Probability-Possibility Hybrid Systems for Merging Technical Indices

Probability-Possibility Hybrid Systems for Merging Technical Indices

Alya Itani Jean Marc Le Caillec  Bassel Solaiman  Ali Hamié 

École Nationale Supérieures Des Télécommunications de Bretagne Lab-STICC, CNRS, UMR 6285 reasCID/SFIIS Technopôle Brest-Iroise, CS 83818, 29285 Brest Cedex, France

Arts, Sciences & Technology University In Lebanon Faculty of Sciences & Fine Arts PO Box 113-7504 Cola, Beirut, Lebanon

Page: 
401-419
|
DOI: 
https://doi.org/10.3166/TS.31.401-419
Received: 
N/A
|
Accepted: 
N/A
|
Published: 
31 December 2014
| Citation

OPEN ACCESS

Abstract: 

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. 

Keywords: 

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

1. Introduction
2. Technical Analysis
3. Probability Possibility General System
4. Decision Support Systems
5. Systems Performances Comparison and Evaluation
6. Further Proposed Enhancements
7. Conclusion
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