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Comparative Study Of Financial Crisis Early Warning Model Based On Social Responsibility

Posted on:2016-02-08Degree:MasterType:Thesis
Country:ChinaCandidate:L M XuFull Text:PDF
GTID:2309330461951383Subject:Technical Economics and Management
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With the constant improvement of China’s market economy system and the rising of the activity of the stock market, the listed companies have become the important pillar of the national economy in our country, no matter from the number or the scale to weighing the listed companies. The producing and operation condition of the listed companies have become more complex and uncertain between the interwoven contradictions of stock market data refreshed and the low speed of the economy among the world, which means the probability of the listed companies is getting higher and higher when dealing with the financial crisis and bankruptcy liquidation. All the interwoven contradictions seriously affected the healthy development of China’s listed companies and the stability of the securities market in China, and it may cause a great shock or the loss of the national economy. In view of this, that establishing a scientific and effective financial crisis warning system can predict the potential crisis in the process of production and operation not only can greatly increase the enterprise’s ability to resist risk and competitiveness, but also likely to turn crises into opportunities to enterprise development by leaps and bounds, so as to ensure the benign development of listed companies in our country.This paper proves that the early-warning model is higher accuracy when Introduce the index of corporate social responsibility, on the basis of using BP neural network model respectively studied the sample data analysis.This thesis consists of five chapters:The first chapter mainly introduces the research background and purpose, and the foundation of financial crisis warning about the literature review both in our country and abroad, putting forward the specific methods to establish the early- warning index system;The second chapter mainly analyzes the definition of the concept of financial crisis theories both in our country and abroad, and states the features of the financial crisis early-warning theory and the financial crisis, on the basis of six theories including the theory of enterprise crisis management, the theory of financial management, the theory of risk management, the theory of strategic management, the theory of enterprise life cycle theory and the enterprise diagnosis theory,and chapter reviews the theories related to corporate social responsibility and social responsibility index, on the basis of previous research to establish the index system of corporate social responsibility from the perspective of feasibility, and selects seven variables putting into the system of financial crisis;The third chapter discusses the principle of index selection, and establishes a financial crisis early- warning index system based on this principle; The fourth In determining the sample and data preprocessing based on the BP neural network model and the traditional model were compared(where the traditional model means BP neural network model does not include the social responsibility indicators), which showed Based model and accuracy higher social responsibility indicators; The fifth chapter summarizes the results of the research, discussing the deficiency and the direction of the future research.The innovation points of this paper:1.Introducing a new indicator variable, which enriches the financial crisis early-warning indicator system and guide the development of the financial crisis early-warning index system; 2.Comparison of model-based and traditional models between social responsibility indicators not only get a new relationship between early warning and early warning accuracy of years, but also examines the advantages of the two models of early warning, and the results prove that the model of this paper warning higher accuracy.
Keywords/Search Tags:CSR, Financial Crisis, The BP Neural Network
PDF Full Text Request
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