Leveraging Natural Language Processing for Stock Portfolio Enhancement: A Study on Investors’ Perception and Practical Applications

This study investigates the role of Natural Language Processing (NLP) in improving stock portfolio management, focusing on investor perceptions and the practical use of NLP-based tools. Traditional methods of financial analysis often fail to fully capture the complexities of market behavior and investor sentiment. This research explores how NLP can fill this gap by processing unstructured data—such as financial news, social media posts, and company reports—to offer enhanced insights for investment decision-making. Using a mixed-methods approach, the study integrates quantitative surveys with qualitative interviews involving investors, fund managers, and financial analysts. Statistical techniques, including One-Way ANOVA and Chi-Square tests, were applied to assess awareness, perceptions, and the effectiveness of NLP tools compared to conventional approaches. The findings reveal that awareness of NLP varies among investors, with younger and academically trained individuals demonstrating higher familiarity. Regular users of NLP tools recognized their value in analyzing market sentiment and improving risk assessment. The research also identifies key challenges to NLP adoption, such as technical difficulties, data quality issues, and high implementation costs. Nonetheless, frequent users remain optimistic about NLP’s future in financial analysis. The study concludes that NLP has strong potential to complement traditional stock analysis methods, offering deeper insights into market trends and investor behavior. It contributes to the growing body of fintech research and provides actionable recommendations to encourage wider adoption of NLP in the finance sector.