Machine learning improves accounting: discussion, implementation and research opportunities


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.

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  1. 1.

    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 rt 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).

  2. 2.

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

  3. 3.

    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.


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I thank Xuan Peng and Ting Sun for being extremely patient and offering many of the critical insights that ultimately led to this discussion, and, especially, conducting additional analyses for purposes of discussion. I also thank Edwige Cheynel, Iván Marinovic, Eric Floyd, Wenqiang Pan, and Allan Timmerman for many fireside discussions that helped mature the ideas contained here and most gratefully thank Joey Engelberg for creating the discussion group that awoke my interest in machine learning.

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Appendix: Random Forests with scikit-learn

Appendix: Random Forests with scikit-learn


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Bertomeu, J. Machine learning improves accounting: discussion, implementation and research opportunities. Rev Account Stud 25, 1135–1155 (2020).

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  • Machine learning
  • Accounting
  • Estimates
  • Modelling

JEL Classification

  • C4
  • C5
  • G3
  • M2
  • M4