Machine Learning-Based Financial Statement Analysis

56 Pages Posted: 27 Jan 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: January 15, 2020

Abstract

This paper explores the application of machine learning methods to financial statement analysis. We investigate whether a range of models in the machine learning repertoire are capable of forecasting the sign and magnitude of abnormal stock returns around earnings announcements based on financial statement data alone. We find random forests and recurrent neural networks to outperform deep neural networks and linear models such as OLS and Lasso. Using the models' predictions in an investment strategy we find that random forests dominate all other models and that non-linear methods perform relatively better for predictions of extreme market reactions, while the linear methods are relatively better in predicting moderate market reactions. Analysing the underlying economic drivers of the performance of the random forests, we find that the models select as most important predictors accounting variables commonly used 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 (January 15, 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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