Adaptable Individualized Investment Recommendation System

• Published in: 2024 International Conference on Electronic Systems and Intelligent Computing (ICESIC) pp. 325 - 330
• Main Authors: Fatima, N. Sabiyath, Noor Alleema, N., Mahesh, C., Umanesan, R., Senthil, K., Kumar, P. Santhosh
• Format: Conference Proceeding
• Language: English
• Published: IEEE 22.11.2024
• Subjects: Investment; Pattern matching; Portfolio; Portfolios; Random forests; Recommender systems; Risk Apetite; Sentiment; Social networking (online); Stock Market; Stock markets; Support vector machines; Uncertainty
• DOI: 10.1109/ICESIC61777.2024.10846428

Internetization has played a massive role in the rapid development and growth of the financial investment industry. Due to the rapid development, there is a demand for more financial knowledge services to help make well-informed investments. Accessing and conducting investments through online financial platforms has made the field highly accessible. This has made the investment recommendation system a popular research area. Personalized recommendations will help the user make investments that are safe for their financial situation and also suit their particular needs and interests. This work proposes a method where investment recommendations are given based on three key factors. The user interest sectors, risk appetite-based clustering, and market sentiment of the stock. These factors will be calculated by the use of Lasso regression, SVM, stepwise regression, generalized boosted regression, random forest, ridge regression and fuzzy clustering. Based on these three factors, the proposed method results in investment recommendations made for users that are personalized to the specific user. This allows existing investors to gain new opportunities and new investors to start investing with lower amount of risk using the information available.