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Extracting Formations from Long Financial Time Series Using Data Mining

Listed author(s):
  • Stella Karagianni
  • Thanasis Sfetsos
  • Costas Siriopoulos
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    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.

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    Article provided by ULB -- Universite Libre de Bruxelles in its journal Brussels economic review.

    Volume (Year): 53 (2010)
    Issue (Month): 2 ()
    Pages: 273-293

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    Handle: RePEc:bxr:bxrceb:2013/80944
    Note: Numéro Spécial « Special Issue on Nonlinear Financial Analysis :Editorial Introduction » Guest Editor :Catherine Kyrtsou
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