Extracting Formations from Long Financial Time Series Using Data Mining
AbstractTechnical analysis has become a custom decision support tool for traders and analysts, though not widely accepted by the academic community. It is based on the identification of a series of well-defined formations appearing over irregular intervals. The same principle forms the basis for the application of data mining methodologies as a tool to discover hidden patterns that exist in a time series, which is achieved by a detailed breakdown of historic information. This paper introduces a methodology for the discovery of formations that exist within a time series and have high probability of reoccurrence. The methodology was developed in an efficient manner requiring only a small number of user-specified parameters. Its two main stages are (a) a modified bottom-up segmentation algorithm with an optimization stage to reach the optimal number of segments, and (b) a rule extraction algorithm. The developed methodology is tested on two major financial series, the daily closing values of the SP500 Index and the GB Pound to US Dollar exchange rates.
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Bibliographic InfoArticle provided by ULB -- Universite Libre de Bruxelles in its journal Brussels economic review.
Volume (Year): 53 (2010)
Issue (Month): 2 ()
Note: Numéro Spécial « Special Issue on Nonlinear Financial Analysis :Editorial Introduction » Guest Editor :Catherine Kyrtsou
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Technical analysis; Data mining; Exchange rates; Stock market; Pattern recognition; Rule extraction;
Find related papers by JEL classification:
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- C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
- F31 - International Economics - - International Finance - - - Foreign Exchange
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