Table of Contents
Advances in Artificial Neural Systems
Volume 2014, Article ID 246487, 7 pages
Research Article

A Hybrid Intelligent Method of Predicting Stock Returns

Woxsen School of Business, Sadasivpet, Kamkol, Hyderabad 502291, India

Received 16 May 2014; Revised 26 August 2014; Accepted 26 August 2014; Published 7 September 2014

Academic Editor: Ozgur Kisi

Copyright © 2014 Akhter Mohiuddin Rather. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.