A premium, placement-grade finance course — corporate finance, valuation & modelling, M&A and LBOs, equity research, investment banking, PE/VC, derivatives, portfolio theory and AI, plus a full framework library, 12 detailed Indian case studies, and role-by-role interview prep. Everything you need to crack a top finance placement.
Nine modules take you from corporate-finance theory through valuation and modelling, deal-making (M&A and LBOs), the buy-side and sell-side careers, capital markets, portfolio and risk theory, behavioural finance, and AI. Then a 14-framework library and 12 deep Indian case studies give you interview ammunition, and the placements module breaks down every major finance role with skills, questions, salaries and growth paths. Use the search bar to jump to any concept.
Module 1 · Corporate Finance & Capital Structure
Corporate finance answers three big questions for a firm: what to invest in (capital budgeting), how to fund it (capital structure), and how to return cash (dividend policy). The single objective tying them together is maximising long-term shareholder value.
1.1 Capital structure theory
How much debt vs equity should a firm use? The theory progression every MBA must know:
Modigliani-Miller (no taxes): in a perfect world, capital structure is irrelevant — firm value depends on assets, not financing.
MM with taxes: because interest is tax-deductible, debt adds value via the tax shield — implying (unrealistically) 100% debt is optimal.
Trade-off theory: the real optimum balances the tax shield against rising financial-distress / bankruptcy costs — there's a sweet spot.
Pecking-order theory: firms prefer internal funds first, then debt, then equity last (issuing equity signals the stock may be overvalued).
1.2 Leverage & its effects
Operating leverage measures how fixed costs amplify the effect of sales changes on operating profit; financial leverage measures how debt amplifies the effect on EPS. Leverage cuts both ways — it magnifies gains in good times and losses in bad. A key metric is the interest coverage ratio (EBIT ÷ interest) — how comfortably a firm services its debt.
1.3 Dividend policy
How should a firm return cash — dividends or buybacks? Dividends give steady cash and signal confidence/stability; buybacks reduce share count (boosting EPS), are flexible, and can be tax-efficient. Signalling theory says cutting a dividend is read as bad news, so firms are reluctant to raise dividends they can't sustain. Mature, cash-rich firms pay more; high-growth firms reinvest instead.
💡 Interview gold: “Is there an optimal capital structure?” → Yes, per trade-off theory: the debt level that minimises WACC and maximises value, balancing the tax shield against distress costs. Quote MM as the theoretical starting point.
Module 2 · Valuation & Financial Modelling
2.1 The three valuation methods
DCF (intrinsic)
Discount projected free cash flows + terminal value at WACC. Most rigorous, most assumption-sensitive.
Comparable companies
Apply peer trading multiples (EV/EBITDA, P/E) to the target. Market-grounded, quick.
Precedent transactions
Multiples paid in past M&A deals. Includes a control premium; useful for deal pricing.
Good analysts triangulate all three into a “football field” valuation range rather than trusting any single number. DCF tends to give the highest theoretical value; precedent transactions often the highest practical (due to control premiums); comps the market-anchored middle.
2.2 The DCF, in depth
There are two DCF flavours: FCFF (free cash flow to the firm — to all investors, discounted at WACC, yields enterprise value) and FCFE (free cash flow to equity — discounted at cost of equity, yields equity value directly). FCFF is more common in interviews. The terminal value (often 60–80% of total value) uses either the Gordon growth method (FCF×(1+g)/(WACC−g)) or an exit multiple. Always run a sensitivity table on WACC and growth — the output swings widely.
2.3 The 3-statement model
The foundation of all modelling: a single Excel workbook where the income statement, balance sheet and cash flow statement are fully linked, so changing one assumption (say, revenue growth) flows correctly through all three and the balance sheet still balances. Building blocks: revenue drivers, margin assumptions, a debt schedule (with interest), a working-capital schedule, and a depreciation/capex schedule. Master this and DCF, LBO and M&A models are extensions of it.
EV = Σ FCF/(1+WACC)ᵗ + TV/(1+WACC)ⁿ | Equity Value = EV − Net Debt
2.4 Modelling best practices
Separate inputs (blue), formulas (black) and links — colour-code for clarity.
No hardcoded numbers inside formulas; drive everything from an assumptions tab.
Keep it flexible with scenarios/sensitivities; avoid circularity traps (interest ↔ cash).
⚠ Interview trap: “What's the biggest flaw of a DCF?” → It's extremely sensitive to assumptions (growth, WACC, terminal value) — “garbage in, garbage out.” Always cross-check with comparables.
Module 3 · M&A & Leveraged Buyouts
3.1 Mergers & Acquisitions
M&A is how companies grow inorganically. Types: horizontal (same industry — consolidation), vertical (supplier/distributor — supply-chain control), and conglomerate (unrelated — diversification). The driving logic is synergy: the combined entity is worth more than the parts, via cost synergies (eliminating duplication) or revenue synergies (cross-selling, scale).
Deal economics hinge on the control premium (acquirers pay above market to gain control), the method of payment (cash vs stock), and whether the deal is accretive or dilutive to the acquirer's EPS. The sobering reality, well documented in research: most M&A destroys value — through overpaying, overestimated synergies and poor integration. India has seen both winners and cautionary tales.
3.2 Accretion / Dilution
A favourite IB interview topic: a deal is accretive if the acquirer's post-deal EPS rises, dilutive if it falls. The quick rule for an all-stock deal: if the acquirer's P/E is higher than the target's, the deal is generally accretive (and vice versa). Cash deals depend on the after-tax cost of debt/cash vs the target's earnings yield.
3.3 The Leveraged Buyout (LBO)
An LBO is buying a company using mostly debt, where the target's own cash flows repay that debt over time. The private-equity sponsor puts in a slice of equity, loads on debt, improves the business, pays down debt, and exits in 3–7 years via sale or IPO. Returns come from three levers:
Debt paydown
Using cash flows to repay debt increases the equity slice (like paying down a home loan).
EBITDA growth
Improving operations/margins grows the business value.
Multiple expansion
Exiting at a higher EV/EBITDA than entry (less reliable, market-dependent).
The ideal LBO target has stable, predictable cash flows, low existing debt, strong assets and improvement potential. Returns are measured by IRR and MOIC (multiple of invested capital). Leverage is the magic — and the danger: it amplifies returns but raises the risk of distress if cash flows disappoint.
💡 Interview gold: “Why does an LBO generate high returns?” → Leverage amplifies equity returns, debt paydown converts enterprise value into equity, plus operational improvement and any multiple expansion. “Best LBO target?” → stable cash flows, low debt, room to improve.
Module 4 · Investment Banking, Equity Research & PE/VC
The glamour roles of finance — understand what each actually does (the realities matter in interviews).
4.1 Investment Banking (sell-side advisory)
IBs advise companies on raising capital (IPOs, debt/equity issues) and M&A, earning fees. Divided into ECM (equity capital markets), DCM (debt capital markets) and Advisory/M&A. Analysts build models, pitch books and run deal processes. It's prestigious and well-paid but famously demanding on hours.
4.2 Equity Research (sell-side analysis)
ER analysts study companies/sectors, build models, and publish buy/hold/sell recommendations with target prices for investor clients. The craft is forming a differentiated view vs consensus and defending it. Strong writing, modelling and sector knowledge matter most.
4.3 Private Equity & Venture Capital (buy-side)
Private Equity
Invests fund capital to buy mature companies (often via LBOs), improves them, and exits for a return. Focus: cash flows, control, operational value creation.
Venture Capital
Invests in early-stage startups for minority stakes, betting on a few big winners to cover many failures (the power law). Focus: team, market size, growth.
PE/VC funds make money via “2 and 20” — roughly a 2% management fee plus 20% of profits (carried interest) above a hurdle. The key distinction to articulate: sell-side (IB, ER) advises and earns fees; buy-side (PE, VC, asset management) invests capital and earns returns. India's startup and deal boom has made these roles increasingly accessible to MBAs.
💡 Interview line: “IB vs PE?” → IB is advisory/fee-based and execution-heavy; PE is principal investing — you own and improve businesses with your fund's capital and are judged on returns (IRR/MOIC), not fees.
Module 5 · Fixed Income & Derivatives
5.1 Fixed income deep-dive
Bonds are priced as the PV of coupons + face value. Beyond the basics, advanced concepts: duration (price sensitivity to a 1% rate change) and convexity (the curvature correcting duration for large moves); the yield curve (yields across maturities) — an inverted curve (short rates above long) is a classic recession signal; and credit spreads (the extra yield over risk-free for default risk). The master rule never changes: price and yield move inversely.
5.2 Options & the Greeks
An option's value has two parts: intrinsic value (in-the-money amount) + time value (premium for remaining uncertainty). Pricing is driven by the spot price, strike, time to expiry, volatility and rates — formalised by the Black-Scholes model. Sensitivity measures, the Greeks, are essential vocabulary:
Delta — sensitivity to the underlying's price.
Gamma — rate of change of delta.
Theta — time decay (value lost as expiry nears).
Vega — sensitivity to volatility.
Key payoff insight: an option buyer has limited downside (the premium) and large upside; a seller/writer has limited upside (the premium) and large-to-unlimited downside. Higher volatility raises option prices (more chance of finishing deep in-the-money). Common strategies — covered calls, protective puts, spreads — combine options to shape risk/reward.
⚠ India context: Index/F&O option trading is huge in India, and regulators have flagged heavy retail losses. In interviews, emphasise options as risk-management tools, and respect the leverage that makes them dangerous for speculation.
Module 6 · Portfolio & Risk Management
6.1 Modern Portfolio Theory (Markowitz)
Harry Markowitz's insight: what matters is not a single asset's risk but its contribution to portfolio risk. By combining assets that aren't perfectly correlated, you can reduce risk for a given return — diversification as the only “free lunch.” The set of optimal risk-return combinations is the efficient frontier; rational investors pick a point on it matching their risk appetite.
6.2 CAPM & systematic risk
Risk splits into systematic (market-wide, undiversifiable — captured by beta) and unsystematic (company-specific, diversifiable). The CAPM says investors are only rewarded for systematic risk: Expected return = Rf + β(Rm − Rf). It underpins the cost of equity in every DCF, linking risk directly to required return and valuation.
6.3 Performance & risk metrics
Sharpe ratio — excess return per unit of total risk (volatility).
Alpha — return above what the market/CAPM predicts (manager skill).
VaR — max expected loss at a confidence level over a period.
Std deviation / Beta — total vs market risk.
Risk is managed through diversification, asset allocation, hedging (derivatives), position limits and capital buffers. The Efficient Market Hypothesis (EMH) claims prices already reflect all information (so beating the market consistently is hard) — a debate that leads straight into behavioural finance, where those “rational” assumptions break down.
💡 Interview gold: “Why isn't a stock's own volatility its whole risk story?” → Because unsystematic risk is diversifiable; investors are only compensated (via CAPM) for systematic risk, measured by beta. This connects MPT, CAPM and WACC.
Module 7 · Behavioural Finance
Classical finance assumes rational investors and efficient markets. Behavioural finance (Kahneman, Tversky, Thaler, Shiller) shows that real humans are predictably irrational — and those biases move markets, creating bubbles and crashes. The biases every finance candidate should name:
Loss aversion
Losses hurt ~2x more than equal gains please — leading investors to hold losers too long and sell winners too early (the disposition effect).
Overconfidence
Overestimating one's knowledge/skill — causing over-trading and concentrated bets.
Herding
Following the crowd — fuelling bubbles (dot-com, crypto manias) and panics.
Anchoring
Fixating on a reference point (a purchase price) instead of current fundamentals.
Confirmation bias
Seeking evidence that supports an existing view and ignoring the rest.
Recency & mental accounting
Over-weighting recent events; treating money differently by arbitrary “buckets.”
Why it matters for placements: it explains market anomalies that the EMH can't, informs value investing (exploiting others' irrationality), and underpins “nudge”-style product design in fintech. Even Benjamin Graham's “Mr. Market” — a manic-depressive who offers wild prices daily — is a behavioural-finance parable worth quoting.
✅ Interview line: “Are markets efficient?” → A balanced answer: mostly efficient in the long run, but behavioural biases create short-term mispricings and bubbles — which is exactly what active and value investors try to exploit.
Module 8 · Fintech & AI in Finance
Technology is reshaping finance, and India sits at the frontier. Demonstrating fluency here is a real differentiator in placements.
8.1 India's fintech revolution
India built world-leading digital public infrastructure — UPI (real-time payments at massive scale), Aadhaar (digital identity), and account aggregators — enabling a fintech boom in payments, lending, wealthtech (Zerodha, Groww) and insurtech. Concepts to know: digital lending, neo-banking, embedded finance, and robo-advisory.
8.2 Where AI is used in finance
Credit scoring & underwriting — ML models assess risk using alternative data, expanding access.
Fraud detection & AML — real-time anomaly detection on transactions.
Algorithmic & quant trading — models execute strategies at speed and scale.
Robo-advisory & personalisation — automated portfolio advice for retail.
Research & productivity — LLMs summarise filings, draft notes and accelerate analysis.
8.3 LLMs as the analyst's co-pilot — ChatGPT, Claude, Gemini
Tools like ChatGPT, Claude and Gemini now help analysts summarise annual reports and earnings calls, draft research, explain concepts, write Excel formulas/VBA, and brainstorm — compressing hours of grunt work. The skill is prompting well and, critically, verifying outputs: models can hallucinate numbers, so every figure must be checked against the source. Treat AI as a fast junior analyst whose work you always review.
⚠ The caution: In finance, accuracy is everything. AI raises real concerns — hallucinated figures, data privacy, model risk and regulatory scrutiny. The interview-ready view: “AI multiplies an analyst's productivity, but judgement, verification and accountability stay human.”
Module 9 · Framework Library
Fourteen frameworks every finance aspirant must own. For each: definition, example, how to use it in an interview, why it matters for placements, and the mistakes that expose a shallow answer. Click to expand.
1. DCF (Discounted Cash Flow)
Definition: Value = PV of projected free cash flows + terminal value, discounted at WACC.
Example: Valuing an FMCG firm by projecting 5 years of FCF + a Gordon-growth terminal value.
Interview usage: The default for “value this company”; be ready to “walk me through a DCF.”
Placement relevance: Core to IB, ER and PE technicals.
Common mistakes: Over-optimistic growth, ignoring terminal-value dominance, no sensitivity check.
2. WACC (Weighted Average Cost of Capital)
Definition: Blended, tax-adjusted cost of debt and equity, weighted by their proportions.
Example: 60% equity at 14% + 40% debt at 9%×(1−25%) → WACC ≈ 11.1%.
Interview usage: The discount rate in a DCF; explaining why debt is cheaper.
Placement relevance: Valuation and capital-structure questions.
Common mistakes: Forgetting the tax shield on debt; using book instead of market weights.
Interview usage: Conceptual options questions and the Greeks.
Placement relevance: Derivatives, treasury, risk and quant roles.
Common mistakes: Forgetting its assumptions (no dividends, constant volatility, European-style) limit real-world use.
Indian Case Studies
Twelve companies that define Indian finance. Each card covers the business model, the financial story, the strategy, a notable event, and the interview questions you'll face. Pick 3–4 to know cold for placements.
1. HDFC Bank
India's largest private bank · quality benchmark
Business model: Earns net interest margin (lend high, borrow cheap via deposits) plus fee income across retail, corporate and (post-merger) mortgage lending.
Financial story: Decades of consistent ~18–20% earnings growth, low NPAs, high CASA and strong ROA/ROE earned it a premium P/B for years.
Strategy: Disciplined underwriting, tech-led scale, and the transformational merger with parent HDFC Ltd creating a banking giant.
Notable event: The HDFC–HDFC Bank merger — managing the larger balance sheet, CASA and margin impact is a live case-study topic.
Interview Qs: “How do you value HDFC Bank?” (P/B + ROE). “Why the premium?” (consistency, asset quality). “Impact of the merger on NIM and CASA?”
2. Reliance Industries
Conglomerate · capital allocation at scale
Business model: A conglomerate spanning oil-to-chemicals (cash engine), Jio (telecom/digital) and Reliance Retail — a “sum-of-the-parts” story.
Financial story: Used cash flows from legacy energy to fund massive capex in Jio and Retail, then deleveraged via landmark stake sales to global investors.
Strategy: Bold, debt-funded bets followed by aggressive de-leveraging; value unlocking by potentially listing Jio and Retail separately.
Notable event: Turning net-debt-free via the Jio Platforms/Retail fundraises — a classic capital-structure and SOTP valuation case.
Interview Qs: “How would you value a conglomerate like Reliance?” → sum-of-the-parts (SOTP), with a holding-company discount. “Why did it deleverage and how?”
3. State Bank of India (SBI)
PSU banking giant · scale & turnaround
Business model: India's largest bank by assets and the dominant public-sector lender, with unmatched branch reach and deposit franchise plus subsidiaries (SBI Cards, SBI Life, SBI MF).
Financial story: Battled high NPAs in the 2010s corporate-loan cycle, then improved asset quality and profitability as the cycle turned — a turnaround story.
Strategy: Leverage scale and CASA, digitise (YONO app), and unlock value via listed subsidiaries.
Notable event: The PSU-bank NPA clean-up and recovery — a case in credit cycles and asset-quality recognition (AQR).
Interview Qs: “PSU vs private banks — valuation gap and why?” “How do credit cycles drive bank NPAs?” “Value of SBI's subsidiaries (SOTP)?”
4. ICICI Bank
Private bank · re-rating story
Business model: A large private bank in retail and corporate lending, with a strong digital franchise (iMobile) and valuable subsidiaries (ICICI Pru Life, ICICI Lombard, ICICI Securities).
Financial story: After a period of higher corporate NPAs, a strategic pivot to granular retail lending and risk discipline drove a strong improvement in ROE and a market re-rating.
Strategy: “Fair-to-customer, fair-to-bank,” risk-calibrated growth, and a focus on core operating profit.
Notable event: The leadership change and subsequent turnaround in asset quality and returns — a governance-and-strategy case.
Interview Qs: “Why did ICICI re-rate?” (asset-quality fix + retail mix + ROE). “How do you analyse a bank?” (CAMELS, P/B, ROE).
5. Bajaj Finance
NBFC powerhouse · consumer lending
Business model: A leading NBFC in consumer durables financing (the “no-cost EMI” pioneer), personal loans, SME and more — earning a lending spread plus fees.
Financial story: Years of high AUM growth, strong NIM, controlled credit costs and high ROE earned a rich valuation.
Strategy: Point-of-sale distribution, data-driven fast underwriting, deep cross-sell to a huge customer base, and an omnichannel push.
Notable event: Sustained premium-multiple growth — and the ever-present NBFC question of cost of funds and asset quality through cycles.
Interview Qs: “Bank vs NBFC risk?” (funding/liquidity). “Why the premium valuation?” “What's the key vulnerability?” (cost of funds, credit cycle).
6. Zerodha
Bootstrapped fintech · disruptor
Business model: India's largest retail broker — zero-brokerage equity delivery, flat per-trade pricing, earning from F&O, interest and float.
Financial story: Famously profitable and bootstrapped (no external funding) — a rare, capital-efficient fintech.
Strategy: A low-cost, tech-first model plus the Varsity education platform built a huge user base at near-zero marketing spend.
Notable event: Disrupting full-service brokers and reshaping retail investing economics in India.
Interview Qs: “How does a zero-brokerage firm earn?” (F&O, float). “What's the regulatory risk?” (F&O rule changes). “Why bootstrapping?” (capital efficiency, control).
7. LIC
Insurance giant · India's biggest IPO
Business model: The state-owned life-insurance behemoth — collects premiums, manages an enormous investment corpus, pays claims, earning on the spread and float.
Financial story: Unmatched scale and trust, but losing share to nimble private insurers; valued on embedded value, not P/E.
Strategy: Leverage brand and agent army while shifting toward digital and higher-margin products.
Notable event: Its 2022 listing — India's largest-ever IPO — and the lesson in pricing a giant on embedded value.
Interview Qs: “How are insurers valued?” (embedded value + VNB, not P/E). “Challenges for LIC?” (private competition, agent-heavy model, digital shift).
8. Paytm (One97)
Fintech · valuation vs profitability
Business model: A fintech platform — payments (UPI/wallet), lending distribution, merchant services — monetising via processing, financial-services commissions and devices.
Financial story: A high-profile, high-valuation IPO followed by a sharp fall — the textbook case of pricing growth vs profitability.
Strategy: Build a payments-led super-app, then monetise via higher-margin lending distribution; path-to-profitability focus.
Notable event: The 2021 IPO de-rating and later regulatory action on its payments-bank arm — a lesson in valuation discipline and regulatory risk.
Interview Qs: “How do you value a loss-making fintech?” (forward multiples, unit economics, path to profit). “What does Paytm teach about IPO pricing?”
9. Adani Group
Infrastructure conglomerate · leverage & scrutiny
Business model: An infrastructure-led conglomerate — ports, energy, power, airports, cement — built largely through debt-funded, capital-intensive expansion.
Financial story: Rapid asset and market-cap growth, then a sharp episode of volatility after a short-seller report raised leverage and governance questions.
Strategy: Aggressive bets on long-gestation infrastructure, later emphasising deleveraging and reassuring investors.
Notable event: The 2023 short-seller report and market reaction — a powerful case in leverage, governance and short-selling.
Interview Qs: “What risks does heavy leverage create?” “How does a short seller build a thesis?” “Why does governance affect valuation?”
10. Tata Motors
Cyclical auto · turnaround & deleveraging
Business model: A cyclical auto major — domestic commercial & passenger vehicles, the JLR luxury arm, and a leading EV position in India.
Financial story: A cyclical, high-operating-leverage business that swung from heavy debt and losses to a strong free-cash-flow-led turnaround and deleveraging.
Strategy: Premiumise JLR, lead India's EV transition, and cut net debt aggressively.
Notable event: The JLR acquisition and subsequent cyclical swings — a case in operating leverage and cyclical valuation.
Interview Qs: “How do you value a cyclical company?” (mid-cycle earnings, EV/EBITDA, avoid peak/trough P/E). “What is operating leverage?”
11. Infosys
IT services · cash machine & payouts
Business model: An asset-light global IT-services firm earning high margins and strong free cash flow, with negligible debt and large cash balances.
Financial story: Consistent cash generation funds generous dividends and buybacks — a model of shareholder returns and capital-allocation discipline.
Strategy: Move up the value chain (digital, AI services), protect margins, and return surplus cash to shareholders.
Notable event: Repeated large buybacks and high payout ratios — a case in capital allocation and returning cash.
Interview Qs: “Dividend vs buyback — which and why?” “Why do IT firms hold so much cash and return it?” “How do you value an asset-light services firm?”
12. Asian Paints
Quality compounder · premium multiple
Business model: India's dominant paints company with high return ratios, low debt, strong free cash flow and pricing power — a classic “quality compounder.”
Financial story: Consistently high ROCE/ROE and steady growth earned a persistently rich P/E — the market pays up for quality and predictability.
Strategy: Distribution & data moat, in-store tinting, working-capital efficiency, and expansion into the home-décor ecosystem.
Notable event: Facing new large entrants testing its moat and pricing power — a case in moats, return ratios and quality valuation.
Interview Qs: “Why does a paints company trade at a tech-like P/E?” (ROCE, moat, consistency). “Is a high P/E always expensive?” (quality + growth justify it).
Placements · Finance Roles Decoded
Seven major finance career tracks — what each role really does, the skills tested, the questions asked, indicative Indian salary ranges, and how careers progress. Ranges are entry-level indicators that vary by firm tier, city and profile.
1. Investment Banking
Role expectations: Advise companies on raising capital (IPOs, debt/equity) and M&A; build models & pitch books, run deal processes. Prestigious, deal-driven, long hours.
Typical questions: “How would you build a portfolio for this client?” “Explain the Sharpe ratio / diversification.” “How do you manage liquidity?”
Salary range: ₹10–20 LPA entry (MBA/CFA).
Career growth: Analyst/Associate → Portfolio/Relationship Manager → Senior PM → CIO / Head of Treasury.
★ Final Mastery Quiz
Six advanced questions across the whole bible. Click to check yourself.
Q1. The main lever of LBO returns is…
Leverage amplifies equity returns; debt paydown, EBITDA growth and multiple expansion drive the rest.
Q2. CAPM rewards investors for which risk?
Unsystematic risk is diversifiable; only systematic risk (beta) is rewarded.
Q3. In an all-stock deal, it's generally accretive if the acquirer's P/E is…
Acquiring a lower-P/E target with higher-P/E stock is typically accretive to EPS.
Q4. You'd value an insurer like LIC primarily on…
Insurers are valued on embedded value + value of new business, not standard multiples.
Q5. Higher volatility does what to an option's price?
More volatility means a higher chance of finishing deep in-the-money — so premiums rise.
Q6. The behavioural bias of holding losers too long is driven mainly by…
Loss aversion (the disposition effect) makes investors hold losers to avoid realising a loss.
FREE PDF · WORTH ₹10,000
Download the complete Finance Placement Bible as a PDF
All 9 modules, the 14-framework library, 12 Indian case studies and the role-by-role placement guide — your entire ₹10,000-grade course in one printable file.
✓ 14 frameworks + 12 case studies
✓ Role guide with salaries & questions
✓ Formulas & modelling references
★ You're Placement-Ready
You've worked through corporate finance and capital structure, valuation and modelling, M&A and LBOs, the sell-side and buy-side careers, fixed income and derivatives, portfolio and risk theory, behavioural finance, and AI — plus a 14-framework library, 12 Indian case studies, and a clear map of every major finance role with its skills, questions, salaries and growth path. This is the depth that separates a strong candidate from a memorable one.
The last mile is execution: rehearse “walk me through a DCF/LBO” out loud, prepare a stock pitch, sharpen Excel, and get real feedback under pressure. Knowledge gets you shortlisted; structured, confident application gets you selected.
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