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

【Author】 Huang, Weige; Gao, Xiang

【Source】APPLIED ECONOMICS LETTERS

【影响因子】1.287

【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.

【Keywords】Bitcoin; high-frequency; investment strategy; LASSO; neural networks

【发表时间】2021

【收录时间】2022-01-02

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