A Hybrid Intelligent Method of Predicting Stock Returns. (7th September 2014)

• Record Type: Journal Article
• Authors: Rather, Akhter Mohiuddin
• Other Names: Kisi Ozgur Academic Editor.
• Is Part Of: Advances in artificial neural systems. (2014)
• Publication Date: 2014-09-07
• Subjects: Neural networks (Computer science) -- Periodicals; Neural networks (Computer science); Periodicals; Electronic journals; 006.32
• DOI: 10.1155/2014/246487
• Languages: English
• ISSNs: 1687-7594

Abstract

Abstract : 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.