Share This Article:

Predicting Stock Prices Using Polynomial Classifiers: The Case of Dubai Financial Market

Abstract Full-Text HTML Download Download as PDF (Size:308KB) PP. 82-89
DOI: 10.4236/jilsa.2011.32010    7,124 Downloads   15,946 Views   Citations

ABSTRACT

Predicting stock price movements is a challenging task for academicians and practitioners. In particular, forecasting price movements in emerging markets seems to be more elusive because they are usually more volatile often accompa-nied by thin trading-volumes and they are susceptible to more manipulation compared to mature markets. Technical analysis of stocks and commodities has become a science on its own; quantitative methods and techniques have been applied by many practitioners to forecast price movements. Lagging and sometimes leading technical indicators pro-vide rich quantitative tools for traders and investors in their attempt to gain advantage when making investment or trading decisions. Artificial Neural Networks (ANN) have been used widely in predicting stock prices because of their capability in capturing the non-linearity that often exists in price movements. Recently, Polynomial Classifiers (PC) have been applied to various recognition and classification application and showed favorable results in terms of recog-nition rates and computational complexity as compared to ANN. In this paper, we present two prediction models for predicting securities’ prices. The first model was developed using back propagation feed forward neural networks. The second model was developed using polynomial classifiers (PC), as a first time application for PC to be used in stock prices prediction. The inputs to both models were identical, and both models were trained and tested on the same data. The study was conducted on Dubai Financial Market as an emerging market and applied to two of the market’s leading stocks. In general, both models achieved very good results in terms of mean absolute error percentage. Both models show an average error around 1.5% predicting the next day price, an average error of 2.5% when predicting second day price, and an average error of 4% when predicted the third day price.

Conflicts of Interest

The authors declare no conflicts of interest.

Cite this paper

K. Assaleh, H. El-Baz and S. Al-Salkhadi, "Predicting Stock Prices Using Polynomial Classifiers: The Case of Dubai Financial Market," Journal of Intelligent Learning Systems and Applications, Vol. 3 No. 2, 2011, pp. 82-89. doi: 10.4236/jilsa.2011.32010.

References

[1] E. Fama, “Efficient Capital Markets: A Review of Theory and Empirical Work,” Journal of Finance, Vol. 25, No. 2, 1970, pp. 383-417. doi:10.2307/2325486
[2] J. Yao, C. Tan and H. Poh, “Neural Networks for Technical Analysis: A Study on KLCI,” International Journal of Theoretical and Applied Finance, Vol. 2, No. 2, 1999, pp. 221-241. doi:10.1142/S0219024999000145
[3] D. Brownstone, “Using Percentage Accuracy to Measure Neural Network Predictions in Stock Market Movements,” Neurocomputing, Vol. 10, No. 3, 1996, pp. 237-250. doi:10.1016/0925-2312(95)00052-6
[4] K. H. Lee and G. S. Jo, “Expert System for Predicting Stock Market Timing Using a Candlestick Chart,” Expert Systems with Applications, Vol. 16, No. 4, 1999, pp. 357-364. doi:10.1016/S0957-4174(99)00011-1
[5] W. Leigh, M. Paz and R. Purvis, “An Anaysis of a Hybrid Neural Network and Pattern Recognition Technique for Predicting Short-Term Increases in the NYSE Composite Index,” Omega, Vol. 30, No. 2, 2002, pp. 69-76. doi:10.1016/S0305-0483(01)00057-3
[6] R. Choudhry and K. Garg, “A Hybrid Machine Learning System for Stock Market Forecasting,” World Academy of Science, Engineering and Technology, Vol. 39, 2008, pp. 315-318.
[7] G. Armano, M. Marchesi and A. Murru, “A Hqybrid Genetic-Neural Architecture for Stock Indexes Forecasting,” Information Sciences, Vol. 170, No. 1, 2005, pp. 3-33. doi:10.1016/j.ins.2003.03.023
[8] J. Yao and C. Tan, “A Case Study on Using Neural Networks to Perform Technical Forecasting of Forex,” Neurocomputing, Vol. 34, No. 1-4, 2000, pp. 79-98. doi:10.1016/S0925-2312(00)00300-3
[9] D. Senol and M. Oztuman, “Stock Price Direction Prediction Using Artificial Neural Network Approach: The Case of Turkey,” Journal of Artificial Intelligence, Vol. 1, No. 2, 2008, pp. 70-77. doi:10.3923/jai.2008.70.77
[10] A.-S. Chen, M. Leung and H. Daouk, “Application of Neural Networks to an Emerging Financial Market: Forecasting and Trading the Taiwan Stock Index,” Computers & Operations Research, Vol. 30, No. 6, 2003, pp. 901-923. doi:10.1016/S0305-0548(02)00037-0
[11] D. Enke and S. Thawornwong, “The Use of Data Mining and Neural Networks for Forecasting Stock Market Returns,” Expert Systems with Applications, Vol. 29, No. 4, 2005, pp. 927-940. doi:10.1016/j.eswa.2005.06.024
[12] Q. Cao, K. Leggio and M. Schniederjans, “A Comparison between Fama and French’s Model and Artificial Neural Networks in Predicting the Chinese Stock Market,” Computers & Operations Research, Vol. 32, No. 10, 2005, pp. 2499-2512. doi:10.1016/j.cor.2004.03.015
[13] I. Kaastra and M. Boyd, “Designing a Neural Network for Forecasting Financial and Economic Time Series,” Neurocomputing, Vol. 10, No. 3, 1996, pp. 215-236. doi:10.1016/0925-2312(95)00039-9
[14] N. Kohzadi, M. Boyd, B. Kermanshahi and I. A. Kaastra, “Comparison of Artificial Neural Network and Time Series Models for Forecasting Commodity Prices,” Neurocomputting, Vol. 10, No. 2, 1996, pp. 169-181. doi:10.1016/0925-2312(95)00020-8
[15] D. Olsona and C. Mossmanb, “Neural Network Forecasts of Canadian Stock Returns Using Accounting Ratios,” International Journal of Forecasting, Vol. 19, No. 3, 2003, pp. 453-465. doi:10.1016/S0169-2070(02)00058-4
[16] K. Assaleh and M. Al-Rousan, “Recognition of Arabic Sign Language Alphabet Using Polynomial Classifiers,” EURASIP Journal of Applied Signal Processing, Vol. 2005, No. 13, 2005, pp. 2136-2145. doi:10.1155/ASP.2005.2136
[17] K. T. Assaleh and W. M. Campbell, “Speaker Identification using a Polynomial-Based Classifier,” Fifth International Symposium on Signal Processing and Its Applications ISSPA, Brisbane, Vol. 1, August 1999, pp. 115-118.
[18] W. M. Campbell, K. T. Assaleh and C. C. Broun, “Speaker Recognition with Polynomial Classifiers,” IEEE Transactions on Speech and Audio Processing, Vol. 10, No. 4, May 2002, pp. 205-212. doi:10.1109/TSA.2002.1011533
[19] C.-L. Liu and H. Sako, “Class-Specific Feature Polynomial Classifier for Pattern Classification and Its Application to Handwritten Numeral Recognition,” Pattern Recognition, Vol. 39, No. 4, 2006, pp. 669-681. doi:10.1016/j.patcog.2005.04.021
[20] I. Deiab, K. Assaleh and F. Hammad, “On Modeling of Tool Wear Using Sensor Fusion and Polynomial Classifiers,” Mechanical Systems and Signal Processing, Vol. 23, No. 5, July 2009, pp. 1719-1729. doi:10.1016/j.ymssp.2009.02.001
[21] K. Assaleh and H. Al-Nashash, “A Novel Technique for the Extraction of Fetal ECG Using Polynomial Networks,” IEEE Transactions on Biomedical Engineering, Vol. 52, No. 6, June 2005, pp. 1148-1152. doi:10.1109/TBME.2005.844046

  
comments powered by Disqus

Copyright © 2019 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.