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. 001Walk me through the main hedge fund strategies and what each one is actually betting on.Strategy taxonomyCorephone / first roundMulti-manager platformsFund of funds

   Say this

   Group them by what the return actually comes from, not by asset class. Long-short equity bets on relative company fundamentals, global macro bets on the direction of rates, currencies and commodities, event-driven bets on a corporate action completing, relative value bets on two related prices converging, and stat arb bets on thousands of small statistical edges.

   Then walk it

   1. Long-short equity: long the good business, short the bad one in the same industry. The bet is stock selection, and the sector or market move is meant to cancel out.
   2. Global macro: top-down positions in rates, FX, sovereign credit and commodities, usually expressed in futures and swaps. Discretionary macro is a small number of large, thematic bets; the hit rate is low and the winners are big.
   3. Event-driven: the return depends on an event happening. Merger arb, spin-offs, index inclusions, activist situations, capital structure arbitrage. Timing risk is the main risk, not valuation risk.
   4. Relative value and fixed income arb: long one instrument, short a closely related one, earn the spread as it converges. Individually low risk, so it gets levered, which is where the danger sits.
   5. Distressed: buy the debt of a broken company and get paid through the restructuring, often ending up owning the equity. Long horizon, illiquid, legally intensive.
   6. Stat arb and quant equity: systematic, high breadth, thousands of positions, each with a tiny expected edge. Multi-strategy sits on top of all of these, allocating capital across pods and managing the correlation between them centrally.

   Where candidates lose it

   Listing strategies by instrument instead of by risk. Saying 'equity funds, bond funds, commodity funds' tells the interviewer nothing. The organising question is always what you are being paid for, and the honest way to close is to say most strategies are short some kind of tail: liquidity, correlation or deal completion.

   Expect next

   • Which of those would you want to work in, and why?
   • Which one is most exposed if funding markets freeze?
   • Where does the return in each case come from, in one word each?
2. 002Explain the difference between discretionary and systematic trading.Strategy taxonomyIntermediatetechnicalMan GroupGeneralist · London · 2023

   Say this

   A discretionary manager makes the decision on each trade, using a rules-guided but human judgement. A systematic manager makes the decision once, in code, and then the rules trade without intervention. The real difference is where the human judgement sits: in the position or in the process.

   Then walk it

   1. Discretionary: deep work on few positions. A macro PM might run fifteen expressions of four themes. Breadth is low, so the edge has to be depth of insight.
   2. Systematic: shallow work on many positions. A trend or stat arb book may hold thousands of instruments, each with a small expected edge, and the edge is breadth plus discipline.
   3. The fundamental law of active management is the clean way to say it: information ratio is roughly skill times the square root of breadth. Discretionary buys the skill term, systematic buys the breadth term.
   4. Capacity differs. Systematic strategies hit capacity limits in the market microstructure and can be measured; discretionary capacity is limited by how many names one human can genuinely know.
   5. Failure modes differ too. Discretionary fails through anchoring, averaging down and story-telling. Systematic fails through overfitting, regime change and everyone crowding the same signal.
   6. The blurred middle is where most large firms live now: quantamental. Human thesis, systematic screening, portfolio construction and risk done by the machine. Man Group itself runs both AHL on the systematic side and discretionary equity books, which is worth naming if you are sitting there.

   Where candidates lose it

   Framing it as 'humans versus computers'. Discretionary PMs use enormous amounts of quantitative tooling, and systematic researchers make thousands of judgement calls when specifying a model. Say where the judgement sits instead, and mention the fundamental law if you want to sound like you have thought about it rather than read about it.

   Expect next

   • Which has more capacity, and why?
   • How would you know a systematic strategy had stopped working rather than just having a bad month?
   • Which would you rather work in?

   Reported by candidates at Man Group (Generalist, London, 2023). Source: Wall Street Oasis.

3. 003What is a long-short equity fund actually doing, and where does the return come from?Strategy taxonomyCoretechnicalLong-short equityMulti-manager platforms

   Say this

   It buys the companies it thinks will do better than the market expects and shorts the ones it thinks will do worse, so the return is meant to come from being right about relative fundamentals rather than from the market going up. The shorts are there to fund the longs and to strip out the market move, not just to hedge.

   Then walk it

   1. Simple version: long 100, short 60. Gross is 160, net is 40. The 40 of net gives you some market exposure and the 160 of gross is where the stock selection lives.
   2. The spread is the product. If your longs are up 12 and your shorts are down 4 in a flat market, you made 16 points of gross spread on your book before financing.
   3. Shorts do three jobs: they generate alpha of their own, they neutralise the sector or factor you do not want to bet on, and the proceeds reduce the capital you need for the longs.
   4. Pairs are the purest expression. Long the share gainer, short the share loser in the same end market, and the industry cycle largely cancels.
   5. Where it breaks: in a violent rally the shorts hurt more than the longs help because losses on a short are unbounded and the position grows as it goes against you. That asymmetry is the whole reason short books get smaller when volatility spikes.
   6. And be honest about the fee maths. A 40 percent net exposure fund charging two and twenty needs meaningful spread just to beat a cheap 40/60 equity-cash blend, which is why gross spread and not net return is how these books are judged internally.

   Where candidates lose it

   Describing the short book as insurance. If shorts were only a hedge you would short the index and save the borrow cost and the research time. A long-short fund shorts single names because it thinks it can make money on them, and saying that is what shows you understand the business.

   Expect next

   • How do you choose between shorting a single name and shorting the index?
   • What happens to your book in a factor rotation?
   • What net exposure would you run and why?
4. 004What is global macro, and how is it different from long-short equity?Strategy taxonomyIntermediatetechnicalGlobal macro

   Say this

   Macro trades the price of money and the economy rather than individual companies: rates, currencies, sovereign credit, commodities and index-level equity. The difference from long-short equity is breadth and depth reversed. Macro takes a few large views expressed in liquid derivatives; long-short takes many small company-level views.

   Then walk it

   1. The instruments give it away. Macro lives in futures, swaps, FX forwards and options because those give you enormous notional exposure with a small cash outlay and can be exited in a day.
   2. The unit of analysis is a policy path, not an earnings number. A typical trade is 'the market is pricing three cuts, I think there will be one, so I am short the front end of the curve'.
   3. Expression matters as much as the view in macro. If you think a currency is overvalued, you can short it outright, own a put, or express it through the rate differential. Each has a different carry and a different way of being right on the view and losing money.
   4. Hit rates are low and honest macro managers say so. You might be right on four trades in ten and still have a good year if the winners run and the losers get cut small. That is why stop discipline is cultural in macro and thesis discipline is cultural in equity.
   5. Carry is the silent factor. Many macro trades pay you to wait or cost you to wait, and a trade with negative carry has to be right quickly.
   6. The limitation: because positions are big and liquid, macro books can be fine on the view and get stopped out by positioning and flow. The 2022 gilt episode is the clean example. The direction was right and the path killed people.

   Where candidates lose it

   Talking about macro views with no instrument attached. 'I think inflation stays sticky' is not a trade. The interviewer wants to hear the expression, the carry and the stop. Name the instrument in the same breath as the view.

   Expect next

   • Give me a macro trade you would put on today and how you would express it.
   • What is the carry on that trade?
   • How would you size it, and where would you stop out?
5. 005What is relative value, and give me an example of a relative value trade.Strategy taxonomyIntermediatetechnicalRelative valueFixed income arbitrage

   Say this

   Relative value is long one instrument and short a closely related one, betting that the price relationship between them converges rather than that either one goes up. Because the two legs are similar, the expected return per unit of notional is tiny, so the strategy only pays after leverage.

   Then walk it

   1. Classic example: the cash-futures basis in government bonds. Own the cash bond, short the future, earn the small mispricing as it converges into delivery. Levered ten or twenty times, a few basis points becomes a real return.
   2. Other standard ones: on-the-run versus off-the-run Treasuries, swap spreads, index arbitrage against the basket, convertible bond arb long the convert and short the equity, and capital structure arb long the bond and short the stock.
   3. The economic function is real. These trades supply liquidity and enforce pricing consistency between related markets, which is why the spreads exist at all.
   4. What you are actually short is liquidity and funding. The trade works while you can hold it; it fails when margin calls force you out at the widest point. LTCM in 1998 and the March 2020 Treasury basis unwind are the same story twice.
   5. So the real risk metric is not volatility of the spread, it is how much the spread can widen before your financing is pulled. Haircut, repo term and counterparty diversification are the actual risk controls.
   6. The honest caveat to say out loud: a relative value P&L looks like a beautiful straight line right up until the day it does not. Sharpe ratios computed on the calm period are meaningless without a stress assumption.

   Where candidates lose it

   Calling it arbitrage without leverage or funding in the answer. Unlevered, these spreads are not worth trading. The moment you mention leverage you have to mention repo haircuts and forced unwinds, and a candidate who volunteers that is immediately more credible than one who says 'risk-free'.

   Expect next

   • How much leverage would that basis trade need to be interesting?
   • What happened to the Treasury basis trade in March 2020?
   • How would you size a trade whose volatility is understated by its history?
6. 006What is statistical arbitrage, and why does it need so much capital and infrastructure?Strategy taxonomyIntermediatetechnicalStatistical arbitrageQuantitative hedge funds

   Say this

   Stat arb takes thousands of small, statistically estimated bets on relative price moves, holds them for days to weeks, and relies on breadth rather than conviction. It needs scale because each position's edge is a few basis points, so you only get a reliable return by running many of them at once with costs under tight control.

   Then walk it

   1. The canonical version is cross-sectional mean reversion: rank a universe on a residual signal, go long the bottom decile and short the top decile, rebalance frequently, hold nothing idiosyncratic enough to matter on its own.
   2. The maths is the fundamental law again. If your per-bet edge is small but your bets are numerous and roughly independent, the portfolio Sharpe scales with the square root of the number of bets. Two thousand weak signals beat ten strong ones on a risk-adjusted basis.
   3. That is why infrastructure is the moat: you need clean point-in-time data, survivorship-free universes, a risk model to neutralise factors, a cost model, and an execution stack that can turn over a large book without paying away the edge.
   4. Transaction cost is not a detail, it is the constraint. A signal with 8 basis points of gross edge and 6 basis points of round-trip cost is not a strategy. Most stat arb research time goes into cost and capacity, not into finding signals.
   5. It is also crowded. Many desks run correlated versions of the same value, momentum and reversal residuals, which is why quant equity has coordinated drawdowns. August 2007 is the textbook one: deleveraging in one corner forced liquidation across the whole cohort.
   6. The limitation to state plainly: the alpha decays. A signal that worked for five years will be arbitraged, so the business is really a research pipeline that retires signals as fast as it adds them.

   Where candidates lose it

   Describing it as pairs trading and stopping. Pairs trading is the toy version. What makes it a real strategy is the factor-neutral portfolio construction, the cost model and the research pipeline, and those are what the interviewer wants to hear you mention.

   Expect next

   • How would you test whether a signal is just a repackaged momentum factor?
   • What happened in the August 2007 quant quake?
   • How do you estimate capacity for a signal?

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.