Financial Analysis interview preparation

Questions

100

Traced to a firm

42

Firms

28

Updated

September 2026

Asked at

All firmsMoody's7Bain Capital3SSState Street3AMAres Management2BLBlackRock2DED.E. Shaw2MSMorgan Stanley2Oaktree Capital Management2S&P Global2Bridgewater Associates1Citadel1FTFranklin Templeton1Golub Capital1HWHarris Williams1Houlihan Lokey1J.P. Morgan1Jane Street1MWMarshall Wace1Millennium Management1Morningstar1PIMCO1Sycamore Partners1TSTruist Securities1Two Sigma1Vanguard1WMWellington Management1Wells Fargo Securities1Wolverine Trading1

Topic

All topicsThree statements9Accounting policy and standards5Working capital and cash7Ratio analysis8Forecasting and budgeting9Variance and management reporting7Unit economics and costing8Capital budgeting7Cost of capital and valuation7Markets and rates5Modelling, Excel and data8Business partnering6Brainteasers and estimation4Fit and career10

Level

AnyCoreIntermediateHard

Type

AnyTechnicalCaseBrainteaserMarket viewFit

1. 074You inherit a model from someone who left last week and the CFO wants to use it on Monday. How do you audit it?Modelling, Excel and dataHardcase studyFinancial modellingBig Four

   Say this

   Work from the outside in. First sanity-check the outputs against reality, then trace the three or four numbers that drive them, then stress-test rather than read every cell. You cannot audit 40,000 formulas by Monday, so you audit the ones the answer depends on.

   Then walk it

   1. Start with the outputs. Does revenue growth, margin and cash flow look plausible against history and against the sector? An implied 45 percent EBITDA margin on a distribution business tells you more in ten seconds than an hour of cell tracing.
   2. Then the structural checks: does the balance sheet balance, does the cash flow tie to the cash movement, does historical data reconcile to the published accounts. Any break here and nothing downstream is trustworthy.
   3. Then mechanical scanning. Use formula view or a spreadsheet audit tool to find hard-coded constants inside formulas, inconsistent formulas within a row, broken links to external files, circular references and unintended ranges. A row that is consistent for eleven periods and different in the twelfth is exactly what you are hunting.
   4. Then stress tests, which are the fastest way to find logic errors. Set revenue growth to zero, set it to 50 percent, set price to nil. If EBITDA does not move sensibly, or the balance sheet breaks under a stress, you have found a hard-code or a broken link.
   5. Then trace the top three value drivers back to their source, and only those. For a DCF that is usually WACC, terminal growth and the revenue build. Verify each against a document, not against another cell in the same model.
   6. And I would tell the CFO on Friday what I had and had not verified, in writing, with a short list of numbers I do not yet trust. Presenting an unaudited model as clean is the career risk here, not the model itself.

   Where candidates lose it

   Saying you would check every formula. There is not time and it is the wrong method. Outputs first, structural checks, mechanical scan for inconsistencies, then stress tests. And say you would disclose what you could not verify.

   Expect next

   • What is the fastest way to find a hard-coded number?
   • You find an error that changes the answer 20 percent. What do you do?
   • How would you hand this model on properly?
2. 075Your three-statement model does not balance. How do you find the break?Modelling, Excel and dataIntermediatetechnicalFinancial modellingCorporate FP&A

   Say this

   Find the first period where the imbalance appears, then look at the size of the difference, because the number usually names the culprit. Most breaks are one of four things: net income not flowing to retained earnings, a balance sheet movement missing from the cash flow, a sign error, or dividends and capex mishandled.

   Then walk it

   1. Step one, locate. Check the balance row across all periods and find the first column that breaks. Everything after it is contamination; the error is in that one period.
   2. Step two, read the difference. If it equals net income, retained earnings is not picking up the P&L. If it equals twice something, you have a sign error. If it equals depreciation, the add-back is missing or double-counted. The magnitude is the diagnosis.
   3. Step three, check the two mandatory links: net income flows to retained earnings less dividends, and closing cash from the cash flow statement equals the balance sheet cash line. Those two account for most breaks.
   4. Step four, confirm every balance sheet line has a corresponding cash flow movement. A new line added to the balance sheet, a lease liability, a deferred tax balance, an FX reserve, and not wired into the cash flow, is the classic mid-project break.
   5. Step five, check the debt schedule and capex. Gross versus net movements in borrowings, and a capex figure taken from the P&L depreciation rather than the fixed asset schedule, both produce clean-looking models that do not balance.
   6. And the prevention, which is what I would say last: build the balance check from the very first day and keep it on screen. Models that balance from row one never accumulate a break you have to hunt for later.

   Where candidates lose it

   Hunting cell by cell from the top. Locate the first broken period, then let the size of the difference identify the cause. Not knowing that the difference often equals net income or depreciation is what makes this take an afternoon instead of five minutes.

   Expect next

   • The difference equals the depreciation charge. What is wrong?
   • Where would you put the balance check?
   • What if it balances but the cash flow does not tie?
3. 080How would you make money with social media data?Modelling, Excel and dataHardcase studyTwo SigmaGeneralist · New York · 2024

   Say this

   Find a place where social data leads a number somebody pays for, and prove the lead is real before you monetise it. The two honest routes are forecasting revenue ahead of official data, and selling the cleaned signal to people who trade or plan on it.

   Then walk it

   1. Start from the decision, not the data. Who pays for being two weeks early? A consumer company deciding on promotional spend, a retailer planning inventory, an investor sizing a quarter. Each of those is a different product.
   2. The forecasting route: mention volume, sentiment and follower growth for a brand, mapped against reported quarterly revenue. If mentions of a product line lead revenue by four weeks with a stable relationship, you have a nowcast that beats consensus.
   3. Then validate it properly, because this is where the question is really marked. Back-test out of sample, check the relationship holds across at least two or three cycles, and control for the obvious confounders: a viral post is not demand, a paid campaign inflates mentions, and bot activity is a large share of raw volume.
   4. Then the monetisation models: sell the cleaned data feed, sell a derived index, or use it internally to trade or to plan. The data business scales better and is easier to defend legally; the internal use captures more of the value but only while the signal stays private.
   5. Then the hard constraints, which a good answer names unprompted. Platform terms of service and API restrictions, privacy law, no personal data, no material non-public information, and signal decay: any edge from public data erodes as more people buy the same feed.
   6. So my proposal would be a narrow pilot. One sector, five brands, six quarters of history, one testable claim: can we predict reported revenue better than consensus. If the back-test fails, stop, because most alternative data claims do not survive it.

   Where candidates lose it

   Jumping to 'sentiment analysis predicts stock prices'. Everyone says it and almost nobody validates it. The marks are in the back-test, the confounders such as bots and paid campaigns, the legal constraints, and naming who pays for the output.

   Expect next

   • How would you back-test that claim?
   • What would you do about bot-generated volume?
   • What happens to the edge once you sell the data?

   Reported by candidates at Two Sigma (Generalist, New York, 2024). 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.