Using genetic algorithms to predict financial performance --Evidence from China

Author

Jiang, Yanxia ; Ke, Dagang ; Wang, Yongjun ; Xu, Lida

Author_Institution

Xi´´an Jiaotong Univ., Xi´´an

fYear

2007

fDate

7-10 Oct. 2007

Firstpage

3225

Lastpage

3229

Abstract

This study applies genetic algorithms to select financial statement variables which are used to predict the direction of one-year-ahead earnings change. To evaluate the forecasting ability of GA-based-linear discriminant analysis (GA-LDA), this study compares it with probabilistic neural network and decision tree model. The experiment results show that the GA -LDA model outperforms other classification methods.

Keywords

decision trees; financial management; forecasting theory; genetic algorithms; neural nets; GA-based-linear discriminant analysis; decision tree model; financial forecasting; financial statement variables; genetic algorithms; probabilistic neural network; Asia; Economic forecasting; Genetic algorithms; Industrial economics; Industrial relations; Macroeconomics; Performance analysis; Predictive models; Sensitivity and specificity; Stock markets;

fLanguage

English

Publisher

ieee

Conference_Titel

Systems, Man and Cybernetics, 2007. ISIC. IEEE International Conference on

Conference_Location

Montreal, Que.

Print_ISBN

978-1-4244-0990-7

Electronic_ISBN

978-1-4244-0991-4

Type

conf

DOI

10.1109/ICSMC.2007.4413654

Filename

4413654

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2540257