Predicting financial distress in TSX-listed firms using machine learning algorithms.

ABSTRACT:

Introduction

This study investigates the application of machine learning (ML) algorithms, a subset of artificial intelligence (AI), to predict financial distress in companies. Given the critical need for reliable financial health indicators, this research evaluates the predictive capabilities of various ML techniques on firm-level financial data.

Methods

The dataset comprises financial ratios and firm-specific variables from 464 firms listed on the TSX. Multiple ML models were tested, including decision trees, random forests, support vector machines (SVM), and artificial neural networks (ANN). Recursive feature elimination with cross-validation (RFECV) and bootstrapped CART were also employed to enhance model stability and feature selection.

Results

The findings highl

SUBMITTER: Lokanan ME 

PROVIDER: S-EPMC11631907 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature