Improving financial distress prediction using machine learning: A preliminary study

Faculty of Information and Communication Technology, Universiti Tunku Abdul Rahman, Malaysia

^* Corresponding author: tongdl@utar.edu.my

Abstract

Financial distress is when a company faces significant difficulties meeting its financial obligations and maintaining profitability, leading to bankruptcy, insolvency, and severe economic losses. Therefore, early warning for companies at risk of financial distress is vital for business stakeholders to take timely corrective actions and avoid adverse outcomes. Existing financial distress predictions often rely on historical datasets, incorporating various indicators collected through varied methodologies and experts’ opinions. The challenge arises in discerning which indicators are pivotal for predicting corporate distress, as their influence and relevance may vary. This study proposed a machine learning framework to eliminate variations of different experts’ knowledge when selecting pivotal indicators. Data containing 4006 companies and 204 indicators was extracted from CSMAR. The Chi-square test is employed to select significant indicators. The correlation of these selected indicators is modeled using the C4.5 decision tree. Results showed that this selected feature set is closely aligned with those obtained when utilizing all features in the data. A thorough comparison of the indicators selected by the expert revealed notable distinctions. Features chosen by the Chi-square test are related to financial ratios and also exhibit a pronounced focus on societal attention, shareholding concentration, and market dynamics.