year t. ARMFE = absolute value of the residual management forecast errors for year t − 1. Management
forecast errors are defined as Comp1 from principal component analysis of three variables regarding management forecast errors. ARMFE_SLS = absolute value of residual management forecast error for sales
for year t − 1. ARMFE_OI = absolute value of residual management forecast error for ordinary income for year t − 1. ARMFE_NI = absolute value of residual management forecast error for net income for year t− 1. SIZE = natural log of total assets at the end of fiscal year t − 1. ROA = return on assets for fiscal year t − 1. GROWTH = sales growth for fiscal year t − 1. LOSS = indicator variable with a value of one if thefirm reports a net loss and zero otherwise. LEV = total liabilities divided by total assets. INST = the percentage of institutional ownership at the end of fiscal year t − 1. CROSS = the percentage ofcross-shareholdings at the end of fiscal year t − 1. FOREIGN = the percentage of foreign ownership at the end of fiscal year t − 1. All sequential variables are Winsorized at the 1st and 99th percentiles by year,
and ***, **, and * denote statistical significance at the 0.01, 0.05, and 0.1 levels using a two-tailed t-test.

37

Table 6
Management forecast accuracy and idiosyncratic risk
Expected

RMSE tRMSE tRMSE tRMSE t

sign

Coef.

t-valueCoef.

t-valueCoef.

t-valueCoef.

t-valueConstant
ARMFE t−1?
+

4.638***
0.048 *

15.4391.751

4.633***

15.5694.607***

16.2224.689***

15.904

ARMFE_SLS t−1
ARMFE_OI t−1++

0.055*

1.775
0.044

1.580

ARMFE_NI t−1
SIZE t−1+−−0.247***−8.974−0.245***−9.179−0.242***−9.4780.167**−0.250***2.091−9.527

ROA t−1
GROWTH t−1−−−0.5070.064−0.7600.485−0.4670.051−0.7090.375−0.4750.079−0.7230.618−0.5220.107−0.792
0.852

LOSS t−1
LEV t−1++

0.343***
1.242***

6.199
8.609

0.344***
1.234***

6.006
8.560

0.348***
1.236***

6.204
8.596

0.307***
1.243***

6.024
8.524

INST t−1
CROSS t−1++−0.459***0.139−3.0011.352−0.473***0.125−2.9791.185−0.465***0.140−2.8851.306−0.441***0.141−2.977
1.378

FOREIGN t−1
Year Dummies+

2.124***
Yes

8.442

2.127***
Yes

8.530

2.122***
Yes

8.458

2.096***
Yes

8.744

Adj. R2
Obs.

0.479
7450

0.479
7450

0.479
7450

0.481
7450

Table 6 shows the results of the multivariate regressions of model (3). The table presents the relationship between the absolute value ofresidual management forecast error and idiosyncratic risk. The definitions of each variable are as follows. RMSE = idiosyncratic return
volatility based on the three-factor model in Fama and French (1993) for fiscal year t. ARMFE = absolute value of the residual
management forecast errors for year t − 1. Management forecast errors are defined as Comp1 from principal component analysis of threevariables regarding management forecast errors. ARMFE_SLS = absolute value of residual management forecast error for sales for year t− 1. ARMFE_OI = absolute value of residual management forecast error for ordinary income for year t − 1. ARMFE_NI = absolute valueof residual management forecast error for net income for year t − 1. SIZE = natural log of total assets at the end of fiscal year t − 1. ROA= return on assets for fiscal year t − 1. GROWTH = sales growth for fiscal year t − 1. LOSS = indicator variable with a value of one if thefirm reports a net loss and zero otherwise. LEV = total liabilities divided by total assets. INST = the percentage of institutional ownershipat the end of fiscal year t − 1. CROSS = the percentage of cross-shareholdings at the end of fiscal year t − 1. FOREIGN = the percentageof foreign ownership at the end of fiscal year t − 1. All sequential variables are Winsorized at the 1st and 99th percentiles by year. In thepanel, t-values are corrected for heteroskedasticity as well as for cross-sectional and time-series correlation using a two-way cluster at thefirm and year levels, as proposed by Petersen (2009); ***, **, and * denote statistical significance at the 0.01, 0.05, and 0.1 levels,
respectively, using a two-tailed t-test.

38

Table 7
The effect of firm size on the relationship between management forecast accuracy
and idiosyncratic risk
Expected
sign

RMSE tRMSE tRMSE tt-valueCoef.

t-valueCoef.

t-valueCoef.

t-value

2.122***

17.3601.999***

16.4812.129***

19.1160.205*

1.714
0.400***

3.372
0.483**

2.166-0.374−1.079Constant?

2.102***

18.741

ARMFE t−1
ARMFE_SLS t−1++

0.138*

1.752

ARMFE_OI t−1
ARMFE_NI t−1++

ARMFE × SIZEq1 t−1
ARMFE_SLS × SIZEq1 t−1++

ARMFE_OI × SIZEq1 t−1
ARMFE_NI × SIZEq1 t−1++

ARMFE × SIZEq4 t−1
ARMFE_SLS × SIZEq4 t−1−−

ARMFE_OI × SIZEq4 t−1
ARMFE_NI × SIZEq4 t−1−−

SIZEq1 t−1
SIZEq4 t−1+−

0.702***−0.163***

5.829−3.8360.600***−0.176***

ROA t−1
GROWTH t−1−−−0.1020.070−0.130
0.512

LOSS t−1
LEV t−1++

0.391***
1.005***

INST t−1
CROSS t−1++

FOREIGN t−1
Year Dummies+

Adj. R2Obs.

RMSE tCoef.

−0.067−0.392
0.086

0.492
0.072

−0.239***0.237−3.091-0.319***−2.766−0.547***−4.460−0.606***−2.946

3.537−3.1900.349−0.0191.053−0.3300.781***−0.123**3.474−2.514−0.1740.065−0.235
0.505

0.050
0.000

0.072
0.000

−0.3390.088−0.392
0.689

6.381
7.199

0.383***
0.966***

6.162
6.984

0.406***
0.959***

6.968
7.120

0.343***
0.943***

6.445
6.652

−0.764***0.098−4.3520.857−0.769***0.114−4.1270.955−0.689***0.110−3.5830.935−0.791***
0.068−4.5190.584

1.409***
Yes

6.717

1.360***
Yes

6.375

1.525***
Yes

6.669

1.233***
Yes

6.393

0.445
7450

0.444
7450

0.455
7450

0.444
7450

Table 7 presents the results of the estimation of equation (4) in testing hypothesis IIa. The table presents the effect of firm size on therelationship between the absolute value of residual management forecast error and idiosyncratic risk. The definitions of each variable areas follows. RMSE = idiosyncratic return volatility based on the three-factor model in Fama and French (1993) for fiscal year t. ARMFE =absolute value of the residual management forecast errors for year t − 1. Management forecast errors are defined as Comp1 fromprincipal component analysis of three variables regarding management forecast errors. ARMFE_SLS = absolute value of residual
management forecast error for sales for year t − 1. ARMFE_OI = absolute value of residual management forecast error for ordinary
income for year t − 1. ARMFE_NI = absolute value of residual management forecast error for net income for year t − 1. SIZEq1 =indicator variable set to one if the level of total assets is in the first quartile, where that quartile contains the firms with the lowest total
assets in each year, and zero otherwise. SIZEq4 = indicator variable set to one if the level of total assets is in the fourth quartile, wherethat quartile contains the firms with the highest total assets in each year, and zero otherwise. ROA = return on assets for fiscal year t − 1.
GROWTH = sales growth for fiscal year t − 1. LOSS = indicator variable with a value of one if the firm reports a net loss and zerootherwise. LEV = total liabilities divided by total assets. INST = the percentage of institutional ownership at the end of fiscal year t − 1.
CROSS = the percentage of cross-shareholdings at the end of fiscal year t − 1. FOREIGN = the percentage of foreign ownership at theend of fiscal year t − 1. All sequential variables are Winsorized at the 1st and 99th percentiles by year. In the panel, t-values are corrected
for heteroskedasticity as well as for cross-sectional and time-series correlation using a two-way cluster at the firm and year levels, as
proposed by Petersen (2009); ***, **, and * denote statistical significance at the 0.01, 0.05, and 0.1 levels, respectively, using a
two-tailed t-test.

39

Table 8
The effect of analyst coverage on the relationship between management forecast
accuracy and idiosyncratic risk
Expected

RMSE tRMSE tRMSE tRMSE t

sign

Coef.

t-valueCoef.

t-valueCoef.

t-valueCoef.

t-valueConstant
ARMFE t−1?
+

4.611***
0.079*

18.8081.864

4.606***

19.2474.549***

19.6984.662***

19.405

ARMFE_SLS t−1
ARMFE_OI t−1++

0.098**

2.439
0.099**

2.088

ARMFE_NI t−1
ARMFE × COV t−1++

0.309**

2.328

ARMFE_SLS × COV t−1
ARMFE_OI × COV t−1++

ARMFE_NI × COV t−1
COVt−1

+
+

0.015

0.254

0.016

SIZE t−1
ROA t−1−−−0.246***−0.500−10.603−0.790GROWTH t−1
LOSS t−1

−
+

0.056
0.343***

LEV t−1
INST t−1++

CROSS t−1
FOREIGN t−1++−0.044*−1.750−0.064**−2.097−0.084*−1.751

0.264

0.036

0.591

−0.218**0.049−2.0920.730−0.244***−0.460−11.121−0.745−0.239***−0.439−11.240−0.712−0.248***−0.566−11.295−0.879

0.446
6.192

0.042
0.343***

0.333
5.924

0.070
0.351***

0.584
6.304

0.104
0.286***

0.868
6.017

1.245***−0.453***8.878−3.042

1.234***−0.471***

8.849−3.052

1.233***−0.467***8.882−2.9671.228***−0.436***8.710−3.0250.144
2.144***

1.389
8.068

0.127
2.144***

1.201
8.105

0.142
2.132***

1.322
7.989

0.141
2.105***

1.381
8.317

Year Dummies
Adj. R2

Yes
0.479

Yes
0.479

Yes
0.480

Yes
0.482

Obs.

7,457

7,457

7,457

7,457

Table 8 presents the results of the estimation of equation (4) in testing hypothesis IIa. The table presents the effect of analyst following on
the relationship between the absolute value of residual management forecast error and idiosyncratic risk. The definitions of each variable
are as follows. RMSE = idiosyncratic return volatility based on the three-factor model in Fama and French (1993) for fiscal year t.
ARMFE = absolute value of the residual management forecast errors for year t − 1. Management forecast errors are defined as Comp1
from principal component analysis of three variables regarding management forecast errors. ARMFE_SLS = absolute value of residual
management forecast error for sales for year t − 1. ARMFE_OI = absolute value of residual management forecast error for ordinary
income for year t − 1. ARMFE_NI = absolute value of residual management forecast error for net income for year t − 1. COV = indicatorvariable set to one if one or more analysts actively track and publish opinions on a company and its stock, and zero otherwise. SIZE =natural log of total assets at the end of fiscal year t − 1. ROA = return on assets for fiscal year t − 1. GROWTH = sales growth for fiscal
year t − 1. LOSS = indicator variable with a value of one if the firm reports a net loss and zero otherwise. LEV = total liabilities divided
by total assets. INST = the percentage of institutional ownership at the end of fiscal year t − 1. CROSS = the percentage ofcross-shareholdings at the end of fiscal year t − 1. FOREIGN = the percentage of foreign ownership at the end of fiscal year t − 1. All
sequential variables are Winsorized at the 1st and 99th percentiles by year. In the panel, t-values are corrected for heteroskedasticity as
well as for cross-sectional and time-series correlation using a two-way cluster at the firm and year levels, as proposed by Petersen (2009);***, **, and * denote statistical significance at the 0.01, 0.05, and 0.1 levels, respectively, using a two-tailed t-test.

[2013.10.19.1147]40