Abstract

The Nigerian stock market recently witnessed a continuous drop in the All-Share Index and volume of traded securities. The stock market indices have moved far relative to their previous year's levels and banks and markets suddenly become clearly unstable or strained to the point where it may collapse. In order to forestall future happenings, this work therefore defines a method of training that provides a forecast of stock growth over a period of 52 weeks. The earnings per share, price earnings ratio and the closing prices are calculated. It is resolved that fluctuations can be averted if past knowledge is well studied and made active.

References

• Nwude E. C. (2012), The Crash of The Nigerian Stock Market: What Went Wrong, The Consequences and The Panacea, Developing Country Studies, ISSN 2225-0565, 2, 9, 105- 117.
• Rai, P and Rai, k. (2011), Comparison of Stock Prediction Using Different Neural Network Types, International Journal of Advanced Engineering & Application, January issue, 157-160.
• Afshari H. (2005). Structural measurement of forecasting stock price in Tehran stock exchange, measuring accounting and auditing. Manage. Fac. Tehran Univ. , 32: 103-126.
• Owusu-Nantwi, Victor2 and John K. M. Kuwornu (2011). Analyzing the effect of macroeconomic variables on stock market returns: Evidence from Ghana, Journal of Economics and International Finance, 311, 605–615.
• Obiechina, M. E. (2010), Capital Flows and Financial Crises: Policy Issues and Challenges for Nigeria, Economic and Financial Review, 48(1), 93-112.
• Olaniyi, T. A. and Olabisi, O, Y. (2011), Causes and impacts of global financial crisis on the performance of Nigerian banks (a case study of selected banks), E3 Journal of Business Management and Economics Vol. 2 (4). pp. 164-170.
• Ashamu, S. O. and Abiola, J. (2012), The Impact of Global Financial Crisis on Banking Sector in Nigeria, British Journal of Arts and Social Sciences ISSN: 2046-9578, 4,2, 251-257.
• Jenrola, O. A. and Daisi, O. R. (2012) The Implications of Global Financial Crisis on the Nigerian Capital Market Performance:An Empirical Investigation (2000-2008), European Journal of Humanities and Social Sciences, ISSN 2220-9425, 16,1, 803-819.
• Shar, V. A. (2007). Machine Learning Techniques for Stock Prediction, www. VatsahAShar.
• Avramov D (2002). Stock Return Predictability and Model Uncertainty, Financial Economy, 64, 423-458.
• Gadanecz, B. (2007), "Recent initiatives by the Basel-based committees and groups", BIS Quarterly Review, September.
• Salehi M. , Khodadadi V. , and Abdolkhani, H. (2011). Forecasting stock price using artificial neural networks: A multi-layer perception model - Iranian evidence,Scientific Research and Essays, 6, 19, 4029-4038.
• Shorouq Fathi Eletter, Saad Ghaleb Yaseen and Ghaleb Awad Elrefae (2010). Neuro-Based Artificial Intelligence Model for Loan Decisions, American Journal of Economics and Business Administration 2, 1, 27-34.
• Mitchell David and Pavur Robert, (2002) . Using Modular Neural Networks for Business Decisions, Management Decision 40/1.
• Collins, E. , Ghosh, S. and Svofield, C. (1998), An Application of a multiple Neural Network Learning System to Emulation of Mortgage Underwriting Judgements. , Proceedings of the IEEE International Conference on Neural Networks, 2, 459-466. 16] Aas, K. , Huseby, R. B. and Thune, M. (1999), 'Data mining: A survey', Report, NorwegianComputing Center. ISBN 82-539-0426-6.
• Dutta, S. , and Shekhar, S. (1998), Bond Rating: A Non-conservative Application of Neural Networks. , Proceedings of the IEEE International Conference on Neural networks, 2, 443-452.
• Moody, J. , Saffell, M. , 2001. Learning to Trade via Direct Reinforcement, IEEE Transactions on Neural Networks, 12, 4.