Prediction of Stock Market in Nigeria Using Artificial Neural Network

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Author(s)

Peter Adebayo Idowu 1,* Chris Osakwe 1 Aderonke Anthonia Kayode 1 Emmanuel Rotimi Adagunodo 1

1. Dept. of Computer Science and Engineering, Obafemi Awolowo University, Ile-Ife, Nigeria

* Corresponding author.

DOI: https://doi.org/10.5815/ijisa.2012.11.08

Received: 27 Feb. 2012 / Revised: 12 Jun. 2012 / Accepted: 23 Aug. 2012 / Published: 8 Oct. 2012

Index Terms

Artificial Neural Network, Prediction, Nigerian Stock Exchange, Input Signal

Abstract

Prediction of Nigerian stock market is almost not done by any researcher and is an important factor which can be used to determine the viability of Nigerian stock market. In this paper, the prediction models were developed using Artificial Neural Network. The result of the prediction of Nigerian Stock Exchange (NSE) market index value of selected banks using Artificial Neural Network was presented. The multi-layer feed forward neural network was used, so that each output unit is told what its desired response to input signals ought to be. This work has confirmed the fact that artificial neural network can be used to predict future stock prices. The data collection period is from 2003 to 2006.

Cite This Paper

Peter Adebayo Idowu, Chris Osakwe, Aderonke Anthonia Kayode, Emmanuel Rotimi Adagunodo, "Prediction of Stock Market in Nigeria Using Artificial Neural Network", International Journal of Intelligent Systems and Applications(IJISA), vol.4, no.11, pp.68-74, 2012. DOI:10.5815/ijisa.2012.11.08

Reference

[1]Bishop, C.M. Neural Networks for Pattern Recognition, Oxford: Oxford University Press, 1995

[2]Okobiah, E. Monetary Studies (Theories and Policies), Delta State University, Lagos Centre., 2000.

[3]Pesaran, M.H. and Timermann, M. A Recursive Modelling Approach to Predicting Stock Returns, University of Cambridge Press, Cambridge, 1999.

[4]Anderson, M. and Rosenfeld, E. Neural Networks – Theory and Architecture, Prentice Hall, London, 1989.

[5]Chung, K.K. Financial Forecasting using Neural Networks or Machine Learning Techniques, University of Queensland, Queensland, 2001 {unpublished BSc. Thesis]

[6]Pesaran, M.H. and Timermann, M. A Recursive Modelling Approach to Predicting Stock Returns, University of Cambridge Press, Cambridge, 1999.

[7]Carling, A. Introducing Neural Networks, Sigma Press, England, 1992

[8]Kimoto T., Asakawa K., Yodaa M., Takeoka M. Stock Market prediction System with modular neural network, in proceedings of the International Joint Conference on Neural Network, 1990, pp 1-6.

[9]Mizuno H., Kosaka M., Yajima H., Komoda N. Application of Neural Network to Technical Analysis of Stock Market Prediction, Studies in informatics and Control, 1998, Vol7 No 3 pp 111-120.

[10]Phua P., Ming D. Lin W., Neural Network With genetic Algorithms for Stock prediction, 5th Conference of the Association of Asian Pacific Operations Research Societies, 5-7 July 2000, Singapore

[11]Harvey, B. Stock Market Prediction using Artificial Neural Networks: A macroeconomic Approach, University of Queensland, 1995.

[12]Yildiz B., Use of Artificial Neural Network in prediction of Financial Failures Journal of IMKB, 2001, Vol 5 No 17. 

[13]Sitte, R. and Sitte, J. ‘Analysis of Predictive Ability of Time Delay Neural Networks Applied to the S&P 500 Time Series, Proceedings of the IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews’ , 2000, Vol. 30, No 4, pages 568 - 572.