ACFLN: artificial chemical functional link network for prediction of stock market index

Uncertainty and complexity associated with the stock data make the exact determination of future prices impossible. Successful prediction of a stock future price requires an efficient prediction system. This paper proposes an artificial chemical reaction optimization based functional link network termed as ACFLN for stock market forecasting. The efficiency of the proposed model has been evaluated by forecasting five real stock market prices such as BSE, DJIA, NASDAQ, TAIEX and FTSE. Different experiments are conducted to evaluate the performance of the proposed model such as forecasting the stock price 1 day ahead, 1 week ahead, and 1 month ahead. Data is obtained for all the working days in a year and for each data the said experiments are conducted. From simulation studies, it is revealed that the proposed model achieves better forecasting accuracies over others.