Development and performance evaluation of FLANN based model for forecasting of stock markets

Abstract

A trigonometric functional link artificial neural network (FLANN) model for short (one day) as well as long term (one month, two months) prediction of stock price of leading stock market indices: DJIA and S&P 500 is developed in this paper. The proposed FLANN model employs the least mean square (LMS) as well as the recursive least square (RLS) algorithms in different experiments to train the weights of the model. The historical index data transformed into various technical indicators as well as macro economic data as fundamental factors are considered as inputs to the proposed models. The mean absolute percentage error (MAPE) with respect to actual stock prices is selected as the performance index to gauge the quality of prediction of the models. Extensive simulation and test results show that the application of FLANN to the stock market prediction problem gives out results which are comparable to other neural network models. In addition the proposed models are structurally simple and requires less computation during training and testing as the model contains only one neuron and one layer. Between the two models proposed the FLANN–RLS requires substantially less experiments to train compared to the LMS based model. This feature makes the RLS-based FLANN model more suitable for online prediction.