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Financial reporting complexity and investor underreaction to 10-K information

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Abstract

We study the immediate and delayed market reaction to U.S. Securities and Exchange Commission (SEC) EDGAR 10-K filings. Unusual trading volumes and stock-price movements are documented during the days around the 10-K filing dates. The abnormal price movements are positively associated with future accounting profitability, indicating that 10-K reports contain useful information about future firm performance. In addition, investors’ reaction to 10-K information seems sluggish, as demonstrated by the stock-price drift during the 12-month period after 10-K filing. We find that investors’ underreaction tends to be stronger for firms with more complex 10-K reports.

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Notes

  1. We thank the referee for suggesting the last test.

  2. See, for example, SEC Rule 421(b), 421(d), and Release Nos. 33-7497, 34-39593, IC-23011.

  3. Mandatory electronic filing was fully phased in at the end of 1995. SB stands for small business, which, according to the SEC, refers mainly to entities with revenue less than $25 million. There is no difference in substance between form 10-K and 10-K405, except where the Rule 405 box on the facing page of the Form 10-K is checked. Checking the box indicates that no disclosure of delinquent ownership reports is required. This classification was discontinued in 2002 after the SEC determined that the use of this designation by companies was inconsistent and unreliable.

  4. To the extent that any other significant public disclosure event also takes place during the 3-day window, our measure of 10-K information content will be noisy.

  5. The high variation in FDR works against finding significant underreaction because, to avoid hindsight bias, we use the FDR distribution in the prior year to form portfolios.

  6. We use a five-group partition, as opposed to the usual 10-group partition, to ensure that firms in the top and bottom groups are comparable to firms in the other groups. The hedge portfolio return is larger and more significant with a 10-group portfolio construction.

  7. This is done to avoid hindsight bias. Using the current year distribution to form portfolios gives similar results.

  8. We report value-weighted portfolio return to reduce the impact of small firms. The hedge returns are larger and more significant with equal-weighted portfolio return calculation.

  9. Chordia and Shivakumar (2006) argue that price momentum is subsumed by a measure of investor underreaction to earnings news. Hence, it is not clear whether the momentum variable represents a risk factor or a measure of underreaction to other news. Therefore, we report this separately from the results using the Fama and French three-factor model.

  10. In conducting the calendar time portfolio analysis, we also vary the length of the holding period from 1 to 12 months. The hedge returns are generally positive, and the statistical significance does not show a monotonic increase as the holding period lengthens. This further indicates that the significant long run BHAR is not simply due to compounding.

  11. We also measure earnings surprise based on three-day abnormal stock return around earnings announcement (see, for example, Brandt et al. 2007). Making the measure of earnings news consistent with FDR reduces the concern that the different predictive power of FDR and SUE is due to the difference in the way news is measured. The result shows that FDR is still positive and highly significant.

  12. To further examine how much of the predictive power of COMPLEX is due to industry differences, we calculate industry-neutral information complexity measure which takes a value of 1 if the length of a firm’s 10-K exceeds the median of all 10-K reports in the same industry during the same year. Using this measure in regression (4), the coefficient on FDR * COMPLEX remains highly significant.

  13. Brav and Heaton (2002) show that when investors are uncertain about information structure, rational leaning can lead to a pattern of underreaction that varies with the level of uncertainty.

  14. We thank Feng Li for providing us with the risk measure.

  15. We use the 2-digit SIC industry classification. At least 10 firms in each industry are used to estimate the Dechow and Dechiv (2002) model. We also require at least four years of historical data to estimate the standard deviation of residuals.

  16. We also redefined the information uncertainty measures into dummy variables similar to the way COMPLEX is defined. The significance of the information uncertainty variables gets weaker. FDR * COMPLEX remains highly significant.

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Acknowledgments

This paper has benefited greatly from comments and suggestions of an anonymous referee, Patricia Dechow, Shai Levi, and Katherine Schipper (editor). The editorial assistance of Joseph Cadora and the financial support of the Center for Financial Reporting and Management at University of California are gratefully acknowledged.

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Authors and Affiliations

  1. Barclays Global Investors, 45 Fremont Street, San Francisco, CA, 94105, USA

    Haifeng You

  2. Haas School of Business, University of California, 545 Student Services Building, SPC 1900, Berkeley, CA, 94720, USA

    Xiao-jun Zhang

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  1. Haifeng You
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Correspondence to Xiao-jun Zhang.

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You, H., Zhang, Xj. Financial reporting complexity and investor underreaction to 10-K information. Rev Account Stud 14, 559–586 (2009). https://doi.org/10.1007/s11142-008-9083-2

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