article

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

  • Applied Economics Letters
  • Taylor & Francis
Research footprint

At a glance

الاستشهادات
6
المراجع
13
Comments
0
Paper overview

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.

Record transparency

Publication details

DOI
10.1080/13504851.2021.1908512
OpenAlex
W3151550579
Document type
article
Language
EN
Source
Applied Economics Letters
Last metadata update
المجتمع

Comments

تسجيل الدخول للانضمام إلى النقاش.

  1. لا توجد تعليقات بعد. ابدأ النقاش.