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Predicting Stock Returns and Optimizing Portfolios: An Analysis of 15 Technology Companies Based on ARIMA, GARCH and Monte Carlo Simulation

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

Author

Listed:
  • Yifan Liu

    (Southwestern University of Finance and Economics, International Economics and Trade)

Abstract

Traditionally, portfolio management focused on using historical averages and qualitative assessments of market trends. However, with more data available and improvements in computational power, more complex methods have been developed. This study aims to analyze the stock prices of 15 technology companies spanning from 2016 to 2023, creating a training set to calculate returns. Utilizing ARIMA and GARCH models, this study predicted stock returns for the subsequent years, 2023–2024. These models are used to capture the trends and volatility in the stock prices, providing valuable insights into potential future performance. To further enhance the analysis, this study employed Monte Carlo simulation to evaluate various portfolio combinations. This approach enabled to assess the risk and return characteristics of different investment strategies, ultimately identifying the most representative strategy for the given dataset. The findings contribute to the field of financial forecasting and portfolio optimization, highlighting the potential of advanced statistical techniques in predicting stock returns and informing investment decisions.

Suggested Citation

  • Yifan Liu, 2025. "Predicting Stock Returns and Optimizing Portfolios: An Analysis of 15 Technology Companies Based on ARIMA, GARCH and Monte Carlo Simulation," 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 641-648, Springer.
    • Handle: RePEc:spr:advbcp:978-94-6463-652-9_66
    DOI: 10.2991/978-94-6463-652-9_66
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