Cowboying Stock Market Herds with Robot Traders
Jaqueson Galimberti,
Nicolas Suhadolnik and
Sergio Da Silva
Computational Economics, 2017, vol. 50, issue 3, No 3, 393-423
Abstract:
Abstract One explanation for large stock market fluctuations is its tendency to herd behavior. We put forward an agent-based model where instabilities are the result of liquidity imbalances amplified by local interactions through imitation, and calibrate the model to match some key statistics of actual daily returns. We show that an “aggregate market-maker” type of liquidity injection is not successful in stabilizing prices due to the complex nature of the stock market. To offset liquidity shortages, we propose the use of locally triggered contrarian rules, and show that these mechanisms are effective in preventing extreme returns in our artificial stock market.
Keywords: Herding; Robot trading; Financial regulation; Agent-based model (search for similar items in EconPapers)
JEL-codes: C63 G02 (search for similar items in EconPapers)
Date: 2017
References: Add references at CitEc
Citations:
Downloads: (external link)
http://link.springer.com/10.1007/s10614-016-9591-2 Abstract (text/html)
Access to the full text of the articles in this series is restricted.
Related works:
Working Paper: Cowboying Stock Market Herds with Robot Traders (2016) 
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:50:y:2017:i:3:d:10.1007_s10614-016-9591-2
Ordering information: This journal article can be ordered from
http://www.springer. ... ry/journal/10614/PS2
DOI: 10.1007/s10614-016-9591-2
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 () and Springer Nature Abstracting and Indexing ().