Have Quants Taken over Wall Street?

Updated: Apr 28

At Goldman [Sachs] the number of people engaged in trading shares has fallen from a peak of six hundred in 2000 to just two today.

- The Economist, “Too Squid to Fail,” 29th October 2016

Have Quants Taken Over Wall Street?

The short answer to the title question is ‘yes.’ But first, you might be wondering ‘what is a quant?’

A quant, or quantitative analyst, is someone who specialises in developing and deploying mathematical models to solve complex financial problems. They have been around for a long time and play a very interesting role in the financial sector.

Quants have traditionally been characterised as geeky outsiders. They have Masters or PhDs in non-finance related subjects and as a result, during the heyday of MBAs in the 1980s, quants were shunned for their lack of understanding of how the business worked and would operate in the back office of banks and investment firms. However, since then, they have crept into the heart of the money-making business.

There are two parallel trends that I went to identify: the first being the rise of computing, and the second being the emergence of exotic instruments. By doing this I hope to explain the rapid rise of this new class of financiers. This will hopefully shed light on why the current financial landscape looks the way it does.

The Story Begins

In 1986, the Managing Director of New York based investment firm, First Boston, was fired for losing $100 million on a deal. Following his dismissal, Larry Fink became obsessed with creating a system which would provide him with a market edge. He and his colleagues retreated to the hills of East Wenatchee. There, in a series of giant warehouses, they built a huge data centre and a piece of incredibly powerful software. They called this software Aladdin, like the eponymous character, they sought to make their dreams come true.

BlackRock, the company Larry Fink cofounded in 1988 and now directs, is the largest asset management firm in the world with over $7 trillion assets under management. It provides various services, from advisory to technology and licensing. But its success is predicated on the notion of secure risk management through the power of mathematics, informed by data. Aladdin, the piece of software at the heart of BlackRock, is comprised of 25 million lines of code and analyses unfathomable amounts of data about the past in order to make informed decisions about the future. Software like this is called a black box and its inner workings are a closely guarded secret. Bearing in mind that it informs trillions of dollars’ worth of investment decisions this comes as no surprise. Aladdin is the product of quants, and it’s BlackRock’s edge. Now, as the largest asset management firm in the world, BlackRock stands as a monolith thanks to the power of computing.

In 1982, another company was set up by a man who would come to represent the typical quant. Jim Simons is an extraordinary man and his company, Renaissance Technologies (RenTech), would go on to be the most successful hedge fund ever. Born in 1938, he grew up wanting to be a mathematician. He started his career in academia and proved himself to be an innovator at the forefront of his discipline. He briefly worked for the NSA as a codebreaker during the height of the Cold War. After writing a letter to the New York Times opposing the escalation of the war in Vietnam, directly contradicting his superiors, Simons was fired. Following his dismissal from the NSA he returned to academia to publish Chern-Simons theory, which helped lay the foundations of String Theory, only to subsequently establish one of the most successful hedge funds of all time.

When he founded RenTech, Simons had no previous economic or finance background. He was a pure mathematician. This explains why RenTech’s success was not grounded in economic theory but rather on raw data and computation. The team of physicists and mathematicians that established RenTech kickstarted the quant revolution, with the predictive power of their technology yielding incredible amounts of wealth for their clients. RenTech’s Medallion fund has the best record in investing history returning more than 66% annualised returns over a 30-year period from 1988 to 2018.

BlackRock and RenTech are two very interesting and successful companies. They are examples of how mathematics and computer science can be used to generate tremendous wealth. Now, quants have become highly sought after, as firms are always seeking that competitive edge.

The Rise of Exotic Instruments

The second theme I wish to pull out is the rise of exotic instruments. One example of such instruments is collateralised debt obligations (CDOs). They are highly complex financial products and were first introduced in 1987 by Drexel Burnham Lambert bank. The idea was that banks could put together a portfolio of unattractive assets (in this case “junk bonds”), which together would form a safe investment. This idea is called securitisation. You provide the investor with a “safe” return by pooling various types of contractual debt. This could be credit-card loans, mortgages or any other asset which could generate a “safe” cash-flow to be sold to investors in the form of CDOs or bonds. The important role that CDOs played in the collapse of the American housing market, which precipitated the Global Financial Crisis, is well known. The point I wish to make is that CDOs were an example of complex financial instruments that could be used to generate tremendous wealth.

Enter Michael Osinski. During the 1990s, Michael worked for Lehman Brothers and was instrumental in developing BondTalk. BondTalk was a sophisticated piece of computer software that would model the mortgage securitisation process. This software helped the world’s major banks turn subprime mortgages into attractive financial instruments (CDOs). But in 2007 the world witnessed a credit crunch followed by the crash, with the collapse of Lehman Brothers in 2008, and the subsequent recession.

I do not blame Mike Osinski. The Financial Crisis was far too complex an event to blame on any one individual or even a class of individuals (in this case quants). The point I am making is that these two trends helped propel quants to the forefront of the money-making industry. The rise of computer driven investment technology helped quants enter roles at established firms. They were then instrumental in providing the key pieces of technology, both software and mainframe architecture that would help the big banks generate tremendous wealth by utilising the development of complex financial instruments.

But What Now for Quants?

Quants rose, and had their moment in the sun, have they now retreated back to their academic departments? Well, that is not the way the financial sector has responded to 2008. As I indicated above, I don’t think it is a necessary or pragmatic response. With the growing emphasis on machine learning, Artificial Intelligence and Big Data, quants are in more demand than ever. There are signs everywhere you look. One indicator is that the number of programming languages with dedicated financial analysis packages has exploded. Another indicator is the geography of Wall Street itself. In 2018 Deutsche Bank, the last remaining big institutional bank on Wall Street, announced that it would be moving its headquarters to various smaller hubs across the US. Over the last decade or so firms have been moving their front office up-town and their back office to newer and cheaper spaces in New Jersey. These established institutions are emulating the example of more dynamic investment firms and hedge funds. This signals the end of an era.

But what do quants do in our post 2008 world? Well, pretty much anything. They could be part of a team that builds and maintains the key pieces of investment architecture, whether that is the physical exchange or maintaining server utility at the firm. They could help develop or tweak black boxes or other trading algorithms. They may be used to help construct mathematical models of interest rates, bond pricing, or they could be involved in drawing up derivative contracts.

There is a myriad of potential career paths for quants to pursue. I think that, given the importance of data in an age of information and the challenge of finding new ways to generate positive alpha (above benchmark returns), it's clear that these not-so-new kids on the block are here to stay.