Sales & Trading Interview Questions

The overriding theme in global macro today is the stubbornness of core services inflation, which has forced central banks to maintain higher terminal interest rates than the market originally priced for early 2026. Looking at fixed income, the US 2-year Treasury yield is trading near 4.30 per cent (verify), and the UK 2-year Gilt is reflecting similar upward pressure near 4.15 per cent (verify). This flattening of the yield curve indicates that fixed-income participants are anticipating a growth slowdown later in the year. In the equity markets, this higher-for-longer environment has triggered a rotation away from highly leveraged growth stocks into cash-generative value sectors, notably defense and energy. For a sales desk, the optimal actionable idea right now centres on advising corporate clients to hedge their upcoming debt refinancing rounds through interest rate swaps before further yield extension occurs. Simultaneously, for institutional asset managers, I would pitch a long position in short-duration inflation-linked bonds. This captures the persistent core inflation prints while insulating the client's portfolio from the capital losses inherent in longer-duration assets if central banks remain hawkish.

I expect the Federal Reserve to maintain the Federal Funds Rate within its current tight band, while the Bank of England will likely implement one further 25-basis-point hike over the coming two quarters. The structural drivers behind this are persistent wage growth within the services sector and an expansionary fiscal stance from governments, which continuously offsets monetary tightening efforts. The broader market is currently pricing in a gradual easing cycle toward the end of the year, but I believe this curve is overly optimistic. Core inflation metrics remain stuck well above the 2.0 per cent target. For instance, recent annualized prints are hovering around 2.8 per cent in the US and 3.1 per cent in the UK. Therefore, the risk is asymmetric to the upside. Central banks will choose to induce a mild economic slowdown rather than risk unanchoring long-term inflation expectations. This means short-term rates will stay elevated, maintaining a flat or inverted yield curve structure through the majority of the year.

The fundamental difference lies in the symmetry of the financial obligation and the resulting payoff profile. A futures contract is a legally binding bilateral agreement where the buyer and the seller are mutually obligated to exchange an underlying asset at a predetermined price on a specific future date. Because of this, the risk profile is linear and symmetric. As the underlying spot price rises or falls, one party gains and the other loses in exact proportion, managed daily via variation margin accounts. Conversely, an option contract introduces structural asymmetry. The buyer purchases a right, but not an obligation, to buy via a call, or sell via a put, the underlying asset. The seller, however, retains the total obligation to perform if the buyer chooses to exercise. This creates a non-linear payoff structure. The option buyer faces a capped, predetermined downside limited exclusively to the upfront premium paid, while enjoying theoretically infinite upside on a call. The option seller faces the inverse profile, earning a capped maximum return equal to the premium collected, while taking on substantial, sometimes unlimited, risk.

While a market maker aims to capture the bid-ask spread by continuously quoting prices on both sides of the market, they are constantly exposed to three primary institutional risks. The first is inventory risk, which occurs because a market maker must hold a temporary long or short position to facilitate client flow. If a client sells a large block of stock to the desk, and the broader market immediately drops before the trader can hedge or pass that inventory on, the desk takes a direct mark-to-market capital loss. The second, and most structurally dangerous, is adverse selection risk, often referred to as trading against toxic flow. This happens when the market maker executes against an institutional participant who possesses superior information or algorithmic execution capabilities. The market maker fills the order, only to watch the price immediately trend strongly in the counterparty's favour, meaning the market maker bought right before a collapse or sold right before a rally. The third is liquidity risk, where during periods of extreme macro stress, matching counter-flow completely vanishes. The bid-ask spread widens dramatically, volatility spikes, and the market maker is left holding illiquid, highly volatile risk blocks that cannot be cleared or hedged effectively in the interdealer broker market.

To calculate 17 times 18, I will break it down by multiplying 17 by 20 and then subtracting 17 times 2. First, 17 multiplied by 20 is equal to 340. Second, 17 multiplied by 2 is equal to 34. Finally, subtracting 34 from 340 yields a final result of 306. Alternatively, I can use the difference of squares approach, treating it as 17.5 squared minus 0.5 squared, which translates to 306.25 minus 0.25, giving exactly 306.

When rolling two independent, six-sided dice, the total sample space of possible outcomes is 6 multiplied by 6, which equals 36 total permutations. To find the probability of rolling a total of 7, we must count the number of favourable outcomes. Seven is the most statistically frequent sum because it has the highest number of combinations. Those exact pairs are: 1 and 6, 2 and 5, 3 and 4, 4 and 3, 5 and 2, and 6 and 1. This gives us exactly 6 distinct favourable permutations. To calculate the final probability, we divide the 6 favourable outcomes by the 36 total possible outcomes. This simplifies down to a fraction of 1 over 6, which converts mathematically to approximately 16.67 per cent.

I am pursuing Sales & Trading because I want my day-to-day work tied directly to live global markets. Unlike traditional investment banking divisions where project cycles span several months and rely on qualitative presentations, the trading floor operates on real-time data and objective accountability. I want to be in a position where my preparation, macro thesis, and execution speed are tested by the market every single hour. During my academic career and market mock simulations, I discovered that I operate with high cognitive clarity when forced to make rapid choices with incomplete information. For example, when an unexpected economic print drops, you do not have days to build a pitchbook; you have seconds to adjust your risk quotes or get on the phone to explain the structural implications to your institutional clients. This fast-paced environment aligns perfectly with my analytical strengths and my interest in macroeconomic policy. I am specifically drawn to the market-making side because managing inventory and pricing risk requires deep mathematical discipline mixed with a split-second understanding of market psychology.

Losing money is an inevitable structural cost of running a risk book. The moment a trade moves against me and hits my predetermined risk or stop-loss limit, my first action is to execute the exit immediately and accept the loss. On a professional floor, letting ego interfere by widening a stop-loss or doubling down to break even is how a minor loss scales into a desk-ending disaster. Once the risk is flat, I approach the loss with absolute objectivity. I run a structured review to isolate why the loss occurred. I ask myself two specific questions: First, did I execute a sound, statistically positive expected-value strategy that simply landed on a negative outcome due to market randomness? If yes, I accept it as a normal business expense. Second, did I miss a structural data point, miscalculate asset correlation, or let cognitive bias distort my entry? If the thesis itself was flawed, I document the error, adjust my pricing models, and update my risk parameters to ensure that specific structural blind spot is permanently closed.