Research On Financial Distress Dynamic Prediction Of Listed Company In China

With the deepening reform of the market economic system and the development of the capital market in our country, the enterprise, not only gain opportunities, but also confronted with countless risk. As to the listed companies in our country, the situation that they are occupied in "ST" plate and even forced to quit listing because of financial crisis occurs frequently. If a company gets into financial distress, it not only jeopardizes the survival and development of itself, but also brings huge losses of the inventors and creditors. Any financial distress is a process of gradual deterioration. It is very important for firms to detect the signal of financial distress timely, forecast financial distress, and take effective measures in the bud. Enterprise's financial early-warning as the barometer of the economical operation and indicator lamp which enterprises managed, carrying on research to it not only has high academic values, but also has using value.Many scholars at home and abroad have been studied on the financial distress early warning and established the early-warning models, but most early-warning models are based on the company's static data as samples, without considering the time-series characteristics of financial indicators. The companies' financial crisis was not a sudden occurrence, but rather an evolving process. The model's effectiveness is poor which based on the annual data. It did not take the history of the cumulative value of financial indicators of the impact on the present into account. If the enterprise's overall financial performance is normal with a single period of poor performance, the financial situation of enterprises will soon be back to normal. This temporary deviation from normal should not be classified as a financial distress company. But the static model does not consider the historical impact will classify the normal companies as crisis ones. Therefore, this paper uses quarterly data of listed companies to build dynamic financial crisis early-warning model, taking the cumulative effect of the history of financial indicators into account, with a view to early to predict a listed company's financial anomalies. The empirical research tests the differences between financial indicators and the data's stationary. Then use vector auto-regressive moving average model and the exponentially weighted moving average model to build a financial distress dynamic early-warning model of the listing company. Finally, the usefulness of the model is tested. Upon the examination, the result of the model is satisfactory with high accuracy rate. The model can predict the financial distress earlier.