Extracting Formations from Long Financial Time Series Using Data Mining

Stella Karagianni, Thanasis Sfetsos and Costas Siriopoulos ()

Brussels Economic Review, 2010, vol. 53, issue 2, 273-293

Abstract: 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.

Keywords: Technical analysis; Data mining; Exchange rates; Stock market; Pattern recognition; Rule extraction (search for similar items in EconPapers)
JEL-codes: C22 C63 F31 (search for similar items in EconPapers)
Date: 2010
Note: Numéro Spécial « Special Issue on Nonlinear Financial Analysis :Editorial Introduction » Guest Editor :Catherine Kyrtsou
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