Explanation of binarized tick data using investor sentiment and genetic learning

Takashi Yamada () and Kazuhiro Ueda ()
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Takashi Yamada: University of Tokyo
Kazuhiro Ueda: University of Tokyo

A chapter in Practical Fruits of Econophysics, 2006, pp 205-209 from Springer

Abstract: Summary This paper attempts to clarify some time series properties of binarized tick data by investor sentiment and genetic algorithm. For this purpose, first we explore the conditions for genetic algorithm to describe investor sentiment. Then we calculate auto-correlations and conditional probabilities using binarized sample paths generated by estimated models of investor sentiment. The most fitted parameter set of genetic algorithm have the following implications: First, a herd behavior is likely to emerge. Second, traders try to perceive brand-new information even if it is not completely correct.

Keywords: investor sentiment; genetic learning; binarized time series (search for similar items in EconPapers)
Date: 2006
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-4-431-28915-9_37

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DOI: 10.1007/4-431-28915-1_37

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