15 Common Valuation Interview Q&As

There are three primary approaches: trading comparables, precedent transactions, and discounted cash flow analysis. Trading comps — also called public market comps — value the company based on what similar public companies trade at in the market today; you apply sector multiples like EV/EBITDA or NTM P/E to your subject company's financials to derive a range of implied values, and since these are minority-stake, liquid market prices, they represent the floor of what a company is worth in most M&A contexts. Precedent transactions look at what acquirers actually paid for comparable companies in completed deals; these multiples embed a control premium — typically 25–40% above the unaffected stock price — reflecting synergies and strategic value, so they tend to be higher than trading comps. The DCF builds a 5–10 year projection of free cash flows, discounts them back at WACC, and adds a terminal value; it represents intrinsic value independent of market sentiment, but it's highly sensitive to assumptions, especially the terminal value which typically represents 70–85% of total DCF value. In practice, you triangulate across all three: comps first as a market check, precedents as M&A context, and DCF as an intrinsic anchor — with the football field showing the full range.

You lean on DCF when the comp set is thin or unreliable — for instance, a company operating in a niche vertical with no direct public peers, or a company going through a transformation where trailing EBITDA is depressed and not representative of normalized earnings power. DCF is also valuable when you want to isolate intrinsic value away from broader market sentiment: if the sector is overvalued due to a bubble or undervalued due to macro dislocation, comps will reflect that distortion while a DCF won't — assuming your projections are sound. A third case is when you're modeling specific operational improvements, like a turnaround or a carve-out where the business looks nothing like public peers today but has a well-defined path to margin expansion; the DCF lets you capture that trajectory explicitly in the model rather than relying on a static market multiple. The tradeoff is that DCF is only as good as your assumptions — garbage in, garbage out — so you always cross-check with comps to make sure your implied multiples are in a reasonable range relative to the market.

The fundamental distinction is capital structure neutrality. EV/EBITDA sits above the interest line — EBITDA is pre-interest, pre-tax, and pre-D&A, so the multiple captures the operating business regardless of how it's financed; two companies with identical operations but different debt levels will have similar EV/EBITDA multiples, making it the right tool for cross-company comparison in M&A. P/E, by contrast, is an equity-level multiple — it divides the share price (equity value per share) by earnings per share, which is after interest and after tax. More leverage means higher interest expense, which reduces net income and thus EPS; a heavily levered company can have a lower P/E than an unlevered peer despite identical operating performance, which makes the comparison misleading. P/E is most useful when comparing companies with similar capital structures in the same sector — financial services, for example, where EV/EBITDA doesn't apply cleanly because interest income is part of revenue and debt is part of the product. In M&A, you almost always use EV-based multiples for the reasons above; P/E is more commonly used in equity research and public market analysis.

The core problem is that the terminal value — which already accounts for 70–85% of DCF value in mature companies — becomes even more dominant for early-stage businesses, sometimes representing 95%+ of total value, because near-term free cash flows are either negative or negligible. Projecting FCF five to ten years out for a company with no established customer base, unclear unit economics, or pre-revenue operations introduces enormous uncertainty; small changes in the year-5 margin assumption can swing equity value by 50%. The WACC is also unreliable — it requires a beta, a debt rating, and a stable capital structure, none of which exist for early-stage companies; you end up using public comps' betas which may bear no resemblance to the risk profile of the actual business. For these reasons, practitioners typically value early-stage companies using revenue multiples (EV/Revenue) benchmarked against high-growth comps, or comparable acquisition precedents in the same category, or a venture-style cash-on-cash return analysis for PE and growth equity buyers. The DCF can still serve as a sanity check on what growth rate is implied by a given valuation, but it's rarely the primary methodology.

A high EV/EBITDA multiple signals that the market expects more value from this business than its current earnings suggest — most commonly because of high expected growth, a durable competitive moat, or an asset-light model where EBITDA understates true cash generation (low capex relative to depreciation). For example, software companies trade at 20–30x EV/EBITDA while industrial manufacturers might trade at 7–9x, reflecting the difference in recurring revenue, margin expansion potential, and capital intensity. You always interpret multiples in context: a 12x multiple is rich for a slow-growth business but cheap for a category leader with 30% organic growth. Importantly, a premium can also reflect overvaluation — investor enthusiasm that has gotten ahead of fundamentals — which is why you cross-reference the implied multiple against sector benchmarks, growth-adjusted multiples like EV/EBITDA-to-growth, and the company's own historical trading range. In an interview, never just say "high multiple = good company" without acknowledging that it could also mean the market has priced in a lot of growth that still needs to materialize.

The primary screen is industry and business model — you want companies operating in the same sector, with similar revenue mix, similar gross margin profile, and similar go-to-market motion. After the industry screen, you narrow by size: a rule of thumb is 0.5x to 2x the subject company on revenue or market cap, because micro-cap and mega-cap companies trade at structural premiums and discounts that would distort your multiple range. Geography matters too — a European industrial trading at a discount to its US peers because of macro factors shouldn't anchor your multiple range for a US-listed subject. Once you have the long list, you tighten further: growth rate (a 5% grower shouldn't anchor a 20% grower's multiple), margin profile (a 10% EBITDA margin company trades very differently from a 35% EBITDA margin company in the same sector), and any obvious structural differences like seasonality or working capital intensity. You also verify that all comps have publicly traded equity and recent analyst coverage — illiquid names can have stale or distorted multiples. The final output is typically 6–12 companies with a clear narrative for why each belongs in the set.

A control premium is the amount an acquirer pays above the target company's unaffected public market stock price — "unaffected" meaning before any leak of the deal or market speculation. It represents the extra value the buyer is willing to pay for the right to control the business: set strategy, allocate capital, realize synergies, and determine exit timing. Historically, control premiums in public M&A transactions average 25–40%, though they vary significantly by deal type, sector, and market conditions; a hostile bid might command a higher premium while a negotiated all-cash deal in a soft market might come in at 20%. The premium is economically justified when the acquirer can generate synergies — cost cuts, revenue uplift, tax optimization, financing improvements — that more than offset the premium paid above market. This is why precedent transactions consistently produce higher multiples than trading comps: transactions reflect control value, while trading comps reflect minority, marketable value. When an investment banker presents a football field, the precedents bar is always above the comps bar, and that spread is the implied control premium range for the sector.

Most comparable companies don't share the same fiscal year-end as your subject company or as each other — a retailer might have a January fiscal year-end, a tech company December, and a defense contractor September. If you're comparing LTM EBITDA across a comp set without adjusting, you're mixing different 12-month periods, which introduces noise when economic conditions or seasonality differ across those windows. Calendarization converts each company's reported fiscal year data to a common calendar year period — usually December — so you're comparing apples to apples. The formula is straightforward: if a company's fiscal year ends in March, their CY figure is built by blending the stub period from the prior FY with the stub from the next FY, weighted by months. Specifically: CY Metric = (months of FY1 that fall in the CY / 12) × FY1 Metric + (months of FY2 that fall in the CY / 12) × FY2 Metric. In practice, this is most relevant for LTM and NTM calendarization in comps; DCF models typically just run on whatever fiscal year convention the subject company uses.

The first limitation is staleness: M&A markets move in cycles, and a transaction from five years ago may reflect a very different interest rate environment, credit availability, and sector multiple regime; a 2021 tech deal done at 30x EV/EBITDA tells you little about what a buyer would pay today. The second is deal scarcity — for niche industries or small market caps, there may be only two or three relevant deals over the past decade, which isn't enough to build a credible range; one strategic outlier with unusual synergies can skew the entire set. The third is deal-specific factors that inflate the multiple beyond what a typical buyer would pay: a bidding war, a hostile defense scenario, or an acquirer willing to pay a massive premium for a single asset they absolutely needed. Payment terms also matter — all-stock deals often carry inflated apparent multiples because the target accepted acquirer equity at a premium during a hot market; cash deals are a cleaner read. Finally, private company transactions often don't disclose full financial details, so you may be working with incomplete data and estimated multiples that carry significant error bars.

WACC is the weighted average of the cost of equity and the after-tax cost of debt, where the weights are the market value proportions of each in the capital structure. Cost of equity is calculated using CAPM: Risk-Free Rate + Beta × Equity Risk Premium. The risk-free rate is typically the 10-year US Treasury yield (around 4.2–4.5% today); the equity risk premium is usually 5–6% based on historical US market excess returns; and beta measures systematic risk relative to the market — you'll unlever the betas of comparable public companies to remove their capital structure, then re-lever at your target structure. Cost of debt is simpler: take the pre-tax interest rate on the company's debt (from their credit agreements or implied by their bond yield) and multiply by (1 − tax rate) to reflect the interest tax shield. Then weight each component: if a company has $3B equity market cap and $1B debt, equity is 75% of total capital and debt is 25%, so WACC = 75% × Cost of Equity + 25% × After-Tax Cost of Debt. In practice, WACC for investment-grade US companies runs 8–11% — always sanity-check your output against the sector average.

You use both and cross-check them, because they answer slightly different questions. The Gordon Growth Model (also called the perpetuity growth model) assumes the company grows its free cash flow at a stable, modest rate forever — typically GDP-level growth, 2–3% — and calculates terminal value as: Terminal FCF / (WACC − g). This is theoretically clean but extremely sensitive to the growth rate assumption; increasing g from 2% to 3% with a 10% WACC increases terminal value by 25%. The exit multiple method assumes the company is sold at the end of the projection period at a market multiple — typically EV/EBITDA — consistent with what comparable mature businesses trade at today. This is more market-anchored and less sensitive to minor assumption changes, which is why many practitioners prefer it as the primary method. In any well-constructed DCF you build both, present them side-by-side, and use the cross-check as a signal: if GGM implies a 15x exit multiple but your exit multiple assumption is 10x, something is off in your inputs. A divergence greater than 20% should prompt a review of your WACC, growth rate, or both. Most sell-side DCFs use the exit multiple as primary and GGM as the triangulation.

DCF value decreases — this is one of the most fundamental relationships in valuation. A higher discount rate reduces the present value of every future cash flow, including the terminal value, because each dollar of future earnings is being divided by a larger discount factor. The relationship is nonlinear: the terminal value, which represents the lion's share of total DCF value, is especially sensitive. Terminal value under the exit multiple method is discounted back at the full WACC for n years, so even a 50-basis-point increase in WACC — say from 10.0% to 10.5% — can reduce equity value by 10–15% in a typical mid-cap model. This is why DCF sensitivity tables always include a WACC axis: a 9%–11% WACC range against a 10x–14x exit multiple will give you the full valuation range under reasonable assumptions, and that spread is often $10–$20 per share. In interview context, the follow-up is usually "so what would make WACC go up?" — answer: higher risk-free rates (rising rate environment), higher beta (more cyclical/uncertain business), higher credit spread (more leverage or deteriorating credit quality), or a shift in capital structure toward more equity (equity is more expensive than debt on an after-tax basis for most companies).

A merger is accretive when the acquirer's pro forma EPS after the transaction is higher than its standalone EPS — meaning the acquisition adds to earnings per share rather than diluting it. The fundamental driver is the relationship between acquisition multiple and acquirer valuation multiple: if the acquirer trades at 20x P/E and acquires a target at 12x P/E (using stock), the earnings yield on the acquired business (1/12 = 8.3%) exceeds the cost of equity issuance (1/20 = 5.0%), so each dollar of acquired earnings generates more EPS than the dilution from issuing shares. In a cash deal, accretion depends on whether the after-tax cost of debt (the interest on acquisition financing) is lower than the earnings yield of the target. Synergies are the other lever: even a dilutive deal at face value can become accretive if cost synergies — headcount reduction, facility consolidation, vendor renegotiation — are large enough to offset the premium. A $200M net income target acquired for $4B (20x P/E) looks dilutive against an acquirer at 15x, but $80M of post-tax synergies changes the effective acquisition multiple and turns it accretive. Importantly, accretion/dilution analysis is a short-term earnings test — not a value creation test. A deal can be accretive and still destroy value if the acquirer overpays.

Goodwill arises in purchase price accounting when the price paid for a company exceeds the fair value of its net identifiable assets. After a deal closes, the acquirer must perform purchase price allocation (PPA): step through the target's balance sheet, mark all tangible assets (PP&E, inventory, receivables) to fair value, identify and value intangible assets that weren't on the balance sheet (brand names, customer relationships, technology, non-compete agreements), then subtract all liabilities at fair value. If the purchase price exceeds all of that — tangible assets plus identified intangibles minus liabilities — the residual is goodwill, representing things like workforce quality, customer loyalty, and business synergies that can't be discretely valued. The formula is: Goodwill = Purchase Price − (Fair Value of Net Tangible Assets + Identified Intangibles − Liabilities). Under US GAAP, goodwill is not amortized; instead it sits on the balance sheet indefinitely and is tested annually for impairment — if the business deteriorates and carrying value exceeds fair value, the company takes a write-down. Under IFRS, the treatment is the same for listed companies. In an interview, know that a large goodwill write-down is a red flag that the acquirer overpaid relative to what the business actually delivered.

Strategic buyers can justify a higher price because they're not buying the standalone business — they're buying the combined entity after synergies are captured. A strategic acquirer can take $80M in cost synergies (shared headcount, facilities, G&A) and $40M in revenue synergies (cross-sell, distribution leverage), effectively acquiring $120M of incremental EBITDA on top of the target's existing earnings base; if they apply the same EV/EBITDA multiple to the synergy-adjusted EBITDA, the maximum justifiable price is significantly higher. A PE firm, by contrast, is constrained to standalone economics: they can't take synergies because they have no platform to integrate into (unless it's a bolt-on to an existing portfolio company), and their return analysis is anchored by two hard constraints — debt capacity and return hurdle. Debt capacity limits how much leverage they can put on the deal (typically 4–7x EBITDA depending on sector and credit markets), which caps the equity check; and the return hurdle (20%+ IRR) limits the entry multiple because they need to exit at a sufficient premium to generate those returns over 5 years. The net result: in a competitive process, a strategic buyer will almost always outbid a PE firm on an asset where synergies exist, which is why most large-cap M&A is strategic-on-strategic — PE tends to win assets that are underperforming, operationally complex, or out-of-favor with strategic acquirers.

For any valuation methodology question, structure your answer in three beats: (1) what it is and how it works mechanically, (2) when you use it and why it's appropriate in that context, and (3) its key limitation or what would make you weight it less heavily. Interviewers aren't testing whether you can recite definitions — they're testing whether you can reason about tradeoffs. The candidate who says "DCF can be unreliable when the terminal value dominates" demonstrates judgment; the one who just walks through the formula demonstrates memorization. Every answer in this guide is built on that three-beat structure.