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dc.contributor.authorBEKIROS, Stelios D.
dc.date.accessioned2016-01-12T15:17:39Z
dc.date.available2016-01-12T15:17:39Z
dc.date.issued2015
dc.identifier.citationJournal of empirical finance, 2015, Vol. 30, pp. 34-49en
dc.identifier.issn0927-5398
dc.identifier.urihttps://hdl.handle.net/1814/38372
dc.description.abstractUntil recently economists focused on structural models that were constrained by a lack of high-frequency data and theoretical deficiencies. Little academic research has been invested in actually trying to build successful real-time trading models for the high-frequency foreign exchange market, which is characterized by inherent complexity and heterogeneity. The present work opens new directions for inference on market efficiency in an attempt to account for the use of technical analysis by practitioners over many years now. This paper presents a heuristic model that efficiently emulates the dynamic learning of intraday traders. The proposed setup incorporates agent beliefs, preferences and expectations while it integrates the calibration of technical rules by means of adaptive training. The study focuses on EUR/USD which is the most liquid and widely traded currency pair. The data consist of a very large tick-by-tick sample of bid and ask prices covering many trading periods to enhance robustness in the results. The efficiency of a technical trading strategy based on the proposed model is investigated in terms of directional predictability. The heuristic learning system is compared against many non-linear models, a random walk and a buy & hold strategy. Based on statistical testing it is shown that, with the inclusion of transaction costs, the profitability of the new model is consistently superior. These findings provide evidence of technical predictability under incomplete information and can be justified by invoking the existence of heterogeneity caused by many factors affecting market microstructure. Overall, the results suggest that the proposed model can be used to improve upon traditional technical analysis approaches.en
dc.language.isoenen
dc.relation.ispartofJournal of empirical financeen
dc.subjectIntraday exchange ratesen
dc.subjectMarket heterogeneityen
dc.subjectTechnical tradingen
dc.subjectHeuristic learningen
dc.subjectF31en
dc.subjectG17en
dc.subjectG14en
dc.subjectC45en
dc.subjectC58en
dc.titleHeuristic learning in intraday trading under uncertaintyen
dc.typeArticleen
dc.identifier.doi10.1016/j.jempfin.2014.11.002
dc.identifier.volume30en
dc.identifier.startpage34en
dc.identifier.endpage49en


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