2011-7
The effect of non-financial risk information on
the evaluation of implied cost of capitals
Norio Kitagawa
Hyonok Kim
Masatoshi Goto

The effect of non-financial risk information on
the evaluation of implied cost of capitals1

Norio Kitagawa
Graduate School of Business Administration
Kobe University

Hyonok Kim
Faculty of Business Administration
Tokyo Keizai University

Masatoshi Goto
Graduate School of Business Administration
Kobe University

First version: January 2011

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This research was partially supported by the Ministry of Education, Culture, Sports, Science and Technology(MEXT), Grant-in-Aid for Scientific Research (Research B: 21330110) and Grant-in-Aid for Young Scientists(Research B: 21730370).

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The effect of non-financial risk information on
the evaluation of implied cost of capitals

Abstract

The purpose of this paper is to examine the effect of voluntary disclosure of ‘businessrisk’ information (hereafter referred to as ‘risk information’), which is a significant
determinant of the information environment, on estimating the cost of capital. Recently,
some studies indicate that the reliability of the cost of capital estimation differs
according to the accounting standards and the information environment of the firm (e.g.
Chen et al., 2004; Easton and Monahan, 2005). On the basis of their studies, we predictthat the cost of capital will be more precise in firms that proactively disclose risk
information voluntarily. Our results suggest that the implied cost of capital reflects risk
more appropriately in firms with a high ‘business risk’ disclosure level.

Keywords: implied cost of capital; risk factors; non-financial risk information

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The effect of non-financial risk information on
the evaluation of implied cost of capitals
1. Introduction

The purpose of this paper is to examine the effect of the ‘business risk’ information(hereafter referred to as ‘risk information’) disclosed in Japanese annual reports on theaccuracy of the implied cost of capital.
Cost of capital cannot be accurately calculated, because the risk premium cannot bedirectly observed. Hence, attempts have been made to estimate the cost of capital using
various methods, such as those using post-realized returns, and more specificallyleading calculations using the Capital Asset Pricing Model (CAPM), the Fama-French
Three Factor Model (1992, 1993) or other models. In recent years, a method attracting
special attention uses the ex ante earnings, book value of equity of equity and the
expected value of the dividend to back calculate the cost of capital from the residual
income model and the abnormal earnings growth valuation model. The cost of capital
calculated using the latter methods is collectively referred to as the implied cost ofcapital, because it represents the implicit cost of capital assumed by market participant.
Much concern has been placed in the past on the problem of estimating the mostreliable cost of capital using various estimation models, because the true value of thecost of capital is unobservable and the variety of methods used to calculate it. A seriesof studies triggered by Gode and Mohanram (2003) conducted a comparative analysis
on the accuracy of the cost of capital estimated using an alternative model by comparing
the correlation between the known risk factor and the realized return. However, theopinions of research to date are divided regarding the merits of the cost of capital. In
recent years, some research shows that the reliability of the cost of capital differs
depending on the information environment in which it is placed (e.g. Easton andMonahan, 2005). This paper will analyze the effect of voluntary disclosure of risk
information, which is a significant determinant of the information environment, on
estimating the cost of capital.
3

Risk information mentioned here is qualitative information that cannot be expressedin figures. It is, for example, the term used to refer to “the risk related to investing in
next-generation technology” or “the risk related to shifting to new products.” Thefinancial information used in prior studies is quantitative information, but risk
information is voluntarily disclosed non-financial information that is useful wheninterpreting quantitative information. In other words, the voluntary disclosure of risk
information allows more accurate corporate assessments, because private corporate
information becomes known to investors. Here, we predict that a more highly reliable
cost of capital can be estimated for firms that proactively release risk information.
We evaluate the cost of capital as highly reliable when the following conditions are
met. First, the known risk factors significantly correlate with expected sign. Second, theR-square is high in a multiple regression model in which the cost of capital is adependent variable and the risk factor is an independent variable. The cost of capitaluses 1) the model proposed by Gebhardt et al. (2001), 2) the model proposed by Ohlson
and Juettner-Nauroth (2005), 3) the EP ratio, 4) the PEG ratio proposed by Easton(2004), and 5) the adjusted PEG ratio proposed by Easton (2004). We selected 1)
market beta, 2) unsystematic risk, 3) earnings volatility, 4) leverage, 5) corporate size,6) long-term earnings growth, and 7) book-to-market ratio in the known risk factors.
The results of our analysis show a strong correlation expected sign in the known risk
factor and the cost of capital, which was estimated using models based on the abnormal
earnings growth valuation model, when comparing firms with a high business risk
disclosure level to those with a low disclosure level. Moreover, firms with a highbusiness risk disclosure level demonstrated the higher explanatory power of the multiple
regression model, which uses the cost of capital as a dependent variable and the risk
factor as an independent variable.
The content disclosed and format details are unspecified by the risk information
disclosure laws in Japan and are practically at the discretion of the firm. From theperspective of the capital market, knowing the influence of risk information on theaccuracy of the cost of capital estimation will substantiate the significance of risk

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information disclosure and is expected to contribute to discussions in the planning of asystem for more detailed disclosure laws.
The remainder of this paper is organized as follows. Section 2 reviews previous
research and develops the hypothesis. Section 3 presents the research design, confirms
the five cost of capitals and seven risk factors used in this paper, and explains themethod of identifying proactive firms that voluntarily disclose risk information. Section
4 presents the sample and basic statistics. Section 5 reports the main results. Section 6
conducts additional analysis taking into consideration the corporate stance towards risk
management. Section 7 summarizes the conclusions and explains the limitations of thispaper.

2. Prior studies and hypothesis development

As mentioned above, since the true value of the cost of capital is not observable, it is
not possible to compare estimation models for the cost of capital easily. Therefore, thepreceding studies compare them in establishing many valuation criteria. To be more
specific, the cost of capital are evaluated by measuring the correlation with observablerisk factors and realized stock returns.

In other words, the cost of capital which shows

expected signs and a higher correlation with risk factors and realized stock returns isregarded as desirable. However, their evidence is not consistent.
For example, Gode and Mohanram (2003) compared costs of capital derived from themodel of Ohlson and Juettner-Nauroth (2005), the model of Gebhardt et al. (2001), andthe model of Liu et al. (2002). They compared costs of capital using the following threemethods. First, they examined the correlation with risk factors. Secondly, theyexamined the correlation with a risk premium computed by multiplying a realized valueof risk factors and a coefficient obtained with a regression of the risk premium in theprevious year on risk factors in the previous year. Thirdly, they examined thecorrelation with a realized stock return. And they show that all costs of capital have apositive correlation with conventional risk factors (such as earnings volatility,5

variability in stock returns, and leverage), and have a negative correlation with analysts
coverage. In particular, the correlation of cost of capital derived from the model of GLS
model was found to be higher than the model of OJ model, thus they conclude that GLS
model is superior to OJ model. 2
In contrast, Botosan and Plumlee (2005) compared the costs of capital by focusing on
differences in assumptions of terminal value. They inferred a cost of capital from fivemodels proposed by Botosan and Plumlee (2002)，Gebhardt et al. (2001)，Gordon and
Gordon (1997)，Gode and Mohanram (2003)，and Easton(2004).3 They alsomeasured the correlation with risk factors such as a market beta, leverage, information
risks, market value, book-to-market ratio, and growth in expected earnings. As a result,it was suggested that the models presented in Botosan and Plumlee (2002) and Easton(2004) showed the most consistent correlation with risk factors. Meanwhile, it waspointed out that the cost of capital inferred from the model of Gebhardt et al. (2001) did
not show a consistent correlation with risk factors.
Recently, some studies indicate that the cost of capital politic differs according to theaccounting standards and the information environment of the firm. For example, Chenet al. (2004) compared the cost of capital in the Gebhardt et al. (2001) and the Ohlson
and Juettner-Nauroth (2005) models for seven countries (USA, UK, Australia, Canada,
Japan, Germany and France). The results showed that the cost of capital in the Gebhardt
et al. (2001) method was highly reliable in environments that sustained clean surplus
relations in the financial reports. Adversely, the Ohlson and Juettner-Nauroth (2005)
model could estimate a valid cost of capital in environments that did not necessarily
sustain clean surplus relations. Moreover, Easton and Monahan (2005) compared thecost of capital based on its correlation with the realized return. The subject of the2

However, as the result varies depending on whether loss firms are included in the calculation of industry median(Gebhardt et al. 2001) or not (Liu et al. 2002), it is necessary to interpret the superiority of the residual income model
to a limited extent. On the contrary, Gode and Mohanram (2008) concluded that the cost of capital inferred from
Ohlson and Juettner-Nauroth (2005) shows a more robust result.
3
Assumptions regarding terminal value in each study are as follows: 1) Botosan and Plumlee (2002): analysts’
forecast on terminal value is equal to a market forecast; 2) Gebhardt et al.(2001): ROE exceeding the prediction
horizon converges on industry median. 3) Gordon and Gordon (1997): ROE exceeding the prediction horizon
approximates a cost of capital. 4) Gode and Mohanram (2003): Corporate abnormal earnings growth converges on theeconomic level if exceeding the prediction horizon; 5) Easton (2004): Corporate abnormal earnings growth is zero if
exceeding the prediction horizon.

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analyses is the cost of capital derived from seven models, and all confirm that thecorrelation with the realized return, contrary to expectations, was negative. From this, it
can be concluded that the implied cost of capital has a low reliability as the measured
value for expected return. However, they also note the following two points. First, when
the analyst consensus regarding the long-term growth forecast is low, the reliability of
the cost of capital estimated using the Claus and Thomas (2001) model is higher.
Second, when the ex-post calculated analyst forecast error is small, all costs of capitaland the realized returns show a positive correlation, as expected. Hence, they concluded
that the merit of the cost of capital is influenced by the analyst forecast error.
This paper elaborates on these types of research and verifies that the voluntary
disclosure of risk information by firms influences the cost of capital politic. If firms
voluntarily disclose information, the degree that this information is effectively reflected
in securities prices is higher, because private corporate information becomes known toinvestors. In other words, the firm’s information is rapidly and suitably reflected instock prices. Hence, we predict that the cost of capital would be more precise in firmsthat proactively disclose risk information voluntarily.

3. Research design

The research design of our study relies on that of Gode and Mohanram (2003), andBotosan and Plumlee (2005) to compare the cost of capital using the following two
methods. In the first method, we calculated the exemplary risk factor that is thought togain general support and compared its correlative strength with the cost of capital. In thesecond method, we estimated the multiple regression model in which the cost of capitalis a dependent variable and the risk factor is an independent variable, and compared themodel’s explanatory ability. In this paper, we assessed the ideal cost of capital, whichreflects the risk, as one the higher the correlation with the risk factor and the higher theR-square of the multiple regression model.

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We also distinguish, in this paper, between firms that disclose many items of businessrisk-related information from those that disclose few items and verify whether adifference in the aforementioned comparative merits arises between the two. If thedisclosure of risk information is useful as supplementary information for estimating risk,
the correlation between the cost of capital and the risk factor, and the R-square in themultiple regression model must be high in firms that disclose information.
The following explains the calculation method of the variables used in this paper
related to 3.1) the cost of capital, 3.2) the risk factor, and 3.3) the disclosure of risk
information.

3.1 Implied cost of capital
1) Gebhardt et al. (2001) Model
As mentioned above, the Gebhardt et al. (2001) model for inferring the cost of capitalis based on the residual income model, and can be represented by equation (1) below.
Here, P0 is a stock price at 0, BV t is forecasted book value per share at period t ,FROE t is forecasted ROE at period t , and x denotes the cost of capital. As

forecasted future earnings are available to a limited extent, the forecasted earnings andthe earnings growth ratio are used explicitly for three years. The values after this period
are assumed to converge on a industry median. The forecast period of 12 years is
adopted in this case.

P0  BV0 

11
FROEi  x
FROE1  x
FROE2  x
BV0 
BV1  BVi 12
1  x 
1  x 
1  x ii 3

FROE12  x
x1  x 

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(1)

BV11

GLS model requires forecasted values of ROE, book value per share, and dividend pershare. The forecasted ROEs for one year and two years ahead are computed by using theforecasted EPS (reported by IBES). Therefore, when the forecasted EPS at t is definedas FEPS t , the forecasted ROE for i years ahead is expressed as FEPS t i BVt i 1 . The
8forecasted earnings for three years ahead, FEPS3, is computed by multiplying theforecasted earnings for two year ahead, FEPS2, and the long-term growth reported by
IBES around the same time of this forecasted value. From four terms to 12 years ahead,a certain amount is deducted every year from the forecasted value for three terms aheadto the median of the industry to which the company belongs. Here, the industry medianis the median value of values of the industry to which the company belongs (Nikkei
industry code is used here) experienced over the past 10 years up to the present date. To
be more specific, it is obtained by computing a median for each year over the past 10
years, then calculating the median of that 10-year period. It is incorporated in the modelfrom the viewpoint of measuring how much profitability the company may have in the
future by using the industry median.
The forecasted book value per share is obtained by using the clean surplus
relationship, adding the forecasted EPS in the next year to the current book value pershare, BV0, and subtracting the forecasted dividend per share. In other words, theforecasted book value for term t is expressed by the following equation: BVt = BVt-1 +FEPSt − DPSt. At this time, the necessary forecasted dividend per share is computedby using a dividend payout ratio.
Specifically, on the assumption that the dividend payout ratio is invariable, theforecasted dividend is computed by multiplying the dividend payout ratio calculated in
the current term and the forecasted EPS. Consequently, the expression to calculate theforecasted dividend per share for term t is FEPS t  DPS 0 EPS 0 .
The cost of capital is computed by solving the above polynomial equation for x .
This polynomial equation may have multiple solutions, and in this case is solved using
the Muller method.4

2) Ohlson and Juettner-Nauroth (2005) Model

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The Muller method is described in general textbooks to explain the algorithm. Here, “Scientific and Engineering
Computation” by Hayato Togawa, SAIENSU-SHA Co., Ltd. 1992, is referred to. In addition to the Muller method,
it can also be solved with the Newton method and the Traub method. The solution of a nonlinear equation can becomputed, but it is not possible to limit to only one solution; it is just one of many solutions. Under the Mullermethod, in cases that multiple solutions are obtained, the smallest one is adopted.

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The Ohlson and Juettner-Nauroth (2005) model is expressed as equation (2). The
definition of variables are the same as in Gebhardt et al. (2001). Although γ plays animportant role in this expression, how to decide its value is not definitively shown, evenin Ohlson and Juettner-Nauroth (2005). They refer that, taking the value of EPS for
example, the value is EPSt+1 = γEPSt, where γ>1.5 In this study, we assume that γ is
1.03.6x  A  A2 
FEPS1  FEPS 2 －  1P0
 FEPS1(2)where,ADPS1 
1   1 
2
P0 

3) Modified PEG ratio, PEG ratio, and EP ratio in Easton (2004)
In this study, we decided to also examine the modified PEG ratio and PEG ratio
proposed by Easton (2004) in order to weigh the impacts of γ and dividends involved in
Ohlson and Juettner-Nauroth (2005). The modified PEG ratio can be expressed byequation (3).7 As seen in this expression, a modified PEG ratio is obtained byhypothesizing γ＝１ based on the condition that the abnormal earnings growth in the
Ohlson and Juettner-Nauroth (2005) model is constant. Furthermore, the PEG ratio isbased on the assumption of DPS＝０, in addition to the assumption in the modified
PEG ratio. The PEG ratio can be expressed by equation (4).
xFEPS 2  xDPS1  FEPS1
P0

(3)

5

However, if γ＞１, the future value of EPS calculated by EPSt+1=γEPSt diverges.
Ohlson and Juettner-Nauroth (2005) stated that it is possible to unify γ of all firms within the range of 1.03~1.05.
They explain as follows (p. 359): “Perhaps the most logical interpretation is that the limit growth should correspondto the very long run steady state in which a firm’s growth in expected earnings equals the growth in expected GNP. It
follows that one can argue that γ should be the same for all firms in the range of 1.03 to 1.05.”
7
Some preceding studies modify the model by using the forecasted earnings for four and five terms ahead (e.g.,
Botosan and Plumlee 2005). However, as there are few companies which announce the forecasted earnings for fourand five terms ahead, this estimation model is used here.
6

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x

FEPS 2  FEPS1
P0(4)

We also examine the EP ratio. The EP ratio is also regarded as a form of theabnormal earnings growth valuation model, and is computed by equation (5). The EP
ratio assumes the abnormal earnings growth = 0. In other words, the EP ratio is a model
implying that, if forecasted earnings for the next year are available, a satisfactory
corporate evaluation is possible.
x

FEPS1
P0

（5）

3.2 Risk factors
1) Beta
Prior studies use market beta as a risk factor. Their argument is based on CAPM
which predicts a positive association between a firm’s market beta and the risk premium.
In addition, Several studies show an association between market beta and the risk
premium (e.g., Gordon and Gordon 1997; Harris and Marston 1992; Marston and Harris
1993; Harris et al. 2002). According to preceding studies, we estimate market beta
(hereinafter referred to as beta), using data on daily stock returns over the past year fromthe announcement date of analysts’ forecasts by IBES.

2) Unsystematic risk
Although some studies find no statistical relationship between unsystematic risk andexpected return (see, Pratt and Grabowski 2008, p.169), many prior studies show apositive association between unsystematic risk and future stock returns (e.g., Malkiel
and Xu 1997). We extract unsystematic risk with the following procedures. That is, weestimate the regression model in which daily stock returns in the previous year are adependent variable and stock returns of market portfolios are an independent variable,
and used the variance of the residuals obtained from the regression as a proxy forunsystematic risk (hereinafter referred to as Unsyst).

113) Earnings variability
Graham et al. (2007) provide survey evidence that corporate executives prefer smoothearnings, in part because they believe that higher earnings volatility increases the cost ofcapital. Francis et al. (2004) shows a positive relation between earnings volatility andexpected returns. We also predict a positive association between earnings variability andrisk premiums. In prior studies, Gebhardt et al. (2001) and Gode and Mohanram (2003)
measured earnings variability using the following procedures. First, they computed thefollowing variables expressing earnings variability: 1) the mean absolute error ofanalyst forecasts over the past five years; 2) the coefficient of variation in EPS; and 3)
the dispersion of analysts’ forecasts. Then, using factor analysis, they identified a single
variable from these three variables, and used it as a criterion in expressing earnings
variability. In this study, we measured earnings variability using the standard deviationof earnings over the past five years (hereinafter referred to as Earnvar).

4) Leverage
Modigliani and Miller (1958) demonstrate that the risk premium can be expressed anincreasing function of leverage. Fama and French (1992) show a positive association
between leverage and realized stock returns. Dhaliwal et al. (2006) also find evidence
that the equity risk premium is positively related with leverage. According to precedingstudies, we use leverage measured as the ratio of the book value long-term debt to themarket value of equity (herein after referred to as Leverage) as a risk factor. We predicta positive association between the risk premium and leverage.

5) Size
Numerous Studies have shown the negative association between market capitalization
and realized returns (e.g., Fama and French 1992; Berk 1995). In addition, market
capitalization can be a risk factor as a proxy for the information environment, since theinformation environment is affected by many factors, including trading volume, firmsize, bid-ask spreads, and institutional factor, and these factors are highly correlatedwith each other. Prior studies show that firms that are better connected with information12

intermediaries, such as analysts and institutional investors, have lower risk premiums
because easy availability of information lowers the information asymmetry between afirm and its investors, and lowers the informational risk for investors (e.g., Demsetz
1968; Copeland and Galai 1983; Glosten and Milgrom 1985; Amihud and Mendelson
1986; Diamond and Verrecchia 1991; Brennan and Swaminathan 1993; Handa and Linn
1993; Coles et al. 1995; Clarkson et al. 1996; Botosan 1997; Healy and Palepu 1999).8
Therefore, we use the firm size measured by the log of the market value of equity(hereinafter referred to as Size) as a risk factor. According to preceding studies, we
expect a negative association between the size and the risk premium.

6) Long-term growth in expected earnings
Gebhardt et al. (2001) and Gode and Mohanram (2003) use the long-term growth in
expected earnings from IBES as a proxy for market mispricing, and predict a negative
correlation between the risk premium and long-term growth. Their argument is based on
two phenomena. First, based on La Porta (1996), they argue that analysts are
overoptimistic for high-growth firms and their stock prices are too high, which results ina low risk premium. Gebhardt et al. (2001) explain the second reason for a negative
association between long-term growth and the risk premium as follows. Residual
income models assume that ROE reverts to the industry median ROE. If the industry
median ROE is lower than the analysts’ estimate of a firm’s long-run ROE, then thesefirms will appear to have a higher price and a lower risk premium. Therefore, a negative
association between the risk premium and long-term growth is expected. In this study,
we use the forecasted long-term growth reported by IBES to define variables regarding
the long-term growth in expected earnings (hereinafter referred to as LTG).

7) Book-to-market (BM) ratio
Gebhardt et al. (2001) and Gode and Mohanram (2003) control for the book-tomarket ratio as measured by the log of the ratio of shareholders’ equity to the market
8

Based on these arguments, Botosan and Plumlee (2005) use both information risk and firm size as a risk factor.
Information risk is measured as the width of the range between Value Line’s minimum and maximum price forecasts
scaled by the midpoint of range, and firm size is measured as the market value of equity..

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value of equity. This is consistent with Fama and French (1992) and Berk et al. (1999).
A high book-to-market (BM) ratio could reflect lower growth opportunities and lower
accounting conservatism. As argued by Gode and Mohanram (2003), it is difficult toconclude how the combination of these factors will influence the risk premium.
According to preceding studies, however, we expect a positive correlation between the
BM ratio and the risk premium. In this study, we estimate a variable of book-to-market
ratio (hereinafter referred to as BM) as the log of the ratio of shareholders’ equity to themarket value of equity.

3.3 Risk disclosure level
The business risk disclosure-related variable varies as shown in equation (6) below
according to Kim(2008). Here, Risks is the number of risk information items disclosed
in the annual security report. Median is the median number of the risk items in the same
industry. In other words, the value calculated by deducting the industry average numberof disclosed items from the number of disclosed items by the sample firms is thebusiness risk variable. Subtracting the industry average varies the number of disclosure
items by industry and avoids the industry influencing the relevant variable.
The business risk disclosure-related variable used in this paper is slightly different to
the one used by Kim(2008) with Risk being divided by total assets. The reason beingthat the number of disclosed items could represent the size of the firm, assuming thatthe larger the firm the more thorough their disclosure of non-financial information.

 Risks  Risks   median ln Assets  Assets 

RiskDisclevel  ln

Firms judged to proactively disclose risk have a RiskDisclevel value larger than the
median, and those that do not have a value lower than the median.

4. Sample and descriptive statistics
14(6)

4.1 Sample
The sample selection criteria for this paper are as follows.
1) The firm is a listed company between 2004 and 2007 (excluding
financial institutions).
2) The IBES simultaneously releases the firm’s EPS estimate for the next financial
year, and the EPS estimate for the financial year after next, and Long-term Growthestimate.
3) Risk-related information is released in the annual security report.
4) The consolidated financial statements necessary to calculate the cost of capital andthe known risk factor, and the stock price data are available.
The industry of the sample firms was identified using the Nikkei industry
classification. The IBES EPS estimates are released monthly, but the firms publishedvary every month. The estimates are published irregularly and it seems they are
published when some information became available. Hence, in principal, we used
estimate data published in August of each year in our analysis. If the earnings forecast
was not released in August, we backtracked from August until the most recent
settlement month and substituted the estimate in cases where the relevant information
was released. Moreover, we obtained the business risk-related information from theannual security reports. We obtained the data relating to the consolidated financial
statements from Nikkei Media Marketing’s Nikkei Financial Data CD-ROM and DVD
editions. We obtained the stock price data from Nikkei Media Marketing’s Nikkei

Portfolio Master.
Using these, we removed from the sample firms for which we could not estimate thecost of capital. In the GEB model, we removed from the sample firms for which we
obtained a negative result or multiple results. We also removed from the sample firms
for which the polynomial calculation did not converge in the result. On the other hand,firms were excluded if they did not meet the condition stated in the OJ model, PEG ratio
and revised PEG ratio that the EPS estimate for the financial year after next be higher
than the EPS estimate for the next financial year. Moreover, γ= 1.03 is set in the OJ
15

model, so some samples did not produce a result. As a result, the final sample was 1,019
firms.

4.2 Descriptive statistics
Table 1 is the basic sample statistics after being processed for outliers.9 In Panel A,
the GEB model cost of capital (hereafter referred to as GLS) is low compared to othercosts of capital. Conversely, the OJ model cost of capital (hereafter referred to as OJ) isrelatively high. The Easton (2004) PEG ratio (hereafter referred to as PEG) and revised
PEG ratio (hereafter referred to as MPEG) levels are lower than OJ by the shareignoring the abnormal earnings growth valuation and allocation, but are higher than
GLS.10 Moreover, when comparing Panel B and Panel C, the cost of capital level inPanel C is larger. This suggests a higher trend in firms with a high business risk
disclosure level than ones with a low level.
Table 2 Panel A shows the correlation between the costs of capital. Overall, the costsof capital show a positive correlation, but the correlation with EP shows a negative
correlation. Moreover, the correlation between PEG and MPEG is high, but theircorrelation with OJ is relatively low. The correlation between OJ and GLS remains
3.2%.11 Conversely, Panel B shows the correlation between the risk factors. Basically,
it indicates the expected signexpected sign and the adjusted result, but the correlation
with LTG differed from the estimate. Moreover, the size of the coefficient is at the mostBeta’s and Earnvar ‘s -0.403.12

9

In this study, a value which is lower than 1 percentile (higher than 99 percentile) of each variable
is regarded as outlier, and it is replaced by 1 percentile (99 percentile).
10
Other preceding studies also observe such a tendency. In Gode and Mohanram (2003), the meanvalue of GLS is 3.2% , in comparison with 5.1% of OJ. In Botosan and Plumlee(2005), while GLS
is 1.0% and PEG is 5.0%, OJ is 6.6%. In Easton and Monahan (2005), GLS is 10.9%, PEG is
11.0%, and MPEG is 12.2%. In Guay et al. (2006), GLS is 9.9%, PEG is 13.2%, and OJ is 13.4%.
11
In preceding studies, the correlation between GLS and OJ is quite high. For example, Gode andMohanram(2003) and Botosan and Plumlee (2005) observed the correlation as high as 36%. In theanalysis of this study, the correlation between GLS and OJ is almost 40% in the first half of thesampling period. For this reason, it has been found that the correlation is significantly dependent on
the adopted sampling period.
12
We calculated the VIF (variance Inflation factor) statistics and conclude that there is no problem
of multicorrelation in our models.

16

5. Results

5.1 Univariate analyses
Table 3 Panel A shows the correlation matrix of the risk factor with each cost ofcapital. There are two variables that are statistically significant and correspond to the
expected sign in GLS and OJ. In GLS, Unsyst and Size indicate the expected correlation
for the expected sign in Leverage, BM and OJ.13 Only LTG corresponds to the
expected sign in EP.14 Conversely, when focusing on PEG and MPEG, the six risk
factors excluding LTG are statistically significant and correspond to the expected sign,and the correlation is the most coherent. Overall, it is likely that PEG and MPEG
indicate the best correlation with the risk factor.
Table 3 Panel B shows the comparative analysis results, splitting the sample into the
low risk disclosure level group (LG) and the high risk disclosure level group (HG).
Looking at PEG and MPEG, HG has more significantly correlated risk factors than LG.
The number does not differ in OJ, but the correlation coefficient is large in HG. Thismeans that the cost of capital of firms with a high business risk disclosure level reflect
the appropriate risk. However, the number significant risk factors in GLS are identical,
and the correlation coefficient is smaller in HG. Hence, opposite results will be obtained
to costs of capital based on the abnormal earnings growth valuation model.

5.2 Multivariate analyses
Table 4 Panel A shows the cost of capital of each model as dependent variables andthe estimated results of the multiple regression model in which the seven risk factors are
13

Beta and Unsyst in GLS, as well as BM in OJ, show a significant correlation with unexpected
sign. In EP, Beta, Unsyst, Earnvars, and Leverage have unexpected signs. We conduct additional
examination of Beta and Unsyste, changing the estimation period. We adopted as estimation period
180days prior to forecast data announcement by IBES, and three months prior to its announcement.
However, the remarkable differences from main results are not found.
14
LTG in EP is contrary to the expectation that LTG and the cost of capital mostly have a significant
and positive correlation. Although the LTG sign forecast is based on Gode and Mohanram(2003),
even in their analysis, the same tendency is observed. Therefore, our result is consistent withpreceding studies.

17

independent variables. GLS has the highest R-square of the five costs of capital.
However, these results must be carefully interpreted, because the relatively small
number of three risk factors is significantly relevant and matches the expected sign. If
we emphasize the integrity of the agreement with the risk factors, we see that MPEG is
the best. MPEG has the expected signs for all risk factors, excluding LTG, and issignificantly relevant.
Conversely, Table 4 Panel B shows the results, splitting the sample into LG and HG.
Excluding GLS, the R-square for HG is higher than LG, and HG is significantly
relevant and has more expected signs matching the risk factors. These results suggestthat proactive disclosure of risk information increases the reliability of the cost ofcapital as risk yardstick. The largest difference between LG and HG is MPEG. HG issignificant for the three factors—Unsyst, Size, and BM—agreeing as expected, and theR-square is 6.69% higher. This compares favorably with GLS, which has the highest Rsquare.

6. Additional analyses
Previous research notes that the influence of risk disclosure differs depending on thefirm’s level of risk management. Kim(2008) shows that investors see through the firm’s
stance on risk management and assess risk information. In other words, he showed that
disclosing risk information eases the asymmetric diversity of information and isconnected to a reduction in the cost of capital for firms which are positive towards risk
management only.
As additional verification, we split the sample based on the firm’s level of risk
management, and conducted a comparative analysis as in the previous section. In
compliance with Kim(2008), we used the management earnings forecast error with theproxy variable that shows the firm’s level of risk management. The specific calculation
method is shown in equation (7) below.

18ForecastEr ror  FEarnings tt 1  Earnings t 
FEarnings tt 1  Earnings t median Assets t 1
Assets t 1



(7)

FEarnings indicates the earnings forecast for the next financial year (i.e., this financialyear) released in the financial results by the firm at the beginning of the year. Earnings
is the this year’s actual earnings. Assets is the total assets at the beginning of the year.
Median is the industry median. The small forecast error group is defined as firms with
the Forecast Error in equation (7) falling below the median, and the large forecast errorgroup as firms above the median. If we follow Kim’s hypothesis (2009), the former canbe regarded as firms with good risk management, and the latter regarded as ones withinsufficient risk management.
Table 5 Panel A shows the analysis results using the small forecast error group. The
R-square is higher for HG than LG in all models, excluding GLS. However, there is nota large difference in the risk factors significant for agreeing as expected. Conversely,
Table 5 Panel B shows the analysis results using the large forecast error group. The Rsquares, excluding GLS, are higher for HG. Furthermore, HG has more risk factors
significant for agreeing as expected, excluding GLS.
Comparing Panel A with Panel B, the difference in the R-square between LG and HG
is larger in Panel B. Moreover, the difference in the number of significant risk factors
when comparing LG and HG is larger in Panel B. This suggests that the increase in theaccuracy of costs of capital calculated on risk information disclosure is large for firmsthat are not proactive regarding risk management.

7. Conclusions
This paper examine the influence of risk information disclosure in annual reports on
the reliability of estimated implied costs of capital.
Previous research specified that if firms voluntarily disclose information, the level ofinfluence the information has on securities prices is effectively higher, because private
corporate information becomes known to investors. Hence, it is assumed that the cost of19

capital more appropriately reflects the firm’s risk through voluntary risk information
disclosure. In this paper, we assumed and tested the hypothesis that the costs of capitalof firms with high risk disclosure levels was highly reliable in appropriately reflecting
risk compared to those with low disclosure levels. We evaluated the reliability of thecost of capital from two perspectives: 1) correlation of known risk factors, and 2) the Rsquare in the multiple regression model in which the cost of capital is a dependent
variable and the risk factor is an independent variable.
Our results show the strong correlations with expected sign between the costs ofcapital and known risk factors when comparing firms that proactively disclose risk
information with those that do not. Moreover, the R-square in the multiple regression
model is higher for firms that proactively disclosed risk information. This evidence
demonstrates that voluntary risk information disclosure transmits private corporate
information to investors and makes it possible to estimate more accurate costs of capital.
Furthermore, additional analysis shows that these analysis results are more
predominantly seen in firms with large management earnings forecast errors. In otherwords, our result suggest that the influence of the voluntary risk information disclosureon estimating the cost of capital is larger for firms that do not take a proactive stancetowards risk management.
However, some issues in this paper remain unanalyzed. The first issue is theinterpretation of the GLS analysis results. We obtained opposite results for costs ofcapital in GLS. A possible reason is that GLS includes long-term earnings forecasts in
the input variables, but we did not obtain evidence that directly suggests this. The
second issue is to clarify the interpretation regarding the influence of a firm’s stancetowards risk management its cost of capital. The analysis in this paper found that thelevel of improvement risk information disclosure has on the accuracy of the cost ofcapital is higher in cases focusing on firms that do not take a proactive stance towards
risk management. This analysis cannot offer a clear interpretation of these results, so
further testing is necessary in the future. Lastly, we present another issue of analysis
focusing on an alternative cost of capital. Research in recent years specifies that the
problem with the assumptions surrounding the cost of capital to date can be resolved by20

simultaneously estimating the earnings growth and the discount rate. Future research
may also include analyzing how analysis results using this new cost of capital differ
from the five types of cost of capital used in this paper.
[2011.2.16 1023]

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26

Table 1

Descriptive statistics

Panel A: Pooled Sample
GLS

OJ

EP

PEG

MPEG

Beta

UnsystEarnvarLeverage

Size

LTG

BM

Score

mean

0.041

0.155

0.045

0.086

0.078

0.964

3.008

2.026

0.403

26.41215.561

0.642

0.028

sd

0.026

0.285

0.042

0.040

0.048

0.319

1.996

5.345

0.509

1.298

19.339

0.282

0.184

p25

0.028

0.079

0.035

0.063

0.052

0.742

1.734

0.332

0.074

25.457

5.550

0.442

-0.090

p50

0.037

0.100

0.048

0.081

0.073

0.961

2.606

0.631

0.205

26.44511.300

0.592

0.000

p75

0.047

0.131

0.062

0.101

0.093

1.179

3.709

1.519

0.511

27.30118.800

0.805

0.099

Panel B: Low Score Sample
GLS

OJ

EP

PEG

MPEG

Beta

UnsystEarnvarLeverage

Size

LTG

BM

Score

mean

0.037

0.136

0.045

0.083

0.074

0.951

3.012

2.009

0.365

26.15115.4500.639-0.087

sd

0.017

0.257

0.038

0.034

0.040

0.325

1.898

5.401

0.495

1.197

17.800

0.275

0.085

p25

0.026

0.080

0.036

0.063

0.051

0.723

1.736

0.314

0.064

25.210

5.900

0.438

-0.141

p50

0.035

0.099

0.048

0.080

0.071

0.957

2.632

0.557

0.170

26.083

11.4200.594-0.084

p75

0.045

0.124

0.061

0.098

0.089

1.173

3.669

1.341

0.411

27.00719.0000.799-0.025

Panel C: High Score Sample
GLS

OJ

EP

PEG

MPEG

Beta

UnsystEarnvarLeverage

Size

LTG

BM

Score

mean

0.045

0.168

0.045

0.088

0.080

0.979

2.912

2.036

0.441

26.69915.587

0.639

0.142

sd

0.031

0.285

0.044

0.044

0.053

0.312

1.764

5.293

0.521

1.312

20.448

0.283

0.185

p25

0.029

0.078

0.034

0.063

0.053

0.765

1.714

0.359

0.100

25.754

5.300

0.444

0.020

p50

0.038

0.100

0.048

0.082

0.075

0.977

2.546

0.707

0.242

26.711

11.000

0.590

0.098

p75

0.048

0.136

0.063

0.104

0.097

1.188

3.715

1.643

0.584

27.59318.000

0.800

0.224

Note:
Low Score Sample: sample with a low business risk disclosure level. High Score Sample: sample with a high business risk disclosure level. GLS: the cost of capital inferred using Gebhardt et al.(2001)
model. OJ: the cost of capital inferred using Ohlson and Juettner-Nauroth(2005) model. EP: expected earnings to price ratio. PEG: PEG ratio proposed by Easton (2004). MPEG: modified PEG ratio
proposed by Easton (2004). Beta: market beta using daily stock returns over the past 1 year from the announcement date of analysts’ forecasts by IBES. Unsyst: unsystematic risk as measured by theresidual from the regression over the previous year of a firm’s daily return on the daily market return. Earnvar: earning variability using the standard deviation of earnings over the past five years.
Leverage: leverage as the ratio of the book value long-term debt to the market value of equity. Size: the natural log of the market value of equity. LTG: forecasted long-term growth reported by IBES todefine variables regarding the long-term growth in expected earnings. BM: book-to-market ratio as the ratio of shareholders’ equity to the market value of equity.

27

Table 2

Correlation among the cost of capitals or risk factors

Panel A: Correlations among the cost of capitals
GLS
OJ
EP
PEG
GLS
1.000
0.060
0.085
0.145
OJ
0.032
1.000
-0.1420.800
EP-0.002 -0.078
1.000-0.116
PEG
0.118
0.143
-0.283
1.000
MPEG
0.125
0.030
-0.322
0.901

MPEG
0.178
0.520
-0.1170.876
1.000

Panel B: Correlations among the risk factors
Beta Unsyst Earnvar Leverage
Beta
1.000
0.257
0.403
0.095
Unsyst
0.265
1.000
0.198
-0.188
Earnvar
0.126
0.043
1.000
0.265
Leverage-0.048 -0.160
0.135
1.000
Size
0.143 -0.376-0.019
0.232
LTG
0.212
0.130
0.067
-0.023BM-0.277 -0.158
0.031
0.165

Size
0.159
-0.363-0.0460.210
1.000-0.060-0.303

LTG
0.183
0.149
0.129
-0.015-0.0551.000-0.156BM-0.283-0.198-0.0350.254-0.300-0.2101.000

Note:
GLS: the cost of capital inferred using Gebhardt et al.(2001) model. OJ: the cost of capital inferred using Ohlson and Juettner-Nauroth(2005) model. EP: expected earnings to price ratio. PEG: PEG ratio
proposed by Easton (2004). MPEG: modified PEG ratio proposed by Easton (2004). Beta: market beta using daily stock returns over the past 1 year from the announcement date of analysts’ forecasts by
IBES. Unsyst: unsystematic risk as measured by the residual from the regression over the previous year of a firm’s daily return on the daily market return. Earnvar: earning variability using the standard
deviation of earnings over the past five years. Leverage: leverage as the ratio of the book value long-term debt to the market value of equity. Size: the natural log of the market value of equity. LTG:
forecasted long-term growth reported by IBES to define variables regarding the long-term growth in expected earnings. BM: book-to-market ratio as the ratio of shareholders’ equity to the market valueof equity.
Spearman (Pearson) correlations are above (below) the diagonal.

28

Table 3

Correlation matrix between cost of capital and risk factors

Panel A: Pooled sample
Predicted
sign
Beta
(＋)

Unsyst(＋)

Earnvar(＋)

Leverage

(＋)

Size

(－)

LTG

(－)

BM

(＋)

Obs.

GLS

OJ

EP

PEG

MPEG

-0.010(0.73)-0.075***(0.01)-0.021(0.46)
0.271***(0.00)
0.137***(0.00)
0.132***(0.00)
0.179***(0.00)-0.064**(0.04)
0.154***(0.00)-0.028(0.37)
0.047(0.13)-0.147***(0.00)
0.051*(0.10)
0.000(0.99)

0.033(0.25)-0.195***(0.00)-0.082***(0.00)-0.060**(0.04)
0.048*(0.09)-0.047*(0.10)
0.015(0.60)

0.059*(0.05)
0.201***(0.00)
0.094***(0.00)
0.128***(0.00)-0.222***(0.00)
0.144***(0.00)
0.112***(0.00)

0.128***(0.00)
0.166***(0.00)
0.110***(0.00)
0.132***(0.00)-0.114***(0.00)
0.156***(0.00)
0.073***(0.02)

1186

1019

1195

1066

1069

29

Panel B: Comparison between high score sample and low score sample
Predicted

Beta

UnsystEarnvarLeverage

Size

LTG

BM

Obs.

GLS

sign

Low

(＋)

-0.104**

(＋)

(＋)

(＋)

(－)

(－)

(＋)

OJ
High

EP

PEG

Low

High

Low

High

Low

0.057

-0.031-0.109**-0.0180.079*

0.042(0.01)(0.17)(0.49)(0.01)(0.65)(0.06)-0.110***-0.103**0.123***

0.189***-0.084 **(0.00)(0.01)(0.00)(0.00)-0.041-0.012-0.017(0.32)(0.76)

0.382***

MPEG
High

Low

High

0.067

0.110**

0.142***(0.33)(0.13)(0.01)(0.00)-0.215***0.133***

0.204***

0.055

0.191***(0.04)(0.00)(0.00)(0.00)(0.20)(0.00)-0.040-0.077*-0.094**

0.097**

0.081*

0.119**

0.102**(0.69)(0.37)(0.06)(0.02)(0.02)(0.06)(0.01)(0.02)

0.226***

0.044

0.068

-0.175***0.015

0.147***

0.123***

0.170***

0.123***(0.00)(0.00)(0.32)(0.13)(0.00)(0.72)(0.00)(0.00)(0.00)(0.00)

0.064

0.167***-0.110**-0.174***-0.086**

0.124***-0.174***-0.263***-0.006(0.12)(0.00)(0.01)(0.00)(0.03)(0.00)(0.00)(0.00)(0.90)(0.00)

0.029

0.188***

0.023-0.005-0.099**0.011

0.143***

0.123***

0.124***

0.129***(0.48)(0.00)(0.60)(0.91)(0.01)(0.78)(0.00)(0.00)(0.00)(0.00)

0.287***

0.111**-0.015

0.020

0.053

-0.0350.024

0.190***-0.016

0.153***(0.00)(0.01)(0.73)(0.66)(0.19)(0.39)(0.57)(0.00)(0.70)(0.00)

599

587

520

499

604

591

547

519

548

521

0.171***Note:
Low Score Sample: sample with a low business risk disclosure level. High Score Sample: sample with a high business risk disclosure level. GLS: the cost of capital inferred using Gebhardt et al.(2001)
model. OJ: the cost of capital inferred using Ohlson and Juettner-Nauroth(2005) model. EP: expected earnings to price ratio. PEG: PEG ratio proposed by Easton (2004). MPEG: modified PEG ratio
proposed by Easton (2004). Beta: market beta using daily stock returns over the past 1 year from the announcement date of analysts’ forecasts by IBES. Unsyst: unsystematic risk as measured by theresidual from the regression over the previous year of a firm’s daily return on the daily market return. Earnvar: earning variability using the standard deviation of earnings over the past five years.
Leverage: leverage as the ratio of the book value long-term debt to the market value of equity. Size: the natural log of the market value of equity. LTG: forecasted long-term growth reported by IBES todefine variables regarding the long-term growth in expected earnings. BM: book-to-market ratio as the ratio of shareholders’ equity to the market value of equity.
t-statistics are provided in parentheses. They are based on White’s (1980) heteroskedasticity-consistent standard errors and covariance.
*** Statistically significant at the 0.01 level of significance using a two-tailed t-test. ** Statistically significant at the 0.05 level of significance using a two-tailed t-test. * Statistically significant at the
0.1 level of significance using a two-tailed t-test.

30

Table 4

The result of the multivariate analyses

Panel A: Pooled sample
Predicted
signIntercept
(＋/－)

Beta

(＋)

Unsyst(＋)

Earnvar(＋)

Leverage

(＋)

Size

(－)

LTG

(－)

BM

(＋)

Obs.
R-squared

GLS

OJ

EP

PEG

MPEG

-0.100***(-4.36)0.000(-0.12)0.001***(2.58)-0.000***(-3.60)0.010***(7.64)
0.004***(5.44)
0.000***(3.93)
0.024***(7.28)

0.683***(2.65)-0.083***(-3.08)0.020***(2.62)-0.002***(-2.80)0.056***(3.18)-0.020**(-2.30)0.001(0.91)-0.017(-0.40)0.094**(2.38)
0.016***(3.58)-0.005***(-3.63)-0.001**(-2.38)-0.006(-1.52)-0.002(-1.15)0.000(-1.07)-0.001(-0.10)0.202***(6.33)
0.006(1.51)
0.003***(3.73)
0.000*(1.68)
0.015***(4.49)-0.006***(-4.98)0.000***(2.82)
0.013*(1.72)

0.126***(3.08)
0.016***(3.24)
0.003***(2.76)
0.001**(2.30)
0.015***(4.03)-0.004**(-2.44)0.000**(2.54)
0.014*(1.63)

1186
0.1655

1019
0.05

1195
0.063

1066
0.12661069
0.096631

Panel B: Comparison between high score sample and low score sample
Predicted

GLS

OJ

EP

PEG

MPEG

sign

Low

High

Low

High

Low

High

Low

High

Low

High

Intercept

(＋/－)

Beta

(＋)

Unsyst(＋)

Earnvar(＋)

Leverage

(＋)

Size

(－)

LTG

(－)

BM

(＋)

Year Dummy-0.022(-1.25)0.000(0.11)
0.000(0.87)
0.000***(-2.93)0.011***(5.30)
0.002***(2.60)
0.000(1.32)
0.018***(5.54)
Yes-0.144***(-3.73)0.000(0.10)
0.000(0.58)
0.000**(-1.98)0.010***(5.44)
0.006***(4.42)
0.000***(3.70)
0.025***(4.69)
Yes

0.722*(1.86)-0.042(-1.57)0.014(1.10)-0.001(-1.32)0.039*(1.89)-0.022*(-1.69)0.000(0.63)-0.040(-0.57)
Yes

0.524*(1.84)-0.130**(-2.57)0.033***(2.58)-0.003**(-2.50)0.081**(2.57)-0.014(-1.53)-0.000(-0.29)0.015(0.29)
Yes

0.135**(2.08)
0.005(0.95)-0.003*(-1.79)0.000(-1.48)-0.013*(-1.91)-0.003(-1.29)0.000*(-1.92)0.002(0.29)
Yes

0.063(1.35)
0.020***(3.38)-0.007***(-3.85)-0.001**(-2.05)0.000(-0.06)0.000(-0.28)0.000(0.29)-0.008(-0.97)
Yes

0.234***(5.72)
0.003(0.82)
0.001(1.39)
0.000(1.35)
0.015***(3.13)-0.006***(-4.47)0.000**(2.54)-0.007(-0.71)Yes

0.210***(3.99)
0.010(1.52)
0.004**(2.52)
0.000(1.18)
0.014***(3.02)-0.006***(-3.39)0.000(1.44)
0.029***(2.72)
Yes

0.115**(2.25)
0.012**(2.49)
0.000(0.40)
0.001**(2.01)
0.016***(3.01)-0.002(-1.35)0.000*(1.78)-0.006(-0.53)Yes

0.147**(2.29)
0.023**(2.65)
0.004*(1.86)
0.001*(1.74)
0.014**(2.49)-0.005**(-2.16)0.000(1.30)
0.034**(2.43)
Yes

Obs.

599

587

520

499

604

591

547

519

548

521

R-squared

0.21270.16790.03080.08430.06220.07790.0966

0.15980.06370.1306Note:
Low Score Sample: sample with a low business risk disclosure level. High Score Sample: sample with a high business risk disclosure level. GLS: the cost of capital inferred using Gebhardt et al.(2001)
model. OJ: the cost of capital inferred using Ohlson and Juettner-Nauroth(2005) model. EP: expected earnings to price ratio. PEG: PEG ratio proposed by Easton (2004). MPEG: modified PEG ratio
proposed by Easton (2004). Beta: market beta using daily stock returns over the past 1 year from the announcement date of analysts’ forecasts by IBES. Unsyst: unsystematic risk as measured by theresidual from the regression over the previous year of a firm’s daily return on the daily market return. Earnvar: earning variability using the standard deviation of earnings over the past five years.
Leverage: leverage as the ratio of the book value long-term debt to the market value of equity. Size: the natural log of the market value of equity. LTG: forecasted long-term growth reported by IBES todefine variables regarding the long-term growth in expected earnings. BM: book-to-market ratio as the ratio of shareholders’ equity to the market value of equity.
t-statistics are provided in parentheses. They are based on White’s (1980) heteroskedasticity-consistent standard errors and covariance.
*** Statistically significant at the 0.01 level of significance using a two-tailed t-test. ** Statistically significant at the 0.05 level of significance using a two-tailed t-test. * Statistically significant at the
0.1 level of significance using a two-tailed t-test.

32

Table 5

The result of the multivariate analyses

Panel A: Small forecast error sample
Predicted

GLS

OJ

EP

PEG

MPEG

sign

Low

High

Low

High

Low

High

Low

High

Low

High

Intercept

(＋/－)

Beta

(＋)

Unsyst(＋)

Earnvar(＋)

Leverage

(＋)

Size

(－)

LTG

(－)

BM

(＋)

Year Dummy-0.006(-0.23)-0.002(-0.73)0.000(0.13)
0.000**(-2.02)0.013***(4.79)
0.001(1.41)
0.000(0.24)
0.009**(2.45)
Yes-0.251***(-3.24)-0.008(-1.02)-0.001(-0.26)0.000*(-1.70)0.013***(4.63)
0.010***(4.00)
0.000*(1.68)
0.024***(2.60)
Yes

0.330***(3.11)-0.006(-0.85)0.003(1.33)
0.000(0.96)
0.013**(2.29)-0.009***(-2.67)0.000(1.42)-0.007(-0.34)Yes-0.318(-0.86)-0.030(-0.89)0.035(1.56)
0.000(-0.15)0.027**(2.02)
0.012(0.94)
0.000(-0.85)0.046**(2.03)
Yes

0.113**(2.57)-0.003(-0.57)-0.001(-0.97)0.000*(-1.91)
0.002(0.75)-0.002(-1.35)0.000***(-3.35)0.003(0.47)
Yes

0.191(1.20)
0.018(1.07)-0.013(-1.46)0.000(-0.36)-0.004(-0.54)-0.005(-0.86)0.000(0.69)
0.007(0.63)
Yes

0.245***(3.17)-0.005(-0.85)0.003(1.55)
0.000(0.79)
0.013**(2.56)-0.007***(-2.59)0.000**(2.31)-0.012(-0.66)Yes-0.017(-0.14)0.000(0.02)
0.014**(2.28)
0.000(0.22)
0.017(1.57)
0.002(0.36)
0.000(-0.50)0.022(1.49)
Yes

0.163*(1.95)-0.002(-0.29)0.003(1.57)
0.000(0.97)
0.013**(2.57)-0.004(-1.39)0.000**(2.40)-0.011(-0.58)Yes-0.100(-0.69)0.002(0.17)
0.016**(2.01)
0.000(0.27)
0.019*(1.71)
0.004(0.85)
0.000(-0.71)0.024(1.51)
Yes

Obs.

285

238

244

192

286

238

262

207

262

207

R-squared

0.26590.20830.156

0.22670.10790.11390.12430.17010.10720.174233

Panel B: Large forecast error sample
Predicted

GLS

OJ

EP

PEG

MPEG

sign

Low

High

Low

High

Low

High

Low

High

Low

High

Intercept

(＋/－)

Beta

(＋)

Unsyst(＋)

Earnvar(＋)

Leverage(＋)-0.036(-1.15)0.000(-0.06)0.001**(2.12)
0.000(-1.19)0.007**(2.08)

0.067(1.57)
0.007(1.24)
0.000(-0.09)0.000(-1.40)0.001(0.30)

0.345(0.54)-0.057*(-1.66)0.019(0.95)-0.003(-1.31)0.105(1.53)

0.022(0.04)-0.201**(-1.96)
0.076***(2.59)-0.006***(-2.86)0.284***(3.10)

0.164**(2.05)
0.011(1.52)-0.001(-0.73)0.000(-0.80)-0.011(-0.81)0.127*(1.80)
0.012*(1.79)-0.006**(-2.42)0.000(-0.55)-0.013(-1.00)0.188***(2.95)
0.002(0.30)
0.003***(2.95)
0.001(1.31)
0.009(1.25)

0.288***(3.85)
0.024**(2.43)
0.003(1.60)
0.000(1.12)
0.029***(4.77)

0.083(1.02)
0.011(1.30)
0.003**(2.36)
0.001**(2.17)
0.005(0.69)

Size

(－)

LTG

(－)

BM

(＋)

Year Dummy

0.002*(1.73)
0.000*(1.81)
0.027***(5.43)
Yes-0.002(-1.09)0.000**(2.16)
0.015*(1.92)
Yes-0.008(-0.35)0.001(0.57)-0.054(-0.51)
Yes

0.002(0.09)-0.001(-0.66)0.102(1.11)
Yes-0.005(-1.49)0.000(-1.19)0.003(0.23)
Yes-0.002(-0.93)0.000(-1.18)-0.011(-1.11)
Yes-0.005**(-2.05)0.000(1.23)
0.006(0.60)
Yes-0.010***(-3.52)0.000(1.22)
0.023**(2.33)
Yes-0.002(-0.58)0.000(0.51)
0.007(0.53)
Yes

0.316***(3.43)
0.047***(3.35)
0.003(0.81)
0.001*(1.91)
0.023**(2.05)
0.012***(-3.53)0.000(1.00)
0.018(1.31)
Yes

Obs.

279

229

244

209

282

232

252

212

253

214

R-squared

0.246

0.15860.04430.1962

0.05320.085

0.1346

0.28180.0836

0.2373

Note:
Small forecast error sample: sample with small management forecast error. Large forecast error sample: sample with large management forecast error. Low: sample with a low business risk disclosurelevel. High: sample with a high business risk disclosure level. GLS: the cost of capital inferred using Gebhardt et al.(2001) model. OJ: the cost of capital inferred using Ohlson and JuettnerNauroth(2005) model. EP: expected earnings to price ratio. PEG: PEG ratio proposed by Easton (2004). MPEG: modified PEG ratio proposed by Easton (2004). Beta: market beta using daily stock
returns over the past 1 year from the announcement date of analysts’ forecasts by IBES. Unsyst: unsystematic risk as measured by the residual from the regression over the previous year of a firm’s daily
return on the daily market return. Earnvar: earning variability using the standard deviation of earnings over the past five years. Leverage: leverage as the ratio of the book value long-term debt to themarket value of equity. Size: the natural log of the market value of equity. LTG: forecasted long-term growth reported by IBES to define variables regarding the long-term growth in expected earnings.
BM: book-to-market ratio as the ratio of shareholders’ equity to the market value of equity.
t-statistics are provided in parentheses. They are based on White’s (1980) heteroskedasticity-consistent standard errors and covariance.
*** Statistically significant at the 0.01 level of significance using a two-tailed t-test. ** Statistically significant at the 0.05 level of significance using a two-tailed t-test. * Statistically significant at the
0.1 level of significance using a two-tailed t-test.

34