An XGboost Algorithm Based Model for Financial Risk Prediction

An XGboost Algorithm Based Model for Financial Risk Prediction

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 a...

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Bibliographic Details
Main Authors: Yunsong Xu, Jiaqi Li, Anqi Wu
Format: Article
Language:English
Published: Faculty of Mechanical Engineering in Slavonski Brod, Faculty of Electrical Engineering in Osijek, Faculty of Civil Engineering in Osijek 2024-01-01
Series:Tehnički Vjesnik
Subjects:
machine learning
prediction model
systemic financial risk
XGBoost
Online Access:https://hrcak.srce.hr/file/465257

Internet

https://hrcak.srce.hr/file/465257

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