Quant

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Related to Quants: Qantas

Quant

A person with numerical and computer skills who carries out quantitative analyses of companies.

Quant

Informal for a quantitative analyst. It is more generally applied to any securities analyst who uses quantitative analysis to determine buy or sell signals. Investment advisory firms employ quants to help them make investment decisions on behalf of clients. A quant is also called a rocket scientist. See also: Technical analysis.

quant

A person who has strong skills in mathematics, engineering, or computer science, and who applies those skills to the securities business. For example, a pension fund may employ a quant to put together an optimal portfolio of bonds to meet the fund's future liabilities. Also called rocket scientist.
References in periodicals archive ?
Leigh Drogen, the founder of crowdsourced financial analysis platform Estimize, started organizing quant training for discretionary traders when he heard about portfolio managers going to night classes to learn data science.
To rectify this, Estimize began its Learn to Quant (L2Q) training sessions in association with Jefferies, the investment bank.
To managers, quants value cleverness more than realism and practicality, are unwilling or unable to clearly explain the reasoning underlying their elaborate models and cannot appreciate the many immeasurable factors relevant to most decisions.
Roles and individuals that combine the strengths of quants and managers will be in high demand in the world that is emerging.
The only way to tame the data beast and ultimately have a shot at enjoying success from quantitative R&D," he says, "is through the quant toolbox - the combination of the immediacy, concurrency and multiplicity of data needs that cause so much complexity in trading infrastructures and impede improved value extraction from data.
Until then, quant teams must rely on multiple data stores that specialize in various datasets and crunch through exceedingly large datasets, in multiple physical locations to access the data they need to perform.
Many quants should also benefit from reading this book.
Rishi Narang does an excellent job demystifying how quants work, in an accessible and fun read.
including key models, important formulae, popular contracts, essays and opinions, a history of quantitative finance, sundry lists, the commonest mistakes in quant finance, brainteasers, plenty of straight-talking, the Modellers' Manifesto and lots more.
For example, if a quant is trying to measure the volatility of a stock portfolio with several different stocks, each with its own volatility, S+GARCH can accurately model the time-varying correlations among the stocks as well as their individual volatility and provide the analyst with a single risk measurement for the portfolio.