Learning and predictability via technical analysis: Evidence from bitcoin and stocks with hard‐to‐value fundamentals. Issue 1 (1st June 2020)

• Record Type: Journal Article
• Authors: Detzel, Andrew; Liu, Hong; Strauss, Jack; Zhou, Guofu; Zhu, Yingzi
• Is Part Of: Financial management. Volume 50:Issue 1(2021)
• Page Start: 107
• Page End: 137
• Publication Date: 2020-06-01
• Subjects: Bitcoin -- cryptocurrency -- learning -- technical analysis -- return predictability; Corporations -- Finance -- Periodicals; 658.1505
• DOI: 10.1111/fima.12310
• Languages: English
• ISSNs: 0046-3892

Abstract

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.