A Hybrid LSTM–XGBoost Framework for Multi-Horizon Stock Return Prediction Across Diversified Equity Portfolios
Accurate prediction of equity returns remains a major challenge in computational finance due to the non-stationary, nonlinear, and low signal-to-noise ratio nature of financial time series. This paper proposes a hybrid two-stage architecture that combines a long short-term memory (LSTM) network with an XGBoost gradient...
S. Mostafa, Yahia Ahmed, Farah Darwish et al.
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