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Learning and predictability via technical analysis: Evidence from bitcoin and stocks with hard‐to‐value fundamentals
Author(s) -
Detzel Andrew,
Liu Hong,
Strauss Jack,
Zhou Guofu,
Zhu Yingzi
Publication year - 2020
Publication title -
financial management
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.647
H-Index - 68
eISSN - 1755-053X
pISSN - 0046-3892
DOI - 10.1111/fima.12310
Subject(s) - predictability , sharpe ratio , technical analysis , econometrics , economics , value (mathematics) , financial economics , position (finance) , trading strategy , rational expectations , sample (material) , statistics , mathematics , finance , portfolio , chemistry , chromatography
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.