Constructing Cybersecurity Stocks Portfolio Using AI
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 t...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-11-01
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| Series: | Forecasting |
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| Online Access: | https://www.mdpi.com/2571-9394/6/4/53 |
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| author | Avishay Aiche Zvi Winer Gil Cohen |
| author_facet | Avishay Aiche Zvi Winer Gil Cohen |
| author_sort | Avishay Aiche |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-0b35962e2ce94e85889ef18c3665af23 |
| institution | Kabale University |
| issn | 2571-9394 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Forecasting |
| spelling | doaj-art-0b35962e2ce94e85889ef18c3665af232024-12-27T14:26:49ZengMDPI AGForecasting2571-93942024-11-01641065107710.3390/forecast6040053Constructing Cybersecurity Stocks Portfolio Using AIAvishay Aiche0Zvi Winer1Gil Cohen2Western Galilee College, Acre 2412101, IsraelWestern Galilee College, Acre 2412101, IsraelWestern Galilee College, Acre 2412101, IsraelThis 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.https://www.mdpi.com/2571-9394/6/4/53AI analysiscybersecurity stock analysis |
| spellingShingle | Avishay Aiche Zvi Winer Gil Cohen Constructing Cybersecurity Stocks Portfolio Using AI Forecasting AI analysis cybersecurity stock analysis |
| title | Constructing Cybersecurity Stocks Portfolio Using AI |
| title_full | Constructing Cybersecurity Stocks Portfolio Using AI |
| title_fullStr | Constructing Cybersecurity Stocks Portfolio Using AI |
| title_full_unstemmed | Constructing Cybersecurity Stocks Portfolio Using AI |
| title_short | Constructing Cybersecurity Stocks Portfolio Using AI |
| title_sort | constructing cybersecurity stocks portfolio using ai |
| topic | AI analysis cybersecurity stock analysis |
| url | https://www.mdpi.com/2571-9394/6/4/53 |
| work_keys_str_mv | AT avishayaiche constructingcybersecuritystocksportfoliousingai AT zviwiner constructingcybersecuritystocksportfoliousingai AT gilcohen constructingcybersecuritystocksportfoliousingai |