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In this article, I shall discuss the four standard yield curve dynamics: 1. Bull steepening 2. Bull flattening 3. Bear steepening 4. Bear flattening Bull vs bear markets When referring to the interest rate markets, a bull market is describing a market where interest rates decrease and a bear market is describing a market where interest rates increase. This may seem counterintuitive. However, this is because the markets are expressed in the view of being a bond holder. If the market buys bonds (i.e., a bull market), then the yield of bonds decrease. If the market sells bonds (i.e. a bear market), then the yield of bonds increase. Curve steepening vs flattening The interest rate yield curve can usually quantified by taking differences between long-end and short-end yields. For example: - 2s10s = 10y - 2y - 5s30s = 30y - 5y This describes how steep or flat the curve is. If the 2s10s increases, then the 2s10s yield curve is steepening. If the 2s10s decreases, then the 2s10s yield curve is flattening. Yield curve scenarios What do the following four scenarios actually mean? 1. Bull steepening 2. Bull flattening 3. Bear steepening 4. Bear flattening 1. Bull steepening: Rates decrease and the yield curve steepens Suppose the 2y rate decreases by 5bps and the 10y rate decreases by 2bps. This means the 2s10s has increased by 3bps. The short-end of the curve has rallied more than the long-end and so the curve has bull steepened. 2. Bear steepening: Rates increase and the yield curve steepens Suppose the 2y rate increases by 2bps and the 10y rate increases 5bps. This means teh 2s10s has increased 3bps. The long-end of the curve has sold off more than the short-end and so the curve has bear steepened. 3. Bull flattening: Rates decrease and the yield curve flattens Suppose the 2y rate decreases 2bps and the 10y rate decreases 5bps. This means the 2s10s has decreased 3bps. The long end of the curve has rallied more than the short-end and so the curve has bull flattened. 4. Bear flattening: Rates increase and the yield curve flattens Suppose the 2y rate increases 5bps and the 10y rate increases 2bps. This means 2s10s has decreased 3bps. The short-end of the curve has sold off more than the long-end and so the curve has bear flattened.

A simple example of a high-frequency trading strategy High-frequency trading firms are important to the financial markets because they provide liquidity, allowing market participants easily buy or sell financial instruments. It can also be extremely profitable to be a market maker (I’ve heard rumors of Citadel Securities having a Sharpe ratio in excess of 10 deploying these types of strategies). Exchanges often have contracts with high-frequency trading firms such as Citadel Securities, Jane Street, Optiver, IMC, Susquehanna, etc, to provide liquidity for financial instruments on their exchanges. Market making example Here is a simplified example of how a high-frequency firm can generate profit through market making strategies. Suppose we have the following scenario orderbook on two exchanges A and B, where exchange A has less liquidity than exchange B. Our firm has been assigned as the market maker for exchange A and we currently have a limit order to buy 30 shares at $98 and a limit order to sell 30 shares at $102 (see ← below): Exchange A ask2 $102, quantity 30 ← ask1 $101, quantity 9 bid1 $99, quantity 5 bid2 $98, quantity 30 ← Exchange B ask2 $102, quantity 75 ask1 $101, quantity 50 bid1 $99, quantity 45 bid2 $98, quantity 30 Now suppose there is a large market order to sell 35 shares on exchange A (a market order means to execute at the best market price, regardless of what the price is). Then our limit order to buy 30 shares at $98 will be filled (5 shares filled at $99 with the remaining shares filled at our $98 bid order). So we have just bought 30 shares at $98 and now we need to hedge this exposure to remain market neutral. We can then go to the more liquid exchange B and sell our 30 shares at the best bid price of $99. Therefore, we bought 30 shares at $98 on exchange A and sold 30 shares at $99 on exchange B and so we made ($99-$98) x 30 = $30. This process continuously repeats throughout the trading day. Conclusion Execution speed is vital for the profitability of these strategies. If your execution is not fast enough, the market will move and you will no longer be able to trade at the optimal prices. This is why high-frequency trading firms will go to extremes even to improve their execution speeds by microseconds.

Financial markets can usually be grouped into: Equity, rates, credit, FX and commodities. Within these markets there are many different instruments and derivatives. You will not be expected to know the details of all instruments in all markets, but you will be expected to know your specialised market well. After graduating and joining the industry, you will usually be assigned onto a desk. If you are lucky, you will be allowed to choose but the majority are randonly assigned. It’s possible to switch markets after a few years but it becomes harder as you gain more experience. Linear vs Non-linear - Linear products refer to instruments which have linear payoffs, such as swaps and bonds, etc. These products have little convexity. - Non-linear products refer to instruments which have a convex payoff structures - usually products which involve optionality and volatility (e.g. a call option). Equity - Single stock equities - Single stock equity options - Equity index options - Equity index futures Rates The “rates” market is referring to swaps, government bonds and related instruments. - Interest rate swaps: agreement to exchange cash flows of a fixed rate for a floating rate - Government bonds - Bond futures: futures on bonds. The seller can deliver the cheapest-to-deliver (CTD) bond at delivery. - Swaptions: options on interest rate swaps - Cap / floors: options on a floating rate - Curve spread options: options on the yield curve (e.g. 2s10s = 10y - 2y) Credit The “credit” markets are referring to corporate bonds, credit default swaps and related instruments. “Credit” products usually have a higher yield compared to government bonds as investors need to be compensated for a corporation’s larger default risk. This is known as the credit spread. - Corporate bonds - Credit default swaps: a swap which pays the swap holder in the case of a bond default - Convertible bonds: bonds which can be converted into equity - Callable / puttable bonds: bonds which can be redeemed early by the issuer / holder Foreign Exchange (FX) The FX market is usually closely linked with the swap market to avoid arbitrage opportunities. - Spot FX - Forward FX: agreement to exchange currencies at an FX rate agreed today on a specified date in the future - FX options: options on the FX rate Commodities - Agriculture: Corn, wheat, etc. - Metals: Gold, silver, etc. - Energy: Oil, natural gas, etc. Exotic options / Structured products In addition to the standard products, clients can request for exotic / structured products which have specific payoff structures and risk profiles. Here are some common exotic options: - Binary options: pays 1 if option in in-the-money and 0 otherwise - Barrier options (Knock in): Option comes to existence if barrier is hit - Barrier options (Knock out): Option ceases to exist if the barrier is hit

What does a quant actually do? In general, you can separate the different quant job titles as following: - Quantitative Researcher - Quantitative Analyst - Quantitative Trader - Quantitatigve Strategist However, you should note that you can have the same title as someone else but be doing completely different things. Therefore, it’s important to understand what the role involves before taking a job. In this article, I will break down the different tasks that a “quant” might do. 1. Researching trading strategies When people think of quant roles, they think of the role that involves researching and optimising trading strategies. These quant roles are “alpha” researchers. Their role involves finding ‘alpha’ by researching features for a trading model. These roles involve a lot of statistics and machine learning. High frequency strategies typically involve less complicated models such as linear regression whereas mid-low frequencies strategies might involve machine leanring and deep learning models. 2. Developing pricing models Many exotic derivatives and structured products cannot be priced using the standard derivatives pricing formulas (such as Black-Scholes). These products may have unusual payoffs and conditions and do not have an obvious fair value. As a result, models need to be developed to price these products and to calculate their risks. A quant position in a pricing models team might involve researching ways to improve the pricing and risk models for these exotic products. It may also involve validating existing models. 3. Pricing trades for clients Quants on a trading desk or a client facing team may be involved in pricing specific trades that a client requests. These quants will use the models built by the quants in the pricing team to price the trade and calculate other analytics that the client is interested in (such as the trade profile, richness or cheapness, etc). These quants may also recommend the best trades for the client, given the details of the client request. 4. Developing tools for trading A quant working on, or closely with, a trading desk may be responsible for creating tools for the traders and portfolio managers to analyse market data, scan for best trades, improving the pricing systems and anything else trading related. This role might also involve automing trading processes, and other development work on the trading system or platform. Conclusion In conclusion, a “quant” can do many different things even with the same job title. Therefore, it’s important that you understand what the role involves by asking a lot of questions during the interview.

This article will discuss a few insights into the global financial markets: - Flow vs structured products - Sales, Trading & Structuring Flow products vs Structured products Flow products Flow products are financial instruments that are easy to trade and value. Examples of these include: - Equities - Bonds - FX - Commodities - Vanilla derivatives: Options, Swaps, Futures These products are standardised and liquid to trade. Highly liquid meaning they can easily be bought and sold. Structured products Structured products are financial instruments that are more customised to meet specific payoff structures and risk profiles. Examples include: - Swaptions - Spread options - Convertible bonds - Equity/credit-linked notes Structured products are harder to value and have lower liquidity. They are almost always OTC traded products and traded direct with the other counterparty (rather than through an electronic exchange). Sales, Trading & Structuring Global market jobs are typically separated into three categories: Sales, Trading & Structuring. The standard hours are 7am - 5pm/6pm and you need to be at the desk when the markets are open. Some roles which are not involved with talking or trading directly with the market may work from 9am-7pm. Sales people Sales people will spend the majority of their time chatting and pitching trades to clients. Most of this communication is done via Bloomberg chat. They may also do analysis into the markets to determine trades which look attractive and pitch these trades to the clients. Sales people will work closely with the trading desks who will execute the trades. Traders Traders execute trades, provide market making services and manage the risk of their portfolio. Their trades may be executed in the market on behalf of the client or they may trade directly with the client (they are the trade counterparty). Trading desks have strict risk limits and their job is to hedge away their risk to avoid hitting this limit. A lot of flow products may be traded electronically to which there is little manual intervention by the traders themselves. However, structured products are typically traded over-the-counter (OTC) and so a lot of the trading is done via voice or Bloomberg chat. Structuring Structuring involves providing a trading solution for a client who is looking for a particular payoff and risk profile. It involves constructing the structured product (using a combination of different financial products) and computing the payoff profiles, PnL breakdown and risks. The roles involves pitching using slide decks and modelling and constructing the structured product using Excel or Python.

Having a good CV is the most important part of the recruitment process. The hiring manager / HR may only spend 5 seconds looking at your CV before deciding whether to give you an interview or reject you (imagine how long you’d spend looking at each CV if you had to sift through 200 CV). Therefore, it’s crucial to have a CV that’s clear and easy to read while also highlighting your skills and qualifications. Once you have gotten to the interview stages, then your CV matters less and it’s more important to perform well in the interview. Here are some tips for your CV to help you secure job interviews. - Put the most recent things at the top of CV: If your are still studying, put your education at the top. If you have graduated already, put your work experience at the top - Put your expected year of graduation: Companies want to know when you can potentially start working for them full-time. They will usually only give you an interview or internship if you can start working full-time for them within the next year. - Add any relevant coursework, projects and course modules: These can include: scientific computing, machine learning, algorithms and data analysis. These can be personal projects, part of your degree, or contributions to online communities such as Github or Kaggle. Personal projects are very important especially if you lack relevant work experience. - Tailor your CV to the role: Each role and division will have skills and keywords that they are looking for. - Keep your CV to one page: If you find you cannot fit everything onto one page then your writing is not concise enough and you have included too much irrelevant things. - Don’t put a photo of yourself: You want to get an interview based on your credentials, not how you look. - Don’t add colours: Keep your CV in black and white. - Don’t write paragraphs: You want to write in bullet points so it’s easy to read. Interview Guide - I’m compiling a guide for Quantitative Finance interviews. It will include: - Valuable tips on securing interviews efficiently and avoiding the time-consuming process of applications - A detailed overview of the interview process, including best practices and strategies for success. - A comprehensive interview preparation roadmap - Technical practice questions including: Probability & Statistics, Finance & Derivatives, Python, Algorithms, Linear Algebra, and more. - Insightful questions to ask your interviewer to show your interest. - Proven ways to excel in HR interviews. - My real-life interview experiences + questions that were asked. If you’d like to ace your next Quant interview, join the waiting list in the bio.

Bonuses are usually significant proportion of your total compensation. Bonus numbers can range from a x multiples of your salary in good years to zero in bad years (depending on what role you have). Each firm will have their own policies but in general, your bonus will usually be determined by a mixture of your boss, senior managers and HR. You can expect the following factors to affect your performance-based bonus number: - How the firm / region performs - How your team / division performs - How you individually perform 1. How the firm / region performs The most important factor is how the firm performs. As you’d probably expect, the company needs to make money in order to pay bonuses. How the firm performs in a given year can be attributed to the level of financial markets activity (out of the firm’s control) and their own operations (within the firm’s control). If financial markets are busy, this tends to result in more trades and thus more revenue for the firm. Your performance-based bonus is also determined by how your region performs. This is arguably more important than how the firm performs globally. If your region does not perform well even if the firm performs well global, then the bonuses in your region are not going to be good. 2. How your team / division performs It also matters how your team / division contributed towards the revenue generated in your region. If your team performs well and the region performs well, then you will receive a good bonus, and vice versa. 3. Your individual performance Lastly, your individual performance will affect your performance-based bonus. However, your individual performance is not as big a contributor towards your bonus compared to how well your region and team performs. If you perform well but your region and division loses money, then the firm will still have no money to pay you a good bonus. Individual performance evaluations are determined by a mixture of feedback from your boss and team as well as colleagues in other teams. Other things to mention - Some firms have a sign-on bonus. This is sometimes negiotiable. - Some firms may force you to give back your bonus if you leave the company shortly after receiving your bonus. - Senior employees may receive part of their bonuses in the form of stock or options. These may need to be vested over a period of time.

A few book recommendations to get started with quantitative trading strategies. Use these books to create personal projects and impress your interviewers. 1. Algorithmic Trading This is the best book for beginners to get started with quantitative trading strategies. Dr Ernest P. Chan is highly renown in the industry. The book walks through simple strategies such as mean-reversion and momentum. It is the first quant book that I read and I highly recommend it. 2. High-Frequency Trading Great book which provides an insight on the mysteries of high-frequency trading and market making. One of my key takeaways from this book is that high-frequency trading strategies can have a Sharpe ratio greater than 12. The reasoning is as follows: Suppose you have a strategy with +EV returns distribution. The more trades you make (i.e HFT), the more your PnL distribution will converge to the +EV returns distribution. Simply the Law of Large Numbers. 3. Inside the Black Box This book is highly recommended by everyone in the industry. It is less technical but it gives a great insight on the process of research to implementation of a trading strategy.

Practical tips for studying quant interview questions 1. Know a little about a lot A general principle for interviews is to know a little bit about a broad range of topics. You want to be able to explain simply about a lot of different topics that the interviewer may ask you about. This approach is more practical than having a very deep knowledge in only one area. You can never predict what is going to be asked in an interview so having a broad knowledge base increases your chances of being able to answer (at least partially) any questions your interviewer might ask. It is also unlikely you will have to write proofs for every question you get asked. 2. Don’t spend more than 15 mins on a single question when studying When I first started preparaing for quant interviews I made the mistake of getting stuck on a question for one hour before giving up and looking at the solution. As a result, I didn’t manage to get through many questions and didn’t improve any of my interview performances. A more practical approach to studying the interview questions is to look at the solution after 5-10 mins if you are completely stuck on how to start solving the question. You want to look at the solution, understand it, and then revisit it at a later date. This approach allows you to go through more questions in a shorter amount of time and gain an intuition on how to solve them. Once you begin gaining an intuition, it becomes much easier to solve the many different types of question. 3. Keep track of the interview questions What gets tracked, gets improved. You want to keep track of the interview questions you have studied so you know which ones you were able to complete and which ones need to be revisited. I would suggest to set up a simple spreadsheet in google sheets or excel with the following columns: - Name of question - Type of question (probability, finance, etc) - Page number (so it’s easy for you to find the question again) - Notes - 1st attempt (correct, incorrect, needs revisiting, etc) - 2nd attempt Feel free to adjust the column headers to your needs.

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