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Cross-cryptocurrency return predictability

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  • Guo, Li
  • Sang, Bo
  • Tu, Jun
  • Wang, Yu

Abstract

Using data from Binance, we find strong evidence of cross-cryptocurrency return predictability. The lagged returns of other cryptocurrencies serve as significant predictors of focal cryptocurrencies. The results are robust across various methods, including the adaptive LASSO and principal component analysis. Furthermore, a long-short portfolio formed on the past returns of cryptocurrencies can generate a sizable return out-of-sample after accounting for transaction costs. Overall, our findings corroborate cross-cryptocurrency return predictability and are consistent with the spillover effect mechanism, where common shocks among cryptocurrencies coupled with the limited attention of investors lead to slow information diffusion across coins.

Suggested Citation

  • Guo, Li & Sang, Bo & Tu, Jun & Wang, Yu, 2024. "Cross-cryptocurrency return predictability," Journal of Economic Dynamics and Control, Elsevier, vol. 163(C).
  • Handle: RePEc:eee:dyncon:v:163:y:2024:i:c:s0165188924000551
    DOI: 10.1016/j.jedc.2024.104863
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    Keywords

    Cryptocurrency; Return predictability; Information spillover; Adaptive LASSO;
    All these keywords.

    JEL classification:

    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G40 - Financial Economics - - Behavioral Finance - - - General

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