Forecasting S&P 500 index using artificial neural networks and design of experiments

Journal of Industrial Engineering International

دوره 9، شماره 1، اسفند 2013   

نویسندگان

Seyed Taghi Akhavan Niaki^* ؛ Saeid Hoseinzade

Department of Industrial Engineering, Sharif University of Technology, Azaadi Ave, P.O. Box 11155–9414, 1458889694, Tehran, Iran

چکیده

The main objective of this research is to forecast the daily direction of Standard & Poor's 500 (S&P 500) index using an artificial neural network (ANN). In order to select the most influential features (factors) of the proposed ANN that affect the daily direction of S&P 500 (the response), design of experiments are conducted to determine the statistically significant factors among 27 potential financial and economical variables along with a feature defined as the number of nodes of the ANN. The results of employing the proposed methodology show that the ANN that uses the most influential features is able to forecast the daily direction of S&P 500 significantly better than the traditional logit model. Furthermore, experimental results of employing the proposed ANN on the trades in a test period indicate that ANN could significantly improve the trading profit as compared with the buy-and-hold strategy.

کلیدواژه‌ها

S&P 500 index؛ Financial time series؛ Artificial neural networks؛ Design of experiments؛ Analysis of variance؛ Logit model