Quantitative Finance & Financial Engineering
Quantitative finance, also known as mathematical finance and financial mathematics, is a field of applied mathematics related to mathematical modeling of financial markets. In general, mathematical finance takes observed market prices as input to derive and expand mathematical or numerical models without necessarily establishing a connection with financial theory. It requires mathematical consistency, not compatibility with economic theory. For example, a financial economist might study the structural reasons why a company might have a certain stock price, while a financial mathematician might consider a stock price as given and try to obtain the corresponding value of a derivative using stochastic calculus.
Financial engineering is almost same as quantitative finance. One difference is that financial engineering is an applied discipline, whereas quantitative finance can be applied or theoretical or both of them. Quantitative analysts or financial engineers working in the field of finance are commonly referred to as quants.
Computational finance is the use of computer science to solve quantitative problems in the financial field. Most quants or financial engineers write their own algorithms and code for the most part, but they can be subdisciplines of their own.
Different Type of Quantitative Positions
Quantitative positions design and run complex models that allow financial companies to price and trade securities. They are mostly employed by investment banks and hedge funds, but are sometimes also hired by commercial banks, insurance companies and management consulting firms. In addition to financial software and information providers.
Financial Engineer
Financial engineers are the people commonly referred to when using the term “quantitative analyst.” They are often tasked with figuring out how to get the product that the sales team sells to customers in large banks and price it right. This includes stochastic calculus and risk-neutral pricing tools, as well as the ability to implement models in existing libraries built in languages like C++, Python, R, or MATLAB.
Financial engineers are often found in classes of fixed income and foreign exchange assets where derivatives are prevalent. Financial engineers often have a background in physics or engineering using modeling techniques to implement new financial products.
Quantitative trader
Quantitative traders are generally mentioned as “top of the food chain” in the quantitative financial industry. This is because they generate transaction returns for hiring companies such as banks or quantitative hedge funds. Quantitative traders spend their time designing algorithms to search for alpha. It generates more returns than returns as a component of standard stock market fluctuations. These algorithms often have heavy econometrics, statistical, or machine learning characteristics, so quantitative traders often have a doctorate degree in artificial intelligence or applied mathematics.
Quantitative Researcher
Quantitative researchers are generally pure mathematicians or PhD in Stochastic Calculus. They are often hired by alternative research firms or larger hedge funds, and conceptualize valuation strategies, develop and continuously improve upon mathematical models, and help translate algorithms into code. Quantitative researchers also conduct research and statistical analysis to build and refine monetization systems for trading signals.
Quantitative Developer
In general, there are two types of quantitative developers in the financial industry. The first type works closely with other quantitative analysts to implement and optimize financial models. In practice, this means taking the prototype code from MATLAB, R, or Python and rewriting it in another language like C++ or Java. These quants are often close to money and reside in the front office of the investment bank.
The second type of quantitative developer deals with financial pricing data and trading system architecture. They will code the raw infrastructure to enable quant analysts/traders to run models and earn money. In practice, this means connecting the database to “business logic” and mediation APIs. In an investment bank, this could mean the maintenance work of a large legacy system or, if employed by a fund, work on a “greenfield” project involving a new trading algorithm. In banks this is usually the middle office role.
Qualifications and Skills
Education
A long-term career as a quantitative analyst generally requires a graduate degree in a quantitative field such as finance, economics, mathematics, or statistics. Degrees in theoretical physics, engineering, computer science, and other fields that deliver high-level training in mathematical modeling and other advanced quantitative techniques may also be acceptable. Some doctorate-level professionals who want to transition into the financial industry from quantitative careers in non-finance fields choose to return to school to earn a master’s degree in majors such as financial engineering or mathematical finance.
Unmatched Quantitative Skills
Almost all banking requires some degree of quantitative skill, but for quants it should be on a completely different level. The role is closer to research than to mainstream finance. This is why most quantum roles in top companies are occupied by PhD degrees in physics, mathematics, or equivalent.
When it comes to statistics, calculus, probability theory and other mathematical concepts, you have to be really exceptional.
Programming Skills
Quants must have a high level of programming skills. Some kind of experience with C++, Python, R or MATLAB is almost a must. Even if you’re not in a development role, you still need to be able to write code to do quantitative analysis.
If you take on a development-centric role, programming and coding will be full. First you need to try to understand the trader’s needs. Then we test and deliver the product. Finally, you need to provide ongoing support and monitor performance in real time.
How to be a Quant
Quants come from a variety of backgrounds, including science, academia, engineering, software development, and finance. This means you have more than one way to get involved and more than one role to play.
Unlike most other banking roles, it’s also much easier to get into quantitative finance from a non-financial role. For example, if you have good experience developing math software applications, you may be more desirable for hiring managers than for other bankers. Similarly, if you have worked as a researcher in some high volume field, you can also be highly desirable for many funds and institutions.
