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In the United States, quant trading positions are most prevalent in big financial hubs such as New York and Chicago, and areas where hedge funds tend to cluster, such as Boston, Massachusetts, and Stamford, Connecticut. Globally, quant traders may find employment opportunities in major financial hubs such as London, Hong Kong, Singapore, Tokyo, and Sydney, among other regional financial centers.

Compensation in the field of finance tends to be very high. According to Bureau of Labor Statistics data, the median annual pay for financial analysts in 2022 was $95,080, while the highest 10% earned more than $169,940. However, in the field of quantitative analysis, it is not uncommon to find positions with posted salaries of $250,000 or more. With bonuses, the actual salary could top $500,000 per year. As with most careers, the more experience you have, the higher a salary you can command. Hedge funds or other trading firms generally pay the most, while an entry-level quant position may earn only $125,000 or $150,000.

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If you are a quant trader, you will generally earn the highest salary working for a hedge fund. For example, based on the Selby Jennings' North American quant team salary and bonus survey for 2020, a graduate with a Ph.D. in a STEM field (science, technology engineering, or mathematics) could earn between $300,000 and $400,000 in total compensation (combined salary and bonus) at a top hedge fund or independent trading firm.

Most firms require at least a master's degree, or preferably a Ph.D., in a quantitative subject (mathematics, economics, finance, or statistics). Master's degrees in financial engineering or computational finance may also be effective entry points for careers as a quant trader.

If you hold an MBA degree, you will likely also need a very strong mathematical or computational skill set, in addition to some solid experience in the real world in order to be hired as a quant trader.

Alongside their educational requirements, quant traders must also have advanced software skills. C++ is typically used for high-frequency trading applications, and offline statistical analysis would be performed in MATLAB, SAS, S-PLUS, or a similar package. Pricing knowledge may also be embedded in trading tools created with Java, .NET or VBA, and are often integrated with Excel.

Certain aspects of statistics are the backbone of quantitative trading, including regression theory and time-series analysis. Electronic engineering techniques such as Fourier analysis and wavelet analysis are also utilized in quantitative analysis. Most of the statistics concepts you will need to understand to work in quant trading is so advanced that it is not taught at an undergraduate level. For this reason, it is important to pursue advanced study in statistics (namely Ph.D. coursework).

Quantitative analysis is the use of mathematical and statistical methods in finance and investment management. Those working in the field are quantitative analysts (quants). Quants tend to specialize in specific areas which may include derivative structuring or pricing, risk management, investment management and other related finance occupations. The occupation is similar to those in industrial mathematics in other industries.[1] The process usually consists of searching vast databases for patterns, such as correlations among liquid assets or price-movement patterns (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.[2] 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.

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. 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 specialized economics journals).[4] 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 It calculus, management of risk in a quantifiable manner underlies much of the modern theory.

Quantitative analysts often come from financial mathematics, financial engineering, applied mathematics, physics or engineering backgrounds, and quantitative analysis is a major source of employment for people with financial mathematics master's degrees, or with mathematics and physics PhD degrees.

Typically, a quantitative analyst will also need extensive skills in computer programming, most commonly C, C++, Java, R, MATLAB, Mathematica, and Python.Data science and machine learning analysis and modelling methods are being increasingly employed in portfolio performance and portfolio risk modelling,[11][12] and as such data science and machine learning Master's graduates are also hired as quantitative analysts.

This demand for quantitative analysts has led to the 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, applied mathematics, machine learning, and financial analysis are becoming more popular with students and with employers. See Master of Quantitative Finance for general discussion.

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.See also structurer.

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.[6][13] 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.[6] 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 "[build] the Supercollider of Finance".[14][15]

In the aftermath of the financial crisis[2008], 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; see model risk. 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.

Post crisis, 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.

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, as mentioned, this has changed.

Quantitative developers, sometimes called quantitative software engineers, or quantitative engineers, 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 engineers and quantitative analysts. The term is also sometimes used outside the finance industry to refer to those working at the intersection of software engineering and quantitative research.

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. Themajority 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. e24fc04721