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Using unstructured and qualitative disclosures to explain accruals

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  • Frankel, Richard
  • Jennings, Jared
  • Lee, Joshua

Abstract

We examine the usefulness of support vector regressions (SVRs) in assessing the content of unstructured, qualitative disclosures by relating MD&A-based SVR-accrual estimates (MD&A accruals) to actual accruals. We find that MD&A accruals explain a statistically and economically significant portion of firm-level accruals and identify more persistent accruals. We find that the explanatory power of MD&A accruals is higher for more readable 10-Ks, thereby providing evidence for the construct validity of the readability measures. To highlight the flexibility of the SVR method, we apply it to other dependent variables and disclosures. We find that MD&A-based cash-flow forecasts produced by SVR predict next period’s cash flows. We apply SVR to conference call transcripts and find accruals estimates have similar explanatory power to MD&A accruals. Finally, the explanatory power of MD&A accruals increases between 1994 and 2013.

Suggested Citation

  • Frankel, Richard & Jennings, Jared & Lee, Joshua, 2016. "Using unstructured and qualitative disclosures to explain accruals," Journal of Accounting and Economics, Elsevier, vol. 62(2), pages 209-227.
    • Handle: RePEc:eee:jaecon:v:62:y:2016:i:2:p:209-227
    DOI: 10.1016/j.jacceco.2016.07.003
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    References listed on IDEAS

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    1. David F. Larcker & Anastasia A. Zakolyukina, 2012. "Detecting Deceptive Discussions in Conference Calls," Journal of Accounting Research, Wiley Blackwell, vol. 50(2), pages 495-540, May.
    2. Tim Loughran & Bill Mcdonald, 2014. "Measuring Readability in Financial Disclosures," Journal of Finance, American Finance Association, vol. 69(4), pages 1643-1671, August.
    3. Richardson, Scott A. & Sloan, Richard G. & Soliman, Mark T. & Tuna, Irem, 2005. "Accrual reliability, earnings persistence and stock prices," Journal of Accounting and Economics, Elsevier, vol. 39(3), pages 437-485, September.
    4. Dechow, Patricia & Ge, Weili & Schrand, Catherine, 2010. "Understanding earnings quality: A review of the proxies, their determinants and their consequences," Journal of Accounting and Economics, Elsevier, vol. 50(2-3), pages 344-401, December.
    5. Angela K. Davis & Jeremy M. Piger & Lisa M. Sedor, 2012. "Beyond the Numbers: Measuring the Information Content of Earnings Press Release Language," Contemporary Accounting Research, John Wiley & Sons, vol. 29(3), pages 845-868, September.
    6. Li, Feng, 2008. "Annual report readability, current earnings, and earnings persistence," Journal of Accounting and Economics, Elsevier, vol. 45(2-3), pages 221-247, August.
    7. Price, S. McKay & Doran, James S. & Peterson, David R. & Bliss, Barbara A., 2012. "Earnings conference calls and stock returns: The incremental informativeness of textual tone," Journal of Banking & Finance, Elsevier, vol. 36(4), pages 992-1011.
    8. Paul C. Tetlock & Maytal Saar‐Tsechansky & Sofus Macskassy, 2008. "More Than Words: Quantifying Language to Measure Firms' Fundamentals," Journal of Finance, American Finance Association, vol. 63(3), pages 1437-1467, June.
    9. Kristian D. Allee & Matthew D. Deangelis, 2015. "The Structure of Voluntary Disclosure Narratives: Evidence from Tone Dispersion," Journal of Accounting Research, Wiley Blackwell, vol. 53(2), pages 241-274, May.
    10. Li, Feng & Minnis, Michael & Nagar, Venky & Rajan, Madhav, 2014. "Knowledge, compensation, and firm value: An empirical analysis of firm communication," Journal of Accounting and Economics, Elsevier, vol. 58(1), pages 96-116.
    11. Core, John E., 2001. "A review of the empirical disclosure literature: discussion," Journal of Accounting and Economics, Elsevier, vol. 31(1-3), pages 441-456, September.
    12. Feng Li, 2010. "The Information Content of Forward‐Looking Statements in Corporate Filings—A Naïve Bayesian Machine Learning Approach," Journal of Accounting Research, Wiley Blackwell, vol. 48(5), pages 1049-1102, December.
    13. Stephen V. Brown & Jennifer Wu Tucker, 2011. "Large‐Sample Evidence on Firms’ Year‐over‐Year MD&A Modifications," Journal of Accounting Research, Wiley Blackwell, vol. 49(2), pages 309-346, May.
    14. Dechow, Patricia M., 1994. "Accounting earnings and cash flows as measures of firm performance : The role of accounting accruals," Journal of Accounting and Economics, Elsevier, vol. 18(1), pages 3-42, July.
    15. Ball, Ray & Shivakumar, Lakshmanan, 2005. "Earnings quality in UK private firms: comparative loss recognition timeliness," Journal of Accounting and Economics, Elsevier, vol. 39(1), pages 83-128, February.
    16. Mitchell A. Petersen, 2009. "Estimating Standard Errors in Finance Panel Data Sets: Comparing Approaches," The Review of Financial Studies, Society for Financial Studies, vol. 22(1), pages 435-480, January.
    17. Patrick Bajari & Denis Nekipelov & Stephen P. Ryan & Miaoyu Yang, 2015. "Machine Learning Methods for Demand Estimation," American Economic Review, American Economic Association, vol. 105(5), pages 481-485, May.
    18. Yang Bao & Anindya Datta, 2014. "Simultaneously Discovering and Quantifying Risk Types from Textual Risk Disclosures," Management Science, INFORMS, vol. 60(6), pages 1371-1391, June.
    19. Nelson, Karen K. & Barth, Mary E. & Cram, Donald, 2001. "Accruals and the Prediction of Future Cash Flows," Research Papers 1594r, Stanford University, Graduate School of Business.
    20. Paul C. Tetlock, 2007. "Giving Content to Investor Sentiment: The Role of Media in the Stock Market," Journal of Finance, American Finance Association, vol. 62(3), pages 1139-1168, June.
    21. Tim Loughran & Bill Mcdonald, 2011. "When Is a Liability Not a Liability? Textual Analysis, Dictionaries, and 10‐Ks," Journal of Finance, American Finance Association, vol. 66(1), pages 35-65, February.
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    Cited by:

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    2. John Donovan & Jared Jennings & Kevin Koharki & Joshua Lee, 2021. "Measuring credit risk using qualitative disclosure," Review of Accounting Studies, Springer, vol. 26(2), pages 815-863, June.
    3. Mushtaq, Rizwan & Gull, Ammar Ali & Shahab, Yasir & Derouiche, Imen, 2022. "Do financial performance indicators predict 10-K text sentiments? An application of artificial intelligence," Research in International Business and Finance, Elsevier, vol. 61(C).
    4. Rjiba, Hatem & Saadi, Samir & Boubaker, Sabri & Ding, Xiaoya (Sara), 2021. "Annual report readability and the cost of equity capital," Journal of Corporate Finance, Elsevier, vol. 67(C).
    5. Hoberg, Gerard, 2016. "Discussion of using unstructured and qualitative disclosures to explain accruals," Journal of Accounting and Economics, Elsevier, vol. 62(2), pages 228-233.
    6. Li, Ken, 2022. "Textual fundamentals in earnings press releases," Advances in accounting, Elsevier, vol. 57(C).
    7. Claudine Mangen & Alexia Paduano & Bianca Paduano & Jessica Hadzurik & Juliano Leggio & Kayla Russo, 2020. "Smoke and Mirrors? Disclosures in the Marijuana Industry in Canada," Accounting Perspectives, John Wiley & Sons, vol. 19(3), pages 149-179, September.
    8. Matthew Bamber & Santhosh Abraham, 2020. "On the “Realities” of Investor‐Manager Interactivity: Baudrillard, Hyperreality, and Management Q&A Sessions†," Contemporary Accounting Research, John Wiley & Sons, vol. 37(2), pages 1290-1325, June.
    9. Xi Chen & Yang Ha (Tony) Cho & Yiwei Dou & Baruch Lev, 2022. "Predicting Future Earnings Changes Using Machine Learning and Detailed Financial Data," Journal of Accounting Research, Wiley Blackwell, vol. 60(2), pages 467-515, May.
    10. Blankespoor, Elizabeth & deHaan, Ed & Marinovic, Iván, 2020. "Disclosure processing costs, investors’ information choice, and equity market outcomes: A review," Journal of Accounting and Economics, Elsevier, vol. 70(2).
    11. Richard Frankel & Jared Jennings & Joshua Lee, 2022. "Disclosure Sentiment: Machine Learning vs. Dictionary Methods," Management Science, INFORMS, vol. 68(7), pages 5514-5532, July.
    12. Durnev, Art & Mangen, Claudine, 2020. "The spillover effects of MD&A disclosures for real investment: The role of industry competition," Journal of Accounting and Economics, Elsevier, vol. 70(1).
    13. Chad R. Larson & Richard Sloan & Jenny Zha Giedt, 2018. "Defining, measuring, and modeling accruals: a guide for researchers," Review of Accounting Studies, Springer, vol. 23(3), pages 827-871, September.

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    More about this item

    Keywords

    M40; M41; Textual analysis; Support vector regressions; Disclosure; Accruals;
    All these keywords.

    JEL classification:

    • M40 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Accounting - - - General
    • M41 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Accounting - - - Accounting

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