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. 041What is the difference between alpha and beta, and how do you actually separate them?Performance and alphaIntermediatetechnicalMulti-manager platformsAsset management

   Say this

   Beta is the return you get for taking market risk, which anyone can buy for a few basis points. Alpha is what is left after you strip out every systematic exposure you were paid to take. You separate them by regressing the return stream on the factors and looking at the intercept.

   Then walk it

   1. The single-factor version: regress fund returns on market returns. The slope is beta, the intercept is alpha, the residual is unexplained. A fund with 0.6 beta in a market up 20 percent earned 12 points from beta before any skill.
   2. The problem is that a single factor flatters almost everyone. Add size, value, momentum, quality and low volatility and a lot of claimed alpha becomes a known factor tilt. That is the entire point of the Fama-French and Carhart extensions.
   3. For hedge funds you go further and add the exposures specific to the strategy: credit spreads, volatility, term premium, and for merger arb a factor that is essentially short the market's tail. Fung and Hsieh style models exist precisely because hedge fund returns are non-linear.
   4. The practical version inside a fund is attribution rather than regression. Decompose today's P&L into market, sector, style and residual, and only the residual counts as stock picking.
   5. The subtlety worth saying: alpha is model dependent. It is defined as what your factor model cannot explain, so a better model shrinks it. Alpha today is often just a factor nobody has named yet, which is roughly the history of quantitative finance.
   6. And beware short samples. With three years of monthly data the standard error on alpha is large enough that a 3 percent annual alpha is statistically indistinguishable from luck. Saying that is what separates a candidate who has run the regression from one who has read about it.

   Where candidates lose it

   Defining alpha as 'outperformance versus the index'. That is beta plus a benchmark choice. Alpha is residual to a factor model, which means it depends on which factors you include, and any honest answer says so. Also mention the sample-size problem: it is the fastest way to sound like you have handled real return data.

   Expect next

   • How many years of data do you need to prove an alpha is real?
   • Is a small-cap tilt alpha or beta?
   • How would you decompose a long-short fund's returns?
2. 043What is the difference between the Sharpe ratio and the information ratio?Performance and alphaIntermediatetechnicalAsset managementQuantitative hedge funds

   Say this

   Sharpe measures excess return over cash divided by total volatility. Information ratio measures excess return over a benchmark divided by tracking error. Sharpe asks whether the whole portfolio was worth owning; information ratio asks whether the active decisions added value.

   Then walk it

   1. The numerator differs in what you subtract. Sharpe subtracts the risk-free rate. IR subtracts the benchmark, so all the market return is stripped out and only the active bet remains.
   2. The denominator differs too. Sharpe uses the volatility of total excess return; IR uses tracking error, the volatility of the difference from the benchmark.
   3. For a genuinely market-neutral hedge fund the two converge, because cash is effectively the benchmark. For a long-only manager they are very different: a fund can have a strong Sharpe from owning equities and a terrible IR from poor stock selection.
   4. The fundamental law of active management lives here: IR is roughly the information coefficient times the square root of breadth. It tells you a manager can raise IR either by being more skilful per decision or by making more independent decisions, which is the entire argument for systematic investing.
   5. Practical benchmark: a long-only manager with an IR of 0.5 sustained over ten years is genuinely good. Most active managers do not clear it after fees, which is why the fee compression happened.
   6. The shared weakness: both use volatility as risk and both need long samples to say anything. And IR is hostage to the benchmark choice, so a manager can improve their IR by picking a benchmark they are structurally tilted away from. Always ask what the benchmark is before reading the number.

   Where candidates lose it

   Saying they are basically the same thing, or getting tracking error and volatility confused. The distinction is what you subtract and what you divide by, and the good answer adds the benchmark-gaming point. For a market-neutral fund saying they converge shows you understand the definitions rather than memorising two formulas.

   Expect next

   • For a market-neutral fund, which would you quote and why?
   • What is a good information ratio over ten years?
   • How can a manager game their information ratio?
3. 045How do you attribute a fund's P&L?Performance and alphaIntermediatetechnicalMulti-manager platformsRisk management

   Say this

   Split it into the pieces that correspond to decisions someone made. Market, sector, style factor, then idiosyncratic stock selection, then financing and trading costs. The residual after the systematic pieces is the only part that is evidence of skill.

   Then walk it

   1. Start with market: beta-adjusted net exposure times the index return. That is the part you would have earned with no stock selection at all.
   2. Then sector or industry: the net weight in each industry times that industry's return relative to the index. This catches the PM who is really making a sector call and calling it stock picking.
   3. Then style factors from the risk model: growth, value, momentum, size, quality, volatility. Each has a net loading and a factor return, so each has a P&L line.
   4. Then the residual, which is stock selection. Split it long and short, because a book that makes all its money on the long side in a rising market has not demonstrated a short-selling capability and that matters for how much gross it should run.
   5. Then the costs that are frequently ignored: borrow fees, dividends paid on shorts, financing spread on the leverage, and realised trading cost. On a 300 percent gross book these can be well over a hundred basis points a year.
   6. The caveat to state: attribution is model dependent and the pieces do not add up cleanly. There is always an interaction and residual term, factor returns are estimated, and a PM can dispute the classification of a name. So I would use it to ask questions rather than to settle them.

   Where candidates lose it

   Attributing to positions rather than to risks. Listing the top five winners and losers is a P&L report, not an attribution. The point is to isolate whether the money came from decisions the PM is paid for. Remember to include financing and borrow costs; candidates almost always leave them out and they are large on a levered book.

   Expect next

   • What if all the alpha is on the long side?
   • How would you attribute a macro book instead?
   • What are the biggest cost lines on a levered long-short book?
4. 046What is a good hit rate, and why is that not the right question?Performance and alphaIntermediatetechnicalLong-short equityGlobal macro

   Say this

   Around 55 percent is genuinely good for a fundamental book, and many excellent macro managers run below 50. It is the wrong question on its own because P&L is hit rate times average win against miss rate times average loss. Expectancy, not accuracy, is what pays.

   Then walk it

   1. Write the identity out: expectancy equals p times average win minus one minus p times average loss. You can be right 40 percent of the time with a three to one win-loss ratio and run a very good book.
   2. That is exactly the trend-following and macro profile. Lots of small losses cut quickly, a few large winners held. The discipline is entirely in the size of the losses, not in the frequency of the wins.
   3. Fundamental long-short is the other shape: higher hit rate, smaller average win, because positions are researched and sized before entry rather than added to as they work.
   4. So the diagnostic questions are better than the hit rate. What is my average winner versus average loser, do I cut losers faster than winners, and am I adding to winners? A high hit rate with a losing book means you take profits early and let losses run, which is the most common retail-shaped mistake in a professional seat.
   5. Sample size again. Twenty positions tells you nothing about hit rate. You need hundreds of independent decisions before the number means anything, which is another reason systematic books can measure themselves and discretionary books mostly cannot.
   6. The honest limitation: hit rate is easy to game by how you define a decision. Was a position you added to three times one call or four? Without a consistent definition, the number is a story rather than a statistic.

   Where candidates lose it

   Quoting a high hit rate as though it were the goal. Interviewers use this to see whether you think in expectancy. Also watch the trap in the other direction: saying hit rate does not matter at all. It matters, it just has to be read with the win-loss ratio alongside it.

   Expect next

   • What is your win-loss ratio on your own investing?
   • Why do macro managers tolerate a sub-50 percent hit rate?
   • How would you know if you were cutting winners too early?

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.