(Re-)Imag(in)ing Price Trends

• 成果类型: Article
• 署名作者: Jiang, Jingwen; Kelly, Bryan; Xiu, Dacheng
• 署名单位: University of Chicago; Yale University; Yale University
• 刊物名称: JOURNAL OF FINANCE
• ISSN/ISSBN: 0022-1082
• DOI: 10.1111/jofi.13268
• 发表日期: 2023
• 页码: 3193-3249
• 关键词: technical analysis; INFORMATION; returns

摘要

We reconsider trend-based predictability by employing flexible learning methods to identify price patterns that are highly predictive of returns, as opposed to testing predefined patterns like momentum or reversal. Our predictor data are stock-level price charts, allowing us to extract the most predictive price patterns using machine learning image analysis techniques. These patterns differ significantly from commonly analyzed trend signals, yield more accurate return predictions, enable more profitable investment strategies, and demonstrate robustness across specifications. Remarkably, they exhibit context independence, as short-term patterns perform well on longer time scales, and patterns learned from U.S. stocks prove effective in international markets.