Work

An XGboost Algorithm Based Model for Financial Risk Prediction

Year: 2024

Type: article

Abstract: This study presents a novel financial risk prediction model utilizing the XGboost algorithm, analyzing macroeconomic data from the Jorda-Schularic-Taylor database.Our method achieves an 84.77% accuracy rate in predicting systemic financial risks.Unlike traditional models, this model combines the anomaly detection algorithm with the XGboost model, solving the possible "gray sample" problem and improving predictive accuracy.The model's feature importance analysis reveals key indicators, providing insights into the dynamics of financial risk occurrence.Finally, the systemic financial risk score is used to comprehensively evaluate a country's systemic financial risk level, offering a robust risk assessment and monitoring tool.This research enhances the application of machine learning in financial risk prediction, offering a reference for improving risk identification and prevention.

Source: Tehnicki vjesnik - Technical Gazette

DOI: 10.17559/tv-20231021001043

Authors: Xu, Yunsong, Li, Jiaqi, Wu, Anqi

Institutions: Beijing Language and Culture University, Central University of Finance and Economics, East China University of Political Science and Law

Language: English

Topic: Financial Distress and Bankruptcy Prediction

Subfield: Accounting

Field: Business, Management and Accounting

Domain: Social Sciences

Open Access status: gold

Estimated APC paid: $431

Funders: Beijing Language and Culture University, Fundamental Research Funds for the Central Universities