Skip to main content
Log in

Regulation fair disclosure and the cost of equity capital

  • Published:
Review of Accounting Studies Aims and scope Submit manuscript

Abstract

We examine the effect of Regulation Fair Disclosure (Reg FD) on the cost of equity capital. We find some evidence that (1) the cost of capital declines in the post-Reg FD period relative to the pre-Reg FD period, on average, for a broad cross-section of US firms, (2) the decrease in the cost of capital post Reg FD is mainly for medium and large firms but is insignificant for small firms, and (3) the decrease in the cost of capital post Reg FD is systematically related to firm characteristics indicative of selective disclosure before Reg FD. In contrast, we find little evidence of a decrease in the cost of capital for American Depositary Receipts and US-listed foreign firms, which are legally exempt from Reg FD. Overall, our findings do not support a conclusion in recent studies that the cost of capital has increased post Reg FD and, if anything, suggest the opposite.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1

Similar content being viewed by others

Notes

  1. In the “Written Statement Concerning Regulation Fair Disclosure” (SEC 2001), the SEC states that, “[t]o the extent that some investors decide not to participate in our markets as a result [of selective disclosure], the markets lose a measure of liquidity and efficiency, and costs of raising equity capital are increased.”

  2. Indeed, regulators believe that high accounting and disclosure standards reduce the cost of capital. For example, Arthur Levitt, the former SEC chairman, stated: “[T]he truth is, high accounting standards lower the cost of capital” (Levitt 1998). Neel Foster, a former member of the Financial Accounting Standards Board, stated: “[I]n the context of financial reporting, the end result is that better disclosure results in a lower cost of capital” (Foster 2003). Lastly, the FASB’s Steering Committee on Business Reporting Research Project stated, in its 2001 report, that a basic premise underlying its report is that “improving disclosures makes the capital allocation process more efficient and reduces the average cost of capital” (FASB 2001).

  3. Specifically, they form three portfolios based on firm size and regress value-weighted monthly portfolio returns on the Fama and French (1996) three factors. Gomes et al. (2007, Table 7) find that the loading on SMB, a factor related to size, is significantly larger in the post-Reg FD period than in the pre-Reg FD period for small firms and, to a lesser extent, medium firms but remains unchanged for large firms.

  4. Given that we find a reduction in the cost of capital post Reg FD, a natural question is why firms did not voluntarily practice fair disclosure before Reg FD. One potential explanation is that, without a mandatory requirement, it is difficult for firms to convince investors that they are committed to fair disclosure. Another explanation is that there are costs associated with fair disclosure. For example, firms could face higher proprietary costs when disclosing complex and detailed information to the public, as compared to financial analysts. Consequently, some firms may find that private (that is, selective) disclosure is their first best choice, public (that is, fair) disclosure second best choice, and no disclosure last choice. These firms would choose fair disclosure after, but not before, Reg FD.

  5. Selective disclosure is akin to insider trading, because both practices allow a privileged few to benefit at the expense of the investing public. The adverse economic effects of the two practices are essentially the same (SEC 2000). Thus, curtailing selective disclosure should have a similar effect on the cost of capital as enforcing insider trading laws studied in Bhattacharya and Daouk (2002).

  6. Our sample period ends at the end of March 2002 to avoid an overlap with the Sarbanes–Oxley act, which was passed in July 2002.

  7. Note that we use subscript t to refer to both a quarter and a year. Year t corresponds to four quarters ending in quarter t (that is, quarter t − 3 to quarter t). The subscript t in Eqs. 3 and 6 refers to a year and thus B t–1 in Eq. 6 corresponds to quarter t − 4.

  8. Similar to Easton and Sommers (2007, fn. 20), we use adjusted B t , rather than B t , when estimating Eq. 6. Adjusted B t is calculated as B t minus year t net income (sum of quarterly net income (quarterly data #69) in quarter t − 3 to quarter t) plus EPS t (sum of quarterly earnings before extraordinary items (quarterly data #8) in quarter t − 3 to quarter t).

  9. We also plot the cost of capital and the corresponding risk premium for small, medium, and large firms, respectively. In general, the patterns for medium and large firms are similar to that for the full sample in Fig. 1. However, there is much less variation in the cost of capital and the risk premium over time for small firms.

  10. The residuals in Eqs. 8 and 9 are potentially serially correlated. We calculate t-statistics using the Newey and West (1987) adjusted standard errors to correct for such time series correlation.

  11. The negative adjusted R 2 for small firms (−0.135) is due to an implicit constraint in Eq. 8 that the coefficient on R Ft be equal to one. When we estimate a modified Eq. 8 where R Ft is moved to the right hand side, the adjusted R 2 for small firms is 0.290, and the coefficient on R Ft is insignificantly different from zero.

  12. The market-to-book ratio is the ratio between market value of equity at the end of September 2000 from CRSP and book value of equity (Compustat annual data #60) of the fiscal year ending immediately before October 2000. R&D expenditures are research and development expenditures (annual data #46) of the fiscal year ending immediately before October 2000, scaled by market value of equity at the end of September 2000. Missing R&D expenditures are set to zero. The level of institutional ownership is from Thomson Financial Ownership database (13f filing) measured at the end of September 2000. Firms not covered in the ownership database are assumed to have zero institutional ownership.

  13. One disadvantage of using the constant sample is that it contains predominantly large and mature firms and the resulting sample size is smaller. Consequently, using the constant sample reduces the generalizability of the paper’s findings. We choose to report our main results using the full sample in order to examine the effect of Reg FD on the cost of capital for a broad cross-section of firms.

  14. Untabulated results mentioned in the paper are available from the authors upon request.

  15. Our results are not qualitatively changed when we use each individual measure or the median of these three cost of capital measures.

  16. Variables β MKTit, β SMBit, and β HMLit are factor loadings for the three risk factors in Fama and French (1996) and are estimated for firm i in quarter t by regressing excess returns on the Fama and French three factors using monthly returns in the 60 months (require at least 24 months) prior to the last month in quarter t. Variables LogMVit and LogBMit are the natural logarithm of market value of equity and the book-to-market ratio, respectively, measured at the end of the prior fiscal year (Fama and French 1992; Gebhardt et al. 2001; Gode and Mohanram 2003). We include the price momentum (MMTit) to control for a potential bias in estimated implied cost of equity induced by analysts’ sluggish earnings forecasts (Guay et al. 2003). We measure MMTit as the natural logarithm of one plus the compound returns over the 12 months prior to the last month in quarter t. We measure leverage (Levit) as the natural logarithm of one plus the ratio of total long-term debt to the market value of equity measured at the end of the prior fiscal year. Analyst forecast error (Ferrit) is included to control for a potential bias in estimated cost of equity capital due to biases in analyst forecasts (for example, Claus and Thomas 2001; Hail and Leuz 2006), which is defined as the difference between the consensus analyst forecast at the end of quarter t for the forthcoming year and the I/B/E/S reported actual earnings, scaled by the stock price at the end of the prior fiscal year. When the I/B/E/S reported actual earnings are missing, we use actual earnings from Compustat. We include analyst forecasted long-term growth rate (Growthit) to control for a positive relation between the implied cost of capital and long-term growth rate documented in Gebhardt et al. (2001) and Gode and Mohanram (2003), and Growthit is measured as the analyst consensus long-term earnings growth rate from I/B/E/S. If that rate is missing, we calculate the earnings growth rate implied from the 3- and 2-year-ahead earnings forecasts. We include analyst forecast dispersion (LogDispit) because Gebhardt et al. (2001) and Dhaliwal et al. (2005) find a negative relation between implied cost of capital and analyst forecast dispersion. We measure LogDispit as the natural logarithm of one plus the standard deviation of analyst earnings forecasts at the end of quarter t for the forthcoming year, scaled by the absolute value of the consensus earnings forecast. LogDispit is missing for observations with less than two analysts following at the end of quarter t. Instead of excluding these observations, we set LogDispit to zero and control for this condition by including an indicator variable (Less2it), which is set to one if the number of analyst forecasts is less than two and zero otherwise. Finally, we control for industry effects by including lagged industry risk premium (IndRPit–4), calculated as the difference between the median cost of capital in each of the Fama and French (1997) industries at the end of the prior year and the corresponding risk-free rate.

  17. Our findings in Tables 4 and 5 and Panel C of Table 6 are qualitatively unchanged when we adjust standard errors for clustering at both firm and time level (Petersen 2009).

  18. The χ 2 tests are based on robust standard errors corrected for heteroscedasticity and clustering at the firm level with all five (three) regressions estimated simultaneously. This approach allows for correlations of error terms across sub-samples.

  19. Note that Richardson et al. (2004, pp. 906–907) find a negative relation between analyst’s earnings forecast errors (FESC = actual − forecast) or forecast pessimism (PESS = 1 if FESC is nonnegative and 0 otherwise) and the book-to-market ratio. This translates into a negative relation between forecast optimism (=forecast − actual) and the market-to-book ratio. Consistent with Richardson et al., we also find that forecast errors are negatively correlated with the market-to-book ratios in our sample.

  20. Although ADRs are legally exempt from Reg FD, some ADRs might voluntarily comply with Reg FD. Crawley et al. (2008) find that 40% of the active cross-listed firms that responded to their survey voluntarily adopted Reg FD. Moreover, Reg FD adopters enjoy a significant reduction in the cost of equity capital, as evidenced by a decrease in the bid-ask spread and share price volatility and an increase in share turnover, relative to non-adopters. Francis et al. (2006, p. 274), however, suggest that ADRs did not fully comply with Reg FD and continued to share private information with analysts post Reg FD. Thus, if there is a reduction in the cost of capital post Reg FD for ADRs, the magnitude of the reduction should be smaller than that observed for US firms. Gomes et al. (2007, pp. 329–330) find no difference between ADRs and US firms in terms of changes in analyst following and in use of earnings pre-announcements in the post-Reg FD period relative to pre-Reg FD period; they do not examine whether there is a difference between ADRs and US firms in the change in the cost of capital post Reg FD.

  21. Many ADR firms do not have quarterly financial reports. For these ADRs, we obtain data from their semi-annual or annual reports.

  22. On surface, the results for small US firms in Table 6 are not consistent with the small sub-sample results in Table 2. However, we find that the 2,047 firm-quarter observations in the “small” US firm portfolio in Table 6, Panel A, actually comprise 767 small firms, 1,138 medium firms, and 142 large firms in Table 2. That is, the “small” US firms in Table 6, Panel A, are in fact mostly medium and large firms.

References

  • Bailey, W., Li, H., Mao, C., & Zhong, R. (2003). Regulation fair disclosure and earnings information: Market, analyst, and corporate responses. Journal of Finance, 58, 2487–2514.

    Article  Google Scholar 

  • Bhattacharya, U., & Daouk, H. (2002). The world price of insider trading. Journal of Finance, 57, 75–108.

    Article  Google Scholar 

  • Botosan, C. (1997). Disclosure level and the cost of equity capital. The Accounting Review, 72, 323–349.

    Google Scholar 

  • Bushee, B. (1998). The influence of institutional investors on myopic R&D investment behavior. The Accounting Review, 73, 305–333.

    Google Scholar 

  • Bushee, B., Matsumoto, D., & Miller, G. (2004). Managerial and investor responses to disclosure regulation: The case of Reg FD and conference calls. The Accounting Review, 79, 617–643.

    Article  Google Scholar 

  • Claus, J., & Thomas, J. (2001). Equity premia as low as three percent? Evidence from analysts’ earnings forecasts for domestic and international stock markets. Journal of Finance, 56, 1629–1666.

    Article  Google Scholar 

  • Crawley, M., Ke, B., & Yu, Y. (2008). Causes and consequences of cross-listed firms’ voluntary adoption of US securities laws: The case of regulation fair disclosure. Working paper, University of Texas at Austin and Pennsylvania State University.

  • Daske, H. (2006). Economic benefits of adopting IFRS or US-GAAP—have the expected cost of equity capital really decreased? Journal of Business Finance and Accounting, 33, 329–373.

    Article  Google Scholar 

  • Dhaliwal, D., Krull, L., & Li, O. (2007). Did the 2003 tax act reduce the cost of equity capital? Journal of Accounting and Economics, 43, 121–150.

    Article  Google Scholar 

  • Dhaliwal, D., Krull, L., Li, O., & Moser, W. (2005). Dividend taxes and implied cost of equity capital. Journal of Accounting Research, 43, 675–708.

    Article  Google Scholar 

  • Duarte, J., Han, X., Harford, J., & Young, L. (2008). Information asymmetry, information dissemination and the effect of regulation FD on the cost of capital. Journal of Financial Economics, 87, 24–44.

    Google Scholar 

  • Easley, D., Hvidkjaer, S., & O’Hara, M. (2002). Is information risk a determinant of asset returns? Journal of Finance, 57, 2185–2221.

    Article  Google Scholar 

  • Easley, D., & O’Hara, M. (2004). Information and the cost of capital. Journal of Finance, 59, 1553–1583.

    Article  Google Scholar 

  • Easton, P. (2004). PE ratios, PEG ratios, and estimating the implied expected rate of return on equity capital. The Accounting Review, 79, 73–95.

    Article  Google Scholar 

  • Easton, P. (2006). Use of forecasts of earnings to estimate and compare cost of capital across regimes. Journal of Business Finance and Accounting, 33, 374–394.

    Article  Google Scholar 

  • Easton, P., & Sommers, G. (2007). Effect of analysts’ optimism on estimates of the expected rate of return implied by earnings forecasts. Journal of Accounting Research, 45, 983–1015.

    Article  Google Scholar 

  • Easton, P., Taylor, G., Shroff, P., & Sougiannis, T. (2002). Using forecasts of earnings to simultaneously estimate growth and the rate of returns on equity investment. Journal of Accounting Research, 40, 657–675.

    Article  Google Scholar 

  • Eleswarapu, V., Thompson, R., & Venkataraman, K. (2004). The impact of regulation fair disclosure: Trading costs and information asymmetry. Journal of Financial and Quantitative Analysis, 39, 209–225.

    Article  Google Scholar 

  • Elton, E. (1999). Expected return, realized return, and asset pricing tests. Journal of Finance, 54, 1199–1220.

    Article  Google Scholar 

  • Fama, E., & French, K. (1992). The cross-section of expected stock returns. Journal of Finance, 47, 427–465.

    Article  Google Scholar 

  • Fama, E., & French, K. (1996). Multifactor explanations of asset pricing anomalies. Journal of Finance, 51, 55–84.

    Article  Google Scholar 

  • Fama, E., & French, K. (1997). Industry costs of equity. Journal of Financial Economics, 43, 153–193.

    Article  Google Scholar 

  • Financial Accounting Standards Board (FASB). (2001). Improving business reporting: Insights into enhancing voluntary disclosures, the Steering Committee on Business Reporting Research Project. Available at http://www.fasb.org/brrp/BRRP2.PDF.

  • Foster, N. (2003). The FASB and the capital markets. Available at http://www.fasb.org/articles&reports/Foster_FASBReport.pdf.

  • Francis, J., Nanda, D., & Wang, X. (2006). Re-examining the effects of regulation fair disclosure using foreign listed firms to control for concurrent shocks. Journal of Accounting and Economics, 41, 271–292.

    Article  Google Scholar 

  • Gebhardt, W., Lee, C., & Swaminathan, B. (2001). Toward an implied cost of capital. Journal of Accounting Research, 39, 135–176.

    Article  Google Scholar 

  • Gintschel, A., & Markov, S. (2004). The effectiveness of regulation FD. Journal of Accounting and Economics, 37, 293–314.

    Article  Google Scholar 

  • Gode, D., & Mohanram, P. (2003). Inferring cost of capital using the Ohlson–Juettner model. Review of Accounting Studies, 8, 399–431.

    Article  Google Scholar 

  • Gomes, A., Gorton, G., & Madureira, L. (2007). SEC regulation fair disclosure, information, and the cost of capital. Journal of Corporate Finance, 13, 300–334.

    Article  Google Scholar 

  • Guay, W., Kothari, S. P., & Shu, S. (2003). Properties of implied cost of capital using analysts’ forecasts. Working paper, Wharton school, MIT, and Boston College.

  • Hail, L., & Leuz, C. (2006). International differences in the cost of capital: Do legal institutions and securities regulation matter? Journal of Accounting Research, 44, 485–531.

    Article  Google Scholar 

  • Hassett, K. (2000). Outlaw selective disclosure? No, the more information the better. Wall Street Journal (August 10): A18.

  • Heflin, F., Subramanyam, K. R., & Zhang, Y. (2003). Regulation FD and the financial information environment: Early evidence. The Accounting Review, 78, 1–37.

    Article  Google Scholar 

  • Hutton, A. (2005). The determinants of managerial earnings guidance prior to regulation fair disclosure and bias in analysts’ earnings forecasts. Contemporary Accounting Research, 22, 867–914.

    Article  Google Scholar 

  • Irani, A., & Karamanou, I. (2003). Regulation fair disclosure, analyst following, and analyst forecast dispersion. Accounting Horizon, 17, 15–29.

    Article  Google Scholar 

  • Ke, B., & Petroni, K. (2004). How informed are actively trading institutional investors? Evidence from their trading behavior before a break in a string of consecutive earnings increases. Journal of Accounting Research, 42, 895–927.

    Article  Google Scholar 

  • Ke, B., Petroni, K., & Yu, Y. (2008). The effect of regulation FD on transient institutional investors’ trading behavior. Journal of Accounting Research, 46, 853–883.

    Article  Google Scholar 

  • Levitt, A. (1998). The importance of high quality accounting standards. Accounting Horizons, 12, 79–82.

    Google Scholar 

  • Matsumoto, D. (1999). Management’s incentives to guide analysts’ forecasts. Working paper, University of Washington.

  • Mohanram, P., & Rajgopal, S. (2009). Is PIN priced risk? Journal of Accounting and Economics, 47, 226–243.

    Article  Google Scholar 

  • Newey, W., & West, K. (1987). A simple, positive semi-definite, heteroscedastic and autocorrelation consistent covariance matrix. Econometrica, 55, 703–708.

    Article  Google Scholar 

  • Ohlson, J., & Juettner-Nauroth, B. (2005). Expected EPS and EPS growth as determinants of value. Review of Accounting Studies, 10, 349–365.

    Article  Google Scholar 

  • Opdyke, J. (2000). The big chill: Street feels effect of ‘fair disclosure’ rule—regulation is altering the way analysts approach their jobs. Wall Street Journal (October 23): C1.

  • Petersen, M. (2009). Estimating standard errors in finance panel data sets: Comparing approaches. Review of Financial Studies, 22, 435–480.

    Article  Google Scholar 

  • Richardson, S., Teoh, S., & Wysocki, P. (2004). The walk-down to beatable analyst forecasts: The role of equity issuance and insider trading incentives. Contemporary Accounting Research, 21, 885–924.

    Article  Google Scholar 

  • Securities and Exchange Commission (SEC). (2000). Final rule: Selective disclosure and insider trading. Available at http://www.sec.gov/rules/final/33-7881.htm.

  • Securities and Exchange Commission (SEC). (2001). Written statement concerning regulation fair disclosure. Available at http://www.sec.gov/news/testimony/051701wssec.htm.

  • Sidhu, B., Smith, T., Whaley, R., & Willis, R. (2008). Regulation fair disclosure and the cost of adverse selection. Journal of Accounting Research, 46, 697–728.

    Article  Google Scholar 

  • Smith, C., & Watts, R. (1992). The investment opportunity set and corporate financing, dividends, and compensation policies. Journal of Financial Economics, 32, 262–292.

    Article  Google Scholar 

  • Verdi, R. (2005). Information environment and the cost of equity capital. Working paper, University of Pennsylvania.

Download references

Acknowledgments

We gratefully acknowledge the helpful comments and suggestions from Peter Easton (the editor), two anonymous referees, Anwer Ahmed, Daniel Cohen, Dan Collins, Randy Elder, Shane Heitzman, Jack Hughes, David Hulse, Mo Hussein, Susan Krische, Oliver Li, Alfred Liu, Mary Lea McAnally, Kofi Okyere, Tom Omer, George Plesko, Ed Swanson, Senyo Tse, Connie Weaver, Jian Zhou, David Ziebart, and seminar participants at Georgetown University, London Business School, Santa Clara University, SUNY at Binghamton, Syracuse University, Texas A&M University, University of Connecticut, University of Kentucky, the 2006 American Accounting Association Annual Meeting, and the 2006 NTU International Conference on Finance. We thank Thomson Financial for providing analyst forecast data through the Institutional Brokers Estimate System (I/B/E/S) and Brian Bushee for generously providing us his institutional investor classification data. Zhihong Chen acknowledges the financial support provided by the City University of Hong Kong (#7200077).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Dan S. Dhaliwal.

Appendix: Firm-specific estimation of the implied cost of equity capital

Appendix: Firm-specific estimation of the implied cost of equity capital

We first define the following variables used in the three firm-specific cost of capital estimation models.

P t :

Stock price of a firm’s common share at time t

E t [.]:

Expectation based on information at time t

DPS t+τ :

Future dividends per share for time t + τ

B t :

Book value of equity per share from the most recent available financial statement at time t

EPS t+τ :

Median earnings forecasts per share from I/B/E/S or derived earnings forecasts per share for time t + τ

POUT:

Forecasted dividend payout ratio. We use a firm’s previous fiscal year’s dividend payout ratio to measure the forecasted payout ratio. If earnings are negative, we assume a return on assets of 6% to calculate earnings

1.1 Gebhardt et al. (2001)—GLS model

The GLS model is derived from the dividend discount model

$$ P_{t} = \sum\limits_{\tau = 1}^{\infty } {{\frac{{E_{t} \left[ {{\text{DPS}}_{t + \tau } } \right]}}{{\left( {1 + R_{\text{GLS}} } \right)^{\tau } }}}} . $$
(GLS1)

Under the “clean surplus” assumption, that is, \( B_{t + \tau } = B_{t + \tau - 1} + {\text{EPS}}_{t + \tau } - DPS_{t + \tau } \), the above equation can be rewritten as

$$ P_{t} = B_{t} + \sum\limits_{\tau = 1}^{T - 1} {{\frac{{E_{t} \left[ {{\text{EPS}}_{t + \tau } - R_{\text{GLS}} \times B_{t + \tau - 1} } \right]}}{{\left( {1 + R_{\text{GLS}} } \right)^{\tau } }}}} + \sum\limits_{\tau = T}^{\infty } {{\frac{{E_{t} \left[ {{\text{EPS}}_{t + \tau } - R_{\text{GLS}} \times B_{t + \tau - 1} } \right]}}{{\left( {1 + R_{\text{GLS}} } \right)^{\tau } }}}} $$
(GLS2)

or

$$ P_{t} = B_{t} + \sum\limits_{\tau = 1}^{T - 1} {{\frac{{E_{t} \left[ {{\text{ROE}}_{t + \tau } - R_{\text{GLS}} } \right] \times B_{t + \tau - 1} }}{{\left( {1 + R_{\text{GLS}} } \right)^{\tau } }}}} + \sum\limits_{\tau = T}^{\infty } {{\frac{{E_{t} \left[ {{\text{ROE}}_{t + \tau } - R_{\text{GLS}} } \right] \times B_{t + \tau - 1} }}{{\left( {1 + R_{\text{GLS}} } \right)^{\tau } }}}} , $$
(GLS3)

where ROEt+τ is the return on equity for time t + τ. Assuming that the abnormal earnings at time T, that is, \( \left[ {{\text{ROE}}_{t + T} - R_{\text{GLS}} } \right] \times B_{t + T - 1} \), becomes a perpetuity from T onward, Eq. GLS3 can be rewritten as

$$ P_{t} = B_{t} + \sum\limits_{\tau = 1}^{T - 1} {{\frac{{E_{t} \left[ {{\text{ROE}}_{t + \tau } - R_{\text{GLS}} } \right] \times B_{t + \tau - 1} }}{{\left( {1 + R_{\text{GLS}} } \right)^{\tau } }}}} + {\frac{{E_{t} \left[ {{\text{ROE}}_{t + T} - R_{\text{GLS}} } \right] \times B_{t + T - 1} }}{{\left( {1 + R_{\text{GLS}} } \right)^{T - 1} R_{\text{GLS}} }}}. $$
(GLS4)

To estimate Eq. GLS4, Gebhardt et al. (2001) first use I/B/E/S analysts’ earnings forecasts for the next 3 years as the expected earnings from year t + 1 to year t + 3. They then measure the expected earnings by assuming that the future return on equity declines linearly to an equilibrium return on equity from year t + 4 to year t + T. This equilibrium return on equity is a moving median return on equity for all firms in the same industry in the past 10 years. The return on equity (ROE) is calculated as income before extraordinary items (Compustat data item #18) scaled by the lagged total book value of equity (Compustat data item #60). We classify all firms into 48 industries defined by Fama and French (1997). Firm-year observations with a negative return on equity are eliminated in the calculation. The future book value of equity is estimated by assuming the clean surplus relation, that is, \( B_{t + \tau } = B_{t + \tau - 1} + {\text{EPS}}_{t + \tau } - {\text{DPS}}_{t + \tau } \). The future dividend, DPS t+τ , is calculated by multiplying EPS t+τ by the payout ratio POUT. Following Gebhardt et al. (2001), we set T = 12.

1.2 Claus and Thomas (2001)—CT model

The CT model is similar to the GLS model, except that it assumes that the abnormal earnings after 5 years grow at a rate of g lt in perpetuity. Under this assumption, we have

$$ P_{t} = B_{t} + \sum\limits_{\tau = 1}^{5} {{\frac{{E_{t} \left[ {{\text{EPS}}_{t + \tau } - R_{\text{CT}} \times B_{t + \tau - 1} } \right]}}{{\left( {1 + R_{\text{CT}} } \right)^{\tau } }}}} + {\frac{{E_{t} \left[ {{\text{EPS}}_{t + 5} - R_{\text{CT}} \times B_{t + 4} } \right]\left( {1 + g_{\text{lt}} } \right)}}{{\left( {1 + R_{\text{CT}} } \right)^{5} \left( {R_{\text{CT}} - g_{\text{lt}} } \right)}}}. $$
(CT1)

To estimate Eq. CT1, Claus and Thomas (2001) use the I/B/E/S analysts’ earnings forecasts to derive the abnormal earnings for year t + 1 to year t + 5. Earnings forecasts for year t + 4 and year t + 5 are derived from earnings forecasts for year t + 3 and the long-term earnings growth rate. If the long-term earnings growth rate is missing in I/B/E/S, an implied earnings growth rate from EPSt+2 and EPSt+3 is used instead. The long-term abnormal earnings growth rate (g lt) is calculated using contemporaneous risk-free rate (the yield on the 10-year Treasury bonds) minus 3%. The future book value of equity is also estimated by assuming the clean surplus relation, that is, \( B_{t + \tau } = B_{t + \tau - 1} + {\text{EPS}}_{t + \tau } - {\text{DPS}}_{t + \tau } \). The future dividend DPS t+τ is calculated by multiplying EPS t+τ by the payout ratio POUT.

1.3 Gode and Mohanram (2003)—GM model

The GM model is derived from the Ohlson and Juettner-Nauroth (2005) model, which in turn is based on the dividend discount model below.

$$ P_{t} = \sum\limits_{\tau = 1}^{\infty } {{\frac{{E_{t} \left[ {{\text{DPS}}_{t + \tau } } \right]}}{{\left( {1 + R_{\text{GM}} } \right)^{\tau } }}}} . $$
(GM1)

Defining the dividend-adjusted earnings (z t ) as \( z_{t} = {{\left[ {{\text{EPS}}_{t + 1} - {\text{EPS}}_{t} - R_{\text{GM}} \left( {{\text{EPS}}_{t} - {\text{DPS}}_{t} } \right)} \right]} \mathord{\left/ {\vphantom {{\left[ {{\text{EPS}}_{t + 1} - {\text{EPS}}_{t} - R_{\text{GM}} \left( {{\text{EPS}}_{t} - {\text{DPS}}_{t} } \right)} \right]} {R_{\text{GM}} }}} \right. \kern-\nulldelimiterspace} {R_{\text{GM}} }} \) and assuming that the sequence of \( \left\{ {z_{t} } \right\}_{t = 1}^{\infty } \) grows asymptotically at a rate of g lt, that is, \( z_{t + 1} = (1 + g_{\text{lt}} )z_{t} \), Eq. GM1 can be rewritten as

$$ P_{t} = {\frac{{E_{t} \left[ {{\text{EPS}}_{t + 1} } \right]}}{{R_{\text{GM}} }}} + {\frac{{E_{t} \left[ {{\text{EPS}}_{t + 2} - {\text{EPS}}_{t + 1} - R_{\text{GM}} \left( {{\text{EPS}}_{t + 1} - {\text{DPS}}_{t + 1} } \right)} \right]}}{{R_{\text{GM}} \left( {R_{\text{GM}} - g_{\text{lt}} } \right)}}}. $$
(GM2)

Solving for R GM, we obtain

$$ R_{\text{GM}} = A + \sqrt {A^{2} + {\frac{{E_{t} \left( {{\text{EPS}}_{t + 1} } \right)}}{{P_{t} }}}\left( {g_{\text{st}} - g_{\text{lt}} } \right)} , $$
(GM3)

where \( A = {\frac{1}{2}}\left[ {g_{\text{lt}} + {\frac{{E_{t} \left( {{\text{DPS}}_{t + 1} } \right)}}{{P_{t} }}}} \right] \) and \( g_{\text{st}} = {\frac{{{\text{EPS}}_{t + 2} - {\text{EPS}}_{t + 1} }}{{{\text{EPS}}_{t + 1} }}} \).

To estimate Eq. GM3, Gode and Mohanram (2003) use the average of the short-term earnings growth rate implied in EPSt+1 and EPSt+2 and the analysts’ forecasts of long-term growth rates as their measure of g st. They further require that EPSt+1 > 0 and EPSt+2 > 0. The long-term asymptotic growth rate (g lt) is calculated using the contemporaneous risk-free rate (the yield on the 10-year Treasury bonds) minus 3%. The future dividend DPSt+1 is calculated by multiplying EPSt+1 by the payout ratio POUT.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Chen, Z., Dhaliwal, D.S. & Xie, H. Regulation fair disclosure and the cost of equity capital. Rev Account Stud 15, 106–144 (2010). https://doi.org/10.1007/s11142-009-9115-6

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11142-009-9115-6

Keywords

JEL Classification

Navigation