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Discrete Dynamics in Nature and Society
Volume 2014, Article ID 370280, 10 pages
http://dx.doi.org/10.1155/2014/370280
Research Article

Dynamic Prediction of Financial Distress Based on Kalman Filtering

School of Economics and Management, Southeast University, Nanjing, Jiangsu 211189, China

Received 24 April 2014; Revised 25 June 2014; Accepted 25 June 2014; Published 10 July 2014

Academic Editor: Zhengqiu Zhang

Copyright © 2014 Qian Zhuang and Lianghua Chen. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

The widely used discriminant models currently for financial distress prediction have deficiencies in dynamics. Based on the dynamic nature of corporate financial distress, dynamic prediction models consisting of a process model and a discriminant model, which are used to describe the dynamic process and discriminant rules of financial distress, respectively, is established. The operation of the dynamic prediction is achieved by Kalman filtering algorithm. And a general -step-ahead prediction algorithm based on Kalman filtering is deduced in order for prospective prediction. An empirical study for China’s manufacturing industry has been conducted and the results have proved the accuracy and advance of predicting financial distress in such case.