Authors : Abhishek Bhave

Volume/Issue : Volume 10 - 2025, Issue 8 - August

Google Scholar : https://tinyurl.com/4w3mdbz3

Scribd : https://tinyurl.com/2pbebkjb

DOI : https://doi.org/10.38124/ijisrt/25aug657

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Abstract : Fin Tech innovations are increasingly shaping investment technology by combining advanced valuation models with accessible, web-based financial applications. StockValuation Pro1 is designed to bridge this gap by equipping investors, financial advisors, and investment clubs with professional-grade Discounted Cash Flow (DCF) analysis, equity analysis, and portfolio management tools. The platform supports multiple valuation methodologies—such as P/E, P/S, EV/EBITDA, and P/FCF ratios—alongside customizable five-year revenue and margin projections for bull, base, and bear scenarios. Unlike conventional spreadsheets or static portfolio trackers, StockValuation Pro emphasizes fundamental financial modeling, realtime data integration, and clear valuation metrics to guide disciplined investment decisions. Developed as a modern webbased financial application using React, TypeScript, and Tailwind CSS for the frontend, and Supabase with PostgreSQL for secure backend infrastructure, it incorporates the Finnhub API for live market data and Recharts for interactive analytics. This paper outlines the platform’s features, valuation methodology, technical architecture, and potential role in democratizing access to professional-grade investment analysis within the broader FinTech ecosystem.

Keywords : FinTech, Investment Technology, Discounted Cash Flow (DCF), Stock Valuation, Equity Analysis, Portfolio Management, Investment Analysis, Web-Based Financial Application, Financial Modeling, Real-Time Data, Valuation Metrics, API Integration.

References :

1. A. Damodaran, Investment Valuation: Tools and Techniques for Determining the Value of Any Asset, 3rd ed. Wiley, 2012.
2. E. F. Fama and K. R. French, “Common risk factors in the returns on stocks and bonds,” Journal of Financial Economics, vol. 33, no. 1, pp. 3–56, 1993.
3. M. Belanche, L. Casalo, and C. Flavi´ an, “Artificial intelligence in´ fintech: understanding robo-advisors adoption among users,” Industrial Management & Data Systems, vol. 121, no. 4, pp. 857–875, 2021.