Known in the business as “quants”, quantitative analysts develop and implement complex mathematical models that financial firms use to make decisions about risk management, investments and pricing. Part speculator, part ruthless logician, a quant’s aim is to reduce risk and/or generate profits.
Quantitative Analyst Responsibilities
Responsibilities will differ according to employer (e.g. hedge fund vs. investment bank), product focus (e.g. asset-backed securities vs. commodities) and level of expertise. A quant may be required to:
- Research and analyze market trends and statistics to make modeling decisions
- Develop and implement complex quantitative models (e.g. models for trading equities) and analytical software/tools
- Perform daily statistical analyses (e.g. risk analytics, loan pricing, default risk modeling, etc.) and coding tasks (e.g. pattern recognition or machine learning)
- Detail model specifications and methods of data collection
- Test new models, products and analytics programs
- Maintain and modify analytical models while in use
- Apply or invent independent tools to verify results
- Collaborate with teams of mathematicians, computer engineers and physicists to develop optimal strategies
- Consult with financial industry personnel on trading strategies, market dynamics, trading system performance, etc.
- Generate requirement documentation for software developers
- Present and interpret data results to senior management and clients
There are quants who are experts in a specific area – statistical arbitrage, derivative pricing, quantitative investment management, algorithmic trading or electronic market making – and quants who play to specific strengths.
For example, a shy and retiring “back office” quant (i.e. quantitative modeler/researcher) may be deeply involved in researching and validating statistical models or generating new financial strategies. A high-octane “front office” quant (i.e. quantitative trader) could be working one-on-one with traders, designing stock market algorithms and supplying colleagues with computer-based pricing and trading tools.
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An Interview with a Real Quantitative Analyst
We spoke with Micah Spruill, Co-Founder of Aurora Investment Advisors, about what it’s like to work as a quantitative analyst. Below, Micah discusses the pros and cons of being a quant, the most frequently used programming languages, and his advice to students.
- Being able to apply scientific methods to finance and discovering new ways of viewing and analyzing this type of data.
- Being able to offer investors an investment approach that seeks a better, more true understanding of markets (in both terms of alpha generation and risk management).
- Educating others (especially investors) on the importance of quantitative analysis and why, if used effectively, it is so powerful in comparison to more conventional methods.
The more you know, the more you realize you don’t know. What I mean by this: the deeper you go down a path the more you realize the chance for hidden risks. In finance, especially when relying on models, there is always the thought in the back of your head, “Did we miss something, are we overlooking anything?” and that’s usually a very difficult question to answer. Add this to the responsibility of managing large sums of money and there is always a certain level of stress that exists. I like to view this as “healthy stress” though, because if you don’t have it, you may fall prey to overconfidence.
2018 Quantitative Analyst Average Salaries
Major employers may typically be hedge funds and investment banks because of the understanding and knowledge quantitative analysts have on finances. You’ll also find opportunities with securities and commodities traders, brokerage firms, accounting companies, commercial banks, insurance companies and financial consulting firms. Hedge funds and trading firms tend to pay the best, although compensation packages are often dependent on the firm’s earnings.
Wall Street is where the money is for quants. Reported salaries from LinkedIn average the salary for NY quantitative analysts, as of June 2019 at $101,000. Positions for quantitative traders may tend to run high in major trading/hedge fund centers such as Chicago, Boston and Stamford. If you don’t want the U.S. to be your home base, you might try Hong Kong, London, Sydney, Singapore, Tokyo or Beijing.
Average Salary: $94,051 per year
Median Salary: $84,408 per year
Total Pay Range: $55,176 – $125,365
Senior Quantitative Analyst
Average Salary: $108,004 per year
Note: Salary information from Glassdoor and PayScale was retrieved as of February 2018.
Quantitative Analyst Qualifications
What Kind of Degree Will I Need?
Even for low-level quant positions, employers will expect to see a master’s degree on your résumé. This could be in a targeted program such as quantitative finance, mathematical/computational finance, operations research or financial engineering. Or it could be in a related quantitative field such as physics, statistics or math. Unless you have outstanding math and computational skills and real-world experience in financial analysis, an MBA is not going to cut it.
To qualify for senior-level positions or job openings in top firms, you may need a PhD in math, statistics, physics, computer science, financial engineering or the like. Candidates with a PhD are likely to have demonstrated that they have the ability to work independently on complex research projects – a valuable asset for large hedge funds invested in “blue sky” projects.
What Kind of Skills Will I Need?
- Object-oriented programming
- Big data modeling
- C++ (used for high-frequency trading applications)
- MatLab, SAS, S-PLUS/R (used for statistical analysis)
- Monte Carlo techniques
- Machine learning
- Data mining
- C#/Java, .NET or VBA, Excel
- Calculus (including differential, integral and stochastic)
- Linear algebra and differential equations
- Numerical linear algebra (NLA)
- Probability and statistics
- Game theory
- Portfolio theory
- Equity and interest rate derivatives, including exotics
- Systematic and discretionary trading practices
- Credit-risk products
- Financial modeling
- Analytical Problem-Solving: Employing logic and mathematical/programming tools to tackle abstract financial problems.
- Ability to Work Under Pressure: Making key financial decisions in high-stress situations.
- Independent Research: Working without supervision on potentially insurmountable challenges; employing patience and dogged persistence to complete tasks.
- Concentration: Tolerating long hours working with computer code and data.
Note: Michael Halls-Moore, the founder of QuantStart.com, has put together an excellent Self-Study Plan for Becoming a Quantitative Analyst.
What About Certifications?
There aren’t many certifications explicitly targeted at quants. So before you invest in the CFA or the CQF, ask your professors and professional mentors if certification will have a measurable benefit to your career.
Offered by the CFA Institute, this credential is geared towards investment professionals. To become a CFA charterholder, candidates must complete an independent study program and pass 3 exams. Each exam requires approximately six months of preparation. In addition, charterholders must prove they have four years of qualified, professional work experience in investment decision-making.
Is the CFA worth it? The jury is still out. Some quants (e.g. financial engineers without a PhD who want to boost their qualifications) may choose to pursue the CFA in order to give them an edge over their peers. But it’s not often mentioned in job requirements for quantitative analysts.
The CQF is more of a training course than a certification. This part-time, online financial engineering program is targeted at individuals interested in real-world quantitative finance. That includes derivatives, quantitative trading, insurance, model validation or risk management. Courses, workshops and the final exam can be completed as one six month program or divvied into two 3-month levels.
Jobs Similar to Quantitative Analyst
Quants spend a lot of their time looking at screens. If you’re fascinated by the finance world and would like a broader set of job tasks, your alternatives include:
- Business Analyst
- Financial Analyst
- Investment Banking Analyst
You also have the option to retreat into academia or research, as a pure Mathematician, or buff up your programming skills to investigate engineering roles.
Opportunities for senior-level quants include:
- Portfolio Manager
- Hedge Fund Manager
Quant Developer vs. Quant Analyst
Quantitative developers usually have more advanced object-oriented experience and less math and finance expertise than quantitative analysts.
As Michael Halls-Moore notes in What are the Different Types of Quantitative Analysts?, developers work closely with quant modelers/analysts to take a MatLab/R prototype, optimize it and make it highly fault tolerant for the production environment. They may also be responsible for routine tasks like statistical coding or maintaining large-scale legacy systems.
Although most quant developers are paid less than quant analysts:
“The one extremely high-paying role that sits in the quantitative development arena is that of the star C/C++ developer who understands Unix network programming, low-latency systems and the ins and outs of the Linux Kernel. These individuals can often be found working in the secretive world of Ultra High-Frequency Trading (UHFT), where trade orders are now measured in microseconds.” – Michael Halls-Moore What are the Different Types of Quantitative Analysts?
Quantitative Analyst Job Outlook
Since the BLS doesn’t track specialized data science roles, statistics on quantitative analyst jobs are – ironically – difficult to source. At the moment, the BLS is projecting employment of financial analysts (a rather broad category) to grow 11% from 2016 to 2026, faster than the average for all occupations.
This percentage could well be higher for quantitative analysts. In Quants: The Rocket Scientists of Wall Street, the author suggests that demand for quants would increase due to a number of trends:
- Accelerated growth of hedge funds and automated trading systems
- Increased complexity of liquid and illiquid securities
- Continuing interest in market-neutral investment strategies
- Demand from traders, accountants and sales reps for pricing/risk models
Faced with the twin bogeymen of big data and AI, financial executives are increasingly turning to analytics experts/interpreters for assistance.