The Impact of Data Normalization on Stock Market Prediction: Using SVM and Technical Indicators

Predicting stock index and its movement has never been lack of attention among traders and professional analysts, because of the attractive financial gains. For the last two decades, extensive researches combined technical indicators with machine learning techniques to construct effective prediction models. This study is to investigate the impact of various data normalization methods on using support vector machine (SVM) and technical indicators to predict the price movement of stock index. The experimental results suggested that, the prediction system based on SVM and technical indicators, should carefully choose an appropriate data normalization method so as to avoid its negative influence on prediction accuracy and the processing time on training.

[1]  Ömer Kaan Baykan,et al.  Predicting direction of stock price index movement using artificial neural networks and support vector machines: The sample of the Istanbul Stock Exchange , 2011, Expert Syst. Appl..

[2]  Chih-Jen Lin,et al.  A Practical Guide to Support Vector Classication , 2008 .

[3]  Zyad Shaaban,et al.  Normalization as a Preprocessing Engine for Data Mining and the Approach of Preference Matrix , 2006, 2006 International Conference on Dependability of Computer Systems.

[4]  Ian H. Witten,et al.  The WEKA data mining software: an update , 2009, SKDD.

[5]  Jacinta Chan.  Financial Times Guide to Technical Analysis: How to Trade like a Professional , 2011 .

[6]  Kesari Verma,et al.  Investigations on Impact of Feature Normalization Techniques on Classifier's Performance in Breast Tumor Classification , 2015 .

[7]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[8]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[9]  Isabelle Guyon,et al.  An Introduction to Variable and Feature Selection , 2003, J. Mach. Learn. Res..

[10]  Soushan Wu,et al.  Comparison of support-vector machines and back propagation neural networks in forecasting the six major Asian stock markets , 2006 .

[11]  Jiawei Han,et al.  Data Mining: Concepts and Techniques , 2000 .

[12]  Kyoung-jae Kim,et al.  Financial time series forecasting using support vector machines , 2003, Neurocomputing.

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

[14]  Nello Cristianini,et al.  Support vector machine classification and validation of cancer tissue samples using microarray expression data , 2000, Bioinform..

[15]  Manish Kumar,et al.  Forecasting Stock Index Movement: A Comparison of Support Vector Machines and Random Forest , 2006 .

[16]  S. Achelis.  Technical analysis a to z , 1994 .

[17]  Yuhanis Yusof,et al.  A comparison of normalization techniques in predicting dengue outbreak , 2010 .

[18]  Shouyang Wang,et al.  Forecasting stock market movement direction with support vector machine , 2005, Comput. Oper. Res..

[19]  A. Santhakumaran,et al.  Statistical Normalization and Back Propagationfor Classification , 2011 .

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