Neural networks and forecasting stock price movements-accounting approach: Empirical evidence from Iran


Hossein Naderi, Mojtaba Moradpour, Mehdi Zangeneh and Farzad Khani


Stock market prediction is one of the most important interesting areas of research in business. Stock markets prediction is normally assumed as tedious task since there are many factors influencing the market. The primary objective of this paper is to forecast trend closing price movement of Tehran Stock Exchange (TSE) using financial accounting ratios from year 2003 to year 2008. The proposed study of this paper uses two approaches namely Artificial Neural Networks and multi-layer perceptron. Independent variables are accounting ratios and dependent variable of stock price , so the latter was gathered for the industry of Motor Vehicles and Auto Parts. The results of this study show that neural networks models are useful tools in forecasting stock price movements in emerging markets but multi-layer perception provides better results in term of lowering error terms.


DOI: j.msl.2012.03.019

Keywords: Neural networks ,Forecasting ,TSE ,Multi-layer perceptron

How to cite this paper:

Naderi, H., Moradpour, M., Zangeneh, M & Khani, F. (2012). Neural networks and forecasting stock price movements-accounting approach: Empirical evidence from Iran.Management Science Letters, 2(4), 1417-1424.


References

Brockett, P.L., Cooper, W.W., Golden, L.L., & Xia, X. (1997). A case study in applying neural networks to predicting insolvency for property and casualty insurers. Journal of the Operational Research Society, 48, 1153-1162.

Callen, J.L., Kwan, C.C.Y., Yip, P.C.Y., & Yuan, Y. (1996). Neural network forecasting of quarterly accounting earnings. International Journal of Forecasting, 12(4), 475-482.

Caudill, M. (1993). GRNN and Bear It. AI Expert, 8(5), 28-33.

Chen, A.S., Leung, M.T., & Daouk, H. (2003). Application of neural networks to an emerging financial market: forecasting and trading the Taiwan Stock Index. Computers & Operations Research, 30(6), 901-923.

Desai, V. S., & Bharati, R. (1998). A comparison of linear regression and neural network methods for predicting excess returns on large stocks. Annals of Operations Research, 78, 127-163.

Dhar, V., & Chou, D. (2001). A comparison of nonlinear methods for predicting earnings surprises and returns. IEEE Transaction on neural network, 12(4), 907 – 921.

Ebrahimpour, Kabir, E., Esteky, H., Yousefi, M.R. (2008).A mixture of multilayer perceptron experts network for modeling face/nonface recognition in cortical face processing regions. Expert Systems with Applications, 14(2), 145-156.

Ebrahimpour, R., Nikoo, H., Masoudnia, S., Yousefi, M.R., & Ghaemif, M.S. (2011). Mixture of MLP-experts for trend forecasting of time series: A case study of the Tehran stock exchange. International Journal of Forecasting, 27(3), 804-816.

Haerdle, W. (1990), Applied Nonparametric Regression, Cambridge Univ. Press.

Leung, M.T., Chen, A.S., & Daouk, H. (2000). Forecasting exchange rates using general regression neural networks. Computers & Operations Research, 27(11-12), 1093-1110.

Leung, M.T., Daouk, H., & Chen, A.S. (2000). Forecasting stock indices: a comparison of classification and level estimation models. International Journal of Forecasting, 173-190.

Nadaraya, E.A. (1964). On estimating regression. Theory of Probability Applications, 10, 186-190.

Olson, D., & Mossman, C. (2003). Neural network forecasts of Canadian stock returns using accounting ratios. International Journal of Forecasting, 19(3), 453-465.

Schioler, H. & Hartmann, U. (1992). Mapping neural network derived from the Parzen window estimator. Neural Networks, 5, 903-909.

Specht, D.F. (1968). A practical technique for estimating general regression surfaces. Lockheed report LMSC 6-79-68-6. Defense Technical Information Center AD-672505.Specht, D.F. (1991). A generalized regression neural network. IEEE Transactions on Neural Networks, 2, 568-576.