Research On Listed Tourism Companies Financial Crisis Prewarning Model Based On The Theories Of Grey Neural Network

With the development of the economy and the support of national policy, the tourism industry developed rapidly, and gradually grew into a strategic pillar industry of national economy. With the status of the tourism industry increasing layer by layer, tourism market continuing to expand, more and more enterprises enter into the tourism industry. Listed tourism companies, as an advanced form of expression, occupy an important status in the modern market economy. However, the overall tourism development of listed companies are not optimistic, and its financial position lower than the overall average, many listed companies with varying degrees of loss. Therefore, studying how to use existing research results at home and abroad, combined with the characteristics of tourism companies listed on the financial position to make timely judgments evaluation, is an urgent need to address the problem.In this paper, 26 listed tourism companies in Shanghai and Shenzhen A share market as the research object, to combine existing research results, on the basis of the analysis of the factors that financial crisis caused by tourism companies, the initial induction of the early warning indicators, through the difference in test and correlation analysis, and finally extracted out of the seven most important representative of factors, and as the amount of gray neural network input a set of tourism companies build financial distress prediction model. Many empirical researchs on the domestic non-financial indicators considered small, so this paper introduces the social and public risk factors, business development strategies and other non-financial indicators, making the construction of the model can travel more targeted listed companies to evaluate the status of the financial crisis. At the same time, this combination of small sample size of listed companies in tourism, the characteristics of poor in the amount of information, using a combination of gray system and neural network system, built early warning model. Company operators or management can use the model to detect travel market financial problems, and timely response measures to tackle the problem and improve the company's overall effectiveness.The results show that: (1) characteristics of the financial indicators with tourism and non-financial indicators, in combination, can effectively improve the accuracy of the model to determine rates of early warning; (2) Integrated Application of Grey Model and BP neural network model, one can guarantee at least The early warning indicators provide a higher amount of information content, on the other hand, also guarantees the right to re-input model of objectivity and reliability; (3) Analysis of samples through early warning, timely and effective manner for the company to provide travel managers of listed companies financial situation, and according to the results of financial analysis, provide solutions to the crisis situation of different financial guidance and advice.