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Machine Learning und empirische Rechnungslegungsforschung: Einige Erkenntnisse und offene Fragen
[Machine Learning and Empirical Accounting Research: Some Findings and Open Questions]

Author

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  • Thorsten Sellhorn

    (LMU München)

Abstract

Zusammenfassung Im Zuge der digitalen Transformation von Wirtschaft und Gesellschaft ergeben sich zunehmend Anwendungsfelder für Ansätze des maschinellen Lernens nicht nur in der Rechnungslegungspraxis, sondern auch in der betriebswirtschaftlichen Forschung auf diesem Gebiet. Der nachfolgende Beitrag diskutiert selektiv einige Einsatzgebiete von Machine-Learning-Ansätzen in der Unternehmensberichterstattung, der Abschlussprüfung sowie der Unternehmensanalyse und -bewertung. Zudem werden aktuelle und potenzielle Anwendungen in der empirischen Forschung aufgezeigt sowie limitierende Faktoren diskutiert.

Suggested Citation

  • Thorsten Sellhorn, 2020. "Machine Learning und empirische Rechnungslegungsforschung: Einige Erkenntnisse und offene Fragen [Machine Learning and Empirical Accounting Research: Some Findings and Open Questions]," Schmalenbach Journal of Business Research, Springer, vol. 72(1), pages 49-69, March.
    • Handle: RePEc:spr:sjobre:v:72:y:2020:i:1:d:10.1007_s41471-020-00086-1
    DOI: 10.1007/s41471-020-00086-1
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    1. Quick, Reiner & Marten, K.-U. & Ruhnke, K., 2015. "Wirtschaftsprüfung, Grundlagen des betrieblichen Prüfungswesens nach nationalen und internationalen Normen," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 77034, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    2. Larcker, David F. & Rusticus, Tjomme O., 2010. "On the use of instrumental variables in accounting research," Journal of Accounting and Economics, Elsevier, vol. 49(3), pages 186-205, April.
    3. Ulf Br&?ggemann & J?rg-Markus Hitz & Thorsten Sellhorn, 2013. "Intended and Unintended Consequences of Mandatory IFRS Adoption: A Review of Extant Evidence and Suggestions for Future Research," European Accounting Review, Taylor & Francis Journals, vol. 22(1), pages 1-37, May.
    4. Ian D. Gow & David F. Larcker & Peter C. Reiss, 2016. "Causal Inference in Accounting Research," Journal of Accounting Research, Wiley Blackwell, vol. 54(2), pages 477-523, May.
    5. Edward I. Altman, 1968. "Financial Ratios, Discriminant Analysis And The Prediction Of Corporate Bankruptcy," Journal of Finance, American Finance Association, vol. 23(4), pages 589-609, September.
    6. Gerard Hoberg & Gordon Phillips, 2016. "Text-Based Network Industries and Endogenous Product Differentiation," Journal of Political Economy, University of Chicago Press, vol. 124(5), pages 1423-1465.
    7. Edward I. Altman, 1968. "The Prediction Of Corporate Bankruptcy: A Discriminant Analysis," Journal of Finance, American Finance Association, vol. 23(1), pages 193-194, March.
    8. Beaver, Wh, 1966. "Financial Ratios As Predictors Of Failure - Reply," Journal of Accounting Research, Wiley Blackwell, vol. 4, pages 123-127.
    9. Earley, Christine E., 2015. "Data analytics in auditing: Opportunities and challenges," Business Horizons, Elsevier, vol. 58(5), pages 493-500.
    10. 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.
    11. Adrian Gepp & Martina K. Linnenluecke & Terrence J. O’Neill & Tom Smith, 2018. "Big data techniques in auditing research and practice: Current trends and future opportunities," Journal of Accounting Literature, Emerald Group Publishing Limited, vol. 40(1), pages 102-115, February.
    12. Sendhil Mullainathan & Jann Spiess, 2017. "Machine Learning: An Applied Econometric Approach," Journal of Economic Perspectives, American Economic Association, vol. 31(2), pages 87-106, Spring.
    13. Gow, Ian D. & Larcker, David F. & Reiss, Peter C., 2016. "Causal Inference in Accounting Research," Research Papers 3393, Stanford University, Graduate School of Business.
    14. Robert Bloomfield & Mark W. Nelson & Eugene Soltes, 2016. "Gathering Data for Archival, Field, Survey, and Experimental Accounting Research," Journal of Accounting Research, Wiley Blackwell, vol. 54(2), pages 341-395, May.
    15. Libby, Robert & Bloomfield, Robert & Nelson, Mark W., 2002. "Experimental research in financial accounting," Accounting, Organizations and Society, Elsevier, vol. 27(8), pages 775-810, November.
    16. Gerard Hoberg & Gordon Phillips, 2010. "Product Market Synergies and Competition in Mergers and Acquisitions: A Text-Based Analysis," The Review of Financial Studies, Society for Financial Studies, vol. 23(10), pages 3773-3811, October.
    17. Leamer, Edward E & Leonard, Herman B, 1983. "Reporting the Fragility of Regression Estimates," The Review of Economics and Statistics, MIT Press, vol. 65(2), pages 306-317, May.
    18. Beneish, Messod D., 1997. "Detecting GAAP violation: implications for assessing earnings management among firms with extreme financial performance," Journal of Accounting and Public Policy, Elsevier, vol. 16(3), pages 271-309.
    19. Beaver, Wh, 1966. "Financial Ratios As Predictors Of Failure," Journal of Accounting Research, Wiley Blackwell, vol. 4, pages 71-111.
    20. repec:eme:jal000:j.acclit.2017.05.003 is not listed on IDEAS
    21. Baruch Lev, 2018. "The deteriorating usefulness of financial report information and how to reverse it," Accounting and Business Research, Taylor & Francis Journals, vol. 48(5), pages 465-493, July.
    22. Kexing Ding & Baruch Lev & Xuan Peng & Ting Sun & Miklos A. Vasarhelyi, 2020. "Machine learning improves accounting estimates: evidence from insurance payments," Review of Accounting Studies, Springer, vol. 25(3), pages 1098-1134, September.
    23. Baruch Lev, 2019. "Ending the Accounting-for-Intangibles Status Quo," European Accounting Review, Taylor & Francis Journals, vol. 28(4), pages 713-736, August.
    24. Susan Athey & Guido Imbens, 2015. "A Measure of Robustness to Misspecification," American Economic Review, American Economic Association, vol. 105(5), pages 476-480, May.
    25. Gassen, Joachim, 2014. "Causal inference in empirical archival financial accounting research," Accounting, Organizations and Society, Elsevier, vol. 39(7), pages 535-544.
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