Last Updated on December 29, 2022
As financial securities become increasingly complex, demand has grown steadily for professionals who not only understand the complex mathematical models that price these securities, but who are able to enhance them to generate profits and reduce risk. These individuals are known as quantitative analysts, or simply “quants,” or even the colloquially affectionate “quant geeks.”
Quantitative analysts design and implement complex models that allow financial firms to price and trade securities. They are employed primarily by investment banks and hedge funds, but sometimes also by commercial banks, insurance companies, and management consultancies; in addition to financial software and information providers.
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What Do Quantitative Analysts Do?
Quants that work directly with traders, providing them with pricing or trading tools, are often referred to as “front-office” quants.
In the “back office,” quants validate the models, conduct research, and create new trading strategies. For banks and insurance companies, the work is focused more on risk management than trading strategies. Front-office positions are typically more stressful and demanding but are better compensated.
The high demand for quants is driven by multiple trends:
- The rapid growth of hedge funds and automated trading systems
- The increasing complexity of both liquid and illiquid securities
- The need to give traders, accountants and sales reps access to pricing and risk models
- The ongoing search for market-neutral investment strategies.
Where do Quant Analysts Work?
Quantitative analyst positions are found almost exclusively in major financial centers with trading operations. In the United States, that would be New York and Chicago, and areas where hedge funds tend to cluster, such as Boston, Massachusetts and Stamford, Connecticut.3 Across the Atlantic, London dominates; in Asia, many quants are working in Hong Kong, Singapore, Tokyo, and Sydney, among other regional financial centers.
Despite the heavy concentration in those cities, quants are found all over the world—after all, many global firms analyze and/or trade complex securities, which creates demand for the quant’s brainpower and abilities. But the problem that a quant working in Houston or San Francisco faces is that changing employers most likely would mean changing cities, whereas a quant working in Manhattan should be able to interview for and find a job within a mile or two of their previous one.
What do Quants Earn?
Compensation in the field of finance tends to be very high, and quantitative analysis follows this trend.45 It is not uncommon to find positions with posted salaries of $250,000 or more, and when you add in bonuses, a quant likely could earn $500,000+ per year. As with most careers, the key to landing the high-paying jobs is a resume filled with experience, including with well-known employers, as well as reliance on recruiting firms and professional networking for opportunities.
The highest-paid positions are with hedge funds or other trading firms, and part of the compensation depends on the firm’s earnings, also known as the profit and loss (P&L). At the other end of the pay scale, an entry-level quant position may earn only $125,000 or $150,000, but this type of position provides a fast learning curve and plenty of room for future growth in both responsibilities and salary.
Also, some of the lower-paid quant positions likely would be primarily quant developers, which is more of a software-development position where the individual is not required to have as much math and financial expertise. An excellent quant developer could certainly earn $250,000, but that’s about as high as the compensation package generally would go.
Despite the high pay level, some quants do complain that they are “second-class citizens” on Wall Street and don’t earn the multimillion-dollar salaries that top hedge fund managers or investment bankers command. As you can see, financial success is always relative.
Quant major roles
Quant Analyst – A.k.a. “financial engineer”. This is the traditional concept of the “quant”, at least up until relatively recently. Such quants are employed to put a price on complex “exotic” derivatives contracts. This was extremely popular during the years leading uo to the financial crisis of 2007/2008. It also lead to the prevalance of the Masters in Financial Engineering (MFE) postgraduate course now offered by many universities.
Skills required in this arena include stochastic calculus, stochastic time series modelling and programming in a statically-typed language such as C++, C# or Java. Another type of role that has cropped up in the last few years is the “model val” quant. These roles typically require checking other models for validity, as opposed to developing models directly.
Quant Developer – The “quant dev” is a programmer at heart. The role is extremely varied and recruiters tend to “pad out” more traditional IT roles with the word “quant”, when there is really very little quantitative work in such roles. The true quant developer will likely be in the more senior end of the “middle office” of an investment bank or even in the “front office” (i.e. close to the money!).
On the buy side (asset management), quant devs will generally be building trading infrastructure, designing analytics/reporting engines or taking a prototype quantitative model and optimising it for execution speed. Generally quant devs will have a computer science background, but sometimes will have a more mathematical skillset with substantial programming experience. Quant devs write software in a wide variety of languages from C, C++, C#, Java through to Python, Julia, Go and Haskell.
Quant Trader/Researcher – This is generally the most coveted role in the quant hierarchy. This is primarily because it involves pay that is (usually) directly linked to performance (which in this case means trading returns). In some firms it also provides the most flexibility, and possibly interest, due to its collegiate/research nature. The skills required to be a quant trader are essentially linked with the models used to generate returns.
Almost certainly it will involve a strong statistical skillset, as well as time series modelling, signals analysis and, more recently, machine learning and Bayesian statistics. A PhD and/or post-doc experience, with a strong academic publishing record, are generally prerequisites for the top quant trading positions. Within the category of quant trader I will add “high frequency trader”. This is an individual who excels at low-level systems engineering, network latency analysis or optimised hardware programming. Such individuals are often drawn from EEE backgrounds (see below).
Quant Risk Manager – Since the financial crisis there has been a significant emphasis placed on trade-, portfolio- and firm-wide risk management. Many quants are now employed to measure and minimise risk in such institutions. Such risk assessment is not particularly straightforward, especially in firms that have multiple levels of risk exposure across counter-parties, obscure derivatives contracts and incorrect model usage.
A background in advanced statistics is highly valued for roles in quantitative risk management. These skills can be obtained on a mathematics degree or even an interdisciplinary course such as MORSE. The latter involves a mixture of mathematics, operations research, statistics and economics and thus provides a good grounding in the quantitative and qualitative risks that financial firms are exposed to.
Best quantitative finance undergraduate programs
Massachusetts Institute of Technology Cambridge, MA
#1inQuantitative Analysis
#2inBusiness Programs
Though the Massachusetts Institute of Technology may be best known for its math, science and engineering education, this private research university also offers architecture, humanities, management and social science programs. The school is located in Cambridge, Massachusetts, just across the Charles River from downtown Boston.
Carnegie Mellon University Pittsburgh, PA
#2inQuantitative Analysis
#7inBusiness Programs
Carnegie Mellon University, a private institution in Pittsburgh, is the country’s only school founded by industrialist and philanthropist Andrew Carnegie. The school specializes in academic areas including engineering, business, computer science and fine arts.
University of Pennsylvania Philadelphia, PA
#3inQuantitative Analysis (tie)
#1inBusiness Programs
Founded by Benjamin Franklin, the University of Pennsylvania is a private institution in the University City neighborhood of Philadelphia, Pennsylvania. Students can study in one of four schools that grant undergraduate degrees: Arts and Sciences, Nursing, Engineering and Applied Sciences, and Wharton.
University of Texas at Austin Austin, TX
#3inQuantitative Analysis (tie)
#5inBusiness Programs (tie)
The University of Texas at Austin is one of the largest schools in the nation. It’s divided into 13 schools and colleges, the biggest of which is the College of Liberal Arts. It also has highly ranked graduate programs, including the McCombs School of Business, Cockrell School of Engineering and School of Nursing.
Students can participate in more than 1,000 clubs and organizations or in the sizable UT Greek system. The university has several student media outlets, and its sports teams are notorious competitors in the Division I Big 12 Conference. UT also offers hundreds of study abroad programs, with the most popular destinations being Spain, Italy, the United Kingdom, France and China. Freshmen do not have to live on campus.
Georgia Institute of Technology Atlanta, GA
#5inQuantitative Analysis
#19inBusiness Programs (tie)
Georgia Tech, located in the heart of Atlanta, offers a wide range of student activities. The Georgia Tech Yellow Jackets, an NCAA Division I team, compete in the Atlantic Coast Conference and have a fierce rivalry with the University of Georgia. Since 1961, the football team has been led onto the field at home games by the Ramblin’ Wreck, a restored 1930 Model A Ford Sport Coupe.
Georgia Tech has a small but vibrant Greek community. Freshmen are offered housing, but aren’t required to live on campus. In addition to its campuses in Atlanta and Savannah, Georgia Tech has campuses in France, Ireland, Costa Rica, Singapore and China.
New York University New York, NY
#6inQuantitative Analysis
#5inBusiness Programs (tie)
New York University’s primary campus is located in the lively Greenwich Village neighborhood of Manhattan. NYU is a true city school, with no borders separating a distinct campus from the streets of the Big Apple. Students are guaranteed housing for all four years in the many residence halls throughout Manhattan, but many upperclassmen choose to live off campus in apartments around the city.
NYU has a small but active Greek life with more than 30 fraternity and sorority chapters. There are more than 300 student organizations on campus, such as WNYU, the student radio station which streams online and broadcasts on a local FM channel to the university community.
Cornell University Ithaca, NY
#7inQuantitative Analysis (tie)
#8inBusiness Programs (tie)
Cornell University, a private school in Ithaca, New York, has 14 colleges and schools. Each admits its own students, though every graduate receives a degree from Cornell University. The university has more than 1,000 student organizations on campus.
University of California–BerkeleyBerkeley, CA
#7inQuantitative Analysis (tie)
#3inBusiness Programs
The University of California—Berkeley overlooks the San Francisco Bay in Berkeley, Calif. Students at this public school have more than 1,000 groups to get involved in, including more than 60 fraternity and sorority chapters.
University of Michigan–Ann ArborAnn Arbor, MI
#9inQuantitative Analysis
#4inBusiness Programs
The university boasts of Ann Arbor, only 45 minutes from Detroit, as one of the best college towns in the U.S. Freshmen are guaranteed housing but not required to live on campus. Students can join one of the school’s more than 1,500 student organizations or 62 Greek chapters. Athletics play a central role at Michigan, including the football team’s fierce rivalry with Ohio State.
Michigan also offers highly ranked graduate programs, including the Stephen M. Ross School of Business, College of Engineering, Law School and Medical School, in addition to the well-regarded School of Dentistry and Taubman College for Architecture and Urban Planning. The University of Michigan Hospitals and Health Centers is ranked among the top hospitals in the country.
Arizona State University Tempe, AZ
#10inQuantitative Analysis
#23inBusiness Programs (tie)
Arizona State University’s Tempe campus offers more than 200 research-based programs in the arts, business, engineering and more. The campus is located just outside of Phoenix, in the suburb of Tempe, Arizona.
Best degree programme for a quant
Mathematics
Why do I consider mathematics to be the “best” degree programme for a quant? Simply put, many quant roles require a substantial grounding in mathematics and mathematical modelling. Unfortunately, if you wish to work in a quantitative role, then there is no getting around having to learn some hard mathematics!
A good mathematical degree, with strong options choices, will cover all of the areas needed by a practising quant. These include real analysis, probability theory, frequentist an, ordinary and partial differential equations, optimisation, mathematical modelling and vector calculus.
Nearly all high-end mathematics degrees include some form of optional programming courses, some of which use more modern languages such as Python, MatLab and R.
In addition, the level of rigour promoted by a mathematics degree produces good candidates for further postgraduate research work. An additional benefit of learning “how to learn mathematics” is that it makes it somewhat easier to jump to other fields, as often the subject-specific mathematics can be the barrier to learning a new subject.
That being said, mathematics is not an easy course to take. It requires a substantial commitment to obtain grades that would impress a recruiter (and hiring firm!). Mathematics is not something that can easily be “sailed through”. It requires deep thought and a substantial awareness of many disparate areas. However, it is an extremely rewarding degree to take and can “open your eyes” to some very interesting abstract concepts.
Mathematics is not an easy course to take. It requires a substantial commitment to obtain grades that would impress a hiring firm. However, it is probably the most suitable degree to choose to become a quant.
As I stated above, if I had to recommend one particular course, I would choose mathematics.
What quant roles lead naturally from a mathematics degree or appropriate postgraduate course? Predominantly a quant analyst (financial engineer) or quant researcher/trader. It is also possible to head into a quant risk analyst role, although it is likely some specific postgraduate training would be required.
Note also that it is possible to become a quant developer after doing a mathematics degree (that’s what I did, after all!), although I had quite an extensive programming background that I carried out in addition to my degree.
Theoretical Physics
Physics is the study of how the universe works at the smallest and largest scales. Theoretical physics, in particular, is concerned with development of models that attempt to predict, and infer, behaviour of physical phenomena. Such skills are very similar to those of a quant researcher or financial engineer, who is constantly attempting to try and model complex stochastic phenomena.
There is a substantial overlap of material covered by a mathematics degree and a theoretical physics degree. However, the manner in which the material is presented is far more practical and does not provide as much rigour as that of a mathematics degree. Such rigour is generally unnecessary when modelling so if you are more interested in “how things work” then you might find physics more appropriate.
On a theoretical physics course you will learn about classical mechanics, including Lagrangian and Hamiltonian dynamics, electromagnetism, quantum mechanics, special and general relativity, cosmology, statistical physics, particle physics and perhaps more advanced courses such as quantum field theory and string theory.
Crucially, these modules will teach you about mathematical modelling via ordinary differential equations, vector calculus, partial differential equations, statistics, linear algebra, linear analysis and probably some programming (albeit lilkely in Fortran or C, although you may be lucky enough to use MatLab or Python).
Theoretical physics is almost perfect for quant research and you will see a lot of physicists becoming quant researchers.
Hence a theoretical physics degree will cover nearly everything you might need to learn in a mathematics degree, but with an emphasis on applications to physical phenomena and less of a concentration on theorems and proofs. For certain quant roles, this may even be more optimal.
What quant roles are most appropriate for a theoretical physicist? As with mathematics, the two most appropriate roles are a quant researcher/trader and perhaps a quant analyst. The former requires intuitive modelling skills. Theoretical physics is almost perfect for this and you will see a lot of physicists becoming quant researchers. The latter will evidently require some stochastic analysis experience, which for a physicist, will likely come at the postgraduate level, unless their course allowed optional mathematics modules to be taken.
Once again, risk analyst or quant dev roles are also appropriate assuming a strong statistics or programming background, possibly in addition to your degree modules. This is often carried out at postgraduate level.
Computer Science
There is a misconception in general society that computer science involves going to university to learn “coding”. This is particular pervasive due to the immense rise of technology startup culture and web development. However, this notion is incorrect, and somewhat harmful.
Computer science is actually a subset of applied mathematics, dealing with the particular mathematical areas involved in computation. In addition a modern computer science degree involves a substantial amount of computer architecture design, computer hardware engineering, software engineering, compiler design, algorithmic design/complexity as well as database design.
There is a misconception in general society that computer science involves going to university to learn “coding”. However, this notion is incorrect.
A lot of these skills are now seen as “outdated” by a technology startup culture that values rapid iteration and abstraction from the hardware. However, in quantitative finance, the above topics are precisely those that allow various quant shops to give them an edge in a highly competitive sector. Hence a computer science degree should NOT be considered an anachronism in todays job market.
A good junior quant developer, who has aspirations to lead their own team some day, will need to be extremely well versed in the above topics if they are to work in the more lucrative (and arguably more interesting) areas of quant finance.
Quantitative trading and pricing infrastructure involves some extremely interesting areas of computer science and engineering including high availability (redundancy), low-latency applications, large codebase design and refactoring, robust high-frequency systems, GPU processing farms and other high-performance computation (HPC) applications, real-time high-throughput responsive analytics engines as well as database cluster design and monitoring.
In addition, being a quant dev either via a consultancy structure or as a direct employee can be extremely lucrative, particularly after a career spent in a specialisation or niche.
As a side-benefit, because there is an under-supply of strong software developers, it is straightforward to jump back-and-forth between financial services and technology startup roles, particularly in finance/tech hubs such as New York or London. Hence there is strong job security in being a quantitative developer with a strong computer science background.
Electrical & Electronic Engineering
As with computer science there is a misconception that electrical and electronic engineering (EEE) students spend the majority of their time soldering electrical circuits and poring over component datasheets. While this is clearly part of the degree course, in actual fact top EEE courses are often quite theoretical and have a strong mathematical component associated with them.
Typical courses include embedded programming (in a language like C or Assembly), digital signal processing (which is highly valued in higher frequency trading), hardware optimisation design (including usage of tools such as FPGA) and robust/high availability system design. These are all skills useful in high-frequency trading or as quant developers programming low-latency high-availability architecture for trading applications.
Postgraduate “Triple E” students are great candidates for eventual roles in high-frequency trading firms.
“Triple E” students are great candidates for eventual roles in high-frequency trading firms. After an undergraduate degree in EEE, it is common for postgraduate students to specialse in specific embedded hardware implementations that provide substantial experience with low-latency optimisation and high-throughput. These skills are the natural domain of the high-frequency trader and as such EEE students are often in demand from these (rather secretive!) firms.
