Quantitative analysts (also called “quants”) are professionals specializing in the design, development, and implementation of algorithms and mathematical or statistical models intended to solve complex financial problems. In their work, quantitative analysts apply a blend of techniques and knowledge from multiple disciplines including finance, economics, mathematics, statistics, and computer science.
The quantitative analyst job is relatively new because quantitative analysis only started to rapidly grow in the second half of the 20th century when more sophisticated quantitative methods (e.g., stochastic calculus) were introduced in finance. The mass computerization of finance operations and the introduction of complex securities further fueled the demand for quantitative analysts. The recent popularity of data science and machine learning creates new opportunities for people interested in the job of a quantitative analyst.
Quantitative analysts work in risk management, securities trading, and asset management. The primary employers of quantitative analysts are large financial institutions such as hedge funds, investment banks, commercial banks, and insurance companies.
Educational Requirements for Quantitative Analysts
Due to the complexity of the work and challenging work environment, the educational requirements for quants are extremely high. The majority of quantitative analysts possess advanced degrees (Master’s or Ph.D. degrees) in quantitative disciplines, including mathematics, physics, engineering, and computer science.
Candidates with advanced degrees in financial engineering or quantitative finance are also considered. Although there are fewer people with a finance and economics educational background among quants, individuals with the necessary knowledge and programming experience can land the position.
The rapid introduction of data science and machine learning in finance also increased the demand for candidates with the relevant educational background in said fields. Potential quants should possess experience in coding and knowledge of programming languages such as C++, Java, Python (check out CFI’s Python for Finance Course), R, and MATLAB.
Quantitative analysts may choose the area in which they want to specialize. Some of the possible paths include:
Risk management: In risk management, quantitative analysts develop models to analyze the risks associated with investment or trading positions.
Front office quant: A quantitative analyst involved in the operations of the front office (sales and trading). The analysts are primarily involved in determining profitable opportunities and leveraging quantitative methods in trading strategies.
Algorithmic trading: Quants are primarily responsible for developing and maintaining complex securities trading algorithms.
Library quantitative analysis: Quants analyze and validate the existing models and ensure that the models provide relevant information in an efficient manner.
CFI is the official provider of the Financial Modeling & Valuation Analyst (FMVA)™ designation for financial analysts. From here, we recommend continuing to build out your knowledge and understanding of more corporate finance topics such as: