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Machine learning and the cross-section of cryptocurrency returns

Author

Listed:
  • Cakici, Nusret
  • Shahzad, Syed Jawad Hussain
  • Będowska-Sójka, Barbara
  • Zaremba, Adam
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.

Suggested Citation

  • Cakici, Nusret & Shahzad, Syed Jawad Hussain & Będowska-Sójka, Barbara & Zaremba, Adam, 2024. "Machine learning and the cross-section of cryptocurrency returns," International Review of Financial Analysis, Elsevier, vol. 94(C).
    • Handle: RePEc:eee:finana:v:94:y:2024:i:c:s1057521924001765
    DOI: 10.1016/j.irfa.2024.103244
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    More about this item

    Keywords

    Cryptocurrency markets; Machine learning; Return predictability; Limits to arbitrage; Asset pricing; The cross-section of returns;
    All these keywords.

    JEL classification:

    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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    Access and download statistics

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