M.S. Mathematics and Statistics vs. M.S. Financial Economics

[LTCM]

HI everyone,

I'm new to these forums and have found it to be a pretty dauting task to sort through all the info provided.

Right now I'm trying to decide between two programs I have been admitted to. A M.S. in Mathematics and Statistics at Georgetown, and a Masters in Financial Economics at Rotman, University of Toronto. I just finished my undergrad degree in economics and math.

I want to get into trading, and am wondering what you would recommend to be the better program? The cost and length of the programs are essentially equivalent. With the Rotman program, there is room for elective courses in the actuarial science/stats and the math department, along with some good applied courses (Risk Modelling and Trading) at Rotman, and an econometrics and financial econometrics sequence. At Georgetown though, there is only one elective at the business school, along with a course in financial mathematics. At the same time, the Georgetown program will provide the better quantitatve background, with PDE, probability, numerical analysis courses, etc. I was in contact with a student there and he says the grads with a finance interest have done well getting interviews with hedge funds in NYC. One actually interened at NASA too which is pretty sweet.

I guess another question that is (very) implicit in what I have written above, is wheter there is a career in trading for financial economics graduates, or in the next few years, will everything be algorithmic trading that requires a more quant background?

Thanks a lot, anything is appreciated.

 

[MLBrandow]

LTCM,

I actually stumbled into a very similar problem just yesterday. My stats professor extended an invite to consider the Statistics graduate program, and I believe he's going to offer me a TA-ship, which is something I don't have in FinMath program I was accepted into.

I am seriously considering making the switch, although I, too would be very interested to hear some comments regarding this matter.

When do you have to make your decision by?

 

[alain]

If you go to Statistics, try to stay close to the finance side. Learn as much as you can about econometrics, time series, and things like stat arb. Stats won't cover anything about pricing though. You will be on your own for that.

 

[LTCM]

LTCM,

I actually stumbled into a very similar problem just yesterday. My stats professor extended an invite to consider the Statistics graduate program, and I believe he's going to offer me a TA-ship, which is something I don't have in FinMath program I was accepted into.

I am seriously considering making the switch, although I, too would be very interested to hear some comments regarding this matter.

When do you have to make your decision by?

I actually sent in a deposit for the masters in financial economics at Toronto a month ago, but the more I read online, the more convinced I am that the math/stats M.S. would be the better choice. The major problem I am having is, as I said, that of the 9 courses I would take for the MS, only 2 would have anything to do with finance.

You seem to be in a good spot though with the financial math program - it's a perfect mix. Which school? Florida for both? I didn't have time to write the GRE so couldn't apply to a lot of programs deadline.

 

[MLBrandow]

LTCM,

My predicament is as follows:

FSU Financial Math Program, sticker price

-OR-

FSU Applied Statistics Program, $24000 TA-Ship (2yr) plus full tuition waiver.


Neither provides reliable placement, HOWEVER, I found out today that the dean of the Finance department at FSU, with whom I have been meeting regularly for the past year or so to discuss taking upper level finance classes (because I am not in the major, so he has to personally add me) is the "go-to" guy for internships and recruiters. Basically, he is my "in" in the financial networking department.


At this point, I am leaning very heavily toward the Stats Program. It even seems like they will be willing to let me start in the fall as an UNDERGRADUATE so that I can finish up my second bachelor's degree (Mathematics). This is basically a best-case scenario, and they are laying it all on the table right now.



In speaking with some people about the comparison, it seems like the Financial Math is certainly the more niche degree, as it is very specific for a certain type of job and therefore it is a tunneled curriculum which hones very specific skills. This is great if you are 100% sure you want to be a quant.

The Applied Statistics (or Math Statistics in your case) is more general, but is a much more inclusive degree. Personally, I want to work more in the business decision-making side rather than the programming side, and so I think my decision may be clearer than I am letting myself onto presently.

I would look to meet with professors in either department you are applying if possible, or meet with professors at your school in analogous departments and hear their opinions. I think this will help you reach a decision more quickly.

Best of luck and I would be interested to hear your decision when you make it.

 

[bigbadwolf]

In speaking with some people about the comparison, it seems like the Financial Math is certainly the more niche degree, as it is very specific for a certain type of job and therefore it is a tunneled curriculum which hones very specific skills. This is great if you are 100% sure you want to be a quant.

And you know the jobs will be there and that you will be able to snag one of them. Otherwise, keep your options open -- as you seem to be doing. Even if financial engineering recruits fewer people, thee will still be a need in various sectors for experts in statistics, stochastic theory, PDEs, digitial signals processing, scientific computing, and numerical analysis. Why slam the door unnecessarily on these options by taking a degree that is simply too specialised, in an area with uncertain job prospects?

 

[olgenue]

If you go to Statistics, try to stay close to the finance side. Learn as much as you can about econometrics, time series, and things like stat arb. Stats won't cover anything about pricing though. You will be on your own for that.

Agree with Alain that stats won't teach anything about pricing. Also, since you majored in math, I assume you are familiar with rigorous proofs and theorems in certain pure math classes. I feel that anyone who survives and does well in these classes is able to learn stats fairly comfortably on his/her own.
On the other hand, pricing is not so straightforward and easy to learn by oneself because one not only needs to know the mathematical underpinnings but also needs to repeatedly get his/her hands dirty with the coding and implementation.
If I were in your shoes, I would choose the program that would enable me to learn the "harder" things like pricing and math in school and sacrifice the easier things which I can learn on my own later.
That is not to say all stats are relatively easy. Certain topics like Stochastic Differential Equations which fuses statistics, with real analysis and with differential equations is a rigorous mathematical subject in itself.

 

[Yuriy]

As far as I understand, there are many more job opportunities for people with a Stats degree than for those with Fin Math.

If you have a Ph.D., or even M.S., in Stats you can find jobs in finance. Yes, they won't involve pricing options, but might involve other things. At the same time you can also look for jobs in pharmaceutical industry, government, and academia.

You can do a Stats degree with some flavor of MFE (like Stochastic Calculus, C++, Portfolio Management, Financial Time Series). There are jobs in finance that don't require knowing how to price Bermudian options but require you to know factor analysis. So there is always an opportunity.

 

[MLBrandow]

alain and others,

The following is my prospective Applied Statistics program curriculum:


[FONT=&quot]FALL 2008[/FONT]
[FONT=&quot]STA 5166. Statistics in Applications I (3).[/FONT][FONT=&quot] Prerequisite: MAC 2313. Comparison of two treatments, random sampling, randomization and blocking with two comparisons, statistical inference for means, variances, proportions and frequencies, and analysis of variance.[/FONT]
[FONT=&quot]STA 5326. Distribution Theory and Inference (3).[/FONT][FONT=&quot] Prerequisite: MAC 2313; at least one previous course in statistics or probability. Introduction to probability, random variables, distributions, limit laws, conditional distributions, and expectations.[/FONT]
[FONT=&quot]ECO 5416. Econometrics I (3).[/FONT][FONT=&quot] This course is an introduction to econometric methods focusing on the statistical foundation for estimation and inference in the classical regression model.[/FONT]

[FONT=&quot]SPRING 2009[/FONT]
[FONT=&quot]STA 5167. Statistics in Applications II (3).[/FONT][FONT=&quot] Prerequisite: STA 5166. Special designs in analysis of variance, linear and nonlinear regression, least squares and weighted least squares, case analysis, model building, nonleast squares estimation.[/FONT]
[FONT=&quot]STA 5325. Mathematical Statistics (3).[/FONT][FONT=&quot] Prerequisites: STA 4442 or 5440 and either MAC 2313 or STA 5326. Sufficiency, point estimation, confidence intervals, hypothesis testing, regression, linear models, Bayesian models.[/FONT]
[FONT=&quot]ECO 5423. Econometrics II (3).[/FONT][FONT=&quot] Prerequisite: ECO 5416 or permission of instructor. This course considers extensions of the classical regression model. Topics include nonlinear least squares, instrumental variables estimation, and generalized least squares.[/FONT]
[FONT=&quot]ECO 5281. Financial Economics I (3). [/FONT][FONT=&quot]This course is intended to provide a comprehensive introduction to the field of financial economics. The class focuses on static and dynamic consumption based on asset pricing models and a few elementary applications. The class is designed to set up the framework for models with production, financial institutions and monetary policy issues, which will be the basis for more advanced work.[/FONT]

[FONT=&quot]FALL 2009[/FONT]
[FONT=&quot]STA 5168. Statistics in Applications III (3).[/FONT][FONT=&quot] Prerequisite: STA 5167. Response surface methods, repeated measures and split-plot designs, basic log-linear and logit models for two-way and multiway tables, and multinomial response models.[/FONT]
[FONT=&quot]STA 5856. Time Series and Forecasting Methods (3).[/FONT][FONT=&quot] Prerequisite: STA 5126, QMB 3200, or equivalent. Autoregressive, moving average and mixed models, autocovariance and autocorrelation functions, model identification, forecasting techniques, seasonal model identification estimation and forecasting, intervention and transfer function model identification, estimation and forecasting.[/FONT]
[FONT=&quot]ECO 5282. Financial Economics II (3). [/FONT][FONT=&quot]This course focuses on three broad areas: production-based asset pricing theory and corporate finance; financial intermediation; and monetary theory and policy. Particular emphasis is placed on the economic role played by commercial banks in private information economies, and on the effect of Federal Reserve policy on financial markets.[/FONT]

[FONT=&quot]SPRING 2010[/FONT]
[FONT=&quot]STA 5507. Applied Nonparametric Statistics (3).[/FONT][FONT=&quot] Prerequisite: A course in statistics above STA 1013 or consent of instructor. Applications of nonparametric tests, estimates, confidence intervals, multiple comparison procedures, multivariate nonparametric methods, and nonparametric methods for censored data.[/FONT]
[FONT=&quot]STA 5666. Statistics for Quality and Productivity (3).[/FONT][FONT=&quot] Prerequisites: STA 5167 or consent of the instructor, and either STA 4322 or 5126. Statistics for quality control and productivity; graphical methods; control charts; design and experiment for product and process improvement.[/FONT]
[FONT=&quot]STA 5707. Applied Multivariate Analysis (3).[/FONT][FONT=&quot] Prerequisite: One of STA 5167, 5207, or 5327. Inference about mean vectors and covariance matrices, canonical correlation, principal components, discriminant analysis, cluster analysis, computer techniques.[/FONT]
[FONT=&quot]STA 5939. Introduction to Statistical Consulting (3).[/FONT][FONT=&quot] (S/U grade only.) Prerequisites: STA 5167 or 5327. Formulation of statistical problems from client information; the analysis of complex data sets by computer; practical consulting experience.[/FONT][FONT=&quot]
[/FONT]

I would be interested to hear your thoughts (and others') on this curriculum.

 

[Yuriy]

alain and others,

The following is my prospective Applied Statistics program curriculum:

This is a more or less classic statistics curriculum. It opens opportunities to work in many different areas. However, you might want to bring more finance. You need a course on Introductory Finance and, preferably, on Investments/Portfolio Management. Also, a C++ course would be a great addition and a course on Stochastic Calculus or at least on Stochastic Processes (if your Stats dept does not have it try looking in IE/OR or EE departments) needs to be taken as well. Financial Economics courses won't help much, so you might consider dropping both from your schedule. Stat 5507 and 5666 might not be vey useful.

 

[Sanket Patel]

Michael,

the Financial Economics is great - especially if Dr. Beaumont is teaching it. He is a master pedagogue. But as Yuriy pointed, the class is 100% theory. It's a great class to take to gain an understanding of the roots of Finance via the general equilibrium and arbitrage pricing theory. But as far as practicality is concerned, there is probably isn''t much in the class that you can directly apply into quant work.

 

[IlyaKEightSix]

Financial math or statistics? From my personal experience...

The closer you get to simply dollars and cents, the more boring it becomes. Plus, the dollars and cents can be picked up extremely quickly. It's the technical aspects that need more learning--and are far more fun as well, simply because of being challenged in your area of strength.

I'd go with statistics any day--more challenging, and *far* more fun in my experience.