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Constructing Cybersecurity Stocks Portfolio Using AI

Author

Listed:
  • Avishay Aiche

    (Western Galilee College, Acre 2412101, Israel)

  • Zvi Winer

    (Western Galilee College, Acre 2412101, Israel)

  • Gil Cohen

    (Western Galilee College, Acre 2412101, Israel)

Abstract

This study explores the application of artificial intelligence, specifically ChatGPT-4o, in constructing and managing a portfolio of cybersecurity stocks over the period from Q1 2018 to Q1 2024. Leveraging advanced machine learning models, fundamental analysis, sentiment analysis, and optimization techniques, the AI-driven portfolio significantly outperformed leading cybersecurity ETFs, as well as broader market indices such as the Nasdaq 100 (QQQ) and S&P 500 (SPY). The methodology employed included data collection, stock filtering, predictive modeling using Random Forests and Support Vector Machines (SVMs), sentiment analysis through natural language processing (NLP), and portfolio optimization using Mean-Variance Optimization (MVO), with quarterly rebalancing to ensure responsiveness to evolving market conditions. The AI-selected portfolio achieved a total return of 273%, with strong risk-adjusted performance as demonstrated by key metrics such as the Sharpe ratio, highlighting the effectiveness of an AI-based approach in navigating market complexities and generating superior returns. The results of this study indicate that AI-driven portfolio management can uncover investment opportunities that traditional methods may overlook, offering a competitive edge in the cybersecurity sector and promising enhanced predictive accuracy, efficiency, and overall investment success as AI technologies continue to evolve.

Suggested Citation

  • Avishay Aiche & Zvi Winer & Gil Cohen, 2024. "Constructing Cybersecurity Stocks Portfolio Using AI," Forecasting, MDPI, vol. 6(4), pages 1-13, November.
    • Handle: RePEc:gam:jforec:v:6:y:2024:i:4:p:53-1077:d:1524593
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    References listed on IDEAS

    as
    1. Haenlein, Michael & Kaplan, Andreas, 2021. "Artificial intelligence and robotics: Shaking up the business world and society at large," Journal of Business Research, Elsevier, vol. 124(C), pages 405-407.
    2. Dimitri Percia David & Alain Mermoud & S'ebastien Gillard, 2021. "Cyber-Security Investment in the Context of Disruptive Technologies: Extension of the Gordon-Loeb Model," Papers 2112.04310, arXiv.org.
    3. Fischer, Thomas & Krauss, Christopher, 2018. "Deep learning with long short-term memory networks for financial market predictions," European Journal of Operational Research, Elsevier, vol. 270(2), pages 654-669.
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