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

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  • Weige Huang
  • Xiang Gao

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.

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  • Weige Huang & Xiang Gao, 2022. "LASSO-based high-frequency return predictors for profitable Bitcoin investment," Applied Economics Letters, Taylor & Francis Journals, vol. 29(12), pages 1079-1083, July.
    • Handle: RePEc:taf:apeclt:v:29:y:2022:i:12:p:1079-1083
    DOI: 10.1080/13504851.2021.1908512
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    Cited by:

    1. Weige Huang & Xiang Gao, 2023. "Forecasting Bitcoin Futures: A Lasso-BMA Two-Step Predictor Selection for Investment and Hedging Strategies," SAGE Open, , vol. 13(1), pages 21582440231, January.

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