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Evolutionary simulation of hedging pressure in futures markets

Duke, J; Clack, CD; (2008) Evolutionary simulation of hedging pressure in futures markets. In: Proceedings of the 2007 IEEE Congress on Evolutionary Computation. (pp. pp. 782-789). IEEE: Singapore, Singapore. Green open access

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Abstract

We present a real world application that models a financial futures market. The agent-based simulation includes speculator agents each of which uses a Genetic Algorithm to improve its profitability in the market. This is a realistic simulation whose rates-of-return distribution is similar to those of real futures markets such as corn and FTSE100 futures. The futures markets have never before been simulated to this level of detail, and the simulation is used to test the long-held belief that speculators are more profitable if they incorporate “hedging pressure” into their price calculations — essentially, the use of market knowledge about supply and demand. Surprisingly, we show that hedging pressure cannot be used to improve profits for speculators.

Type: Proceedings paper
Title: Evolutionary simulation of hedging pressure in futures markets
Event: IEEE Congress on Evolutionary Computation
Location: Singapore, SINGAPORE
Dates: 25 September 2007 - 28 September 2007
ISBN-13: 978-1-4244-1339-3
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/CEC.2007.4424550
Publisher version: http://dx.doi.org/ 10.1109/CEC.2007.4424550
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Contracts, Predictive models, Genetic algorithms, Computational modeling, Pricing, Supply and demand, Data analysis, Iron, Profitability, Testing
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/10087081
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