What question did this study set out to answer?

To develop a hybrid neural network model that improves stock return predictions using high-frequency data.

Deep learning and stock returns: neural networks combining sequential information and cross-sectional information

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Randomized trial demonstrates improved stock return prediction in financial markets, suggesting enhanced decision-making capabilities.

To develop a hybrid neural network model that improves stock return predictions using high-frequency data.
Proposed a novel LSTM-MLP hybrid architecture for predictive modeling.
Utilized high-frequency intraday data from the TAQ database for analysis.
Measured performance against Random Forest and XGBoost models in terms of turnover ratio and profitability.
The hybrid model achieved a turnover ratio of 43.7%, significantly lower than Random Forest (77.5%) and XGBoost (72.2%).
Demonstrated superior out-of-sample returns when adjusted for trading costs.
Captured unique return patterns not accounted for by existing models, providing valuable insights for portfolio management.

Chen et al. (2026) studied this question.

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