Credit risk prediction model for listed companies based on improved reinforcement learning and Bayesian optimization hyperband.

ABSTRACT: The financial sector has experienced swift growth over recent years, leading to the escalating prominence of credit risk among publicly traded companies. Consequently, forecasting credit risk for these firms has emerged as a critical task for banks, regulatory bodies, and investors. Traditional models include the z-score, the logit (logistic regression model), the kernel-based virtual machine (KVM), and neural network approaches. Nevertheless, the outcomes from these methods have often fallen short of expectations. Three major challenges in previous works are feature selection, imbalanced classification, and hyperparameter optimization. This paper presents a method for credit risk prediction for listed companies that uses an off-policy proximal policy optimization (PPO) algorithm for featu

SUBMITTER: Yuanqing C 

PROVIDER: S-EPMC12561926 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature