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.

中文翻译：

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（重新）想象价格趋势

我们通过采用灵活的学习方法来重新考虑基于趋势的可预测性，以识别高度预测回报的价格模式，而不是测试动量或反转​​等预定义模式。我们的预测数据是股票级别的价格图表，使我们能够使用机器学习图像分析技术提取最具预测性的价格模式。这些模式与通常分析的趋势信号显着不同，可以产生更准确的回报预测，实现更有利可图的投资策略，并展示跨规范的稳健性。值得注意的是，它们表现出环境独立性，因为短期模式在较长时间范围内表现良好，而从美国股市学到的模式在国际市场上证明是有效的。