MBA Finance · Equity Research & Investment Analyst Professional
Finance student building real skills — financial modelling, DCF valuation, derivatives strategy, and Python-powered analysis. Aspiring CFA candidate on a mission to understand how markets price businesses versus what they're actually worth.
From Physics to finance — a deliberate pivot built on curiosity, real projects, and a drive to understand how markets work.
"I started in marketing. Then I opened a balance sheet — and never looked back."
Today I'm an MBA Finance student at Welingkar, interning at a stock broking firm where I work on derivatives strategy, market analysis, and risk-reward setups. Along the way I've built DCF models, run sector analyses, and developed a deep curiosity for how businesses are priced by markets versus what they're actually worth.
I'm working toward a career in Equity Research or Investment Banking — roles where analytical rigour and market awareness matter more than anything else. Still learning, but learning fast.
My edge is unusual: a Physics background that trained me to model systems and quantify uncertainty, 16 months of real business experience in performance marketing, a MICA certification in digital marketing, and a genuine obsession with valuation. I'm also an aspiring CFA candidate, building toward the rigour the charter demands.
A structured map of finance learning — from financial statements to derivatives and quantitative risk models.
Real projects built with actual data, genuine methodologies, and original conclusions — not textbook exercises.
📌 DCF intrinsic value: ₹193/share — stock overvalued by 59% at CMP ₹308.
📌 EBITDA margin declined 21.3% (FY21) → 11.6% (FY25) — structural compression.
📌 ROIC fell 45.8% (FY21) → 20.7% (FY25) — diminishing returns on new capital.
📌 Near debt-free (₹150 Cr) — strong balance sheet but premium unjustified.
📌 Monte Carlo VaR (95%): max single-day loss of 3.21%.
📌 Football field: ₹139–₹277 — CMP above every methodology.
| Method | Value | vs CMP |
|---|---|---|
| DCF Base | ₹193 | OV 59% |
| DCF Bull | ₹245 | OV 26% |
| EV/EBITDA Comps | ₹174 | OV 77% |
| P/E Comps | ₹235 | OV 31% |
yfinance💡 First real Python-in-finance workflow — automation eliminates human error in repeated calculations, not just saves time.
💡 OLS via scipy.stats.linregress gives identical results to Excel SLOPE() — validated against GGL beta of 0.90.
💡 Built modularly — change the STOCKS list, full peer beta table regenerates under 2 minutes.
💡 Butterfly spreads in practice: buy wing1 + wing2 − 2× sell body. Cost = max loss. Strike gap = profit zone width.
💡 State persistence in stateless systems — each GitHub Actions run is fresh, so trade state must be written and re-loaded each cycle.
💡 Real Greeks behave differently from theory — Theta accelerates near expiry, IV spikes inflate strategy costs with flat underlying.
💡 Built with AI assistance — used as a force-multiplier, not a substitute for understanding the strategy logic.
💡 Large dataset handling (12K+ rows) — same skill applies to Bloomberg exports, equity universes and financial databases.
💡 NLP-driven filtering mirrors analyst information processing — extract, filter, rank. Similar to building stock screeners.
💡 Shows cross-domain ability: AI + data engineering + product thinking — differentiates a candidate who can build, not just analyse.
💡 Power BI is widely used in IB and corporate finance for management reporting, financial dashboards, and deal tracking.
💡 Data modelling skills — relationships, calculated fields — map directly to integrated 3-statement modelling.
💡 Visual storytelling is a core IB skill — presenting complex numbers clearly for senior stakeholders in pitch books and CIMs.
MBA Finance student targeting Investment Banking and Equity Research. Aspiring CFA candidate.
Request Full Resume →Open to internship opportunities, IB/ER analyst roles, and finance conversations. Mumbai based.
Whether you're a recruiter, fellow analyst, or someone curious about finance — I'd love to connect. I respond within 24 hours.