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 ...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.展开更多
目的针对陇海铁路沿线密集的黄土滑坡灾害,对其振陷系数提出一种新的预测方法。方法以滑带黄土动三轴试验资料为基础,运用MATLAB建立滑带黄土振陷的BP(error back propagationneural network)神经网络预测模型,并与多元线性回归方法建...目的针对陇海铁路沿线密集的黄土滑坡灾害,对其振陷系数提出一种新的预测方法。方法以滑带黄土动三轴试验资料为基础,运用MATLAB建立滑带黄土振陷的BP(error back propagationneural network)神经网络预测模型,并与多元线性回归方法建立的模型进行误差对比分析。结果BP神经网络模型预测的结果要比多元线性回归模型预测的更准确。结论滑带黄土振陷预测的BP神经网络模型是一种比较理想的预测方法,对黄土地区的滑坡稳定性评价和铁路地基沉降的分析具有重要的价值。展开更多
文摘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.
文摘目的针对陇海铁路沿线密集的黄土滑坡灾害,对其振陷系数提出一种新的预测方法。方法以滑带黄土动三轴试验资料为基础,运用MATLAB建立滑带黄土振陷的BP(error back propagationneural network)神经网络预测模型,并与多元线性回归方法建立的模型进行误差对比分析。结果BP神经网络模型预测的结果要比多元线性回归模型预测的更准确。结论滑带黄土振陷预测的BP神经网络模型是一种比较理想的预测方法,对黄土地区的滑坡稳定性评价和铁路地基沉降的分析具有重要的价值。
