Quantitative analysts are in demand in virtually every industry, from government offices and software firms to Wall Street hedge funds and healthcare. Regardless of their specific work setting, they generally have a few things in common: mathematical chops, computer programming know-how, and advanced degrees to prove it.
When Robert K. Merton won the Nobel Prize in Economics in 1997 for his work on the Black-Scholes model—a mathematical formula for estimating the variation over time of financial instruments—demand in the finance industry for professionals with mathematics, statistics, and computer programming expertise was only just starting to explode.
At the time, Wall Street was booming, and firms scrambled to add financial analysts who could combine new technology with complex mathematics to create innovative financial products and trading strategies. These new hires, who had the skills to marry programming languages and large data sets, became known as quantitative analysts—"quants" for short.
Since the emergence of quantitative analysts in the finance sector, high-frequency trading (HFT)—a trading method that uses powerful computer programs to transact a large number of orders in fractions of a second—has all but swallowed a market once characterized by broker-dealers buying and selling securities.
There's no doubt that these new trading strategies and the computational finance pros who use them have pushed the industry in a new direction. The investment banking and financial services company Morgan Stanley reported to the Financial Times that quant strategies managed at least $1.5 trillion in 2019. That same year, JPMorgan estimated that only about 10 percent of U.S. equity trading was done by traditional investors.
In an era in which hedge funds, banks, and other trading firms are constantly mining datasets for an edge, quants are poised to take advantage of data science developments while also eliminating cognitive bias in the quest for returns. As big data continues to transform the finance landscape, institutions increasingly call on quants' computational finance skills to maintain their competitive edge.
With the demand for quant-specific skills ever on the rise, you may be wondering how to become a quantitative analyst—a career known for high stakes and hefty compensation. We'll answer that while also covering these questions:
Quantitative analysts design and implement complex models that allow firms to price and trade certificates or other financial instruments of monetary value. They're primarily employed by investment banks and hedge funds but may also find work with commercial banks, insurance companies, and management consultancies. Other employers include engineering firms, financial software and information providers, and government agencies.
Quants who work directly with the salespeople and traders at investment banks are often referred to as "front-office" quants. This role is responsible for determining prices, managing risk, and identifying profitable opportunities. It typically puts a greater emphasis on developing solutions to specific problems than on detailed financial modeling.
"Back office" analysts are typically the"checks and balances" of firms, ensuring that people handle their organizations' money and assets appropriately and honestly. In this role, quants validate models, conduct research, and create new trading strategies that rely on quantifiable information that can be backtested for accuracy.
Compensation in the finance industry tends to be very high—and quantitative analyst salaries are no different. According to Glassdoor, financial analysts in this role pull in an average salary of $106,751 per year.
Glassdoor data also indicate that a robust resume can open doors to quant positions with salaries that are double, and sometimes even triple, the average. The highest-paid positions are often senior- and management-level roles, and their titles reflect that.
The Bureau of Labor Statistics (BLS) reports that some sectors are more lucrative than others. In 2018, the median annual wage for financial analysts employed in securities, commodity contracts, and other financial investment industries was roughly $20,000 to $30,000 more than those working in other top industries.
While policies on annual bonuses vary from employer to employer, the employment search engine Indeed reports that quants are awarded an average annual bonus of $10,000. PayScale highlights similar data, indicating that quantitative analysts can expect an average bonus of $9,982 per year.
However, Glassdoor paints a slightly different story. It publishes data from anonymous quantitative financial analysts who report receiving bonuses as high as $65,000, and even $100,000. In comparison, the average U.S. worker earned an annual bonus of $1,797 in 2017.
Traders can work for a financial institution, where they buy and sell financial instruments such as stocks, bonds, commodities, derivatives, and mutual funds. They can also be self-employed, making trades with their money or credit and keeping all profits for themselves.
According to Glassdoor, financial traders make an average salary of $123,598 per year, which is slightly higher than the site reported average for quantitative analyst annual wages.
Additionally, financial trading is a front-office specialization, meaning that traders working for financial institutions are client-facing and mostly responsible for directly generating revenue for their employers. Working on the front line is typically more stressful and demanding—but also tends to offer better compensation.
Due to the complex nature of their work, quantitative analysts face stringent education requirements. Most firms require candidates to have a master's degree in a quantitative discipline such as:
Some employers require candidates pursuing senior-level positions to have a Ph.D. or similar level of education in a subject area that requires solving problems from a mathematical or statistical perspective. Depending on a doctorate holder's specific educational background, their skills might include extensive insight into high-frequency trading algorithms, a mastery of object-oriented programming, or a thorough understanding of machine learning as it applies to financial datasets.
Although students who complete a master's degree or Ph.D. in to become a quantitative analyst have undergraduate degrees in a variety of majors, most hold a bachelor's degree in a subject area known for providing practical quantitative skills, such as statistics, finance, or economics.
Those pursuing a quantitative master's degree with a bachelor's degree in a non-quantitative field, such as liberal arts or the humanities, may need to make up for their lack of training in calculus, algebra, computer science, and physics—courses that typically make up the foundation of any STEM major or career.
Some graduate schools allow students to complete prerequisite courses during the first year in the program while others require students to get prerequisite courses out of the way before enrolling. In the latter case, students might seek out accredited online courses or complete prerequisite classes at a local community college. They may also have the option to enroll in a summer intensive course provided by the grad school they plan to attend.
There is no prescribed pathway to this career, but there are typical routes to success. Many prospective quants complete a bachelor's degree in a quantitative field, then begin working in entry-level or junior analyst roles. From there, they usually return to school or move to related positions, such as investment analyst or other research functions.
There aren't many certifications specifically targeted at quants. Some in the field seek out designation from the Financial Industry Regulatory Authority (FINRA), which acts as the main licensing organization for the securities industry.
Though voluntary, these certifications are also desired by many employers:
__The International Association for Quantitative Finance (IAQF) offers a host of resources for those with strong analytical skills and data analytics know-how to gain an understanding of the financial engineering world, which covers the types of work both financial engineers and quants do.
Quantocracy is another great resource for quants. It offers a finely-tuned mashup of algorithmic and quantitative trading blog posts featuring thought-provoking insight and ideas, as well as complex arguments concerning the quant domain.
Additionally, Alpha Architect's blog is a go-to when it comes to research-based posts and test-based strategies. Produced on behalf of an investment firm that pursues accounts and index strategies for exchange-traded funds, the site also offers an amazing collection of white papers that are available free of cost.
As a renowned hedge fund manager, author, and Northwestern University faculty, Dr. Ernest Chan runs the __popular blog Quantitative Trading, which covers everything from algorithmic trading and book reviews to factor modeling and trading strategies.
Aspiring quants should know that a typical work schedule is hard to nail down, as professionals in this field are usually expected to be flexible. They may be called upon to take care of various production systems before the market opens or after it closes, or to complete projects that require rapid turnaround time (and thus, lots of work before and after regular business hours).
One Reddit thread focusing on typical quant workweek covers the experiences of professionals employed across a variety of industries. Some note a standard nine-to-five while others highlight a schedule that requires 12-hour days. One quant who specializes in risk management and is employed by a major bank holding company responded that hours tend to be reasonable but vary through the seasons.
Lastly, beginners in the field should know that success in this profession requires a unique combination of hard and soft skills. In terms of measurable qualities, in-depth mathematical knowledge, programming skills, and trading experience are just a few essentials they'll need to work with some large and highly complex datasets.
But with these traits, they should also have an innovative mindset that allows them to approach financial markets and algorithms with a willingness to make creative decisions that break away from conventional mathematical models. In a similar vein, they should have a curiosity that drives their understanding of the "what" and "why."
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