Research On The Financial Crisis Warning Model Of Real Estate Listed Companies In China

Financial crisis early warning has always belonged to the forefront of the financial management theory, but from the research point of view at home and abroad in recent decades, most of the research is focused on all listed companies,and does not distinguish between specific industries. Difference in profession makes one feel worlds apart, the financial data of the different industries will have their own unique characteristics, and also their own will be somewhat relied on. Therefore, the financial crisis early warning research by industry is necessary, and helpful to promote the further development of the theory of financial crisis in our country. Real estate is a leading and pillar industry in national economy, which has the characteristics of high investment, high risk, high-profit. Compared with other industries, real estate has clear industry characteristics; plus, in recent years,Chinese high house prices and real estate control policy which is frequently introduced, pose serious challenges to the real estate developers. Therefore, the latent, early warning and prevention of financial crisis in the real estate should be given more attention. The real estate financial crisis early warning system is imperative to establish.Based on what have mentioned above, the paper at the angle of combining theory and practice, using the methods of literature review, comparative research and statistical analysis research to build a scientific and reasonable company financial crisis warning system. First of all, on the basis of literature research at home and abroad, have defined and reviewed the related theory of financial crisis, financial crisis warning system and warning indicators. In addition to that, in the comparison and analysis of model have determined the institute selection method in this paper. Secondly select the sample and warning indicators, With the help of significance test and the principal component analysis to eliminate indicators. Retained final explanation variables of not related but significant variables. Finally, in combination with non-financial information and the main factor extracted by component analysis using logistics regression to build warning system, and through test to prove the scientific rationality of proposed model.