The Financial Engineering Degree and 5 recommendations for improving quantitative finance programs

Do you agree with the recommendations suggested by the author?

  • Strongly agree

    Votes: 28 51.9%
  • Somewhat agree

    Votes: 23 42.6%
  • Somewhat disagree

    Votes: 3 5.6%
  • Strongly disagree

    Votes: 0 0.0%

  • Total voters
    54

Andy Nguyen

Member
Teri Geske is a lecturer and Executive in Residence at the UCLA Anderson MFE Program. She share with QuantNet a few years ago about "Top 10 Things Practitioners Really Want from Financial Engineers". She recently wrote an article on GARP to reflect on what she has observed about the financial engineering programs the last few years. Here is her 5 recommendations for improving quantitative finance programs.

1. Agree on a Name.

Despite its growing acceptance in the financial community, there is still no consensus about what these degrees are called. Does it matter? I believe so. Some schools call the degree a "Master of Financial Engineering" and they call their graduates "Financial Engineers"; others offer a "Master of Computational Finance" or a "Master of Quantitative Finance," and use various acronyms to refer to their graduates. This inconsistent labeling should be fixed, as it would help to improve recognition of, and professional respect for, the degree-holders.[prbreak][/prbreak]

Just as law school graduates unambiguously have a law degree and B-school students earn MBAs, graduates with a master's degree in financial engineering would benefit from having a widely recognized credential. Interestingly, the International Association of Financial Engineers (IAFE) recently changed its name to the International Association for Quantitative Finance (IAQF) -- perhaps suggesting that the term "engineering" has a negative connotation, equating "to engineer" with "to manipulate."

My personal view is that "engineering" in its best sense is what students in these programs are taught to do: to use concepts from mathematics and physics to build models (with financial data as raw materials), and to test the models, adjust them and put them to use in the real world.

For the remainder of this article, I will refer to the degree as an MFE, and holders of these degrees as financial engineers, leaving it to others to make a final decision on an official name.

2. Remember the Laws of Supply and Demand.

I believe financial engineering is a career path with staying power, but not one with unlimited growth potential. If more and more universities add MFE programs, the number of graduates supplied by MFE programs may soon exceed the number of graduates the industry can absorb.

The truth is, with an annual tuition of $50,000 or more, these programs are a potential source of profits for their universities. Indeed, they are an appealing prospect for B-schools, as well as math, engineering and computer science departments that are facing budget constraints and pressure to control undergraduate tuition.

Existing programs are tempted to increase class sizes, because the marginal cost of an additional student is virtually nil up to a certain point. But not only is there a finite demand for MFE graduates, there is also a finite supply of qualified applicants. If supply outstrips demand, some MFE Programs -- especially new ones with little name recognition -- will have to reach lower into the applicant pool, diluting the quality and reputation of those programs.

My advice to MFE program directors and prospective applicants and employers: choose quality over quantity; the employers that hire your students certainly will.

3. Don't Dumb It Down.

MFE programs can face pressure from students to focus on what is "hot" at the moment. Recently, I have noticed an increase in the number of applicants interested in an MFE because they believe it will give them quick access to a job with a firm that does statistical arbitrage ("stat arb") trading. So, they focus on whether a program offers a stat arb "training class," without much concern for the rest of the curriculum.

There's nothing wrong with stat arb; indeed, there are compelling arguments to be made that finding and exploiting these statistical anomalies helps to make financial markets more efficient. However, if all a potential applicant wants from an MFE program is a "how to" manual for what he or she thinks is a surefire way to beat the market, I suggest looking elsewhere. There are many books available on this subject, not to mention YouTube videos. (To would-be stat arb traders: before you rush to view those YouTube videos, remember that no trader is going to reveal a profitable strategy to you, and don't forget to account for transaction costs before you conclude you've found a real moneymaker.)

Will some MFE graduates go on to make a lot of money from trading? Sure. But to MFE program directors, I suggest that while we need to be responsive to students' interests, MFE programs shouldn't cater to the headline-grabbing topic at the expense of providing solid training in rigorous theories and practices, because today's hot topic will be replaced by another, and our alumni will not know what to do when it does. The experience of attending a distinguished university with top-notch academic scholars never loses its relevance.

4. Expand Your Horizons.

A recent Wall Street Journal article noted that elite MBA programs (Harvard, Stanford, etc.) are sending more of their new graduates to jobs within tech firms than to finance jobs, for the first time in, well, ever. There are various reasons cited, a number of which would not be applicable to MFE programs and students, but what struck me is the idea is that many of the skills taught in a rigorous MFE program can be extremely useful to tech-related and other businesses.

One obvious example: MFE programs teach (or at least they should teach) econometric analysis, which Wikipedia defines as "the application of mathematics, statistical methods and, more recently, computer science, to economic data." This is strikingly similar to the Wikipedia definition of "Predictive Analytics" (a buzz-term associated with "Big Data"): "Predictive analytics encompasses a variety of techniques, from statistics, modeling, machine learning and data mining, that analyze current and historical facts to make predictions about future, or otherwise unknown, events."

Clearly, financial engineering students are taught skills that are applicable beyond banks and investment management, and MFE programs should explore this opportunity. To satisfy the best employers in these industries, MFE programs need to attract students with creative problem-solving abilities as well as top-notch math skills.

I often tell students that financial engineering can be frustrating to people who need the predictable outcomes offered by chemistry or biology, because, most of the time, quantitative finance jobs involve tackling problems or analyzing data without much of a roadmap. This takes a certain tolerance for ambiguity and an ability to articulate and debate the merits of different ideas.

MFE admissions committees should look for students who can not only solve partial differential equations (PDEs) but who have genuine intellectual curiosity along with those strong math skills; they will make the best financial engineers, and will be marketable to companies outside of the financial services industry.

5. Embrace Change.

OK, it's a cliché, but it definitely applies here. Randomness and change are defining traits of financial markets and of the models designed to capture their characteristics. Just as financial models and investment strategies tend to have a limited half-life (they need to be modified, updated and improved over time to retain their usefulness), MFE programs must also constantly evolve.

The global financial crisis hit not long after the MFE degree had just started to gain recognition (certainly a 3+-sigma event in the lifetime of an academic discipline). Among the consequences of the crisis: a marked reduction in the risk-taking ability and profitability of the bank trading desks that had been the biggest employers of financial engineering talent pre-crisis, as well as increased scrutiny of, and costs associated with, trading derivatives.

The upshot is that there is simply less need to design the types of exotic structures that attracted certain students to MFE programs in the first place. Does this mean MFE programs are now less useful than they were before? I don't think so -- in fact, I believe overall the financial industry will require more people with quantitative analytical skills, not fewer.

Quants will be the go-to people who will measure risks in complex global markets, identify opportunities and create solutions for investors, for corporations and for the banks that serve them. (MBAs, with few exceptions, do not have the skills for this kind of work.) However, there will be less demand for financial engineers to create convoluted, multi-layered derivatives-based products designed to exploit regulatory gaps or investor naiveté.

Strong MFE programs will adapt. They should continue to teach the academic theories that are the foundation of quantitative finance, but should also seek input from the industry, including their own alumni, about how to improve. Moreover, they should involve practitioners to give students a real-world view, to teach them the "vocabulary" of the industry and to emphasize those applications that are most relevant to the changing marketplace for financial engineering talent.

Teri Geske is a lecturer and Executive in Residence at the UCLA Anderson MFE Program, which she joined in 2009. She oversees the MFE Applied Finance Project, delivers weekly lectures on current topics in financial markets and advises prospective and current students about career options. She also does consulting work on investment and risk analytics and teaches Corporate Finance at Mount St. Mary's College. Prior to joining the MFE Program, she worked for more than 20 years in the financial services industry.
 

wenbo

Member
C++ Student
Looks like most people who voted agree on the premises of the article. The issue is that many programs don't see the problem and have little incentive to change. As such, I don't see we even get past item 1.
I voted somewhat agree. The fact is that I agree with the last three items while don't agree with the first two.
I don't understand why the MFE s should have a uniform name because IMO different programs have different understandings or philosophy regarding quantitative finance/ financial engineering. For example, I just read the brochure of FSRM at Rutgers in which it states the program emphasizes a "bottom up" pedagogy in data inspection, data mining, simulation and modeling and it seems like the program doesn't agree with the "top down" modeling approach such as PDE which is being used in most of the MFE programs. I think the name FSRM fits the program's philosophy very well. If all the programs share a uniform name, it might be more difficult to distinguish them. The reputation of a program will become slightly more important, a situation which would be harder for new program.

For the law of supply and demand. If I were the director of any MFE program, I would not know what the item 2 bring to me. It is more like a kind of feeling of Ms. Geske than a suggestion.

PS: I have no relation with FSRM at Rutgers, lol~
 

Aaronnejad

Member
C++ Student
I don't believe that there is a need for a uniform name. This is not a professional qualification, but rather an academic discipline. Computational Finance should be different to financial engineering of CF has a stronger programing component to it.
 
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