%0 Journal Article
%T A Hybrid Intelligent Method of Predicting Stock Returns
%A Akhter Mohiuddin Rather
%J Advances in Artificial Neural Systems
%D 2014
%I Hindawi Publishing Corporation
%R 10.1155/2014/246487
%X This paper proposes a novel method for predicting stock returns by means of a hybrid intelligent model. Initially predictions are obtained by a linear model, and thereby prediction errors are collected and fed into a recurrent neural network which is actually an autoregressive moving reference neural network. Recurrent neural network results in minimized prediction errors because of nonlinear processing and also because of its configuration. These prediction errors are used to obtain final predictions by summation method as well as by multiplication method. The proposed model is thus hybrid of both a linear and a nonlinear model. The model has been tested on stock data obtained from National Stock Exchange of India. The results indicate that the proposed model can be a promising approach in predicting future stock movements. 1. Introduction Prediction of stock returns has attracted many researchers in the past and at present it is still an emerging area both in academia and in industry. Mathematically, the techniques involved in obtaining prediction of stock returns can be broadly classified into two categories. The first category involves linear models such as autoregressive moving average models, exponential smoothing, linear trend prediction, random walk model, generalized autoregressive conditional heteroskedasticity, and stochastic volatility model [1]. The second category involves those models which are based on artificial intelligence such as artificial neural networks (ANNs) [2], support vector machines [3], genetic algorithms (GA), and particle swarm optimization (PSO) [4]. Linear models have a common limitation associated with them, that is, their linear feature which prevents them from detecting nonlinear patterns of data. Due to instability in stock market, the stock data is volatile in nature; thus, linear models are unable to detect nonlinear patterns of such data. Nonlinear models overcome the limitations of linear models, as ANNs embody useful nonlinear functions which are able to detect nonlinear patterns of data [5]. As a consequence, prediction performance improves by using nonlinear models [6, 7]. A lot of work has been done in this field; for instance, radial basis neural network was used for stock prediction of Shanghai Stock Exchange, wherein artificial fish swarm optimization was introduced so as to optimize radial basis function [8]. In time series prediction, ANNs have received overwhelming attention from researchers. For instance, Freitas et al. [9], Wang et al. [10], Khashei and Bijari [11], Chen et al. [12], and Jain and
%U http://www.hindawi.com/journals/aans/2014/246487/