Machine learning and the cross-section of cryptocurrency returns

Nusret Cakici, Syed Jawad Hussain Shahzad, Barbara Będowska-Sójka and Adam Zaremba

International Review of Financial Analysis, 2024, vol. 94, issue C

Abstract: We employ a repertoire of machine learning models to investigate the cross-sectional return predictability in cryptocurrency markets. While all methods generate substantial economic gains—unlike in other asset classes—the benefits from model complexity are limited. Return predictability derives mainly from a handful of simple characteristics, such as market price, past alpha, illiquidity, and momentum. Contrary to the stock market, abnormal returns in cryptocurrencies originate from the long leg of the trade and persist over time. Furthermore, despite high portfolio turnover, most machine learning strategies remain profitable after trading costs. However, alphas are concentrated in hard-to-trade assets and critically depend on harvesting extreme returns on small, illiquid, and volatile coins.

Keywords: Cryptocurrency markets; Machine learning; Return predictability; Limits to arbitrage; Asset pricing; The cross-section of returns (search for similar items in EconPapers)
JEL-codes: G11 G12 G17 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:finana:v:94:y:2024:i:c:s1057521924001765

DOI: 10.1016/j.irfa.2024.103244

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