Learning and predictability via technical analysis: Evidence from bitcoin and stocks with hard‐to‐value fundamentals
提出一个均衡模型，说明技术分析如何通过理性学习内生形成，并发现价格与移动平均线的比率能预测比特币日回报，基于此的交易策略产生显著超额收益，类似结果也适用于小盘股、年轻公司等基本面难估值的股票。
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.