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(Re‐)Imag(in)ing Price Trends

Author

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  • JINGWEN JIANG
  • BRYAN KELLY
  • DACHENG XIU

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.

Suggested Citation

  • Jingwen Jiang & Bryan Kelly & Dacheng Xiu, 2023. "(Re‐)Imag(in)ing Price Trends," Journal of Finance, American Finance Association, vol. 78(6), pages 3193-3249, December.
    • Handle: RePEc:bla:jfinan:v:78:y:2023:i:6:p:3193-3249
    DOI: 10.1111/jofi.13268
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    Cited by:

    1. Yuan, Ying & Qu, Yong & Wang, Tianyang, 2025. "Predicting risk premiums: A constraint-based model," Journal of Empirical Finance, Elsevier, vol. 83(C).
    2. Nechvátalová, Lenka, 2025. "Autoencoder asset pricing models and economic restrictions — international evidence," International Review of Financial Analysis, Elsevier, vol. 107(C).
    3. Yong Zhang & Xinxiao Wu & Yunde Jia & Che Sun, 2026. "Game-Theoretic Modeling of Heterogeneous Investor Interactions for Stock Price Forecasting," Papers 2605.23953, arXiv.org.
    4. Xuefeng Gao & Mengying He & Xuedong He & Jiale Zha, 2025. "Factor-Based Conditional Diffusion Model for Contextual Portfolio Optimization," Papers 2509.22088, arXiv.org, revised Jun 2026.
    5. Saketh Aleti & Tim Bollerslev & Mathias Siggaard, 2025. "Intraday Market Return Predictability Culled from the Factor Zoo," Management Science, INFORMS, vol. 71(9), pages 7731-7751, September.
    6. Beckmeyer, Heiner & Wiedemann, Timo, 2025. "All Days Are Not Created Equal: Understanding Momentum by Learning to Weight Past Returns," Journal of Banking & Finance, Elsevier, vol. 181(C).
    7. Yeonchan Kang & Doojin Ryu & Robert I. Webb, 2025. "How well do machine learning models in finance work?," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 11(1), pages 1-30, December.
    8. Maung, Kenwin & Swanson, Norman R., 2025. "A survey of models and methods used for forecasting when investing in financial markets," International Journal of Forecasting, Elsevier, vol. 41(4), pages 1355-1382.
    9. Viet Trinh, 2025. "A Comprehensive Review: Applicability of Deep Neural Networks in Business Decision Making and Market Prediction Investment," Papers 2502.00151, arXiv.org.
    10. Sophia Zhengzi Li & Yushan Tang, 2025. "Automated Volatility Forecasting," Management Science, INFORMS, vol. 71(7), pages 6248-6274, July.

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