Some top Financial Engineering schools have begun to offer free MOOCs. While few offer certification – these promise a taster of otherwise very costly Masters’ programs. Enough to if know they’re up your street, how you cope with their quantitative aspects, and perhaps also to spice up an application or interview.

Attack of the Quants

If you’ve never heard of a quant you’ve been living under a rock… and a different rock to me. They even have their own film. But perhaps you recognise the more descriptive term, ‘rocket scientists’, coined during the 80s to describe the science and engineering postgraduates investment banks started to hire around then. “Quant” is a nod towards the highly quantitative nature of those areas. And the moniker Quantitative Finance seems to have been invented to distinguish the sexy “finance of the quants” – chock full of equations, Greek squiggles and the allure of million-dollar bonuses – from the boring domain of balance sheets and accounting policies.

Quants: The Alchemists of Wall Street

It almost seems that where there is a university, there’s a Financial Engineering Masters program. 

Over the years, numerous universities (and employers) noticed a gap: MBAs weren’t quantitative enough, and PhDs too time consuming, demanding and expensive to churn out people with the skills employers were demanding in sufficient numbers. Like finance, education abhors a vacuum where money can be made. And so a plethora of Masters-level courses sprang into being to fill the gap.  You thought Limerick was a type of humorous poem with an AABBA rhyme scheme? You’re absolutely right… but long before acquiring that meaning, it was a place in Ireland… and now you can study Computational Finance there. Or in Hoboken, Ramat Gan, Besiktas, Tulsa…  It almost seems that where there is a university, there’s a Financial Engineering Masters program.

But wait: is Computational Finance the same as Financial Engineering? And what about Quantitative Finance, Financial Mathematics, and Mathematics of (and/or in) Finance? Well, there are probably subtle differences. But it just looks like one thing the quant community has failed to manage – despite all those four-digit IQs – is to agree on a consistent name for what’s fast becoming its de facto entry qualification. So I’ll risking appalling the odd purist and just call these courses MFEs – Masters in Financial Engineering – below.

MFEs – Masters in Financial Engineering 

Show Me The Money

Obviously, with graduates commanding high salaries, MFEs don’t come cheap. Tuition costs for the better-regarded courses in the US run to $40-80,000. So to be honest, when I saw that Columbia University – which offers a top-10-ranked MFE – had launched two Financial Engineering and Risk Management courses in October 2013, I was more than a little surprised. But it turned out that University of Washington – with its slightly lower-ranked, less expensive program – had beaten them to it by a full year. And now the reason for this article: more recently, other schools have joined the Financial Engineering MOOC party. Enough in fact that it looks possible to get a solid introduction to the field, for the cost of only your time and perhaps a carefully chosen textbook or two. The options are so numerous, they fill a table:

University	Title
Cal Tech	BEM1105x: Pricing Options with Mathematical Models
Chicago	Asset Pricing Part I
Chicago	Asset Pricing Part II
Columbia	Financial Engineering and Risk Management Part I
Columbia	Financial Engineering and Risk Management Part II
Georgia Tech	Computational Investing, Part I
Georgia Tech	CS 7545, Machine Learning for Trading
Michigan	Introduction to Finance
Stanford	Stocks and Bonds: Risk and Returns
Washington	Mathematical Methods for Quantitative Finance
Washington	Introduction to Computational Finance and Financial Econometrics
Yale	Financial Markets

Of these, Yale’s Financial Markets seems one of the least technical, providing an overview without a heavy quantitative bias. But that’s important: without some intuition, making sense of the equations when they inevitably arise is doubly difficult. This course ran for the first time in October 2014, but the Professor – Nobel Prize winner Robert Schiller – is such a luminary that it seems predestined to repeat. But Michigan’s Introduction to Finance offers a decent alternative – with the advantage of a known start date in the near future and a SoA (free/verified options).

Stanford’s Stocks and Bonds: Risk and Returns looks like it might be slightly more technical, recommending modest exposure to some key financial concepts, but with no explicit mathematical prerequisites 

Stanford’s Stocks and Bonds: Risk and Returns looks like it might be slightly more technical, recommending modest exposure to some key financial concepts, but with no explicit mathematical prerequisites. Columbia’s Financial Engineering and Risk Management courses have picked up some very favourable reviews. They seem to start from a similar level, but the course description does mention the word stochastic (fancy talk for random) which suggests they might get a bit more technical – eg with a bit of numerical modelling.

From there, the entry bar gets a little higher: Chicago’s Asset Pricing courses call for single and multivariable calculus, simple differential equations, matrix algebra, and basic statistics, some programming skills, and some exposure to economics and econometrics. Cal Tech’s Pricing Options with Mathematical Models – which focuses on a narrow but conceptually crucial area of quantitative finance – makes similar demands. Unique among the Quantitative Finance courses at this level, this one will offer a Statement of Accomplishment.

Chicago’s Asset Pricing courses call for single and multivariable calculus, simple differential equations, matrix algebra, and basic statistics, some programming skills, and some exposure to economics and econometrics. 

Fortunately, Washington’s two course offerings Mathematical Methods for Quantitative Finance, Introduction to Computational Finance and Financial Econometrics – seem pretty good places to pick up some of this background knowledge. The former covers material similar to the Dan Stefanica’s much acclaimed A Primer For the Mathematics of Financial Engineering which only assumes entry-level college calculus courses. At a pinch, Ohio State’s self-paced Calculus One could stand in for these. The latter focuses on econometrics – with the added kicker of applied econometrics in R. That means dealing with numbers, which have the advantage of being more concrete than equations. It also seems to cover some basic statistical theory, linear algebra and finance: all things that would come in handy for the Chicago & Cal Tech Courses. (So too does John Hopkin’s Mathematical Biostatistics Boot Camp.  Heavy on statistics and light on bio, this shares a recommended text – Mathematical Statistics and Data Analysis – with the Cal Tech options pricing course.)

In theory there is no difference between theory and practice, but in practice there is, and that difference can break the bank.  

This leaves the Georgia Tech courses. Machine Learning for Trading remains an unknown quantity for now. But the topic is more directly applicable than much of what’s described above. And as one of Georgia Tech’s online CS Masters courses the courseware should be available for free – with paid options for a certificate and possibly college credit.  Computational Investing may offer hints about the approach this course take. This comes to quantitative finance from an unusual angle: computation (done in Python) rather than mathematical theory. For the sole goal of designing automated trading algorithms, this makes a lot of sense: In theory there is no difference between theory and practice, but in practice there is, and that difference can break the bank. So concentrating on backtesting with historical data with little regard for theory has some merit. Although the early iterations of this course were, frankly, panned by reviewers, Professor Balch deserves some credit for sticking to his guns: after four runs, the course should be much improved the next time it’s scheduled.

Back to the Future

While the range and scope of Financial Engineering MOOCs has broadened over the last year or two, they still offer only baby steps into the field; perhap something comparable to the first semester of an on-campus MFE. 

While the range and scope of Financial Engineering MOOCs has broadened over the last year or two, they still offer only baby steps into the field; perhap something comparable to the first semester of an on-campus MFE. And scant few offer certification, even for a price. That’s understandable: top schools seem to value MOOCs as marketing tools, but fear diluting the value of profitable course offerings. But Cal Tech’s breaking of the ranks to offer of a free SoA might just signal a change. For Cal Tech at least, the benefits of offering certification – perhaps in terms of marketing or intake screening, seem to outweigh any perceived risks. So other schools may – and even an MFE will be loathe to estimate this probability – start to feel the same way, and follow suit, and/or offer or MOOCs covering more advanced topics from their MFE programs.

This opens up the tantalising possibility that within a few years the knowledge necessary to get started on one of the more intellectually stimulating – not to mention lucrative – career paths to have emerged in the last few decades may soon be available in a browser near you, price of admission: time, brains, and gumption. It’s a wonderful world.