[3] Quantitative finance 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".
[6] Considered the "Father of Quantitative Investing",[6] Thorp sought to predict and simulate blackjack, a card-game he played in Las Vegas casinos.
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.
[10] The various short-rate models (beginning with Vasicek in 1977), and the more general HJM Framework (1987), relatedly allowed for an extension to fixed income and interest rate derivatives.
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.
Front office work favours a higher speed to quality ratio, with a greater emphasis on solutions to specific problems than detailed modeling.
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.
Two cases are: XVA specialists, responsible for managing counterparty risk as well as (minimizing) the capital requirements under Basel III; and structurers, tasked with the design and manufacture of client specific solutions.
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.
[7][12] 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.
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.
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.
The MV group might well be seen as a superset of the quantitative operations in a financial institution, since it must deal with new and advanced models and trading techniques from across the firm.
Before the crisis however, the pay structure in all firms was such that MV groups struggle to attract and retain adequate staff, often with talented quantitative analysts leaving at the first opportunity.
Financial markets are complex systems in which traditional assumptions, such as independence and normal distribution of returns, are frequently challenged by empirical evidence.
The majority of quantitative analysts have received little formal education in mainstream economics, and often apply a mindset drawn from the physical sciences.
These skills include (but are not limited to) advanced statistics, linear algebra and partial differential equations as well as solutions to these based upon numerical analysis.
Commonly used numerical methods are: A typical problem for a mathematically oriented quantitative analyst would be to develop a model for pricing, hedging, and risk-managing a complex derivative product.
Likewise, masters programs in operations research, computational statistics, applied mathematics and industrial engineering may offer a quantitative finance specialization.