An Empirical Study On Financial Early-warning For Listed Real Estate Companies

As an important risk management tool, which sends warning signal to identify and predict the company's financial crisis for investment managers, financial forecasting model has gradually became one of the hot issues in the company's finance. The research of designing efficient financial distress prediction model to find these hidden dangers undoubtedly is of great theoretical and practical significance.This thesis selects the real estate industry, which is a high-risk, high investment pillar industry, as the research object. With the standardized information disclosure in the stock market, the state's macro-economic data, and the non-financial indicators information related to the real estate industry, it constructs a reasonable warning model for the stakeholders to analysis and judges the business operations and financial position of the listed companies.Firstly, it describes the mechanism of financial crisis, Specific analysis various factors that impact the financial crisis of the listed real estate companies and the origin and development of the early warning technology of financial crisis. Second, it summarizes the research results of financial warning models at home and abroad, compares the various research methods. Then establishes a system of indictors include financial and non-financial indictors on the basis of the summary of the characteristics about real estate. Lastly, it sets up the Logistic model with specific samples and the system of indictors. Furthermore it gives an empirical analysis of the results of those models.The empirical study concludes that the forecast model performs well in predicting, and those indicators, including the cash flow indicators, the shareholding structure indicators and the annual report disclosed indicators, can improve the accuracy of prediction clearly. What's more, the model constructed with shareholding structure indicators and annual report disclosed indicators can provide effective assistance for different stakeholders.