article

Exploring Trading Strategies and Their Effects in the Foreign Exchange Market

Authors:

Maria FasliAuthors Info & Claims

Computational Intelligence, Volume 33, Issue 2

Pages 280 - 307

https://doi.org/10.1111/coin.12085

Published: 01 May 2017 Publication History

Abstract

One of the most critical issues that developers face in developing automatic systems for electronic markets is that of endowing the agents with appropriate trading strategies. In this article, we examine the problem in the foreign exchange FX market, and we use an agent-based market simulation to examine which trading strategies lead to market states in which the stylized facts statistical properties of the simulation match those of the FX market transactions data. Our goal is to explore the emergence of the stylized facts, when the simulated market is populated with agents using different strategies: a variation of the zero intelligence with a constraint strategy, the zero-intelligence directional-change event strategy, and a genetic programming-based strategy. A series of experiments were conducted, and the results were compared with those of a high-frequency FX transaction data set. Our results show that the zero-intelligence directional-change event agents best reproduce and explain the properties observed in the FX market transactions data. Our study suggests that the observed stylized facts could be the result of introducing a threshold that triggers the agents to respond to periodic patterns in the price time series. The results can be used to develop decision support systems for the FX market.

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Cited By

Long XKampouridis MKanellopoulos P(2022)Genetic Programming for Combining Directional Changes Indicators in International Stock MarketsParallel Problem Solving from Nature – PPSN XVII10.1007/978-3-031-14721-0_3(33-47)Online publication date: 10-Sep-2022
https://dl.acm.org/doi/10.1007/978-3-031-14721-0_3
Aloud M(2020)The role of attribute selection in Deep ANNs learning framework for high‐frequency financial tradingInternational Journal of Intelligent Systems in Accounting and Finance Management10.1002/isaf.146627:2(43-54)Online publication date: 12-Mar-2020
https://dl.acm.org/doi/10.1002/isaf.1466

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cover image Computational Intelligence

Computational Intelligence Volume 33, Issue 2

May 2017

186 pages

ISSN:0824-7935

EISSN:1467-8640

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Blackwell Publishers, Inc.

United States

John Wiley & Sons, Inc.

United States

Publication History

Published: 01 May 2017

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Cited By

Long XKampouridis MKanellopoulos P(2022)Genetic Programming for Combining Directional Changes Indicators in International Stock MarketsParallel Problem Solving from Nature – PPSN XVII10.1007/978-3-031-14721-0_3(33-47)Online publication date: 10-Sep-2022
https://dl.acm.org/doi/10.1007/978-3-031-14721-0_3
Aloud M(2020)The role of attribute selection in Deep ANNs learning framework for high‐frequency financial tradingInternational Journal of Intelligent Systems in Accounting and Finance Management10.1002/isaf.146627:2(43-54)Online publication date: 12-Mar-2020
https://dl.acm.org/doi/10.1002/isaf.1466

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