A Decision Tree- Rough Set Hybrid System for Stock Market Trend Prediction

of stock market trends has been an area of great interest both to those who wish to profit by trading stocks in the stock market and for researchers attempting to uncover the information hidden in the stock market data. Applications of data mining techniques for stock market prediction, is an area of research which has been receiving a lot of attention recently. This work presents the design and performance evaluation of a hybrid decision tree- rough set based system for predicting the next days" trend in the Bombay Stock Exchange (BSE- SENSEX). Technical indicators are used in the present study to extract features from the historical SENSEX data. C4.5 decision tree is then used to select the relevant features and a rough set based system is then used to induce rules from the extracted features. Performance of the hybrid rough set based system is compared to that of an artificial neural network based trend prediction system and a naive bayes based trend predictor. It is observed from the results that the proposed system outperforms both the neural network based system and the naive bayes based trend prediction system.

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

[2]  Ying Wu,et al.  Forecast studies for financial markets using technical analysis , 2005, 2005 International Conference on Control and Automation.

[3]  William F. Eng,et al.  The technical analysis of stocks, options & futures : advanced trading systems and techniques , 1988 .

[4]  Yi-Ping Chen,et al.  The Efficacy of Neural Networks and Simple Technical Indicators in Predicting Stock Markets , 2007, 2007 International Conference on Convergence Information Technology (ICCIT 2007).

[5]  Jiawei Han,et al.  Data Mining: Concepts and Techniques, Second Edition , 2006, The Morgan Kaufmann series in data management systems.

[6]  Laxman M. Waghmare,et al.  Algorithmic Trading (AT) - Framework for Futuristic Intelligent Human Interaction with Small Investors , 2010 .

[7]  Jar-Long Wang,et al.  Stock market trading rule discovery using two-layer bias decision tree , 2006, Expert Syst. Appl..

[8]  Kimon P. Valavanis,et al.  Surveying stock market forecasting techniques - Part II: Soft computing methods , 2009, Expert Syst. Appl..

[9]  Ching-Hsue Cheng,et al.  Fuzzy time series model based on probabilistic approach and rough set rule induction for empirical research in stock markets , 2008, Data Knowl. Eng..

[10]  Jian Pei,et al.  Data Mining: Concepts and Techniques, 3rd edition , 2006 .

[11]  Pei-Chann Chang,et al.  A neural network with a case based dynamic window for stock trading prediction , 2009, Expert Syst. Appl..

[12]  Mu-Hsing Kuo,et al.  An ETF Trading Decision Support System by Using Neural Network and Technical Indicators , 2006, The 2006 IEEE International Joint Conference on Neural Network Proceedings.

[13]  Qiang Shen,et al.  Computational Intelligence and Feature Selection - Rough and Fuzzy Approaches , 2008, IEEE Press series on computational intelligence.

[14]  Kuang Yu Huang,et al.  A hybrid model for stock market forecasting and portfolio selection based on ARX, grey system and RS theories , 2009, Expert Syst. Appl..

[15]  Muh-Cherng Wu,et al.  An effective application of decision tree to stock trading , 2006, Expert Syst. Appl..

[16]  K. N. Subramanya,et al.  A review on Data Mining Applications to the Performance of Stock Marketing , 2010 .