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

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

JEL CLASSIFICATION:

• G12
• G15

Disclosure statement

No potential conflict of interest was reported by the author(s).

Acknowledgement

This work is supported by the General Program of National Natural Science Foundation of China (No. 72071211).

Notes

¹ There could be cases when timestamps go unrecorded or prices jump around, and they happen occasionally in a day for several reasons, e.g., the exchange or its API was down, trades did not exist, there are unforeseen technical errors in data reporting or gathering. We choose not to backfill these blank data points.

² The standard deviation of Bitcoin return in the training sample is 0.0511; 0.0377 for the test sample.