IDEAS home Printed from https://ideas.repec.org/a/gam/jforec/v6y2024i4p53-1077d1524593.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2571-9394/6/4/53/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2571-9394/6/4/53/
    Download Restriction: no
    ---><---

    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Wei Dai & Yuan An & Wen Long, 2021. "Price change prediction of ultra high frequency financial data based on temporal convolutional network," Papers 2107.00261, arXiv.org.
    2. Kamaladdin Fataliyev & Aneesh Chivukula & Mukesh Prasad & Wei Liu, 2021. "Stock Market Analysis with Text Data: A Review," Papers 2106.12985, arXiv.org, revised Jul 2021.
    3. Yoganathan, Vignesh & Osburg, Victoria-Sophie, 2024. "The mind in the machine: Estimating mind perception's effect on user satisfaction with voice-based conversational agents," Journal of Business Research, Elsevier, vol. 175(C).
    4. Maggie Wang, Yazhu & Matook, Sabine & Dennis, Alan R., 2024. "Unintended consequences of humanoid service robots: A case study of public service organizations," Journal of Business Research, Elsevier, vol. 174(C).
    5. Sina Montazeri & Akram Mirzaeinia & Haseebullah Jumakhan & Amir Mirzaeinia, 2024. "CNN-DRL for Scalable Actions in Finance," Papers 2401.06179, arXiv.org.
    6. Rad, Hossein & Low, Rand Kwong Yew & Miffre, Joëlle & Faff, Robert, 2023. "The commodity risk premium and neural networks," Journal of Empirical Finance, Elsevier, vol. 74(C).
    7. Noura Metawa & Mohamemd I. Alghamdi & Ibrahim M. El-Hasnony & Mohamed Elhoseny, 2021. "Return Rate Prediction in Blockchain Financial Products Using Deep Learning," Sustainability, MDPI, vol. 13(21), pages 1-16, October.
    8. Kentaro Imajo & Kentaro Minami & Katsuya Ito & Kei Nakagawa, 2020. "Deep Portfolio Optimization via Distributional Prediction of Residual Factors," Papers 2012.07245, arXiv.org.
    9. Ali Atiah Alzahrani, 2025. "Multi-Agent Regime-Conditioned Diffusion (MARCD) for CVaR-Constrained Portfolio Decisions," Papers 2510.10807, arXiv.org, revised Nov 2025.
    10. James Wallbridge, 2020. "Transformers for Limit Order Books," Papers 2003.00130, arXiv.org.
    11. Burka, Dávid & Puppe, Clemens & Szepesváry, László & Tasnádi, Attila, 2022. "Voting: A machine learning approach," European Journal of Operational Research, Elsevier, vol. 299(3), pages 1003-1017.
    12. Chi Chen & Li Zhao & Wei Cao & Jiang Bian & Chunxiao Xing, 2020. "Trimming the Sail: A Second-order Learning Paradigm for Stock Prediction," Papers 2002.06878, arXiv.org.
    13. Barua, Ronil & Sharma, Anil K., 2022. "Dynamic Black Litterman portfolios with views derived via CNN-BiLSTM predictions," Finance Research Letters, Elsevier, vol. 49(C).
    14. Ergenç Cansu & Aktaş Rafet, 2025. "A Supervised Machine Learning in Financial Forecasting: Identifying Effective Models for the BIST100 Index," Review of Economic Perspectives, Sciendo, vol. 25(1), pages 66-90.
    15. Heirati, Nima & Pitardi, Valentina & Wirtz, Jochen & Jayawardhena, Chanaka & Kunz, Werner & Paluch, Stefanie, 2025. "Unintended consequences of service robots – Recent progress and future research directions," Journal of Business Research, Elsevier, vol. 194(C).
    16. Mohammad Zoynul Abedin & Mahmudul Hasan Moon & M. Kabir Hassan & Petr Hajek, 2025. "Deep learning-based exchange rate prediction during the COVID-19 pandemic," Annals of Operations Research, Springer, vol. 345(2), pages 1335-1386, February.
    17. Zhou, Hao & Kalev, Petko S., 2019. "Algorithmic and high frequency trading in Asia-Pacific, now and the future," Pacific-Basin Finance Journal, Elsevier, vol. 53(C), pages 186-207.
    18. Bartosz Bieganowski & Robert Ślepaczuk, 2024. "Supervised Autoencoder MLP for Financial Time Series Forecasting," Working Papers 2024-03, Faculty of Economic Sciences, University of Warsaw.
    19. Wang, Yijun & Andreeva, Galina & Martin-Barragan, Belen, 2023. "Machine learning approaches to forecasting cryptocurrency volatility: Considering internal and external determinants," International Review of Financial Analysis, Elsevier, vol. 90(C).
    20. Wei Pan & Jide Li & Xiaoqiang Li, 2020. "Portfolio Learning Based on Deep Learning," Future Internet, MDPI, vol. 12(11), pages 1-13, November.

    More about this item

    Keywords

    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jforec:v:6:y:2024:i:4:p:53-1077:d:1524593. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.