βΆWhat's the difference between DCF, multiples valuation, and precedent transactions?
DCF (discounted cash flow) = intrinsic value by projecting free cash flow years 1-5+, discounting at WACC (weighted average cost of capital). Used by PE, equity research, startups for fundraising. Multiples (EV/EBITDA, P/E) = valuation by comparing to peers (5ΓEBITDA for a SaaS), quick and market-driven but relies on comparable companies existing. Precedent = analyzing past M&A deals for similar companies (what did Facebook pay for WhatsApp?). In practice: all three together. DCF = upside view, multiples = what the market pays, precedent = deal-making reality check.
βΆHow do I build a three-statement model (P&L, Balance Sheet, Cash Flow)?
Start with P&L (revenue β expenses β EBIT β net income). Revenue is the hardest β drive it from units sold Γ price, not a magic number. COGS and OpEx should scale with revenue (% of revenue). Then Balance Sheet: assets (cash, receivables, inventory, PP&E) = liabilities (payables, debt) + equity. Cash Flow connects them: net income + depreciation (non-cash expense) + changes in working capital + capital expenditure. The three link: CF from ops uses net income; CF from investing uses PP&E changes; CF from financing uses debt/equity changes. Most common error: forgetting that net income β cash. Accruals (receivables, payables) matter.
βΆWhat's a sensitivity table and when should I use it?
Sensitivity = showing how output (e.g. DCF valuation, ROIC) changes when you tweak one or two inputs. Example: DCF value if revenue grows 5% vs 10% vs 15% (one-way), or revenue growth + WACC (two-way table). Used to stress-test your assumptions. If small changes in growth rate cause 50% valuation swings, your model is fragile or the business is risky. Best practice: show base case + bull/bear cases (not just table) to tell a story. Scenario analysis (best/base/worst case) is the narrative version; sensitivity is the quantitative version.
βΆInvestment Banking vs FP&A vs Startup CFO β what's the modeling difference?
IB: high polish, 100+ page pitch books with accretion/dilution models, LBO waterfall, precedent M&A tables. Speed and persuasion matter. FP&A: monthly/quarterly forecast of company's P&L/cash, variance analysis (why did actuals miss plan), rolling 13-week cash forecast. Accuracy and speed matter. Startup CFO: cap table (who owns what % post-funding), unit economics (CAC, LTV, payback period), quick burn runway calculation, series A valuation scenario. Fundraising narrative matters. All three need Excel skill; IB needs transaction experience, FP&A needs budgeting discipline, startup needs founder instinct.
βΆHow AI and automation are changing financial modeling in 2026?
Templates for standard scenarios (LBO, SaaS valuation) are fully automated by tools like Causal and Mosaic (fill in revenue growth, burn rate, get DCF + cash runway). Power Query and VBA are being replaced by Python notebooks in data-heavy shops. For the next 5 years: modeling skill = 70% judgment (what assumptions matter, what's the story) + 30% tool execution. Excel will stay dominant for customized deals; AI-native tools (Mosaic, ChatGPT plugins) are winning on speed for standardized models. Career: analysts who can code (Python, SQL) are more valuable than pure Excel experts. Learn SQL and basic Python alongside Excel.
βΆWhat are the top mistakes in financial modeling?
(1) Hard-coding numbers instead of formulas β makes auditing/changing assumptions nightmare. Always use cell references. (2) Circular references by accident β happens when forecasts feed back into inputs. Modelers catch these early. (3) Wrong depreciation method (straight-line vs accelerated affects cash flow). (4) Forgetting interest expense on debt β inflates EBIT and net income. (5) Revenue assumptions divorced from unit drivers (units Γ price, not a % of overall GDP). (6) Not stress-testing assumptions β 'best case only' doesn't survive board review. (7) Mixing currencies without conversion β Excel cell format β actual conversion. Always show math.
βΆHow do I use financial models for fundraising as a founder?
Investors want to see unit economics (CAC, LTV, payback) and a 5-year P&L with clear revenue drivers (users, ARPU, churn). Red flags: revenue growing 200% but no explanation (hiring? product feature? market expansion?), or burn rate going up while revenue is flat. Build three cases: conservative (30% growth), base (100% YoY growth), bull (200%+ growth). Show it takes $2.5M to reach cash flow positive by Year 3, so you're raising $5M with buffer. Use comps: if SaaS industry has 40% rule (growth rate % + FCF margin %), show how you hit it. Don't oversell; investors know 5-year forecasts are fiction. The model is the conversation-starter, not the truth.