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Drivers of the next-minute Bitcoin price using sparse regressions

Author

Listed:
  • Ikhlaas Gurrib
  • Firuz Kamalov
  • Olga Starkova
  • Elgilani Eltahir Elshareif
  • Davide Contu

Abstract

Purpose - 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.

Suggested Citation

  • Ikhlaas Gurrib & Firuz Kamalov & Olga Starkova & Elgilani Eltahir Elshareif & Davide Contu, 2023. "Drivers of the next-minute Bitcoin price using sparse regressions," Studies in Economics and Finance, Emerald Group Publishing Limited, vol. 41(2), pages 410-431, October.
  • Handle: RePEc:eme:sefpps:sef-04-2023-0182
    DOI: 10.1108/SEF-04-2023-0182
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    More about this item

    Keywords

    High frequency; Lasso; Ridge; Bitcoin price prediction; Cross-market information; G12; G15; G17;
    All these keywords.

    JEL classification:

    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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