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

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.

[1]  William M. Campbell,et al.  Speaker recognition with polynomial classifiers , 2002, IEEE Trans. Speech Audio Process..

[2]  William M. Campbell,et al.  Speaker identification using a polynomial-based classifier , 1999, ISSPA '99. Proceedings of the Fifth International Symposium on Signal Processing and its Applications (IEEE Cat. No.99EX359).

[3]  Hiroshi Sako,et al.  Class-specific feature polynomial classifier for pattern classification and its application to handwritten numeral recognition , 2006, Pattern Recognit..

[4]  Michele Marchesi,et al.  A hybrid genetic-neural architecture for stock indexes forecasting , 2005, Inf. Sci..

[5]  Hasan Al-Nashash,et al.  A novel technique for the extraction of fetal ECG using polynomial networks , 2005, IEEE Transactions on Biomedical Engineering.

[6]  Khaled Assaleh,et al.  On modeling of tool wear using sensor fusion and polynomial classifiers , 2009 .

[7]  An-Sing Chen,et al.  Application of Neural Networks to an Emerging Financial Market: Forecasting and Trading the Taiwan Stock Index , 2001, Comput. Oper. Res..

[8]  Kyungjik Lee,et al.  Expert system for predicting stock market timing using a candlestick chart , 1999 .

[9]  Nowrouz Kohzadi,et al.  A comparison of artificial neural network and time series models for forecasting commodity prices , 1996, Neurocomputing.

[10]  Milton S. Boyd,et al.  Designing a neural network for forecasting financial and economic time series , 1996, Neurocomputing.

[11]  E. Fama.  EFFICIENT CAPITAL MARKETS: A REVIEW OF THEORY AND EMPIRICAL WORK* , 1970 .

[12]  C. Tan,et al.  NEURAL NETWORKS FOR TECHNICAL ANALYSIS: A STUDY ON KLCI , 1999 .

[13]  Khaled Assaleh,et al.  Recognition of Arabic Sign Language Alphabet Using Polynomial Classifiers , 2005, EURASIP J. Adv. Signal Process..

[14]  Jingtao Yao,et al.  A case study on using neural networks to perform technical forecasting of forex , 2000, Neurocomputing.

[15]  Meltem Ozturan,et al.  Stock Price Direction Prediction Using Artificial Neural Network Approach: The Case of Turkey , 2008 .

[16]  Marc J. Schniederjans,et al.  A comparison between Fama and French's model and artificial neural networks in predicting the Chinese stock market , 2005, Comput. Oper. Res..

[17]  Russell L. Purvis,et al.  An analysis of a hybrid neural network and pattern recognition technique for predicting short-term increases in the NYSE composite index , 2002 .

[18]  Rohit Choudhry,et al.  A Hybrid Machine Learning System for Stock Market Forecasting , 2008 .

[19]  David Brownstone,et al.  Using percentage accuracy to measure neural network predictions in Stock Market movements , 1996, Neurocomputing.

[20]  Dennis Olson,et al.  Neural network forecasts of Canadian stock returns using accounting ratios , 2003 .

[21]  David Enke,et al.  The use of data mining and neural networks for forecasting stock market returns , 2005, Expert Syst. Appl..