Quantitative analyst

A quantitative analyst is a person who works in finance using numerical or quantitative techniques. Similar work is done in most other modern industries, but the work is not called quantitative analysis. In the investment industry, people who perform quantitative analysis are frequently called quants. See List of quantitative analysts.

Although the original quants were concerned with risk management and derivatives pricing, the meaning of the term has expanded over time to include those individuals involved in almost any application of mathematics in finance. Examples include statistical arbitrage, algorithmic trading and electronic market making.

History

Robert C. Merton, a pioneer of quantitative analysis, introduced stochastic calculus into the study of finance.

Quantitative finance started in the U.S. in the 1930s as some astute investors began using mathematical formulae to price stocks and bonds.

Harry Markowitz's 1952 Ph.D thesis "Portfolio Selection" was one of the first papers to formally adapt mathematical concepts to finance. Markowitz formalized a notion of mean return and covariances for common stocks which allowed him to quantify the concept of "diversification" in a market. He showed how to compute the mean return and variance for a given portfolio and argued that investors should hold only those portfolios whose variance is minimal among all portfolios with a given mean return. Although the language of finance now involves Itō calculus, minimization of risk in a quantifiable manner underlies much of the modern theory.

In 1969 Robert Merton introduced stochastic calculus into the study of finance. Merton was motivated by the desire to understand how prices are set in financial markets, which is the classical economics question of "equilibrium," and in later papers he used the machinery of stochastic calculus to begin investigation of this issue.

At the same time as Merton's work and with Merton's assistance, Fischer Black and Myron Scholes were developing their option pricing formula, which led to winning the 1997 Nobel Prize in Economics. It provided a solution for a practical problem, that of finding a fair price for a European call option, i.e., the right to buy one share of a given stock at a specified price and time. Such options are frequently purchased by investors as a risk-hedging device. In 1981, Harrison and Pliska used the general theory of continuous-time stochastic processes to put the Black-Scholes option pricing formula on a solid theoretical basis, and as a result, showed how to price numerous other "derivative" securities.

Education

Quants often come from physics, engineering or mathematics backgrounds rather than economics related fields, and quant finance is a major source of employment for people with physics and mathematics Ph.D's. Typically, a quant will also need extensive skills in computer programming, most commonly C++.

This demand for quants has led to the creation of specialized Masters and PhD courses in mathematical finance, computational finance, and/or financial reinsurance. In particular, Masters degrees in Mathematical Finance, financial engineering and financial analysis are becoming more popular with students and with employers. London's Cass Business School was the pioneer of quantitative finance programs in Europe, with its MSc Quantitative Finance as well as the MSc Financial Mathematics and MSc Mathematical Trading and Finance programs providing some leading global research. Carnegie Mellon's Tepper School of Business, which created the Masters degree in financial engineering, reported a 21% increase in applicants to their MS in Computational Finance program, which is on top of a 48% increase in the year before. These Masters level programs are generally one year in length and more focused than the broader MBA degree. The largest quant training program is the Wilmott Certificate in Quantitative Finance , directed by Paul Wilmott.

Front Office Quant

In trading & sales operations, quants work to determine prices, manage risk, and identify profitable opportunities. Historically this was a distinct activity from trading but the boundary between a desk quant and a quant trader is increasingly blurred, and it is now difficult to enter trading as a profession without at least some quant education. In the field of alogrithmic trading it has reached the point where these is little meaningful difference. Front office work favours a higher speed / quality ratio, with a greater emphasis on solutions to specific problems than detailed modelling. FOQs typically are significantly better paid, than those in back office and risk, and far ahead of those in model validation. This has obvious implications for the quality of decisions at a strategic level. Although highly skilled programmers, time constraints mean that complex decisions are mode using Excel and ad-hoc tools.

Library Quant

Major firms invest large sums in an attempt to produce standard methods of evaluating prices and risk. These differ from front office tools in that Excel is very rare, with most development being in C++, though Java and C# are sometimes used in non-performance critical tasks. LQs spend more time modelling ensuring the analytics are both efficient and correct, though there is tension between LQs and FOQs on the validity of their results. LQs are required to understand techniques such as Monte Carlo, Finite Difference Method, as well as the nature of the products being modelled.

Algorithmic Trading Quant

Often the highest paid form of quant, ATQs make use of methods taken from signal processing, game theory, gambling Kelly Criterion, market micro structure, econometrics and time series analysis. Algotrading includes statistical arbitrage, but includes techniques largely based upon speed of response, to the extent that some ATQs modify hardware and Linux kernels to achieve ultra low latency.

Risk Management

This has grown in importance in recent years, as the credit crisis exposed holes in the mechanisms used to ensure that positions were correctly hedged, though in no bank does the pay in risk approach that in front office. A core technique is Value at Risk, and this is backed up with various forms of stress testing, and direct analysis of the positions and models used by traders.

Model Validation

MV takes the models and methods developed by front office, library and modelling quants and determines their validity and correctness. The MV group might well be seen as a superset of the quant operations in a financial institution, since it must deal with new and advanced new models and trading techniques from across the firm. However the pay structure in all firms is such that MV groups struggle to attract and retain adequate staff, often with talented quants leaving at the first opportunity. This gravely impacts corporate ability to manage model risk, or to ensure that the positions being hold are correctly valued. An MV quant will typically earn a fraction of quants in other groups with similar length of experience.

Mathematical and statistical approaches

Because of their backgrounds quants draw from three forms of mathematics: Statistics & Probability, Calculus centred around Partial Differential Equations and Econometrics. The majority of quants have received little formal education in mainstream economics, and often apply a mindset drawn from the physical sciences. Physicists tend to have significantly less experience of statistical techniques, and thus lean to approaches based upon PDEs, and solutions to these based upon numerical analysis.

A typical problem for a numerically oriented quantitative analyst would be to develop a model for pricing, hedging, and risk-managing a complex derivative product. Mathematically-oriented quants tend to have more of a reliance on numerical analysis, and less of a reliance on statistics and econometrics. These quants tend to be of the psychology that prefers a deterministically "correct" answer, as once there is agreement on input values and market variable dynamics, there is only one correct price for any given security (which can be demonstrated, albeit often inefficiently, through a large volume of Monte Carlo simulations).

A typical problem for a statistically oriented quantitative analyst would be to develop a model for deciding which stocks are relatively expensive and which stocks are relatively cheap. The model might include a company's book value to price ratio, its trailing earnings to price ratio and other accounting factors. An investment manager might implement this analysis by buying the underpriced stocks, selling the overpriced stocks or both. Statistically-oriented quants tend to have more of a reliance on statistics and econometrics, and less of a reliance on sophisticated numerical techniques and object-oriented programming. These quants tend to be of the psychology that enjoys trying to find the best approach to modeling data, and can accept that there is no "right answer" until time has passed and we can retrospectively see how the model performed. Both types of quants demand a strong knowledge of sophisticated mathematics and computer programming proficiency.

One of the principal mathematical tools of quantitative finance is stochastic calculus.

According to a July 2008 Aite Group report, today quants often use alpha generation platforms to help them develop financial models. These software solutions enable quants to centralize and streamline the alpha generation process.

Areas of Work

• Trading strategy development
• Portfolio optimization
• Derivatives pricing and hedging: involves a lot of highly-efficient (usually object-oriented) software development, advanced numerical techniques, and stochastic calculus
• Risk management: involves a lot of time series analysis, calibration, and backtesting
• Credit analysis

Seminal publications

See also: List of quantitative analysts

• 1900 - Louis Bachelier, Théorie de la spéculation
• 1952 - Harry Markowitz, Portfolio Selection, Modern portfolio theory
• 1956 - John Kelly, A New Interpretation of Information Rate
• 1967 - Edward O. Thorp and Sheen Kassouf, Beat the Market
• 1972 - Eugene Fama and Merton Miller, Theory of Finance
• 1973 - Fischer Black and Myron Scholes, The Pricing of Options and Corporate Liabilities and Robert C. Merton, Theory of Rational Option Pricing, Black–Scholes
• 1976 - Fischer Black, The pricing of commodity contracts, Black model
• 1977 - Phelim Boyle, Options: A Monte Carlo Approach, Monte Carlo methods for option pricing
• 1977 - Oldrich Vasicek, An equilibrium characterisation of the term structure, Vasicek model
• 1980 - Lawrence G. McMillan, Options as a Strategic Investment
• 1985 - John C. Cox, Jonathan E. Ingersoll and Stephen Ross, A theory of the term structure of interest rates, Cox-Ingersoll-Ross model
• 1988 - John Hull, Options, futures, and other derivatives (7th edition issued in 2008)
• 1990 - Fischer Black, Emanuel Derman and William Toy, A One-Factor Model of Interest Rates and Its Application to Treasury Bond, Black-Derman-Toy model
• 1996 - Philippe Jorion, Value at Risk
• 1997 - Espen Gaarder Haug, The Complete Guide to Option Pricing Formulas
• 1998 - Paul Wilmott, Derivatives: The Theory and Practice of Financial Engineering
• 2004 - Emanuel Derman, My Life as a Quant: Reflections on Physics and Finance
• 2004 - Steven E. Shreve, Stochastic Calculus for Finance

See also

• List of quantitative analysts
• Patterson, Scott D., The Quants: How a New Breed of Math Whizzes Conquered Wall Street and Nearly Destroyed It, Crown Business, 352 pages, 2010. ISBN 0307453375 ISBN 978-0307453372

External links

• http://iafe.org - International Association of Financial Engineers
• http://www.wilmott.com - famed mainly for its forums, one of the main places where quants talk online.
• http://www.newmogul.com - quantitative finance news site
• http://www.quantspot.com - quantitative finance jobs vertical search engine