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ORIGINAL RESEARCH article

Front. Artif. Intell.
Sec. AI in Finance
Volume 7 - 2024 | doi: 10.3389/frai.2024.1466321
This article is part of the Research Topic Applications of AI and Machine Learning in Finance and Economics View all articles

Predicting Financial Distress in TSX-Listed Firms Using Machine Learning Algorithms

Provisionally accepted
Mark E. Lokanan Mark E. Lokanan *Sana Ramzan Sana Ramzan
  • Royal Roads University, Victoria, Canada

The final, formatted version of the article will be published soon.

    This study explores the use of machine learning (ML) algorithms, a subset of artificial intelligence (AI), including artificial neural networks (ANN), to predict financial distress in companies. The dataset includes financial ratios and firm-level variables from 464 TSX-listed firms. By applying various ML models such as decision trees, random forests, support vector machines (SVM), and artificial neural networks (ANN), this research demonstrates the effectiveness of these techniques in predicting financial distress. The findings reveal that revenue growth, dividend growth, cash-to-current liabilities, and gross profit margins are key predictors of financial distress. The ANN model outperformed other classifiers with an accuracy of 98%, highlighting its potential as a robust tool for distress prediction. The study also integrates recursive feature elimination with cross-validation (RFECV) and bootstrapped CART, offering a novel approach to enhance model stability. These insights provide valuable implications for auditors, regulators, and company management to improve financial health monitoring and fraud detection.

    Keywords: machine learning, artificial intelligence, financial distress, M-score model, Earnings management

    Received: 17 Jul 2024; Accepted: 31 Oct 2024.

    Copyright: © 2024 Lokanan and Ramzan. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Mark E. Lokanan, Royal Roads University, Victoria, Canada

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.