A Reinforcement Leaning Approach to Modelling and Solving Prediction Problems with Application in Bankruptcy & Financial Distress Prediction

Abstract

In this study, the application of Reinforcement Learning (RL) algorithms, specifically Q-Learning and SARSA, is examined in the prediction of financial distress and bankruptcy for non-financial companies in the United States and Canada. The investigation concentrates on the manner in which liquidity-related metrics and solvency indicators have impacted short-term financial health predictions over the past five years. The study aims to evaluate the performance of RL classifiers under a variety of financial distress definitions, with a focus on those that are based on liquidity metrics, using a dataset that includes non-financial information from 4,671 companies. An essential goal is to evaluate the impact of different definitions of financial distress on predictive models' precision and the extent to which RL algorithms adjust to these variations. This entails the examination of liquidity-related measures, including the Current Ratio, Quick Ratio, and Altman Z-score,..., in eight specific scenarios, each of which represents a distinct definition of financial distress. The research aims to answer several questions: What are the effects of varying definitions of financial distress on RL classifiers' performance? What is the role of liquidity-related metrics in identifying and forecasting financial distress? Based on these metrics, what is the performance of Q-Learning and SARSA algorithms in predicting financial distress in U.S. and Canadian companies? The study's objective is to provide valuable insights for businesses, investors, and regulators in corporate environments by contributing to the development of more accurate early warning systems and enhanced risk management tools. The main results show that Q-Learning is better than SARSA at predicting financial distress when using simpler, more stable measures of liquidity. It does this by achieving faster convergence and higher accuracy. However, SARSA works more effectively with more complicated and uncertain financial scenarios, especially those involving cash flow ratios.