A Study Of Financial Crisis Prediction With EVA On The Listed Power Generation Companies

Increasing competition in the market make companies’internal and external risks more and more complex. To raise awareness of the crisis, build early warning model and make the value-based management applied to financial early warning system flexibly has become an indispensable management tool. Currently, research on generating financial early warning of listed companies with EVA is still in an innovative research stage at home and abroad. With traditional financial early warning system introduced EVA compared to the financial early warning system to consider the value of the enterprise value creation and timely warning damage, not only can effectively avoid business losses year after year, while helping long-term development. In the capital-intensive power industry, more prone to inefficient use of assets, wasting a lot of capital, invalid expansion, etc., especially after the financial crisis, China’s economy has been affected to some extent, the power industry is under increasing pressure from all sides,significantly increased financial risk, which is a great reason to invest caused invalid. Enterprise Value damaged assets is the biggest problem a lot of waste generation companies currently represented by the existence of state-owned enterprises, thus speeding up the construction of power generation enterprises financial early warning systems and early warning system was introduced in the financial management indicators EVA value generation companies will benefit as soon as possible sustainable development.With the conclusion of the financial early warning and EVA theory, based on the research results, this paper clarifies the benefits and importance of enterprise applications in the financial early warning EVA. Combined with the power industry with its own characteristics, financial early warning indicator system was constructed containing the EVA. Selection of 45 listed companies in the power industry, the use of factor analysis, financial early warning indicator system and applications built in 2007-2012 no record of 37 ST electricity company’s financial data to build a financial early warning model, and set up four financial early warning alarm degrees. Finally, this paper uses eight power companies’financial data which has the ST record to make a model checking. Model accurately predicted the financial situation of eight companies for non-gifted, and in which there are six companies are after the two-stage warning degree in the first two years which is in the ST’s financial situation indicating that more serious damage corporate value, so that the model has a certain degree of predictability. Finally, this paper makes a financial early warning analysis for 45 power companies in 2013. And the consolidated financial positions of the 45 companies are ranked with their different alarming financial warning.