Crisis Early-Warning Model Based on Exponential Smoothing Forecasting and Pattern Recognition and Its Application to Beijing 2008 Olympic Games

A large number of methods like discriminant analysis, logic analysis, recursive partitioning algorithm have been used in the past for the business failure prediction. Although some of these methods lead to models with a satisfactory ability to discriminate between healthy and bankrupt firms, they suffer from some limitations, often due to only give an alarm, but cannot forecast. This is why we have undertaken a research aiming at weakening these limitations. In this paper, we propose an Exponential Smoothing Forecasting and Pattern Recognition (ESFPR) approach in this study and illustrate how Exponential Smoothing Forecasting and Pattern Recognition can be applied to business failure prediction modeling. The results are very encouraging, and prove the usefulness of the proposed method for bankruptcy prediction. The Exponential Smoothing Forecasting and Pattern Recognition approach discovers relevant subsets of financial characteristics and represents in these terms all important relationships between the image of a firm and its risk of failure.