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The Role Of AI And Machine Learning In Portfolio Management: A Study On Engineering Faculties Investment Strategies In The Bangalore Region

Year: 2025
Type: article
Abstract: This study explores the impact of artificial intelligence (AI) and machine learning (ML) on the investment strategies of engineering faculty members in the Bangalore region. As financial markets become more complex, these technologies present significant opportunities to enhance portfolio management. The primary goal of this research is to examine how faculty members are incorporating AI and ML tools into their personal investment practices to improve portfolio performance, manage risks, and make informed decisions. Additionally, the study highlights gaps in knowledge, accessibility, and application of these technologies, offering important insights into areas needing further development. By conducting surveys and interviews, data will be gathered on faculty members' awareness, usage patterns, and trust in AI and ML-driven investment approaches. The research will evaluate their understanding of the advantages of using AI, such as real-time data analysis, predictive modeling, and automated decision-making, as well as the obstacles they encounter, including a lack of technical expertise or skepticism regarding the reliability of these tools. The findings from this study will help identify current challenges and provide actionable recommendations for improving the adoption of AI and ML in portfolio management among engineering faculty. These insights will also be useful for educational institutions, financial advisors, and technology developers in customizing AI solutions to better align with the investment management needs of academic professionals. This research will contribute to both the academic and investment sectors by bridging the gap between traditional investment methods and technology-driven solutions, ultimately enabling faculty members to make more informed investment decisions.
DOI: 10.64252/9thtr391
Institution: Srinivas University
Language: English
Open Access status: hybrid
OpenAlex ID: W4414917126
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