Comparing Multiclass Classification Algorithms for Financial Distress Prediction

TL;DR

This paper compares various multiclass classification algorithms using a Kaggle dataset to improve financial distress prediction, highlighting their effectiveness in identifying distressed companies for better risk management.

Contribution

It introduces a framework for evaluating multiclass classifiers specifically for financial distress prediction, extending their application beyond traditional fields.

Findings

Certain algorithms outperform others in accuracy.

The framework effectively assesses classifier performance.

Financial distress prediction accuracy improves with specific models.

Abstract

In this study, we explore how to improve the functionality of multiclass classification algorithms. We used a benchmark dataset from Kaggle to create a framework. They have been used in a number of fields, including image recognition, natural language processing, and bioinformatics. This study is focused on the prediction of financial distress in companies in addition to the wider application in multiclass classification. Identifying businesses that are likely to experience financial distress is a crucial task in the fields of finance and risk management. Whenever a business experiences serious challenges keeping its operations going and meeting its financial responsibilities, it is said to be in financial distress. It commonly happens when a company has a sharp and sustained recession in profitability, cash flow issues, or an unsustainable level of debt.