Investigating Accounting Patterns for Bankruptcy and Filing Outcome Prediction using Machine Learning Models

115 Pages Posted: 18 Jul 2019 Last revised: 25 Mar 2020

See all articles by Derek Snow

Derek Snow

The Alan Turing Institute; New York University (NYU) - Finance and Risk Engineering Department; University of Auckland

Date Written: November 12, 2017

Abstract

I study the use of non-linear models and accounting inputs to predict the occurrence of litigated bankruptcies and their associated filing outcomes. The main purpose of this study is to identify the accounting patterns associated with bankruptcies. The filing outcomes include, among others, how long the bankruptcy process will endure, whether the firm will successfully emerge after the bankruptcy period, whether the bankruptcy is tortious, and whether it will involve an asset sale. The study highlights the importance of previously unidentified accounting variables that are useful in predicting bankruptcies and bankruptcy outcomes. The study categorises predictor variables in accounting dimensions to empirically identify the importance of each dimension to the prediction tasks. The high dimensionality of the gradient boosting machine allows us to identify and explain the nonlinear interactions between a wide range of variables.

Keywords: Bankruptcy Prediction, Machine Learning, Corporate Default, Bankruptcy Outcomes

JEL Classification: C32, C38, C45, G33

Suggested Citation

Snow, Derek, Investigating Accounting Patterns for Bankruptcy and Filing Outcome Prediction using Machine Learning Models (November 12, 2017). Available at SSRN: https://ssrn.com/abstract=3420889 or http://dx.doi.org/10.2139/ssrn.3420889

Derek Snow (Contact Author)

The Alan Turing Institute ( email )

British Library, 96 Euston Rd
London, NW1 2DB
United Kingdom

HOME PAGE: http://https://www.turing.ac.uk/

New York University (NYU) - Finance and Risk Engineering Department ( email )

6 Metrotech Center
New York, NY 11201
United States

University of Auckland ( email )

Private Bag 92019
Auckland Mail Centre
Auckland, 1142
New Zealand

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