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Quantitative analyst


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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 of 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: 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. 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. From Wikipedia, the free encyclopedia.

It has been suggested that this article be merged into Quantitative analysis finance. Discuss Proposed since November This section does not cite any sources. Please help improve this section by adding citations to reliable sources. Unsourced material may be challenged and removed. June Learn how and when to remove this template message. My life as a quant: Michael; Pliska, Stanley R. Stochastic Processes and Their Applications. My Life as a Quant. John Wiley and Sons. Retrieved 2 April Option to publish open access".

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The model might include a company's book value to price ratio, its trailing earnings to price ratio, and other accounting factors.

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