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Management forecasts and the cost of equity capital: international evidence

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Abstract

We examine international differences in the effect of management forecasts (which we use to proxy for voluntary disclosure) on the cost of equity capital (COC) across 31 countries. We find that the issuance of management forecasts is associated with a lower COC worldwide but that the effect of management forecasts on the COC depends on country-level institutional factors. Specifically, management forecasts have a stronger effect on the COC in countries with stronger investor protection and better information dissemination and a weaker effect in countries with higher mandatory disclosure requirements. Further analyses reveal that these relations are more pronounced when management forecasts are more frequent, more precise, and more disaggregated. Overall, our findings suggest that the ability of management forecasts to reduce firms’ COC derives not only from country-level factors that enhance the credibility of their forecasts but also from factors that reflect the quality of the information environment in terms of the distribution of news and the availability and quality of alternative information. Thus, investor protection, media penetration, and mandatory disclosure requirements have an important effect on the ability of management forecasts to lower the COC.

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Notes

  1. The exception is Francis et al. (2005). They use disclosure scores from the Center for International Financial Analysis Research (which represent both mandatory and voluntary disclosure) to show that firms from around the world benefit from increased disclosure through a lower COC. In addition, Hope et al. (2013) find that voluntary disclosures made by foreign firms cross-listed in the U.S. are associated with smaller analyst forecast errors and a lower implied COC in the U.S.

  2. Most empirical studies use the U.S. setting to explore factors that influence the credibility of voluntary disclosure or to examine whether short-term market reactions vary with voluntary disclosure credibility (Jennings 1987; Rogers and Stocken 2005; Yang 2012; Ng et al. 2013). Credibility is often measured by past forecast accuracy or is inferred from firm characteristics and management incentives that are likely to influence forecast credibility.

  3. Mercer (2004, 186) defines disclosure credibility as an “investor’s perception of the believability of a particular disclosure” and explains that it “refers to the perception held by investors, not an objective condition of a disclosure.”

  4. Our country-level media penetration measure differs from proxies for the firm-level information environment, which could include the extent of firm-specific media coverage, because firm-level variables are at least partially determined by the firm itself.

  5. See, for example, Bagnoli and Watts (2007), Hui et al. (2009), and Ball et al. (2012).

  6. We collect our sample from Standard & Poor’s (S&P) Capital IQ database. Our sample period begins in 2004 because this is the first year for which S&P Capital IQ systematically covers international management earnings forecasts. S&P Capital IQ is a division of S&P that provides web-based information about firms worldwide (see https://www.capitaliq.com/home.aspx). Because the data collection process requires extensive resources and effort, our sample period ends in 2009.

  7. Specifically, Hirst et al. (2008, p. 317) state: “Second, our review of the literature highlights that the typical study focuses on the main effect of one or more forecast antecedents or characteristics on forecast consequences. Because main effect results are unlikely to hold under all conditions, we argue that researchers should identify and test possible interactions among antecedents or characteristics.”

  8. Lang et al. (2012) measure corporate transparency using earnings management, accounting standards, auditor quality, analyst following, and analyst forecast accuracy.

  9. Consistent with investor protection determining forecast credibility, Radhakrishnan et al. (2012) find that the market reaction to management forecasts is stronger in countries with stronger investor protection, presumably because stronger investor protection helps increase management forecast credibility by reducing management’s incentives to make self-serving disclosures.

  10. Mandatory and voluntary disclosures may also have different foci or different degrees of credibility so that one type of disclosure cannot be replaced by the other (Zhang 2011; Cheng et al. 2013). Some studies (e.g., Einhorn 2005; Bagnoli and Watts 2007; Gigler and Hemmer 2001) show that the sign of the relation between voluntary and mandatory disclosures depends on the characteristics of mandatory disclosures.

  11. The industry-level COC has significant explanatory power for the firm-level COC even after controlling for industry fixed effects. However, our inferences are robust to the omission of this variable.

  12. In alternative specifications, we use signed abnormal accruals and find very similar results. Our inferences are also robust to omitting abnormal accruals from the model.

  13. S&P Capital IQ collects management forecasts from various sources including firm filings with stock exchanges, major financial news media, and subscriptions to commercial sources of financial information. Starting from 2004, Capital IQ provides the text of performance forecasts issued by firm management in the Key Developments data set under “Corporate Guidance.”

  14. If voluntary disclosure rules vary across countries and if this variation is correlated with that of the country-level factors, our results may be confounded. Because we cannot systematically assess the voluntary disclosure rules in each country, we acknowledge this as a limitation of our study.

  15. We use I/B/E/S data from 2005 through 2010 to estimate the COC because we estimate the COC in year t + 1 as a function of voluntary disclosure in year t and we have management forecast data from 2004 through 2009.

  16. We remove all firms from Japan because they are effectively required to make management forecasts (Skinner 1994; Kato et al. 2009).

  17. Specifically, 18, 24, and 6 countries lack information about investor protection, mandatory disclosure requirements, and information dissemination, respectively.

  18. Hail and Leuz (2006) report an average COC of 13.0% across 40 countries and Li (2010) finds an average COC of 11% across 18 European Union (E.U.) countries.

  19. The Pearson correlations calculated at the country level are ρ(investor protection, media) = −0.10, ρ(investor protection, disclosure) = 0.16, and ρ(media, disclosure) = 0.14, but none of these correlations are statistically significant. Correlations at the firm level are ρ(investor protection, media) = 0.18, ρ(investor protection, disclosure) = 0.60, and ρ(media, disclosure) = 0.22, and all are statistically significant at conventional levels.

  20. If greater media penetration induces more and better quality management forecasts, we could also observe a negative coefficient estimate on the interaction term. However, we find that the untabulated Pearson and Spearman correlations between Forecast and Media are −0.14 and −0.09, respectively, suggesting that the effect of Media on the relation between management forecasts and the COC is unlikely to be driven by the media’s impact on management forecast quality.

  21. The only exception is Forecast × Media, which becomes marginally significant (β = 0.008, p = 0.11) in the country-level regression. Note, however, that this country-level regression result may be less informative than results from other specifications because it ignores all firm-level variables that affect the COC.

  22. The coefficient on Forecast becomes positive after we include all institutional factors, but this does not imply that Forecast has a positive effect on the COC because the institutional factors have limited degrees of freedom and the hypothetical case that holds these factors constant (in order to examine the effect of Forecast) is not feasible. Using the coefficient estimates on Forecast and on the three interaction terms from Column I of Table 4, Panel B, as well as the values of the three institutional factors as reported in Table 1, we can calculate the net effect of Forecast on the COC for each country. Here we find that the effect is negative for 24 of the 31 countries (77.4%), representing 89.6% of all firms in our sample.

  23. In addition, economic theory suggests that a pre-commitment to disclosure can reduce the cost of capital (Leuz and Verrecchia 2000) and that managers can credibly signal such commitment by providing more frequent disclosures (Botosan and Harris 2000).

  24. Untabulated analyses reveal that the three forecast characteristics are positively correlated with one another, with correlation coefficients ranging between 0.16 and 0.32. In addition, the three forecast characteristics are negatively correlated with the COC, with coefficients ranging between −0.21 and −0.11.

  25. The only exception is for the regression estimated at the country level, but the small sample size and lower statistical power could be responsible for this loss of significance.

  26. The results for tests in Panel A and Panel B are qualitatively similar when we exclude U.S. firms.

  27. Our inferences are unchanged if we use the sum of forecast frequency over the sample period (rather than the average), as do Baginski and Rakow (2012), but the sum is a noisy measure in our setting because firms appear in our sample for different numbers of years.

  28. This research design allows us to provide direct evidence that low quality voluntary disclosure does not help to lower a firm’s COC, while high quality does. We also estimate the regression using the full sample. Here, untabulated results reveal that the main effect of MF_Quality in Model (1) and the interaction effects for the three country-level factors in Model (2) all remain significant.

  29. Because Predicted forecast is a continuous variable, its coefficient estimate (−0.068) indicates that the COC falls by approximately 1.38% for a one standard deviation increase (0.20, not tabulated) in value.

  30. Specifically, the test results are based on a regression of the second-stage residuals on all exogenous variables including the IVs. If the IVs are valid (i.e., exogenous), their coefficients should be close to zero, and, in particular, n*Rsquare should follow a Chi-square distribution, where n is the number of observations (Larcker and Rusticus 2010, p.192). Untabulated results also reveal that, consistent with the IVs being exogenous, none of the individual IVs are significantly associated with the residuals from the second-stage regression.

  31. The idea here is that if the IVs are valid, the coefficient estimates on the IVs should be similar in magnitude and sign (Larcker and Rusticus 2010, p. 201).

  32. In untabulated analyses, we find that our main inferences are also robust to the use of an alternative method to adjust analyst forecast errors developed by Gode and Mohanram (2013).

  33. According to Tables 4 through 6 from Larocque (2013), for the measures that we use, the COC falls by an average of 2.03% from 2004 through 2006 (which are the three years that overlap with our sample period).

  34. Although we can remove most of the error in analyst forecasts, we still do not observe a positive and significant association between the COC and future realized returns after controlling for cash flow news. This is a common problem for research using the implied COC, and we acknowledge this as a caveat of this study.

  35. In untabulated analyses, we include these different measures of market efficiency in the original regression models and find that our inferences hold.

  36. Realized returns are a noisy and biased measure of the expected COC (Elton 1999; Pastor and Stambaugh 1999), but in an international setting, this measure is preferable to an alternative based on the Fama-French three-factor model because the latter does not perform well in developed countries in Europe and Asia (Fama and French 2012), nor does it explain stock returns in emerging markets (Hou et al. 2011).

  37. Lang and Stice-Lawrence (2015) construct country-level measures related to four characteristics of firms’ annual reports: the length of the reports, the degree of boilerplate language, the comparability of reports with those of peer firms, and a FOG-based measure of readability. We exclude the last measure because, as Lang and Stice-Lawrence (2015, 114) point out, it may reflect the complexity of the underlying economics in addition to the complexity of the annual report. Consequently, high readability based on the FOG-based measure does not necessarily indicate poor financial reporting quality.

  38. A related concern is that the adoption of IRFS has led to substantial homogeneity of mandatory reporting practices across E.U. countries. To address this concern, we partition all countries in Lang and Stice-Lawrence (2015) into two groups—the E.U. country group and the non-E.U. country group. F-tests reveal that the variances of two of the three characteristics we use to proxy for mandatory disclosure requirements (specifically, length of the annual report and the amount of boilerplate language) are not significantly different across the two groups. Overall, these statistics suggest that there is a reasonable degree of heterogeneity in the financial reporting practices of E.U. countries even after the adoption of IFRS.

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Acknowledgements

We thank Stephen Penman (editor), two anonymous reviewers, Zhaoyang Gu, Ole-Kristian Hope, James Myers, Roy Schmardebeck, T.J. Wong, and participants at the 2014 Asian Finance Conference, the 2014 Chinese Accounting Professors’ Association of North America Conference, the 2014 European Accounting Association Meeting, and the 2014 Journal of International Accounting Research Conference for helpful comments and suggestions. Linda Myers gratefully acknowledges financial support from the Halsam Chair of Business at the University of Tennessee, Knoxville, and from the Garrison/Wilson Chair while at the University of Arkansas.

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Appendices

Appendix

Table 12 Variable definitions

Measurement of the Implied Cost of Equity Capital

We follow Hail and Leuz (2006) and Cao et al. (2014) and estimate the implied COC using four models. The variables used in the models are defined below.

p t = current stock price, measured as the closing price on the trading day before the measurement of e t .

bv t = current book value of equity per share, measured at the beginning of the fiscal year when t = 0 and estimated from the models when t > 0.

e t = expected future EPS for year t, measured as the consensus analyst forecast in the sixth month of the fiscal year t.

d t  = expected future dividends per share for year t, measured as year t-1’s actual dividends.

g = economic growth, set to r f  − 3%, where r f is the interest rate on a 10-year Treasury bill measured in June of the given year.

glt = consensus analyst forecast for the long-term growth rate from I/B/E/S.

k = the average dividend payout ratio over the past three years.

We extract bv t and k from S&P Capital IQ. p 0 and bv 0 are adjusted for stock splits. We obtain p t. and analyst earnings forecasts (e t+1 , e t+2 , e t+3 , e t+4 , and e t+5 , g lt ) from I/B/E/S. They are adjusted for stock splits. We require nonmissing values for e t+1 and e t+2 , and that e t+2  > e t+1 . All data items are converted to U.S. dollars.

  1. (1)

    The measure from Claus and Thomas (2001) (r CT )

$$ {p}_t={bv}_t+\sum_{\tau =1}^5\frac{e_{t+\tau}-{r}_{CT}\times {bv}_t}{{\left(1+{r}_{CT}\right)}^{t+\tau}}+\frac{\left({e}_{t+5}-{r}_{CT}\times {bv}_{t+4}\right)\times \left(1+ g\right)}{\left({r}_{CT}- g\right){\left(1+{r}_{CT}\right)}^5} $$
(3)

bv t  = bv t − 1 + e t  − e t  × k.

If e t+3 , e t+4 , and e t+5 are missing, they are replaced using the formula e t+1  = e t  × (1 + g lt ).

  1. (2)

    The measure from Gebhardt et al. (2001) (r GLS )

$$ {p}_t={bv}_t+\sum_{\tau =1}^3\frac{e_{t+\tau}-{r}_{GLS}\times {bv}_{t+\tau -1}}{{\left(1+{r}_{GLS}\right)}^{\tau}}+\sum_{\tau =4}^{11}\frac{\overline{ROE_{t+\tau}}-{r}_{GLS}\times {bv}_{t+\tau -1}}{{\left(1+{r}_{GLS}\right)}^{\tau}}+\frac{\overline{ROE_{t+12}}-{r}_{GLS}\times {bv}_{t+11}\ }{r_{GLS}\times {\left(1+{r}_{GLS}\right)}^{11}} $$
(4)

\( \overset{-}{ROE_t}=\frac{1}{I}\sum_{i=1}^I\overset{-}{ROE_{t, i}} \) , where I is the total number of firms in firm i’s industry.

ROE t,i  = e t,i /bv t,i

$$ {\mathrm{bv}}_{\mathrm{t}}={\mathrm{bv}}_{\mathrm{t}-1}+{\mathrm{e}}_{\mathrm{t}}-{\mathrm{e}}_{\mathrm{t}}\times \mathrm{k} $$
  1. (3)

    The measure from Gode and Mohanram (2003) and Easton and Monahan (2005), based on the model from Ohlson and Juettner-Nauroth (2005) ( r OJN )

$$ {p}_t=\frac{e_{t+1}}{r_{OJN}}+\frac{{e ps}_{t+2}-{ e ps}_{t+1}-{r}_{OJN}\times \left({ e ps}_{t+1}-{dps}_{t+1}\right)}{r_{OJN}\times \left({r}_{OJN}- g\right)} $$
(5)
  1. (4)

    The modified PEG ratio model by Easton (2004) ( r PEG )

$$ {p}_t=\left({e}_{t+2}+{r}_{PEG}\times {d}_{t+1}-{e}_{t+1}\right)/{r}_{PEG}^2 $$
(6)

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Cao, Y., Myers, L.A., Tsang, A. et al. Management forecasts and the cost of equity capital: international evidence. Rev Account Stud 22, 791–838 (2017). https://doi.org/10.1007/s11142-017-9391-5

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