Stock Market Movement Direction Prediction Using Three Algorithms

Authors

  • Gunter Senyurt Department of Computer Engineering, Ishik University, Erbil, Iraq
  • Abdulkadir Subasi Department of Computer Engineering, Burch University, Sarajevo, Bosnia and Herzegovina

Keywords:

Price Movement Direction, CART, C4.5, Random Forest, Forecasting, Stock Market

Abstract

One of the highly challenging businesses today is the task of forecasting the market movements by  examining the financial time series data as correctly as possible in order to hedge against the almost  incalculable risk involved and to yield better profits for investors. If there was a highly credible estimation  technique available giving better results than the traditional statistical tools for financial markets, it would be a great asset for trading decision makers of all kinds such as speculators, arbitrageurs, portfolio fund 
managers and even individual investors. In this study CART, C4.5 and Random Forest algorithms were used  to predict the movement direction of a 10 year Istanbul Stock Exchange index (XU-100). Ten technical market indicators such as momentum, MACD and RSI were used in this study as the feature set.

References

Biau G., Devroye L., & Lugosi G. (2008a). Consistency of random forests and other averaging

classifiers. Journal of Machine Learning Research, 9, 2015-2033.

Biau G., Devroye L., & Lugosi G. (2008b). On the layered nearest neighbour estimate, the bagged

nearest neighbour estimate and the random forest method in regression and classification,

Technical report, Universite Paris 6.

Breiman, L., Frydman., Olshen, R.A., & Stone, C.J. (1984). Classification and Regression Trees.

London: Chapman and Hall.

Breiman, L. (2001). Random forests, Machine Learning. Kluwer Academic Publishers, 45, 532.

Breiman, L. (2002). Manual on setting up, using, and understanding Random Forests v3.1,

Technical Report, Retrieved from http://oz.berkeley.edu/users/breiman.

Buyukbebeci, E. (2009). Comparison of MARS, CMARS and CART in predicting default

probabilities for emerging markets, Master Thesis, METU, Ankara.

David G.T., Bani, M., & Adrian F.M. (1998). A bayesian cart algorithm. Biometrika, 85(2), 363-377.

Denison, D., Mallick, B., Smith, F. (1998). Automatic Bayesian Curve Fitting. Journal of the

Royal Statistical Society. Series B (Statistical Methodology), 60(2), 333-350.

Devaney, S. (1994). The Usefulness of Financial Ratios as Predictors of Household Insolvency:

Two Perspectives. Financial Counseling and Planning, 5, 15-24.

Dietterich T.G. (2000). Ensemble methods in machine learning, Lecture Notes in Computer

Science. Springer-Verlag, 1-15.

Efron B., & Tibshirani R. J. (1993). An Introduction to the Bootstrap. New York: Chapman and

Hall.

Friedman, J.H. (1991). Multivariate adaptive regression splines. The Annals of Statistics, 19 (1),1-

141.

Frydman, H., Olshen, R.A., & Stone, C.J. (1984). Classification and Regression Trees. New York,

London: Chapman and Hall.

Iscanoglu, A. (2005). Credit Scoring Methods and Accuracy Ratio, Master Thesis, METU, Ankara.

Jiang S., &Yu W. (2009), A Combination Classification Algorithm Based on Outlier Detection and

C4.5. Springer Publications.

Kara Y., Boyacioglu M.A., & Baykan O.K. (2010). Predicting direction of stock price index

movement using artificial neural networks and support vector machines: The sample of the

Istanbul Stock Exchange. Expert Systems with Applications, 38, 5311-5319.

Kolyshkina I., & Brookes R. (2002). Data Mining Approaches to Modeling Insurance Risk, Report,

Price Waterhouse Coopers.

Lee T., Chiu C., Chou Y., & Lu C. (2006). Mining the customer credit using classification and

regression tree and multivariate adaptive regression splines. Computational Statistics & Data

Analysis, 50, 1113-1130.

Lin Y., & Jeon Y. (2006). Random forests and adaptive nearest neighbours. Journal of American

Statistical Association, 101, 578-590.

Mazid M., Ali S., & Tickle K. (2010). Improved C4.5 Algorithm for rule based classification,

Recent Advances in Artificial Intelligence, Knowledge Engineering and Data Bases,

Australia.

Polat K., & Gunes, S. (2009). A novel hybrid intelligent method based on C4.5 decision tree

classifier and one against-all approach for multi-class classification problems. Expert

Systems with Applications, 36, 1587-1592.

Yang, X.Y. (2009). Decision tree induction with constrained number of leaf node, Master Thesis,

National Central University, Taiwan.

Yu M., & Ai T.H. (2009). Study of RS data classification based on rough sets and C4.5 algorithm,

In Proceedings of the Society of Photo-Optical Instrumentation Engineers (SPIE)

Conference Series.

WEKA (1999-2010). Waikato Environment for Knowledge Analysis, Version 3.7.3. The

University of Waikato Hamilton, New Zealand.

Downloads

Published

2015-12-01

Issue

Section

Articles

How to Cite

Senyurt, G., & Subasi, A. (2015). Stock Market Movement Direction Prediction Using Three Algorithms. EURASIAN JOURNAL OF SCIENCE AND ENGINEERING, 1(1), 16-20. https://eajse.tiu.edu.iq/index.php/eajse/article/view/187

Similar Articles

1-10 of 141

You may also start an advanced similarity search for this article.