Empirical modeling reveals robust price pattern predictions across U.S. equities from 1992 to 2022, indicating that visual machine learning improves financial forecasting and interpretability.
We devise an innovative technical analysis strategy that leverages combinations of technical indicators and machine learning techniques. Our models use image representations generated by visualizing the time-series data of individual stocks over a specific period to identify price patterns. To avoid data snooping, we carefully curtail the size of our training sets and shuffle the sample sets between models. Our approach yields highly reliable and impactful predictions on the U.S. stock market from January 1992 to December 2022 compared to multiple benchmarks, including momentum and reversal, traditional technical trading rules, industrial portfolios, and the Fama–French five-factor model. Using the GradCAM method, we visualize the attention maps of model predictions, providing strong interpretability to our approach of technical analysis.
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Yi et al. (2026) studied this question.
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