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Regression genetic programming for estimating trend end in foreign exchange market

Adegboye, Adesola, Kampouridis, Michael, Johnson, Colin G. (2018) Regression genetic programming for estimating trend end in foreign exchange market. In: 2017 IEEE Symposium Series on Computational Intelligence (SSCI) Proceedings. . IEEE ISBN 978-1-5386-2727-3. E-ISBN 978-1-5386-2726-6. (doi:10.1109/SSCI.2017.8280833)

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http://dx.doi.org/10.1109/SSCI.2017.8280833

Abstract

Most forecasting algorithms use a physical time scale for studying price movement in financial markets, making the flow of physical time discontinuous. The use of a physical time scale can make companies oblivious to significant activities in the market, which poses a risk. Directional changes is a different and newer approach, which uses an event-based time scale. This approach summarises data into alternating trends called upward directional change and downward directional change. Each of these trends are further dismembered into directional change (DC) event and overshoot (OS) event. We present a genetic programming (GP) algorithm that evolves equations that express linear and non-linear relationships between the length of DC and OS events in a given dataset. This allows us to have an expectation when a trend will reverse, which can lead to increased profitability. This novel trend reversal estimation approach is then used as part of a DC-based trading strategy. We aim to appraise whether the new knowledge can lead to greater excess return. We assess the efficiency of the modified trading strategy on 250 different datasets from five different currency pairs, consisting of intraday data from the foreign exchange (Forex) spot market. Results show that our algorithm is able to return profitable trading strategies and statistically outperform state-of-the-art financial trading strategies, such as technical analysis, buy and hold and other DC-based trading strategies.

Item Type: Conference or workshop item (Proceeding)
DOI/Identification number: 10.1109/SSCI.2017.8280833
Divisions: Faculties > Sciences > School of Computing
Faculties > Sciences > School of Computing > Computational Intelligence Group
Faculties > Sciences > School of Computing > Data Science
Depositing User: Michael Kampouridis
Date Deposited: 11 Sep 2017 15:43 UTC
Last Modified: 29 May 2019 19:31 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/63368 (The current URI for this page, for reference purposes)
Kampouridis, Michael: https://orcid.org/0000-0003-0047-7565
Johnson, Colin G.: https://orcid.org/0000-0002-9236-6581
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