Machine Learning-Based Financial Statement Analysis

69 Pages Posted: 27 Jan 2020 Last revised: 3 Dec 2020

See all articles by Amir Amel-Zadeh

Amir Amel-Zadeh

University of Oxford - Said Business School

Jan-Peter Calliess

Oxford-Man Institute of Quantitative Finance

Daniel Kaiser

University of Oxford - Oxford-Man Institute of Quantitative Finance

Stephen Roberts

University of Oxford - Oxford-Man Institute of Quantitative Finance

Date Written: November 25, 2020

Abstract

This paper explores the application of machine learning methods to financial statement analysis. We compare a range of models in the machine learning repertoire in their ability to predict the sign and magnitude of abnormal stock returns around earnings announcements based on past financial statement data alone. Random Forests produce the most accurate forecasts and the highest abnormal returns. (Nonlinear) neural network-based models perform relatively better for predictions of extreme market reactions, while the linear methods are relatively better in predicting moderate market reactions. Long-short portfolios based on model predictions generate sizable abnormal returns, which seem to decay over time. Abnormal returns are robust to various risk factors and load in expected ways on size, value and accruals. Analysing the underlying economic drivers of the performance of the Random Forests, we find that the models select as most important predictors financial variables required to forecast free cash flows and firm characteristics that are known cross-sectional predictors of stock returns.

Keywords: financial statement analysis, fundamental value, machine learning, earnings announcement, accounting-based anomalies, prediction

JEL Classification: G12, G14, M41

Suggested Citation

Amel-Zadeh, Amir and Calliess, Jan-Peter and Kaiser, Daniel and Roberts, Stephen, Machine Learning-Based Financial Statement Analysis (November 25, 2020). Available at SSRN: https://ssrn.com/abstract=3520684 or http://dx.doi.org/10.2139/ssrn.3520684

Amir Amel-Zadeh (Contact Author)

University of Oxford - Said Business School ( email )

Park End Street
Oxford, OX1 1HP
Great Britain

Jan-Peter Calliess

Oxford-Man Institute of Quantitative Finance ( email )

Eagle House, Walton Well Road
Oxford, Oxfordshire OX2 6ED
United Kingdom

Daniel Kaiser

University of Oxford - Oxford-Man Institute of Quantitative Finance ( email )

Eagle House
Walton Well Road
Oxford, Oxfordshire OX2 6ED
United Kingdom

Stephen Roberts

University of Oxford - Oxford-Man Institute of Quantitative Finance ( email )

Eagle House
Walton Well Road
Oxford, Oxfordshire OX2 6ED
United Kingdom

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