Understand the engine
Revenue streams, competitive position, customer base, unit economics and management incentives.
A practical equity-research process built around financial statements, operating drivers, valuation and risk. This website documents how I turn public information into a clear investment view.
Each report starts with the underlying business, rather than the share-price chart. I use consistent assumptions, source logs and sensitivity analysis so conclusions are transparent and challengeable.
Revenue streams, competitive position, customer base, unit economics and management incentives.
Historical growth, margins, cash conversion, returns on capital, debt and working-capital trends.
Forecast drivers into a DCF and relative valuation, then test the conclusion with sensitivities.
Identify catalysts, bear cases, key risks and what would change the investment view.
Analyse market structure and confluence using a TradingView strategy I coded to support disciplined entries and risk management.
Browse covered companies, review the investment case and see the work that sits behind each conclusion — from financial drivers to valuation, catalysts, risks and charting context.
Educational portfolio content only. Each report should be refreshed using primary filings and current market data before any investment decision.
My models use a driver-led forecast: revenue by segment or volume/price where available, margins, tax, capex, working capital and free cash flow. I compare the implied value from a DCF with trading multiples, then pressure-test the main assumptions.
This is the first project in my finance and data-analysis portfolio. Future iterations will add full company reports, downloadable model templates and Python notebooks.