Quantitative analysis (finance)
Quantitative analysis in finance refers to the application of mathematical and statistical methods to problems in financial markets and investment management. Professionals in this field are known as quantitative analysts or quants.
Quants typically specialize in areas such as derivative structuring and pricing, risk management, portfolio management, and other finance-related activities. The role is analogous to that of specialists in industrial mathematics working in non-financial industries.
Quantitative analysis often involves examining large datasets to identify patterns, such as correlations among liquid assets or price dynamics, including strategies based on trend following or mean reversion.
Although the original quantitative analysts were "sell side quants" from market maker firms, concerned with derivatives pricing and risk management, the meaning of the term has expanded over time to include those individuals involved in almost any application of mathematical finance, including the buy side. Applied quantitative analysis is commonly associated with quantitative investment management which includes a variety of methods such as statistical arbitrage, algorithmic trading, and electronic trading.
Some of the larger investment managers using quantitative analysis include Renaissance Technologies, D. E. Shaw & Co., and AQR Capital Management.
History
started in 1900 with Louis Bachelier's doctoral thesis "Theory of Speculation", which provided a model to price options under a normal distribution.Jules Regnault had posited already in 1863 that stock prices can be modelled as a random walk, suggesting "in a more literary form, the conceptual setting for the application of probability to stockmarket operations".
It was, however, only in the years 1960-1970 that the "merit of was recognized"
as options pricing theory was developed.
Harry Markowitz's 1952 doctoral thesis "Portfolio Selection" and its published version was one of the first efforts in economics journals 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.
Thus, although the language of finance now involves Itô calculus, management of risk in a quantifiable manner underlies much of the modern theory.
Modern quantitative investment management was first introduced from the research of Edward Thorp, a mathematics professor at New Mexico State University and University of California, Irvine. Considered the "Father of Quantitative Investing", Thorp sought to predict and simulate blackjack, a card-game he played in Las Vegas casinos. He was able to create a system, known broadly as card counting, which used probability theory and statistical analysis to successfully win blackjack games. His research was subsequently used during the 1980s and 1990s by investment management firms seeking to generate systematic and consistent returns in the U.S. stock market.
The field has grown to incorporate numerous approaches and techniques; see, Post-modern portfolio theory,.
In 1965, Paul Samuelson introduced stochastic calculus into the study of finance. In 1969, Robert Merton promoted continuous stochastic calculus and continuous-time processes. 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 developed the Black–Scholes model, which was awarded the 1997 Nobel Memorial Prize in Economic Sciences. 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 model on a solid theoretical basis, and showed how to price numerous other derivative securities, laying the groundwork for the development of the fundamental theorem of asset pricing. The various short-rate models, and the more general HJM Framework, relatedly allowed for an extension to fixed income and interest rate derivatives. Similarly, and in parallel, models were developed for various other underpinnings and applications, including credit derivatives, exotic derivatives, real options, and employee stock options. Quants are thus involved in pricing and hedging a wide range of securities – asset-backed, government, and corporate – additional to classic derivatives; see contingent claim analysis.
Emanuel Derman's 2004 book My Life as a Quant helped to both make the role of a quantitative analyst better known outside of finance, and to popularize the abbreviation "quant" for a quantitative analyst.
After the 2008 financial crisis, considerations regarding counterparty credit risk were incorporated into the modelling, previously performed in an entirely "risk neutral world", entailing three major developments; see :
Option pricing and hedging inhere the relevant volatility surface - to some extent, equity-option prices have incorporated the volatility smile since the 1987 crash - and banks then apply "surface aware" local- or stochastic volatility models;
The risk neutral value is adjusted for the impact of counter-party credit risk via a credit valuation adjustment, or CVA, as well as various of the other XVA;
For discounting, the OIS curve is used for the "risk free rate", as opposed to LIBOR as previously, and, relatedly, quants must model under a "multi-curve framework"
.
Types
Front office quantitative analyst
In sales and trading, quantitative analysts work to determine prices, manage risk, and identify profitable opportunities. Historically this was a distinct activity from trading but the boundary between a desk quantitative analyst and a quantitative trader is increasingly blurred, and it is now difficult to enter trading as a profession without at least some quantitative analysis education.Front office work favours a higher speed to quality ratio, with a greater emphasis on solutions to specific problems than detailed modeling. FOQs typically are significantly better paid than those in back office, risk, and model validation. Although highly skilled analysts, FOQs frequently lack software engineering experience or formal training, and bound by time constraints and business pressures, tactical solutions are often adopted.
Increasingly, quants are attached to specific desks. Two cases are: XVA specialists, responsible for managing counterparty risk as well as the capital requirements under Basel III; and structurers, tasked with the design and manufacture of client specific solutions.
Quantitative investment management
Quantitative analysis is used extensively by asset managers. Some, such as FQ, AQR or Barclays, rely almost exclusively on quantitative strategies while others, such as PIMCO, BlackRock or Citadel use a mix of quantitative and fundamental methods.One of the first quantitative investment funds to launch was based in Santa Fe, New Mexico and began trading in 1991 under the name Prediction Company. By the late-1990s, Prediction Company began using statistical arbitrage to secure investment returns, along with three other funds at the time, Renaissance Technologies and D. E. Shaw & Co, both based in New York. Prediction hired scientists and computer programmers from the neighboring Los Alamos National Laboratory to create sophisticated statistical models using "industrial-strength computers" in order to " the Supercollider of Finance".
Machine learning models are now capable of identifying complex patterns in financial market data. With the aid of artificial intelligence, investors are increasingly turning to deep learning techniques to forecast and analyze trends in stock and foreign exchange markets.
See.
Library quantitative analysis
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, C# and Python are sometimes used in non-performance critical tasks. LQs spend more time modeling 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 methods and finite difference methods, as well as the nature of the products being modeled.Algorithmic trading quantitative analyst
Often the highest paid form of Quant, ATQs make use of methods taken from signal processing, game theory, gambling Kelly criterion, market microstructure, econometrics, and time series analysis.Risk management
This area has grown in importance in recent years, as the credit crisis exposed holes in the mechanisms used to ensure that positions were correctly hedged; see FRTB,.A core technique continues to be value at risk
- applying both the parametric and "Historical" approaches, as well as Conditional value at risk and Extreme value theory -
while this is supplemented with various forms of stress test, expected shortfall methodologies, economic capital analysis, direct analysis of the positions at the desk level,
and, [|as below], assessment of the models used by the bank's various divisions.