Why a Masters in Finance Won't Make You a Quant Trader

By Michael Halls-Moore on November 28th, 2013

One of the biggest misunderstandings of the quant finance landscape is that by taking an expensive Masters in Financial Engineering (MFE) program from a top school it will easily lead to a high-paying quantitative trading role at a fund. In this article I want to outline why a MFE is not an ideal choice for entry level quant fund roles and discuss other routes into such careers.

What Is A MFE Course?

The first thing we need to understand is exactly what skills and methods are taught on current Masters in Financial Engineering programs. I've had a detailed look at the syllabus content of the top MFE programs (as rated by QuantNet) and the topics generally fall into the categories of:

  • Stochastic Calculus & Black Scholes - The core of nearly all MFE courses is a solid grounding in stochastic calculus techniques as applied to options pricing theory, the theory of Black-Scholes and subsequent models.
  • Fixed Income Derivatives - Fixed income derivatives modelling is an advanced area of options pricing, requiring more sophisticated mathematical tools and models.
  • Numerical Options Pricing - Monte Carlo and Finite Difference Method techniques are often used to numerically price options. These courses tend to have a strong practical/computational component.
  • Portfolio Optimisation - Modern Portfolio Theory is an extremely important part of the asset management landscape and as such it gets a lot of attention in MFE courses.
  • Corporate Finance/Accounting - These tend to be optional courses in most MFE programs, but because of the pervasive nature of investment banking jobs they are still considered highly useful skillsets.
  • Risk Management - Many programs provide technically detailed courses on market, counter-party/credit and operational risk for both banking and asset management firms.
  • Entrepreneurial Finance - Private equity, venture capital & technology startups are hot topics recently with the rise of entrepreneurial hotspots such as Silicon Valley (in San Francisco) and Silicon Roundabout (in London).
  • Time Series Analysis/Forecasting/Regression - These are probably the most important courses for pure quantitative trading research. However, they don't seem to have as much prominence as the courses outlined above.
  • Programming - Most courses provide a computational aspect. Often this is in C++ or VBA. Sometimes this includes modelling languages such as MatLab.

This is a well-rounded education in advanced financial engineering principles. For many roles in finance (particularly investing banking and treasury departments) this is an extremely useful set of skills. However, for reasons we will outline below this is not a particularly useful set for pure quantitative trading work.

We should also consider how the courses themselves are being marketed via the universities. Some courses directly reference quantitative funds as a potential career opportunity, while others make very little mention of the asset management industry.

This is what Columbia (in the US) say about their Master of Science Program in Financial Engineering:

"Financial Engineering is a multidisciplinary field involving financial theory, the methods of engineering, the tools of mathematics and the practice of programming. The Financial Engineering Program at Columbia University provides full-time training in the application of engineering methodologies and quantitative methods to finance. It is designed for students who wish to obtain positions in the securities, banking, and financial management and consulting industries, or as quantitative analysts in corporate treasury and finance departments of general manufacturing and service firms."

This is what Princeton (in the US) say about their Master in Finance degree:

"The Master in Finance program is intended to prepare students for a wide range of careers both inside and outside the financial industry, including financial engineering and risk management, quantitative asset management, macroeconomic and financial forecasting, quantitative trading, and applied research."

This is what Imperial College (in the UK) say about their MSc Risk Management and Financial Engineering course:

"Students who complete the MSc Risk Management and Financial Engineering programme successfully will be able to demonstrate a detailed knowledge of fundamental finance theories and models including their derivation and their use and context in the measurement and management of risk; apply mathematical tools to complex financial problems relating to risk measurement, risk management and risk pricing; use a range of programming tools to develop live implementations of financial models and use these implementations in practice; apply econometric theory and software to analyse and evaluate investment decisions and data, and draw valid conclusions."

Disclosure: I was a PhD student in the Aeronautics Department at Imperial College between 2005-2008.

Now that we have an idea of what is taught on a Masters in Financial Engineering course we are going to consider how quantitative hedge funds operate so we can gain insight into their hiring process.

What Do Quant Funds Do?

In order to understand why MFE courses are unattractive qualifications to quant hedge funds, we need to understand how a quant fund operates and what it looks for when undergoing a hiring cycle.

A quantitative hedge fund makes money through a common hedge fund structure known as "2 and 20". This basically means that if £100million is invested with the fund then each year the fund receives a 2% management fee (the "2") and then a 20% performance fee (the "20") of the money under management. For example, if the fund managed to achieve a 20% gross return on the assets under management (AUM) in a year then it would receive £2million as a management fee and 20% of the 20%, i.e. £4million as a performance fee for a total of £6million in fees.

Thus it is clear that in order to survive a fund needs significant AUM and consistently needs to generate returns above some predetermined benchmark. Consistent high returns attract more investment leading to a larger "2 and 20" while poor returns lead to redemption of capital and sometimes a closing of the fund.

Hence the goal of all quant funds is to keep hunting for new strategies that provide a reasonable consistency of returns while being able to manage risk in such a manner as to avoid large drawdowns.

This motivates the need to find individuals who can "walk the walk" in the sense of being able to either demonstrate a consistent prior trading track record or a strong research record of methods that can readily be applied to financial markets (such as forecasting techniques or machine learning methods) to provide new strategies.

Thus it is now apparent that hiring teams of MFEs are not likely to fulfill either of those two needs, almost by definition, as they are being taught methods that are well-known within the quant trading community and thus are not likely to produce outperformance.

What Do Quant Funds Look For?

I've actually written about this topic quite extensively before so I will point you to the relevant articles and then summarise below:

Primarily, quant hedge funds are interested in individuals who are able to comfortably read research papers, assess the quality of such work, implement the models associated with the research and then build upon it to create profitable trading strategies. In addition they need infrastructure/teams to support these individuals.

Thus the most important criteria looked at by hedge funds when hiring for trading positions is generally some form of publication or research record (or other evidence of research capability) and/or a former trading track record as well as strong programming/modelling skills.

As stated above, quant funds are always looking for new methods and generally work on the bleeding edge of any research area they are interested in. Thus it makes very little sense for them to hire a large group of taught MFE students as they are often unable to tangibly demonstrate the capability to perform independent research or possess a solid quant trading track record. This is exactly why a PhD is often a requirement at the top firms.

Since the majority of quantitative trading is based on statistical learning and analysis of pricing series any background in machine learning, forecasting, time series analysis, signals analysis, complex systems or to some extent stochastic processes, is attractive.

Note that stochastic calculus (and expertise in it) is often not as appealing to quant funds, unless their strategies directly relate to the pricing of derivatives securities. Usually the latter is the domain of the investment banking industry rather than the asset management space.

Quant funds also run extremely sophisticated IT facilities and hire strong quantitative developers to create their trading infrastructure. In high-frequency trading the lines are blurred between strategy and execution. As such, research into Computer Science topics is highly valued. See the article above on getting a job in HFT for more information.

Why Take A MFE Then?

This is not to suggest that MFEs are without merit. MFEs provide a solid grounding in financial principles and often lead to very lucrative careers in investment banking, risk management, corporate finance and quantitative analysis (i.e. financial engineering).

In addition, MFE courses are a fantastic springboard into other areas of academic research such as PhD programs in finance. Having a deep understanding of forecasting after a PhD program in statistical learning becomes very attractive to quant funds and as such MFE programs provide an important stepping stone.

Just be aware that a Masters in Finance is unlikely to lead towards a quant trading job directly and as such you should adjust your career aspirations accordingly.

If you would like to discuss your personal quant finance career situation with me please send me an email at mike@quantstart.com.

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