What is Quantitative Investing?
Quantitative investing is an area of investment that employs mathematical methods, probability and statistical analysis, and data analytics, including techniques from machine learning, to develop trading strategies, construct portfolios, and manage risk. While quantitative investing relies heavily on technology, particularly in the form of algorithmic and high frequency trading, human analysis still plays a role in developing and testing investment strategies and monitoring markets for trends and opportunities.
How Does Quantitative Investment Management Work?
Quantitative investment management makes use of a variety of well-developed models for equity and fixed income markets, as well as currencies, commodities, and structured products. These models are designed to solve problems related to asset pricing and hedging, risk analytics, and portfolio optimization. Some models focus on equity valuation and derivatives calculations, including the Greeks, while others handle interest rates and bond yield curves, for example. Quantitative investing also takes broader market movements into consideration, with an interest in evaluating trends, studying regime changes, and analyzing the volatility of asset price and market movements. Quant models also play a role in assessing risk of various types, including market risk in general, idiosyncratic risk (related to a particular asset), and risk associated with lending or investing including counterparty and credit risk. Quant methods are used to meet regulatory capital requirements through stress testing and valuation adjustments, for example.
The models used in quantitative investing require a solid understanding of probability and statistics, advanced mathematics, such as stochastic calculus, and facility with numerical and computational methods. The use of computer simulation is common in quant investing and includes Monte Carlo methods and a variety of machine learning techniques for the analysis of traditional financial market data and alternative data sets.
Skills Needed for Quantitative Investing
The professional skills required for a job in quantitative investing fall into three main categories:
Mathematical skills – Advanced knowledge of probability, statistics, linear algebra, calculus, and differential equations, including PDEs and SDEs and their roles in mathematical modeling.
Programming skills – The Python language has become prominent in data analysis and machine learning, while languages such as C and C++ have been important historically for applications that require speed and efficiency in execution.
Financial skills – Quant finance professionals should have a solid understanding of asset pricing and trading practices, portfolio and risk management tools and techniques, and the models used to assess opportunities for investing, divesting, or hedging under various market conditions.
Quantitative analysts typically have a bachelor’s degree, and often a master’s degree in mathematics, statistics, physics, computer science, engineering, or operations research. Many also have a PhD in a similar field and may pursue professional education options through online courses.
Quantitative Investing and the CQF
The Certificate in Quantitative Finance (CQF) is a premiere offering in quantitative investing, with a series of modules covering the mathematical foundations, programming skills and financial expertise that are necessary for success in the financial industry. The CQF provides a rigorous quantitative education online, with a flexible learning approach geared towards prospective and existing quant professionals who wish to augment their skillsets and pursue their careers simultaneously.
Upon completion of the program, CQF alumni have access to further educational offerings through the CQF’s Lifelong Learning portal. They also are welcome to participate in networking activities and make use of the CQF Careers Services, for example, among other long-term benefits of the program.
The Future of Quantitative Investing
In recent years, machine learning and data science have had a significant impact on the field of quantitative investing, particularly with the rise of Python for data analysis. In the future, innovations such as quantum computing are also likely to shape the financial landscape. Quant professionals must keep their skills current and watch for emerging opportunities in the exciting field of quant finance.
Find out more about the CQF
Ready to enhance your quantitative investing skills? Download a brochure today to discover how the CQF could help you gain the skills you need to go further in your career.