Quantitative Analyst (Quant)

A quantitative analyst, commonly called a quant, is a finance professional who uses mathematical models, statistical analysis and computer programming to solve problems in markets. Typical tasks include pricing derivatives, measuring the risk of a portfolio, finding patterns in market data and building the systems that trade on them. The work belongs to the field of quantitative finance.

Quants are employed by investment banks, hedge funds, asset managers, proprietary trading firms, insurers, exchanges and regulators. Many hold advanced degrees in mathematics, physics, statistics, engineering or computer science, and the role differs from that of a traditional analyst, who studies companies and economies through financial statements and judgement.

• Desk or front-office quants. They sit with traders and build the pricing and hedging models used on a trading desk, such as models for options and structured products.
• Quantitative researchers. They search for predictive signals in data and design the strategies used in quantitative trading. This is the dominant role at systematic hedge funds.
• Quantitative developers. They write and maintain the production code, including pricing libraries, data pipelines and execution systems, often in C++ or Python.
• Risk quants. They build the models that measure market risk, credit risk and capital requirements, including value at risk.
• Model validation quants. They independently review and test models built by others before the models are approved for use.

The boundaries are loose. At small firms one person may cover research, coding and trading.

Derivatives quants work mainly with probability theory and the mathematics of a stochastic process. The Black-Scholes-Merton model is the starting point, and more complex products are valued with numerical methods such as Monte Carlo simulation. The sensitivities of a price to its inputs, known as the option Greeks, are a standard output.

Research quants rely more on statistics. Their methods include regression, time series analysis, optimization and, increasingly, machine learning. A proposed strategy is tested by backtesting it on historical data, and much of the craft lies in avoiding results that only appear to work because the model was fitted too closely to the past.

The intellectual roots go back to Louis Bachelier, whose 1900 thesis modelled price changes as a random process. The modern profession took shape after 1973, when Fischer Black, Myron Scholes and Robert Merton published their option pricing work and the Chicago Board Options Exchange opened. Banks needed people who could implement and extend the new mathematics.

During the 1980s Wall Street firms began hiring physicists and mathematicians in numbers, a group the press nicknamed rocket scientists. Emanuel Derman, a physicist who moved to Goldman Sachs in 1985, described the transition in his 2004 memoir My Life as a Quant. Edward Thorp, a mathematics professor who applied probability first to blackjack and then to convertible securities, is often cited as an early quantitative investor. From the 1990s onward the growth of electronic markets and cheap computing shifted demand toward statistical research and automated trading.

Every model simplifies reality, and a firm that relies on a flawed model can lose money or misstate its risk. Quantitative models were criticised after the failure of Long-Term Capital Management in 1998 and again after the 2008 financial crisis, when models for mortgage-linked securities underestimated how strongly defaults could move together. In August 2007 many equity funds using similar statistical strategies suffered sudden losses at the same time, an episode showing that widely shared models can create crowded positions.

Regulators responded by requiring formal model governance. In the United States the Federal Reserve and the Office of the Comptroller of the Currency issued supervisory guidance on model risk management in 2011, known as SR 11-7, which expects banks to validate models independently and to understand their limits.

Entry usually requires strong skills in probability, linear algebra, statistics and programming, shown through a quantitative degree and technical interviews. Pay at trading firms is closely tied to the profit of the desk or strategy. The profession has been reshaped several times by changes in markets: the decline of exotic derivatives after 2008 reduced demand for pricing specialists, while the rise of data-driven investing and of the hedge fund industry increased demand for researchers and engineers.