Drivers of the next-minute Bitcoin price using sparse regressions
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.
Keywords
Acknowledgements
The authors thank the reviewers for their constructive comments.
Funding: Not applicable.
Availability of data and materials: The data sets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.
Competing interests: The authors report that they have no competing interests to declare.
Citation
Gurrib, I., Kamalov, F., Starkova, O., Elshareif, E.E. and Contu, D. (2023), "Drivers of the next-minute Bitcoin price using sparse regressions", Studies in Economics and Finance, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/SEF-04-2023-0182
Publisher
:Emerald Publishing Limited
Copyright © 2023, Emerald Publishing Limited