Equity Research interview preparation

Questions

100

Traced to a firm

72

Firms

45

Updated

September 2026

Asked at

All firmsMorningstar12Man Group6Balyasny Asset Management5BLBlackRock5FTFranklin Templeton5MSCI5Jefferies4CSCredit Suisse3Fidelity Investments3Moody's3Perella Weinberg Partners3Point723S&P Global3The Vanguard Group3WMWellington Management3Advent International2Apollo Global Management2Bank of America2Carlyle Group2DED.E. Shaw2Houlihan Lokey2HSBC2Piper Sandler2Sequoia Capital2SSState Street2Viking Global Investors2WBWilliam Blair2ACAQR Capital Management1BGBaupost Group1BMBNY Mellon1Centerview Partners1Coatue Management1Goldman Sachs1GSGuggenheim Securities1HWHarris Williams1Insight Partners1Invesco1Mizuho1Moelis & Company1MSMorgan Stanley1PIMCO1SCSchroders1Scotiabank1T. Rowe Price1TSTruist Securities1

Topic

All topicsResearch process9Stock pitch6Company analysis8Investment philosophy5Valuation14Modelling2Portfolio and risk8Macro8Sector knowledge2Accounting8Career and fit12Industry knowledge6Quantitative research1Sector: technology3Sector: consumer1Sector: healthcare1Sector: energy1Sector: financials2Sector: industrials1Case and estimation2

Level

AnyCoreIntermediateHard

Type

AnyTechnicalCaseFitBrainteaserMarket view

1. 028How would you perform a whitespace analysis?Company analysisHardtechnicalViking Global InvestorsQuantitative Research · New York · 2024

   Say this

   Map what the company currently sells to whom, map the total set of customers and products it could serve, and the gap between them is the whitespace. Then test whether the company can actually reach it.

   Then walk it

   1. Build the current position first: revenue split by product, by customer segment, by geography. You need the base to measure the gap from.
   2. Then size the addressable set honestly, bottom-up. Number of potential customers times realistic spend per customer, not a top-down market report number that includes everyone.
   3. The whitespace is the difference: customers in the addressable set who do not buy, and products the existing customers buy from someone else. The second is usually the higher-probability opportunity, because the relationship already exists.
   4. Then the feasibility test, which is where most whitespace analyses fail. Does the company have the product, the sales capacity and the right to win? Whitespace that requires capability it does not have is not an opportunity, it is a wish.
   5. Then quantify what the market is paying for. If the current price implies the company captures a third of the whitespace, and your work says it captures a tenth, you have a short. That conversion from market map to expectation is the actual investment output.

   Where candidates lose it

   Producing a large total addressable market number and calling it whitespace. The analytical value is entirely in the feasibility filter and in converting the result into what is priced in.

   Expect next

   • How do you size a market bottom-up?
   • How much of that is in the price?
   • How would you verify penetration rates?

   Reported by candidates at Viking Global Investors (Quantitative Research, New York, 2024). Source: Wall Street Oasis.

2. 029How do you construct a factor, and how would you decide whether it is real?Quantitative researchHardtechnicalACAQR Capital ManagementInvestment Research · New York · 2021

   Say this

   Define the signal, rank the universe on it, form long-short portfolios from the extremes, and measure the spread after controlling for known factors. It is real only if it survives transaction costs, out-of-sample testing and an economic explanation.

   Then walk it

   1. Construction: choose the metric, neutralise for size, sector and region so you are not just picking up a sector bet, then sort into quantiles and go long the top and short the bottom with rebalancing at a defined frequency.
   2. Measure the spread return, its volatility, the information ratio, and the turnover it requires. Turnover matters because a signal that needs daily rebalancing can be profitable on paper and unprofitable after costs.
   3. Control for known factors. If your new signal's returns disappear once you regress against value, momentum, quality and size, you have rediscovered an existing factor with a new name.
   4. Then the tests that actually matter: out-of-sample and out-of-region performance, stability across sub-periods, and how many specifications you tried before finding this one. Data mining is the default explanation for any new factor.
   5. And demand an economic story. A factor should be compensation for a risk, or exploitation of a behavioural bias, or a structural constraint on other investors. Without that, decay after publication is the base case.
   6. The honest position: most published factors do not survive replication, so the prior on any new one should be skeptical.

   Where candidates lose it

   Describing the mechanics with no discussion of multiple testing and data mining. The intellectual content of modern factor research is that backtests are easy and robustness is hard. Say so.

   Expect next

   • How would you optimise the construction?
   • Why do factors decay after publication?
   • How would you know if you had overfitted?

   Reported by candidates at AQR Capital Management (Investment Research, New York, 2021). Source: Wall Street Oasis.

3. 031How do you decide when to sell?Portfolio and riskHardsuperdayAsset managementHedge funds

   Say this

   Three reasons and only three: the thesis played out and the price reflects it, the thesis is broken, or something better came along. Never sell because the price fell, and never hold because you are down.

   Then walk it

   1. Thesis achieved: the variant view became consensus and the upside to your revised target is no longer compelling. This is the happy case and people systematically sell too early here.
   2. Thesis broken: the specific thing you said would happen did not, or a fact you relied on turned out false. This should trigger a sale regardless of price, and it is where writing down the falsifier in advance pays for itself.
   3. Better use of capital: opportunity cost. In a concentrated portfolio every new idea must displace something, which imposes useful discipline.
   4. What is not a reason: the price fell, so it is cheaper now. That is only a reason to buy more if the thesis is intact, and only if you have checked rather than assumed.
   5. The behavioural safeguards: a written thesis with falsifiers, a scheduled review after every result, and a rule that you re-underwrite a position from scratch rather than defending your existing note.
   6. And separate trimming from selling. Reducing on valuation while the thesis compounds is a different decision from exiting, and conflating them is how people sell their best ideas.

   Where candidates lose it

   Giving a price-based rule like a fixed stop loss as the whole answer. For fundamental investing, the sell decision is thesis-based. Stops are a risk management overlay, not a research judgement, and saying only that reveals a trader's frame in a research seat.

   Expect next

   • How do you avoid selling winners too early?
   • Do you use stop losses?
   • How do you re-underwrite a position?
4. 032How do you size a position?Portfolio and riskHardsuperdayHedge fundsAsset management

   Say this

   By conviction and by downside, not by expected upside. The question is how much you lose if you are wrong, multiplied by how likely that is, against the portfolio's tolerance for that loss.

   Then walk it

   1. Start from the downside. If the bear case is minus 40 percent and you would be uncomfortable losing more than 2 percent of the fund on one name, the position is capped at about 5 percent.
   2. Then conviction, which really means how confident you are in the analysis and how falsifiable it is. A thesis with a clear near-term test supports a larger position than one that depends on a five-year structural view.
   3. Then correlation. Three positions expressing the same macro view are one position. Sizing has to be done at the portfolio level or you accumulate hidden concentration.
   4. Then liquidity: how many days of average volume is the position, and can you exit it in a stressed market? Illiquidity is a real constraint on size regardless of conviction.
   5. The Kelly criterion is the theoretical frame, but full Kelly is far too aggressive in practice because you cannot estimate probabilities that precisely. Most investors run a fraction of it, and saying that shows you know the theory and its limits.
   6. In a multi-manager seat, most of this is imposed by the risk system anyway, and the analyst's job is to argue for the sizing within those limits.

   Where candidates lose it

   Sizing by upside. Everyone's best idea has the most upside, and sizing on that alone is how funds blow up. Downside and correlation are the content of a real answer.

   Expect next

   • What is your maximum position size?
   • How do you handle correlated positions?
   • Would you add to a loser?
5. 035Tell me about a time someone questioned your integrity.Career and fitHardsuperdayBMBNY MellonEquity Research · New York · 2022

   Say this

   Choose a case where the challenge was reasonable given what the other person could see, and where you resolved it by showing your work. The point is how you respond to being doubted, not that you were vindicated.

   Then walk it

   1. Pick something real but bounded: a number in your analysis that someone thought was wrong, a result that looked too good, a process someone thought you had skipped.
   2. Explain why the doubt was reasonable from their position. Starting with 'they were being unfair' reads badly and misses the point of the question.
   3. Then the response: you showed the working, walked them through the source data, or invited them to check it. Transparency rather than argument.
   4. Then the outcome, and if you had made an error, say so. Admitting a mistake here is stronger than a clean vindication, because it shows how you behave when you are actually wrong.
   5. Then the lasting change: how you document or communicate differently now. In research, where your entire product is a claim, being auditable is the whole professional standard.

   Where candidates lose it

   Getting defensive in the retelling, or choosing an example so serious that it raises new questions. Regulated firms ask this to see whether you respond to scrutiny with openness or with resistance.

   Expect next

   • What if you had actually been wrong?
   • Tell me about an ethical dilemma you faced.
   • How do you make your work auditable?

   Reported by candidates at BNY Mellon (Equity Research, New York, 2022). Source: Wall Street Oasis.

6. 037How would you model a subscription software business, and what metrics matter?Sector: technologyHardtechnicalInsight PartnersSoftware · New York · 2022Piper SandlerInvestment Banking · Burlingame · 2026

   Say this

   Model the recurring revenue base by cohort rather than the income statement. Opening ARR, plus new, plus expansion, less churn and downgrades, gives closing ARR. Everything else follows from that roll-forward.

   Then walk it

   1. The ARR bridge is the model. Once you have opening ARR, new bookings, expansion and churn, revenue is largely determined, because recognised revenue is a lagging function of the contracted base.
   2. Key metrics: net revenue retention, gross retention, gross margin, customer acquisition cost payback, and the rule of forty which is growth plus free cash flow margin.
   3. Net retention above 110 percent is the single most important number, because it means the installed base grows without selling anything new. That is what justifies a high revenue multiple.
   4. Watch the gap between billings, revenue and deferred revenue. Billings lead revenue, so a slowdown shows up in billings a quarter or two before it hits the reported line. That is often where the variant view lives.
   5. Cost side: gross margin tells you how much real compute or support sits in cost of revenue, sales and marketing efficiency tells you whether growth is bought or earned, and R&D as a share of revenue tells you about future product.
   6. And take stock-based compensation seriously, because in software it is large enough to determine whether the company is profitable at all.

   Where candidates lose it

   Modelling revenue directly and ignoring the ARR bridge and deferred revenue. Also quoting the rule of forty without knowing whether it uses free cash flow margin or operating margin, since the two give very different answers.

   Expect next

   • What is the rule of forty?
   • Why do billings lead revenue?
   • What net retention would justify a 10 times revenue multiple?

   Reported by candidates at Insight Partners (Software, New York, 2022); Piper Sandler (Investment Banking, Burlingame, 2026). Source: Wall Street Oasis.

7. 040How would you analyse a pharmaceutical company?Sector: healthcareHardtechnicalMoelis & CompanyMergers and Acquisitions · Los Angeles · 2022GSGuggenheim SecuritiesHealthcare · London · 2026

   Say this

   Value it asset by asset. The marketed drugs are annuities running to patent expiry, the pipeline is a set of probability-weighted options, and the two are valued completely differently.

   Then walk it

   1. Marketed products: forecast each drug's sales to its loss of exclusivity date, then model the cliff. Generic entry typically removes 70 to 90 percent of small-molecule revenue within a year or two; biologics erode more slowly because biosimilars are harder.
   2. Pipeline: for each candidate, size the patient population, price, penetration and duration, then apply probability of success by phase. Roughly 60 to 70 percent from Phase III, around 30 percent from Phase II, low single digits preclinical.
   3. Sum the parts and add net cash. The output is a range, because a single readout can move the value by a factor.
   4. Then the structural questions: the patent cliff schedule over the next five years, R&D productivity measured as approvals per dollar spent, and whether the company can acquire its way out of a gap.
   5. Pricing and reimbursement risk is the sector's macro. Policy on drug pricing can reset the whole group's multiple independently of any company's execution.
   6. The practical framing for a note: what percentage of current revenue loses exclusivity within five years, and does the pipeline plus reasonable business development replace it? That one question drives most pharma investment cases.

   Where candidates lose it

   Applying a single P/E to the whole company. A pharma is a portfolio of expiring annuities plus options, and blending them into one multiple hides the cliff, which is the entire risk.

   Expect next

   • How do you handle the patent cliff?
   • What probability would you use for a Phase II asset?
   • Which is riskier, biologics or small molecules?

   Reported by candidates at Moelis & Company (Mergers and Acquisitions, Los Angeles, 2022); Guggenheim Securities (Healthcare, London, 2026). Source: Wall Street Oasis.

8. 041How would you analyse an energy or commodity producer?Sector: energyHardtechnicalFTFranklin TempletonOil and Gas · San Mateo · 2024Perella Weinberg PartnersInvestment Banking · Houston · 2025

   Say this

   Position on the cost curve first, then reserves and production, then the balance sheet. The commodity price is the same for everyone, so the only company-specific variables are cost, volume and leverage.

   Then walk it

   1. Cost position is everything. A producer in the bottom quartile of the cost curve survives the trough and buys assets cheaply; a high-cost producer is a leveraged bet on the price.
   2. Reserves and reserve life: how long can they produce at current rates, what is the finding and development cost per barrel, and what is the decline rate on existing wells. Shale declines fast, so maintenance capital expenditure is enormous relative to conventional.
   3. Never value it on a spot price. Use a normalised or strip-based deck and show sensitivity across a price range. A low P/E on peak prices is the classic cyclical value trap.
   4. Balance sheet and hedging: leverage against trough cash flow, not current cash flow, and what percentage of next year's production is already hedged and at what price.
   5. Then capital discipline, which has become the sector's main equity story: are they returning cash or reinvesting into growth at the top of the cycle? The market now pays a premium for discipline.
   6. And the long-run structural question on terminal value: what do you assume about demand in twenty years? That assumption, not this year's earnings, is what most energy disagreements are actually about.

   Where candidates lose it

   Valuing on trailing earnings at current prices. Cyclicals invert the normal multiple logic: high multiples at the trough and low multiples at the peak are the correct pattern, not an anomaly.

   Expect next

   • What price deck would you use?
   • How do you normalise a cyclical?
   • How does hedging change your view?

   Reported by candidates at Franklin Templeton (Oil and Gas, San Mateo, 2024); Perella Weinberg Partners (Investment Banking, Houston, 2025). Source: Wall Street Oasis.

9. 042How do you normalise earnings for a cyclical company?ValuationHardtechnicalFTFranklin TempletonOil and Gas · San Mateo · 2024

   Say this

   Estimate what the business earns through an average cycle, not at either extreme. Take mid-cycle volumes and mid-cycle margins, adjusted for any structural change since the last cycle, and value that.

   Then walk it

   1. Method one: average the margin over a full cycle, usually seven to ten years, and apply it to current revenue. Simple and defensible.
   2. Method two: estimate mid-cycle volume and mid-cycle price separately, then rebuild the income statement. More work, but it lets you adjust each independently.
   3. Method three: normalise on the balance sheet instead, using return on invested capital through the cycle applied to today's capital base. Useful when volumes have changed structurally.
   4. The critical adjustment: has anything structural changed since the last cycle? Capacity closures, consolidation, a new cost position, or demand substitution mean history is not a clean guide. This is where the analysis is.
   5. Then apply a mid-cycle multiple to the normalised figure. The common error is applying a peak multiple to normalised earnings, or a normalised multiple to peak earnings; the two must be consistent.
   6. And show the earnings range rather than a point. For cyclicals the honest output is a value at trough, mid and peak, with a probability view on where in the cycle we are.

   Where candidates lose it

   Normalising the earnings but not the multiple, or ignoring structural change and treating the last cycle's average as destiny. Consistency between the earnings base and the multiple is the whole discipline.

   Expect next

   • How do you know where in the cycle you are?
   • What has structurally changed in that industry?
   • Why do cyclicals look cheapest at the top?

   Reported by candidates at Franklin Templeton (Oil and Gas, San Mateo, 2024). Source: Wall Street Oasis.

10. 044What is a reverse DCF and why would you use one?ValuationHardtechnicalLong-only asset management

    Say this

    You hold the market price fixed and solve for the assumptions it implies, instead of producing your own value. It converts valuation from a number into a testable statement about what the market believes.

    Then walk it

    1. Mechanically: set the DCF output equal to the current market capitalisation, then solve for the revenue growth or margin that makes it balance, holding everything else at reasonable levels.
    2. The output is a sentence like: at this price the market is assuming 12 percent revenue growth for a decade and a 25 percent terminal margin.
    3. Then you can make a judgement that is actually falsifiable. Has any company in this industry sustained 12 percent for a decade? How many did, historically? That turns an opinion into a base rate question.
    4. It also removes the main criticism of a forward DCF, which is that you can produce any number you want by choosing assumptions. Here the market chose them; you are only judging them.
    5. It is especially useful for expensive growth stocks, where a conventional DCF is unpersuasive to anyone who does not already share your assumptions.
    6. The limitation: it still depends on your discount rate and terminal assumption, so it constrains the problem rather than solving it. But 'the price implies something only three companies in history have achieved' is a far stronger argument than 'my model says it is worth less'.

    Where candidates lose it

    Describing it as just a DCF run backwards without explaining why it is more persuasive. The point is rhetorical as much as analytical: it shifts the burden of proof onto the market's assumptions.

    Expect next

    • What does the reverse DCF say about a stock you follow?
    • How many companies sustain that growth rate historically?
    • What are its limitations?

Firm tags come from public, anonymous candidate reports on Wall Street Oasis: strong signal, not sworn testimony. Firms are named as the places a question was reported, not as partners of Fin Maverick. Answers are written for this page to show how to think out loud; they are not scripts to recite.