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. 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?
2. 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.

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.