Cross-Market Robustness of CNN + PPO for Multi-Stock Trading: Evidence from the United States, China, and India

Jizhi Wang ()
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Jizhi Wang: Huazhong University of Science and Technology, School of Economics

A chapter in Proceedings of the 2026 11th International Conference on Financial Innovation and Economic Development (ICFIED 2026), 2026, pp 269-280 from Springer

Abstract: Abstract This study examines the external validity of a convolutional neural network (CNN) feature extractor trained with Proximal Policy Optimization (PPO) for multi-stock daily trading across three equity universes: S&P50 (United States), SSE50 (China), and Nifty50 (India). This model was trained on 2023 data and evaluated for out-of-sample performance in 2024, using the same preprocessing, features, hyperparameters, and training protocol. It studied the sensitivity to lookback (10–50 trading days) with a window-expansion technique, and benchmark the CNN+PPO approach against a multilayer perceptron (MLP) baseline. The U.S. sample showed a clear advantage for CNN in shorter windows (particularly 20 days), as evidenced by increased risk-adjusted returns and more localized salience on recent channels. The magnitude of these gains is mixed on SSE50 and non-existent or negative on Nifty50. Complementary gradient saliency and permutation-importance diagnostics suggest that these cross-market differences in performance and saliency can be traced to a mismatch between the convolutional inductive bias and the presence or absence of short, cross-sectional motifs. Results suggest that architecture choice and deployment should be data-driven and informed by market diagnostics and feature engineering.

Keywords: deep reinforcement learning; PPO; convolutional neural networks; algorithmic trading; domain transfer; interpretability (search for similar items in EconPapers)
Date: 2026
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DOI: 10.2991/978-94-6239-642-5_28

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