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Review of Accounting Studies

Erschienen in:

13.08.2020 | Invited Submission

Machine learning improves accounting: discussion, implementation and research opportunities

verfasst von: Jeremy Bertomeu

Erschienen in: Review of Accounting Studies | Ausgabe 3/2020

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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.

Vorheriger Artikel Machine learning improves accounting estimates: evidence from insurance payments

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This would be desirable if, for example, a decision-maker bears a quadratic loss \(\mathbb {E}((g-r_{t})^{2}|h^{t})\) when making a decision based on g. This representation is a normalization to the extent that we can always define r_t as the quantity whose first moment is of interest to a decision-maker: if the decision-maker has a loss function \(\mathbb {E}(L(g,r_{t})|h_{t})\) with an optimum given by the first-order condition \(\mathbb {E}(L_{1}(g^{ML}(h^{t}),r_{t})|h_{t})=0\), we can redefine the (implied) quantity of interest as \(r_{t}^{\prime }\equiv L_{1}(g^{ML}(h^{t}),r_{t})+g^{ML}(h^{t})\), which satisfies (1).

I thank Ting Sun for sharing this analysis for purposes of discussion.

To illustrate, suppose that we (minimally) wish to separate a sample into three subsamples, a training sample to fit the model, a validation sample to select the hyperparamaters of the model, and a test sample to assess performance. The original firms are firms observed over a full time-series. To select these subsamples in a manner that did not imply any time or firm-level correlations, we would have to first divide periods in subsamples 1, 2 and 3 and then subdivide the firms in groups a, b, and c, dividing the entire sample as 1a, 2b and 3c but dropping all other subgroups to avoid correlations. Assuming each group is equally sized, this would imply a data loss of 2/3.

Bao, Y., Ke, B., Li, B., Yu, Y.J., & Zhang, J. (2019). Detecting accounting fraud in publicly traded us firms using a machine learning approach. Available at SSRN 2670703.

Barth, M.E., Li, K., & McClure, C. (2019). Evolution in value relevance of accounting information.

Bertomeu, J., Beyer, A., & Taylor, D.J. (2016). From casual to causal inference in accounting research: The need for theoretical foundations. Foundations and Trends in Accounting, 10(2-4), 262–313.CrossRef

Bertomeu, J., Cheynel, E., Floyd, E., & Pan, W. (2019). Using machine learning to detect misstatements. Available at SSRN 3496297.

Binz, O, Katherine, S, & Stanridge, K. (2020). What can analysts learn from artificial intelligence about fundamental analysis?.

Chemla, G., & Hennessy, C.A. (2019). Rational expectations and the paradox of policy-relevant natural experiments. Journal of Monetary Economics.

Dechow, P.M., & Skinner, D.J. (2000). Earnings management: Reconciling the views of accounting academics, practitioners, and regulators. Accounting Horizons, 14(2), 235–250.CrossRef

Dechow, P., Ge, W., & Schrand, C. (2010). Understanding earnings quality: A review of the proxies, their determinants and their consequences. Journal of Accounting and Economics, 50(2-3), 344–401.CrossRef

Deng, H. (2019). Interpreting tree ensembles with intreess. International Journal of Data Science and Analytics, 7 (4), 277–287.CrossRef

Ding, K., Lev, B., Peng, X., Sun, T., & Vasarhelyi, M.A. (2019). Machine learning improves accounting estimates. Review of Accounting Studies, forth.

Elliott, G, & Timmermann, A. (2013). Handbook of economic forecasting. Elsevier.

Gerakos, J.J., Richard Hahn, P., Kovrijnykh, A., & Zhou, F. (2016). Prediction versus inducement and the informational efficiency of going concern opinions. Available at SSRN 2802971.

Gu, Z., & Wu, J.S. (2003). Earnings skewness and analyst forecast bias. Journal of Accounting and Economics, 35(1), 5–29.CrossRef

Horowitz, J.L. (2001). The bootstrap. In Handbook of econometrics, (Vol. 5 pp. 3159–3228): Elsevier.

Hugon, A., Kumar, A., & Lin, A.-P. (2016). Analysts, macroeconomic news, and the benefit of active in-house economists. The Accounting Review, 91(2), 513–534.CrossRef

Li, F. (2010). The information content of forward-looking statements in corporate filings-a naïve bayesian machine learning approach. Journal of Accounting Research, 48(5), 1049–1102.CrossRef

Mohri, M., Rostamizadeh, A., & Talwalkar, A. (2018). Foundations of machine learning. MIT Press.

Pagan, A., & Ullah, A. (1999). Nonparametric econometrics. Cambridge University Press.

Perols, J. (2011). Financial statement fraud detection: An analysis of statistical and machine learning algorithms. Auditing: A Journal of Practice & Theory, 30(2), 19–50.CrossRef

Perols, J.L., Bowen, R.M., Zimmermann, C., & Samba, B. (2017). Finding needles in a haystack: Using data analytics to improve fraud prediction. The Accounting Review, 92(2), 221–245.CrossRef

Sun, T. (2019). Applying deep learning to audit procedures: An illustrative framework. Accounting Horizons, 33(3), 89–109.CrossRef

Wager, S., & Athey, S. (2018). Estimation and inference of heterogeneous treatment effects using random forests. Journal of the American Statistical Association, 113(523), 1228–1242.CrossRef

Watts, R.L. (2003). Conservatism in accounting part ii: Evidence and research opportunities. Accounting Horizons, 17(4), 287–301.CrossRef

Titel: Machine learning improves accounting: discussion, implementation and research opportunities
verfasst von: Jeremy Bertomeu
Publikationsdatum: 13.08.2020
Verlag: Springer US
Erschienen in: Review of Accounting Studies / Ausgabe 3/2020
Print ISSN: 1380-6653
Elektronische ISSN: 1573-7136
DOI: https://doi.org/10.1007/s11142-020-09554-9

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