Mutual Fund Mastery interview preparation

Questions

100

Traced to a firm

32

Firms

19

Updated

September 2026

Asked at

All firmsVanguard5BLBlackRock3FTFranklin Templeton3Invesco3PIMCO3Fidelity Investments2J.P. Morgan2Morningstar2Neuberger Berman2SCSchroders2T. Rowe Price2Amundi1BMBNY Mellon1Goldman Sachs1Man Group1Northern Trust1SSState Street1Sycamore Partners1WMWellington Management1

Topic

All topicsFund structure and regulation7NAV and operations6Scheme categorisation4Equity schemes5Debt schemes7Risk, liquidity and disclosure7Index funds and ETFs6Hybrid and solution schemes3Costs, plans and commissions6SIP and investor mechanics5Performance measurement6Taxation5Distribution, compliance and NISM5Portfolio construction and advice5Estimation and numeracy5Markets and industry6Career and fit12

Level

AnyCoreIntermediateHard

Type

AnyTechnicalCaseFitBrainteaserMarket view

1. 058Why do point-to-point returns mislead, and what are rolling returns?Performance measurementHardtechnicalIndian AMCsProduct and strategy roles

   Say this

   A point-to-point return depends entirely on the two dates you picked, and fund marketing picks them. Rolling returns compute the return over a fixed window starting on every single day in the history, so you get a distribution of outcomes instead of one lucky path.

   Then walk it

   1. The problem in one example: a five-year return measured from March 2020 starts at the Covid bottom. Almost any Indian equity fund looks extraordinary. Move the start date back three months and the same fund looks ordinary.
   2. Rolling returns fix the start-date bias. For three-year rolling returns over ten years you get roughly 1,800 overlapping three-year observations, each annualised.
   3. What you then look at is the distribution: the median, which is a fairer central estimate than any single window; the worst observation, which tells you the pain a real investor could have experienced; and the proportion of windows that beat the benchmark or cleared, say, 12 percent.
   4. That consistency measure is the useful output. A fund that beat its index in 70 percent of three-year windows is a different proposition from one that beat it in 40 percent but happens to lead the one-year table today.
   5. Rolling returns also expose manager change. If the strong windows all start before a manager left, the distribution will show it while a point-to-point number will not.
   6. Two honest limitations: overlapping windows are highly autocorrelated, so 1,800 observations are nowhere near 1,800 independent data points, and rolling returns still say nothing about whether the strategy will work in the next regime. They fix selection bias, not the fundamental problem of a short Indian track record.

   Where candidates lose it

   Describing rolling returns as an averaging technique and stopping. The point is the distribution — median, worst case and hit rate — and the reason is start-date bias. And do not oversell them: overlapping windows are statistically dependent, and saying so is what a research interviewer is waiting for.

   Expect next

   • How many independent observations do you really have?
   • What would you look at other than the median?
   • How do you handle a fund manager change in the history?
2. 059How would you evaluate whether a fund manager is any good?Performance measurementHardsuperdayFund research and ratingsIndian AMCs

   Say this

   Start with whether the returns came from where he says they came from, then whether that source is repeatable. Performance is the last thing I look at, not the first, because five years of Indian equity data cannot distinguish skill from luck on its own.

   Then walk it

   1. First, the process. What does he claim to do, and does the portfolio show it? A manager who says he buys quality compounders and holds 70 stocks with 80 percent annual turnover is doing something else, and the gap between the story and the portfolio is the most reliable red flag in fund research.
   2. Second, attribution. Split the excess return into allocation and selection. If three years of outperformance came from being overweight one sector that happened to run, that is a bet, not a skill, and it will reverse.
   3. Third, consistency through rolling returns rather than a point-to-point number, plus behaviour in the two or three worst quarters. Downside capture tells you more about a process than upside capture does.
   4. Fourth, the operational facts that ruin otherwise good analysis: how long has he actually run this fund, how much AUM does he manage across schemes, how many other funds does he run, and has the strategy survived a size increase? A small cap manager who was excellent at 2,000 crore may be structurally unable to repeat it at 25,000 crore.
   5. Fifth, incentives and stability. Fund manager tenure in Indian AMCs is shorter than most track records, SEBI now requires part of key employees' compensation to be paid in units of the schemes they manage, and team depth matters more than the star.
   6. The honest conclusion I would give: with fifteen or twenty years of monthly data you can detect skill statistically; with five you cannot. So weight the process, the attribution and the constraints heavily, and treat the return series as corroboration rather than proof.

   Where candidates lose it

   Ranking managers by three-year or five-year returns. That is what the public does and it is why investor returns lag fund returns. The answer that lands names the statistical problem out loud — five years cannot separate skill from luck — and then explains what you look at instead.

   Expect next

   • How much history would you need to be statistically confident?
   • What would make you sell a fund?
   • How do you handle a manager who has just changed?
3. 060What is alpha, and how do you know it is skill rather than just beta?Performance measurementHardtechnicalIndian AMCsFund research and ratings

   Say this

   Alpha is the return left over after you account for the risk the manager took. Raw outperformance is not alpha — if a fund beat the Nifty by 4 percent while running a beta of 1.3 in a rising market, the market gave him most of it and the correct alpha is close to zero.

   Then walk it

   1. Formally, Jensen's alpha is the fund return minus the return the capital asset pricing model predicts for its beta. Run the regression, and alpha is the intercept.
   2. The single-factor version is not enough in practice. Once you add size, value, momentum and quality factors, most Indian mid and small cap outperformance turns out to be a size and momentum tilt rather than stock selection.
   3. So the test is: regress the fund's excess returns on the factors it is plausibly exposed to, and see what survives. If nothing survives, the manager is running a factor portfolio at active fees, and you can buy that exposure in a smart beta index fund for a fraction of the cost.
   4. Statistical significance matters and is usually ignored. With five years of monthly data, an alpha of 2 percent a year will typically have a t-statistic well below 2. You cannot reject luck, and you should say so.
   5. Also check whether the alpha is in the right place. Alpha from a handful of large positions is a concentrated bet; alpha spread across the book, repeated in different market conditions, looks more like process.
   6. And the survivorship problem. The funds you are analysing are the ones that survived. Merged and closed schemes are gone from the database, which biases every category average upward — in India that effect got a boost from the 2017 merger wave.

   Where candidates lose it

   Equating alpha with beating the benchmark. That is the core error. Also, be ready to admit the statistical weakness: a candidate who claims a five-year alpha proves skill has revealed they have never run the regression.

   Expect next

   • What does a factor regression on an Indian mid cap fund usually show?
   • How does survivorship bias affect category averages?
   • What t-statistic would convince you?
4. 061Sharpe, Sortino, information ratio, Treynor. Which would you report to a client and which to an investment committee?Performance measurementHardtechnicalIndian AMCsFund research and ratings

   Say this

   Sharpe for a client, because it answers the only question they care about: return per unit of total risk. Information ratio for the committee, because it measures the manager against his benchmark rather than against cash, which is what you are actually paying him for.

   Then walk it

   1. Sharpe: excess return over the risk-free rate divided by standard deviation of returns. Simple, universal, and it treats upside and downside volatility identically — which is its main flaw.
   2. Sortino: the same idea but the denominator only counts downside deviation. Better for asymmetric strategies, so it flatters an arbitrage or a covered-call fund and is the right measure for anything with a skewed return profile.
   3. Information ratio: active return divided by tracking error. This is the manager-skill measure, because it asks how much excess return he generated per unit of deviation from the benchmark. A closet indexer can have a good Sharpe and a terrible information ratio.
   4. Treynor: excess return divided by beta rather than total volatility. Relevant when the fund is one sleeve of a diversified portfolio, so only systematic risk matters. Rarely used in Indian retail reporting.
   5. Practical numbers for calibration: a long-run Sharpe of 0.5 to 0.7 is normal for an Indian equity fund over a full cycle, and an information ratio above 0.5 sustained over five years is genuinely good. Anyone quoting a Sharpe of 2 on an equity fund has measured a bull market.
   6. The shared limitation, which I would state before being asked: all four assume returns are roughly normal and stable, all four are computed on a short sample, and all four can be gamed by choosing the period. They are screening tools, not verdicts.

   Where candidates lose it

   Reciting four formulas with no view on which to use where. The differentiator is knowing that Sharpe measures against cash and information ratio measures against the benchmark, so only the second one tells you whether the active fee was earned.

   Expect next

   • A fund has a high Sharpe and a low information ratio. What is going on?
   • Which would you use for an arbitrage fund?
   • What Sharpe would make you suspicious?

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.