Hybrid nonlinear adaptive scheme for stock market prediction using feedback FLANN and factor analysis

Accurate and effective stock price prediction is very important for potential investors in deciding investment strategy. Data mining techniques have been applied to stock market prediction in recent literature. Factor analysis (FA), a powerful statistical attributes reduction technique, is chosen to select the inputs of the model from the raw data. A feedback type of the functional link artificial neural network (FFLANN) with recursive least square (RLS) training is proposed as a potential prediction model. Comparative performance measures obtained through simulation experiments of Principal component analysis (PCA) and Discrete wavelet transform (DWT) based methods for two stock indices demonstrate that the proposed model is a better alternative for prediction of stock indices with respect to several performance measures. For comparison purposes multilayer artificial neural network (MLANN), radial basis function neural network (RBFNN) and support vector machine (SVM) based models are also simulated under similar conditions and it is observed that the proposed model is superior to MLANN, RBFNN and SVM based prediction models. Further, the involvement of low computational complexity and reduced training time of the model will be better suited for online prediction purpose.