OPEN ACCESS
The goal of any trader is to buy low and sell high, and thus make maximum revenue with minimum risk of loss. In the financial market, prices change on daily bases, leaving traders with confusion about what decision to take, hold, buy, or sell, and when to take it. Many market analysis techniques were introduced to help traders take a winning decision at the right time, one of which was technical analysis. Technical analysis uses indicators to forecast trend and price movements. Therefore this analysis technique aids traders with the decision making process. This analysis technique has shown great success, which made it the resort of most financial traders. However, the efficiency of this type of analysis is affected by many factors, putting into it a great deal of uncertainty, ambiguity and vagueness. In this study, three decision support systems based on a hybrid probabilisticpossibilistic general approach, are proposed and tested on historical prices of the EuroStoXX50, and the CAC40 indices. The following systems take advantage of the statistical claims of probability on historical data, the interpretability and uncertainty handling competences of possibility theory, and the foreseeing abilities of technical indicators, all merged together to arm traders with a reliable daily decision that assures risk-discounted revenue, with a contribution of efficiently taking advantage of multiple indicators.
RÉSUMÉ
Le but d’un gestionnaire de portefeuille est d’acheter bas et de vendre haut afin d’optimiser les rendements et de réduire les risques de perte. Face aux changements quotidiens, les gestionnaires doivent régulièrement prendre une décision de vendre, d’acheter ou de conserver leurs titres. De nombreuses techniques d’analyses ont été introduites afin de prendre la bonne décision. L’analyse technique, la plus utilisée, est basée sur des indicateurs financiers qui permettent de définir les tendances et de prévoir les variations des valeurs liquidatives des titres. Ces indicateurs financiers aident le gestionnaire dans la prise de décisions. Les succès effectifs ainsi que la simplicité de mise en œuvre de cette approche expliquent le fort intérêt suscité parmi les gestionnaires de portefeuilles. Cependant l’efficacité de cette approche est diminuée par plusieurs facteurs tels que l’incertitude, l’ambiguïté ou l’imprécision de l’information fournie par ces indicateurs. Dans cet article, trois systèmes de décision basés sur une approche hybride probabilité-possibilité sont proposés et testés sur des données historiques de l’EuroStoXX50 et le CAC40. Le système proposé a pour avantage l’utilisation conjointe de l’extraction de l’information à partir des données historiques grâce aux probabilités et de la gestion de l’incertitude/imprécision de cette information grâce aux approches possibilistes. L’objectif est de fournir aux gestionnaires une décision plus robuste par fusion de plusieurs indicateurs que celle fournie par les indicateurs séparément.
technical analysis, technical indicators, probability, possibility fusion, probability-possibility Dubois-Prade transformation, Kullback Leibler divergence
MOTS-CLÉS
analyse technique, indicateurs techniques, probabilités, fusion possibiliste, transformation probabilité possibilité de Dubois-Prade, Kullback Leibler
Abraham A. and Nath B. (2000). Hybrid Intelligent Systems Design: A Review of a Decade of Research, IEEE Transactions on Systems, Man & Cybernetics (Part-C).
Achelis S. B. (2001). Technical Analysis from A to Z, McGraw Hill New York.
Carlsson C., and Fuller R. (1996). A Neuro-Fuzzy System for Portfolio Evaluation, Cybernetics and System Research, p. 296-299.
Cheung W., and Kaymak U. (2007). A Fuzzy Logic Based Trading System, Erasmus Institute, The Netherlands.
Dempster M. A. H., and Jone C. M. (2000). The Profitability of Intra-Day Fx Trading Using Technical Indicators, Research papers in Management Studies-University of Cambridge Judge Institute of Management Studies WP.
Dubois D., Foulloy L., Mauris G., Prade H. (2004). Probability-Possibility Transformations, Triangular Fuzzy Sets, and Probabilistic Inequalities, Reliable computing 10, p. 273-297.
Edwards R.D., Magee J., and Bassetti W. H. C. (2007). Technical Analysis of Stock Trends, CRC.
Kahneman D., Slovic P., and Tversky A. (1982). Judgment under Uncertainty: Heuristics and Biases, Cambridge University Press.
Hiemstra Y. (1994). A Stock Market Forecasting Support System Based on Fuzzy Logic, IEEE, p. 281-287.
Kullback S., and Leibler R. A. (1951). On Information Sufficiency, The Annals of Mathematical Statistics, vol. 22, n° 1., p. 79-86.
Lin C. S., Haider K., and Chi-Chung H. (2002). Can the Neuro Fuzzy Model Predict Stock Indexes Better Than Its Rivals?
Lo A. W., and MacKinlay A. C. (1988). Stock Market Prices Do Not Follow Random Walks: Evidence from a Simple Specification Test, Review of financial studies, p. 41-66.
Neely C., Weller P., and Dittmar R. (1997). Is Technical Analysis in the Foreign Exchange Market Profitable? A Genetic Programming Approach, Journal of Financial and Quantitative Analysis, 32.
Pantazopoulos K. N., Tsoukalas L. H., Bourbakis N. G., Brun M. J., and. Houstis E. N. (1998). Financial Prediction and Trading Strategies Using Neurofuzzy Approaches, Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions, p. 520-531.
Van den Berg J., Kaymak U., and Van den Bergh W. M. (2004). Financial Markets Analysis by Using a Probabilistic Fuzzy Modelling Approach, International Journal of Approximate Reasoning, p. 291-305.
Xu D., Kaymak U. (2008). Value-at-Risk Estimation by Using Probabilistic Fuzzy Systems, IEEE, p. 2109-2116.
Zhou X. S., and Dong M. (2004) Can Fuzzy Logic Make Technical Analysis 20/20?, Financial Analysts Journal, p. 54-75.