Discovering Traders’ Heterogeneous Behavior in High-Frequency Financial Data

Ya Chi Huang, Chueh Yung Tsao*

*Corresponding author for this work

Research output: Contribution to journalJournal Article peer-review

3 Scopus citations

Abstract

This paper develops a utility-based heterogeneous agent model for empirically investigating intraday traders’ behaviors. Two types of agents, which consist of fundamental traders and technical analysts, are considered in the proposed model. They differ in the expectation of future asset returns and the perceived risk. This paper incorporates the unique characteristics of high-frequency data into the model for the purpose of having a reliable and accurate empirical result. In particular, a two-test procedure is developed to test the market fractions hypothesis that distinguishes the heterogeneous agent model from the representative agent model. The proposed heterogeneous agent model is estimated on the Taiwan Stock Exchange data. The results suggest that fundamental traders expect the correction of over- or under-pricing in the future. Technical analysts act as contrarian traders. Technical analysts also believe that buyer-initiated (seller-initiated) trading will further raise (lower) future prices. The bid-ask spread has a crucial effect on the investment risk for the technical analysts. Moreover, technical analysts are short-sighted, have less market fraction, but perform slightly better.

Original languageEnglish
Pages (from-to)821-846
Number of pages26
JournalComputational Economics
Volume51
Issue number4
DOIs
StatePublished - 01 04 2018

Bibliographical note

Publisher Copyright:
© 2017, Springer Science+Business Media New York.

Keywords

  • Heterogeneous agent model
  • High-frequency financial data
  • Market microstructure

Fingerprint

Dive into the research topics of 'Discovering Traders’ Heterogeneous Behavior in High-Frequency Financial Data'. Together they form a unique fingerprint.

Cite this