A method for automatic stock trading combining technical analysis and nearest neighbor classification

In this paper we propose and analyze a novel method for automatic stock trading which combines technical analysis and the nearest neighbor classification. Our first and foremost objective is to study the feasibility of the practical use of an intelligent prediction system exclusively based on the history of daily stock closing prices and volumes. To this end we propose a technique that consists of a combination of a nearest neighbor classifier and some well known tools of technical analysis, namely, stop loss, stop gain and RSI filter. For assessing the potential use of the proposed method in practice we compared the results obtained to the results that would be obtained by adopting a buy-and-hold strategy. The key performance measure in this comparison was profitability. The proposed method was shown to generate considerable higher profits than buy-and-hold for most of the companies, with few buy operations generated and, consequently, minimizing the risk of market exposure.

[1]  Kyung-shik Shin,et al.  A hybrid approach based on neural networks and genetic algorithms for detecting temporal patterns in stock markets , 2007, Appl. Soft Comput..

[2]  Bruce J Vanstone,et al.  A survey of the application of soft computing to investment and financial trading , 2003 .

[3]  Loris Nanni,et al.  Multi-resolution subspace for financial trading , 2006, Pattern Recognit. Lett..

[4]  M.H. Hassoun,et al.  Fundamentals of Artificial Neural Networks , 1996, Proceedings of the IEEE.

[5]  Jacek Mandziuk,et al.  Neuro-evolutionary approach to stock market prediction , 2007, 2007 International Joint Conference on Neural Networks.

[6]  H. White,et al.  Economic prediction using neural networks: the case of IBM daily stock returns , 1988, IEEE 1988 International Conference on Neural Networks.

[7]  Zehong Yang,et al.  Intelligent stock trading system by turning point confirming and probabilistic reasoning , 2008, Expert Syst. Appl..

[8]  Adriano Lorena Inácio de Oliveira,et al.  Adjusting Weights and Architecture of Neural Networks through PSO with Time-Varying Parameters and Early Stopping , 2008, 2008 10th Brazilian Symposium on Neural Networks.

[9]  Peter E. Hart,et al.  Nearest neighbor pattern classification , 1967, IEEE Trans. Inf. Theory.

[10]  R. Haugen,et al.  The New Finance: The Case Against Efficient Markets , 1995 .

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

[12]  Russell L. Purvis,et al.  Trading With a Stock Chart Heuristic , 2008, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[13]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

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

[15]  Shu-Wei Hsu,et al.  The Construction of Stock_s Portfolios by Using Particle Swarm Optimization , 2007, Second International Conference on Innovative Computing, Informatio and Control (ICICIC 2007).

[16]  Byung Ro Moon,et al.  A Hybrid Neurogenetic Approach for Stock Forecasting , 2007, IEEE Transactions on Neural Networks.

[17]  Xiang Li,et al.  A Stock Pattern Recognition Algorithm Based on Neural Networks , 2007, Third International Conference on Natural Computation (ICNC 2007).

[18]  Ying Sai,et al.  Mining Stock Market Tendency by RS-Based Support Vector Machines , 2007 .

[19]  Francis Eng Hock Tay,et al.  Support vector machine with adaptive parameters in financial time series forecasting , 2003, IEEE Trans. Neural Networks.

[20]  Chee Kheong Siew,et al.  Extreme learning machine: Theory and applications , 2006, Neurocomputing.

[21]  Mark O. Afolabi,et al.  Predicting Stock Prices Using a Hybrid Kohonen Self Organizing Map (SOM) , 2007, 2007 40th Annual Hawaii International Conference on System Sciences (HICSS'07).