(Re‐)Imag(in)ing Price Trends
用机器学习图像分析技术从股票价格图中提取预测回报的模式，发现这些模式不同于常见趋势信号，预测更准、策略更赚钱，且在不同市场和时间尺度上表现稳健。
ABSTRACT 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.