# Comparing Multiclass Classification Algorithms for Financial Distress Prediction

arXiv: https://arxiv.org/abs/2307.03908 · HTML: https://mlsanity.com/paper/2307.03908/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 12 references that are in the map.
- First posted on arXiv 8 Jul 2023.

## Builds on (most cited first, 4 of 4)

- [Metrics for Multi-Class Classification: an Overview](https://mlsanity.com/paper/2008.05756/) (2020)
- [Application of Deep Q-Network in Portfolio Management](https://mlsanity.com/paper/2003.06365/) (2020)
- [Implementing the Deep Q-Network](https://mlsanity.com/paper/1711.07478/) (2017)
- [Solving for multi-class: a survey and synthesis](https://mlsanity.com/paper/1809.05929/) (2018)

## Abstract (arXiv)

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.