An Adaptive Second Order Neural Network with Genetic-Algorithm-based Training (ASONN-GA) to Forecast the Closing Prices of the Stock Market

Sarat Chandra Nayak, Bijan Bihari Misra and Himansu Sekhar Behera
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Sarat Chandra Nayak: Department Computer Science Engineering and Information Technology, Veer Surendra Sai University of Technology, Burla, India
Bijan Bihari Misra: Department of Information Technology, Silicon Institute of Technology, Bhubaneswar, India
Himansu Sekhar Behera: Veer Surendra Sai University of Technology, Burla, India

International Journal of Applied Metaheuristic Computing (IJAMC), 2016, vol. 7, issue 2, 39-57

Abstract: Successful prediction of stock indices could yield significant profit and hence require an efficient prediction system. Higher order neural networks (HONN) have several advantages over traditional neural networks such as stronger approximation, higher fault tolerance capacity and faster convergence characteristics. This paper proposes an adaptive single layer second order neural network with genetic algorithm based training (ASONN-GA) applied to forecast daily closing prices of the stock market. For comparative study of performance, two conventional neural based models such as a recurrent neural network (RNN) and a multilayer perceptron (MLP) have been developed. The optimal network parameters for all the three models are tuned by genetic algorithm (GA). The efficiencies of the models have been evaluated by forecasting the one-day-ahead closing prices of real stock markets. From simulation studies, it is revealed that the ASONN-GA model achieve better forecasting accuracy over other two models.

Date: 2016
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