Rocket Scientist

What is a Rocket Scientist?

Rocket scientist is a term coined by traditional traders for a person with a math and statistical research background doing quantitative work in investing and finance. The term dates to the 1970’s and was used tongue-in-cheek when Wall Street firms began employing researchers without finance or trading backgrounds to use computers to conduct extensive quantitative research alongside traditional securities analysts.

Understanding Rocket Scientists

Wall Street expanded its reliance on these specialists—typically referred to as “quants”—as finance and trading became heavily automated and access to big data increased. While quantitative research can be applied to any style of investing, i.e., growth or value, its application in the securities industry has expanded along with the rise of factor investing. Initially thought of as a separate approach to investing that would help reduce human emotion in decision making, quantitative methods are now used across the industry and included within, as opposed to separately from, most investment strategies.

Quantitative Analysis is Now the Norm

An early example of the use of rocket scientists in asset management would be when a successful trader wanted to quantify her investment ideas and test the potential effectiveness of a strategy going forward. Having traditionally selected value stocks, for example, based on a fundamental strategy, a manager might hire an analyst with a Ph.D. and a background in theoretical physics ( A.K.A., “rocket science”) to create a model that tests the contribution to returns of hundreds or thousands of factors and correlations over long periods of time in multiple market scenarios. As the quant builds complex models for the backtesting of the manager’s strategy, she also learns the investment business, potentially evolving from rocket scientist to securities analyst and portfolio manager.

In recent decades quants have been integral to the development of synthetic products and derivatives including swaps. The models used by robo advisors to create investment portfolios and provide advice are also based on quantitative financial research. High frequency trading and other automated, algorithmic trading programs are direct outgrowths of the application of quantitative methods and computer models to investing and trading. 

The extent to which quantitative and factor investing may contribute to potential market volatility when bypassing the checks and balances of human decision making remains a topic of intense debate. Quantitative, program trading was widely blamed for the Black Monday 1987 market crash and for having contributed to other more recent incidents of extreme market volatility such as the flash crash of 2010. The role of the modern, complex, and often opaque derivatives, swaps, and synthetic debt instruments, made possible by quantitative methods, to the causes, transmission, and uncertainty of the global financial crisis and Great Recession has also led to criticism of quantitative investing.

Proponents point out that market crashes also occurred before the introduction of modern quantitative methods, that their use may actually help overcome some of the impact of human psychology, emotion, and cognitive bias in the financial sector, and that the rapid, certain reaction of model-based trading programs can accelerate market adjustments and enhance efficiency. Regardless, quantitative trading is here to stay as the established norm in modern financial markets.