If there’s one thing that finance executives hate, it’s risk. At least, unquantified risk. Naturally, the art of investment revolves around making bets the house is likely to win. It all starts with quantifying revenues, expenditures, profits, and losses, and developing trends, then placing bets where the return is likely to be higher than if that same money stayed in a given benchmark index: it’s known as alpha, the holy grail.
Over the years, financial professionals have gotten pretty good at predicting how businesses will perform. The stock market keeps going up, after all.
But something companies are finding hard to control for is political risk. Governments shift, policies change, and suddenly investments that looked solid from a purely business perspective are whacked by tariffs or taxes or social unrest.
- Grand Canyon University - B.S. in Business Information Systems and M.S. in Data Science
- SNHU - A.S. in Data Analytics, B.S. in Computer Science, B.S. in Data Analytics, and M.S. in Data Analytics
- Syracuse University - M.S. in Applied Data Science: GRE Waivers available
- UC Berkeley - Master of Information and Data Science Online - Bachelor's Degree Required.
- Syracuse University - Master of Information Management Online
So it was a big deal when, in 2016, MasterCard announced that it was patenting certain analytical methods to predict political risks from credit card transaction data. Data scientists at the company had mined millions of transaction records and found an algorithm that could forecast a coup or other destabilizing political events.
The algorithm represents a new frontier in the field of finance, but could be just the tip of the iceberg. One thing’s for sure, the future of the finance industry will be built by people with master’s degrees in financial analytics.
Where You’ll Work and What You’ll Earn: Jobs and Salaries in Financial Analytics
It’s important to distinguish financial analytics from financial analysis, particularly when you’re looking into suitable master’s programs. A master’s degree in financial analysis is typically aimed squarely at preparing you to earn the Chartered Financial Analyst credential from the CFA Institute, a certification that is prized by investment managers and traders. But the intensive preparation for the CFA is of little use to data scientists who plan to crunch numbers in the back office for most of their career—studying security laws and ethics doesn’t help you learn R or SQL.
Like any business with a product to move, financial services companies are turning to big data to push their marketing efforts. Financial analytics is helping them identify likely customers and the best products to offer them. Analytics also helps with collections, personalizing payment plans and recovery to ensure that an investment firm doesn’t have to absorb the losses for a client’s bad bet.
Analytics is also offering a renaissance in evaluating investment opportunities. Although all the traditional numbers still get crunched when looking at stocks, data scientists are also exploring innovative ways to evaluate good bets in the market. Many of these methods are closely guarded secrets in the industry, but others are beginning to trickle out, or otherwise being created by data scientists outside the industry. A 2016 paper by a Stanford university student, for example, identified an actionable model for evaluating stock picks by analyzing social media data.
In some respects, financial analysis is among the most straightforward of data science careers. Input data is typically normalized and validated before you ever get your hands on it, coming from transactional systems that are required by law to be accurate and rigorously policed for any imprecision. The questions you will be asked to answer are equally straightforward: will certain business trends continue under a certain set of givens? Can profitability be increased by altering other variables? What are the risks associated with particular investments or financial positions?
To answer these questions, you’ll have the full armory of data science techniques at your disposal. Machine learning, statistical inference, and advanced visualization techniques will help you make your case.
A wide array of jobs are available to people with master’s degrees in financial analytics, including:
- Criteria Officer
- Data Analytics Manager
- Modeling and Analytics Officer
- Quantitative Financial Analytics Officer
- Risk Officer
- Financial/Credit Analyst
The companies with these opportunities range from health insurers to investment groups to banks to auction houses. Government agencies also have a vested interest in understanding how financial systems operate and in making predictions about the outcomes of fiscal and economic policy. The Securities and Exchange Commission (SEC) relies on financial analytics professionals to keep markets honest and ensure that enforcement policy is effective in reducing the risk of economic catastrophe.
Data scientists working in financial analytics are on the cutting edge of developing more and more innovative techniques for melding traditional financial data with other large data sets that are now available to improve predictive analysis in the finance industry. They are also among the best paid positions in data science; according to job search engine Indeed.com, even federal financial analytics professionals can make more than $130,000 a year. Private sector salaries are a whole lot higher.
Choosing a Master’s Degree Program in Financial Analytics
For better or worse, the centers of finance in the United States are New York and Chicago, respectively hosting the New York Stock Exchange and the CBOT (Chicago Board of Trade). Enrolling in a master’s program near either location increases the chances of learning from instructors who are up to speed on the trends and instruments in the finance industry, and could be your ticket to internship options with companies at the center of the industry.
Regardless of location, the more exposure you get to real-world financial information, the more valuable your education will be. Look for schools offering labs with live Bloomberg terminals and access to trading information, and which offer internships at major institutions.
Like most data science degrees, good financial analytics programs will have a healthy dose of programming and data modeling in the curriculum. They should also have a greater-than-usual focus on machine learning and artificial intelligence methods used for data processing. With the emphasis on high-frequency, automated trading algorithms dominating markets today, it’s important to understand these concepts even if you are not designing them personally.
It’s also wise to look for programs that offer core courses or electives in:
- Time-series analysis
- Data mining principles and procedures
- Bayesian analysis techniques
- Real-time analytics
- Portfolio and risk management
Of course, like any advanced degree program, you may also want to ensure that online options are available for remote learning. It’s not always possible or easy to pick up and relocate to a new state for a specialized program, particularly not if you are already gainfully employed. By selecting an online financial analytics master’s degree, you can gain all the advantages that come with being connected to the finance world without necessarily having to relocate to the expensive cities where they’re located.