Abstract
Using the adoption of SFAS 142 as an exogenous shock, we examine the effect of changes in financial reporting on firms’ internal information environment. We argue that complying with SFAS 142 induces managers to acquire new information and therefore improves their information sets. Interviews with executives and auditors confirm this argument. Using a difference-in-differences design, we find that firms affected by SFAS 142 (i.e., treatment firms) experience an improvement in management forecast accuracy in the post-SFAS 142 period. The increase is smaller for those with stronger monitoring in the pre-SFAS 142 period and greater for those with a higher likelihood of goodwill impairment. Furthermore, treatment firms with improvements in management forecast accuracy have higher M&A quality, internal capital allocation efficiency, and performance in the post-SFAS142 period. Overall, our findings indicate that changes in external financial reporting can lead to better corporate decisions via their impact on the internal information environment.
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Notes
Studies find that firms with high financial reporting quality have better investment efficiency (Biddle and Hilary 2006; McNichols and Stubben 2008; Biddle et al. 2009). These studies usually attribute the finding to the notion that high financial reporting quality reduces both moral hazard and adverse selection problems.
We argue that managers obtain new information during the goodwill impairment tests. Whether that information is provided in financial reporting in a timely fashion is a separate issue. Thus our finding that firms experience an improvement in their internal information environment after SFAS 142 does not contradict firms’ incentives to delay impairment charges.
SFAS 143 requires the recognition of the fair value of asset retirement obligation liabilities for tangible long-lived assets. Guinn et al. (2005) find that only 10% of U.S. public companies are affected by this standard and that, among those affected, the reported changes in their financial statements were relatively minor.
The majority of these executives are from financial industries, and the sample firms are from nonfinancial industries. Thus one caveat is that the insights obtained from the interviews might not apply to firms in nonfinancial industries.
Comiskey and Mulford (2010) review 10 K filings of a sample of U.S. firms and find that the most frequently used methods in estimating the fair value of the reporting units are the present value of future cash flows and market multiples or a weighted average of the two.
For example, when describing the approach for the impairment test, HP states, in its 2012 annual report: “HP calculates the fair value of a reporting unit based on the present value of estimated future cash flows. Cash flow projections are based on management’s estimates of revenue growth rates and operating margins, taking into consideration industry and market conditions. The discount rate used is based on the weighted-average cost of capital adjusted for the relevant risk associated with business-specific characteristics and the uncertainty related to the business’s ability to execute on the projected cash flows.”
These data are based on managers’ responses to the initial adoption of SFAS 142. Reason (2003) argues that the demand for valuation consultants is expected to continue after the initial adoption year. These discussions imply an increase in the reliance on external valuation consultants after the adoption of SFAS 142.
Studies have used management forecast accuracy to capture the quality of managers’ information sets in other settings, such as internal control (Feng et al. 2009), enterprise systems implementation (Dorantes et al. 2013), capital investments (Goodman et al. 2014), and tax avoidance (Gallemore and Labro 2015). Ittner and Michels (2017) validate the use of management forecasts as a proxy for firms’ internal information, based on survey responses to firms’ risk-based forecasting and planning.
This argument is consistent with the findings in prior research on the effect of corporate governance on corporate disclosure. For example, Beasley (1996), Dechow et al. (1996), and Klein (2002) find that corporate governance characteristics are positively associated with financial reporting quality. Furthermore, A**kya et al. (2005) and Karamanou and Vafeas (2005) find that firms with greater board independence provide forecasts with higher quality.
By the same token, firms with stronger governance in the post-SFAS 142 period implement SFAS 142 more rigorously and thus should improve management forecast accuracy more. An untabulated analysis confirms this conjecture. Note that board independence is not very sticky during our sample period, due to exchange and SOX requirements on majority board independence.
The inferences are the same when we use a different measurement window for the pre- and post-SFAS 142 periods (e.g., four or five years).
In untabulated analyses, we find that our inferences remain the same when we include in our control group the 54 firm-years that have goodwill in the pre-SFAS 142 period but no goodwill in the post-SFAS 142 period.
Requiring the sample firms to issue at least one management forecast in each of the six years leads to the same inferences.
In addition, our results hold after controlling for the time trend. Separately, adding the interaction terms of the Treatment variable with all the control variables leads to the same inferences; note that for this analysis, we replace firm fixed effects with industry fixed effects to avoid multicollinearity problems.
While empirical and anecdotal evidence suggests that firms typically exclude one-time items from their earnings forecasts, to ensure that our results are not driven by the inclusion of goodwill impairment charges, we hand-collect a subsample of earnings forecasts to verify that the impairment charge is excluded from both the earnings forecast and actual earnings reported in First Call. Moreover, our inferences remain when we exclude firm-years with goodwill impairments during the sample period.
In untabulated analyses, we exclude firm fixed effects and include the main effect of Treatment. The inferences remain the same.
The similarity between the two groups in other characteristics is likely due to our sampling requirement that both the treatment and control groups issue earnings forecasts at least once in both the pre- and post-SFAS 142 periods. Research suggests that forecasting firms are typically larger than the average Compustat firm. For example, the average market capitalization is $5.6 billion for our sample firms but only $3.8 billion for the average firm in Compustat/CRSP merged dataset.
As shown in Table 3, control firms do not experience an improvement in forecast accuracy, after controlling for other factors that likely affect forecast accuracy.
In untabulated analyses, we use two alternative matching approaches, coarsened exact matching and entropy balancing, and obtain the same inferences. Similarly, the inferences continue to hold if we conduct the analyses by matching treatment firm with control firm on (1) industry and size, (2) industry and performance, or (3) size and performance. We do not use all three dimensions at the same time because the resulting sample is too small to conduct meaningful analyses.
We thank Feng Li for sharing his competition data at http://webuser.bus.umich.edu/feng/. Li et al. (2013) provide a comprehensive validation test of the competition measure. We also conduct a validation test and find that the competition measure is negatively associated with future profitability, after controlling for current profitability for our sample firms.
We thank the anonymous reviewers for this suggestion. Because the number of firm-years with goodwill impairment is very small, we combined the cases of fixed asset impairment with goodwill impairment when estimating the impairment probability model.
We also examine whether SFAS 142 reduces the likelihood of firms having an earnings restatement in untabulated analyses. We differentiate between restatements due to unintentional clerical errors and those due to frauds. While we find that treatment firms are less likely to have a restatement due to clerical errors in the post-SFAS 142 period, we do not find that there is an incremental change in the likelihood of frauds for treatment firms. This is not surprising since goodwill impairment tests are conducted at the reporting-unit level and fraud-related restatements are usually related to top executives’ incentives.
Other intangibles arise primarily from companies’ purchased intangible assets and the allocation of purchase price as a result of mergers and acquisitions. The amount of other intangibles is not trivial, as shown in Appendix B. Firms reporting other intangibles in some years but not in others are excluded from the sample to increase the power of the test. Also, we require pseudo-treatment and pseudo-control firms to issue at least one management forecast in both the pre- and post-SFAS 142 periods.
Note that we require treatment (control) firms to have (not have) goodwill throughout the sample period and pseudo-treatment (control) firms to have (not have) other intangibles throughout the sample period. The firms that have goodwill in some years but not in other years are excluded from the main analysis. Similarly, the firms that have other intangibles in some years but not in others are excluded from the falsification test. As such, the overlap between the two classifications is not very high. Approximately 37% (47%) of the observations in the pseudo-treatment (control) group are also classified as treatment (control) firms in the main analysis.
Our inferences do not change when we choose 1998 as an alternative pseudo-adoption year.
During the dot-com bubble, treatment firms engaged in more acquisitions and likely wrote off goodwill in the post-SFAS 142 period. Therefore one may argue that they experience an improvement in forecast accuracy in the post-SFAS 142 period because they were overvalued during the bubble period or because bad acquisitions were written off after the bubble. However, to the extent that the dot-com bubble affects high-tech firms most, we obtain similar results after we exclude from our sample the firms in high-tech industries (three-digit SIC codes of 357 or 737). The inferences also remain the same after we remove firms that are likely overvalued during the pre-SFAS 142 period—the firms with the change in average annual returns from the pre- and post-SFAS 142 period in the bottom 10%, 20%, 30%, or 40% of the sample distribution.
Both actual and forecasted earnings are before goodwill impairment. Thus the results are not consistent with the notion that managers use goodwill impairment to manage earnings.
We focus on M&A quality, not the overall investment quality, because goodwill primarily arises from the synergy of acquisitions, and thus managers likely pay particular attention to M&A quality during the impairment tests.
References
AICPA. (1970). Accounting Principles Board (APB) Opinions No. 17: Intangible Assets.
A**kya, B., Bhojraj, S., & Sengupta, P. (2005). The association between outside directors, institutional investors, and the properties of management earnings forecasts. Journal of Accounting Research, 43, 343–376.
Beasley, M. (1996). An empirical analysis of the relation between the board of director composition and financial statement fraud. The Accounting Review, 71, 443–465.
Berger, P., & Hann, R. (2003). The impact of SFAS 131 on information and monitoring. Journal of Accounting Research, 41, 163–223.
Berger, P., & Hann, R. (2007). Segment profitability and the proprietary and agency costs of disclosure. The Accounting Review, 82, 869–906.
Biddle, G., & Hilary, G. (2006). Accounting quality and firm-level capital investment. The Accounting Review, 81, 963–982.
Biddle, G., Hilary, G., & Verdi, R. (2009). How does financial reporting quality relate to investment efficiency? Journal of Accounting and Economics, 48, 112–131.
Chen, C., Kohlbeck, M., & Warfield, T. (2008). Timeliness of impairment recognition: Evidence from the initial adoption of SFAS 142. Advances in Accounting, 24, 72–81.
Chen, W., Shroff, P., & Zhang, I. (2013). Consequences of booking market-driven goodwill impairments. Working Paper: University of Minnesota.
Cheng, Q., Luo, T., & Yue, H. (2013). Managerial incentives and management forecast precision. The Accounting Review, 88, 1575–1602.
Cho, Y. J. (2015). Segment disclosure transparency and internal capital market efficiency: Evidence from SFAS No. 131. Journal of Accounting Research, 53, 669–723.
Comiskey, E., & Mulford, C. (2010). Goodwill, triggering events, and impairment accounting. Managerial Finance, 36, 746–767.
Dechow, P., Sloan, R., & Sweeney, A. (1996). Causes and consequences of earnings management: An analysis of firms subject to enforcement actions by the SEC. Contemporary Accounting Research, 13, 1–36.
Dorantes, C., Li, C., Peters, G., & Richardson, V. (2013). The effect of enterprise systems implementation on the firm information environment. Contemporary Accounting Research, 30, 1427–1461.
FASB (Financial Accounting Standards Board). (1995). Statement of Financial Accounting Standards No. 121: Accounting for the impairment of long-lived assets and for long-lived assets to be disposed of.
FASB (Financial Accounting Standards Board). (2001). Statement of Financial Accounting Standards No. 142: Goodwill and other intangible assets.
Feng, M., Li, C., & McVay, S. (2009). Internal control and management guidance. Journal of Accounting and Economics, 48, 190–209.
Francis, J., Hanna, J., & Vincent, L. (1996). Causes and effects of discretionary asset write-offs. Journal of Accounting Research, 38, 117–134.
Frankel, R., McNichols, M., & Wilson, P. (1995). Discretionary disclosure and external financing. The Accounting Review, 70, 135–150.
Gallemore, J., & Labro, E. (2015). The importance of the internal information environment for tax avoidance. Journal of Accounting and Economics, 60, 149–167.
Goodman, T., Neamtiu, M., Shroff, N., & White, H. (2014). Management forecast quality and capital investment decisions. The Accounting Review, 89, 331–365.
Graham, J., Harvey, C., & Rajgopal, S. (2005). The economic implications of corporate financial reporting. Journal of Accounting and Economics, 40, 3–73.
Graham, J., Hanlon, M., & Shevlin, T. (2011). Real effects of accounting rules: Evidence from multinational firms’ investment location and profit repatriation decisions. Journal of Accounting Research, 49, 137–185.
Guinn, R., Schroeder, R., & Sevin, S. (2005). Accounting for asset retirement obligations: Understanding the financial statement impact. The CPA Journal, 75(12), 30–36.
Hayn, C., & Hughes, J. (2006). Leading indicators of goodwill impairment. Journal of Accounting, Auditing, and Finance, 21, 223–265.
Hemmer, T., & Labro, E. (2008). On the optimal relation between the properties of managerial and financial reporting systems. Journal of Accounting Research, 46, 1209–1240.
Holtzman, M. P. & Sinnett, W. M. (2009). Goodwill impairments. https://www.duffandphelps.com/assets/import/uploads/z-press-room-news/import/www.duffandphelps.com/sitecollectiondocuments/2009_goodwill_impairments.pdf .
Hope, O., & Thomas, W. (2008). Managerial empire building and firm disclosures. Journal of Accounting Research, 46, 591–626.
Huefner, R., & Largay, J. (2004). The effect of the new goodwill accounting rules on financial statements. The CPA Journal, 74(10), 30–35.
Hutton, A. (2005). Determinants of managerial earnings guidance prior to Regulation Fair Disclosure and bias in analysts’ earnings forecasts. Contemporary Accounting Research, 22, 867–914.
Ittner, C., & Michels, J. (2017). Risk-based forecasting and planning and management earnings forecasts. Review of Accounting Studies, 22, 1005–1047.
Karamanou, I., & Vafeas, N. (2005). The association between corporate boards, audit committees, and management earnings forecasts: An empirical analysis. Journal of Accounting Research, 43, 453–486.
Kasznik, R. (1999). On the association between voluntary disclosure and earnings management. Journal of Accounting Research, 37, 57–81.
Klein, A. (2002). Audit committee, board of director characteristics, and earnings management. Journal of Accounting and Economics, 33, 375–400.
Lewis, E., Lippitt, J., & Mastracchio, N. (2001). Users’ comments on SFAS 141 and 142 on business combinations and goodwill. The CPA Journal, 71(10), 26–30.
Li, K., & Sloan, R. (2017). Has goodwill accounting gone bad? Review of Accounting Studies, 22, 964–1003.
Li, Z., Shroff, P., Venkataraman, R., & Zhang, X. (2011). Causes and consequences of goodwill impairment losses. Review of Accounting Studies, 16, 745–778.
Li, F., Lundholm, R., & Minnis, M. (2013). A measure of competition based on 10-K filings. Journal of Accounting Research, 51, 399–436.
Malmendier, U., & Tate, G. (2008). Who makes acquisitions? CEO overconfidence and the market’s reaction. Journal of Financial Economics, 89, 20–43.
McNichols, M., & Stubben, S. (2008). Does earnings management affect firms’ investment decisions? The Accounting Review, 83, 1571–1603.
Miller, G. (2002). Earnings performance and discretionary disclosure. Journal of Accounting Research, 40, 173–204.
Ramanna, K. (2008). The implications of unverifiable fair-value accounting: Evidence from the political economy of goodwill accounting. Journal of Accounting and Economics, 45, 253–281.
Ramanna, K., & Watts, R. (2012). Evidence on the use of unverifiable estimates in required goodwill impairment. Review of Accounting Studies, 17, 749–780.
Reason, T. (2003). Goodwill to all pieces: Are companies properly valuing and assigning acquired intangibles to business units? CFO Magazine. http://ww2.cfo.com/banking-capital-markets/2003/07/goodwill-to-all-pieces/ .
Riedl, E. (2004). An examination of long-lived asset impairments. The Accounting Review, 79, 823–852.
Rogers, J., & Stocken, P. (2005). Credibility of management forecasts. The Accounting Review, 80, 1233–1260.
Shroff, N. (2017). Corporate investment and changes in GAAP. Review of Accounting Studies, 22, 1–63.
Williams, P. (1996). The relation between a prior earnings forecast by management and analyst response to a current management forecast. The Accounting Review, 71, 103–115.
Zang, Y. (2008). Discretionary behavior with respect to the adoption of SFAS142 and the behavior or security prices. Review of Accounting and Finance, 7, 38–68.
Acknowledgements
We thank Patty Dechow (editor), Sandip Dhole, Mark Evans, Gerald Lobo, Shuqing Luo, Mary Jane Rabier, Sugata Roychowdhury, Stephen Stubben, Terry Warfield, two anonymous referees, and workshop and conference participants from Hong Kong University of Science and Technology, INSEAD, National Taiwan University, Singapore Management University, University of Melbourne, University of Wisconsin-Madison, 2014 AAA Annual Meeting, 2014 SMU SOAR Symposium, 2015 FARS Mid-year Meeting, 2015 EAA Annual Congress, and 2015 CAAA Conference for helpful comments, and Dengq** Zheng and Wei Ting Loh for research assistance. We thank financial support from the School of Accountancy Research Center (SOAR) at Singapore Management University. The authors gratefully acknowledge funding from the Lee Kong Chian Fellowship.
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Cheng, Q., Cho, Y.J. & Yang, H. Financial reporting changes and the internal information environment: Evidence from SFAS 142. Rev Account Stud 23, 347–383 (2018). https://doi.org/10.1007/s11142-017-9437-8
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DOI: https://doi.org/10.1007/s11142-017-9437-8