Title :
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
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;
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
DOI :
10.1109/ICSMC.2007.4413654
