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Corporation financial distress prediction with deep learning: analysis of public listed companies in Malaysia

Zulkifli Halim (Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA Pahang, Raub, Malaysia)
Shuhaida Mohamed Shuhidan (Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA, Shah Alam, Malaysia) (Accounting Research Institute, Universiti Teknologi MARA, Shah Alam, Malaysia)
Zuraidah Mohd Sanusi (Accounting Research Institute, Universiti Teknologi MARA, Shah Alam, Malaysia)

Business Process Management Journal

ISSN: 1463-7154

Article publication date: 19 February 2021

Issue publication date: 3 August 2021

739

Abstract

Purpose

In the previous study of financial distress prediction, deep learning techniques performed better than traditional techniques over time-series data. This study investigates the performance of deep learning models: recurrent neural network, long short-term memory and gated recurrent unit for the financial distress prediction among the Malaysian public listed corporation over the time-series data. This study also compares the performance of logistic regression, support vector machine, neural network, decision tree and the deep learning models on single-year data.

Design/methodology/approach

The data used are the financial data of public listed companies that been classified as PN17 status (distress) and non-PN17 (not distress) in Malaysia. This study was conducted using machine learning library of Python programming language.

Findings

The findings indicate that all deep learning models used for this study achieved 90% accuracy and above with long short-term memory (LSTM) and gated recurrent unit (GRU) getting 93% accuracy. In addition, deep learning models consistently have good performance compared to the other models over single-year data. The results show LSTM and GRU getting 90% and recurrent neural network (RNN) 88% accuracy. The results also show that LSTM and GRU get better precision and recall compared to RNN. The findings of this study show that the deep learning approach will lead to better performance in financial distress prediction studies. To be added, time-series data should be highlighted in any financial distress prediction studies since it has a big impact on credit risk assessment.

Research limitations/implications

The first limitation of this study is the hyperparameter tuning only applied for deep learning models. Secondly, the time-series data are only used for deep learning models since the other models optimally fit on single-year data.

Practical implications

This study proposes recommendations that deep learning is a new approach that will lead to better performance in financial distress prediction studies. Besides that, time-series data should be highlighted in any financial distress prediction studies since the data have a big impact on the assessment of credit risk.

Originality/value

To the best of authors' knowledge, this article is the first study that uses the gated recurrent unit in financial distress prediction studies based on time-series data for Malaysian public listed companies. The findings of this study can help financial institutions/investors to find a better and accurate approach for credit risk assessment.

Keywords

Acknowledgements

The authors gratefully acknowledge the financial grant 600-IRMI/ARI 5/3(035/2019) given by Accounting Research Institute (ARI), Universiti Teknologi MARA through the Ministry of Education Malaysia and Faculty of Computer and Mathematical Sciences for the support and resources.

Citation

Halim, Z., Shuhidan, S.M. and Sanusi, Z.M. (2021), "Corporation financial distress prediction with deep learning: analysis of public listed companies in Malaysia", Business Process Management Journal, Vol. 27 No. 4, pp. 1163-1178. https://doi.org/10.1108/BPMJ-06-2020-0273

Publisher

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Emerald Publishing Limited

Copyright © 2021, Emerald Publishing Limited

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