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

被引：60

作者：

Detzel, Andrew ^{[1
]}

Liu, Hong ^{[2
]}

Strauss, Jack ^{[1
]}

Zhou, Guofu ^{[2
]}

Zhu, Yingzi ^{[3
]}

机构：

[1] Univ Denver, Daniels Sch Business, Denver, CO USA

[2] Washington Univ, Olin Sch Business, St Louis, WA USA

[3] Tsinghua Univ, Sch Econ & Management, Dept Reconstruct Sci, Beijing, Peoples R China

来源：

FINANCIAL MANAGEMENT | 2021年 / 50卷 / 01期

基金：

中国国家自然科学基金;

关键词：

Bitcoin; cryptocurrency; learning; technical analysis; return predictability; INVESTOR SENTIMENT; CROSS-SECTION; RETURNS; INFORMATION; PREMIUM; MARKETS; PROFITABILITY; PERFORMANCE; ECONOMICS; FORECASTS;

D O I：

10.1111/fima.12310

中图分类号：

F8 [财政、金融];

学科分类号：

0202 ;

摘要：

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.

引用

页码：107 / 137

页数：31

共 88 条

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