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A **quantitative analyst** (or, in financial jargon, a **quant**) is a person who specializes in the application of mathematical and statistical methods to financial and risk management problems. The occupation is similar to those in industrial mathematics in other industries.^{ [1] }

- History
- Education
- Types
- Front office quantitative analyst
- Quantitative investment management
- Library quantitative analysis
- Algorithmic trading quantitative analyst
- Risk management
- Innovation
- Model validation
- Quantitative developer
- Mathematical and statistical approaches
- Techniques
- Academic and technical field journals
- Areas of work
- Seminal publications
- See also
- References
- Further reading
- External links

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 mathematics in finance, including the buy side.^{ [2] } Examples include statistical arbitrage, quantitative investment management, algorithmic trading, and electronic market making.

**Sell side** is a term used in the financial services industry. The three main markets for this selling are the stock, bond, and foreign exchange market. It is a general term that indicates a firm that sells investment services to asset management firms, typically referred to as the buy side, or corporate entities. One important note, the sell side and the buy side work hand in hand and each side could not exist without the other. These services encompass a broad range of activities, including broking/dealing, investment banking, advisory functions, and investment research.

In finance, a **derivative** is a contract that *derives* its value from the performance of an underlying entity. This underlying entity can be an asset, index, or interest rate, and is often simply called the "underlying." Derivatives can be used for a number of purposes, including insuring against price movements (hedging), increasing exposure to price movements for speculation or getting access to otherwise hard-to-trade assets or markets. Some of the more common derivatives include forwards, futures, options, swaps, and variations of these such as synthetic collateralized debt obligations and credit default swaps. Most derivatives are traded over-the-counter (off-exchange) or on an exchange such as the New York Stock Exchange, while most insurance contracts have developed into a separate industry. In the United States, after the financial crisis of 2007–2009, there has been increased pressure to move derivatives to trade on exchanges. Derivatives are one of the three main categories of financial instruments, the other two being stocks and debt. The oldest example of a derivative in history, attested to by Aristotle, is thought to be a contract transaction of olives, entered into by ancient Greek philosopher Thales, who made a profit in the exchange. Bucket shops, outlawed a century ago, are a more recent historical example.

**Risk management** is the identification, evaluation, and prioritization of risks followed by coordinated and economical application of resources to minimize, monitor, and control the probability or impact of unfortunate events or to maximize the realization of opportunities.

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.

**Louis Jean-Baptiste Alphonse Bachelier** was a French mathematician at the turn of the 20th century. He is credited with being the first person to model the stochastic process now called Brownian motion, as part of his PhD thesis *The Theory of Speculation*.

In finance, an **option** is a contract which gives the buyer the right, but not the obligation, to buy or sell an underlying asset or instrument at a specified strike price prior to or on a specified date, depending on the form of the option. The strike price may be set by reference to the spot price of the underlying security or commodity on the day an option is taken out, or it may be fixed at a discount or at a premium. The seller has the corresponding obligation to fulfill the transaction – to sell or buy – if the buyer (owner) "exercises" the option. An option that conveys to the owner the right to buy at a specific price is referred to as a call; an option that conveys the right of the owner to sell at a specific price is referred to as a put. Both are commonly traded, but the call option is more frequently discussed.

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 (mathematics was until then confined to mathematics, statistics or specialized economics journals).^{ [3] } 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, management of risk in a quantifiable manner underlies much of the modern theory.

**Harry Max Markowitz** is an American economist, and a recipient of the 1989 John von Neumann Theory Prize and the 1990 Nobel Memorial Prize in Economic Sciences.

A **thesis** or **dissertation** is a document submitted in support of candidature for an academic degree or professional qualification presenting the author's research and findings. In some contexts, the word "thesis" or a cognate is used for part of a bachelor's or master's course, while "dissertation" is normally applied to a doctorate, while in other contexts, the reverse is true. The term **graduate thesis** is sometimes used to refer to both master's theses and doctoral dissertations.

In 1965 Paul Samuelson introduced stochastic calculus into the study of finance.^{ [4] }^{ [5] } 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.

**Paul Anthony Samuelson** was an American economist and the first American to win the Nobel Memorial Prize in Economic Sciences. The Swedish Royal Academies stated, when awarding the prize in 1970, that he "has done more than any other contemporary economist to raise the level of scientific analysis in economic theory". Economic historian Randall E. Parker has called him the "Father of Modern Economics", and *The New York Times* considered him to be the "foremost academic economist of the 20th century".

**Stochastic calculus** is a branch of mathematics that operates on stochastic processes. It allows a consistent theory of integration to be defined for integrals of stochastic processes with respect to stochastic processes. It is used to model systems that behave randomly.

**Robert Cox Merton** is an American economist, Nobel Memorial Prize in Economic Sciences laureate, and professor at the MIT Sloan School of Management, known for his pioneering contributions to continuous-time finance, especially the first continuous-time option pricing model, the Black–Scholes formula. In 1993 Merton co-founded hedge fund Long-Term Capital Management. In 1997 he received the Nobel Prize for his contributions in Economics.

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.^{ [6] }

**Fischer Sheffey Black** was an American economist, best known as one of the authors of the famous Black–Scholes equation.

**Myron Samuel Scholes** is a Canadian-American financial economist. Scholes is the Frank E. Buck Professor of Finance, Emeritus, at the Stanford Graduate School of Business, Nobel Laureate in Economic Sciences, and co-originator of the Black–Scholes options pricing model. Scholes is currently the chairman of the Board of Economic Advisers of Stamos Capital Partners. Previously he served as the chairman of Platinum Grove Asset Management and on the Dimensional Fund Advisors board of directors, American Century Mutual Fund board of directors and the Cutwater Advisory Board. He was a principal and limited partner at Long-Term Capital Management, L.P. and a managing director at Salomon Brothers. Other positions Scholes held include the Edward Eagle Brown Professor of Finance at the University of Chicago, senior research fellow at the Hoover Institution, director of the Center for Research in Security Prices, and professor of finance at MIT’s Sloan School of Management. Scholes earned his PhD at the University of Chicago.

The **Black–Scholes** or **Black–Scholes–Merton model** is a mathematical model for the dynamics of a financial market containing derivative investment instruments. From the partial differential equation in the model, known as the Black–Scholes equation, one can deduce the **Black–Scholes formula**, which gives a theoretical estimate of the price of European-style options and shows that the option has a *unique* price regardless of the risk of the security and its expected return. The formula led to a boom in options trading and provided mathematical legitimacy to the activities of the Chicago Board Options Exchange and other options markets around the world. It is widely used, although often with adjustments and corrections, by options market participants.

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.^{ [7] }

Quantitative analysts often come from applied mathematics, physics or engineering backgrounds rather than economics-related fields, and quantitative analysis is a major source of employment for people with mathematics and physics PhD degrees, or with financial mathematics masters degrees. Typically, a quantitative analyst will also need extensive skills in computer programming, most commonly C, C++, Java, R, MATLAB, Mathematica, Python.

This demand for quantitative analysts has led to a resurgence in demand for actuarial qualifications as well as creation of specialized Masters and PhD courses in financial engineering, mathematical finance, computational finance, and/or financial reinsurance. In particular, Master's degrees in mathematical finance, financial engineering, operations research, computational statistics, machine learning, and financial analysis are becoming more popular with students and with employers. See Master of Quantitative Finance; Master of Financial Economics.

Data science and machine learning analysis and modelling methods are being increasingly employed in portfolio performance and portfolio risk modelling,^{ [8] }^{ [9] } and as such data science and machine learning Master's graduates are also in demand as quantitative analysts.

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In sales & 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. In the field of algorithmic trading it has reached the point where there is little meaningful difference. 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.

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.

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 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.

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. Algorithmic trading 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.

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 test (financial), economic capital analysis and direct analysis of the positions and models used by various bank's divisions.

In the aftermath of the financial crisis, there surfaced the recognition that quantitative valuation methods were generally too narrow in their approach. An agreed upon fix adopted by numerous financial institutions has been to improve collaboration.

Model validation (MV) takes the models and methods developed by front office, library, and modeling quantitative analysts and determines their validity and correctness. 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. This gravely impacted corporate ability to manage model risk, or to ensure that the positions being held were correctly valued. An MV quantitative analyst would typically earn a fraction of quantitative analysts in other groups with similar length of experience. In the years following the crisis, this has changed. Regulators now typically talk directly to the quants in the middle office such as the model validators, and since profits highly depend on the regulatory infrastructure, model validation has gained in weight and importance with respect to the quants in the front office.

Quantitative developers are computer specialists that assist, implement and maintain the quantitative models. They tend to be highly specialised language technicians that bridge the gap between software developer and quantitative analysts.

Because of their backgrounds, quantitative analysts draw from various forms of mathematics: statistics and probability, calculus centered around partial differential equations, linear algebra, discrete mathematics, and econometrics. Some on the buy side may use machine learning. The majority of quantitative analysts have received little formal education in mainstream economics, and often apply a mindset drawn from the physical sciences. Quants use mathematical skills learned from diverse fields such as computer science, physics and engineering. 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:

- Finite difference method – used to solve partial differential equations;
- Monte Carlo method – Also used to solve partial differential equations, but Monte Carlo simulation is also common in risk management;
- Ordinary least squares – used to estimate parameters in statistical regression analysis;
- Spline interpolation – used to interpolate values from spot and forward interest rates curves, and volatility smiles;
- Bisection, Newton, and Secant methods – used to find the roots, maxima and minima of functions (e.g. internal rate of return.)

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. These quantitative analysts tend to rely more on numerical analysis than statistics and econometrics. The mindset is to prefer 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 quantitative analysts 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 quantitative analysts 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 quantitative analysts demand a strong knowledge of sophisticated mathematics and computer programming proficiency.

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

- Society for Industrial and Applied Mathematics (SIAM) Journal on Financial Mathematics
- The Journal of Portfolio Management
^{ [10] } - Quantitative Finance
^{ [11] } - Risk Magazine
- Wilmott Magazine
- Finance and Stochastics
^{ [12] }

- Trading strategy development
- Portfolio optimization
- Derivatives pricing and hedging: involves software development, advanced numerical techniques, and stochastic calculus.
- Risk management: involves a lot of time series analysis, calibration, and backtesting.
- Credit analysis
- Asset liability management
- Structured finance and securitization
- Asset pricing
- Portfolio management

- 1900 – Louis Bachelier,
*Théorie de la spéculation* - 1938 – Frederick Macaulay,
*The Movements of Interest Rates. Bond Yields and Stock Prices in the United States since 1856*, pp. 44–53, Bond duration - 1944 – Kiyosi Itô, "Stochastic Integral", Proceedings of the Imperial Academy, 20(8), pp. 519–524
- 1952 – Harry Markowitz,
*Portfolio Selection*, Modern portfolio theory - 1956 – John Kelly,
*A New Interpretation of Information Rate* - 1958 – Franco Modigliani and Merton Miller,
*The Cost of Capital, Corporation Finance and the Theory of Investment*, Modigliani–Miller theorem and Corporate finance - 1964 – William F. Sharpe,
*Capital asset prices: A theory of market equilibrium under conditions of risk*, Capital asset pricing model - 1965 – John Lintner,
*The Valuation of Risk Assets and the Selection of Risky Investments in Stock Portfolios and Capital Budgets*, Capital asset pricing model - 1967 – Edward O. Thorp and Sheen Kassouf,
*Beat the Market* - 1972 – Eugene Fama and Merton Miller,
*Theory of Finance* - 1972 – Martin L. Leibowitz and Sydney Homer,
*Inside the Yield Book*, Fixed income analysis - 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 – Oldřich Vašíček,
*An equilibrium characterisation of the term structure*, Vasicek model - 1979 – John Carrington Cox; Stephen Ross; Mark Rubinstein,
*Option pricing: A simplified approach*, Binomial options pricing model and Lattice model - 1980 – Lawrence G. McMillan,
*Options as a Strategic Investment* - 1982 – Barr Rosenberg and Andrew Rudd,
*Factor-Related and Specific Returns of Common Stocks: Serial Correlation and Market Inefficiency*, Journal of Finance, May 1982 V. 37: #2 - 1982 – Robert Engle
*Autoregressive Conditional Heteroskedasticity With Estimates of the Variance of U.K. Inflation,*Seminal paper in ARCH family of models GARCH - 1985 – John C. Cox, Jonathan E. Ingersoll and Stephen Ross,
*A theory of the term structure of interest rates*, Cox–Ingersoll–Ross model - 1987 – Giovanni Barone-Adesi and Robert Whaley (June 1987). "Efficient analytic approximation of American option values". Journal of Finance. 42 (2): 301–20. Most widely used approximation for pricing American options.
- 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 - 1990 – John Hull and Alan White, "Pricing interest-rate derivative securities", The Review of Financial Studies, Vol 3, No. 4 (1990) Hull-White model
- 1991 – Ioannis Karatzas & Steven E. Shreve.
*Brownian motion and stochastic calculus*. - 1992 – Fischer Black and Robert Litterman: Global Portfolio Optimization, Financial Analysts Journal, September 1992, pp. 28–43 JSTOR 4479577 Black-Litterman model
- 1994 – J.P. Morgan RiskMetrics Group, RiskMetrics Technical Document, 1996.
- 2002 – Patrick Hagan, Deep Kumar, Andrew Lesniewski, Diana Woodward,
*Managing Smile Risk', Wilmott Magazine, January 2002, SABR volatility model.* - 2004 – Emanuel Derman,
*My Life as a Quant: Reflections on Physics and Finance*

**Fundamental analysis**, in accounting and finance, is the analysis of a business's financial statements ; health; and competitors and markets. It also considers the overall state of the economy and factors including interest rates, production, earnings, employment, GDP, housing, manufacturing and management. There are two basic approaches that can be used: bottom up analysis and top down analysis. These terms are used to distinguish such analysis from other types of investment analysis, such as quantitative and technical.

**Financial economics** is the branch of economics characterized by a "concentration on monetary activities", in which "money of one type or another is likely to appear on *both sides* of a trade". Its concern is thus the interrelation of financial variables, such as prices, interest rates and shares, as opposed to those concerning the real economy. It has two main areas of focus: asset pricing and corporate finance; the first being the perspective of providers of capital, i.e. investors, and the second of users of capital.

**Real options valuation**, also often termed **real options analysis**, applies option valuation techniques to capital budgeting decisions. A **real option** itself, is the right—but not the obligation—to undertake certain business initiatives, such as deferring, abandoning, expanding, staging, or contracting a capital investment project. For example, the opportunity to invest in the expansion of a firm's factory, or alternatively to sell the factory, is a real call or put option, respectively.

**Financial engineering** is a multidisciplinary field involving financial theory, methods of engineering, tools of mathematics and the practice of programming. It has also been defined as the application of technical methods, especially from mathematical finance and computational finance, in the practice of finance. Despite its name, financial engineering does not belong to any of the fields in traditional professional engineering even though many financial engineers have studied engineering beforehand and many universities offering a postgraduate degree in this field require applicants to have a background in engineering as well. In the United States, the Accreditation Board for Engineering and Technology (ABET) does not accredit financial engineering degrees. In the United States, financial engineering programs are accredited by the *International Association of Quantitative Finance*.

**Financial modeling** is the task of building an abstract representation of a real world financial situation. This is a mathematical model designed to represent the performance of a financial asset or portfolio of a business, project, or any other investment.

**Computational finance** is a branch of applied computer science that deals with problems of practical interest in finance. Some slightly different definitions are the study of data and algorithms currently used in finance and the mathematics of computer programs that realize financial models or systems.

In mathematical finance, the **Black–Derman–Toy model** (**BDT**) is a popular short rate model used in the pricing of bond options, swaptions and other interest rate derivatives; see Lattice model (finance) #Interest rate derivatives. It is a one-factor model; that is, a single stochastic factor—the short rate—determines the future evolution of all interest rates. It was the first model to combine the mean-reverting behaviour of the short rate with the lognormal distribution, and is still widely used.

The following outline is provided as an overview of and topical guide to finance:

A **masters degree in quantitative finance** concerns the application of mathematical methods to the solution of problems in financial economics. There are several like-titled degrees which may further focus on financial engineering, financial risk management, computational finance and/or mathematical finance.

A **basket option** is a financial derivative, more specifically an exotic option, whose underlying is a weighted sum or average of different assets that have been grouped together in a basket. For example, an index option, where a number of stocks have been grouped together in an index and the option is based on the price of the index.

**Neil A. Chriss** is a mathematician, academic, hedge fund manager, philanthropist and a founding board member of the charity organization "Math for America" which seeks to improve math education in the United States. Chriss also serves on the board of trustees of the Institute for Advanced Study.

In finance, **model risk** is the risk of loss resulting from using insufficiently accurate models to make decisions, originally and frequently in the context of valuing financial securities. However, model risk is more and more prevalent in activities other than financial securities valuation, such as assigning consumer credit scores, real-time probability prediction of fraudulent credit card transactions, and computing the probability of air flight passenger being a terrorist. Rebonato in 2002 defines model risk as "the risk of occurrence of a significant difference between the mark-to-model value of a complex and/or illiquid instrument, and the price at which the same instrument is revealed to have traded in the market".

A **master's degree in Financial Economics** provides a rigorous understanding of theoretical finance and the economic framework upon which that theory is based. The degree is postgraduate, and usually incorporates a thesis or research component. Programs may be offered jointly by the business school and the economics department.

**Portfolio optimization** is the process of selecting the best portfolio, out of the set of all portfolios being considered, according to some objective. The objective typically maximizes factors such as expected return, and minimizes costs like financial risk. Factors being considered may range from tangible to intangible.

**Mathematical finance**, also known as **quantitative finance**, is a field of applied mathematics, concerned with mathematical modeling of financial markets. Generally, mathematical finance will derive and extend the mathematical or numerical models without necessarily establishing a link to financial theory, taking observed market prices as input. Mathematical consistency is required, not compatibility with economic theory. Thus, for example, while a financial economist might study the structural reasons why a company may have a certain share price, a financial mathematician may take the share price as a given, and attempt to use stochastic calculus to obtain the corresponding value of derivatives of the stock. The fundamental theorem of arbitrage-free pricing is one of the key theorems in mathematical finance, while the Black–Scholes equation and formula are amongst the key results.

- ↑ See Definition in the Society for Applied and Industrial Mathematics http://www.siam.org/about/pdf/brochure.pdf
- ↑ Derman, E. (2004). My life as a quant: reflections on physics and finance. John Wiley & Sons.
- ↑ Markowitz, H. (1952). "Portfolio Selection".
*Journal of Finance*.**7**(1): 77–91. doi:10.1111/j.1540-6261.1952.tb01525.x. - ↑ Samuelson, P. A. (1965). "Rational Theory of Warrant Pricing".
*Industrial Management Review*.**6**(2): 13–32. - ↑ Henry McKean the co-founder of stochastic calculus (along with Kiyosi Itô) wrote the appendix: see McKean, H. P. Jr. (1965). "Appendix (to Samuelson): a free boundary problem for the heat equation arising from a problem of mathematical economics".
*Industrial Management Review*.**6**(2): 32–39. - ↑ Harrison, J. Michael; Pliska, Stanley R. (1981). "Martingales and Stochastic Integrals in the Theory of Continuous Trading".
*Stochastic Processes and Their Applications*.**11**(3): 215–260. doi:10.1016/0304-4149(81)90026-0. - ↑ Derman, Emanuel (2004).
*My Life as a Quant*. John Wiley and Sons. - ↑ "Machine Learning in Finance: Theory and Applications".
*marketsmedia.com*. 22 January 2013. Retrieved 2 April 2018. - ↑ "A Machine-Learning View of Quantitative Finance" (PDF).
*qminitiative.org*. - ↑ "The Journal of Portfolio Management".
*jpm.iijournals.com*. Retrieved 2019-02-02. - ↑ http://www.tandfonline.com/toc/rquf20/current%7C
- ↑ "Finance and Stochastics – incl. Option to publish open access".

- Bernstein, Peter L. (1992)
*Capital Ideas: The Improbable Origins of Modern Wall Street* - Bernstein, Peter L. (2007)
*Capital Ideas Evolving* - Derman, Emanuel (2007)
*My Life as a Quant*ISBN 0-470-19273-9 - Patterson, Scott D. (2010).
*The Quants: How a New Breed of Math Whizzes Conquered Wall Street and Nearly Destroyed It*. Crown Business, 352 pages. ISBN 0-307-45337-5 ISBN 978-0-307-45337-2. Amazon page for book via Patterson and Thorp interview on Fresh Air, Feb. 1, 2010, including excerpt "Chapter 2: The Godfather: Ed Thorp". Also, an excerpt from "Chapter 10: The August Factor", in the January 23, 2010*Wall Street Journal*. - Read, Colin (2012)
*Rise of the Quants: (Great Minds in Finance Series) ISBN 023027417X*

- http://sqa-us.org – Society of Quantitative Analysts
- http://www.q-group.org/—Q-Group Institute for Quantitative Research in Finance
- http://cqa.org – CQA—Chicago Quantitative Alliance
- http://qwafafew.org/ – QWAFAFEW – Quantitative Work Alliance for Finance Education and Wisdom
- http://prmia.org – PRMIA—Professional Risk Managers Industry Association
- http://iaqf.org – International Association of Quantitative Finance
- http://www.lqg.org.uk/ – London Quant Group
- http://quant.stackexchange.com – question and answer site for quantitative finance

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