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Predicting equity market price impact with performance weighted ensembles of random forests

Predicting equity market price impact with performance weighted ensembles of random forests
Predicting equity market price impact with performance weighted ensembles of random forests
For many players in financial markets, the price impact of their trading activity represents a large proportion of their transaction costs. This paper proposes a novel machine learning method for predicting the price impact of order book events. Specifically, we introduce a prediction system based on performance weighted ensembles of random forests. The system’s performance is benchmarked using ensembles of other popular regression algorithms including: liner regression, neural networks and support vector regression using depth-of-book data from the BATS Chi-X exchange. The results show that recency-weighted ensembles of random forests produce over 15% greater prediction accuracy on out-of-sample data, for 5 out of 6 timeframes studied, compared with all benchmarks
1-8
Booth, Ash
e23d78c8-4b8c-421c-962f-b875136b8e25
Gerding, Enrico
d9e92ee5-1a8c-4467-a689-8363e7743362
McGroarty, Frank
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Booth, Ash
e23d78c8-4b8c-421c-962f-b875136b8e25
Gerding, Enrico
d9e92ee5-1a8c-4467-a689-8363e7743362
McGroarty, Frank
693a5396-8e01-4d68-8973-d74184c03072

Booth, Ash, Gerding, Enrico and McGroarty, Frank (2014) Predicting equity market price impact with performance weighted ensembles of random forests. Computational Intelligence for Financial Engineering and Economics (CIFEr), United Kingdom. 27 - 28 Mar 2014. pp. 1-8 .

Record type: Conference or Workshop Item (Paper)

Abstract

For many players in financial markets, the price impact of their trading activity represents a large proportion of their transaction costs. This paper proposes a novel machine learning method for predicting the price impact of order book events. Specifically, we introduce a prediction system based on performance weighted ensembles of random forests. The system’s performance is benchmarked using ensembles of other popular regression algorithms including: liner regression, neural networks and support vector regression using depth-of-book data from the BATS Chi-X exchange. The results show that recency-weighted ensembles of random forests produce over 15% greater prediction accuracy on out-of-sample data, for 5 out of 6 timeframes studied, compared with all benchmarks

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More information

Published date: 2014
Venue - Dates: Computational Intelligence for Financial Engineering and Economics (CIFEr), United Kingdom, 2014-03-27 - 2014-03-28
Organisations: Centre for Digital, Interactive & Data Driven Marketing, Agents, Interactions & Complexity

Identifiers

Local EPrints ID: 363655
URI: http://eprints.soton.ac.uk/id/eprint/363655
PURE UUID: 52b0d5e0-d3c7-4454-87a9-d41bfcc3a326
ORCID for Enrico Gerding: ORCID iD orcid.org/0000-0001-7200-552X
ORCID for Frank McGroarty: ORCID iD orcid.org/0000-0003-2962-0927

Catalogue record

Date deposited: 28 Mar 2014 14:56
Last modified: 17 Dec 2019 01:48

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