A hybrid FLANN and adaptive differential evolution model for forecasting of stock market indices

摘要

This paper presents a computationally efficient functional link artificial neural network (CEFLANN) based adaptive model for financial time series prediction of leading Indian stock market indices. Financial time-series data are usually non-stationary and volatile in nature. The proposed adaptive CEFLANN based model employs the least mean square (LMS) algorithm with a new cost function to train the weights of the networks. The mean absolute percentage error (MAPE) with respect to actual stock prices is selected as the performance index to estimate the quality of prediction. The CEFLANN model inputs are chosen from the past stock prices of different market sectors along with technical indicators to determine best stock trend prediction one day ahead in time. Further to improve the performance of the CEFLANN model, weights are optimized using an adaptive differential evolution (DE) algorithm and its overall prediction performance is compared with the improved LMS algorithm showing the effectiveness of the DE in producing more accurate forecast. We have selected different combinations of important technical indicators to have a strong control on changes in stock indices.