Research on Financial Distress Predicting of Listed Real Estate Companies using Soft Voting Ensemble Learning
In this study,taking Chinese listed real estate companies as research objects,20 financial indicators reflecting solvency,profitability,operating capacity and leverage ratios are selected,and the financial data of 137 companies are collected for the period of 1991-2021,and a ensemble models based on soft voting is constructed to predict the financial distress of Chinese list-ed real estate companies in the one-year and two-year periods.The results show that the AUC value of the ensemble models are 0.946 and 0.880 for one-year and two-year advance prediction,respectively,which are higher compared with the best-performing single classifier.The explanatory power of the input variables in the ensemble models are analyzed by the SHAP explanatory model.Prediction accuracy is heavily influenced by ROS、ROE、ART、TIE and ROA,irrespective of the one-year or two-year advance pe-riod.The models proposed in this study predict the financial distress of real estate companies with high accuracy and identify the main variables related to forecasting.The study is intended to assist real estate companies and other relevant stakeholders to prevent financial failures through early warning.
Real Estate CompaniesFinancial DistressEnsemble LearningSoft VotingShapley Value
国家科技期刊平台
NSTL
万方数据
维普
