IDEAS home Printed from https://ideas.repec.org/a/eee/jaecon/v62y2016i2p209-227.html
   My bibliography  Save this article

Using unstructured and qualitative disclosures to explain accruals

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0165410116300532
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.jacceco.2016.07.003?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. 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.
    14. 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.
    15. 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.
    16. 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.
    17. 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.
    18. 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.
    19. 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.
    20. 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.
    21. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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).
    2. 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.
    3. 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.
    4. Iatridis, George Emmanuel & Pappas, Kostas & Walker, Martin, 2022. "Narrative disclosure quality and the timeliness of goodwill impairments," The British Accounting Review, Elsevier, vol. 54(2).
    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. 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).
    7. 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.
    8. 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.
    9. 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).
    10. Richard Frankel & Jared Jennings & Joshua Lee, 2022. "Disclosure Sentiment: Machine Learning vs. Dictionary Methods," Management Science, INFORMS, vol. 68(7), pages 5514-5532, July.
    11. 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.
    12. Li, Ken, 2022. "Textual fundamentals in earnings press releases," Advances in accounting, Elsevier, vol. 57(C).
    13. 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).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Richard Frankel & Jared Jennings & Joshua Lee, 2022. "Disclosure Sentiment: Machine Learning vs. Dictionary Methods," Management Science, INFORMS, vol. 68(7), pages 5514-5532, July.
    2. Ingrid E. Fisher & Margaret R. Garnsey & Mark E. Hughes, 2016. "Natural Language Processing in Accounting, Auditing and Finance: A Synthesis of the Literature with a Roadmap for Future Research," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 23(3), pages 157-214, July.
    3. 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).
    4. Christina Bannier & Thomas Pauls & Andreas Walter, 2019. "Content analysis of business communication: introducing a German dictionary," Journal of Business Economics, Springer, vol. 89(1), pages 79-123, February.
    5. Tim Loughran & Bill Mcdonald, 2016. "Textual Analysis in Accounting and Finance: A Survey," Journal of Accounting Research, Wiley Blackwell, vol. 54(4), pages 1187-1230, September.
    6. Blau, Benjamin M. & DeLisle, Jared R. & Price, S. McKay, 2015. "Do sophisticated investors interpret earnings conference call tone differently than investors at large? Evidence from short sales," Journal of Corporate Finance, Elsevier, vol. 31(C), pages 203-219.
    7. Beattie, Vivien, 2014. "Accounting narratives and the narrative turn in accounting research: Issues, theory, methodology, methods and a research framework," The British Accounting Review, Elsevier, vol. 46(2), pages 111-134.
    8. Wolfgang Breuer & Andreas Knetsch & Astrid Juliane Salzmann, 2020. "What Does It Mean When Managers Talk About Trust?," Journal of Business Ethics, Springer, vol. 166(3), pages 473-488, October.
    9. Ardia, David & Bluteau, Keven & Boudt, Kris, 2022. "Media abnormal tone, earnings announcements, and the stock market," Journal of Financial Markets, Elsevier, vol. 61(C).
    10. Senave, Elseline & Jans, Mieke J. & Srivastava, Rajendra P., 2023. "The application of text mining in accounting," International Journal of Accounting Information Systems, Elsevier, vol. 50(C).
    11. Craja, Patricia & Kim, Alisa & Lessmann, Stefan, 2020. "Deep Learning application for fraud detection in financial statements," IRTG 1792 Discussion Papers 2020-007, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    12. Kelvin K. F. Law & Lillian F. Mills, 2015. "Taxes and Financial Constraints: Evidence from Linguistic Cues," Journal of Accounting Research, Wiley Blackwell, vol. 53(4), pages 777-819, September.
    13. 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.
    14. Yan Luo & Linying Zhou, 2020. "Textual tone in corporate financial disclosures: a survey of the literature," International Journal of Disclosure and Governance, Palgrave Macmillan, vol. 17(2), pages 101-110, September.
    15. James P. Ryans, 2021. "Textual classification of SEC comment letters," Review of Accounting Studies, Springer, vol. 26(1), pages 37-80, March.
    16. 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.
    17. Allee, Kristian D. & Do, Chuong & Sterin, Mikhail, 2021. "Product market competition, disclosure framing, and casting in earnings conference calls," Journal of Accounting and Economics, Elsevier, vol. 72(1).
    18. Wanli Li & Tiantian Yan & Yue Li & Ziqiao Yan, 2023. "Earnings management and CSR report tone: Evidence from China," Corporate Social Responsibility and Environmental Management, John Wiley & Sons, vol. 30(4), pages 1883-1902, July.
    19. 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.
    20. Nadine Gatzert & Dinah Heidinger, 2020. "An Empirical Analysis of Market Reactions to the First Solvency and Financial Condition Reports in the European Insurance Industry," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 87(2), pages 407-436, June.

    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

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:jaecon:v:62:y:2016:i:2:p:209-227. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/jae .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.