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Improving trend reversal estimation in forex markets under a directional changes paradigm with classification algorithms

Adegboye, Adesola, Kampouridis, Michael, Otero, Fernando E.B. (2021) Improving trend reversal estimation in forex markets under a directional changes paradigm with classification algorithms. International Journal of Intelligent Systems, . ISSN 0884-8173. E-ISSN 1098-111X. (doi:10.1002/int.22601) (Access to this publication is currently restricted. You may be able to access a copy if URLs are provided) (KAR id:89886)

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Language: English

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https://doi.org/10.1002/int.22601

Abstract

The majority of forecasting methods use a physical time scale for studying price fluctuations of financial markets. Using physical time scales can make companies oblivious to significant activities in the market as the flow of time is discontinuous, which could translate to missed profitable opportunities or risk exposure. Directional changes (DC) has gained attention in the recent years by translating physical time series to event-based series. Under this framework, trend reversals can be predicted by using the length of events. Having this knowledge allows traders to take an action before such reversals happen and thus increase their profitability. In this paper, we investigate how classification algorithms can be incorporated in the process of predicting trend reversals to create DC-based trading strategies. The effect of the proposed trend reversal estimation is measured on 20 foreign exchange markets over a 10-month period in a total of 1000 data sets. We compare our results across 16 algorithms, both DC and non-DC based, such as technical analysis and buy-and-hold. Our findings show that the introduction of classification leads to return higher profit and statistically outperform all other trading strategies.

Item Type: Article
DOI/Identification number: 10.1002/int.22601
Subjects: Q Science > Q Science (General) > Q335 Artificial intelligence
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Depositing User: Fernando Otero
Date Deposited: 24 Aug 2021 08:21 UTC
Last Modified: 25 Aug 2021 10:11 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/89886 (The current URI for this page, for reference purposes)
Kampouridis, Michael: https://orcid.org/0000-0003-0047-7565
Otero, Fernando E.B.: https://orcid.org/0000-0003-2172-297X
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