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Learning and predictability via technical analysis: Evidence from bitcoin and stocks with hard‐to‐value fundamentals

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  • Andrew Detzel
  • Hong Liu
  • Jack Strauss
  • Guofu Zhou
  • Yingzi Zhu

Abstract

What predicts returns on assets with “hard‐to‐value” fundamentals such as Bitcoin and stocks in new industries? We are the first to propose an equilibrium model that shows how technical analysis can arise endogenously via rational learning, providing a theoretical foundation for using technical analysis in practice. We document that ratios of prices to their moving averages forecast daily Bitcoin returns in and out of sample. Trading strategies based on these ratios generate an economically significant alpha and Sharpe ratio gains relative to a buy‐and‐hold position. Similar results hold for small‐cap, young‐firm, and low analyst‐coverage stocks as well as NASDAQ stocks during the dotcom era.

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  • Andrew Detzel & Hong Liu & Jack Strauss & Guofu Zhou & Yingzi Zhu, 2021. "Learning and predictability via technical analysis: Evidence from bitcoin and stocks with hard‐to‐value fundamentals," Financial Management, Financial Management Association International, vol. 50(1), pages 107-137, March.
    • Handle: RePEc:bla:finmgt:v:50:y:2021:i:1:p:107-137
    DOI: 10.1111/fima.12310
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    14. Feng, Wenjun & Zhang, Zhengjun, 2023. "Risk-weighted cryptocurrency indices," Finance Research Letters, Elsevier, vol. 51(C).
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