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@article{209380,
        author = {Prathmesh Bhoyar and Shreya Mahajan and Vedant Zod and Ishani Karemore and Karishma Urkude},
        title = {FinSight AI: An Integrated Platform for Stock Market and Mutual Fund Recommendation},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {5},
        pages = {2051-2057},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=209380},
        abstract = {FinSight AI is a Streamlit application that brings historical stock analysis and personalized mutual fund recommendation into one local tool. This paper describes what the repository really contains and where its limits lie. A stock dashboard and a fund recommender share a home page and a Python calculation engine, and everything runs from two static CSV files: 117,884 daily stock records for 336 names in 20 countries (January 2025 to May 2026), and 771 mutual fund records with 35 columns. A questionnaire produces a 0–100 risk score, which sets an Equity, Hybrid and Debt mix. Funds are then ranked with a six-factor weighted score, and corpus growth is projected at fixed rates of 8%, 10% and 12%. The analytics are descriptive and rule-based. There is no live feed, forecasting model, trading, database or login, and the source of both datasets is undocumented. We list the validation and governance work still needed before the outputs could inform real decisions.},
        keywords = {Financial Analytics, Stock Dashboard, Mutual Fund Recommendation, Risk Profiling, Goal-Based Allocation, Decision Support, Rule-Based Systems},
        month = {October},
        }