An artificial bee colony algorithm based efficient prediction model for stock market indices

The ABC algorithm is a new meta-heuristic approach, having the advantages of memory, multi-characters, local search, and a solution improvement mechanism. It can be used to identify a high quality optimal solution and offer a balance between complexity and performance, thus optimizing forecasting effectiveness. This paper proposes an efficient prediction model for forecasting of short and long range stock market prices of two well know stock indices, S&P 500 and DJIA using a simple adaptive linear combiner (ALC), whose weights are trained using artificial bee colony (ABC) algorithm. The Model is simulated in terms of mean square error (MSE) and extensive simulation study reveals that the performance of the proposed model with the test input patterns is more efficient, accurate than the PSO and GA based trained models.