A definition of quant by Mark Joshi is that quant designs and implements mathematical models for the pricing of derivates, assessment of risk or predicting market movements. Quants implement models that focus on financial relationships. Perhaps the most famous of these is the Black-Scholes option pricing formula, which describes the relationship between the prices of two financial instruments that have a particular connection. Derivates for which quants design models are financial instruments whose values depend on (or are determined by) the future value of some quantity.
In 1973 CBOE – Chicago Board Options Exchange was open. But even before, Pierre Fermat mathematician was maybe the first quant. Solving some problems related to the game of chance. As mentioned, mathematics about options is about controlling uncertainty and in 1997 Scholes and Merton (Black died before and Nobel only goes to living) won Nobel prize.
David Leinweber – President of Leinweber & Co.
He had a chance to work with PDP-1, computer that was built for civilians in 1970s. He also joined RAND doing nice civilian work such as AI inspired analysis of econometric models for the Department of Energy. Later he joined Interference and work on product called Automated Reasoning Tool. But his step into finance was done when he presented on finance conference in LA on AI. His main message was that computers are pretty good at manipulating other computers and that people should do better things than that.
Later in his career one of the products he marketed was MarketMind, it was thermonuclear weapon for technical analysis. He met Evan Schulman that thought him a lot about market microstructure and various incentives of different participants.
He founded firm Codexa and try to exploit available information’s. Markets are not instantaneously and perfectly efficient. Insights, and the ability to execute them rapidly in ever-faster electronic markets, will continue to be rewarded.
Ronald N. Kahn – Global Heads of Advanced Equity Strategies, Barclays Global Investors
He fantasized about being physicist during 1920s. The quantum mechanical revolution was just starting. He majored in physics and focused on cosmology. Later he joined BARRA. It was one of the centers of quant finance revolution. BARRA stands for Barr Rosenberg and Associates. Company was organized around unifying framework of thinking. They sold risk models. He worked on interest rate options. They realized that convexity-based strategies do not beat market. In another project they realize that LBO (RJR Nabisco) caused a significant transfer of wealth from bondholders to stockholders. In 1958, Franco Modigliani and Merton Miller had shown that under idealized circumstances the value of the firm was independent of leverage. Shifting between equity and debt just shuffled claims on firm earnings between equity holders and bond holders.
In 1990 under influence of Richard Grinold they tried to put together framework for active management of portfolios. Due to fundamental law, quantitative active strategies tend to make many small bets as opposed to a few concentrated bets.
As he moved on in career, he believes that consistent outperformance requires constant innovation.
Gregg E. Berman – Strategic Business development, RiskMetrics Group
He would hesitate to call anyone a quant. One can certainly be quantitative, but that’s an attribute of a person, not a career description. He graduated from MIT. He was in academics (Princeton) when a wall street recruiting agency placed a random call to the department asking if anyone was interested in working for a boutique hedge fund. He went to the other side. He joined ED&F Man. He liked analytical part of job, but not the trading one. He wanted to go to JP Morgan and RiskMetrics Group was about to spin off from JP Morgan and he joined them.
Evan Schulman – Chairman, Upstream Technologies, LLC
In university years he wanted to use computer intensive statistical techniques to exploit market inefficiencies. Since this was not that easy, another question was if investment process can be improved. He joined Keystone Group. He was working on understanding and improving investment process, because transaction costs were so high at that time that fund managers didn’t not have sufficient information to cover them. He then moved to Batterymarch. They used computer to apply quality control to the investment process by ensuring diversification, controlling transaction costs and looking for areas of market, that were priced inefficiently.
During his career he realized, he was not suited for work in a large organization. During his career he also realized that Fischer was right – more effective capital is more capital.
Leslie Rahl – President, Capital Market Risk Advisors
She loved everything about MIT. She had a lot of interview with technology firms, but choose to enter Citibank’s management training program. When CBOT (Chicago Board of Trade) opened in 1983, she was asked to work on options. She was able to build and interest-rate caps business. In the pricing model they build in correlation to Black-Scholes formula, she was responsible for two parameters: yield-curve shape and volatility.
For her a quant is someone whose eyes don’t glaze over when they see a formula. Someone for whom numbers speak as loudly as words. Someone who is extremely logical.
Thomas C. Wilson – Chief Insurance Risk Officer, ING Group
He never found a mathematical proof intuitive or elegant or even vaguely inspiring, but rather, more often than not, a slightly painful exercise. His contribution to industry is more about forming the question than the application of any specific quantitative techniques. The early 1990’s marked the market risk era. During this period, the banking industry developed economic capital or value-at-risk (VaR) models. Derivate business grew in 80s and early 90s and as consequences a new and innovative regulatory framework was developed by the Basel Committee on Banking Supervision. The late 1990s was the credit risk era. At that time portfolio level analysis of credits were not advanced. In 1997 he developed a credit portfolio model named CreditPortfolioView.
When in 1990 corporate loan market commoditized a lot of institutions moved into trading business. Developing Raroc models that helped companies compare risk-adjusted returns of different business lines or loans. Shareholders require greater returns from companies that have more systemic risk.
Lessons he learned are: get the question that is relevant for today’s world right, build your intuition before building your model, trust your intuition, challenge your intuition, be as good at communication as you are at the theory and the model is always wrong, but that doesn’t make it useless.
Neil Chriss – Former Managing Director of Quantitative Strategies, SAC Capital Management, LLC
As a young boy he was playing with mathematics and questions like Riemann Hypothesis. The Glass Bead Game was a favorite novel among his mathematician friends. In mathematics research you cannot see the summit. In fact, the summit may not even be there.
He wrote a game D’Fuse. And by doing it, realize that he likes to write programs. He joined Fermilab, joining a research team that was trying to use neural networks to analyze nuclear collision data.
In 1993 he went to study in Toronto and at that time he for the first time seriously considered quant finance. He found mathematics of the Black-Scholes options pricing formula interesting. Working with Victor Ginzburg, he learned to take extremely complicated ideas and explain them clearly and concisely.
In Morgan Stanley he was working in the quant research group. He tried to understand also trading business. He tried to build model to make their business more efficient. At each point in time, the stock portfolio has two costs associated with it: a risk cost and a market impact cost. The risk cost is the theoretical cost associated with holding a risky position that you do not want to be holding. The transaction cost is the cost associated with the market impact of the position’s changing through time. The total cost is the sum of the transaction costs and the risk cost appropriately adjusted by a risk aversion parameter, which controls for how urgently you want to reduce risk. The idea was to model program trading as an optimization problem over trading trajectories.
He moved to Goldman Sachs Asset Management to work on overall portfolio management activities and to develop a new trading strategy.
Then he moved to SAC Capital Management.
Quant approach requires good technology and effective risk management.
Peter Carr – Head of Quantitative Financial Research, Bloomberg
He studied economics and accounting. He graduated from UCLA in 1988. He worked in Morgan Stanley and Bank of America.
Thanks to the insights of heavyweights like Robert C. Merton and Stephen A. Ross, we have powerful framework with which to tackle fascinating problems. Although many academics think otherwise, mathematical finances are in its infancy with lots of low-hanging fruit to savor.
Marko Anson – CEO, Hermes Pensions Management Ltd. CEO, British Telecommunications Pension Scheme
He became quant to ask and answer questions why and how. One can look at venture capital investments as call options on the future success of the new ventures.
In empirical research, quants often talk about noise in the data. Quantitative skills are like foreign language. The more you use the skills, the more honed they become. Conversely, failure to apply the skills on a regular basis leads to a slow dissipation.
He realized that breakthroughs in the financial markets ware regulatory in 1930s and 1940s and quantitative in last 40 years – CAPM of Sharpe (1964), Lintner (1965), Mossin (1966), The Black-Scholes (1973),….
The only thing one can predict with certainty regarding the financial markets is that they are uncertain. Not every economic observation can be distilled down into a convenient analytical format.
Social Security system in USA faces bankruptcy until 2050. LDI (liability driven investment) is the new buzzword around pension funds today.
Bjorn Flesaker – Senior Quant, Bloomberg L.P.
Garbage Can Model applies quite well also to the typical sell-side derivates quant group and its immediate environment. He was raised in environment that valued education as a way to get a job. In Berkley he studied Heath-Jarrow-Morton model of interest rate dynamics and interest rate derivate pricing.
Working in the field he realized that sometimes better information can be bad for actors, since they all react more competitive and not cooperative. He joined Merrill Lynch. And later he moved to Bloomberg as a hands-on research quant.
He became quant by accident. His entrance into finance was with Nikko Securities’ European operation. He needed to learn quickly things like forward Kolmogorov equations, Monte Carlo simulation techniques and general derivates replication theory.
Next, he moved to NatWest. There he learned to build models transparently and that quants never own their models. Anybody who wants to make it as a quant, need to spend some time in a model validation group. There he played with Sobol’ numbers. Later he moved to Commerzbank Securities and ABN AMRO.
Andrew Davidson – President, Andrews Davidson & Co., Inc.
He is not really a quant, he can spout quant sounding sentences, but when it comes to true quantness, he falls a bit short. When he was young, he treated mathematics as philosophical issues, not so much as instructions.
When he was working with mortgages, he was interested in two questions: what is it worth and how do you hedge it. He also realized that how traders and other participants describe the operation of the market and how market it truly operates, are two different things. Price is the most reliable market information. To succeed in MBS, you need to know financial theory, but you also need to study data. In mortgage market challenge is to understand mathematics of it, but also to understand relationships between lenders, borrowers and investors.
Andrew B. Weisman – Managing Director, Merrill Lynch
As student he chooses economics and philosophy, gaining a foundation in econometrics, statistics, calculus and formal logic. He joined Shearson/Lehman Brothers. Then he moved to Bankers Trust. In both companies he worked with foreign exchange trading operations. The main game here is to borrow in low-yield currencies and lend (buy) in high-yield currencies. Using tolls like MVO (mean variance optimization) and VaR (value at risk).
Clifford S. Asness – Managing and Founding Principal, AQR Capital Management, LLC
He joined University of Chicago, working as assistant of Geme Fama and Ken French’s. He started working as Goldman Sachs in the asset management division. There they realized that they can treat countries as portfolios of individual stocks. He believed that they created some great models, but they were also lucky and that didn’t hurt his career. So, based on that he, together with some friend from Goldman started AQR. They set up their fund and they had their share of rough going.
Some of his thinking was around ideas like: quants are data miners, quants use black boxes, LTCM’s blow up shows the limits of quantitative investing, quants are driving with the rear-view mirror. Good quant investment managers can be thought of as a financial economist who have codified their beliefs into a repeatable process.
Stephen Kealhofer – Managing Partner, Diversified Credit Investments
When he was working on capital structure and LBO and spinoffs. There was some mysterious force keeping companies from being completely debt financed. They called it the cost of financial distress.
Later he tried to look for models of reviewing default predictions. Since there was not a lot of data on this subject, this was hard. Both Oldrich and Merton models had some blind spots in them. When he was observing credit origination and management process in banks, he could see flaws in this process. A banker formed an opinion, but could not quantify the process that led to that opinion. So that is the reason that no real portfolio analysis of credits was possible.
He tried to use this knowledge to create loan-pooling venture, but it didn’t work. Next attempt was to start a business focused on creating a commercial risk service. KVM Corporation was the name of the business. The model was based on expected default frequency (EDF) and loss given default (LGD) estimation.
They were not sure if they were selling models, data or service or some combination to banks. In time it became clear that they were valued primarily as change agents. Finance is a beautiful field. It touches everything (license to explore the world), there is more data in finance than in any other field and the path from analysis to the implementation of business ides is incredibly fast.
Julian Shaw – Head Risk Management & Quantitative Research Permal Group
At University of Toronto they have him Levy and Sarnat’s Portfolio Theory and told him to write them a mean-variance optimization program. His mentor was Myron Gordon. Gordon is the developer of the Gordon Growth Model. Model of development of capitalist societies via simulated investment strategies. From his work a new investment criterion emerged – maximization of the probability of long-run survival. Julian learned with him that finance is more than just pricing model and arbitrage. Finance is concerned with the way people organize production and distribution, which is prerequisite for human welfare.
He moved to Gordon Capital. There he learned that when things broke, it is usually the ones with best legal documents that come out on top. He spent a lot of his career extracting the financial essence of complex legal documentation. Legal documents usual do mean what they say.
Next stop for him was CIBC, moral there was independence without expertise is worse than useless. Next stop Barclays Capital. There he worked as risk manager. Good risk management works with business to create the infrastructure needed to support new products. Arguments about pricing, hedging and risking should take place before trading begins, not after.
In his work, he worked on problems with CDO. At the end he realized that sometimes business model is more important than the quantitative model.
What do you call a quant who works on really important financial problems? A businessperson. Identifying and analyzing the key components of business problems and choosing the right techniques to model them are the hardest parts of quantitative financial analysis. Quantitative finance is a craft and a trade. It is barely engineering and it is certainly not a science.
He realized that some hedge fund managers have talent, most do not. Track records are most of the times consequence of luck.
Steve Allen – Deputy Director, Masters Program in Mathematics in Finance, Courant Institute of Mathematical Science, New York University
His education was primarily focused on theoretical mathematics. He landed a job with Chase Manhattan Bank. When Chase moved into foreign exchange options, he was working on Black-Scholes formula that believed to have weakness in lognormal distributions, continuous trading and lack of transaction costs. He showed that it can actually be used for successful hedging even if you don’t assume continuous trading and you keep frequency of hedging with controllable transaction costs.
Mark Kritzman – President and CEO – Windham Capital Management, LLC
Life of a quanta can be summed up by Woody Allen quote that 90 percent of life is showing up. He started to work in insurance. Then he moved to investment department of AT&T, they have 53 Million USD pension fund. Then he moved to Bankers Trust, where he focused on the development of risk model. Next, he set up New Amsterdam Partners with friends.
One of his application of quantitative training was Surplus Insurance. Idea to insure not all pension fund assets, but only pension plan’s surplus instead. At that time mid-1980s portfolio insurance was quite developed. Another idea was Within-Horizon Risk. He developed solution with Don Rich. Full-Scale Optimization is next idea. In the field of Efficient Trading he worked on a variant of Sharpe’s algorithm for portfolio optimization.
The successful quant will combine mathematical proficiency with an appreciation for economics and financial theory.
Bruce I. Jacobs and Kenneth N. Levy – Principals, Jacobs Levy Equity Management
Bruce worked at the Rand Institute, where math and computer science were applied to real-world problems. Ken joined family business and design and code systems for sales forecasting and inventory. Bruce learned in school about efficient market hypothesis, capital asset pricing model and Modigliani-Miller. They worked together at Prudential.
They founded company together in 1986. Their investment approach is based on a philosophy or market complexity. Equity market returns are driven by complex combination of company fundamentals, economic conditions and behavioral factors. They tried to investigate all the markets inefficiencies in the literature and to uncover new ones. They had hard time finding some customers but eventually some pension funds managers took a chance with them. They build their investment strategies based on their research and also tools to help them use their specific security selection models. They were one of the first funds moving into short selling.
Systematic risks can be shared (with diversification) and it can be shifted (with options), but it cannot be eliminated.
Tanya Styblo Beder – Chairman, SBCC
She wanted to be a musician, but her talent was not following her dreams. In Yale she became interested in game theory and operation research.
When she joined First Boston, she worked on analysis of financial statement. And later she worked on M&A. M&A questions moved from “how much company is worth” to “how much will it cost to buy it”. Later she worked on swaps.
Leaving First Boston and starting job as one-person consultant.
Allan Malz – Head of Risk Management, Clinton Group
Mathematics took a while to tighten its grip on him. He graduated in Munich University and then moved to New York to work at the Federal Reserve Bank of New York. He learned about risk management there.
Later he moved to Credit Suisse First Boston as fixed income risk management. He then moved to RiskMetrics Group, spinoff from J.P. Morgan and then to Clinton Group.
He was fortunate to work in Fed during Golden Age of Supervision, at RiskMetrics during and after technology bubble and at the hedge fund during heyday of hedge funds.
Peter Muller – Senior Advisor, Morgan Stanley
He built and oversee a group at Morgan Stanley called Process Driven Trading that trades firm capital using quantitative models.
In early years he worked at BARRA. They were using math to help analyze investment decisions for large institutional portfolio managers. At that time poker was developing into habit for him.
Later he joined Morgan Stanley. The most important thing about Morgan Stanley is that it is meritocracy. If you make money you have power and respect. If you don’t, make sure you are useful to someone who is making it.
Andrew J. Sterge – President AJ Sterge (a division of Magnetar Capital, LLC)
At Cornell he discovered game theory. He thinks that both game theory and options are perfect examples of math applications in action.
His entrance in financial world was at Cooper Neff. Most quants for him are derivates specialists. He thinks that resolution of uncertainty is a function of information arrival. Company was bought by BNP Paribas.
You cannot make yourself a successful quant without implementation. Becoming a quant is not an individual sport.
John F. (Jack) Marshall – Senior Principal of Marshall, Tucker & Associates, LLC, and Vice Chairman of the International Securities Exchange
His specialty is future markets. He started consulting early. Mainly on derivates.