Construction and evaluation of financial distress early warning model based on machine learning

Abstract

As the economy of our nation has expanded, the harm resulting from the financial risks associated with listed corporations has intensified, significantly impeding the survival and expansion of businesses. We must create a reliable financial crisis early warning system to prevent financial risks from endangering the business. This article aims to analyse the development of financial early warning models that use machine learning techniques. The model employs the Archimedes optimization method (AOA) method to optimize the parameters of SVM and selects 20 financial risk assessment indices as the input to anticipate the financial crises. The results suggest that the proposed model surpasses current models in terms of both prediction accuracy and resilience. They also highlight the ensemble model's greater predictive capacity when compared to individual algorithms, highlighting its efficacy in spotting early warning signs of financial instability in a variety of market scenarios. Financial organisations and governments may improve their risk management procedures and prevent future crises by incorporating machine learning techniques into the early warning system.