Hedge Funds interview preparation

Questions

100

Traced to a firm

39

Firms

16

Updated

September 2026

Asked at

All firmsMan Group10Balyasny Asset Management7Bridgewater Associates3DED.E. Shaw3Apollo Global Management2KKR2Oaktree Capital Management2Point722SCSquarepoint Capital2ACAQR Capital Management1BGBaupost Group1Coatue Management1HPS Investment Partners1Northern Trust1Viking Global Investors1Wolverine Trading1

Topic

All topicsStrategy taxonomy8Stock pitch10Short selling6Portfolio construction8Risk and drawdown8Performance and alpha7Event-driven and merger arb8Distressed and credit5Fund structure and economics7Financing, NAV and operations6Compliance and research process5Quant and systematic6India and Category III AIFs5Career and fit11

Level

AnyCoreIntermediateHard

Type

AnyTechnicalMarket viewBrainteaserCaseFit

1. 071How would you value an illiquid position for the monthly NAV?Financing, NAV and operationsHardsuperdayDistressed debtFund administration

   Say this

   By a written policy applied consistently, not by judgement each month. Hierarchy: an observable transaction price if there is one, then broker quotes, then a model calibrated to comparable market data, with the manager's own view last. Then document it, get it reviewed independently, and disclose the level.

   Then walk it

   1. Start at the top of the fair value hierarchy. Level one is an exchange price and there is nothing to decide. Level two is observable inputs, so indicative broker quotes on a similar bond or a recent trade in the same issuer's other paper. Level three is a model with unobservable inputs, which is where the real work and the real risk sit.
   2. For a private or restructured equity stake, a defensible approach is a multiple on the latest reported earnings against listed comparables, plus an illiquidity discount, cross-checked against the last primary round or any secondary transaction.
   3. For a stressed loan, recovery analysis: enterprise value, the waterfall, then a discount rate reflecting the time and uncertainty of the process. Mark the claim, not the face amount.
   4. Governance is most of the answer. Broker quotes from at least two independent sources where possible, a pricing committee that signs off, a written valuation policy reviewed by the board, the administrator striking the NAV rather than the manager, and an annual audit that tests the level three marks.
   5. Consistency matters more than precision, and this is the part to say out loud. A defensible method applied every month is better than a more accurate method applied when it suits. Changing methodology when a mark is inconvenient is the classic abuse.
   6. Then the disclosures that let an investor judge: percentage of NAV in each level, the discount applied, and a sensitivity showing what NAV would be at plus or minus a reasonable range on the key input. If the position is large and genuinely unmarkable, the right answer may be to side-pocket it rather than to invent a number.

   Where candidates lose it

   Giving a valuation technique and no governance. The question is really about controls: who marks it, who checks the marker, and how you stop the method from changing when the number is unhelpful. Also remember to mention that side-pocketing can be the correct answer rather than marking something you cannot mark.

   Expect next

   • What if the two broker quotes differ by 15 points?
   • Who signs off on a level three mark?
   • When should a position be side-pocketed instead?
2. 072What is the difference between holding a position in cash equity and through a swap?Financing, NAV and operationsIntermediatetechnicalLong-short equityPrime brokerage

   Say this

   Cash equity means you own the shares. A total return swap gives you the same economic exposure without owning them: the counterparty holds the stock and pays you the return, you pay a financing spread. The economics are similar, the ownership, disclosure, financing and counterparty risk are not.

   Then walk it

   1. Mechanically, the swap pays you price return plus dividends and you pay a floating rate plus a spread, against posted collateral. Your exposure is the full notional while your outlay is the margin, which is where the leverage comes from.
   2. Reasons funds use swaps: access to markets where direct ownership is restricted or operationally painful, notably parts of Asia and specifically the foreign investor route into some Indian instruments; simpler shorting because the borrow is embedded in the counterparty's book; and a single financing relationship across many positions.
   3. Disclosure is the big structural difference and the one interviewers probe. Economic exposure through a swap has historically not triggered the same beneficial ownership disclosure as shares, which is how large positions can be built quietly. Rules have tightened after several incidents, but the asymmetry is the reason the instrument is popular for stake-building.
   4. You also give up the shareholder rights. No vote, no ability to engage, no standing in a restructuring. For an activist or an event-driven investor that can matter more than the financing saving.
   5. Counterparty risk replaces settlement simplicity. If the dealer fails you are an unsecured creditor for the mark-to-market above your collateral, so you care about the dealer's credit and about netting agreements.
   6. Archegos is the case study that ties it together: enormous swap-based exposure spread across several dealers, each of whom could not see the whole position, with margin that proved thin. The lesson is that swap leverage plus fragmented dealer visibility hides concentration from everyone including the dealers.

   Where candidates lose it

   Saying they are economically identical and stopping. The differences that matter in practice are disclosure, voting rights, counterparty exposure and how the margin is set. Naming the Archegos dynamic shows you understand why regulators now care, rather than just how the instrument is priced.

   Expect next

   • Why would you use a swap rather than buying the stock?
   • What is the counterparty risk on a swap, exactly?
   • Why do swaps matter for stake-building disclosure?
3. 073Describe a recent Excel project you worked on and the tools you used.Financing, NAV and operationsCorephone / first roundNorthern TrustHedge Fund · Chicago · 2022

   Say this

   Pick one real thing you built, say what problem it solved, name the specific functions and how it was structured, and finish with what it saved or caught. Concrete and modest beats a list of features you have heard of.

   Then walk it

   1. Structure it as problem, build, result. 'The team was reconciling two position files by eye, about 400 lines, twice a week. I built a reconciliation sheet that matched on a composite key and flagged breaks by type. It went from an hour to about two minutes and it caught a duplicated trade in the first week.'
   2. Name the tools specifically and correctly: INDEX and MATCH or XLOOKUP rather than nested VLOOKUPs, SUMIFS, a proper table structure, conditional formatting for breaks, Power Query for the import, pivots for the summary. If you wrote VBA or a Python script to pull the data, say so and say why.
   3. Say something about structure, because that is what separates a modeller from a spreadsheet user. Inputs on one sheet, calculations on another, outputs on a third. No hardcoded numbers inside formulas. Consistent row logic across columns so a formula copies cleanly.
   4. Mention the controls you built in, which is the whole point in a fund administration or middle office seat: a check row that must sum to zero, a tie-out to the source total, and an error flag if an input is missing.
   5. Have a version-control answer ready. Dated file names at minimum, a change log, and a documented assumptions tab. Operational teams care about this more than about clever formulas.
   6. Then the honest limitation: say what you would do differently now, usually that the process belonged in a database or a script rather than a spreadsheet, and that you would add input validation. That reads as someone who has maintained their own work rather than handed it over.

   Where candidates lose it

   Listing Excel functions as a skills inventory. Interviewers hear that constantly and it proves nothing. One specific artifact with a measurable outcome and a built-in check is worth more. And be ready to be tested: if you claim Power Query or advanced modelling, expect to be asked to explain exactly how you used it.

   Expect next

   • How did you make sure it was right?
   • What would you do differently now?
   • How comfortable are you with Python or SQL for the same job?

   Reported by candidates at Northern Trust (Hedge Fund, Chicago, 2022). Source: Wall Street Oasis.

4. 074You are on an expert network call and the expert starts describing their current employer's unreported quarter. What do you do?Compliance and research processHardsuperdayMulti-manager platformsCompliance

   Say this

   Stop the call immediately, say clearly that you cannot receive that information, and end it rather than steer it. Then report it to compliance the same day, document what was said, and let them decide whether the name goes on a restricted list. You do not get to make that judgement yourself.

   Then walk it

   1. Interrupt, do not redirect. 'I have to stop you there. I cannot discuss unreported financial results for your employer.' Trying to move the conversation along while having already heard it does not help you.
   2. End the call. Continuing after a breach, even on other topics, looks like you kept fishing, and the call is recorded or logged by the network.
   3. Report it immediately and in writing to compliance, with the date, the expert, the network, and what was said. Self-reporting is the single most protective thing you can do, and delaying it is what turns a mistake into a career-ending problem.
   4. Expect the consequence and accept it: compliance will likely restrict the name, meaning nobody at the firm can trade it until they clear it. Even if you were already long, you may be frozen. That is the correct outcome.
   5. Say the structural controls that should have prevented it, because it shows you understand the framework rather than just the etiquette. Pre-approved question lists, chaperoned calls, prohibitions on speaking to current employees of public companies you cover, mandatory network training, and post-call logging.
   6. Then the honest point about incentives. The information would have been valuable, and that is exactly why the rule has to be absolute rather than a judgement call in the moment. Every insider trading case involving expert networks, including the ones that put people in prison, started with someone deciding this once was probably fine.

   Where candidates lose it

   Giving a soft answer: 'I would change the subject' or 'I would not use it in my model'. Both are wrong. Once you possess material non-public information you are restricted whether you use it or not. The three required beats are stop, end, report. And never say you would check with the PM first; compliance is the escalation path.

   Expect next

   • What if your PM already has a large position in that name?
   • What is the firm's obligation once you report it?
   • How do you structure expert calls to avoid this?
5. 075What is material non-public information, and where exactly is the line?Compliance and research processHardtechnicalComplianceMulti-manager platforms

   Say this

   Material means a reasonable investor would consider it important in deciding whether to trade, which in practice means it would move the price. Non-public means it has not been broadly disseminated. Both tests have to be met, and the difficulty in real life is almost never the definition, it is the mosaic question.

   Then walk it

   1. Materiality examples that are clearly over the line: unreported results or guidance, an unannounced deal, a pending regulatory decision, a major customer loss, a CEO departure, an unannounced buyback.
   2. Non-public means not broadly disseminated. A fact told to one hedge fund, or sitting in a document that was not distributed, is non-public even if it was not marked confidential. Being told it by accident does not make it public.
   3. The legitimate discipline is the mosaic theory: assembling many individually non-material, public or lawfully obtained pieces into a conclusion nobody else has. Channel checks, satellite imagery, credit card panels, job postings, pricing scrapes. That is the entire alternative data industry and it is legal precisely because no single piece is material and non-public.
   4. Where the line actually blurs: a supplier telling you their shipments to a customer are down sharply. Non-public, arguably material, and the supplier may have a duty. The test involves how you got it and whether anyone breached a duty in passing it on, which is the misappropriation and tipping analysis.
   5. So the practical rules are procedural, not intellectual. Restricted and watch lists, pre-clearance of personal trades, chaperoned expert calls, information barriers between pods at platforms, and a habit of escalating anything ambiguous rather than resolving it yourself.
   6. The honest thing to say: the rule is asymmetric on purpose. The cost of escalating something harmless is an hour of compliance time; the cost of being wrong is criminal. So I would rather be the analyst compliance hears from too often than the one they hear about from a regulator.

   Where candidates lose it

   Trying to look sophisticated by arguing about grey areas. Interviewers are checking your instinct, and the correct instinct is to escalate rather than adjudicate. Do mention the mosaic theory, because it shows you know where the legitimate edge lives, but pair it with the procedural controls.

   Expect next

   • Is a sell-side analyst's unpublished view MNPI?
   • How do information barriers work between pods?
   • Where does alternative data cross the line?
6. 076How can you make a financial model detailed enough to be useful but simple enough that you can cover a lot of companies?Compliance and research processHardtechnicalBalyasny Asset ManagementEquity Research · New York · 2026

   Say this

   Model the two or three drivers that actually move the stock in detail and leave everything else as a ratio. The rule I use is that a line gets its own build only if a reasonable disagreement about it changes my target price by more than a few percent. Everything else is a percentage of sales.

   Then walk it

   1. Start from the drivers, not the statements. For a subscription business that is subscribers, ARPU and net retention. For a retailer it is store count, sales per square foot and gross margin. Those get real builds with monthly or segment granularity.
   2. Everything else gets a ratio: SG&A as a percentage of sales, D&A off a simple schedule, working capital on days, capex as a percentage of sales, tax at the guided rate. Resist the urge to build a full three-statement cascade for a name you are screening.
   3. Standardise the template across the coverage universe. Same rows, same order, same colour convention for inputs, same output block. Then updating twenty models after earnings is a mechanical exercise, and you can compare names line by line without re-reading each file.
   4. Tier the coverage explicitly. Five or six core names get deep models with segment detail and a channel-check overlay; twenty to thirty monitored names get a driver model with consensus alongside; the rest get a screen. Coverage breadth comes from the tiering, not from making every model thinner.
   5. Build the comparison in rather than bolting it on. Every model should show consensus next to my numbers and the implied valuation at a range of multiples, because the output I actually need is the gap versus the street, not a standalone forecast.
   6. The limitation to state: a simplified model will miss the thing that was in the footnote, so the trade-off is real. I manage it by re-reading the filings on the core names properly and accepting that on tier three I am running a screen, not a thesis. Pretending a thin model is a deep one is how people get caught.

   Where candidates lose it

   Answering 'keep it simple' with no decision rule. The interviewer wants the criterion you use to choose what gets detail. The materiality test, the driver-versus-ratio split and the tiered coverage model are the substance. And say the cost of simplification honestly, because at a platform you will be asked to cover more names than you can model deeply.

   Expect next

   • How many names can you genuinely cover properly?
   • What would you always model in detail regardless of the sector?
   • How do you update twenty models in an earnings week?

   Reported by candidates at Balyasny Asset Management (Equity Research, New York, 2026). Source: Wall Street Oasis.

7. 077Walk me through your research process on a new name.Compliance and research processIntermediatetechnicalLong-short equityMulti-manager platforms

   Say this

   I work backwards from the question that decides the stock. Understand the business and what the price implies, find the one or two variables the thesis turns on, then spend almost all the time on those. The goal is not to know everything, it is to have an edge on the thing that matters.

   Then walk it

   1. Day one is the price. What does today's valuation imply about growth, margin and duration? A reverse DCF or an implied-multiple check tells me what I have to disagree with, which stops me doing a month of work on a stock that is fairly priced.
   2. Then the primary documents: the last three annual reports, the segment notes, the accounting policies, and the last eight quarterly transcripts read back to front so I can see which promises were kept. Filings before sell-side notes, always.
   3. Then the industry structure. Who are the competitors, where does the profit pool sit, who has pricing power, what are the barriers, what is the customer's alternative. This is where the durability question gets answered and it is what business judgement actually means.
   4. Then identify the crux and state it as a question with a number attached. 'Does gross margin reach 42 percent by 2028?' Then the work plan follows: channel checks, pricing data, competitor disclosure, supplier commentary, whatever bears on that number specifically.
   5. Then build the model to the drivers, put consensus next to my numbers, and write a one-page thesis with the variant view, the catalyst, the bear case with a price, the falsifiers and the sizing. If I cannot write it in one page, I have not found the crux.
   6. Then the falsification step, which is the part that separates research from advocacy: go and find the best bear argument, ideally from someone short the name, and see if it survives. And say the honest limitation, that time is the constraint, so tier three names get a screen and a model rather than this whole process.

   Where candidates lose it

   Describing a linear process that ends with a recommendation. A hedge fund process starts with what the price implies and converges on one crux. Candidates who say 'read the 10-K, build a model, do comps, make a recommendation' have described a training programme, not a research process. Name the crux and the falsification step.

   Expect next

   • How long does that take and what do you cut when you have two days?
   • What is the crux on a name you are following now?
   • How do you find the best bear argument?
8. 078Here are the financial statements of three unnamed companies. Work out what kind of business each one is.Compliance and research processHardcase studyHPS Investment PartnersSpecial Situations · London · 2021

   Say this

   I would read four things in order and narrate as I go: the asset side of the balance sheet, the shape of the cost structure, the working capital cycle, and the capital intensity. Those four together identify a business model within a couple of guesses, and the reasoning is what is being graded, not the final label.

   Then walk it

   1. The balance sheet tells you most of it. Heavy PP&E means manufacturing, utilities, telecom or hotels. Heavy inventory with no PP&E means a retailer or a distributor. Almost no assets but large receivables means services or consulting. Large intangibles and goodwill means an acquisitive or a software business. A balance sheet dominated by financial assets and matched liabilities means a bank, an insurer or a lender.
   2. Then margins and their shape. Gross margin above 70 with heavy sales and marketing is software. Gross margin in single digits on huge revenue is distribution, commodity trading or grocery. High EBITDA margin with heavy depreciation is infrastructure-like, so telecom, towers, pipelines.
   3. Then the working capital cycle, which is the most diagnostic single item. Negative working capital with large payables and fast inventory turns is a supermarket or a restaurant chain. Large deferred revenue is subscription software. Long receivables and inventory days is heavy industry or project work. Receivables that are the business are financial services.
   4. Then capital intensity and leverage. Capex above 15 percent of sales with high depreciation says utility, telecom or semiconductor fab. Very high leverage with stable margins says regulated or contracted cash flows. High leverage with cyclical margins says a leveraged buyout.
   5. Then cross-check with one specific tell per hypothesis. A retailer has operating leases now capitalised as right-of-use assets. An insurer has technical reserves. A hotel has both heavy PP&E and high operating leverage. Say the tell you are looking for before you look for it.
   6. Then commit and quantify your confidence: 'Company A is a grocery retailer, and I am confident because of negative working capital, 25 percent gross margin, 3 percent EBIT margin and inventory turning in under 30 days. If I am wrong, it is a food distributor, and the way to tell them apart is store-level assets versus warehouse assets.' Naming the alternative and the discriminating test is what a credit interviewer is looking for.

   Where candidates lose it

   Guessing early and then defending it. This is a pure reasoning exercise: narrate the evidence in order and let the conclusion fall out. Also, do not neglect the working capital cycle, which is more diagnostic than the income statement. And always name your second-best hypothesis and the test that would separate them.

   Expect next

   • Which of the three would you lend to, and on what terms?
   • Which has the most operating leverage?
   • What single extra disclosure would you ask for?

   Reported by candidates at HPS Investment Partners (Special Situations, London, 2021). Source: Wall Street Oasis.

9. 079Explain the construction of a factor. Why that method, and how would you optimise it?Quant and systematicHardtechnicalACAQR Capital ManagementInvestment Research · New York · 2021

   Say this

   Take value as the example. Define the signal, in this case book to price or a composite of several value measures; clean and winsorise it; standardise it cross-sectionally within industry; then build a long-short portfolio from the ranks, usually top minus bottom quintile, weighted and rebalanced on a defined schedule. Every one of those steps is a choice, and the choices matter as much as the signal.

   Then walk it

   1. Signal definition first, and use a composite rather than a single ratio. Book to price, earnings to price, cash flow to price and sales to enterprise value capture the same idea with different noise, so the average is more robust than any one. That is the main argument for composites over single metrics.
   2. Then the cleaning: point-in-time data with the correct reporting lag so you are not using numbers before they were published, delisted returns included so you are not survivorship biased, winsorise or rank-transform the outliers, and handle negative book values explicitly.
   3. Then neutralisation. Standardise within industry, because a raw value screen just buys banks and sells software. Neutralise size too, or the factor becomes a small-cap bet. The choice of what to neutralise defines what the factor actually measures.
   4. Then portfolio construction: quintile or decile spreads, equal weight versus value weight, rebalance monthly or quarterly. Equal weight shows a stronger factor premium and is much harder to trade. Say that trade-off out loud, because it is where academic factors and investable factors part company.
   5. On optimisation, define the objective honestly: maximise net-of-cost information ratio, not gross return, with constraints on turnover, capacity and exposure to other factors. Then use cross-validation across time and across regions rather than optimising a single sample.
   6. And say the limitation before being asked, because this is the real question inside the question. With enough parameters you can produce any backtest you like. The defences are economic priors before data mining, a small number of specification choices, out-of-sample and out-of-region testing, sensitivity analysis showing the result is not knife-edge, and a documented count of how many specifications you tried. A factor that only works with one lookback and one weighting scheme is a coincidence.

   Where candidates lose it

   Describing the signal and skipping the construction choices. Neutralisation, point-in-time data and the equal-versus-value weighting decision are where the real work is. And on 'how would you optimise it', a candidate who does not immediately raise overfitting has failed the question at a firm built on factor research.

   Expect next

   • How would you know you had overfitted?
   • Why neutralise by industry?
   • How would you test whether your new factor is distinct from momentum?

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

10. 080What are the assumptions of linear regression?Quant and systematicIntermediatetechnicalSCSquarepoint CapitalHedge Fund · Montreal · 2024

    Say this

    Linearity in the parameters, exogenous errors with zero conditional mean, no perfect multicollinearity, homoscedastic and uncorrelated errors, and for exact small-sample inference, normally distributed errors. The first two give you unbiasedness; the rest are about whether your standard errors mean anything.

    Then walk it

    1. Separate the tiers, because that is what distinguishes someone who has used regression from someone who memorised a list. Linearity and exogeneity are needed for the coefficients to be unbiased. Homoscedasticity and no autocorrelation are needed for the usual standard errors to be correct. Normality is only needed for exact t and F inference in small samples.
    2. So a violation of homoscedasticity does not bias your beta, it biases your confidence in it. That distinction matters enormously in practice: you can still use the estimate, you just cannot trust the t-statistic.
    3. In financial time series the assumptions that actually break are autocorrelation and heteroscedasticity, because volatility clusters and returns overlap. The standard fixes are Newey-West or White standard errors, and clustered errors in panel data.
    4. Endogeneity is the serious one. If a regressor is correlated with the error, the coefficient is biased and no standard error fix helps. In finance this usually arises from omitted variables or from a feedback loop where price affects the supposed predictor.
    5. Multicollinearity does not bias anything, it just inflates variances, so coefficients become unstable and flip sign between samples. That is very common with factor exposures, and the tell is a large R-squared with no individually significant coefficient.
    6. Practical additions I would name: outliers dominate least squares because it minimises squared errors, so winsorise or use robust regression; and out-of-sample performance matters more than any in-sample diagnostic, because for a trading signal I care about prediction, not about the p-value.

    Where candidates lose it

    Reciting the list without saying what each assumption buys you. Tiering them into unbiasedness versus valid inference is the differentiator. Also, do not claim normality of the dependent variable is required; it is normality of the errors, and only for small-sample inference.

    Expect next

    • Which assumption is most often violated in financial data, and what do you do about it?
    • What is the consequence of multicollinearity?
    • How would you detect endogeneity?

    Reported by candidates at Squarepoint Capital (Hedge Fund, Montreal, 2024). Source: Wall Street Oasis.

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.