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Machine learning improves accounting: discussion, implementation and research opportunities

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  • Jeremy Bertomeu

    (Washington University)

Abstract

Machine learning has been growing in importance in empirical accounting research. In this opinion piece, I review the unique challenges of going beyond prediction and leveraging these tools into generalizable conceptual insights. Taking as springboard “Machine learning improves accounting estimates” presented at the 2019 Conference of the Review of Accounting Studies, I propose a conceptual framework with various testable implications. I also develop implementation considerations panels with accounting data, such as colinearities between accounting numbers or suitable choices of validation and test samples to mitigate between-sample correlations. Lastly, I offer a personal viewpoint toward embracing the many low-hanging opportunities to bring the methodology into major unanswered accounting questions.

Suggested Citation

  • Jeremy Bertomeu, 2020. "Machine learning improves accounting: discussion, implementation and research opportunities," Review of Accounting Studies, Springer, vol. 25(3), pages 1135-1155, September.
    • Handle: RePEc:spr:reaccs:v:25:y:2020:i:3:d:10.1007_s11142-020-09554-9
    DOI: 10.1007/s11142-020-09554-9
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    References listed on IDEAS

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    1. Pagan,Adrian & Ullah,Aman, 1999. "Nonparametric Econometrics," Cambridge Books, Cambridge University Press, number 9780521355643, October.
    2. Stefan Wager & Susan Athey, 2018. "Estimation and Inference of Heterogeneous Treatment Effects using Random Forests," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 113(523), pages 1228-1242, July.
    3. 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.
    4. Gu, Zhaoyang & Wu, Joanna Shuang, 2003. "Earnings skewness and analyst forecast bias," Journal of Accounting and Economics, Elsevier, vol. 35(1), pages 5-29, April.
    5. G. Elliott & C. Granger & A. Timmermann (ed.), 2013. "Handbook of Economic Forecasting," Handbook of Economic Forecasting, Elsevier, edition 1, volume 2, number 2.
    6. 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.
    7. Bertomeu, Jeremy & Beyer, Anne & Taylor, Daniel J., 2016. "From Casual to Causal Inference in Accounting Research: The Need for Theoretical Foundations," Foundations and Trends(R) in Accounting, now publishers, vol. 10(2-4), pages 262-313, August.
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    Citations

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    Cited by:

    1. Scott Wentland & Gary Cornwall & Jeremy G. Moulton, 2023. "For What It's Worth: Measuring Land Value in the Era of Big Data and Machine Learning," BEA Papers 0115, Bureau of Economic Analysis.
    2. Iwona Posadzińska & Małgorzata Grzeszczak, 2022. "Management Accounting System in the Management of an Intelligent Energy Sector Enterprise," Energies, MDPI, vol. 15(20), pages 1-17, October.
    3. Md Jahidur Rahman & Hongtao Zhu, 2023. "Predicting accounting fraud using imbalanced ensemble learning classifiers – evidence from China," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 63(3), pages 3455-3486, September.
    4. repec:bea:wpaper:0209 is not listed on IDEAS
    5. Rainer Lueg, 2022. "Constructs for Assessing Integrated Reports—Testing the Predictive Validity of a Taxonomy for Organization Size, Industry, and Performance," Sustainability, MDPI, vol. 14(12), pages 1-13, June.
    6. Xin Xu & Feng Xiong & Zhe An, 2023. "Using Machine Learning to Predict Corporate Fraud: Evidence Based on the GONE Framework," Journal of Business Ethics, Springer, vol. 186(1), pages 137-158, August.
    7. Achakzai, Muhammad Atif Khan & Peng, Juan, 2023. "Detecting financial statement fraud using dynamic ensemble machine learning," International Review of Financial Analysis, Elsevier, vol. 89(C).
    8. Mika Ylinen & Mikko Ranta, 2024. "Employer ratings in social media and firm performance: Evidence from an explainable machine learning approach," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 64(1), pages 247-276, March.

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    More about this item

    Keywords

    Machine learning; Accounting; Estimates; Modelling;
    All these keywords.

    JEL classification:

    • C4 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics
    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling
    • G3 - Financial Economics - - Corporate Finance and Governance
    • M2 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Economics
    • M4 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Accounting

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