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

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type	http://eprints.org/ontology/ArticleEPrint http://eprints.org/ontology/EPrint Article Academic Article
seeAlso	HTML Summary of #89886 Improving trend reversal estimation in forex markets under a directional changes paradigm with classification algorithms
sameAs	Improving trend reversal estimation in forex markets under a directional changes paradigm with classification algorithms
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http://eprints.org/ontology/hasDocument	Improving trend reversal estimation in forex markets under a directional changes paradigm with classification algorithms (PDF) Improving trend reversal estimation in forex markets under a directional changes paradigm with classification algorithms (Other) Improving trend reversal estimation in forex markets under a directional changes paradigm with classification algorithms (Other) Improving trend reversal estimation in forex markets under a directional changes paradigm with classification algorithms (Other) Improving trend reversal estimation in forex markets under a directional changes paradigm with classification algorithms (Other) Improving trend reversal estimation in forex markets under a directional changes paradigm with classification algorithms (Other)
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Title	Improving trend reversal estimation in forex markets under a directional changes paradigm with classification algorithms
described by	https://demo.openlinksw.com/about/id/entity/https/raw.githubusercontent.com/annajordanous/CO644Files/main/export_kar_RDFN3.n3
Date	2021-08-08
Creator	Adesola Adegboye Fernando E.B. Otero Michael Kampouridis
status	peer reviewed published
Publisher	Wiley
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.
Is Part Of	International Journal of Intelligent Systems https://kar.kent.ac.uk/id/repository
Subject	Q335 Artificial intelligence
list of authors	https://kar.kent.ac.uk/id/eprint/89886#authors
is topic of	https://raw.githubusercontent.com/annajordanous/CO644Files/main/export_kar_RDFN3.n3
is primary topic of	HTML Summary of #89886 Improving trend reversal estimation in forex markets under a directional changes paradigm with classification algorithms

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