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Extracting Formations from Long Financial Time Series Using Data Mining

par Karagianni, Stella;Sfetsos, Thanasis;Siriopoulos, Costas
Référence Brussels economic review, 53, 2, page (273-293)
Publication Publié, 2010

Article révisé par les pairs

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Résumé :

Technical 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.