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2009 Quantnet Ranking of Financial Engineering (MFE), Quantitative Finance Programs

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The 2011 ranking of MFE program is here

2009 QUANTNETWORK RANKING OF FINANCIAL ENGINEERING / MATHEMATICAL FINANCE MS PROGRAMS

NEW YORK, Sept, 25, 2009 - The financial engineering news organization QuantNetwork proudly presents the first quantitatively based ranking of Financial Engineering / Mathematical Finance MS Programs located in North America. The ranking is based on publicly available data from Xmarks, and uses a proprietary analysis algorithm developed by QuantNetwork.

More information about the ranking and the methodology can be found at
2009 QuantNetwork Ranking of Financial Engineering/Mathematical Finance MS Programs

Twenty-three programs were ranked. The results are reported in groups of five, listing the programs alphabetically by school within each group.
2009 Quantnet MFE rankings.gif
 
This is excellent. Just what is needed in the world of FE.
 
I just read the methodology used for the rankings, and if I understand it right, it seems to be based a great deal on the popularity of the sites of each school. I'm finding it difficult to connect how that would be relevant to how strong each program is. I would think a more suitable ranking methodology would place emphasis on quantifiable attributes like placement (internship/ FT), starting salaries, location, etc since most people get an MFE to be able to find a job in quant finance
 
The methodology highlights an important fact which appears even more clearly when the algorithm is applied to business schools or other topics:
the strong programs appear to the top of each others list of similar sites, but not necessarily to the top of the lists of similar sites of the weaker programs.

It is not bookmark counting, and its applicability tested well on ranking in very different areas, the ranking of business schools being one example that can be compared against benchmarks.

Ranking based on placement data would be best, but very few programs make this data publicly available. The alternative was an unbiased ranking based on a general algorithm.
If only the information was made available from all programs. That is the whole reason of using a third party database.
 
Congrats to Baruch's high ranking on the 2009 QuantNetwork Ranking of MFE programs!

I'm sure these rankings are accurate and unbiased..... after all QuantNetwork is a "financial engineering news organization" with no ties to any program in this list.....Right ?
 
Congrats to Baruch's high ranking on the 2009 QuantNetwork Ranking of MFE programs!

I'm sure these rankings are accurate and unbiased..... after all QuantNetwork is a "financial engineering news organization" with no ties to any program in this list.....Right ?
It's very easy to make assumption without a understanding of the algorithm. We'll let the data and methodology speak for themselves.
We are proud of the work being done on QuantNetwork to help members who are interested in financial engineering, regardless of which program they decide to study in.
 
It's very easy to make assumption without a understanding of the algorithm. We'll let the data and methodology speak for themselves.
We are proud of the work being done on QuantNetwork to help members who are interested in financial engineering, regardless of which program they decide to study in.

This is true, and I agree 100%.

I'm in Canada (and my preference would be to attend UofT's MMF program) yet I feel like I've learned a lot from everyone here about financial engineering.
 
UCB is ranked much below what it should be.
It is actually in Top 2-3 if not the best.
 
Using the same criteria that you used to rank UCB, would you be able to consistently rank all programs?
If your resulting ranking is a list of programs ordered by where you think they should be, it's personal opinion, not a robust ranking methodology.

I agree with you on the whole, but I am sensitive about UCB( the error in ranking is too big). But I think you can survey the opinion among students about this one. I know hardly any person who has ranked Stanford finance program above the Berkeley program. When both programs have been offered, people have invariably preferred UCB.
 
Not sure why NYU ranking is so low either. That's supposed to be one of the best schools in Applied Math.
 
All the negative issues have already been raised and QN folks have tried to answer them on GD forum. I am not satisfied with the answers but I appreciate their effort.
 
University of Toronto at the bottom? Is not John Hull teaches over there?
 
University of Toronto at the bottom? Is not John Hull teaches over there?

John Hull teaches at Rotman, the business school of UToronto, while the MMF isn't part of the business school as far as i know.
 
I think people are overlooking the fact that the rankings were based on an algorithm and not opinions based upon reputation or the fact that professor ABC teaches at school XYZ.
 
I believe there is an informational page labelled "Methodology". Are you just lazy or have you abandoned your intellectual curiosity?
 
From the forum, UCLA's MFE program seems to be discusses a lot, but it isn't listed in the top 23. Is there a list of 24-50 that is coming out in the near future?
 
The Elephant in the Room of FE Programs

NYU-Poly FRE: The Elephant in the Room of Graduate Financial Engineering Programs
By Philip Maymin
Assistant Professor of Finance and Risk Engineering at NYU-Polytechnic Institute


How should we rank financial engineering programs? They have different names (quantitative finance, computational finance, mathematical finance), are housed in different schools (math, business, engineering), and for the most part are relatively young, so there is not a lot of history.

We could rank them by research, perhaps by the number of downloads of research papers as tracked by the industry-standard Social Science Research Network. By that measure, the NYU-Poly FRE would be top of the list. With more than 51,000 downloads, it dwarfs just about any other program. It even surpasses many entire schools; for example, thats more downloads than for all of the faculty in all of the departments of Carnegie Mellons Tepper School of Business, across its entire lifetime.

We could rank them by admitted class size, to distinguish large programs from small. By that measure, NYU-Poly FRE would be an order of magnitude above the rest. While most financial engineering programs graduate about 25-35 students per year, we average about 350.

We could try to split the problem into smaller groups and rank programs within the same type of department, for example all quantitative finance programs, then all computational finance programs, and so on. Here, NYU-Poly FRE is a bit lonely, because there is no one else in our group. No other department incorporates risk engineering into its financial engineering program.

Having a department of both finance and risk engineering is a unique and important distinction. Indeed, it was one of the reasons mathematician, trader, and best-selling author Nassim Nicholas Taleb decided to leave NYU-Courant last year to come to NYU-Poly as a Distinguished Professor of Risk Engineering: he wrote that he only did so after he verified that there was not a single economist in our entire building. Insurance and finance have recently started to merge in the real world but we have been preparing students for the convergence for over a decade, largely due to the efforts of Department Head Charles Tapiero, who has personally published over 350 papers and thirteen textbooks in topics of finance, insurance, and risk.

NYU-Poly FRE was the second financial engineering program in the world when it launched in 1995, and the first to have its curriculum certified by the International Association for Financial Engineers (the IAFE no longer certifies FE programs). By any objective measure, we are off the charts. We really are the elephant in the room of financial engineering programs.

Yet we are routinely excluded from rankings of financial engineering programs. Why? Perhaps because it is hard enough to compare the little programs to each other. Its one thing to compare grapes to grapes, but what do you when theres a watermelon?

Well, how about counting the number of Google hits? If we search for the name of the institution offering the degree and the name of the department, we can rank the programs based on how many matches Google finds. That table is presented below, for data as of October 2009.

C++:
   [B]Count    Google Search Term[/B]
 122,000    NYU Polytechnic Institute "Finance and Risk Engineering"
  95,400    NYU Courant Institute "Mathematics in Finance"
  77,700    Columbia University "Mathematics of Finance"
  41,600    Stanford University "Financial Mathematics"
  35,600    Princeton University "Financial Engineering"
  29,700    Columbia University "Financial Engineering"
  20,200    University of Michigan "Financial Engineering"
  18,100    Cornell University "Financial Engineering"
  17,000    University of California at Berkeley "Financial Engineering"
  14,700    Boston University "Mathematical Finance"
  14,400    Claremont Graduate School "Financial Engineering"
  12,500    Carnegie Mellon University "Computational Finance"
  10,800    University of Toronto "Mathematical Finance"
   9,790    University of Chicago "Financial Mathematics"
   9,480    University of Southern California "Mathematical Finance"
   8,430    Rutgers University "Mathematical Finance"
   7,430    Baruch College "Financial Engineering"
   7,340    Rutgers University "Quantitative Finance"
   5,600    Georgia Institute of Technology "Quantitative and Computational Finance"
   5,310    Florida State University "Financial Mathematics"
   5,180    Kent State University "Financial Engineering"
   5,130    North Carolina State University "Financial Mathematics"
   3,780    Purdue University "Computational Finance"

For more information see the Quantnet Wiki on NYU-Poly FRE:
NYU Poly - QuantNetwork Wiki

An easier-to-read PDF version of this document is attached below.
 

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