A Dynamic Prediction Model of Financial Distress in the Financial Sharing Environment

Abstract

The dynamic prediction of financial distress can monitor the financial status of an enterprise in real time and provide evidence for financial analysts. However, currently, there are few studies concerning the dynamic prediction of financial distress in the financial sharing environment, so in order to fill this research gap, this study established a dynamic prediction model of financial distress in the financial sharing environment. Firstly, this study employed the analytic hierarchy process (AHP) and entropy weight theory to determine an index system for the dynamic evaluation of financial distress in the financial sharing environment and gave the weight assignment method of the evaluation indexes. Then, based on the probabilistic neural network (PNN), this study constructed a dynamic prediction model of financial distress and used experimental results to verify the effectiveness and feasibility of the constructed model.