LASSO-based high-frequency return predictors for profitable Bitcoin investment

Weige Huang and Xiang Gao

Applied Economics Letters, 2022, vol. 29, issue 12, 1079-1083

Abstract: This article explores the Bitcoin return predictability of variables constructed from one-minute high-frequency Bitcoin trading data. During the training period of 2012–2018, LASSO is used to pick out the most powerful predictors. We then use predictors selected by LASSO to predict the Bitcoin returns in the 2018–2019 test sample. An investment strategy based on the return predictions outperforms a simple buy-and-hold strategy and other strategies based on the prediction of Ordinary Least Squares and Neural Networks.

Date: 2022
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DOI: 10.1080/13504851.2021.1908512

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