Forecasting stock price index is one of the major challenges in the trade market for investors. Time series data for prediction are difficult to manipulate, but can be focused as segments to discover interesting patterns. In this paper we use several functional link artificial neural networks to get such patterns for predicting stock indices. The novel architecture of functional link artificial neural network with working principle of different models are provided to achieve best forecasting and classification with increase in accuracy of prediction and decrease in training time. Various FLANN models with different polynomials are investigated using different Indian stock indices like IBM, BSE, Oracle, & RIL. The main absolute percentage error (MAPE), sum squared error (SSE) and the standard deviation error (SDE) have been considered to measure the performance of the different FLANN models. In this paper we have presented the result using Reliance Industries Limited (RIL) stock data between 22/12/1999 to 30/12/2011 on closed price of every trading day.
Keywords-Artificial Neural Network (ANN); Functional Link ANN (FLANN); Power Functional Link ANN (PFLANN); Laguerre Functional Link ANN (LFLANN); Legendre Functional Link ANN (LeFLANN); Chebyshev Functional Link ANN (CFLANN); Absolute Percentage Error (MAPE); Sum of Squared
Error (SSE); Standard Deviation of Error (SDE)I.978-1-4673-2272-0/12/$31.00
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 nonstationary 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.
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