Title: Fluctuation prediction of stock market index by adaptive evolutionary higher order neural networks

Authors: Sarat Chandra Nayak; Bijan Bihari Misra; Himansu Sekhar Behera

Addresses: Department of Computer Science and Engineering, Silicon Institute of Technology, Bhubaneswar, Odisha – 751024, India ' Department of Information Technology, Silicon Institute of Technology, Bhubaneswar, Odisha – 751024, India ' Department of Computer Science Engineering and Information Technology, Veer Surendra Sai University of Technology, Burla, Odisha – 768018, India

Abstract: The stock market is complex and dynamic in nature, and has been a subject of research for modelling its random fluctuations. Higher order neural network (HONN) has the ability to expand the input representation space, perform high learning capabilities and have been utilised to solve many complex data mining problems. To capture the extreme volatility, nonlinearity and uncertainty associated with stock data, this paper compares two adaptive evolutionary optimisation-based Pi-Sigma neural networks (AE-PSNN), for prediction of closing prices of five real stock markets. For this experimental study, BSE, DJIA, NASDAQ, FTSE and TAIEX stock indices are employed for short, medium and long term predictions. The performance of the AE-PSNN models has been compared with that of a gradient descent-based PSNN (GD-PSNN) model and found to be superior in terms of prediction accuracy and prediction of change in direction.

Keywords: stock market prediction; higher order neural networks; HONN; data mining; evolutionary optimisation; Pi-Sigma neural networks; PSNN; particle swarm optimisation; PSO; genetic algorithms; GAs; gradient descent; stock market fluctuation; stock market volatility; stock markets.

DOI: 10.1504/IJSI.2016.081152

International Journal of Swarm Intelligence, 2016 Vol.2 No.2/3/4, pp.229 - 253

Received: 07 Jul 2015
Accepted: 09 Jun 2016

Published online: 24 Dec 2016 *

Full-text access for editors Access for subscribers Purchase this article Comment on this article