Are Information Attributes Priced? Christine A. Botosan* Associate Professor of Accounting C. Roland Christensen Faculty Fellow Email: [email protected]Marlene A. Plumlee* a Associate Professor of Accounting Email: [email protected]*David Eccles School of Business University of Utah Salt Lake City, UT 84112 a corresponding author January 2006 We wish to thank Stephen Brown for his generous assistance in the calculation of the PIN variable used in this study. We also wish to thank the workshop participants at the University College Dublin,
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Are Information Attributes Priced?
Christine A. Botosan*Associate Professor of Accounting
*David Eccles School of BusinessUniversity of Utah
Salt Lake City, UT 84112a corresponding author
January 2006
We wish to thank Stephen Brown for his generous assistance in the calculation of the PIN variable used in this study. We also wish to thank the workshop participants at the University College Dublin, University of Utah, New York University, Toronto University, Wharton and University of Wisconsin-Madison for their helpful comments. The authors gratefully acknowledge the financial support of the David Eccles School of Business and the contribution of I/B/E/S Inc. for providing earnings per share forecast data, available through the Institutional Brokers Estimate System. These data have been provided as part of a broad academic program to encourage earnings expectations research.
Easley and O’Hara (EO) (2004) model the impact of information attributes on the cost of equity
capital. We empirically test three implications of the EO model and document results consistent with its
predictions. Specifically we find that cost of equity capital is increasing in the proportion of the
information set that is private versus public, decreasing in the fraction of investors who are informed and
decreasing in the overall precision of the information set. Accordingly we conclude that Easley and
O’Hara’s conjecture that public and private information have a role to play in affecting firms’ required
returns is supported by the data.
1. Introduction
Easley and O’Hara (EO) (2004) model the impact of information attributes on the cost of equity
capital. They conclude that cost of equity capital is affected by the following attributes of information: (1)
the proportion of the information set that is private versus public (hereafter composition), (2) the fraction
of investors who are informed (hereafter dissemination), and (3) the overall precision of the information
set (hereafter precision). EO demonstrate that cost of equity capital is increasing in the composition of the
information set and decreasing in its dissemination and precision. We empirically test these three
implications of the EO model and document results consistent with the model’s predictions.
We employ two alternative proxies for the cost of equity capital – rDIVPREM and rPEGPREM. rDIVPREM is
derived from the dividend discount model and is the internal rate of return that equates a firm’s current
stock price to analysts’ forecasts of future dividends and target price (Botosan and Plumlee (2002);
Botosan et al. (2004)). rPEGPREM is similarly derived from the dividend discount model, but after imposing
the assumption that both dividends prior to the earnings forecasts and growth in abnormal earnings
beyond the forecast horizon are zero (Ohlson and Juettner-Nauroth (OJ) (2003); Easton (2004)). Botosan
and Plumlee (2005) assess the empirical validity of five alternative methods of estimating cost of equity
capital including rDIVPREM and rPEGPREM and conclude that, among those examined, only these estimates are
predictably and robustly related to risk.1
Our proxies for the composition and precision of information are drawn from Barron et al. (BKLS)
(1998). BKLS demonstrate how observable attributes of analysts’ forecasts can be employed to estimate
the precision of the analysts’ public and private information sets. We employ these measures to derive
estimates of the overall precision of the information set (labeled PRECIS), and the proportion of the
information set that is private versus public (labeled COMPOS). We compute a proxy for the fraction of
1 For other research that examines possible proxies for expected cost of equity capital see Botosan (1997), Gebhardt
et al. (2001), and Gode and Mohanram (2003).
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investors who are informed (labeled DISSEM) using the estimated arrival rates of informed and
uninformed investors, both of which are components of the probability of an informed investor (i.e. PIN)
metric developed in Easley et al. (1997).
We find that both proxies for cost of equity capital are increasing in COMPOS and decreasing in
DISSEM and PRECIS, consistent with the EO model and EO’s conjecture that information attributes are
priced. The magnitudes of our coefficients suggest that an increase in COMPOS of 10 points (e.g. from
20% to 30%) is associated with an increase in cost of equity capital of about 7 basis points, whereas a
similar increase in DISSEM is associated with a decrease in cost of equity capital of about 38 basis
points, on average. In addition, the sample firm providing the most precise information enjoys a cost of
equity capital that is 114 basis points lower than the sample firm providing the least precise information.2
One implication of our findings is that managers can realize a lower cost of equity capital by reducing
private information relative to public information. Most existing research (including the EO model)
assumes that public information supplants private information, which suggests that managers might
realize cost of equity capital benefits by providing more public disclosures. However, a relatively early
stream of research suggests that some types of public disclosure might generate private information (see
Barron et al. (2005), and Botosan et al. (2004)), indicating that further research is needed to help
managers evaluate their optimal reporting strategy. Another implication of our findings is that managers
can procure lower costs of equity capital by adopting corporate reporting strategies that mitigate
investors’ costs of becoming informed thereby encouraging greater dissemination of private information.
For example, managers might increase the transparency of their disclosures to reduce investors’
information processing costs. Managers might also hold conference calls or host analyst “road-shows” to
encourage a greater analyst following. Finally, managers can achieve cost of equity capital benefits by
choosing accounting policies and disclosure practices that increase the overall precision of information.
Our study contributes to a growing body of empirical literature which focuses on the association
between information and cost of equity capital. For example, several papers examine the relationship
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between corporate financial reporting practices and cost of equity capital (see Botosan (1997), Botosan
and Plumlee (2002), Brown et al. (2004), and Richardson and Welker (2001)). In addition, a number of
papers relate attributes of earnings to cost of equity capital (see Affleck-Graves et al. (2002), Francis et al.
(2004), Hribar and Jenkins (2004), and Mikhail et al. (2004)). Common to all of these studies is a focus
on public information. Two more recent studies focus on proxies for private information and so are most
closely related to this endeavor. First, Botosan et al. (BPX) (2004) show that rDIVPREM is increasing in the
precision of private information, but decreasing in the precision of public information. Second, Easley et
al. (EHO) (2002) document a positive association between realized returns and PIN, their proxy for
COMPOS.
Our paper extends this stream of research in general and the research conducted by BPX and EHO in
particular, in several respects. First, BPX focus on the separate impacts of public and private information
precision on cost of equity capital, whereas our study considers the relationship between overall precision
and cost of equity capital in tandem with proxies for composition and dissemination. Second, EHO
employ realized returns for cost of equity capital. In contrast we employ measures of implied cost of
equity capital in the analysis. Third, ours is the first study to consider the relationship between cost of
equity capital and all three of the information attributes suggested by the EO model.
We organize the remainder of our paper as follows. We outline the theory that underlies our
hypotheses and discuss prior research related to this study in Section I. We describe our research design
and empirical proxies in Section II, and our sample and descriptive statistics in Section III. We present the
results of our analysis in Section IV, and Section V concludes the paper.
2. Hypotheses development and prior research
2.1. Hypotheses development
Easley and O’Hara (2004) (EO) develop a multi-asset, rational expectations, equilibrium asset-pricing
model that incorporates public and private information, as well as informed and uninformed risk-averse
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investors. Within this framework EO consider the impact of cross-sectional differences in (1) the
composition of information between public and private information, (2) the dissemination of private
information across traders, and (3) the overall precision of information on firms’ costs of equity capital.3
In the EO model uninformed investors perceive stocks to be risky due to information risk and demand
higher returns to compensate for this additional risk. In contrast, informed traders perceive less
information risk and, therefore, are willing to take larger positions in securities about which they are
informed. Trading by informed investors can have two beneficial effects on the firm’s cost of equity
capital. First, since informed investors take larger positions in the firm’s stock, demand for the firm’s
securities may be increased thereby reducing the cost of equity capital. Second, uninformed investors
partially infer private information from stock price; they perceive less information risk when the trading
activities of informed investors reveal their private information with greater precision.
The impact of the composition, dissemination, and precision of information on cost of equity capital
results from the interplay among the effects outlined above. With respect to the composition of the
information set, EO demonstrate that stocks with more private information and less public information
face a higher cost of equity capital. This is because uniformed investors can not perfectly infer private
information from stock price, such that firms with relatively more private information are viewed as more
risky by uninformed investors and are charged a higher cost of equity capital as a result. This gives rise to
our first hypothesis, stated below.
H1: Cost of equity capital is increasing in the proportion of information that is private.
With respect to the dissemination of information, EO demonstrate that when private information is
more widely disseminated across investors, cost of equity of capital is reduced via the demand effect and
the information revelation effect. First, when more investors are informed, demand for the stock is
greater, price is higher, and cost of equity capital is lower. Second, when more investors are informed
their private information is revealed to uninformed investors with greater precision. This makes the stock
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less risky for uninformed traders and further reduces the cost of capital. This gives rise to our second
hypothesis.
H2: Cost of equity capital is decreasing in the dissemination of private information across investors.
With respect to the overall precision of information, EO demonstrate that greater precision lowers cost
of equity capital by making the stock less risky for the uninformed investors. Uninformed investors
perceive less information risk because the public information they observe directly and the private
information revealed to them indirectly via stock price are both more precise. This gives rise to our third,
and final, hypothesis.
H3: Cost of equity capital is decreasing in the overall precision of information.
2.2. Prior Research
A large body of empirical research investigates the association between public information and cost of
equity capital. One segment of this research broaches this issue indirectly by examining the effect of
disclosure on variables believed to be related to cost of equity capital. For example, Frankel et al. (1995)
find that managers of firms that access the capital markets provide management earnings forecasts more
frequently. Welker (1995) and Leuz and Verrecchia (2000) document a negative association between
disclosure levels and bid-ask spreads. Healy et al. (1999) find that firms that increase disclosure
experience an increase in stock performance, institutional ownership, and analyst following, and a
decrease in bid-ask spreads. Brown et al. (2004) find that a policy of regularly holding conference calls
mitigates information asymmetry. Finally, Affleck-Graves et al. (2002) demonstrate a favorable
association between earnings predictability and reduced information asymmetry.
Another segment of this research broaches the association between public information and cost of
equity capital by examining the effect of disclosure on the cost of raising equity capital via a secondary
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offering or on estimates of cost of equity capital. For example, Lang and Lundholm (2000) conclude that
hyping a stock in anticipation of a secondary offering increases price and allows the firm to raise capital
at a lower cost. Botosan (1997) finds that among firms with low analyst following, greater annual report
disclosure is associated with a lower cost of equity capital and Botosan and Plumlee (2002) extend this
result to large, heavily followed firms. Finally, several recent papers document a negative association
between earnings “quality” and cost of equity capital (e.g. Francis et al. (2004), Hribar and Jenkins
(2004), and Mikhail et al. (2004)).
All of the research discussed above focuses on public information. Two more recent studies, Botosan
et al. (2004) (BPX) and Easley et al. (2002) (EHO), consider the effects of public and private information
on cost of equity capital, and, as such, are most closely related to this study. BPX use separate empirical
proxies for the precision of public and private information to examine the effect of private information
precision on cost of equity capital, after controlling for the negative association between cost of equity
capital and public information established in the prior literature. BPX find that cost of equity capital is
increasing in the precision of private information and that the precision of private information is
positively correlated with the precision of public information. They find that, for the average firm, the
cost of capital reduction achieved through more precise public information is almost entirely offset by the
cost of capital increase associated with more precise private information.
BPX consider the precision of private and public information as separate constructs. While the EO
model allows the precisions of private and public information to differ, the model is silent as to the
separate effects of each of these precisions on cost of equity capital. Moreover, BPX’s finding of a
positive correlation between the precisions of private and public information is not consistent with EO’s
assumption that the precisions of private and public information are perfect substitutes. BPX do not
examine the effect of overall precision or dissemination of information on cost of equity capital, nor do
they examine the impact of composition in tandem with precision or dissemination.
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EHO test the hypothesis put forward in EO regarding the composition of information. Consistent with
the EO model, EHO document a strong positive association between realized returns, their proxy for the
cost of equity capital, and PIN, their proxy for the fraction of information that is private. But, prior
research suggests that realized returns are not a powerful proxy for cost of equity capital when sample
size is limited in large part because “information about future cash flows is the dominant factor driving
firm-level stock returns” (Voulteenaho (2002)).
This may explain why EHO find no association between cost of equity capital and beta and book-to-
price and a positive association with firm size. Moreover, EHO’s PIN proxy for composition might also
capture dissemination. This is a significant issue because composition and dissemination have opposite
effects on cost of equity capital in the EO model. Finally, EHO focus on one information attribute –
composition. If composition, dissemination and/or precision are correlated, including one attribute in the
analysis without controlling for the other attributes may result in a correlated omitted variables bias.
Our study complements and extends existing research by (1) employing implied cost of equity capital
estimates in the analysis, (2) employing an alternative proxy for composition that is suggested by the EO
model, and (3) examining all three information attributes simultaneously.
3. Research design and empirical proxies
3.1. Empirical model
To examine the relationship between cost of equity capital and the composition, dissemination and
precision of information we estimate the following regression equation.
(1)
Where: rit = equity risk premium (i.e. cost of equity capital less the risk free rate) for firm i, year t. BETAit = market model beta for firm i, year t.
LGROWit = log of long range expected growth in earnings, year t.LMKVLit = log of market value of common equity for firm i, year t.BPit = book-to-price for firm i, year t.COMPOSit = percentage of total precision attributed to private information for firm i, year t.
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DISSEMit = percentage of trades by informed traders. PRECISit = total information precision for firm i, year t.
Based on the theory set forth in EO, we hypothesize that the coefficient on COMPOS (γ5) is positive,
and the coefficients on DISSEM (γ6) and PRECIS (γ7) are negative.
We include market beta, growth, firm size, and book-to-price in the analysis to control for other
sources of risk that could confound our analysis, and to validate our proxy for cost of equity capital. We
expect the coefficient on BETA to be positive since the Capital Asset Pricing Model indicates that cost of
equity capital is increasing in market beta.4 Beaver et al. (1970) argue that abnormal earnings streams
derived from growth opportunities are more risky and La Porta (1996) provides empirical evidence that
growth and risk are positively related. Accordingly we expect to observe a positive coefficient on
LGROW. Berk (1995) argues that, market value of equity (book-to-price) is inversely (positively)
associated with risk in general, and that cost of equity capital is negatively related to market value of
equity and positively related to book-to-price in an incomplete model of expected returns. Thus, we
expect the coefficient on LMKVL to be negative and the coefficient on BP to be positive. The procedures
we employ in estimating our variables are described in detail below.
3.2. Empirical proxies – cost of equity capital and control variables
3.2.1. Cost of equity capital (rDIVPREM and rPEGPREM)
The dependent variable in our model is the expected risk premium, or cost of equity capital net of the
risk free rate of interest. Botosan and Plumlee (2005) evaluate the construct validity of five popular
methods of estimating firm-specific cost of equity capital and find that the target price method and the
price-earnings-growth (PEG) method generate estimates (rDIVPREM and rPEGPREM, respectively), which are
consistently and predictably related to risk, while the alternative methods do not. Based on their results,
BP conclude that researchers requiring firm-specific estimates of expected cost of equity capital are
4 See Litner (1965), Mossin (1966) and Sharpe (1964).
10
justified in using either rDIVPREM or rPEGPREM to proxy for cost of equity capital. To assess the robustness of
our results to the proxy employed, we estimate cost of equity capital using both methods.
The target price method estimates the internal rate of return that equates current stock price to the
present value of forecasted dividends and target price. It employs the short-horizon form of the dividend
discount formula given in equation (2). In this specification of the dividend discount model the forecasted
terminal value truncates the infinite series of future cash flows at the end of year 5.
(2)
Where: = price at time t=0 or t=5. rDIV = estimated cost of equity capital.
= the expectations operator.
= dividends per share, t=1 to 5.
The data and procedures we employ in estimating rDIV mirror those employed by Botosan and Plumlee
(2005). Dividend forecasts for the current fiscal year (i.e., t=1), the following fiscal year (i.e., t=2), the
long run (i.e., t=5), and maximum and minimum long-run target price estimates are collected from
forecasts published by Value Line during the third quarter of the calendar year. These data are collected
from the Value Line database, available in machine-readable form.
Value Line does not provide dividend forecasts for years 3 and 4. Accordingly, we assume linear
growth in dividends from year 2 to year 5, and interpolate between these years to generate dividend
forecasts for years 3 and 4. Forecasted target price is the 50th percentile of Value Line’s forecasted long-
run price range. Current stock price (P0) equals the stock price reported on CRSP on the Value Line
publication date or closest date thereafter within 3 days of publication.
We use the values for P0, E0[P5] and the E0[dt]’s (t=1 to 5) in a numerical approximation program that
identifies the annual firm-specific rDIV that equates the right and left-hand sides of the equation to within a
$0.005 difference between the actual- and fitted-value of P0.5 rDIVPREM is rDIV less the risk free rate of
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interest. We use the 5-year Treasury Constant Maturity Rate as of the end of the month in which the
expected cost of equity capital estimates are determined as our estimate of the risk free rate of interest.
We collect these data from the US Federal Reserve at www.federalreserve.gov.
The primary assumption underlying this method is that analysts’ forecasts of future dividends and
target prices accord with those of market participants. If this assumption is violated, the link between
current stock price and analysts’ forecasts of future cash flows is strained and the link between the
resulting estimates of cost of equity capital and the underlying construct is weakened. This mitigates
against finding results.
Since cost of equity capital is inherently unobservable and Botosan and Plumlee (2005) conclude that
the PEG method also produces estimates that behave as if they capture cross-sectional variation in cost of
equity capital, we triangulate our analysis by examining the estimates produced by the PEG method as
well. Accordingly, our second estimate of cost of equity capital is based on the formula below, drawn
from Easton (2004).
(3)
Where: rPEG = estimated cost of equity capital. E0 = the expectations operator.epst = earning per share at time t.
This formula is derived from a special case of the dividend discount model that assumes no changes in
abnormal earnings beyond the forecast horizon, and no dividend payments prior to the earnings forecasts.
Consistent with Botosan and Plumlee (2005), we use long-run earnings forecasts (eps5 and eps4) in place
of eps2 and eps1 in the above model for two reasons. First, in some instances eps2 is less than eps1, but in
no instance is eps5 less than eps4. Since we cannot solve the model if eps2 is less than eps1 using eps5 and
eps4 maximizes our sample size. Second, and more importantly, using long-run earnings forecasts
increases the likelihood that changes in abnormal earnings beyond the forecast horizon will equal zero.
rPEGPREM is rPEG less the risk free rate of interest.
a rDIVPREM is the estimated risk premium based on the target price method (BP 2005). rPEGPREM is the estimated risk premium based on the PEG method (Easton 2004). BETA is capital market beta estimated via the market model with a minimum of 30 monthly returns over the 60 months prior to June 30th of the year expected cost of equity capital is estimated using a value weighted NYSE/AMEX market index return. GROW is the Value Line long-range earnings growth forecasts. MKVL is the market value of equity as of the most recent quarter prior to the date cost of equity is calculated, stated in millions of dollars. BP is the book value of common equity scaled by the market value of common equity, both measured at the end of the most recent quarter prior to June 30th of the year cost of equity capital is estimated. COMPOS is the proportion of overall precision attributed to private information measured as PRIVATE/(PUBLIC + PRIVATE), where PUBLIC is the precision of analysts’ public information set and PRIVATE is the precision of analysts’ private information set, both based on the BKLS method. DISSEM is the dissemination of private information across traders measured the number of informed traders (μ), scaled by the sum of the informed and uninformed traders (μ+2), drawn from the calculation of PIN (EEOW (2001)). PRECIS is total information precision calculated as PUBLIC + PRIVATE. The table contains means, medians, 25th percentiles, 75th percentiles, and standard deviations of the variables included in the regressions for the 3,896 firm-year observations from 1993-2003. All statistics are calculated from the sample pooled across 11 years.
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Table 2Average cross-sectional correlations of firm characteristics
rDIVPREM rPEGPREM BETA LGROW LMKVL BP COMPOS DISSEMrPEGPREM 0.682
(11/0)1.00
BETA 0.144(8/0)
0.279(11/0)
1.000
LGROW 0.287(11/0)
0.653(11/0)
0.316(11/0)
1.000
LMKVL -0.231(0/11)
-0.321(0/11)
-0.046(0/3)
-0.134(0/8)
1.000
BP 0.135(9/0)
0.237(11/0)
-0.075(0/4)
-0.108(0/7)
-0.365(0/11)
1.00
COMPOS 0.117(9/0)
0.146(8/0)
-0.062(1/4)
-0.025(3/3)
-0.187(0/7)
0.267(10/0)
1.00
DISSEM 0.090(10/0)
0.171(10/0)
0.083(2/0)
0.119(9/0)
-0.793(0/11)
0.239(8/0)
0.127(6/0)
1.00
RPRECIS -0.068(0/4)
-0.121(0/9)
0.042(3/0)
0.038(3/0)
0.180(7/0)
-0.291(0/10)
-0.359(0/11)
-0.115(0/6)
a rDIVPREM is the estimated risk premium based on the target price method (BP 2005). rPEGPREM is the estimated risk premium based on the PEG method (Easton 2004). BETA is capital market beta estimated via the market model with a minimum of 30 monthly returns over the 60 months prior to June 30th of the year expected cost of equity capital is estimated using a value weighted NYSE/AMEX market index return. GROW is the Value Line long-range earnings growth forecasts. MKVL is the market value of equity as of the most recent quarter prior to the date cost of equity is calculated, stated in millions of dollars. BP is the book value of common equity scaled by the market value of common equity, both measured at the end of the most recent quarter prior to June 30th of the year cost of equity capital is estimated. COMPOS is the proportion of overall precision attributed to private information measured as PRIVATE/(PUBLIC + PRIVATE), where PUBLIC is the precision of analysts’ public information set and PRIVATE is the precision of analysts’ private information set, both based on the BKLS method. DISSEM is the dissemination of private information across traders measured the number of informed traders (μ), scaled by the sum of the informed and uninformed traders (μ+2), drawn from the calculation of PIN (EEOW (2001)). PRECIS is total information precision calculated as PUBLIC + PRIVATE. The table contains the time-series means of annual bivariate rank correlations of the variables included in the regressions for the 3,896 firm-year observations from 1993-2003. The numbers in parentheses are the number of years (out of eleven) that the annual correlation coefficient is significantly positive/negative.
26
Table 3Time-series averages of the coefficients in 11 annual cross-sectional regressions (1993-2003).
Panel A: Regressions using rDIVPREM (estimated risk premium based on the target price method) as the proxy for risk.
BETA(+)
LGROW(+)
LMKVL(-)
BP(+)
COMPOS(+)
DISSEM(-)
RPRECIS(-)
Avg. Adj. R2
0.017(4.56)**
0.037(9.76)**
-0.018(-3.40)**
0.007(0.88)
0.007(2.17)*
-0.121(-2.96)**
-0.010(-3.69)**
21.0%
Panel B: Regressions using rPEGPREM (estimated risk premium based on the PEG method) as the proxy for risk.
BETA(+)
LGROW(+)
LMKVL(-)
BP(+)
COMPOS(+)
DISSEM(-)
RPRECIS(-)
Avg. Adj. R2
0.006(3.95)**
0.049(10.85)**
-0.006(-3.74)**
0.022(3.97)**
0.007(5.57)**
-0.038(-2.98)**
-0.014(-3.25)**
59.2%
The sample includes 3,896 firm-year observations from 1993-2003. The t-statistics are based on the standard error of the weighted coefficient estimates across the 11 years (Fama and MacBeth 1973). In calculating the t-statistics, the coefficients are weighted by the square root of the annual sample size to adjust for differences in the number of observations on a year-by-year basis and adjusted for autocorrelation in the annual coefficients
based on an AR(1) autocorrelation structure. Standard errors are multiplied by an adjustment factor, , where n is the number
of years (11) and is the first-order autocorrelation of the annual coefficient estimates (Abarbanell and Bernard, 2000). The dependent variable in Panel A is the estimated risk premium based on the target price method (BP 2005) (rDIVPREM). The dependent variable in Panel B is the estimated risk premium based on the PEG method (Easton 2004) (rPEGPREM). BETA is capital market beta estimated via the market model with a minimum of 30 monthly returns over the 60 months prior to June 30th of the year expected cost of equity capital is estimated using a value weighted NYSE/AMEX market index return. LGROW is natural log of the Value Line long-range earnings growth forecasts. LMKVL is the natural log of the market value of equity as of the most recent quarter prior to the date cost of equity is calculated. BP is the book value of common equity scaled by the market value of common equity, both measured at the end of the most recent quarter prior to June 30th of the year cost of equity capital is estimated. COMPOS is the proportion of overall precision attributed to private information measured as PRIVATE/(PUBLIC + PRIVATE), where PUBLIC is the precision of analysts’ public information set and PRIVATE is the precision of analysts’ private information set, both based on the BKLS method. DISSEM is the dissemination of private information across traders measured the number of informed traders (μ), scaled by the sum of the informed and uninformed traders (μ+2), drawn from the calculation of PIN (EEOW (2001)). RPRECIS is the fractional rank of total information precision calculated as PUBLIC + PRIVATE. T-values are given in parentheses.** (*) denotes significant at the 0.01 (0.05) level or better, < (1-tailed t-test).
27
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2 The figures quoted in the text are from the rPEGPREM regression. The corresponding figures from the rDIVPREM
regression are 7 basis points (for COMPOS), 121 basis points (for DISSEM), and 100 basis points (for RPRECIS).
5 We make appropriate adjustments for fractions of years and the portion of the current fiscal-year dividend forecast
distributed to investors prior to the forecast date. Botosan and Plumlee (2005) describe these adjustments in detail.
6 We eliminate 36 observations from our sample because long- range growth in earnings is in excess of 100%. In
each case, period two forecasted earnings per share is small and negative and the long-range earnings per share is
relatively large and positive. Our conclusions are not altered if these observations are included in our analyses,
although we ultimately eliminate several of the 36 observations as they are influential observations in the
regressions.
8 The form of the log likelihood function we estimate is given in EEOW (2001). It is
9 BP estimate rDIV using three alternative points in the target price range (the 50th percentile, the 25th percentile, and
the minimum value) and find their results are robust to all. BP employ rDIV estimated with the 25th percentile value in
their primary tests to reduce the magnitude of the average estimate; we employ the 50th percentile because doing so
maximizes our sample size.
3 EO also conclude that the existence of some information (even if it is all private) yields a lower cost of equity
capital than no information at all. We do not investigate the fourth prediction since some information exists for all of
the firms included in our analysis.
10 Mean (median) values for μ and (the components of DISSEM) are 88.7 (60.1) and 128.5 (67.5), respectively.
These values are greater than the mean values documented in Brown et al. (2001) (mean μ = 34.5 and mean =46.9)
and Easley, et al. (2002) (mean μ = 31.1 and mean =24.0). Our higher values are consistent with our estimation
31
method, which truncates observations with a large number of buys and sells to a lesser extent, and with a more
recent sample period characterized by greater trade volume. Our sample period ends in 2003, while the Brown et al.
and Easley et al. sample periods end in 1996 and 1998, respectively.