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Taming crypto anomalies: A Lasso-type factor model

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  • Li, Jinchuan
  • Zhu, Yifeng

Abstract

In this paper, we examine the performance of 49 cryptocurrency market anomalies in terms of their in-sample, out-of-sample, and full sample performance following the work of Liu et al. (2022). We find that, despite the similarities in anomaly performance between the in-sample and overall sample periods, there are noticeable changes in the behavior of out-of-sample anomalies. This includes the disappearance of size effect and the appearance of left-tail risk effect. Consequently, by using the Iterative Double Selection Lasso method, we construct a new three-factor model — DS3, which consists of the market factor (MKT), the two-week momentum factor (MOM2), and the residual momentum factor (RMOM). In comparison to CPT3 (Liu et al., 2022) and IPCA3 (Bianchi and Babiak, 2025), our DS3 model exhibits a certain advantage in explaining anomalies.

Suggested Citation

  • Li, Jinchuan & Zhu, Yifeng, 2026. "Taming crypto anomalies: A Lasso-type factor model," Research in International Business and Finance, Elsevier, vol. 83(C).
    • Handle: RePEc:eee:riibaf:v:83:y:2026:i:c:s0275531926000255
    DOI: 10.1016/j.ribaf.2026.103298
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    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

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