An evolution of Quantiative Finance, and how to position yourself for the future

Posted on 08 February 2021

​​Having been in the search world for around 16 years I have seen many changes in the Financial services landscape. I started my career in London partnering with Private Equity & Hedge Funds in the City, pre the Global Financial Crisis this was an incredible place to cut your teeth, I managed to continue in this world for the best part of my career, the approach adopted by many firms buy and sell side gradually changed from fundamental research/analysis to quantitative research, this is where I now spend most of my time, partnering with Global Financial Institutions looking to have the edge over the market by employing the best minds and strategies available! Whilst there is a global crisis everywhere right now, potential future employees and employers still need to build careers and teams. With that in mind I wanted to do some market research on the evolution of the quant world and how students and future captains of industry can position themselves to gain the most sort after skills and, in turn, the top employment options globally.

The Past:

Just as automation and mechanisation were the cornerstones of the Industrial Revolution at the turn of the 19th century, modern finance theory, quantitative models, and econometric techniques provide the foundation that has revolutionised the investment management industry over the last 20 years (Bloch 2014).

Model quantitative finance arguably starts with Harry Markowitz’s seminal work in 1952 (Markowitz 1952), which first introduced portfolio selection using a quantitative optimisation procedure that balances the trade-off between risk and return. Markowitz’s work laid the ground for the capital asset pricing model (CAPM). The most fundamental general equilibrium theory in modern finance, CAPM states that the expected value of the excess return of any asset is proportional to the excess return of the total investable market, where the constant of proportionality is the co-variance between the asset return and the market return. This is the well-known Beta. And the other well-known concept Alpha is also derived from CAPM where the market is not in equilibrium. Beta and Alpha have since been widely used in the financial industry by various portfolio managers. They are still very popular with evolving perspectives, see, e.g., Winther and Steenstrup (2016).

If CAPM laid the building block for pricing fundamental assets, then the work by Black–Scholes–Merton, see Black and Scholes (1973) and Merton (1973), gave rise to the celebrated option pricing. Their work later inspired the risk-neutral pricing theory which has become the standard framework for derivative pricing. The Black-Scholes option pricing model is so widely used in the option market that the well-know Implied Volatility was actually based on this model, even though the assumption of a constant volatility in the Black-Scholes model has long been proven wrong.

The Present:

Fast forward to the 21st century, the quantitative methods have been developing and evolving tremendously over the past few decades. In the fundamental asset space (equity market), Fama-French’s three-factor (Fama and French 1993) has taken over CAPM and became the gold standard for equity and bond portfolio selection and management. Their works have also created a huge area in the finance literature: the anomalies. Andrew Ang, the former professor of finance at Columbia Business School and current Managing Director of BlackRock, incorporate and extended many of the anomalies studies into his efforts on factor investing (Ang 2014).

While in the derivative space (options and futures), a lot has happened since Black-Scholes-Merton as well. From stochastic volatility to jumps, many more features have been added to the option pricing models to explain the volatility smile/smirk implied from the Black-Scholes model ( As more assumptions of the underlying assets have been relaxed, the more complex the option models become. Analytical solutions for solving option prices gradually disappeared without concerning the industrial practitioners the least as the model computers’ power has much outweighed the benefit of analytical solutions. For example, the numerical approaches like Monto-Carlo simulations, Binary-Trees, PDE, etc can be readily implemented to price the most complex option prices in the blink of an eye.

In recent years, the term Statistical Arbitrage has attracted much attention within the quantitative investment community. Statistical arbitrage is typically referred to as trading strategies that rely on mathematical modelling techniques seeking profit opportunities from pricing inefficiencies (Pole 2007). Machine learning models are increasingly deployed to enhance the Statistical Arbitrage strategies within many major quant hedge funds: Renaissance, Blackrock, WorldQuant, etc.

How to Get Ahead:

From the educational side, there is never lack of demand for a quant degree. Our clients will usually ask for at least an Masters from a top global University and beyond this may request that the candidates have a PhD on top of a Masters. Depending on the firm, further education in Physics, Statistics, Mathematics may be essential to even get an interview. We see a lot of demand in the programming and coding world (especially in Python) that demand more Computer Science qualifications as well as a statistical/mathematical undergrad course. No one said it would be easy!!

The top master degree programs in the financial engineering world are always the dream for students determined to be a quant, below are the courses and Universities that will give you that potential leg up in the USA and UK!



James Maidlow is a Director at Venture Search. Venture Search is an international banking & financial services search firm, combing technology and human skill to enhance all aspects of the hiring process.

Ang, Andrew. 2014. Asset management: A systematic approach to factor investing. aOxford University Press.

Black, Fischer, and Myron Scholes. 1973. “The Pricing of Options and Corporate Liabilities.” Journal of Political Economy 637–654.

Bloch, Daniel Alexandre. 2014. “A practical guide to quantitative portfolio trading.” SSRN 2543802.

Fama, E. F., and K. R. French. 1993. “Common risk factors in the returns on stocks and bonds.” Journal of Financial Economics 3–56.

Markowitz, Harry. 1952. “Portfolio Selection.” The Journal of Finance 77-91.

Merton, Robert C. 1973. “Theory of Rational Option Pricing.” Bell Journal of Economics and Management Science 141–183.

Pole, A. 2007. Statistical arbitrage: algorithmic trading insights and techniques. John Wiley & Sons.

Winther, Kenneth Lillelund, and Søren Resen Steenstrup. 2016. “Smart Beta or Smart Alpha?” The Journal of Investing 85-94.

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