Abstract
We examine the relation between management earnings forecast disclosure policy and the cost of equity capital in a cross-section of 1,355 firms over a 4-year post-Regulation Fair Disclosure period (2001 through 2004). We find evidence of a negative association between the quality of management earnings forecasting policy and cost of equity capital, and we document that the strength of the association is greater for firms with higher disclosure costs and for firms with more relevant quarterly management earnings forecasts. Our results are robust to the use of multiple methods to address both endogeneity and the measurement error in firm-specific estimates of implied cost of equity capital.
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Notes
As noted by Botosan (2006), the effects of voluntary disclosure are to reduce information asymmetry, information risk, or both. We review several analytical models in Section 2 that provide varying predictions about the relations among information risk, information asymmetry, and cost of equity capital.
The difference between single period forecasting behavior and forecasting policy is particularly important for interpretation of evidence on the cost of equity capital effects of management forecasting. Francis et al. (2008) limit their study to 2001, the first year following the passage of Reg FD. Reg FD changed both management forecast disclosure policy and the capital market information environment (Wang 2007). It is highly likely that investors would find it difficult to infer longer-run forecasting behavior from a single year’s forecasting activity immediately after Reg FD’s passage. In fairness to the authors, their study focuses on voluntary disclosures in annual reports and 10-Ks which are far less likely to vary over time, and given the disclosure mechanism, are unaffected by Reg FD. Their supplemental test on management forecasts is based on the same year, and they recognize that management forecasting has greater intertemporal variation.
We use quarterly management earnings forecasts because of both their strong link to security prices (Pownall et al. 1993; Baginski et al. 1993) and the greater tension provided by the fact that quarterly forecasts are more timely, and Botosan and Plumlee (2002) detect the unexpected positive relation between more timely disclosure and cost of equity capital.
Reg FD was implemented on October 23, 2000, in an effort to level the playing field for all investors by eliminating selective disclosure. Prior research supports the argument that Reg FD was successful in reducing the amount of selective disclosure (Gintschel and Markov 2004).
This assertion itself is a subject of debate because, although intuitive and (generally) supported by empirical evidence, alternative analytic models specify conditions under which the assertion will not hold. These alternative models can be found in Diamond (1985), Lundholm (1988, 1991), Bushman (1991), Alles and Lundholm (1993), Kim and Verrecchia (1991, 1994), and McNichols and Trueman (1994). In summary, the conditions that call the assertion into question are the correlation of private and public signal errors, the ability of informed investors to create more information precision with their private information, and the predictability of the disclosure event.
Leuz and Verrecchia (2006) analytically examine the link between information quality, which they define as higher reporting precision, and a firm’s cost of equity capital. They also find that higher quality leads to a lower cost of equity capital, and they also show that this link does not disappear when diverse portfolios are formed. Hughes et al. (2007) show that the Easley and O’Hara (2004) result is driven by underdiversification in a finite economy.
Other papers indirectly link disclosure to cost of equity capital by linking individual disclosure types to various capital market variables (bid/ask spread, volatility, etc.), which proxy for information risk/information asymmetry conditions that likely lead to higher cost of equity capital. The results are mixed. Coller and Yohn (1997) document decreases in bid/ask spread pursuant to management forecast release. Piotroski (2002) finds support of managers’ claims of increased volatility following disclosure.
Mapping the precise path from disclosure to cost of equity capital is a complex task and beyond the scope of this paper. See Bhattacharya et al. (2007) for recent work in this area.
We thank a referee for pointing out these two examples.
Easton (2004, p. 77) notes that the PEG measure is equal to the price-earnings ratio divided by an earnings growth rate. Using a price-to-forward earnings ratio (PE) for stock recommendations requires that a high (low) PE implies a low (high) expected rate of return, but the earnings of next period may not be indicative of the future stream of earnings. Thus, the PEG captures the comparison of the PE ratio and earnings growth rate as a basis for stock recommendations. Easton (2004) refers readers to http://www.fool.com/School/TheFoolRatio.htm for a description of the PEG ratio. This site notes that the PEG ratio may not work for firms in the financial industry because firms in these industries “have low P/E's that virtually never reach their growth rates, mainly because their companies are valued off assets they hold (like oil deposits and real estate) rather than operating earnings.” Accordingly, we exclude financial firms from our primary analyses.
Botosan and Plumlee (2005) also find that another ex ante cost of equity capital estimation approach, the target price method (see Brav et al. 2005), yields estimates of cost of capital that exhibit relatively high correlations with well-known risk factors. We do not employ the target price method in our tests for three reasons. First, the method requires Value Line data, which dramatically reduces sample size. Second, the Value Line sample is dominated by the largest, most followed, less risky, and oldest firms. As argued by Leuz and Verrecchia (2000) it is unlikely that theoretically suggested disclosure effects can be documented in highly developed information environments. Third, if analysts are basing their target prices on the assumption of market inefficiency, then it is not clear that implied rates of return derived from target prices reflect capital market beliefs about cost of equity capital and future cash flows.
One could argue that pre-Reg FD counts are valid measures of public disclosure and therefore are relevant if the disclosure/cost of capital relation is driven by information asymmetry reduction. However, one cannot infer whether a given level of public disclosure is accompanied by private disclosure as well, and the disclosure/cost of capital relation may be driven solely through the reduction in information risk.
Their specific example of a disclosure cost heterogeneity is cross-sectional differences in the sophistication of firms’ investors, which leads to cross-sectional differences in disclosure practices (Dye 1985, 1998), and cross-sectional differences in required return to compensate for information risk.
When we estimate Eq. 3, we also include the exogenous variables from the second stage (Wooldridge 2002). Brown and Hilligeist (2007) and Larcker and Rusticus (2010) employ 2SLS to address these particular sources of endogeneity, and Nikolaev and Van Lent (2005) cite the use of 2SLS as a potential solution. See Barton and Waymire (2004), Cohen (2006), and Heflin et al. (2005) for additional recent examples of the use of 2SLS. Leuz and Verrecchia (2000) model a similar disclosure choice/disclosure outcome as a self-selection problem and use a treatment effects model (see Heckman 1979) to mitigate the effects of disclosure choice. We replicate our analysis using the Heckman approach in supplemental tests.
Age and performance are also related to litigation risk (Kasznik and Lev 1995; Rogers and Stocken 2005). Regulated industries include all communication sectors; electric and other services; water supply; natural gas transmission and distribution sectors; national and state commercial banks; chartered savings institutions; personal credit institutions; life insurance; accident and health insurance; and fire, marine, and casualty insurance. High-tech industries include drug and pharmaceuticals; computers, electronics, computer programming, data processing and other computer related; and research, development, and testing services.
In untabulated tests, we also include leverage (LEV) as a control variable, measured as long-term debt plus any debt in current liabilities divided by total assets, to proxy for the amount of debt in the firm’s capital structure. Botosan and Plumlee (2005) find LEV to be positively associated with cost of equity capital. Likewise, although not identified as an additional risk factor/anomaly by Fama and French (1992), we also include long-term growth rates (LTG) to control for risk associated with growth opportunities for each firm, estimated using the long-term growth rates from I/B/E/S. Gebhardt et al. (2001) and Botosan and Plumlee (2005) find LTG to be positively associated with cost of equity capital. Inclusion of LEV and LTG does not affect our inferences, and we limit our main controls to well-known risk factors identified in the CAPM and by Fama and French.
Again, these results are incremental associations in a multiple regression, and we are not concerned with the signs of the relations, only the joint ability of the instruments to capture management forecast policy choice (Maddala 1977). For example, although the negative sign on number of shareholders is counterintuitive, number of shareholders is highly positively associated with number of analysts (see Table 2), and number of analysts loads heavily in the expected direction in the regression. Thus, the result on number of shareholders is after control for the number of analysts. The results on all variables are consistent when using TOBIT regressions with the exception of HIGHTECH, which loses significance.
The adjusted R 2s of 13.17–15.58% are also relatively high when compared with recent published work. As examples, Brown and Hilligeist (2007) report a pseudo R 2 of 8.2% for their disclosure quality first-stage regression, and Barton and Waymire (2004) report an R 2 of 11% and F-statistic of 4.05 with 14 instruments.
All results are reported after truncating values of independent and dependent variables at the 1st and 99th percentiles. Results also are not affected by the inclusion or exclusion of financial firms. The Table 4 estimation is also replicated using TOBIT in the first stage.
Easton and Monahan (2005) show that higher long-term growth forecasts by analysts are associated with their forecast errors, and thus are a good proxy for financial analyst forecast quality. They show that ex ante cost of equity capital estimates are more reliable, though still fraught with measurement error, in a sub-sample of firms with lower long-term growth forecasts. In a supplemental test (not tabulated), we replicate our PEG-based tests with a sample constructed to match the low long-term growth forecast (and hence more reliable cost of equity capital) sub-sample in Easton and Monahan’s paper. We discard firms with high long-term growth estimates (>15%) to obtain a sample of 884 firms with mean and median long-term growth of 8.8 and 9.2%, respectively. In this sub-sample, the second-stage t-statistic on the management forecast disclosure policy variable is significantly negative, as expected (t = −1.88). We also extended the PEG-based tests by including an interaction of MFDiscPol with the analysts’ long-term growth forecast to examine whether our results are driven by analyst forecast quality. The coefficient on MFDiscPol remains significantly negative, and the coefficient on the interaction term is insignificant.
We instrument each interaction term using the set of instruments for MFDiscPol described earlier except that we do not, for example, use lnHHI as a first stage instrument for its interaction with MFDiscPol. Also, because the variable of primary interest, fitMFDiscPol, is the predicted value from the first stage, it is correlated with a given first-stage variable to the extent that the first-stage variable explains it. The results we report in Table 5 assume that multicollinearity does not affect the coefficient estimates. To make sure that it does not, we (a) re-estimated the regression using OLS (given that OLS estimation yields the same conclusions in other tests), (b) estimated the first-stage of the two-stage least squares with the given cost proxy omitted so that the fitted first-stage variable is not correlated with the cost proxy, and (c) estimated separate regressions for high and low disclosure cost cases. Our conclusions do not vary across these alternative estimation techniques except for the Herfindahl Index variable (proxy for product market competition). The negative significant relation between management forecast disclosure quality and cost of equity capital is similar in both the high and low cost subsamples. However, we use continuous variables to measure disclosure costs in our primary tests, and partitions at the median are made to form the high and low cost groups for the separate regression tests. Thus, the separate regression tests also discard information and ignore the possibility that sufficiently high disclosure costs occur at a place other than the median. We do not test all disclosure cost proxies jointly because we are not interested in incremental effects, and instrumenting each interaction with the same instruments leads to severe collinearity.
Larcker and Rusticus (2008) do provide an over-identifying restrictions test of the appropriateness of the instruments that can be applied when the number of instruments exceeds the number of endogenous regressors. The over-identifying restrictions test regresses the second stage residuals of a 2SLS estimation on all exogenous instruments. If the instruments are valid, then the R 2 from the model should be close to zero. Larcker and Rusticus (2008) note that nR 2 in this test is distributed χ2 with K–L degrees of freedom where K is the number of exogenous variables unique to the first stage, and L is the number of endogenous explanatory variables. We ran this test on the Table 4 model with all controls and obtained an R 2 of 4%. The χ2 statistic is significantly different from zero at the 0.001 level. However, Larcker and Rusticus note that this test nearly always rejects in large samples.
The independent variable of interest in the OLS regression is lnMFDiscPol (described previously). In the interest of brevity, we do not provide the details of the Heckman choice model, which may be found in Heckman (1979). Also, a recent application of the model appears in Feng et al. (2009). The Heckman approach uses a Probit model in the first stage. Therefore, we transform MFDiscPol to obtain a first stage dummy dependent variable MFDiscDummy that equals one if MFDiscPol is greater than its median and zero otherwise. We use all exogenous variables to estimate the first stage.
The inverse Mills ratio is significant in three of the four Heckman-type regressions.
As noted by Francis et al. (2008), the choice between ex ante and ex post (i.e., realized returns) cost of capital proxies remains controversial. Asset pricing tests in the finance literature are based on broad samples and time periods, and thus a caveat is warranted for any study that employs limited samples and time periods in the analysis.
The second stage is a trading strategy based solely on the fitted management forecast policy variable from the first stage, which alternatively could be formulated as a linear regression with a single indicator variable that captures the trading strategy returns. Therefore, no additional exogenous variables appear in the second stage for inclusion in the first stage.
The significance of the book-to-market and size effects fluctuate in this last pair of columns, while the market portfolio return and the earnings quality return remain positive and significant. Francis et al. (2008) find the market portfolio and the book-to-market effect to be insignificant in their tests and note that Core et al. (2008) find that only the book-to-market effect is priced as expected in a longer window monthly returns test.
We use a rate of 12% to estimate cum-dividend earnings and assume that dividends in the current period are equal to dividends in the next four periods. This is consistent with Easton et al. (2002). We use the forecasted EPS for 2005 and 2006 from I/B/E/S and then use growth rates from I/B/E/S to estimate EPS for 2007 and 2008. For firms with only 2005 forecasted EPS available on I/B/E/S, we use the growth rate from I/B/E/S to calculate EPS for years 2006 through 2008. For firms with 2005 and 2006 forecasted EPS but no growth rates available, we calculate their growth rate from the two forecasted earnings numbers and use this rate to estimate 2007 and 2008 EPS. All forecasts are from the I/B/E/S Summary Statistics file at the end of 2004.
Easton (2006) adapts a method in O’Hanlon and Steele (2000) to examine cost of capital differences across regimes. The method, which is similar in spirit to Easton et al. (2002), relies on actual rather than forecasted earnings per share to obtain growth and cost of capital estimates and thus is independent of analyst forecast quality. As an additional test, we estimated the Easton (2006) adaptation of O’Hanlon and Steele (2000) in our sample and obtained similar conclusions (results not tabulated). The estimate of r for low disclosure quality is 11.12%, and the estimate of r for high disclosure quality is a statistically significantly (p < 0.0001) lower 5.2%. While this method does not suffer from potential analyst forecast bias, it does require a choice of “actual” earnings to include in the model (presumably some estimate of permanent actual earnings). Note that, consistent with Easton and Sommers (2007), the estimates of r from the O’Hanlon and Steele (2000) model are lower than estimates from the Easton et al. (2002) model.
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Acknowledgments
We thank Bipin Ajinkya, Scott Atkinson, Ben Ayers, Linda Bamber, Anne Beatty, Christine Botosan, Duane Brandon, Andrew Christie, Dan Cohen, Chris Cornwell, Joel Demski, Jere Francis, Jenny Gaver, Ken Gaver, Steve Hillegeist, Giles Hilary, Ken Klassen, Rick Johnston, Inder Khurana, Andy McLelland, Gary McGill, Steve Monahan, Marlene Plumlee, Kenny Reynolds, Ken Shaw, Doug Skinner, Siew Hong Teoh, Jenny Tucker, Eric Yeung, and workshop participants at Auburn University, Bocconi, the University of Florida, the University of Georgia, INSEAD, Louisiana State University, the University of Missouri-Columbia, The Ohio State University, and the University of Utah for helpful comments on earlier versions of this paper.
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Baginski, S.P., Rakow, K.C. Management earnings forecast disclosure policy and the cost of equity capital. Rev Account Stud 17, 279–321 (2012). https://doi.org/10.1007/s11142-011-9173-4
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DOI: https://doi.org/10.1007/s11142-011-9173-4