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. 013What is your favourite telecom stock?Stock pitchIntermediatetechnicalBalyasny Asset ManagementGeneralist · New York · 2020

   Say this

   Pick one, commit to it, and make the answer about the sector's economics rather than the company's story. Telecom is a capital intensity and pricing-power question: the winner is whoever earns a return above cost of capital on the network they have already built.

   Then walk it

   1. Frame the sector first, in one line. Telecom is a high fixed cost, low marginal cost, heavily regulated oligopoly where the swing variables are subscriber pricing, capex intensity and spectrum cost.
   2. Then the metrics that matter, which are not the ones from other sectors. ARPU, churn, subscriber net adds, capex as a percent of sales, EBITDA less capex, and net debt to EBITDA. Leverage is structurally high, so the equity is a levered bet on ARPU.
   3. Then the actual pick with a number. For instance, a market where three players have replaced four and tariffs are rising: the operator with the lowest cost per gigabyte and the most spectrum gets disproportionate incremental margin because every new subscriber drops through at near-zero marginal cost.
   4. The India angle is genuinely the best telecom case study going, and worth using if you know it. Tariff repair after consolidation moved ARPU up materially, and the equity story became entirely about whether that pricing held while capex rolled off.
   5. Say the bear case in the same breath. Spectrum auctions are a recurring, unavoidable capital call; a price war resets the whole thesis in a quarter; and regulated markets can hand a windfall to the consumer at any point.
   6. Then the hedge, since this is a hedge fund question. Long the share gainer, short the subscale operator with the same spectrum costs and worse coverage. Same regulatory risk, opposite unit economics, and the trade isolates the operating gap.

   Where candidates lose it

   Answering with a household name and a vague 5G story. The interviewer is testing whether you know the sector's unit economics. If you cannot say ARPU, churn and capex intensity for the name you picked, pick a different sector. Also do not say 'I do not follow telecom' and stop; name what you do follow and offer that instead.

   Expect next

   • What is ARPU doing in that market and why?
   • How would you short telecom?
   • How do you value spectrum?

   Reported by candidates at Balyasny Asset Management (Generalist, New York, 2020). Source: Wall Street Oasis.

2. 014What moves a stock?Stock pitchCorephone / first roundBalyasny Asset ManagementEquity Hedge · Chicago · 2021

   Say this

   Only two things: a change in expected cash flows, or a change in the rate those cash flows are discounted at. Everything else on a screen is one of those two arriving through some channel. Over a day, it is the surprise versus expectations rather than the level of the news.

   Then walk it

   1. Numerator effects: revisions to revenue, margin, capex and the duration of growth. The bulk of single-stock moves on results days are revision events, not valuation events.
   2. Denominator effects: risk-free rates, equity risk premium, the stock's own beta and perceived risk. These move whole sectors at once, which is why a long-only manager can be right on the company and wrong on the price.
   3. The crucial refinement for a hedge fund seat: prices move on the delta versus expectations, not on the absolute number. A company can grow earnings 20 percent and fall 10 percent because the buy side expected 25.
   4. Then the flow and positioning layer, which fundamental candidates skip and traders never do. Who owns it, how crowded it is, short interest, index inclusion, lock-up expiries, buybacks, and how the stock is set up into a catalyst.
   5. So on a results day the question is never 'were the numbers good'. It is 'were they better than the buy side whisper, and how was the stock positioned going in'. A beat into a crowded long can still sell off hard.
   6. One number to anchor it: for a long-duration equity, a 100 basis point move in the discount rate can be worth 15 to 20 percent of value with no change at all to the business. That is why rates dominate whole quarters of single-stock performance.

   Where candidates lose it

   Reciting a list of news categories. The answer is a framework with two boxes, and the sophistication is in adding expectations and positioning. Say the phrase 'relative to what was expected' or a hedge fund interviewer will assume you have only ever read sell-side notes.

   Expect next

   • How do you find out what the buy side actually expects?
   • A company beats and the stock falls 8 percent. What happened?
   • How do you think about the valuation drivers of a name?

   Reported by candidates at Balyasny Asset Management (Equity Hedge, Chicago, 2021). Source: Wall Street Oasis.

3. 015How do you think about the valuation drivers of a name?Stock pitchIntermediatetechnicalBalyasny Asset ManagementEquity Hedge · Chicago · 2021

   Say this

   I reduce the multiple to its drivers rather than treating it as a given: growth, return on incremental capital, and risk. Two companies on the same multiple with different reinvestment economics are not priced the same, and that gap is usually where the trade is.

   Then walk it

   1. Start from the identity. Value is this year's cash flow, grown at g, discounted at r. So the multiple is a function of growth, the cost of capital and how much capital the growth consumes.
   2. Reinvestment is the part people skip. Growth is only valuable if the return on incremental invested capital exceeds the cost of capital. A company growing 15 percent at a 6 percent return on capital is destroying value while looking exciting.
   3. So I run three numbers on every name: organic growth, return on incremental capital, and free cash conversion. Those three explain most of the cross-sectional multiple dispersion inside a sector.
   4. Then I use a reverse DCF to make the multiple concrete. At today's price, what growth and margin does the market require? That converts an abstract multiple into a testable forecast I can agree or disagree with.
   5. Then the risk side: earnings duration, cyclicality, customer concentration, and leverage. A levered cyclical deserves a lower multiple on trough earnings, and mechanical peer-multiple comparisons miss that entirely.
   6. The limitation I would state: multiples embed the market's view of duration, which is unobservable. That is why I use the reverse DCF to find the implied assumption rather than arguing that 14 times is cheap because the peer is on 17.

   Where candidates lose it

   Answering with a list of valuation methodologies. The question asks what drives value, not which spreadsheet you build. Growth, return on incremental capital and risk, then a reverse DCF to make it concrete. A candidate who says 'DCF, comps and precedent transactions' has answered a banking question in a hedge fund interview.

   Expect next

   • Two companies in the same industry trade at 12 and 22 times. What could justify that?
   • How do you use a reverse DCF?
   • When is a low multiple a trap?

   Reported by candidates at Balyasny Asset Management (Equity Hedge, Chicago, 2021). Source: Wall Street Oasis.

4. 029How would you hedge a name that does not have a similar public comp?Portfolio constructionHardtechnicalBalyasny Asset ManagementEquity Research · New York · 2026

   Say this

   Decompose the position into the risks you do not want and hedge each one with whatever trades, rather than hunting for a twin. Usually that means a basket: some index or sector for market beta, a factor or style proxy, and then something specific for the commodity, currency or customer exposure.

   Then walk it

   1. First, write down what you are actually exposed to. Market beta, sector, style factors like growth and momentum, one or two macro sensitivities, maybe a single large customer or an input cost. The comp problem disappears once you stop thinking in comps.
   2. Then hedge the biggest exposures with liquid instruments. Index futures for beta, a sector ETF for industry, and a factor ETF or a long-short style basket if the name is a strong growth or momentum expression.
   3. Then go up or down the value chain. If there is no comp, there is usually a supplier, a customer or an input. A specialty chemical company with no peer can often be partly hedged with the feedstock or with the auto OEMs it sells into.
   4. Then a statistical basket as the fallback. Regress the stock on a set of liquid candidates over a sensible window and build a weighted short basket from the loadings. It is crude but it is honest, and platforms do exactly this.
   5. If nothing works, the right answer is to size it smaller. An unhedgeable idiosyncratic risk is a legitimate risk to take, just not at full weight, and saying that is better than inventing a hedge.
   6. Then name the two failure modes: a regression-fitted basket can be a spurious relationship that breaks in the stress you were hedging against, and it needs rebalancing or the loadings drift. I would re-estimate monthly and cap how much of the risk I claim is hedged.

   Where candidates lose it

   Answering 'short the index' and stopping, or inventing a comp that is not really one. At a multi-manager platform this question is about whether you think in risk factors rather than in tickers. And do not miss the escape hatch: sometimes the correct answer is that the risk cannot be hedged and the position should be halved.

   Expect next

   • How would you build that regression basket and over what window?
   • What could go wrong with a statistically fitted hedge?
   • When is the right answer to just size it smaller?

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

5. 035Write a function that returns the n largest drawdowns in a return series.Risk and drawdownHardtechnicalBalyasny Asset ManagementQuantitative Trading · London · 2025

   Say this

   Build the cumulative NAV, walk it once tracking the running peak, and record a drawdown episode whenever the series falls below a peak and then makes a new high. Each episode gets a depth, a start, a trough and a recovery date. Then sort the episodes by depth and return the top n. It is a single linear pass.

   Then walk it

   1. Step one: turn returns into a wealth index, cumulative product of one plus r. Do this before anything else, because drawdowns are multiplicative and summing returns gives the wrong depth.
   2. Step two: running maximum of the wealth index. The drawdown series is wealth divided by running max, minus one, which is zero or negative at every point.
   3. Step three, the part interviewers actually test: segment into episodes. An episode opens when the drawdown series goes below zero and closes when it returns to zero, meaning a new high water mark. Within each episode the trough is the minimum.
   4. Step four: sort episodes by depth, take the first n. Say the complexity: O(T) for the pass plus O(k log k) for the sort, where k is the number of episodes, so linear in practice.
   5. State the edge cases before being asked, because this is where candidates get cut: the series ends while still in a drawdown, so the last episode is unrecovered and you should report it with no recovery date. Also decide whether overlapping nested dips count as one episode or several, and say which convention you are using.
   6. The naive alternative is to take the n most negative points of the drawdown series, and it is wrong: they will all sit inside the same crash. Volunteering why that fails is what shows you understood the question rather than pattern-matched it.

   Where candidates lose it

   Returning the n most negative values of the drawdown series. They cluster in one episode, so you report the same crash n times. The question is really about episode segmentation. Also, sum returns instead of compounding them and every number is wrong. State your episode convention out loud.

   Expect next

   • How would you handle a series that ends mid-drawdown?
   • How would you report time to recovery?
   • How would you do this for a portfolio of a thousand instruments efficiently?

   Reported by candidates at Balyasny Asset Management (Quantitative Trading, London, 2025). Source: Wall Street Oasis.

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. 082Talk me through your research process for a systematic signal. How do you avoid fooling yourself?Quant and systematicHardtechnicalBalyasny Asset ManagementQuantitative Trading · London · 2025

   Say this

   Start with an economic reason the signal should work, then test it in a way that can fail. Hypothesis first, then data preparation, then a simple specification, then out-of-sample and out-of-region validation, then costs, then capacity. The discipline is that the hypothesis comes before the backtest, not after it.

   Then walk it

   1. State the economic mechanism first and write it down before running anything. Who is on the other side, and why are they there? A signal with no story about who is losing money is almost certainly a data artifact.
   2. Then the data work, which is most of the time and all of the risk. Point-in-time data with correct reporting lags, restated figures handled properly, delisted and merged companies included, corporate actions adjusted, and survivorship bias eliminated. Look-ahead bias is the most common silent killer and it always flatters the result.
   3. Then the simplest possible specification. One parameter, no optimisation, sensible defaults. If the effect does not show up in the naive version, it probably is not there. Elaboration after validation, never before.
   4. Then validation that can actually fail: hold out a period you never look at, test in other regions and other asset classes, test across sub-periods, and check that the result is not driven by a handful of stocks or one month. Report the number of specifications you tried, because a t-statistic loses its meaning after the twentieth attempt.
   5. Then costs and capacity, which kill more signals than statistics do. Model spread and impact, compute net-of-cost performance at realistic size, and check whether the signal survives a one-day implementation lag. A signal requiring same-second execution is not a signal for a fundamental-horizon book.
   6. Then the honest self-checks: decide the kill criteria before the test, keep a research log of everything tried including the failures, and have someone else reproduce the pipeline. The uncomfortable truth is that most published anomalies do not replicate out of sample, so my prior on my own new signal should be low.

   Where candidates lose it

   Describing a backtest rather than a research process. The order of operations is the answer: hypothesis, then data hygiene, then a naive test, then validation, then costs. A candidate who does not mention point-in-time data, look-ahead bias and the multiple-testing problem will not get through a quant research interview.

   Expect next

   • How many specifications did you try on your last project?
   • How do you handle restated financials in a backtest?
   • What is your kill criterion for a signal?

   Reported by candidates at Balyasny Asset Management (Quantitative Trading, London, 2025). 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.