EconPapers    
Economics at your fingertips  
 

Discovering Traders’ Heterogeneous Behavior in High-Frequency Financial Data

Ya-Chi Huang () and Chueh-Yung Tsao ()
Additional contact information
Ya-Chi Huang: Lunghwa University of Science and Technology
Chueh-Yung Tsao: Chang Gung University

Computational Economics, 2018, vol. 51, issue 4, 821-846

Abstract: 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.

Keywords: Heterogeneous agent model; High-frequency financial data; Market microstructure (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: Track citations by RSS feed

Downloads: (external link)
http://link.springer.com/10.1007/s10614-016-9643-7 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:kap:compec:v:51:y:2018:i:4:d:10.1007_s10614-016-9643-7

Ordering information: This journal article can be ordered from
http://www.springer. ... ry/journal/10614/PS2

Access Statistics for this article

Computational Economics is currently edited by Hans Amman

More articles in Computational Economics from Springer, Society for Computational Economics Contact information at EDIRC.
Bibliographic data for series maintained by Sonal Shukla ().

 
Page updated 2019-05-15
Handle: RePEc:kap:compec:v:51:y:2018:i:4:d:10.1007_s10614-016-9643-7