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Stock Price Prediction Using a Stacked Heterogeneous Ensemble

Author

Listed:
  • Michael Parker

    (School of Computing, University of Portsmouth, Buckingham Building, Lion Terrace, Portsmouth PO1 3HE, UK)

  • Mani Ghahremani

    (School of Computing, University of Portsmouth, Buckingham Building, Lion Terrace, Portsmouth PO1 3HE, UK)

  • Stavros Shiaeles

    (School of Computing, University of Portsmouth, Buckingham Building, Lion Terrace, Portsmouth PO1 3HE, UK)

Abstract

Forecasting stock price ranges remains a significant challenge because of the non-linear nature of financial data. This study proposes and evaluates a stacking ensemble model for range-based volatility forecasting, using open, high, low, and close (OHLC) prices. The model integrates a diverse, heterogeneous set of base learners, such as statistical (ARIMA), machine learning (Random Forest), and deep learning (LSTM, GRU, Transformer) models, with an XGBoost meta-learner. Applied to several major financial indices and a single stock, the proposed framework demonstrates high predictive accuracy, achieving R 2 scores between 0.9735 and 0.9905. These results highlight the efficacy of a multi-faceted stacking approach in navigating the complexities of financial forecasting.

Suggested Citation

  • Michael Parker & Mani Ghahremani & Stavros Shiaeles, 2025. "Stock Price Prediction Using a Stacked Heterogeneous Ensemble," IJFS, MDPI, vol. 13(4), pages 1-23, October.
    • Handle: RePEc:gam:jijfss:v:13:y:2025:i:4:p:201-:d:1780991
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