(Re‐)Imag(in)ing Price Trends

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 pred...

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Bibliographic Details
Published in:The Journal of finance (New York) 2023-12, Vol.78 (6), p.3193-3249
Main Authors: JIANG, JINGWEN, KELLY, BRYAN, XIU, DACHENG
Format: Article
Language:English
Subjects:
Flexible learning
International markets
Rates of return
Robustness
Stock prices
Trends
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Summary: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.
ISSN:0022-1082
1540-6261
DOI:10.1111/jofi.13268