Hedge Funds puzzles, solved step by step

997 x 1,003 = 999,991 and 67 squared = 4,489. The first is a difference of squares: the numbers sit 3 either side of 1,000, so the product is 1,000,000 minus 9. The second uses the same identity the other way round: 67 squared is 64 x 70 plus 3 squared, which is 4,480 plus 9. Both take one line once you spot the round number nearby.

Why does multiplying around a round number work?

Take a square garden 10 metres a side and reshape it to 13 by 7. The fence is the same length, but the plot shrinks from 100 square metres to 91. It always shrinks by the square of how far you moved each side, here 3 squared, 9. Two numbers spaced equally around a midpoint multiply to the midpoint squared minus the gap squared. For 997 x 1,003 the midpoint is 1,000 and the gap is 3, so the answer is 1,000,000 minus 9.

How do you square a number like 67 in your head?

Push it to a round neighbour and repair the difference. Move 3 down to 64 and 3 up to 70, multiply those, then add back the 3 squared you took away: 64 x 70 = 4,480, plus 9 is 4,489. You choose the gap so one factor is round. The expansion route agrees: 67 is 70 minus 3, so 67 squared is 4,900 - 420 + 9, the same 4,489. Two methods landing on one number is your check.

Why would a fund ask arithmetic at all?

A trader checks prices, spreads and position sizes in their head all day, and a slip costs money before any spreadsheet catches it. Interviewers use speed on arithmetic like this as a proxy for how quickly you would catch a quote that does not add up. Say the identity as you use it, so that if you slip, the interviewer can see where and you can recover out loud.

One round trip makes at most 16; unlimited round trips make 26. For one trade, walk the prices once, carrying the lowest price so far and the best sale against it: buy at 96, sell at 112. For many trades, add every day-on-day rise and skip every fall: 8 + 11 + 7 = 26. Without short selling the falls are simply sat out, never profited from.

How do you find the best single trade without checking every pair?

Imagine walking down a street of shops that all sell the same phone, planning to buy once and sell once further along. You do not need to compare every pair of shops: carry the cheapest price seen so far in your head, and at each shop ask what selling here would make against it. One pass, keeping the running minimum and the best gap found so far, gives the best single trade. Here the running minimum drops to 96 on day 2 and the best gap appears on day 7: 112 - 96 = 16.

Why is the many-trade answer just the sum of the rises?

Any rise from a low to a later high is the sum of the daily steps inside it, and some of those steps may be falls. With no short selling and no costs, the most you can make is the total of every positive day-on-day move, 26 here, because trading only the up steps collects everything a longer trade would and skips its falls. In practice that is three round trips: buy 96, sell 104; buy 99, sell 110; buy 105, sell 112.

What does the interviewer add next?

Costs. Once each round trip costs something, the sum of rises overstates the profit, because small moves stop being worth trading. With a cost of 6 per round trip, the three separate trades net 2 + 5 + 1 = 8, the best two-trade split nets 9, and the single trade from 96 to 112 nets 10, so the single trade now wins. The general version is a short dynamic programme that tracks the best profit on each day while holding and while flat.

161/36, about Rs 4.47. The higher face equals k in 2k minus 1 of the 36 equally likely rolls: 1, 3, 5, 7, 9 and 11 rolls for k from 1 to 6. Multiply each value by its count, add to 161, and divide by 36. The lower face averages 91/36, about 2.53, and the two add to 7, the average total of two dice, which is a quick check.

How many of the 36 rolls give each maximum?

Think of two runners and a prize for the faster time: the winning time is better than a typical single runner's because you always keep the better of two. The higher face is at most k in k x k of the 36 rolls, so it equals exactly k in k squared minus (k minus 1) squared, which is 2k minus 1 rolls. That gives 1 roll with a maximum of 1, 3 with a maximum of 2, and on up to 11 with a maximum of 6. On the grid those cells form L shapes that grow as you move towards the corner.

How do you check 4.47 in ten seconds?

Use the pair. The higher face plus the lower face always equals the total of the two dice, so their averages must add to 7. The lower face is at least k in (7 minus k) squared rolls, which gives an average of 91/36, about 2.53. 4.47 plus 2.53 is 7.00. A second method that lands exactly is what makes an interviewer stop checking your arithmetic and move on to the follow-up.

The follow-up is usually a game. If you could pay to roll one die or to roll two and keep the higher, the second is worth about Rs 0.97 more. That gap, the value of a free second look, is the same idea as an option: the right to choose after seeing the outcome is worth paying for.

1 over root 2, about 0.71. The covariance of X with X + Y is Var(X) plus Cov(X, Y), which is sigma squared plus zero. The standard deviation of X + Y is root 2 times sigma because the variances add. So the correlation is sigma squared over (sigma x root 2 sigma), which is 1/root 2. X explains half the variance of the sum, and the correlation is the square root of that half.

What is the fastest way to set it up?

A two-member team's score is the sum of both players' scores. If the players are equally good and play independently, knowing one player's score tells you something about the team total, but only half the story. Split the covariance: Cov(X, X + Y) = Cov(X, X) + Cov(X, Y) = sigma squared + 0. The variance of the sum is sigma squared + sigma squared = 2 sigma squared, because independent variances add. Correlation is covariance over the product of standard deviations: sigma squared over (sigma x root 2 sigma) = 1/root 2.

Why is the answer not 0.5?

Because 0.5 is the R squaredThe share of one variable variance explained by another; for a simple regression it is the correlation squared., not the correlation. X explains exactly half of the variance of X + Y, and correlation is the square root of the share of variance explained, so it is root 0.5, about 0.707. The geometric picture makes it stick: treat independent variables as arrows at right angles, and correlation as the cosine of the angle between arrows. X + Y sits at 45 degrees to X, and cos 45 is 0.707.

Give the general version to show you own it. If Y has variance k times X's, the correlation is 1/root(1 + k): the more noise you add, the lower it falls. That is the logic behind a noisy signal: a forecast that is half signal and half independent noise, by variance, correlates about 0.71 with the signal, not 0.5.

Net income falls Rs 7.5, cash rises Rs 2.5 and the balance sheet shrinks by Rs 7.5 on both sides. Pre-tax profit falls 10, tax falls 2.5, so net income falls 7.5. The cash flow statement starts at -7.5 and adds back the non-cash 10: cash up 2.5. On the balance sheet, cash is up 2.5 and fixed assets are down 10, so assets fall 7.5, matched by retained earnings down 7.5.

Why does cash go up when an expense goes up?

Imagine your employer lets you deduct the wear on your car from taxable income. No money leaves your pocket for the wear itself, but your tax bill falls. Depreciation is an expense that costs no cash but reduces tax, so the only cash effect is the tax saved: 25% of Rs 10, Rs 2.5. That is the {term('depreciation tax shield', 'The tax saved because depreciation is deductible even though it uses no cash; equal to depreciation times the tax rate.')}, and it is the one number the question is testing.

What order do you walk it in so nothing gets lost?

Income statement first, then cash flow, then balance sheet, one line each. Net income is the bridge: it closes the income statement, opens the cash flow statement, and lands in retained earnings on the balance sheet. Income statement: depreciation +10, pre-tax -10, tax -2.5, net income -7.5. Cash flow: -7.5 plus 10 added back, cash +2.5. Balance sheet: cash +2.5, fixed assets -10, so assets -7.5; retained earnings -7.5, so the two sides move together.

Add one sentence on why a hedge fund analyst cares. Two companies with identical operations can report different earnings because of depreciation choices, while their cash generation differs only by the tax effect. That is one reason investors look at cash flow alongside earnings before trusting a P/E.

They are exactly equal: 40 ml of wine sits in the water glass and 40 ml of water sits in the wine glass. After the first pour the water glass holds 200 ml of water and 50 ml of wine. A 50 ml pour of that mix is one fifth wine, so 10 ml of wine and 40 ml of water go back. Each glass ends at 200 ml, which forces the two amounts to match.

What is the argument that needs no arithmetic?

Two classrooms hold 30 students each. Five walk from room A to room B, and then any five people at all walk back. Both rooms hold 30 again. Every seat in room A left empty by a room A student who stayed away must now be filled by a room B student, so the room B students in room A always equal the room A students in room B. The glasses work the same way, because both finish at 200 ml.

How do the numbers confirm it?

After the first pour the water glass holds 250 ml, of which 50 ml, one fifth, is wine. Stirred evenly, a 50 ml pour back carries 10 ml of wine and 40 ml of water. The wine glass ends with 150 plus 10, 160 ml of wine, and 40 ml of water. The water glass keeps 200 minus 40, 160 ml of water, and 40 ml of wine. Forty and forty.

What does the interviewer learn from how you answer?

Whether you look for something that stays fixed before you reach for arithmetic. The conservation argument survives every variation: uneven stirring, several pours back and forth, any spoon size, as long as both glasses finish at their starting volumes. Desks use the same move on inventory: if two accounts hold the same totals after a string of transfers, whatever left one must have been replaced from the other, whatever the route. Give the one-line argument first, then the 40 and 40 as the check.

Let X be -1, 0 or 1 with equal chances and let Y = X squared. Y is fixed completely once you know X, yet the correlation is exactly zero. The covariance is E[XY] minus E[X]E[Y]; E[X] is 0 and E[XY] is the average of -1, 0 and 1, which is also 0. Correlation measures only straight-line co-movement, and a symmetric U shape has none.

What does correlation actually measure?

Think of a household's electricity bill against the outside temperature. It is high in the coldest months, when the heater runs, high in the hottest, when the air conditioner runs, and low in between. Correlation asks only whether one variable tends to rise along a straight line as the other rises, so a U-shaped link, however tight, can score zero. Over a year balanced around a mild middle, temperature explains the bill almost completely and a straight-line measure misses all of it.

How do you prove the covariance is zero?

Write out the three cases. X averages 0, Y averages 2/3, and XY takes the values -1, 0 and 1, which also average 0, so the covariance E[XY] - E[X]E[Y] is exactly 0. Symmetry does the work: every point to the right of the axis has a mirror image on the left with the same Y, so the upward slope on one side cancels the downward slope on the other. A continuous version works the same way: X normal with mean zero and Y equal to X squared.

Where does this bite on a desk?

Anywhere a payoff depends on the size of a move rather than its direction. A long straddleA call and a put bought at the same strike and expiry, which gains from a large move in either direction. gains from a big move either way, so over moves balanced around zero its return shows little correlation with the stock's return while being driven entirely by it. A risk report built only on correlations would call that position unrelated to the stock. Zero correlation means no straight-line link; only independence means no link at all.

It cannot be done at any finite speed. Averaging 60 mph over the 120-mile round trip means finishing in 2 hours. The outward 60 miles at 30 mph already took 2 hours, so the return leg would have to take no time at all. Driving back at 90 mph, the tempting answer, gives an average of only 45 mph.

Why is 90 mph the wrong instinct?

Averaging 30 and 90 to get 60 treats the two speeds as if they counted equally. They do not, because the car spends far longer at the slow speed. Think of a student who scores 30% on a three-hour paper and 90% on a ten-minute quiz: nobody would call that a 60% performance. Average speed is total distance over total time, so the slow leg carries more weight because it takes up more of the clock.

How do you prove it cannot be done?

Work in time, not speed. At 60 mph, 120 miles takes exactly 2 hours, and the first leg has already spent those 2 hours. Any return speed, however fast, adds some time, which pushes the average below 60 mph. At 90 mph the return takes 40 minutes and the average is 45 mph; at 300 mph it takes 12 minutes and the average is 54.5 mph. The average creeps towards 60 but never reaches it.

Where does the same mistake show up on a desk?

It appears whenever numbers are averaged without the right weights. A position that falls 50% and then rises 50% does not break even, because the second move works on a smaller base; the average that matters is the one weighted the way the thing actually compounds. Speeds over equal distances call for the harmonic mean, which sits below the simple average whenever the numbers differ. It is the same reason that putting a fixed rupee amount into a fund each month buys units at an average cost below the average price over those months.

About Rs 31.6 crore: Rs 10 crore times the square root of 10. If daily P&L is independent from day to day, with the same volatility and a mean near zero, variances add, so ten-day volatility is root 10 times daily volatility, and a normal quantile scales the same way. The rule also needs the positions held unchanged for ten days and a distribution that keeps its shape over the horizon.

Why not ten times the one-day number?

Picture ten friends each tossing a coin for Rs 100. The worst case is the group losing Rs 1,000, but the typical spread of the group's total is nowhere near ten times one person's, because some win while others lose. Independent daily moves partly cancel, so their variances add while their volatilities do not, and volatility grows with the square root of the number of days. Ten times would need every bad day to line up in the same direction, which is exactly what independence rules out. Value at riskThe loss a book should not exceed over a set horizon at a set confidence level, for example one day at 99%. inherits that square root when the distribution is normal.

What has to be true for the rule to hold?

List the assumptions, because that is the real question. Returns must be independent across days, volatility constant, the mean close to zero, the positions unchanged, and the distribution one that keeps its shape when summed, as the normal does. Break any one and the rule drifts. Positive autocorrelation, where bad days follow bad days, makes the true ten-day number larger. A book that is cut after losses makes it smaller. Fat tails make the one-day 99% quantile a poor guide to the ten-day one.

Which way does the error usually run?

In a calm market the rule is a fair approximation. In stress it tends to understate, because volatility rises and losses cluster just when the ten-day horizon matters. The square root of time is a scaling convenience, not a law, so a risk team checks it against ten-day P&L measured directly. The same assumption sits behind the desk habit of multiplying daily volatility by 16 to get an annual figure, 16 being roughly the square root of the trading days in a year; stretch it to 250 days here and you get Rs 158 crore, a number few would trust.