Portfolio Management interview preparation

Questions

100

Traced to a firm

40

Firms

24

Updated

September 2026

Asked at

All firmsBLBlackRock4Vanguard4WMWellington Management4Amundi3ACAQR Capital Management3Neuberger Berman3SCSchroders3Man Group2MSCI2Northern Trust2AllianceBernstein1Apollo Global Management1Blackstone1BMBNY Mellon1Carlyle Group1Fidelity Investments1Goldman Sachs1Invesco1Millennium Management1MSMorgan Stanley1NUNuveen1PIMCO1SSState Street1TPTPG1

Topic

All topicsPortfolio theory5Factor models8Asset allocation11Rebalancing3Portfolio construction7Benchmarks and tracking error5Performance measurement8Risk management6Fixed income and LDI5Currency and global3Implementation and costs5Active versus passive6India markets7Brainteasers5Career and fit16

Level

AnyCoreIntermediateHard

Type

AnyTechnicalCaseMarket viewFitBrainteaser

1. 040What is the Sharpe ratio, and where does it mislead you?Performance measurementCorephone / first roundAsset management

   Say this

   Excess return over the risk-free rate divided by the volatility of that excess return. It is the standard measure of return per unit of total risk. It misleads whenever the return distribution is not symmetric, when returns are smoothed, or when the strategy sells tail risk.

   Then walk it

   1. Compute it on excess returns, not raw returns, and annualise properly: multiply the mean by the number of periods and the standard deviation by the square root of that number. A monthly Sharpe times twelve is wrong by a factor of about 3.5.
   2. Failure one, skew. A strategy that sells options or credit protection has a lovely Sharpe ratio right up to the day it does not. Volatility does not see the left tail, so option selling and carry trades look better than they are.
   3. Failure two, smoothing. Illiquid or appraisal-priced assets have autocorrelated returns, which suppresses measured volatility. A private credit fund reporting a Sharpe of 2 is often reporting the marking policy, not the risk. Correct for it by using the Lo adjustment or by summing lagged betas.
   4. Failure three, the horizon and the sample. Sharpe ratios are noisy: distinguishing a Sharpe of 0.5 from 1.0 with confidence takes many years of data, so ranking managers on three-year Sharpe is close to ranking noise.
   5. Failure four, it ignores whether the risk was systematic. A levered index fund has a decent Sharpe and no skill in it, which is why Sharpe is the wrong tool for evaluating an active manager inside a benchmarked mandate. Information ratio is the right one there.
   6. So I would quote Sharpe as a summary, then show Sortino for asymmetry, maximum drawdown for path, and the return distribution's skew and kurtosis. And if the assets are illiquid, I would say explicitly that the volatility is understated.

   Where candidates lose it

   Forgetting the risk-free rate in the numerator, or annualising by multiplying the ratio rather than scaling mean and standard deviation separately. On the substance, the failure interviewers most want to hear is that Sharpe rewards selling tail risk. A candidate who praises a hedge fund with a Sharpe of 3 and no questions about skew has failed the question.

   Expect next

   • How would you adjust Sharpe for illiquidity?
   • Sharpe or information ratio for an active equity manager?
   • What Sharpe ratio would make you suspicious?
2. 041Take me through Sortino, Treynor, Jensen's alpha and Calmar, and tell me when you would use each.Performance measurementIntermediatetechnicalAsset managementFund selection

   Say this

   They differ in what goes in the denominator, and that choice is the whole point. Sortino uses downside deviation, Treynor uses beta, Jensen's alpha is the regression intercept rather than a ratio, and Calmar uses maximum drawdown. Pick the one whose denominator matches the risk your client actually minds.

   Then walk it

   1. Sortino: excess return over a minimum acceptable return, divided by downside deviation below that threshold. Use it when returns are skewed, so an option-selling or credit strategy is not flattered by treating upside surprises as risk.
   2. Treynor: excess return divided by beta. It measures reward per unit of systematic risk, so it is the right measure for one sleeve inside an already diversified portfolio, where idiosyncratic risk has been diversified away and only beta consumes the portfolio's risk budget.
   3. Jensen's alpha: the intercept from regressing excess portfolio return on excess market return. It is a return number, not a ratio, so it does not scale, and two managers with the same alpha and very different tracking errors are not equally good. Also it is single-factor, so most measured Jensen alpha turns out to be size, value or momentum once you extend the model.
   4. Calmar: annualised return divided by maximum drawdown, usually over three years. Crude, but it speaks the language of an investor who will redeem after a bad run, and for managed futures and hedge funds it is the ratio allocators quote.
   5. Which I would use: information ratio for a benchmarked long-only manager, Sortino and Calmar for absolute return strategies, Treynor for a component inside a portfolio, and a multi-factor alpha rather than Jensen's for any real attribution.
   6. The shared weakness is sample size. All of these are ratios of noisy estimates, and drawdown-based measures are the worst because maximum drawdown is a single realised event, so Calmar effectively has one observation in it.

   Where candidates lose it

   Reciting four formulas without pairing each to a use case. The distinguishing detail is why Treynor uses beta rather than total volatility, and the fact that Jensen's alpha is a single-factor intercept that usually evaporates against a multi-factor model. Also say that maximum drawdown is one realised event, so drawdown ratios are the least statistically reliable of the set.

   Expect next

   • Why does Treynor use beta instead of volatility?
   • What happens to Jensen's alpha when you add momentum to the model?
   • Which would you show a pension trustee?
3. 042Walk me through Brinson attribution.Performance measurementIntermediatetechnicalAsset managementPerformance analysis

   Say this

   It splits excess return into three parts: allocation, from over or underweighting a segment, selection, from picking better securities inside a segment, and interaction, the cross term. Do it segment by segment and the three components sum to the total excess return.

   Then walk it

   1. Set up four numbers per segment: portfolio weight, benchmark weight, portfolio return within the segment, benchmark return within the segment.
   2. Allocation effect equals the weight difference times the benchmark segment return minus the total benchmark return. So you are rewarded for overweighting segments that beat the overall benchmark, which is the correct definition of a good allocation decision.
   3. Selection effect equals the benchmark weight times the difference between your return and the benchmark's return in that segment. It isolates stock picking at neutral weight.
   4. Interaction equals the weight difference times the return difference. It captures the fact that good picking in an overweighted segment gets amplified.
   5. A worked example: you hold 30 percent in technology against a 20 percent benchmark weight, technology returns 15 percent against a total benchmark of 8 percent, and your tech holdings return 18 percent. Allocation is 10 points times 7 percent, so 70 basis points. Selection is 20 percent times 3 percent, 60 basis points. Interaction is 10 percent times 3 percent, 30 basis points.
   6. Two practical warnings. First, the segments must match how decisions are actually made, so sector attribution on a manager who thinks in factors tells you nothing. Second, single-period effects do not simply add up over time, so multi-period attribution needs a linking method such as Carino or Menchero, and the residual from naive compounding confuses a lot of client reports.

   Where candidates lose it

   Getting the allocation formula wrong by using the segment return rather than the segment return minus the total benchmark return. Without that subtraction you credit a manager for overweighting anything that went up, even something that lagged the index. And be ready for the multi-period linking point, because that is the follow-up that catches people who only know the single-period formulas.

   Expect next

   • What is the interaction term really telling you?
   • How do you link attribution across periods?
   • What if the manager does not think in sectors at all?
4. 043What does the interaction effect in Brinson attribution actually mean, and why do some houses drop it?Performance measurementHardsuperdayPerformance analysisInstitutional asset management

   Say this

   It is the joint effect of the two decisions: weight difference times return difference. It has no owner in most investment processes, because nobody deliberately decides to be overweight a sector specifically in order to amplify their stock picking, which is why many houses fold it into selection.

   Then walk it

   1. Mechanically it exists because excess return is the product of two differences, and the product of two differences always leaves a cross term. It is arithmetic, not an insight.
   2. The interpretation problem is organisational. In most firms a strategist sets sector weights and analysts pick stocks. Neither of them made the interaction decision, so attributing it to either one starts an argument rather than settling one.
   3. So the common fix is the two-factor Brinson-Fachler variant, where selection is computed at portfolio weights rather than benchmark weights, which absorbs the interaction into selection. That is defensible when stock selection is the dominant process, since the stock picker's weight decision is part of their job.
   4. It matters most when it is large, and it is large when weight deviations are big and within-segment returns differ a lot: concentrated funds, emerging markets, single-country sleeves. In a mild tracking error portfolio it is a rounding error.
   5. Watch the sign. A large negative interaction means you were overweight the segments where your picking was worst, or underweight where it was best. That is a genuine finding about the process, that the sizing and the research were pulling in opposite directions.
   6. My practical position: report it separately internally, because the sign is diagnostic, and fold it into selection in client reporting, because a line item nobody owns invites the client to ask a question that has no good answer.

   Where candidates lose it

   Defining the formula without explaining why practitioners dislike the term. The answer that lands is organisational: nobody owns the decision. And knowing that Brinson-Fachler computes selection at portfolio weights to absorb it is the detail that separates someone who has run attribution from someone who has read about it.

   Expect next

   • What does a persistently negative interaction tell you about a process?
   • How does Brinson-Fachler differ from the original?
   • When is interaction large enough to care about?
5. 044How does factor-based attribution differ from Brinson, and when would you prefer it?Performance measurementHardsuperdayRisk and quantitative analysisQuantitative asset management

   Say this

   Brinson attributes returns to the segments you allocated between. Factor attribution attributes them to systematic exposures, style, country, industry, currency, with a specific residual. I would prefer factor attribution whenever the decisions are not organised by sector, and always for risk, because Brinson says nothing about risk.

   Then walk it

   1. Brinson is arithmetic on weights and returns. Factor attribution is a regression or a holdings-based risk model: your active exposures times the factor returns, plus the stock-specific residual.
   2. The difference in output is what you can act on. Brinson might say you gained 80 basis points from selection in industrials. A factor model says that 60 of it was a value tilt available for 20 basis points in an ETF and 20 was genuinely specific. Only one of those tells you whether the fee is justified.
   3. Factor attribution also reconciles with the risk system, which Brinson cannot. The same model that forecast your tracking error explains your realised return, so risk and performance speak one language. That is why multi-manager platforms and quant houses run it.
   4. It catches the bets you did not know you had. A portfolio of high-conviction stock picks can be a levered bet on the dollar, or on rates, and the manager may genuinely not know until the decomposition shows it.
   5. The costs and limits: you need a risk model and clean holdings data, the answer depends on the model's factor set, and if the true bet is not in the model it lands in specific return and looks like skill. Two vendors' models will also give different alphas for the same fund.
   6. So in practice I would run both. Brinson because it maps onto how the investment committee makes decisions and clients understand it, factor attribution because it is the only one that tells you whether the excess return was replicable.

   Where candidates lose it

   Framing it as one method being right. They answer different questions, and the interviewer wants to hear that Brinson cannot tell you whether the excess return was replicable while factor attribution can. Also concede that missing factors show up as spurious alpha, because that is the honest limitation of the method you are recommending.

   Expect next

   • What would you do if the factor model shows all the alpha is specific?
   • How do you handle two vendors' models disagreeing?
   • Which would you show the investment committee?
6. 046Sharpe ratio or information ratio, which matters more for an active manager?Performance measurementCorephone / first roundAsset managementInstitutional asset management

   Say this

   Information ratio, if the manager has a benchmark. It measures excess return per unit of active risk, which is exactly what the client is paying for, whereas Sharpe includes the market exposure the client could have bought for a few basis points.

   Then walk it

   1. Definitions side by side: Sharpe is excess return over cash divided by total volatility. Information ratio is excess return over the benchmark divided by tracking error.
   2. The reason IR is the right one for a benchmarked mandate: a long-only equity manager's Sharpe is dominated by the equity risk premium, so in a good decade every manager looks skilled and in a bad one every manager looks useless. IR strips the market out.
   3. Typical scale is worth knowing so you can talk about it credibly. An IR of 0.5 over a full cycle is genuinely good and roughly top quartile, 0.75 is excellent, and anything above 1 sustained over a long period is rare and worth questioning.
   4. Sharpe is the right measure when there is no benchmark: absolute return funds, multi-asset total return, and anything where the client's alternative is cash.
   5. The connection to portfolio construction: IR is what the fundamental law predicts, skill times root breadth, and it is what a tracking error budget is denominated in. If I know a manager's IR I can say how much excess return I should expect for the tracking error I am giving them, which is the arithmetic of hiring them.
   6. The shared caveat: both are estimated from short samples and both are noisy, so I would look at IR alongside consistency, the proportion of rolling periods above benchmark, rather than treating a single point estimate as a fact.

   Where candidates lose it

   Treating them as interchangeable or picking Sharpe for a benchmarked fund. The decisive point is that Sharpe rewards market beta the client could buy for nothing. Also know realistic magnitudes; a candidate who says an IR of 3 is achievable has never looked at real manager data.

   Expect next

   • What information ratio would you expect from a good manager?
   • When is Sharpe the right measure?
   • How does IR link to the tracking error budget?
7. 047Time-weighted or money-weighted returns, which do you report and why?Performance measurementIntermediatetechnicalAsset managementWealth management

   Say this

   Report time-weighted for the manager and money-weighted for the client. Time-weighted strips out the effect of cash flows the manager did not control, which is the fair way to judge the manager. Money-weighted, the IRR, measures what the investor actually earned on the money they had invested.

   Then walk it

   1. Time-weighted chains together sub-period returns, breaking the series at every external cash flow, so a large contribution just before a bad quarter does not penalise the manager. That is why GIPS requires it for composite reporting.
   2. Money-weighted solves for the discount rate that sets the present value of all flows to zero. It is sensitive to timing and size, which is exactly right if you want to know whether the investor's own decisions helped or hurt.
   3. The gap between the two is the behaviour gap, and it is large. Dalbar and Morningstar studies repeatedly find investor returns trail fund returns by 1 to 2 percentage points a year, entirely from buying after good performance and selling after bad.
   4. So the two numbers answer different questions and both belong in a client report: here is what the strategy did, here is what you earned, and here is the difference your timing made. That conversation is far more useful than either number alone.
   5. Where money-weighted is mandatory: private equity, private credit, infrastructure, anything where the manager controls the drawdown schedule. If the manager decides when the capital comes in, they must be judged on the money-weighted outcome.
   6. And the standard caveats: IRR assumes interim distributions are reinvested at the IRR itself, which is rarely true, it can be undefined or multiple with irregular sign changes, and it can be flattered by early distributions or a credit facility that delays the first capital call.

   Where candidates lose it

   Picking one and treating the other as wrong. The useful answer allocates each to the right question and names the case where money-weighted is compulsory, private markets, because the manager controls the timing. Mentioning the behaviour gap with a number turns this from a definition into an insight.

   Expect next

   • Why is IRR the industry standard in private equity?
   • How does a subscription line flatter an IRR?
   • How would you explain the gap to a client who underperformed their own fund?

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.