Big data analytics, order imbalance and the predictability of stock returns

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buir.contributor.orcidŞensoy, Ahmet|0000-0001-7967-5171en_US
dc.citation.epage19en_US
dc.citation.spage1en_US
dc.citation.volumeNumber62en_US
dc.contributor.authorAkyildirim, E.
dc.contributor.authorŞensoy, Ahmet
dc.contributor.authorGulay, G.
dc.contributor.authorCorbet, S.
dc.contributor.authorSalari, Hajar Novin
dc.contributor.bilkentauthorŞensoy, Ahmet
dc.contributor.bilkentauthorSalari, Hajar Novin
dc.date.accessioned2022-02-21T11:29:07Z
dc.date.available2022-02-21T11:29:07Z
dc.date.issued2021-09-24
dc.departmentDepartment of Managementen_US
dc.description.abstractFinancial institutions have adopted big data to a considerable extent to provide better investment decisions. Consequently, high-frequency algorithmic traders use a vast amount of historical data with various statistical models to maximize their trading profits. Until recently, high-frequency algorithmic trading was the domain of institutional traders with access to supercomputers. Nowadays, any investor can potentially make high-frequency trades because of easy access to big data and software to analyze and execute trades. With that in mind, Borsa Istanbul introduced real time big data analytics as a product to its customers. These analytics are derived in real time from order book and trade data and aim to level the playing field between investment firms and retail traders. Using classical benchmark models in the literature, we show that Borsa Istanbul’s order imbalance-based data analytics are useful in predicting both time-series and cross-sectional intraday excess future returns, proving that this product is extremely beneficial to market participants, particularly day traders.en_US
dc.embargo.release2023-09-24
dc.identifier.doi10.1016/j.mulfin.2021.100717en_US
dc.identifier.eissn1873-1309
dc.identifier.issn1042-444X
dc.identifier.urihttp://hdl.handle.net/11693/77540
dc.language.isoEnglishen_US
dc.publisherElsevieren_US
dc.relation.isversionofhttps://doi.org/10.1016/j.mulfin.2021.100717en_US
dc.source.titleJournal of Multinational Financial Managementen_US
dc.subjectFintechen_US
dc.subjectBig dataen_US
dc.subjectData analyticsen_US
dc.subjectOrder imbalanceen_US
dc.subjectAlgorithmic tradingen_US
dc.titleBig data analytics, order imbalance and the predictability of stock returnsen_US
dc.typeArticleen_US
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