Management Forecast Bias and Corporate Social Responsibility Awareness:
Evidence from Japan

Abstract
This study examines the influence of corporate social responsibility (CSR) awareness, measured bycorporate social performance (CSP), on management earnings forecasts and estimated implied costof equity (ICE) capital in the context of Japanese listed firms in the period 2007-2015. The empiricalresults demonstrate that CSP mitigates optimistic forecasts and enhances forecast accuracy after
controlling the firm size effect on CSP. Considering the possibility of excess conservatism inmanagement forecasts to avoid negative public reaction to downward forecast revisions, weinvestigate the effects of CSP on ICE for upward and downward forecast biased firms, respectively,
and find that among the downward forecast bias group, some firms implement excess conservatismin their earnings forecasts. Thus, excess conservatism may be a possible bias in management
earnings forecasts peculiar to the Japanese disclosure system.

Keywords: Corporate Social Responsibility, Management Forecast, Implied Cost of Equity

11. Introduction
Whether or not the implied cost of equity or the internal cost of capital can successfully capture
variations in expected rates of return has been a growing topic among researchers. Many studies of
US firms have been concerned with the estimation of the implied cost of equity using analyst
short-term and long-term earnings forecasts; however, their empirical results have been mixed (Guayet al., 2011). For example, Kang and Sadka (2015) find a negative relation between analyst forecasts
and realized future returns and suggest that analyst forecasts are generally systematically optimistic
earnings forecasts.
In the case of Japanese firms, managers of listed companies are requested to disclose
management forecasts for one-year financial performance data, such as net income and sales, in theirfinancial reports to the stock exchange. Most listed companies actually publish management
forecasts for the next fiscal year when they disclose their annual financial statements. This is one of
the unique points of Japanese financial disclosure. Thus, such managers may be more careful orconservative than analysts in predicting future returns because they have to be accountable for thedisclosed figures. Accordingly, management earnings forecasts for net income are available asproxies of the internal cost of equity for Japanese companies. The accuracy of such forecasts can beseen by analysts as honest reflections of the beliefs of managers as presented to their shareholders.
Gu, Kubota, and Takehara (2017) find that at firms with higher corporate social performance(CSP), the measurement of corporate social responsibility awareness, accounting earnings are lessmanaged in terms of accrual and real-activity based strategies. Their study indicates that corporatesocial responsibility (CSR) activities may improve the earnings quality of Japanese firms and CSR
conscious firms are likely to gain investors’ confidence. Suto and Takehara (2016a) find a negative
relationship between the cost of equity and CSP in the Japanese firms’ context, based on theconditional version of the Fama and French three-factor model (Fama and French, 1997). The
result suggests that CSP may negatively influence the internal rate of return of the firm or implied
cost of equity. According to these prior studies, an interesting research question is whether or not afirm’s CSR awareness can restrain management forecast optimism and enhance management
forecast accuracy as a proxy of the internal rate of return.
The purpose of this study is twofold; to investigate the influence of CSR awareness on a firm’s
estimated implied cost of equity in the context of the listed Japanese firms, and to explore thelinkage between CSR awareness and the quality of management earnings forecasts as a proxy for theinternal rate of return. In this study, we estimate the internal cost of capital based on the residual
income valuation model in Frankel and Lee (1998). Then, we identify the accuracy of the
management forecast of net income in the following fiscal year and management behavioral bias
from discretional forecasting. With respect to the accuracy of the forecast, we analyze the link
between any forecast error found at the end of the fiscal year and the CSP of the firm reported in the2

middle of the fiscal year. An increase in management forecast accuracy could contribute to
mitigating information asymmetry between managers and shareholders regarding future cash flow,
and to improving the firm’s valuation in the market. From a longer-term view, CSR awareness could
encourage management to avoid opportunistic behavior so as to build confidence among investors inthe sustainable development or long-term success of the firm. Therefore, we analyze the link
between optimistic bias of management forecasts (forecast net income > realized net income) and
CSP.
The empirical results show that CSP mitigates both forecast errors and optimistic bias in terms of
net income. The results, from firm size controlled sub-samples, suggest that CSP can reduce implied
cost of equity in firm size groups. Considering the possibility of firms’ implementing discretional
downward forecast bias to avoid negative assessments of management, we divide the sample into
two groups (upward forecast bias and downward forecast bias firms) and find that excessconservative bias may be a notable forecast earnings bias in the context of Japanese listed firms.
This rest of this paper is organized as follows. In Section 2, prior research on CSP and its
background, management forecasting, and the implied cost of equity are discussed. Section 3
develops hypotheses for CSP and the implied cost of equity and for CSP and management forecast
biases. In Section 4, after explaining our methodology and data in the empirical analyses, regression
models and estimated results are presented. Section 5 sums up the empirical results and discusses
implications and future research topics.
2. Prior research and basic concepts
2.1 Corporate social responsibility and risk premium
CSR can be narrow or broad in scope in the management of social issues by corporations.
According to McWilliams et al. (2006), CSR activities have been posited to include incorporatingsocial characteristics or features into products and manufacturing processes (p.2). Many priorstudies have discussed and examined whether CSR aware firms explore business opportunities,
improve organizational efficiency, build the trust of stakeholders, and are rewarded for meeting the
expectations of shareholders by enhancing long-term market value of the corporation. In the years of
discussion and examination of CSR, it has generally become a core concept in corporate
management. In this study, we narrowly define CSR practices as the corporate strategy forsustainability and long-term success of the firm in society.
The current importance and ubiquity of CSR imply that firms now measure the extent to whichin their business strategy they practically handle such responsibility for stakeholders. Thus, CSP istaken as a measure of firm CSR awareness. Wood (1991) defines CSP as a business organization’s
configuration of principles of social responsibility, processes of social responsiveness, and policies,
programs and observable outcomes as they relate to the firm’s social relationships (p. 693). There is3

a good deal of empirical research that examines the relationship between CSP and corporate
financial performance (CFP), focusing on conceptualizing, specifying, and testing this link (e.g.,
Cochran and Wood, 1984; Graves and Waddock, 1994; Russo and Fouts, 1997).
Empirically, numerous studies examine the association between CSP and profitability and theresults are inconclusive, but many prior studies do confirm the risk reduction aspect of CSP(Orlitzky et al., 2003; Van Beurden and Gossling, 2008). Recently, a number of studies have focusedon the financial market’s perception of CSR in the determination of risk premium, and many of themfind a negative relation between CSP and idiosyncratic risk (Boutin-Dufresne and Savaria, 2004;
Girerd-Potin et al., 2014; Mishra and Modi, 2013). Theoretically, CSR could influence the risk
premium in the market and the cost of equity through several channels: reduction of uncertainty of
future business development or business risk, mitigating information asymmetry between managers
and shareholders, and lowering financial constraints (e.g., difficulties accessing external financing),
or friction in the financial market by enhancing trust in management. Responsible firm actions insociety could reduce uncertainty about the firm’s future cash flow in responding to changes in social
demands and emerging new business opportunities in society in the long run.
There are many empirical studies that focus on CSP and risk premiums using US firm data. El
Ghoul et al. (2011) demonstrate a negative relationship between a firm’s CSP and its implied equity
risk premium calculated using four different models. Gregory et al. (2014) explore the risk reducing
effects of CSR and their implications for financial performance and show that CSR performance isassociated with better long-term growth and lower cost of equity. Dhaliwal et al. (2011) examine thebenefits of the voluntary disclosure of CSR practices using several different models of implied costof equity, while Dhaliwal et al. (2012) find that the issuance of standalone CSR reports is associatedwith lower analyst forecast error. More recently, Ioannou and Serafeim (2015) find a positive impact
of CSR on sell-side analyst recommendations. Chen et al. (2014) investigate whether CSR activities
alleviate financial constraints or friction in the market by enhancing investor trust in the managementof the firm and find that stakeholder engagement has been shown to limit the likelihood ofshort-term opportunistic behavior.
In the context of Japanese firms, in the period 2007-2013, Suto and Takehara (2016a) find thatfirm CSP metrics are significantly negatively related to most risk measures, including those that aremarket-based and accounting-based. With respect to CSP dimensions, it is remarkable thatenvironment and social contributions show a strong negative relationship with market-based risk.
Suto and Takehara (2017) investigate the influence of CSP on cost of equity and find that

institutional ownership, which is widely defined as non-individual investors, influences on therelation between CSP and cost of equity and reduces the cost of equity by using CSP as
information.
The firm’s corporate management awareness of the CSR strategy could be a key determinant of4

voluntary non-financial disclosure, which could influence the cost of capital of the firm. Suto andTakehara (2016b) investigate differences in CFP between the respondents and non-respondents of a
CSR survey conducted by Toyo Keizai Inc. among listed Japanese firms. They found that there were
statistically significant differences in CFP and stock ownership structure between the respondents
and non-respondents. They assumed that the respondents are more aware about CSR than the
non-respondents and found the non-respondent firms had lower profitability and higher risks, andless institutional ownership than the responding firms. They further estimated the CSP of
non-respondents under certain assumptions of CSP-CFP relations and confirmed that the CSP of
non-respondents could be much lower than that of respondents. These findings suggest that the
non-respondents with lower corporate performance may have been indifferent to societal changes or
may have intentionally hidden their low CSP.
Thus, CSP could motivate managerial behavior from the long-term perspective of corporate
valuation. If so, the more CSR aware the corporate managers are, the more averse they may be toopportunistic disclosure or to supplying biased information to the market in the short-term.
2.2 Management forecast and internal rate of return
Many studies examining US firm data find that analyst forecasts are biased as a proxy of implied
cost of equity. Kang and Sadka (2015) investigate whether analyst forecasts at the firm level have anegative association with realized returns. The study suggests that analysts tend to have an optimistic
bias. Their study also provides evidence that market participants do not efficiently assess analysts’
biases of firms with a high internal cost of capital and they caution against using analyst forecasts as
a proxy for cost of equity. Guay et al. (2011) examine predictable error in implied cost of equity
estimates resulting from analyst forecasts that are sluggish with respect to information in past stock
returns.
In the US, management earnings forecast disclosure is voluntary. There is a large amount of priorresearch on the information value of management forecasts on stock prices and corporate valuations.
Some studies are concerned with management optimism in earnings forecasts; and, the empiricalresults of these are inconclusive. Koch (2002) finds that optimism tends to relate to firms’ financial
distress, however, Frankel et al. (1995) find no optimistic bias of management forecasts before
disclosure.
In contrast, Japanese companies listed on a stock exchange are required to disclose management
forecasts of financial performance data of the next fiscal year, or the mid-year financial results in theprompt report of the financial statement at the end of each term report, in terms of sales, operating
profits, net income, and dividends. In each quarterly report, the realized financial performance forthe previous quarter is also disclosed. Moreover, a company is required to disclose by Company Law
any important revision of a management forecast. Thus, the mandatory provision of management

5

earnings forecasts is a unique feature of Japan’s financial disclosure system (Ota, 2006).
As Japanese corporate managers are exposed to reputational risk from their forecast errors,
management forecasts from listed Japanese companies may be considered more reliable than that of
their US counterparts in terms of building investor confidence over the long run. On the other hand,
they may tend to portray an optimistic bias in their earnings forecasts or profitable operation in the
short run, especially when in financial distress.
As a research topic, it is of interest for external analysts to analyze the informational value of the
management earnings forecast as a proxy of the internal rate of return, especially for investors in thecapital market. If the management forecast is reliable, investors could use it as a proxy for the cost ofequity in firm valuations. On the other hand, if the management forecast were opportunisticallybiased, it would amplify the uncertainty of the firm valuation and increase the risk investors face.
In the context of Japanese firms, Muramiya (2005) finds a negative relation between theaccuracy of the management earnings forecast and the implied cost of equity estimated with theresidual income model. The result suggests that published management forecasts play an importantrole in the pricing of risk premiums. However, some other studies find an optimistic bias in the
management forecast for firms in financial distress (Ota, 2006; Suda and Shuto, 2001). These results
suggest that in Japan, the information value of the management forecast depends on the financial
situations of the firms.
2.3 Implied cost of equity and internal rate of return
In this study, we estimate the cost of equity of individual firms based on the residual income
valuation model (1).

NI t i  rE BVt i 1(1  rE ) ii 1Vt  BVt  (1)In the valuation equation (1), Vt is an intrinsic value of the firm and NIt and BVt denote net income
and book value of equity at fiscal year t. rE denotes the cost of equity, which we will estimate.
Frankel and Lee (1998) assumed that the residual income streams after fiscal year t are constant.
Under this perpetuity assumption, we obtain the following simple valuation formula (2).1
Vt  BVt 

E[ NI t 1 ]  rE BVt E[ NI t 1 ]  rE BVt(1  rE )(1  rE ) rE

If the stock price is at fair level, namely, when intrinsic value Vt is equal to the market value ofequity MVt, after some calculations, cost of equity is given as the earnings-to-price ratio.

1

This equation (2) is equivalent to the equation (3.1) in Frankel and Lee (1998, p.289).

6(2)

MVt  BVt ( E[ ROE t 1 ]  rE ) BVt ( E[ ROE t 1 ]  rE ) BVt,
1  rE(1  rE ) rE

MVt  E[ ROE t 1 ]  rE E[ ROE t 1 ]  rE , 1 
1  rE(1  rE ) rE
BVt 

 E[ ROE t 1 ]  rE  E[ ROE t 1 ] ,
 1 rErE

BVt E[ NI t 1 ] E[ NI t 1 ]BVt
.
E[ ROE t 1 ] 
rE 
MVt
BVt
MVt
MVt

(3)

Replacing the expected value of NIt+1 in (3) with the management forecast FNIt+1, which becomespublicly available at the time of the prompt report, the implied cost of equity, ICE, here is defined asin (4).
ICE t FNI t 1
MVt

(4)

Let RNIt+1 denote the realized value of NIt+1, which will be revealed one year after the initial forecast.
Then, the accuracy of the manager’s earnings forecast should be defined by the difference between
forecasted EPR and realized EPR. We use a variable named PNIt as a measure of earnings forecast
optimism.
PNIt 

FNIt 1  RNIt 1
MVt

(5)

Since net income is a stochastic variable, PNIt, defined in (5), is the sum of the stochastic forecast
error and the manager’s intentional earnings bias. If we can further assume that the expected value ofthe forecast error for net income is zero, positive PNI means that the manager discloses a more
optimistic earnings forecast and a negative PNI means a more conservative earnings forecast. For
this reason, we pay particular attention to PNI as a measure of management forecast bias in theempirical analyses. It should also be noted that the quality of the earnings forecast will be low if themagnitude of PNI is large. Thus, we use an absolute value for PNI (ABSPNIt=|PNIt|) as a measure of
forecast accuracy.
3. Hypotheses development
3.1 Corporate social responsibility awareness and management forecast biases
As already mentioned, CSR has become a core concept in corporate management for long-term
success and sustainability in today’s changing societal and environmental surroundings. Many
studies discuss and examine whether firms explore business opportunities, improve organizational
efficiency, build stakeholder trust, and successful risk management and are rewarded for meeting the
expectations of shareholders by enhanced long-term corporate market value.
If CSR contributes to adding discipline to corporate governance and to building strong
stakeholder relationships in the long run, higher CSP firms may enhance investor confidence in the7information they disclose in the market and thereby reduce the risk premium demanded by investors.
On the other hand, there are incentives that motivate managers to avert a negative response in termsof market price through profit manipulation in the short-term or to provide optimistic forecasts to
attract investors with myopic decision-making. There could be a potential conflict of interest amongcorporate managers between building shareholder confidence in the long-term and opportunistic
manipulation of earnings in the short-term. Our question is whether high CSR awareness influences
management forecast biases. Actually, CSR practices need corporate resources while, at the sametime, they take time to achieve the expected CSR effects and the results are uncertain. In thissituation, corporate managers can be motivated to pursue myopic behavior to gain short-term
evaluations of their businesses. A more myopic manager may be less aware of CSR strategy andinstead follow opportunistic behaviors.
In this study, we identify two types of management forecast biases; forecast inaccuracy andforecast optimism (conservatism). With respect to inaccuracy, we employ a measure of management
forecast error. A more accurate management forecast for the coming year could contribute to
mitigating information asymmetry between managers and shareholders around future cash flow andthereby improve the firm’s valuation in the market. In the long term, optimistic management
forecasts lessen investor confidence in management and can thus damage long-term success of the
firm.

Similarly, excessive conservative forecasts could stem from management opportunistic

behaviors, which obstruct information asymmetry between management and shareholders and harm
corporate value.
3.2 Hypotheses
When there is no information asymmetry between managers and shareholders, the ex-ante
measure of expected return is a proper proxy for the cost of equity. If realized returns deviate frominvestor expectations, there could be an agency cost due to the asymmetry of information between
managers and shareholders regarding future cash flows. If a firm’s CSR strategy, from a long-term
perspective, provides information to the public that reduces uncertainty about future cash flows, its
internal rate of return could be influenced by firm CSR awareness. Thus, the following hypothesis is
introduced.
Hypothesis 1: CSP has a negative relation with implied cost of equity.
In fact, the implied cost of equity can be influenced by the quality of the management earnings
forecast. For long-term success, rather than a short-term share price increase, managers may choose
to improve forecast accuracy or minimize forecast error and restrain optimistic forecasting totemporarily attract investors. However, when focus is on the short-term or on financial distress,

8managers have to address pressures from myopic demands from the market and forecast accuracy
could be undermined by earnings manipulation. A firm’s CSR awareness may encourage amanagerial behavioral bias that avoids earnings manipulation in the short term. If so, CSP could be arelevant factor in improving forecast accuracy. Thus, hypothesis 2 is introduced.
Hypothesis 2: The higher the CSP of the firm, the more accurate the management earnings
forecast.
CSR awareness could also contribute to restraining any optimistic bias in the management
forecast. Responsible managers would be averse to providing biased information to the market froma long-term view, for such behavior could harm the trust in the firm’s management in the market andits market valuation. Thus, hypothesis 3 is introduced.
Hypothesis 3: The higher the CSP of the firm, the less optimistic its management earnings
forecast.
Restraining optimistic management forecasts, however, may link to discretional conservatism
motivated by the desire to enhance the reputation of management or to avert criticism from notrealizing the forecast. If managers provide optimistic forecasts, there is an upward bias in theearnings forecast. However, among less optimistic forms of forecast bias, there can be some withexcess conservative forecast bias that reduce trust in management. Thus, it is necessary to identify
the effects of CSP on forecast biases between upward-bias and downward-bias firms. Thus,
hypothesis 4 is introduced.
Hypothesis 4: The higher the CSP of the firm, the less conservative the management earnings
forecast.
4. Empirical Analyses
4.1 Data and variables
The sample period of this study is 2007-2015, which covers the longest period of available CSR
data. The CSP data we use in this study were developed by Suto and Takehara (2016a) based on theannual CSR questionnaire survey data by Toyo Keizai Inc.2 The first column of Table 1 shows all

2

Toyo Keizai has conducted nine annual CSR surveys since 2005. The questionnaire is sent to all public

firms on the Tokyo Stock Exchange (TSE) and to the major listed companies on other exchanges in Japan.
We exclude 2005 and 2006 from the analysis as the question format changed significantly after 2006.

9listed firms on the stock exchanges with a fiscal year-ending in March and the second and thirdcolumns show the number of the listed firms which disclose management forecasts and theirestimated implied cost of equity, respectively.
The firms which provide management forecasts include 90.6% of the all listed firms with aMarch fiscal year end for the observed period (=16,632/18,351). Therefore, a large number of thelisted firms disclose management earnings forecasts in Japan. The fourth and fifth columns are thenumbers of the non-respondents and respondents to the Toyo Keizai questionnaire. Of the listed
firms, 33.5% responded to the CSR Survey. Among the 18,351 analyzed firms, 15,791 have bothmanagement forecasts and estimated implied cost of equity, including 6,156 respondents and 12,195
non-respondents to the CSR Survey. Finally, the last column shows the number of the sample firms
for which both the implied cost of equity and CSR data are available. Thus, we select these 5,435
firms as the sample, which covers about 29.6% of all listed firms with a March fiscal year end(=5,435/18,351).
For most Japanese firms, the fiscal year is from April to March and management forecast of one
year net income for the fiscal year is disclosed in the middle of May. The definitions of the study
variables are summarized in Figure 1. The ICE is calculated per (4) in the residual income valuation
model (1). As a measure of management forecast bias, the difference between the management
forecast of net income and realized net income is calculated as follows. The difference between
future net income forecast (FNI) and realized net income (RNI) divided by the market value ofequity is the measure of management optimism (PNI). A higher PNI implies a more optimistic
forecast. The absolute value of the gap between future net income and realized net income (ABPNI)
represents the accuracy of the management forecast. Both of the two variables are standardized withmarket value MV. PNIt is a measure of earnings forecast optimism for fiscal year t and ABSPNIt is ameasure of earnings forecast accuracy for fiscal year t. We use these two variables to identify
discretional manager forecast decision-making and irresponsibility for minimization of forecast
errors. They are proxies for different types of management forecast biases.
As a proxy for CSR awareness, we use a composite measure of CSP, as designed by Suto andTakehara (2016a).3 We introduce four control variables which influence management forecasts;
return on total assets (ROA) as an accounting-based measure of financial performance; debt ratio
3

Suto and Takehara (2016a) use a stakeholder approach to define the dimensions of CSP and develop an

original method to construct CSP indices with the Toyo Keizai questionnaire results. They calculate five
CSP dimensional indices according to stakeholder relationships: employee relations (EMP), social
contributions (SC), security of the firm and product safety (SS), internal governance and risk management(IG) and environmental preservation (ENV). Subsequently, they consolidate these CSP dimensional
indices into a composite index.

10(DR) (=total debt/total assets) as leverage; natural logarithm of market value of equity (lnMV) (inmillion JPY) as firm size; and book-to-market (B/M) value (in %) to identify differences in themarket valuation of the firm.
Table 2 presents the descriptive statistics of the variables for the CSR respondents andnon-respondents. Respondents are characterized by better performance (e.g., higher ROA), larger
size, higher leverage, and higher market value than non-respondents. They also have lower implied
cost of equity (ICE) and lower forecast biases in terms of both forecast optimism (PNI) and accuracy(ABSPNI) than non-respondents.
Table 3 presents the correlation matrix among the variables within respondent groups. CSP has asignificantly negative correlation with ICE (-0.144) and a strongly positive one with firm size MV(0.634), as expected. CSP is also negatively correlated with PNI (-0.025) and ABPNI (-0.068), but
not statistically significant. ICE has a significantly negative correlation with PNI (-0.208), ROA(-0.311), and firm size lnMV (-0.248), and market value B/M (-0.249). Firm size, lnMV, has a strong
positive correlation with performance, ROA (0.227), and higher market valuation, B/M (-0.531), as
expected, and a significantly negative correlation with PNI (-0.128) and an insignificant one with
ABSPNI (-0.032).
PNI has a strongly negative correlation with ROA (-0.556), which suggests that a lower
corporate performance tends to be linked to more management forecast optimism. On the other hand,the correlation of ABPNI with ROA (-0.154) is negative but less so than PNI. B/M is positively
correlated to PNI (0.174), while it has a negative correlation with ABPNI (-0.139). Although
investors are concerned with forecast accuracy, they seem to be more influenced by optimistic
management forecasts.
4.2 Corporate social responsibility-ranked portfolios and awareness
Preliminarily, we categorize the sample firms based on their levels of CSR awareness andconduct analyses to identify the association of CSR awareness with other variables by the difference
test of averages between our portfolios of firms. Table 4 summarizes the results.
First, we categorize the listed firms into respondents and non-respondents to the CSR survey, andrun difference test averages for ICE between the two portfolios of respondents. The non-respondents
are assumed to be less aware of CSR practices and related disclosure than respondents (Suto andTakehara, 2016b). The results for the average ICE are expectedly negative at 1% significance.
Respondents have lower ICE, less forecast biases (lower PNI and ABPNI), higher performance(ROA, ROE), higher creditability, higher market valuation, and larger size than non-respondents.
Second, we categorize respondents into five CSP ranked portfolios and compare the average of thevariables between the lowest CSP portfolio (P1) and the highest CSP portfolio (P5). As shown in
Table 4, the results are completely consistent with those between full sample respondents and11non-respondents. This suggests that higher CSP firms restrain management forecast optimism more,are more concerned with forecast accuracy, and have a lower implied cost of equity.
Next, we classify each CSP-ranked portfolio into five groups of forecast bias in terms of PNI andABPNI, respectively, and compare the average ICE of the cross ranked portfolios. Table 5 presents
25 cross ranked portfolios in five CSP groups and five PNI or ABPNI groups, and calculates theaverage ICE in each of the cells. PNI1 is the highest (most optimistic bias) and PNI5 is lowest (least
optimistic bias). ABSPNI1 is the highest (most inaccurate bias) and ABPNI5 the lowest (least
inaccurate bias). CSP1 is the lowest (least responsible) and CSP5 is the highest (most responsible).
Panel A compares the ICE average in the PNI-CSP cross-portfolios and Panel B compares it in the
ABSPNI-CSP portfolios. First, to identify the ICE and CSP levels by controlling PNI and ABSPNI,
we conduct difference tests of the average ICE between the lowest CSP portfolio (CSP1) and highest
CSP portfolio (CSP5). For both Panels A and B, from the results of the difference tests, we foundthat a higher CSP tends to link to a lower ICE for every PNI or ABSPNI portfolio. Further, therelation between the CSP level and PNI is systematic except for PNI4/CP2 (8.244). Based on the
results, we confirm that a higher CSR awareness tends to link to a lower cost of equity.
Next, to identify the relation between ICE and forecast biases while controlling CSP, we conduct
difference tests of average ICE between the highest PNI portfolio (PNI1) and the lowest PNI
portfolio (PNI5) for each CSP level, and between the highest ABSPNI portfolio (ABSPNI1) and thelowest ABSPNI portfolio (ABSPNI5). In Panel A, if the CSP level is controlled, the relation between
PNI and ICE is unexpectedly negative, demonstrating that higher optimistic bias links to a lower costof equity. This suggests that there may be a high conservative bias link to a high-risk premium. To
comingle this complicated result, it is necessary to identify the influence of optimism andconservatism in the management forecast on cost of equity, respectively. In Panel B, if CSP iscontrolled, ABSPNI has a positive correlation with ICE except for CSP2, however, in the CSP5
portfolio, the result of the difference test is not statistically significant. Based on these results, we
comprehend that forecast accuracy could contribute to reducing the cost of equity, even at the same
CSP level.
4.3 Regression analyses
4.3.1 Regression model
We employed the following regression model to analyze the effects of CSR awareness on thecost of equity.
Y j ,t     1 CSP j ,t   2 ROA j ,t   3 DR j ,t   4 ln MV j ,t   5 BM j ,t
28

8

i 1

i 1

   j Industry i , j ,t    i Yeari , j ,t   j ,t(6)

In the above regression model (6), the dependent variable Yj,t is either the implied cost of equity

12

(ICE), forecast optimism (PNI), or forecast accuracy (ABSPNI). Industryi,j,t is the dummy variable,
which equals 1 if firm j belongs to the i-th industry, and 0 otherwise. We also include a year dummy
variable, Yeari,j,t, in this model. Since there may be endogeneity between CSP and the dependent
variable (ICE, PNI, or ABSPNI), we estimate the parameters in model (6) by a two-stage leastsquares method. We use as instrument variables a lagged one-year composite CSP score (CSPj,t-1)
and NOTSE1 dummy variable, which equals 1 if a firm is not listed on the Tokyo Stock ExchangeFirst Section, and 0 otherwise.
The regression results are summarized in Table 6. The coefficient of CSP on ICE is negative but
not statistically significant, while ICE looks to be influenced by other firm attributes. ICE has asignificantly positive relation with the profitability measure ROA, DR, and B/M, while being
strongly and negatively associated with firm size (lnMV), as expected. As for management forecast
biases, the coefficient of CSP on PNI is significant and negative (-0.247) at 1% significance and on
ABSPNI (-0.091) is significant at the 10% level. According to these estimated coefficients, we
comprehend that CSR has more effect on mitigating inaccuracy than restraining optimistic forecasts.
For the whole sample, hypothesis 1 is rejected, but hypotheses 2 and 3 are not rejected.
Among the firm attributes, ROA has a strongly negative relation with both PNI and ABSPNI,
which suggests that higher performing firms have less incentive to pursue opportunistic earnings
management. Regarding other firm attributes, the coefficients of DR, lnMV, and B/M on ABSPNI
are all significantly negative, however, all variables except for ROA are insignificant for PNI.
4.3.2 Regressions by firm size groups
We confirmed, as presented in Table 3, that firm size is strongly correlated with CSP. Thus, we
divide the sample firms into three size groups (large-cap, mid-cap, and small-cap) in each year
(t=2007,…,2015) and, after excluding firm size (lnMV) from the control variables, conducted
regressions again. The regression results are summarized in Table 7. All groups have negative
coefficients of CSP on implied cost of equity (ICE) and the two bias variables PNI and ABSPNI. For
large-cap and mid-cap groups, CSP has negative coefficients on ICE, significant at the 5% level, andfor the small-cap group, negative and significant at the 10% level.
Regarding the forecast biases for the large-cap group, the coefficients of CSP on both variables
are significant at less than 5%. For the mid-cap, the coefficient of CSP on PNI is statisticalsignificant (p-value=0.62), but for ABSPNI it is not significant (p-value=0.108). For small-cap, in
contrast, the coefficient of CSP on PNI is not significant (p-value=0.279) and that of ABPNI issignificant (p-value=0.001). Thus, if the size effect on CSP is controlled, hypothesis 1 is no longer
rejected. Hypothesis 2 and 3 are also again not rejected, but the results are not strong. Theseempirical results suggest that CSP reduces cost of equity and could contribute to mitigating
management forecast biases.

13

Over all firm size groups, ROA and B/M have strong positive effects on ICE and negative effects
on PNI and ABSPNI. At the same time, ROA and B/M have significantly negative effects on
management forecast biases. Thus, among the firm attributes, except for firm size, corporate
performance and market valuation are key determinants of cost of equity in the market as well as
management forecast behavior.
4.3.3 Forecast optimism and conservatism
The regression results we obtained suggest that CSP could mitigate management forecast biases
in terms of forecast accuracy and discretional behavioral bias. However, as suggested in 4.2, theinfluence of CSP on management forecast biases may not be symmetric with forecast optimism andconservatism. Even if more CSR aware firms provide less optimistic forecasts, it is possible thatsome of these firms may be doing so as they prefer conservative forecasts in order to avert negative
assessments of any downside error of the forecast. Forecast conservatism could be motivated by the
self-protection behavior of managers or by a desire to enhance management’s reputation by
providing a potential positive surprise effect in the market. If so, higher CSP may not contribute toencouraging management forecast decision-making.
To investigate the different forecast behaviors, we classify the sample firms into two groups;
upward bias (PNI>0; forecasted income > realized income) and downward bias (PNI<0; forecasted
income < realized income). We assume that upward biased firms have optimistic biases anddownward biased firms have conservative biases.
We conduct regression analyses for each group. The regression results are demonstrated in Table
8. Panel A presents PNI>0 (upward forecast bias group; optimistic) and Panel B presents PNI<0(downward forecast bias group: conservative). In the regressions, the explanatory variable is
ABSPNI to identify the effect of CSP on degree of optimism and conservatism.
Regarding the upward bias group (optimistic forecast), the coefficient of CSP on ICE is negative
but statistically insignificant. The coefficient of ABSPNI is also negative and statistical insignificant
at less than 10% significance. We see no significant effect of CSP on degree of upward bias.
Regarding the downward bias group, the coefficient of CSP on ICE is significantly positive and thecoefficient of CSP on ABSPNI is also positive but insignificant. For the downward bias group, high
CSP firms tend to have a high cost of equity, while CSP has no significant influence on forecast bias.
Thus, hypothesis 4 is rejected.
When we classify the sample into the upward forecast bias and downward forecast bias firms, therelation between CSP and forecast bias within each group becomes less obvious. According to theresults, the more conservative the forecast, the higher the cost of equity of the firm; this result
suggests that some of the Japanese firms may provide excessive conservative forecasts, as it is foundthat CSP could increase their cost of equity while their CSR practices could contribute to improving

14

their management forecast opportunism as a whole. In terms of the downward bias, the fact that CSP
has a significantly positive relation with ICE in the downward forecast bias group suggests that high
CSP firms may bear high risk premiums in the market. We cannot deny the possibility of excessconservatism among downward bias management forecasts, which then links to the uncertainty of
estimating the firm’s future cash flow.
5 Concluding remarks
This is the first study to examine the influence of CSP on management earnings forecast biases
in the context of Japanese firms for the period 2007-2015. The aim of this study is to investigate two
types of forecast biases: forecast accuracy and forecast optimism (or conservatism). Based on
empirical analyses, we find the following notable results. First, a high CSP links to lower managerial
forecast biases. Second, controlling the size effect on CSP by segmenting the sample into three sizegroups, we find that a high CSP not only mitigates these two forecast biases but it also links to alower implied cost of equity. These empirical results suggest that CSP as a proxy for firm CSR
awareness may enhance forecast accuracy and mitigate opportunistic managerial forecasts. Fromthese results, we comprehend that a CSR strategy for long-term success can contribute toencouraging managerial forecast behavior that increases transparency and business trust andultimately, reduces the implied cost of equity. Third, considering possible excess conservative
management forecast bias to avoid negative assessments of management, we investigate theinfluence of CSP on ICE among upward and downward bias groups. In the downward forecast bias
group, we find that a high CSP may bear a high-risk premium in the market. This finding suggeststhat some firms may pursue excess conservatism in earnings forecasts, while CSP has no significant
influence on mitigating conservatism.
In the management earnings forecast disclosure peculiar to Japan, listed firms are requested todisclose management forecasts for net income and sales, and, moreover, per Japan’s Corporate Law,a certain level of forecast errors must be revised in the firm’s next financial report. Therefore, themanagers at Japanese listed firms seem to be under strong pressure to avoid forecast errors and thus,
in general, choose conservative forecasting rather than optimistic forecasting as downward revisionsof earnings forecasts can be perceived as management failures. High CSP aware firms may be moreconcerned with their reputations as good companies and may thereby tend to be more conservative.
It is very interesting, in the context of the Japanese listed firms, that among firms with downward
bias forecasts, there can be excess conservatism in order to protect managers from the risk ofnegative assessments. We also found, however, that such managerial behavior may induce
information asymmetry between management and investors and thus link to a higher cost of equity
in the market.
Possibly, such excess conservatism may be only a remarkable forecast bias in Japan. According15

to the empirical results of this study, in terms of the excess conservatism in management earnings
forecasts, CSR practices may not contribute to improving the behavioral bias of managers,. Moreresearch is necessary to investigate excess conservatism in management earnings forecast bias
among listed Japanese firms and its determinants. This could be an interesting research topic for
future development.
References:
Van Beurden, P. and T. Gossling (2008), “The worth of values: A literature review on the relation
between social and financial performance,” Journal of Business Ethics, 82 (2), 407-424.
Boutin-Dufresne, F. and P. Savaria (2004), “Corporate social responsibility and financial risk,”
Journal of Investing, 13 (1), 57-66.
Chen, B., I. Ioannou, and G. Serafeim (2014), “Corporate social responsibility and access to finance”,
Strategic Management Journal, Vol. 35 No. 1, pp. 1-23.
Cochran, P.L. and Wood, R.A. (1984), “Corporate social responsibility and financial performance,”
Academy of Management Journal, 27 (1), 42–56.
Dhaliwal, D. S., O. Z. Li, A. Tsang, and G. Y. Yang (2011), “Voluntary nonfinancial disclosure andthe cost of equity capital: The initiation of corporate social responsibility reporting,” The
Accounting Review, 86 (1), 59-100.
Dhaliwal, D. S., S. Radhakrishnan, A. Tsang, and Y. Yong (2012), “Nonfinancial disclosure andanalyst forecast accuracy: International evidence on corporate social responsibility disclosure,”
The Accounting Review, 87 ( 3), 723-759.

Fama, E. F. and K. R. French (1997), “Industry cost of equity”, Journal of Financial Economics,
43, 153-193.
Frankel, R., M. McNichols, and G. C. Wilson (1995), “Discretionary disclosure and externalfinancing,” The Accounting Review, 70 (1), 135-150.
Frankel, R. and C. Lee (1998), “Accounting valuation, market expectation, and cross-section ofstock returns,” Journal of Accounting and Economics, 25, 283-319.
Gebhardt,W. R., C. M . C . Lee and B. Swaminathan (2001), “Toward an implied cost of capital,”
Journal of Accounting Research, 39 (1), 135-176.
El Ghoul, S. E., O. Guedhami, C. Y. Kwok, and D. R. Mishra (2011), “Does corporate social
responsibility affect the cost of capital?” Journal of Banking and Finance, Vol. 35 No. 9, pp.
2388-2406.
Girerd-Potin, I., S. Jimenez-Garces, and P. Louvet (2014), “Which dimensions of social
responsibility concern financial investors?” Journal of Business Ethics, 121 (4), 559-576.
Graves, S. B. and S. A. Waddock (1994), “Institutional ownership and corporate social performance,”
Academy of Management Journal, 37 (4), 1034-1046.
Gregory, A., R. Tharyan, and J. Whittaker (2014), “Corporate social responsibility and firm value:
disaggregating the effects on cash flow, risk and growth,” Journal of Business Ethics, 124 (4),633-657.
Guay, W., S. P. Kothari and S. Shu (2011), “Properties of implied cost of capital using analysts’
forecasts,” Australian Journal of Management, 36 (2) 125–149.
16

Gu, Z, K. Kubota and H. Takehara (2017), “How do normative and compliant CSR affect theearnings quality of Japanese firms?”, to appear in The Japanese Accounting Review, 7.
Ioannou, I. and G. Serafeim (2015), “The impact of corporate social responsibility on investment
recommendations: Analysts’ perceptions and shifting institutional logics,” Strategic
Management Journal, 36 (7), 1053-1081.
Kang, N. and R. Sadka (2015), “Implied cost of capital in the cross-section of stocks,” SSRN
Working Paper, URL: http://dx.doi.org/10.2139/ssrn.2600147.
Koch, A. S. (2002), “Financial distress and the credibility of management earnings forecasts,” GSIA
Working Paper #2000-10.
McWilliam, A., D. S. Siegel and P. M. Wright (2006) “Corporate Social Responsibility: Strategic
Implications”, Journal of Management Studies 43(1), 1-18.
Mishra, S. and S. B. Modi (2013), “Positive and negative corporate social responsibility, financial
leverage, and idiosyncratic risk,” Journal of Business Ethics, 117 (2), 431-448.
Muramiya, K. (2005), “Management forecast accuracy and cost of equity capital,” Securities
Analysts Journal, 43 (9), 83-97 (in Japanese).
Ota, K. (2006), “Analysts’ awareness of systematic bias in management earnings forecasts:
Empirical evidence from Japan”, in International Accounting: Standards, Regulations andFinancial Reporting, edited by Gregoriou G. N. and M. Gaber, 267-294, Elsevier Press, Birlington
MA.
Orlitzky, M., F. L. Schmidt and S. L. Rynes (2003), “Corporate social and financial performance: A
meta-analysis,” Organization Studies, 24 (3), 403-411.
Russo, M.V. and P. A. Fouts (1997), “A resource-based perspective on corporate environmental
performance and profitability,” Academy of Management Journal, 40 (3), 534–559.
Suda, K. and A. Shuto (2001), “Management earnings forecast and discretionary accounting
behavior (Keieisha no riekiyosoku to sairyoteki kaikeikoudou), Sangyou Keiri, 61 (2), 46-56 (inJapanese).
Suto, M. and H. Takehara (2016a), “The link between corporate social performance and financial
performance: empirical evidence from Japanese firms,” International Journal of Corporate
Strategy and Social Responsibility, 1 (1), 4-24.
Suto, M. and H. Takehara, (2016b), “Estimating the hidden corporate social performance ofJapanese firms,” Social Responsibility Journal, 12 (2), 348-362.
Suto, M. and H. Takehara (2017), “CSR and cost of capital: Evidence from Japan,” Social
Responsibility Journal, 13 (4), 798-816.
Wood, D. J. (1991), “Corporate social performance revisited,” Academy of Management Review, 16(4), 691-718.

17

MV j , May  BV j ,t 1 

FNI j ,t  k j , May BV j ,t 1
k j , May

 k j , May 

FNI j ,t
MV j , May

 EPR

ICE j ,t  k j , May

Fiscal Year t (April –March)
Fiscal Year End

DRt Management
Forecast(Middle of May)

TDt
TAt

ROAt 

FNI t  NI t
MV j , May

PNI t Fiscal Year End

NI t(TAt  TAt 1 ) / 2

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May

TAt 1
TDt 1TAt

FNI t(Prompt Report)

TDt

NI t 1

NI t(Financial Report)(Financial Report)

Figure 1. Definition of Variables

18

Table 1. Number of Observations

Year

March Fiscal Management Implied Cost isYear End
Forecast Exists
Estimated

NonRespondents Implied Cost &respondents
to CSR Survey CSR Survey
to CSR Survey

2007

2,138

1,750

1,712

1,513

625

518

2008

2,123

1,785

1,739

1,477

646

556

2009

2,071

1,801

1,543

1,437

634

482

2010

2,023

1,809

1,731

1,372

651

574

2011

1,986

1,820

1,603

1,335

651

535

2012

2,105

1,979

1,918

1,388

717

658

2013

1,976

1,880

1,819

1,267

709

657

2014

1,974

1,896

1,856

1,222

752

711

2015

1,955

1,912

1,870

1,184

771

744

All

18,351

16,632

15,791

12,195

6,156

5,435

19

Table 2. Descriptive Statistics
CSP: Composite measure of Corporate Social Performance, ICE: Implied Cost of Equity, PNI:
Measure of management forecast optimism. ROA: Return on Total Assets, DR: Debt ratio (=total
debt/total assets), lnMV: Natural logarithm of market value of equity8 (in million JPY), B/M:
Book-to-market (in %).
Mean, S.D., 1st Qu., Median and 3rd Qu. denote sample mean, standard deviation, 1st quintile,
median and 3rd quintile, respectively. Sample period is from fiscal year 2007 to 2015.
Mean
CSP
ICE
PNI
ABSPNI
ROA
DR
lnMV
B/M

Respondents
Non-reppondents

S.D.

0.256
-----

1.684
-----

1st Qu.
-1.069-----Median0.420-----

3rd. Qu.
1.682
-----

Respondents

7.004

3.879

4.348

6.362

8.913

Non-reppondents

7.602

4.514

4.471

6.877

9.970

Respondents

2.532

8.074

-1.233

0.126

3.006

Non-reppondents

3.140

9.191

-1.553

0.267

3.991

Respondents

1.496

1.864

0.313

0.808

1.922

Non-reppondents

1.744

2.092

0.374

0.946

2.296

Respondents

2.249

4.906

0.903

2.375

4.277

Non-reppondents

1.697

6.525

0.679

2.228

4.323

Respondents

52.08518.99437.94352.69766.714

Non-reppondents

50.23620.83334.03050.57166.071

Respondents

10.684

1.924

9.216

10.63412.121

Non-reppondents

9.401

1.474

8.359

9.271

10.314

Respondents

1.238

0.751

0.739

1.075

1.554

Non-reppondents

1.422

0.914

0.780

1.254

1.870

20

Table 3. Correlations among Variables
CSP: Composite measure of Corporate Social Performance, ICE: Implied Cost of Equity, PNI:
Measure of management forecast optimism. ROA: Return on Total Assets, DR: Debt ratio (=total
debt/total assets), lnMV: Natural logarithm of market value of equity8 (in million JPY), B/M:
Book-to-market (in %).

CSP
ICE
PNI
ABSPNI

CSP

ICE

PNI

ABSPNI

ROA

DR

lnMV

B/M

1.000

-0.144-0.025-0.068

0.048

0.033

0.634

-0.2491.000-0.208-0.008

0.311

0.132

-0.248

0.227

1.000

0.264

-0.5560.062-0.128

0.174

1.000

-0.154-0.188-0.028-0.1391.000-0.3770.227-0.3971.000-0.032-0.0801.000-0.531

ROA
DR
lnMV
B/M

1.000

21

7.602

0.777

1.744

1.697

2.765

50.236

9.401

1.422

PNI

ABSPNI

ROA

ROE

DR

lnMV

B/M

-----

ICE

CSP

1.238

10.68452.085

4.364

2.249

1.496

0.617

7.004

0.256

NonRespondent
respondents-0.184

1.283

1.850

1.600

0.552

-0.248-0.160-0.598-----(R-NR)

Diff.

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

-----

p -value

1.471

9.157

51.079

2.792

1.700

1.789

0.833

7.913

-2.252

Low CSP

P1

22

1.437

9.708

52.125

4.094

2.049

1.521

0.699

7.540

-0.761P2

1.287

10.53151.801

4.434

2.293

1.508

0.592

7.142

0.413

P3

1.045

11.64652.052

5.780

2.752

1.282

0.403

6.498

1.426

P4

0.951

12.37253.365

4.723

2.453

1.398

0.575

6.004

2.449

High CSP

P5-0.520

3.216

2.286

1.931

0.754

-0.391-0.258-1.909

4.701(P5-P1)

Diff.

0.000

0.000

0.003

0.006

0.001

0.000

0.013

0.000

0.000

p -value

CSP: Composite measure of Corporate Social Performance, ICE: Implied Cost of Equity, PNI: Measure of management forecast optimism. ROA: Returnon Total Assets, DR: Debt ratio (=total debt/total assets), lnMV: Natural logarithm of market value of equity (in million JPY), B/M: Book-to-market(in %).

Table 4. Characteristics of CSP ranked 6 Portfolios

Table 5. Implied Cost of Equity of 25 Portfolios
CSP: Composite measure of Corporate Social Performance, PNI: Measure of management forecast
optimism.
Panel A: CSP and Management Forecast Optimism (PNI) ranked 25 Portfolios
PNI1(high)

PNI2

PNI3

PNI4

PNI5(low)Diff.

p -value

CSP1 (low)

7.450

7.366

7.316

7.887

9.096

-1.646

0.001

CSP2

6.517

6.731

7.178

8.244

9.064

-2.547

0.000

CSP3

6.471

6.685

6.892

7.085

8.295

-1.824

0.000

CSP4

6.370

6.304

5.910

6.484

7.576

-1.206

0.000

CSP5(high)

5.469

5.872

5.898

5.953

6.724

-1.255

0.000

Diff.

1.981

1.494

1.418

1.933

2.372

p -value

0.000

0.000

0.000

0.000

0.000

Panel B: CSP and Management Forecast Accuracy (ABSPNI) ranked 25 Portfolios
ABSPNI1 ABSPNI2 ABSPNI3 ABSPNI4 ABSPNI5(high)

Diff.

p -value(low)

CSP1(low)

8.373

8.043

7.746

7.520

7.509

0.864

0.084

CSP2

7.123

7.575

7.927

7.553

7.626

-0.504

0.237

CSP3

7.466

7.325

7.332

6.700

6.691

0.776

0.052

CSP4

6.950

6.961

6.452

6.146

6.200

0.750

0.022

CSP5(high)

6.080

6.126

5.892

6.008

5.861

0.218

0.435

Diff.

2.293

1.917

1.854

1.512

1.647

p -value

0.000

0.000

0.000

0.000

0.000

23

Table 6. Results of Two-Stage Regression Analyses[Independent Variables] CSP: Composite measure of Corporate Social Performance, ROA: Return on Total
Assets, DR: Debt ratio (=total debt/total assets), lnMV: Natural logarithm of market value of equity8 (inmillion JPY), B/M: Book-to-market (in %).
[Dependent Variables] ICE: Implied Cost of Equity, PNI: Measure of management forecast optimism.

Dependent
Variable

ICE
Coef.

PNI

p -value

Coef.

ABSPNIp -valueCoef.

p -valueIntercept

7.868

0.000

8.651

0.001

3.959

0.000

CSP

-0.0040.939-0.2470.075-0.091

0.002

ROA

0.385

0.000

-1.1780.000-0.218

0.000

DR

0.061

0.000

0.000

0.989

-0.010

0.000

lnMV

-0.513

0.000

0.090

0.473

-0.063

0.011

B/M

0.929

0.000

0.124

0.849

-0.4550.000

Industry Dummy

Yes

Yes

Yes

Yes

Yes

Yes

Year Dummy

Yes

Yes

Yes

Yes

Yes

Yes

Adjusted R

2

0.319

0.501

24

0.338

Table 7. Size Category-wise Two-Stage Regression Results
Panel A. Large-Cap Samples
Dependent
Variable(n=1,583)
ICE

PNI

ABSPNI

Coef.

p -valueCoef.

p -valueCoef.

p -valueIntercept

2.876

0.026

4.521

0.000

3.881

0.000

CSP

-0.2020.011-0.0590.034-0.102

0.004

ROA

0.339

0.000

-0.4460.000-0.257

0.000

DR

0.055

0.000

-0.0270.000-0.020

0.000

B/M

1.167

0.000

-0.6020.004-0.6550.000

Industry Dummy

Yes

Yes

Yes

Yes

Yes

Yes

Year Dummy

Yes

Yes

Yes

Yes

Yes

Yes

Adjusted R

2

0.336

Panel B. Mid-Cap Samples
Dependent
Variable

0.566

0.364

(n=1,499)
ICE

PNI

ABSPNI

Coef.

p -valueCoef.

p -valueCoef.

p -valueIntercept-1.395

0.148

4.835

0.000

3.930

0.001

CSP

-0.2180.000-0.0830.062-0.019

0.108

ROA

0.483

0.000

-0.3970.000-0.200

0.000

DR

0.079

0.000

-0.0170.000-0.009

0.073

B/M

1.268

0.000

-0.3120.001-0.2690.001

Industry Dummy

Yes

Yes

Yes

Yes

Yes

Yes

Year Dummy

Yes

Yes

Yes

Yes

Yes

Yes

Adjusted R

2

0.373

Panel C. Small-Cap Samples
Dependent
Variable

0.513

0.297

(n=1,417)
ICE

PNI

ABSPNI

Coef.

p -valueCoef.

p -valueCoef.

p -valueIntercept

2.433

0.065

1.718

0.000

2.237

0.000

CSP

-0.1310.084-0.0840.279-0.200

0.001

ROA

0.317

0.000

-0.3580.000-0.214

0.000

DR

0.078

0.000

-0.0060.173-0.004

0.125

B/M

1.101

0.000

-0.3090.007-0.4400.000

Industry Dummy

Yes

Yes

Yes

Yes

Yes

Yes

Year Dummy

Yes

Yes

Yes

Yes

Yes

Yes

Adjusted R

2

0.272

0.541

25

0.367

Table 8. Regression Results of Upward Forecast Bias and Downward Forecast Bias
Panel A. PNI>0 Samples: Upward Forecast Bias Group (Optimistic)
Dependent Variable

ICE
Coef.

(n=2,350)

ABSPNIp -valueCoef.

p -valueIntercept

7.820

0.000

3.658

0.000

CSP

-0.0840.335-0.083

0.113

ROA

0.252

0.000

-0.336

0.000

DR

0.050

0.000

-0.012

0.000

lnMV

-0.3860.000-0.001

0.972

B/M

0.543

0.011

-0.3780.010

Industry Dummy

Yes

Yes

Yes

Yes

Year Dummy

Yes

Yes

Yes

Yes

Adjusted R

2

0.259

0.503

Panel B. PNI<=0 Samples: Downward Forecast Bias Group (Conservative)
Dependent Variable

ICE
Coef.

(n=2,149)

ABSPNIp -valueCoef.

p -valueIntercept

4.293

0.067

0.665

0.009

CSP

0.173

0.000

0.001

0.973

ROA

0.704

0.000

0.150

0.000

DR

0.087

0.000

0.003

0.128

lnMV

-0.5090.000-0.070

0.000

B/M

2.229

0.000

-0.0110.887

Industry Dummy

Yes

Yes

Yes

Yes

Year Dummy

Yes

Yes

Yes

Yes

Adjusted R

2

0.422

0.315

26