Can investors’ informed trading predict cryptocurrency returns? Evidence from machine learning

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buir.contributor.authorŞensoy, Ahmet
buir.contributor.orcidŞensoy, Ahmet|0000-0001-7967-5171
dc.citation.epage101683- 22en_US
dc.citation.spage101683- 1en_US
dc.citation.volumeNumber62en_US
dc.contributor.authorWang, Y.
dc.contributor.authorWang, C.
dc.contributor.authorŞensoy, Ahmet
dc.contributor.authorYao, S.
dc.contributor.authorCheng, F.
dc.date.accessioned2023-02-15T12:58:21Z
dc.date.available2023-02-15T12:58:21Z
dc.date.issued2022-05-24
dc.departmentDepartment of Managementen_US
dc.description.abstractAs an emerging asset, cryptocurrencies have attracted more and more attention from investors and researchers in recent years. With the gradual convergence of the investors in cryptocurrency and traditional financial markets, the research on investor trading behavior from the perspective of microstructure has become increasingly important in cryptocurrency market. In this paper, we study whether investors’ informed trading behavior can significantly predict cryptocurrency returns. We use various machine learning algorithms to verify the contribution of informed trading to the predictability of cryptocurrency returns. The results show that informed trading plays a role in the prediction of some individual cryptocurrency returns, but it cannot significantly improve the prediction accuracy in an average sense of the whole market. The lack of market supervision of cryptocurrency market may be the main factor for relatively low efficiency of this market, and policymakers need to pay attention to it.en_US
dc.description.provenanceSubmitted by Ezgi Uğurlu (ezgi.ugurlu@bilkent.edu.tr) on 2023-02-15T12:58:21Z No. of bitstreams: 1 Can_investors’_informed_trading_predict_cryptocurrency_returns_Evidence_from_machine_learning.pdf: 4138047 bytes, checksum: c503937b0bce0163f58249649b3628be (MD5)en
dc.description.provenanceMade available in DSpace on 2023-02-15T12:58:21Z (GMT). No. of bitstreams: 1 Can_investors’_informed_trading_predict_cryptocurrency_returns_Evidence_from_machine_learning.pdf: 4138047 bytes, checksum: c503937b0bce0163f58249649b3628be (MD5) Previous issue date: 2022-05-24en
dc.embargo.release2025-05-24
dc.identifier.doi10.1016/j.ribaf.2022.101683en_US
dc.identifier.eissn1878-3384
dc.identifier.issn0275-5319
dc.identifier.urihttp://hdl.handle.net/11693/111364
dc.language.isoEnglishen_US
dc.publisherElsevier Inc.en_US
dc.relation.isversionofhttps://doi.org/10.1016/j.ribaf.2022.101683en_US
dc.source.titleResearch in International Business and Financeen_US
dc.subjectCryptocurrencyen_US
dc.subjectMachine learningen_US
dc.subjectBehavioral financeen_US
dc.subjectInformed tradingen_US
dc.subjectForecastingen_US
dc.titleCan investors’ informed trading predict cryptocurrency returns? Evidence from machine learningen_US
dc.typeArticleen_US

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