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Stock Forecasting and Portfolio Optimization Based on ARIMA-GARCH, Random Forest and Monte Carlo Models

In: Proceedings of the International Workshop on Navigating the Digital Business Frontier for Sustainable Financial Innovation (ICDEBA 2024)

Author

Listed:
  • Jingyuan San

    (South China Normal University, Department of Software Engineering)

Abstract

Stock forecasting has long been a popular issue in financial markets. Also comparing the effectiveness of different forecasting models is enormously important. In this paper, the prediction accuracy of the traditional Autoregressive Integrated Moving Average-Generalized Autoregressive Conditional Heteroskedasticity (ARIMA-GARCH) model and the machine learning model (Random Forest) was compared. 15 high-tech stocks were selected and predicted through 2 models. This paper then optimized the stock portfolio based on the prediction results. The collected data was pre-processed to generate the model predictions and then the portfolio was optimized using the Monte Carlo algorithm. The ARIMA model used the AIC criterion to select optimal parameters. Additionally, the GARCH model was utilized in conjunction with ARIMA model in order to eliminate the effect of heteroskedasticity. While in the Random Forest model, the training was done by partitioning the training set and the test dataset. The experimental results indicated that the random forest model in machine learning provided more accurate prediction results than the ARIMA-GARCH model. Additionally, the optimized portfolio showed very substantial expected returns.

Suggested Citation

  • Jingyuan San, 2025. "Stock Forecasting and Portfolio Optimization Based on ARIMA-GARCH, Random Forest and Monte Carlo Models," Advances in Economics, Business and Management Research, in: Junfeng Lu (ed.), Proceedings of the International Workshop on Navigating the Digital Business Frontier for Sustainable Financial Innovation (ICDEBA 2024), pages 582-591, Springer.
    • Handle: RePEc:spr:advbcp:978-94-6463-652-9_61
    DOI: 10.2991/978-94-6463-652-9_61
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