Drivers of the next-minute Bitcoin price using sparse regressions

Simple item page
dc.contributor.authorGurrib, Ikhlaas
dc.contributor.authorKamalov, Firuz
dc.contributor.authorStarkova, Olga
dc.contributor.authorElshareif, Elgilani Eltahir
dc.contributor.authorContu, Davide
dc.date.accessioned2023-12-25T13:34:56Z
dc.date.available2023-12-25T13:34:56Z
dc.date.copyright© 2023
dc.date.issued2023
dc.description.abstractPurpose: This paper aims to investigate the role of price-based information from major cryptocurrencies, foreign exchange, equity markets and key commodities in predicting the next-minute Bitcoin (BTC) price. This study answers the following research questions: What is the best sparse regression model to predict the next-minute price of BTC? What are the key drivers of the BTC price in high-frequency trading? Design/methodology/approach: Least absolute shrinkage and selection operator and Ridge regressions are adopted using minute-based open-high-low-close prices, volume and trade count for eight major cryptos, global stock market indices, foreign currency pairs, crude oil and gold price information for February 2020–March 2021. This study also examines whether there was any significant break and how the accuracy of the selected models was impacted. Findings: Findings suggest that Ridge regression is the most effective model for predicting next-minute BTC prices based on BTC-related covariates such as BTC-open, BTC-high and BTC-low, with a moderate amount of regularization. While BTC-based covariates BTC-open and BTC-low were most significant in predicting BTC closing prices during stable periods, BTC-open and BTC-high were most important during volatile periods. Overall findings suggest that BTC’s price information is the most helpful to predict its next-minute closing price after considering various other asset classes’ price information. Originality/value: To the best of the authors’ knowledge, this is the first paper to identify the covariates of major cryptocurrencies and predict the next-minute BTC crypto price, with a focus on both crypto-asset and cross-market information. © 2023, Emerald Publishing Limited.
dc.identifier.citationGurrib, I., Kamalov, F., Starkova, O., Elshareif, E. E., & Contu, D. (2023). Drivers of the next-minute Bitcoin price using sparse regressions. Studies in Economics and Finance. https://doi.org/10.1108/SEF-04-2023-0182
dc.identifier.issn10867376
dc.identifier.urihttps://doi.org/10.1108/SEF-04-2023-0182
dc.identifier.urihttps://hdl.handle.net/20.500.12519/963
dc.publisherEmerald Publishing
dc.relation.ispartofseriesStudies in Economics and Finance
dc.rights.holderCopyright : © 2023, Emerald Publishing Limited.
dc.subjectBitcoin price prediction
dc.subjectCross-market information
dc.subjectHigh frequency
dc.subjectLasso
dc.subjectRidge
dc.titleDrivers of the next-minute Bitcoin price using sparse regressions
dc.typeArticle

Files

Collections

Dr. Muhammad Ikhlaas Gurrib