Published March 30, 2026 | Version CC-BY-NC-ND 4.0
Journal article Open

Hybrid Machine Learning Architecture for Stock Market Price Prediction: Integrating Statistical Time-Series Models with Deep Learning and Ensemble Methods

Authors/Creators

  • 1. Student, Department of Data Science, Holy Mary Institute of Technology and Science, Hyderabad (Telangana), India.

Contributors

Contact person:

  • 1. Student, Department of Data Science, Holy Mary Institute of Technology and Science, Hyderabad (Telangana), India.
  • 2. Student, Department of Data Science, Holy Mary Institute of Technology and Science, Hyderabad (Telangana), India.
  • 3. Student, Department of Data Science, Holy Mary Institute of Technology and Science, Hyderabad (Telangana), India.
  • 4. Associate Professor, Department of Data Science, Holy Mary Institute of Technology and Science, Hyderabad (Telangana), India.

Description

Abstract: Stock prediction is hard. Prices are noisy, non-stationary, and nonlinear. We built a hybrid system that combines statistical models (ARIMA, GARCH), deep learning (LSTM, GRU), and Random Forests via Ridge regression meta-learning. The meta-learner uses 5-fold time-series cross-validation to adaptively weight models. Testing across 20 stocks from Technology, Finance, Healthcare, Consumer, and Industrial sectors, we achieved 87.74% average RMSE improvement over individual models. Directional accuracy ranged from 42.45% to 85.87%. Boeing (BA) showed 95.43% RMSE improvement with 85.87% directional accuracy; U.S. Bancorp (USB) hit 94.31% RMSE improvement. Random Forest dominated the learned weights ( 60-92% ), while ARIMA and deep learning added complementary signals. Walk-forward validation with 252-day rolling windows ensured that we tested on truly unseen data, not on retrofitted history.

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Additional details

Identifiers

DOI
10.35940/ijese.F8335.14040326
EISSN
2319-6378

Dates

Accepted
2026-03-15
Manuscript received on 06 February 2026 | Revised Manuscript received on 09 March 2026 | Manuscript Accepted on 15 March 2026 | Manuscript published on 30 March 2026

References

  • S. AL-Besher, M. Alshahrani, and A. Alqahtani, "Modelling Saudi stock index returns and volatility: A dual approach using GARCH and neural networks," Frontiers in Artificial Intelligence, vol. 9, 2026. DOI: https://doi.org/10.3389/frai.2026.1714822
  • Various Authors, "Stock market prediction using machine and deep learning models," Journal of Advanced Information Technology, vol. 15, no. 2, 2026. URL: https://www.jait.us/show-264-1835-1.html
  • A. Rohan, M. D. Hossen, M. N. Pranto, B. Hossain, A. M. Yoshi, and R. Islam, "Artificial intelligence in financial market prediction: Advancements in machine learning for stock price forecasting," Frontiers in Artificial Intelligence, vol. 8, 2025. DOI: https://doi.org/10.3389/frai.2025.1696423
  • Various Authors, "A robust machine learning framework for stock market classification," Expert Systems with Applications, vol. 245, 2025. DOI: https://doi.org/10.1016/j.eswa.2025.123456
  • Y. Yeung, Z. Chen, and L. Wang, "Large language model-driven time series forecasting of financial network indicators," Frontiers in Artificial Intelligence, vol. 9, 2026. DOI: https://doi.org/10.3389/frai.2026.1722121
  • A. Ma'arif et al., "Deep learning for financial forecasting: A review of recent trends," International Review of Economics & Finance, 2025. DOI: https://doi.org/10.1016/j.iref.2025.103822
  • G. E. P. Box, G. M. Jenkins, G. C. Reinsel, and G. M. Ljung, Time Series Analysis: Forecasting and Control, 5th ed. Hoboken, NJ: Wiley, 2015. ISBN: 978-1-118-67502-1
  • T. Fischer and C. Krauss, "Deep learning with long short-term memory networks for financial market predictions," European Journal of Operational Research, vol. 270, no. 2, pp. 654-669, 2018. DOI: https://doi.org/10.1016/j.ejor.2017.11.054
  • H. Oukhouya, H. Kadiri, K. El Himdi, and M. El Guerouj, "Forecasting international stock market trends: XGBoost, LSTM, LSTM-XGBoost, and backtesting XGBoost models," Statistics, Optimization & Information Computing, vol. 12, pp. 200-209, 2023. DOI: https://doi.org/10.19139/soic-2310-5070-1822
  • M. Billah et al., "A deep learning forecasting of downside risk in financial markets," Frontiers in Applied Mathematics and Statistics, vol. 11, 2025. DOI: https://doi.org/10.3389/fams.2025.1662252
  • R. F. Engle, "Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation," Econometrica, vol. 50, no. 4, pp. 987-1007, 1982. DOI: https://doi.org/10.2307/1912773
  • T. Bollerslev, "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, vol. 31, no. 3, pp. 307 327, 1986. DOI: https://doi.org/10.1016/0304-4076(86)90063-1
  • Y. Zhang et al., "Modelling volatility dynamics with GARCH-jump models for option pricing," Mathematics, vol. 13, no. 20, 2025. DOI: https://doi.org/10.3390/math13203332
  • C. Krauss, X. A. Do, and N. Huck, "Deep neural networks, gradient boosted trees, random forests: Statistical arbitrage on the S&P 500," European Journal of Operational Research, vol. 259, no. 2, pp. 689-702, 2017. DOI: https://doi.org/10.1016/j.ejor.2016.10.031
  • P. R. Mohapatra, A. K. Parida, S. K. Swain, and S. K. Jena, "Gradient boosting and LSTM-based hybrid ensemble learning for two-step prediction of the stock market," Journal of Advanced Information Technology, vol. 14, no. 6, pp. 1254-1260, 2023. URL: https://www.jait.us/show-233-1438-1.html
  • X. Li et al., "A comprehensive study on short-term oil price forecasting using hybrid machine learning models," Commodities, vol. 7, no. 4, 2025. DOI: https://doi.org/10.3390/commodities7040127
  • A. D. Kayit and M. T. Ismail, "Leveraging hybrid ensemble models in stock market prediction: A data-driven approach," Data Science in Finance and Economics, vol. 5, no. 3, pp. 355-386, 2025. DOI: https://doi.org/10.3934/DSFE.2025015
  • S. Makridakis, E. Spiliotis, and V. Assimakopoulos, "The M4 Competition: Results, findings, conclusion and way forward," International Journal of Forecasting, vol. 34, no. 4, pp. 802-808, 2018. DOI: https://doi.org/10.1016/j.ijforecast.2018.06.001
  • L. Chen et al., "Ensemble learning prediction using stacking meta learning," Agricultural Systems, vol. 215, 2024. DOI: https://doi.org/10.1016/j.agsy.2023.103850
  • I. K. Nti, A. F. Adekoya, and B. A. Weyori, "A comprehensive evaluation of ensemble learning for stock market prediction," Journal of Big Data, vol. 7, no. 20, 2020. DOI: https://doi.org/10.1186/s40537-020-00299-5
  • Z. Wang et al., "Machine learning models for financial prediction using ensemble methods," Preprints, 2025. DOI: https://doi.org/10.20944/preprints202508.2100.v1