Stochastic Monte Carlo & Reverse DCF Pipeline for SUN.NS
This engine runs a Monte Carlo Simulation. Instead of guessing a single future, it runs thousands of probabilistic scenarios testing how variations in revenue and margins will impact Sun Pharma's true underlying value.
The core structural inputs (Total Debt, Cash Equivalents, the 10.5% WACC discount rate, and Base Revenue) were automatically extracted from the live LSEG (Refinitiv) API utilizing a 5-year historical baseline.
The model outputs the Intrinsic Target Price. If our simulated Base Case price is lower than Sun Pharma's current market trading price (~₹1,868), the stock is mathematically considered Overvalued (SELL).
90% chance value is higher
Most probable target price
Only 10% chance to exceed
This application is the culmination of a 5-phase automated quantitative finance pipeline.
Built a robust Python extraction layer utilizing the Refinitiv Workspace API. It dynamically pulls 5-years of trailing historical financials, deduplicates the time-series data, and establishes the foundational metrics (Revenue, CapEx, D&A, Debt, Cash).
Constructed a deterministic 5-year Free Cash Flow to Firm (FCFF) forecast. It mathematically computes the Weighted Average Cost of Capital (WACC) using dynamic risk-free rates and market risk premiums to discount future cash flows accurately.
Implemented an institutional-grade Gordon Growth Model (GGM) to calculate the Terminal Value. It then bridges the Enterprise Value down to Equity Value by accounting for the exact net debt position to find the deterministic intrinsic share price.
Upgraded the static DCF into a dynamic probabilistic model using NumPy and SciPy. By injecting standard deviations into core drivers and penalizing cash flows with Net Working Capital (NWC) drag, the model runs 10,000 parallel universes to find the true statistical center of mass.
Wrapped the entire Python algorithmic backend into a blazing fast FastAPI server, and surfaced it to the user via this responsive, glassmorphism-styled Vite frontend to allow for real-time stress-testing and portfolio presentation.