An Early Warning Model of Financial Distress Prediction Based on Logistic-AHP-BP Neural Network Model

• 作者: Yifan Wu
• 来源期刊: 经济管理学刊:中英文版
• 学科: 经济
• 关键词: FINANCIAL DISTRESS Risk of Delisting LOGISTIC Regression BP NEURAL Network Model
• 年，卷（期）: 2018,（2）
• 页码范围: 184-194
• 页数: 11页
• 分类号: F

摘要

Ever since the appearance of"Implementation Measures for Suspending and Terminating the Listing of Loss-making Companies"in 2001,the delisting system has emerged.However,the proportion of delisted companies in China has never exceeded 1% each year.The number of delisted companies in the security market is far less than the number of companies with financial distress.The capital market lacks a good delisting system and investors lack risk identification capabilities.Financial risk is directly related to delisting risk.Therefore,an early warning model of financial distress prediction for China.s stock market can provide guidance to stakeholders such as listed companies and capital markets.This paper first explains the immature delisting system of China.s capital market and the overall high risk of listed companies.financial distress.Then,the paper further elaborates previous research on financial distress prediction model of listed companies and analyzes the advantages and disadvantages of different models.This paper chooses the Analytic Hierarchy Process(AHP)to screen out the main factors that affect the risk of financial distress.The main factors are included in Logistic regression model and BP neural network model for predicting financial distress of listed companies.The overall effect of two models are assessed and compared.Finally,this paper proposes policy implications according to empirical results.