Discovering the ecosystem of an electronic financial market with a dynamic machine-learning method

Shawn Mankad () and George Michailidis
Additional contact information
Shawn Mankad: Department of Statistics, University of Michigan, Michigan, USA, Postal: Department of Statistics, University of Michigan, Michigan, USA
George Michailidis: Department of Statistics, University of Michigan, Michigan, USA, Postal: Department of Statistics, University of Michigan, Michigan, USA

Algorithmic Finance, 2013, vol. 2, issue 2, 151-165

Abstract: Not long ago securities were traded by human traders in face-to-face markets. The ecosystem of an open outcry market was well-known, visible to a human eye, and rigidly prescribed. Now trading is increasingly done in anonymous electronic markets where traders do not have designated functions or mandatory roles. In fact, the traders themselves have been replaced by algorithms (machines) operating with little or no human oversight. While the process of electronic trading is not visible to a human eye, machine-learning methods have been developed to recognize persistent patterns in the data. In this study, we develop a dynamic machine-learning method that designates traders in an anonymous electronic market into five persistent categories: high frequency traders, market makers, opportunistic traders, fundamental traders, and small traders. Our method extends a plaid clustering technique with a smoothing framework that filters out transient patterns. The method is fast, robust, and suitable for a discovering trading ecosystems in a large number of electronic markets

Keywords: trading strategies; high frequency trading; machine learning; clustering (search for similar items in EconPapers)
JEL-codes: H00 (search for similar items in EconPapers)
Date: 2013
References: Add references at CitEc
Citations: View citations in EconPapers (2)

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

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:ris:iosalg:0021

Access Statistics for this article

Algorithmic Finance is currently edited by Phil Maymin

More articles in Algorithmic Finance from IOS Press
Bibliographic data for series maintained by Saskia van Wijngaarden ().