A Model and Empirical Analysis on Financial Distress Forecasting of Listed Companies Based on Least-Square Support Vector Machine

Author

Liu, Chunmei ; Xin, Min

Author_Institution

Sch. of Inf. Manage. & Eng., Shanghai Univ. of Finance & Econ., Shanghai, China

fYear

2009

fDate

20-22 Sept. 2009

Firstpage

1

Lastpage

4

Abstract

This paper applies least-square support vector machine (LS-SVM), a statistic machine learning methods, and establishes a model of financial distress prediction. Based on information of listed companies in Shanghai and Shenzhen during the year 2005 to 2006, the paper gives an empirical analysis of financial distress prediction. Research conclusions show that prediction and self discriminate capability of the prediction model is increasing by year as the coming of financial distress, and the remained discriminate capability is higher than the prediction capability.

Keywords

financial data processing; forecasting theory; learning (artificial intelligence); least squares approximations; support vector machines; LS-SVM; financial distress forecasting; least-square support vector machine; statistic machine learning method; Companies; Economic forecasting; Finance; Information analysis; Information management; Logistics; Neural networks; Predictive models; Support vector machines; Wind forecasting;

fLanguage

English

Publisher

ieee

Conference_Titel

Management and Service Science, 2009. MASS '09. International Conference on

Conference_Location

Wuhan

Print_ISBN

978-1-4244-4638-4

Electronic_ISBN

978-1-4244-4639-1

Type

conf

DOI

10.1109/ICMSS.2009.5301328

Filename

5301328