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Sequence Outlier Detection Based on Chaos Theory and Its Application on Stock Market

  • Chi Xie
  • Zuo Chen
  • Xiang Yu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4223)

Abstract

There are many observable factors that could influence and determine the time series. The dynamic equations of their interaction are always nonlinear, sometimes chaotic. This paper applied phase space reconstruction method to map time series into multi-dimension space based on chaos theory. Extracted from multi-dimension phase space by the method of sequential deviation detection, outlier set was used to construct a decision tree in order to identify the kinds of outliers. According to the results of decision tree, a trading strategy was set up and applied to Chinese stock market. The results show that, even in bear market, the strategy dictated by decision tree brought in considerable yield.

Keywords

Trading Strategy Outlier Detection Chaos Theory Large Lyapunov Exponent Chaotic Time Series 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Chi Xie
    • 1
  • Zuo Chen
    • 1
  • Xiang Yu
    • 1
  1. 1.College of Business AdministrationHunan UniversityChangsha, HunanP.R. China

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