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QuantNet’s 2013-2014 International Guide to Programs in Financial Engineering | quantnet.com 1
International Guide to
Programs in Financial
Engineering
QUANTNET INC.
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Andy Nguyen
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Copyright 2013-2014
by QUANTNET INC.
Welcome
Welcome to the 2013-14 edition of the QuantNet International Guide
to Programs in Financial Engineering.
Our inaugural edition has been an extremely useful companion
guide to the eld of nancial engineering for many students. It has
been downloaded thousands of times since its debut one year ago.
Quantitative nance is ever-evolving due to unprecedented
changes in technology, markets, and political forces. The biggest
changes occurring in the last few years have been the diversity of
employers who hire graduates from quant programs as well as the
emergence of the Big Data movement.
Candidates with strong technical skills will be in high demand
for the foreseeable future, but the required skills and domain
knowledge arerapidly changing, especially so for the nance
industry in the post-Dodd-Frank regulatory reform era.
The aim of this guide is to provide the information you need
toprepare for your immediate goal, be it as a stronger applicant
to thetop graduate programs, a better job applicant, or a more
successfulprofessional.
Today, as the top website for quant education and career
resources, we have served 2.5 million unique visitors since 2010.
Our audience is made up entirelyof MFE applicants, quantitative
nance professionals, academics, andemployers.
We hope that, having reading this guide, you’ll learn more
about the industry and make better-informed decisions on your
educationand career choices. Be sure to visit and join us on
QuantNetto take advantage of our special tools and a community
that helps you connect with employers and network with other
professionals inthe eld.
With best wishes for your career,
Andy Nguyen
andy@quantnet.com
QUANTNET | 2013-2014 GUIDE | quantnet.com2
Contents
A Welcome Message from QuantNet
by Andy Nguyen ........................................................ 1
About Financial Engineering
Big Data in Finance
by Andrew Sheppard .................................................5
What Do Financial Engineers Do?
by Aaron Brown.......................................................12
QuantNet Services Overview ..............................14
So You Want to Be a Financial Engineer?
Preparing for a Career in the Field
by Todd Fahey ..........................................................18
Efcient Ways to Set Up a Successful Career
by Dan Stefanica .....................................................23
READING LIST: Books about Financial
Engineering ...........................................................25
Finding a Master of Financial
Engineering Program
How to Identify a Master in Finance Program
Worth Attending
by Anthony DeAngelis .............................................28
How to Pick an MFE Program by Aaron Brown ..31
2013-14 QuantNet Ranking of Master of
Financial Engineering Programs....................... 34
International List of Education Programs
in Financial Engineering and Quantitative
Finance ...............................................................36
Quant’s Next Top Model
by Rachael Horsewood .............................................43
Understanding the Quantitative Finance
Industry in Asia
by Chyng Wen Tee and Christopher Ting ................49
Word-Class
Excellence
èOne-on-one alumni mentoring for every current student
èLifetime career services for alumni
Student Outcomes & Success
Competition Highlights
Dedicated, Tight-Knit Alumni Community
7%95%
96%113K
27
Admission Rate
Fall 2013
Students
Class of 2012
Job Placement
Class of 2012
Internship Placement
Summer 2013
Average Starting Salary
Class of 2012
TOP EMPLOYERS: Barclays Capital, Goldman Sachs,
ITG, JPMorgan, Morgan Stanley, State Street,
Autonomy Capital, Promontory Financial Group
èRotman International Trading Competition:
1st (2012); 3rd (2013, 2011)
èIAFE Student Competition: 2nd (2013)
èMetaquotes Automated Trading Championship:
2nd (2012)
baruch.cuny.edu/mfe
Two teams of Baruch College MFE students take first and fourth
places in the 2012 Rotman International Trading Competition in Toronto
QuantNet’s 2013-2014 International Guide to Programs in Financial Engineering | quantnet.com 3
Making Sure Your MFE Application Stands Out
by Bill Stanley ..........................................................53
QuantNet MFE Application Tracker ..................55
C++ Programming for Financial Engineering
Online Certicate ...............................................58
Graduate Schools Want Thoughtful,
Well-Rounded Students
by Gwen Stanczak ................................................... 59
READING LIST: Books for Applicants and
Students of Financial Engineering Programs....63
Getting a Job
Job Search Strategies & Interview Techniques:
Questions and Answers by Ellen Reeves ............. 65
Finance Industry Dictates Changes to Job
Market by Ken Abbott ............................................75
QuantNet Salary Survey ................................... 77
Questions Asked at Quant Interviews..............78
Firms that Employ Quants with
MFE Degrees ...................................................... 79
Why Join a Professional Organization?
by Peg DiOrio ...........................................................81
READING LIST: Books to Help Prepare for
Quant Interviews ...............................................83
Appendix
Excel 2007/2010 Shortcuts ................................85
Advertisers Index ........................................87
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of Applied Mathematics
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computational-finance.uw.edu
Seattle Campus and
Worldwide Online
Ideal Blend of Theory and
Applications Curriculum
Top UW and finance industry faculty
Extensive use of R and R Finance Packages
QUANTNET | 2013-2014 GUIDE | quantnet.com4
SECTION 1:
About Financial
Engineering
QuantNet’s 2013-2014 International Guide to Programs in Financial Engineering | quantnet.com 5
Big Data in Finance
By Andrew
Sheppard
echnology has
always been a driver
in nance. That was
true when Nathan
Rothschild (the
eponymous founder of
Rothschild Bank) used carrier
pigeons to relay the news of
Napoleon’s defeat at Waterloo
to London in 1815, something
that was obviously going to
move the London markets, and
an innovation that, at the time,
shortened the transmission
of the outcome of the battle
from days to hours. Technology
as a driver in nance is also
true today, perhaps even more
so. And what’s really driving
nance today, from a technology
perspective, is Big Data (and Big
Compute and Machine Learning
and Data Mining and the Cloud,
as these oftentimes go hand-in-
hand with Big Data).
Which raises the question: What
should a modern day quant
know about Big Data?
In many ways, this is related to
the changing role of the quant.
From my own experience of
having been a quant for 20+
years, you have to reinvent
yourself every three to ve
years or die. These days, the
best quants I see are not just
good at the quantitative stuff
(math and the technical side
of nance), but are also ace
programmers, because the best
and most useful type of quants
build useful tools that can be
used to make more money
while better understanding
and (we hope) managing risk.
Now added to the mix is the
role of Data Scientist. For a
modern-day quant it’s going to
be difcult to avoid nancial Big
Data. Or, turning that statement
around, if you are a modern-
day quant and you aren’t really
rather good with Big Data, you
are handicapping yourself. (I’d
like to say “shooting yourself in
the foot, but that may be a bit
harsh, but not by much). Adapt
and prosper, or die; it’s your
choice. Has life ever been any
different for a quant?
So, what should the modern-day
quant know about Big Data? I’ll
answer that by picking out the
“peaks” of the Big Data
landscape that are particularly
relevant from a quant nance
point of view.
However, before I do that, I
would like to dene what Big
Data is and describe some
characteristics of Big Data,
which I hope will leave us in the
position of knowing what we’re
talking about. Or, at the very
least, for me to know what I am
talking about!
I’m going to give not one, but
two, denitions of Big Data in
nance. The rst is from an end-
user perspective and leverages
Microsoft Excel’s role as the de
facto, front-end-of-choice for
trading desks, risk departments,
and pretty much every layer of
the nancial organization from
front-to-back ofce. If end users
continued on page 7
“If end users have data that doesn’t fit in Excel,
or requires hours for Excel to process,
you typically have a Big Data problem.”
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QuantNet’s 2013-2014 International Guide to Programs in Financial Engineering | quantnet.com 7
have data that doesn’t t in
Excel, or requires hours for Excel
to process, you typically have a
Big Data problem.
The other perspective on Big
Data is from an IT department’s
point of view, and it basically
says that if you are looking at a
data set and the rst thing that
comes to mind is “gosh, this
belongs in Hadoop, then you
have a Big Data problem. Note
that in the second denition,
it is only necessary to initially
think the data belongs in
Hadoop, not that it really does
actually belong in Hadoop (and
much nancial doesn’t, but more
on that later); it’s the sentiment
that is at the core of the second
denition. These are simple and
practical working denitions
for Big Data, and strangely
enough, in more than a few
years of working in nancial Big
Data, I’ve yet to hear someone
disagree with them as useful
working denitions.
Now that we know—or at least
can broadly agree—on what Big
Data is, it’s time to explore the
nature, or character, of data.
Data, generally speaking, is
characterized along four axes:
volume, velocity, variety, and
veracity—also known as the
four V’s for obvious reasons.
Volume is the quantity of data.
Velocity is the rate at which
data is arriving. Variety is how
structured or unstructured the
data is (which, in short, is data
complexity). And Veracity is the
quality and reliability of the data.
(Guess what, data that is clean
and easily and unambiguously
interpreted is better!) Data that
is considered “Big” along any
of these axes is, by denition,
Big Data. This is our third, and
perhaps, most generic denition
of Big Data.
There is also a characteristic
of nancial data that sets
it apart from data in many
other industries, and this is its
relatively short “half-life. The
half-life of data is the time it
takes for the economic value
of the data to halve in value.
To illustrate with a somewhat
fatuous example, I’m going
to give you the choice of two
prices: 1) IBM’s stock price for
yesterday or 2) IBM’s stock price
for tomorrow. Any takers for
yesterday’s stock price? No,
I thought not. And a show of
hands for tomorrow’s price? Yes,
that’s more like it! Clearly, stock
prices, generally speaking, have
a rather short half-life. What this
means from a practical point of
view is that for much nancial
data, if you can’t use it within
a small multiple of its half-life,
you may as well throw it away.
Its value can decay that quickly.
There may be other reasons
for storing the data, such as
regulatory mandates, but you
should always be mindful of the
economic value of the data you
are dealing with and deal with it
accordingly.
What’s driving Big Data?
Financial markets across
all times and all places are
governed by two simple
impulses: greed and fear. Or,
opportunity and risk, to be more
polite. The opportunity to make
a dollar, and the chance to not
continued on page 8
Big Data in Finance continued from page 5
“Financial markets across all times and all places
are governed by two simple impulses: greed and
fear. Or, opportunity and risk, to be more polite.”
“There is also a characteristic of financial data that
sets it apart from data in many other industries,
and this is its relatively short ‘half-life’.”
QUANTNET | 2013-2014 GUIDE | quantnet.com8
lose a dollar. Companies in
nance see great value in Big
Data, otherwise they would stop
using the stuff in a heartbeat.
They also see the chance to
better manage their risks using
more data. And, as mentioned
before, the regulators are hard at
work with new and far-reaching
mandates that generate, and
require the storage of, vast
amounts of new data. What also
drives data growth is technical
capability, such as the price of
disk storage; because if we could
not store and process Big Data
economically, again, we’d stop
doing it.
Also, I’ll give a piece of advice
to budding and existing quants.
And it is this: build a portfolio
of tools that people love to
use. Not only will this endear
you—beyond measure—to
your existing employer and
users, it will also prove to be
an invaluable resource when
it comes to nding your next
employer and next group of
users. If you are a budding quant
without an employer, then build
some demo tools or contribute
to open-source projects in the
nance space. There is a world
of difference, a gulf that is
tremendously wide, between
just talking about something
versus saying, “hey, look, if I can
just ip open my laptop for a
moment I can show you a real-
time simulator I built for bank-
wide CVA calculations that uses
GPUs for compute acceleration”
(or some other tool or technique
that knocks their socks off).
It’s good advice, because I have
successfully used it many times
myself.
Now, back to the “peaks” of the
Big Data landscape.
The perspective I want to give
is a combination of techniques
and tools that a practicing
quant should ideally have at
their ngertips. You don’t have
to be an expert in each, but you
should know enough to know
what techniques and tools to
use in a given situation and
to become expert when the
need arises. In the case where
you need to become expert in
something very quickly, in the
words of a former head trader I
worked for: “You have a week!”
On the trading desk I don’t think
it has ever been otherwise.
In terms of techniques, there
are a number of areas of
importance. Data gathering,
cleaning (also called
“scrubbing”), normalizing
(putting everything on the
same apples-to-apples basis),
storing and management; all
of which I will group together
under “Data Programming”.
And “Data Insights” are ways
of understanding the nature
and character of the data you
are dealing with; you need to
understand your data before you
can intelligently attack it with
analysis. This “insights” step is
often overlooked. “Fools rush in”
is the expression that comes to
mind when people do this.“Data
Analysis” is extracting
meaningful and actionable
information from the data.
This is the way I think when
tackling Big Data problems; if
you don’t nd it useful, feel free
to create your own. But one way
or another, design and build a
Big Data tool chain that works
for you, because an ad hoc set of
tools that you throw together for
each project will leave you in a
world of pain.
Data Programming
In many ways, this is the
plumbing that supports
everything else you want to
do with data. Like real-world
plumbing, you want this to
be tight, clean, and have the
right capacity. No one wants
to be dealing with an ugly
mess on the oor, or have to
metaphorically put their hand in
ontinued on page 9
Big Data in Finance continued from page 7
“In the case where you need to become expert in something very quickly,
in the words of a former head trader I worked for:
‘You have a week!’”
QuantNet’s 2013-2014 International Guide to Programs in Financial Engineering | quantnet.com 9
Big Data in Finance continued from page 8
the toilet bowl to unblock
things! This is something you
just have to get right, otherwise
you won’t get to the insights
and analysis for your data. Also,
something that is very often
overlooked is that few (if any)
data sets are static; data is a
dynamic and living thing, so
an automated mechanism for
updating your data set is a must,
and this mechanism must also
be robust and scale as your data
set gets bigger.
In terms of gathering, cleaning,
and normalizing your data,
scripting languages are very
useful. Languages such as
Python have a rich set of
libraries that make data
manipulation, if not simple,
then at least easier. And don’t be
afraid to use older but still very
useful and powerful tools such
as Awk. You can use traditional
languages such as C++, but
that will be really productive
only after you have built a data
toolbox or developed a DSL
(Domain Specic Language) for
the purpose, in which case you
have effectively created your
own scripting language anyway.
So why not just use something
like Python and save yourself
the effort?
Also, since I’ve now mentioned
DSLs, I would like to say a few
more things about them. DSLs
for Big Data are incredibly
powerful. They are a way of
getting things done very quickly
and succinctly and at a level of
abstraction that end users can
understand; this way you can
provide Big Data tools for end
users to use. This is true not
just for the data programming
part of the data tool chain, but
anywhere in the chain of tools
you use for data insights and
analysis. Start building DSLs for
Big Data and make your life, and
the lives of your users, easier.
People will love you for it!
For data storage and
management, there are plenty
of good databases and tools.
You should build your own only
if doing so is a real competitive
advantage (emphasis on “real”
here). There are plenty of in-
memory databases and NoSQL
databases and relational
databases (yes, some Big Data
really does belong under the
relational model!) that you
should nd one where your
data ts well. Just make sure
that your choice here makes
downstream activities—insights
and analysis—simple and not
hard.
In the area of data storage and
management, one tool that I
must mention specically is
Hadoop, which I will introduce
through a story.
Some years ago I was working
on Big Data on Wall Street and
I would often ask “Have you
looked at Hadoop?” to which
the response was nearly always
“What’s Hadoop?” Fast-forward
from that point by six months,
and I would ask people Are you
working on Big Data?”—often to
the same people as before—and
the answer would be “Yes, we
have a Hadoop project!” In six
months people had gone from
not knowing what Hadoop is to
Hadoop being synonymous with
Big Data! Gosh, things move fast
in nance.
Hadoop is not just a tool for
storing and managing Big Data,
it is in reality an ecosystem of
tools that includes such things
as machine learning (Mahout).
Hadoop is simply a must-have
skill for a quant these days; start
learning it today.
continued on page 10
“. . . there are plenty of good databases and tools.
You should build your own only if doing so is a real
competitive advantage (emphasis on ‘real’ here).”
QUANTNET | 2013-2014 GUIDE | quantnet.com10
Data Insights
Know your data.
That seems a sensible idea,
but it’s amazing how many
people jump into analysis
without even the most basic
knowledge of what they are
dealing with. Before doing
analysis on your data, and
certainly before you start
making important decisions
with your data, you should have
an intimate knowledge of all
aspects and characteristics of
your data. Think of it like this:
if you were going to attack an
enemy on a hilltop over open
ground, wouldn’t you want to
do some reconnaissance rst?
Datareconnaissance, if we can
call it that, will give you a good
picture of the battleeld before
you advance.
Tools I nd useful here are,
again, the scripting languages
and tools used for data
programming. In addition,
data visualization is a very
powerful technique for having
a sense of what your data is
about. Whereas a large data set
presented as a table of numbers
is largely incomprehensible to a
human, the same data
creatively displayed as a
graphic—ideally one that is
interactive and which allows
the user to zoom in and out,
ip, and rotate—can convey
meaning at all scales, large and
small. Tools that are useful for
this type of exploratory work
include MATLAB, Mathematica,
and R, the latter being free and
open-source. These same tools
are very good at extracting
statistical and other summary
measures from your data. You
should also keep an eye out for
new and useful tools that may
make you more productive;
this is general advice for the
whole data tool chain. The data
language Julia is one such tool
that comes to mind and is worth
keeping an eye on.
I’ll close the discussion on data
insights with a word of caution.
Data sets are so large these days
that there is a danger of seeing
patterns in the data that
simply aren’t there. The term
for this isapophenia.
If you have ever looked at
a cloud and seen a ship, a
car, or a face that looks like
your grandmother, you have
experienced apophenia. The
cloud has so many countless
water particles that almost
any pattern can be tted to
them just by altering your
point of view. Make sure this
doesn’t happen with you
and your nancial data.
Apophenia in nancial data
is particularly prevalent
when looking for protable
strategies from the data.
Data Analysis
This, frankly, is the purpose of
Big Data. Data programming and
data insights were just a way
to get you here in an orderly
fashion. Now it’s time to extract
continued on page 11
“ . . . if you build tools that give easy access to Big Data to your end users
(eliminating you as the bottleneck at each stage of the data tool chain),
people will love you for it.”
Big Data in Finance continued from page 9
“You should also keep an eye out
for new and useful tools that may make you
more productive; this is general advice
for the whole data tool chain.”
QuantNet’s 2013-2014 International Guide to Programs in Financial Engineering | quantnet.com 11
Big Data in Finance continued from page 10
value from the data. The data
tool chain all the way up to this
point has been expense, now it’s
time for prot!
When you chose how to store
and manage your Big Data (the
data programming step), you will
have chosen a tool that makes
the analysis easier. Here’s where
the ecosystem of tools around
something such as Hadoop pays
big dividends. Not only does
Hadoop provide good out-of-
the-box tools for analysis, it also
provides tools to build your own
analysis tools.
Hadoop is particularly strong in
this area, but other NoSQL and
relational tools are coming along
very nicely too and denitely
worth looking at.
It’s also the case that Excel and
R have become rather good
front-ends to Big Data; Excel
in particular is a comfortable
and easy-to-use front-end for
end users. And I will repeat a
common theme throughout this
article: if you build tools that
give easy access to Big Data to
your end users (eliminating you
as the bottleneck at each stage
of the data tool chain), people
will love you for it.
Lastly, and this is again
something all too often
overlooked, analysis is also a
source of Big Data. Data feeds
on data, and more Big Data is
often the byproduct of Big Data.
Indeed, sometimes your analysis
may generate data sets that are
larger than your original Big
Data. Just make sure that extra
size is reected in the added
value they bring.
So, where are we today? Big
Data is now a reality in nance
and pervades every nook and
cranny of nancial institutions.
IBM has determined that 90%
of the world’s data was created
in the past two years alone, and
there seems no end in sight to
data growth. From this quant’s
perspective, you had better get
your Big Data skills up to snuff—
and quickly.
To echo again the words of my
former head trader boss, “You
have a week!”, so you’d better
get started soon.
Andrew Sheppard started his career in nance as a quant at Bankers Trust working in
London, then Tokyo, and nally in New York. Andrew has since worked as a consultant,
chief quant, and CTO at various European and U.S. banks and a multi-billion dollar
hedge fund. Since 2010 he has worked as a consultant exclusively in the areas of Big
Data and Big Compute in nance and insurance.
QUANTNET | 2013-2014 GUIDE | quantnet.com12
he function of
nance is to connect
providers of capital
with users of capital.
This can be a simple
process. For example, a venture
capitalist might nd wealthy
individuals to fund start-up
companies. This venture
capitalist might make use of
tools such as a spreadsheet
and quantitative theory such as
discounted cash ow valuation,
but has little need for a
specialized nancial engineer.
Most nance is done in more
complicated ways, using
intermediate institutions such
as banks, exchanges, and special
purpose entities. Many people
with technical skills are needed
to keep this system running. I
do not consider them nancial
engineers, however. They work
in nance and have quantitative
skills, but they are doing niche
jobs for which the eld of
application doesn’t matter
much. Designing databases or
solving equations for a bank is
not essentially different from
doing the same tasks for, say,
a parcel delivery service or an
aircraft manufacturer.
I dene a nancial engineer as
someone using technical skills
in the nance industry whose
work is informed by the end-to-end,
capital provider to capital user,
effects of what he does. It is not
necessarily a better or more
honorable profession than
the specialists who make up
most of the nancial technical
workforce. It does require
different attitudes and skills,
and it presents different
challenges and offers different
rewards.
There are three characteristics
any engineer must have. First,
she must accept reality. She does
not spend effort worrying about
how things might have been
different, or complaining to the
universe or agitating for other
people to change their ways. She
is not concerned with opinions,
untestable propositions, or
abstractions that do not affect
decisions. Second, she must
have a vision for how things
could be better. It need not be an
individual vision, many engineers
function best on group projects,
but random tinkering is not
engineering. Third, she must
have the drive and skills to
accomplish her vision through
her own efforts. She can fail at
the third step and be a failed
engineer. But if she fails at either
of the rst two steps, she’s
something other than an
engineer. The engineer’s prayer
is, “Thank you ____ (ll in what-
ever you feel gratitude toward
for existence) for the universe
and for my eyes, my hands, my
brain. I’ll take it from here.
continued on page 13
What Do Financial
Engineers Do?
Engineering is not merely knowing and being knowledgeable, like a walking
encyclopedia; engineering is not merely analysis; engineering is not merely the
possession of the capacity to get elegant solutions to nonexistent engineering
problems; engineering is practicing the art of the organized forcing of technological
change. Engineers operate at the interface between science and society.
—Gordon Brown
By Aaron Brown
QuantNet’s 2013-2014 International Guide to Programs in Financial Engineering | quantnet.com 13
What Do Financial Engineers Do? continued from page 12
This denition generates two
questions. Do you need a
nancial engineering degree in
order to be a nancial engineer?
The answer is “no”; some of the
greatest engineers in history
were self-educated. However,
a good nancial engineering
program is the most efcient
way to pick up the necessary
knowledge. You would have to
work for many years, in many
areas of nance, to become
familiar with the nancial
system end-to-end through
direct experience. You can learn
most of the technical skills from
books and the Internet, but not
the practical details that are
essential to sound engineering.
Thirty years ago, a quant with
a good general education, a
curious mind, and a diverse
set of industry contacts could
teach himself. Today it would be
extremely difcult.
The second question is whether
there is any reason to enter a
nancial engineering program
if you do not want to be a
nancial engineer. This one is
tricky. The reason employers
hire people from nancial
engineering programs is they
need employees with a breadth
of technical nancial knowledge.
Only a few employers want
engineers of any stripe;
engineers can be disruptive.
Employers are more likely to be
looking for technical specialists
who can move to different areas
as needed, and who might avoid
some tunnel vision mistakes
of a quant without general
nancial training. So you might
be tempted to get a nancial
engineering degree in order to
have a better chance of getting a
job as a technical specialist.
That can work, but I think it’s
rarely a good idea. The trouble
is people like that usually have
short careers in nance. When
you amortize the cost of the
program along with the lost
employment time while in the
program, you might well end up
making more money in a lower-
salaried, less volatile career.
More important, that lower-
salaried career can progress
naturally. You won’t face a mid-
career transition after being
laid off from a high-paying job,
having to start over in some
other eld. Another point is
there are probably cheaper and
easier things you can do to
improve your chances of landing
a technical specialist job in
nance than get an MFE.
What is it like to be a nancial
engineer? I have to start with a
caveat. The world is changing
fast, and the nancial world
is changing faster. It’s easier
to predict functions than
institutional roles. For example,
I’m pretty condent nancial
engineers will be describing the
possible evolution of derivative
prices for many years, and
probably using some kind of
generalized Monte
Carlo to do it. But
I have much less
condence that
they will be doing
it on anything like
a modern dealer
trading desk. Over
my career, I have seen
species of nancial businesses
spring up, evolve, and die out.
Nevertheless, to prevent this
from getting too abstract, I’m
going to use current institutional
terms. Just remember to focus
on the functions of the job,
not how it is embedded in a
nancial business.
Let’s begin with front ofce jobs,
jobs in groups that generate
direct revenue. These are the
most exciting jobs with the best
pay, also the most volatile and
the ones where luck plays
continued on page 15
“What’s it like to be a financial engineer? I have to start with caveat.
The world is changing fast, and the financial world is changing faster.
It’s easier to predict functions than institutional roles.”
Since its beginning in 2003, QuantNet has grown from a small
discussion board for MFE students to a global comprehensive
resource dedicated to nancial engineering. Whether you are
thinking of a graduate degree or certicate, looking to upgrade your
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This certicate is designed for people interested in pursuing graduate studies in
nancial engineering and covers essential C++ topics with applications to nance.
Approximately half of the students who successfully completed the seminar prior
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COMMUNITY
QuantNet is the largest community of MFE applicants and graduates. Every month,
QuantNet.com is frequented by thousands of visitors looking for information
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QUANT PROGRAMS RESOURCES
QuantNet Resources are the website’s latest tool that provides applicants with in-
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The 2013-2014 QuantNet ranking is the most comprehensive ranking to date of
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QuantNet surveyed program administrators, hiring managers and quantitative
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SERVICES OVERVIEW
QuantNet’s 2013-2014 International Guide to Programs in Financial Engineering | quantnet.com 15
What Do Financial Engineers Do? continued from page 13
the greatest role in career
success. They reward aggressive,
condent nancial engineers,
and may require subordinating
personal lives. On the other
hand, they allow the most
professional freedom. Front-
ofce nancial engineers can
choose to work in a wide variety
of circumstances from one-
person start-ups to the largest
companies. A disadvantage
is front-ofce skills are
not transferrable to other
professions. If your professional
rewards and satisfaction are the
biggest things in your life, and if
you are sure nance is for you,
the front ofce may be the best
place to work.
The two forms of revenue in
nance are trading prots and
many avors of fees. Financial
engineers are needed to design
and support trading strategies,
create and manage structured
products, develop software to
be used in pricing or hedging,
and other tasks that combine
aspects of all three functions.
The reason you should have
a nancial engineer for these
tasks is all the revenue is
extracted from a highly complex
and fast-changing nancial
system. It’s not enough to write
good code or solve equations
properly; front-ofce quants
need to build systems that
can thrive in a chaotic and
competitive environment.
Next come back-ofce jobs.
There are a lot more of them
than front ofce jobs, and they
afford better work-life balance.
Success will depend on ability
more than on luck and politics.
Careers will be more predictable
and if you do decide to leave
nance, your acquired back-
ofce skills will have some
value. Your achievements are
likely to have longer useful lives.
Pay and excitement are lower
than in the front ofce. Financial
engineers can play important
roles in back-ofce areas such
as nancial control, especially
risk control, and systems
development.
Back ofce is sometimes
referred to as the “plumbing”
of the nancial system. Like
plumbing, everyone ignores
it when it works, but when it
doesn’t work, life can be very
unpleasant. Also like plumbing,
it’s a lot more complicated than
most people imagine.
Finally there is the middle ofce.
A century ago, there were literal
physical front and back ofces in
brokerage rms, the front ofce
for clients and the back ofce
for clerks. There never was a
real middle ofce. The term was
invented in the 1980s to describe
risk management, because
risk managers used front-
ofce skills, and sometimes
got injected into front-ofce
decisions, but did not generate
revenue directly.
There’s no generally
agreed denition
of the term; some
people include
departments
such as treasury,
information
technology, legal, and
compliance. Financial engineers
are most often found in risk
management and, if you include
them in middle ofce, front-
ofce IT and risk IT.
In some respects—pay, glamour,
and career volatility—middle
ofce is (as you might expect)
midway between front ofce and
back ofce. In another respect,
however, it differs from both
front and back ofce. Middle
ofce requires nancial
engineers. However specialized
a nancial task or institution, it
can be affected by the end-to-end
continued on page 16
“It’s not enough to write good code or solve equations
properly; front-office quants need to build systems that can thrive
in a chaotic and competitive environment.”
QUANTNET | 2013-2014 GUIDE | quantnet.com16
What Do Financial Engineers Do? continued from page 15
chain of capital in which it is
embedded.
The great British engineer Henry
Royce dened the simple ethos
of the profession: “Strive for
perfection in everything you do.
Take the best that exists and
make it better. When it does not
exist, design it. This is a proud
and noble undertaking, for those
with the talent and energy to
attempt it. And today, nance
is one of the most exciting
and useful places to practice
engineering. You take it from
here.
Aaron Brown is risk manager at AQR Capital Management and the current
Global Association of Risk Professionals Risk Manager of the Year. He is the
author of Red-Blooded Risk (Wiley, 2012), The Poker Face of Wall Street (Wiley,
2006, selected one of the 10 best books of 2006 by Business Week) and
A World of Chance (with Reuven and Gabrielle Brenner, Cambridge University
Press, 2008). In his 31-year Wall Street career he has been a trader, portfolio
manager, head of mortgage securities, and risk manager for institutions
including Citigroup and Morgan Stanley. He also served a stint as a nance
professor and was one of the top professional poker players in the world during the
1970s and 80s. He holds degrees in Applied Mathematics from Harvard and Finance and
Statistics from the University of Chicago.
Join the Conversation QuantNet’s more than 20,000 members meet in the Forums to
trade information about everything from general education and
career topics to detailed conversations on day-to-day financial
engineering topics to answers for specific questions asked
during the interview process.
Everyone from well-known
quant professionals to
students new to the industry
can get together to talk about
the latest trends in quantitative finance.
The forums are searchable and allow
you to see which topics are the
most recently updated.
Here’s a sample of the forums
generating chatter now:
Ellen Reeves’ column
Ask Ellen—Job
Hunting and Career
Development Advice
“Q/A with Todd Fahey” oers
the opportunity to chat with
Todd Fahey, a headhunter for
Sheield Haworth, Inc.
“C++ Online Certiicate
contains multiple threads for
anyone interested in learning
more about the course to
enrolled students seeking
help from their peers.
QUANTNET | 2013-2014 GUIDE | quantnet.com18
So You Want to Be a
Financial Engineer?
Preparing for a Career in the Field By Todd Fahey
hen
preparing
for a career
in Financial
Engineering,
it’s helpful to know what you
need to know in order to be
considered a good candidate
for a job, as well as how to be
successful in that role once you
are hired.
First, you should know that the
general utilization of an MFE
degree tends to be oriented
toward quantitative roles on
the desk (i.e., working on the
trading desk and delivering
the models, risk calculators,
etc., directly to the traders
who utilize their products), or
in risk management, model
validation, library control, CVA,
or quantitative development
and programming.
I’ve been a recruiter for more
than 14 years, and have worked
exclusively in quantitative
nance for the last 12 years.
My coverage spans global
investment banks, hedge funds,
proprietary trading companies,
and asset management rms,
focusing on the front-ofce
quant and trading and tech-
nology professionals. The vast
majority of roles that I cover
are automated/systematic/
algorithmic quants and traders
through quantitative software
and systems/platform developers
and quantitative analytics and
modeling on the desk. I will
discuss in more detail how
to prepare yourself for these
roles, and help you focus on the
subjects you need your degree
program to teach you.
While the job market is very soft
for new MFEs hitting the market
to be desk quants, as well as
those in exotics and structured
nance, there is a signicant
need within the CVA, risk, and
quant developer/programming
elds right now. I anticipate this
need will only grow stronger
over time as there is signicant
emphasis on risk and credit
at the moment—and the
foreseeable future—specically
as it relates to the current
regulatory environments both
here and abroad.
The other area that is bright
at the moment is within the
world of automated, algorithmic,
systematic, and quantitative
trading. These roles are highly
competitive for entry-level
professionals. Further, they all
require programming skills in
core languages, along with a
solid knowledge of statistical,
neural network and/or articial
intelligence methods. If this is a
route you are looking to pursue,
you need to know that you will
be facing some ridiculously stiff
competition, and you may be
best served by being open to
relocation outside of the U.S.—
Asia in particular. Also, work
hard on getting solid skills and
experience with C++, Python,
Java, and/or Scala, as these
tend to be the most utilized
programming languages in the
eld.
My personal recommendation if
you’re looking for a job now, in
terms of target companies would
be, in order: hedge funds, asset
management rms, proprietary
trading companies and, nally,
nally, banks. The reasoning
behind this is that banks are in
regulatory hell right now;
proprietary trading companies
could very well have some
continued on page 19
QuantNet’s 2013-2014 International Guide to Programs in Financial Engineering | quantnet.com 19
So You Want to Be a Financial Engineer? continued from page 18
issues with the pending
regulations in the U.S. and the
UK, and there is still a lot of
money waiting for deployment
across the global spectrum right
now. Asset management rms
and hedge funds appear to be
the beneciaries of what we
anticipate over the next 5-10
year stretch.
But how do you prepare for
these jobs? First, it’s helpful to
know what to prepare for in
terms of education, based upon
your interests. For example, if
you desire to pursue a path in
high frequency futures trading,
you should be aware that the
vast majority of these people
do not have PhDs, and some
employers in this eld actually
believe them to be detrimental.
A strong background in
electrical and/or computer
engineering (with a master’s
degree, preferably), very strong
programming skills (C++, Java,
C#, Scala, Python, etc.) and
comfort with very large data
sets is key.
If you are more interested in
the mathematical side, a PhD
is the preference, although
not a necessity (MFEs typically
work in this arena, as well).
Typical coursework for
these careers is Operations
Research, Applied Mathematics,
Mathematics, Theoretical
Physics (not experimental—
not a desirable math track),
Electrical Engineering, Computer
Science or Engineering, and
Mechanical Engineering. If you
decide that this is the path to
pursue, understand that strong
programming is a requirement
and will be done every day. It is
no longer optional. And, if you
can only program in MATLAB,
SAS, S+ or another RAD or
statistical package, you will be
at a disadvantage compared
with those who can program in
advanced languages mentioned
above.
What do you need to know to
make yourself competitive in
the market wherever you choose
to work in the world? Let’s face
it—this is probably the most
competitive eld of employment
outside of professional sports.
As such, talent alone might not
get you in the door. There are
things that you should do in
order to make yourself stand
out from the crowd. Including
some things that may make you
uncomfortable and push you
in directions you may not have
considered prior to pursuing this
career path. I will highlight the
things I believe that will best
start you on the path to success:
Personality and
Communication Skills
Believe it or not, you are not
quite as unique as you might
think you are. Everybody in
this eld is “smart”. The ones
who get jobs—and then
progress upward through the
continued on page 20
“. . . there is still a lot of money waiting
for deployment across the global spectrum right
now. Asset management firms and
hedge funds appear to be the beneficiaries
of what we anticipate over the next
5-10 year stretch.”
“There are things that you should do
in order to make yourself stand out from
the crowd. Including some things that
may make you uncomfortable and push
you in directions you may not have
considered . . .“
QUANTNET | 2013-2014 GUIDE | quantnet.com20
So You Want to Be a Financial Engineer? continued from page 19
ranks—have one commonality:
people (at least someone) like
them. You need to be articulate
and outgoing. Inquisitive, yet
thoughtful.
One way to help your
personality show through would
be to join Toastmasters or a
similar organization. While you
may not ever be in sales or a
rened public speaker, it will
only serve to help differentiate
yourself from being like
everyone else.
Probably the most overlooked
need beyond the technical
skills required in this eld is
the need for communication,
specically, communication in
the English language. English
is the universal language of
nance—the same as if you were
an international airline pilot.
If you are not a native speaker,
it would be extremely helpful
to take communications
courses to help with your
grammar, presentation, and
writing abilities. Even if this is
not part of your curriculum,
outside tutoring would not hurt
you. After all, you may be the
smartest mathematician in the
world, but if you can’t articulate
it so that people understand
you, or if your writing skills are
so atrocious (author included
here . . . ) that it is impossible to
follow in a linear fashion, you’re
severely disadvantaged.
Programming
If you’re not good at it, get good
at it. In almost every role in
quantitative nance you will be
required to program. The better
you are, the easier it will be for
you to land a job in the eld.
Languages to concentrate on
are: C++, Perl, Python, Java,
continued on page 21
RESULTS FROM OTHER MFE/MATH FINANCIAL PROGRAMS
12. University of Chicago (Math Finance)
20. MIT
29. Boston University (Math Finance)
32. Rutgers University (Math Finance)
35. NYU (Math Finance)
37. Boston University (Math Finance)
38. UC Berkeley (Financial Engineering)
41. University of Chicago (Math Finance)
2013 Rotman International
Trading Competition Result
THE 2013 COMPETITION WAS ATTENDED BY MANY TOP MFE PROGRAMS
FROM OVER 40 UNIVERSITIES WORLDWIDE.
THE TOP 5 TEAMS THIS YEAR ARE
1. Laval University (Quebec)
2. Chulalongkorn University (Thailand)
3. Baruch College (Financial Engineering)
3. University of Toronto
5. BI Norwegian Business School
QuantNet’s 2013-2014 International Guide to Programs in Financial Engineering | quantnet.com 21
So You Want to Be a Financial Engineer? continued from page 20
C# / .NET, Scala, Hadoop,
MATLAB (not a substitute for
C++!) and other functional
programming languages.
Economics and Finance
In the world of quantitative
trading, economics and nance
classes are not important—other
than for being a well-rounded
professional at a macro level.
Within the world of quantitative
strategists, there is a chasm.
Most banks and hedge funds
look for those who have a
rigorous math background.
However, there are a number
of hedge funds and asset
managers who look to avoid
those backgrounds. They want
classically trained economists
with PhDs from the major Ivy
League schools. If you don’t have
a PhD from one of those schools
and a top undergrad from the
same level institution—don’t
waste your time. This eld
is ercely competitive and
you need to up your game to
even have an opportunity to
interview.
Math
There are a lot of different areas
within math, but there is one
thing for certain: if you’re going
to be a derivatives quant, you
had best be good at stochastic
calculus. Other areas of note are
linear algebra, spatial geometry,
and familiarity with partial
differential equations and
ordinary differential equations.
Internships
The ability to secure an
internship should be a priority
from the moment you walk
in the door as a freshman in
college. You get to learn about
what these people do on a daily
basis, and you may have an
opportunity for a rotation.
You get to be on the Human
Resource department’s radar—
a big thing once you are ready to
enter the job market.
The best way to nd a job is
to have one in hand as you
get ready to graduate because
you’ve interned at the company
and they feel they need to have
you on their team because you
impressed them so greatly as
an intern. Most
importantly, you
begin to network
with other
professionals in the
eld. People move
often and it is 99.99%
likely that you will
leave your rst job
within ve years. The
saying “It’s not what you
know, it’s who you know, carries
a lot of weight in the hiring
world. Get to know as many
people as you can and actively
engage with your network often.
continued on page 22
“The best way to find a job is to have one in hand as you get ready
to graduate because you’ve interned at the company and
they feel they need to have you on their team because you
impressed them so greatly as an intern. Most importantly, you
begin to network with other professionals in the field.”
“There are many different quantitative
networking groups, conferences, and
symposiums in every financial center to keep
you engaged in the latest trends and ideas,
and also—and I cannot stress the importance
of this enough—the ability to network not only
with your peers, but the level of successful
professional that you all strive to be.”
QUANTNET | 2013-2014 GUIDE | quantnet.com22
So You Want to Be a Financial Engineer? continued from page 21
Continuing Education
If there were ever a time to
recommend staying the distance
if you have your sights on a PhD,
now is the time. Entering the
market later with a PhD may put
you at the top of the candidate
list, as well as position you to job
search in a better market.
Not interested in a PhD?
Not a problem. There are
many different quantitative
networking groups, conferences,
and symposiums in every
nancial center to keep you
engaged in the latest trends and
ideas, and also—and I cannot
stress the importance of this
enough—the ability to network
not only with your peers, but the
level of successful professional
that you all strive to be.
One other thing that you need
to do is read. Voraciously. I’m
not speaking about books,
articles, and literature dedicated
to your eld of endeavor.
I’m speaking of information
ow that is real-time and/or
relevant to recent events. If you
don’t know what is going on
around you, it is hard to have
an opinion about what is going
on around you. Read The Wall
Street Journal, Financial Times, and
other newspapers. Subscribe to
e-zines such as Fierce Finance or
FINAlternatives. Join specic web
communities such as QuantNet,
Wilmott, or Nuclear Phynance.
Read books by Michael Lewis or
other topical books relevant to
nance (I’m personally a huge
fan of Roger Lowenstein’s When
Genius Failed: The Rise and Fall of
Long-Term Capital Management.
Become a well-rounded quant
and you will start to move away
from the pack.
The purpose of this article is to
give you a general overview of
the market, the trends, and what
skills I believe you should have
based upon what positive and
negative stresses I see in the
market now and in the next few
years.
Ultimately, I hope that you
remember the ultimate
lesson here: each of your own
situations and experiences is
unique to you. Clarity of your
path is the most important thing
to you. Keep the goal in mind
as you make your decisions
and, with a bit of luck and good
timing, you will arrive at the
point you’re aiming for.
Todd Fahey is Executive Director, Global Head – Quantitative Strategies Practice, at
Shefeld Haworth, Inc. He has trained quantitative and technical recruiters; published
articles, blogs, and e-zines; and presented at various business schools. He can be
reached at fahey@shefeldhaworth.com. Todd also has a Q&A column on QuantNet
where you can ask him questions about the quant job market.
A note from Todd Fahey: I am an executive search consultant, and my value to companies
is to nd experienced personnel. In order for me to maximize my time and efciency,
I need to look at what is the cost/benet of my time usage. Simply, the majority of my
clients are looking for me to nd them the people that they are willing to pay for my
services and that they would have difculty nding on their own. That means that I
more than tend to look at people who are currently actively working in the industry,
have a minimum of three years of work experience, and carry a numerate degree from
a top university globally. Beyond these criteria, it is a stretch to say that I am willing to
work with an individual who don’t meet the bar. I am always willing to offer advice and
suggestions, but that doesn’t mean that I can necessarily help any one individual.
QuantNet’s 2013-2014 International Guide to Programs in Financial Engineering | quantnet.com 23
Financial Engineering Education as a
Gateway for a Quantitative Finance Career
Efficient Ways to Set Up
a Successful Career By Dan Stefanica
graduate degree
in nancial
engineering is
primarily a way to
start, or advance,
a career in quantitative nance.
It is not only useful academically,
but practically as well by learning
about various career paths and
deciding which best suits your
interests and background, and
by creating opportunities to
compete for the right openings
at the right time.
Based on a long experience
fostering careers of both young
and mid-career students and
alumni, I will briey share
pointers on how to put a master
of nancial engineering (MFE)
graduate degree in the larger
perspective of a successful
career in quantitative nance,
from deciding whether to pursue
an MFE, to shaping your career
path once you graduate.
Make sure you learn about
possible career paths before
deciding to apply for an MFE
program. The comprehensive
QuantNet Quant Internship and
Graduate Recruitment Firms
Listing includes an extensive
list of career options from buy-
side and sell-side employers.
The book The Complete Guide to
Capital Markets for Quantitative
Professionals by Alex Kuznetsov
and Mark Joshi’s On Becoming
a Quant guide are also good
sources of information.
Once the decision to pursue an
MFE is made, a three-pronged
process begins: deciding where
to apply, putting together a
competitive application, and
preparing for graduate studies.
Approach this from a career
goals perspective: nd the
program that best suits and
services your career needs and
then use the time you have before
you start the program to strengthen
and update your background. By
doing so, you will set yourself up
for a successful graduate studies
experience, and for better career
options upon graduation.
There should be no doubt
that programming will be an
important part of your future
work and studies. Improve your
C++ and VBA skills, which will
be valuable both in a highly
quantitative role and in a
business-oriented role.
Brush up on your math skills
d Calculus— My book A Primer
for the Mathematics of Financial
Engineering was written with this
goal in mind.
d Linear algebra—Gil Strang’s
Introduction to Linear Algebra has
a strong numerical avor.
d ProbabilityA Natural
Introduction to Probability Theory
by Ronald Meester is both
intuitive and rigorous.
Learn about financial
instruments in a
quantitative framework
d Salih Neftci’s An Introduction
to the Mathematics of Financial
Derivatives is good background
reading.
continued on page 24
QUANTNET | 2013-2014 GUIDE | quantnet.com24
Efficient Ways to Set Up a Successful Career continued from page 23
d Principles of Financial
Engineering, also by Neftci, gives
an excellent practical view
of quant nance for trading
applications.
I cannot overemphasize the
importance of preparing for
graduate studies from the
moment you decide you want
to pursue them. A strong
and current knowledge of
programming, mathematics,
and fundamentals of nance
when entering the program
is, in my experience, a great
attribute of highly successful
graduates, which translates
into signicantly better career
opportunities.
Once you accept an admission
offer, you have several more
months to prepare for your
studies. It is then time to contact
the program and ask for specic
instructions on how to best use
that time given your particular
set of strengths and background
knowledge.
During your studies, remember
at all times that you are doing
an MFE as a step toward a
quantitative career.
Use the program resources—
networking with graduating
students, talking to industry
professionals teaching in the
program, and consulting with
career advisors—to identify the
areas you would like to work in
by the end of the rst semester
of studies. Find out what skills
are most valued by employers in
those areas, and use this
knowledge to decide which
courses you choose subsequently,
as well as what you need to
emphasize in your studies.
Put your job search in a longer-
term perspective. The goal
should not be just to get a job
upon graduation, but to nd the
right position that will allow
your career to grow over time.
This could mean a rst job
where you will further learn and
grow your set of skills, or taking
a position that could be used
as an apprenticeship toward,
why not, starting your own rm
when the time is right several
years down the road.
And it may all start the moment
you begin preparing for your
MFE graduate studies. You have
more time between when you
decide to pursue an MFE and
when you start the degree, than
between the beginning of your
studies and when you start
interviewing for internships.
That time is precious and, if
used efciently, could make a
big difference.
Good luck!
Dan Stefanica has been the Director of the Masters Program in Financial Engineering
at Baruch College, City University of New York, since its inception in 2002. He teaches
graduate courses on numerical methods for nancial engineering, as well as pre-
program courses on advanced calculus and numerical linear algebra with nancial
applications. He is also the author of A Primer for the Mathematics of Financial Engineering,
QuantNet’s #1 best-selling book three years in a row: 2010, 2011, and 2012.
QuantNet’s 2013-2014 International Guide to Programs in Financial Engineering | quantnet.com 25
On Becoming a Quant
AUTHOR: Mark Joshi
PAGES: 20
FORMAT: Online PDF le
WHY YOU SHOULD READ IT: Mark Joshi’s short
guide advises students who want to become a
quant. He covers the types of quant jobs and
expected salaries, the areas of derivatives quants
work in, the types of employers who hire quants,
what a quant needs to learn, the current job
market, and how to look for a job and what to
expect during the interview.
The Complete Guide to Capital Markets for
Quantitative Professionals
AUTHOR: Alex Kuznetsov
PAGES: 600
FORMAT: Hardcover, Kindle
WHY YOU SHOULD READ IT: This book is a must-
read for those with a background in science and
technology who are thinking of transferring
their skills to the nancial industry. Kuznetsov
details how the nancial industry works, as well
as how different rms make their money. Then
he describes how professional with different
technical backgrounds t into roles within the
industry. A section on technology discusses how
nancial models are created and used.
My Life as a Quant: Reflections on
Physics and Finance
AUTHOR: Emanuel Derman
PAGES: 308
FORMATS: Hardcover, Paperback, Kindle
WHY YOU SHOULD READ IT: This is the book that
introduces “quant” as a profession for generations
of students. Emanuel Derman discusses his
journey as one of the rst high-energy particle
physicists to migrate to Wall Street, and along the
way he analyzes the incompatible personas of
traders and quants. Derman also notes the
dissimilar nature of knowledge in physics and
nance, while offering his thoughts on how to
apply the principles of physics to nancial markets.
The Big Short: Inside the Doomsday Machine
AUTHOR: Michael Lewis
PAGES: 266
FORMAT: Hardcover, Paperback, Kindle, Audio, CD
WHY YOU SHOULD READ IT: Michael Lewis’ #1
best-selling book tells the story of the 2007-2008
nancial crisis and how Wall Street missed the bad
securities being issued backed by the subprime
mortgage-backed securities (MBS) that destroyed
more than $1 trillion in wealth.
Financial Engineering: The Evolution
of a Profession
AUTHOR: Tanya S. Beder
PAGES: 616
FORMAT: Hardcover, Kindle
WHY YOU SHOULD READ IT: Part of the Robert
W. Kolb Series in Finance, Tanya S. Beder has put
together a collection of articles by practitioners
and academics with a dedicated section on the
Financial Engineering degree. This book details
the different participants, developments, and
products of various markets—from xed income,
equity, and derivatives to foreign exchange. Case
studies from companies in different segments of
the industry, a glossary, and a companion website
offer additional information and support for those
interested in nancial engineering.
See more recommended reading at
QUANTNET MASTER READING
LIST FOR QUANTS.
READING LIST: Books about Financial Engineering
apply now: www.poly.edu/Fe2
+ M.S. in Financial Engineering
Diversied program spanning
four industry-focused tracks in:
Risk Finance; Computational Finance;
Technology and Algorithmic Finance;
Financial Markets and Corporate Finance.
Inquire about NYU-Poly’s one-year full-time program,
part-time programs or certicate programs.
Master of science
financial engineering
in a technology-intensive environMent
We give you a solid theoretical foundation. Then we show you how
to put it to practice. Our rigorous, top-tier master’s program will
teach you to solve the most complex problems facing nancial
engineers today and train you to be a leader in global nance.
+ Graduates gain careers as portfolio managers, traders and
quants in global rms like Goldman Sachs and the World Bank.
+ Ranked #8 by Social Science Research Network for
research downloads.
+ International exchanges available with leading universities
in Africa, Europe and Asia.
+ Campus in the heart of New York City’s nancial district.
+ Career services support from the NYU Wasserman Center
for Career Development.
+ International conferences and workshops held regularly.
+ Research Internship Grant Fund, dedicated to raising one million
dollars annually,allows deserving students to pursue research
projects for six months after graduation.
+ Key nancial technologies available to students including 12
Bloomberg terminalsas well as a FINCAD grant worth seven
milliondollars.
+ The rst curriculum to be certied by the International
Association of Financial Engineers.
+ The NYU-Poly M.S. in Financial Engineering was initiated
with the generous support of the Alfred P. Sloan Foundation
and was the second program of its kind, anywhere.
Bridge theory & Practice · gloBal outreach · excellence in research
QuantNet’s 2013-2014 International Guide to Programs in Financial Engineering | quantnet.com 27
SECTION 2:
Finding a Master of
Financial Engineering
Program
QUANTNET | 2013-2014 GUIDE | quantnet.com28
How to Identify a Master
in Finance Program
Worth Attending
By Anthony
DeAngelis
ith the
increased
popularity of
specialized
graduate
degrees in nance (Finance
and Financial Engineering), I
have received countless emails,
all with the question “Is this
program worth attending?”
This is an important question
considering the cost, both in
time and money, that going back
to school entails. With the ever-
increasing number of programs
out there, potential applicants
should take into consideration
the following before making
their decision. These apply to
both domestic and international
students, but I will talk toward
the end about specic things to
look out for as an international
student.
1) What are your goals?
If your goal is to use the MSF/
MFE degree as your second
chance at breaking into
investment banking or trading,
then you have to pick a program
that has strong on-campus
recruiting and a history of
placement in this area. These
programs tend to have highly
ranked undergraduate business
programs and are located near
major metro areas. You can
gure out if a program is for
you by looking at their past
placement stats and seeing if
investment banks and other
trading shops come on campus
for recruiting.
If your goal is to study nance
and break into the nancial
industry, regardless of the
position, then you have a lot
more exibility. Look for schools
that are known in their region
or city, those with a wide range
of recruiting and alumni (front
ofce nance roles), or those
that allow you to work while
attending school. This will
give you the opportunity to
keep gaining experience while
increasing your knowledge and
skill set so that you can move up
in your career or laterally into
that nancial position you are
seeking.
2) Cohort program or
flexible class load?
A few universities offer a lock-
step cohort program. Everyone
takes the same classes at the
same time. Other programs
allow students to specialize
and customize their program. It
is important for you to decide
what kind of educational
experience you want.
A cohort program is the most
simple and direct way to go
about a master’s degree, and it
ensures everyone is qualied
and eligible for the widest
variety of nance roles. You also
build close friendships and a
connection with the campus
and university. The downside
is that you lose the exibility to
continued on page 29
QuantNet’s 2013-2014 International Guide to Programs in Financial Engineering | quantnet.com 29
How to Identify a Master in Finance Program Worth Attending continued from page 28
customize your classes. These
types of programs are ideal for
fresh graduates who might not
know what they want to do or
who need more structure.
On the other hand, if you are
looking for a program that
gives you the opportunity to
choose classes and specialize
in the area of nance that truly
interests you, then you want a
program with exibility.
Flexible programs give you the
opportunity to more effectively
target a narrower eld of
employment, and the upside is
you will be better prepared. In
my experience, this tends to be
optimal for people who have
worked for a little bit of time or
someone who has a clear vision
of what they want to do post-
graduation.
3) Does the program offer
career services?
This is something to focus on
if you recently graduated and
need help obtaining your rst
job. Most specialized master’s
programs have a career ofce or
ofcer that aids in placements
and recruiting. The nuanced
nature of these specialized
programs really dictates the
necessity of having a career
services person focused on the
program. Programs without a
dedicated person rely on the
overall career center, which
can sometimes work, but
oftentimes does not. The lack
of dedicated career services
happens for a variety of
reasons, but mainly because
these specialized master’s
programs are relatively small
compared to undergraduate
business programs and MBA
programs. Because of this you
have career ofcers who don’t
truly understand how to sell
these students, how to work
with them when it comes
to placements and nding a
job, and how to market the
programs.
Pay special attention to this as
this is something that can be
part of your due diligence before
the program begins. I suggest
speaking with or meeting
with the career resources
individual who can tell you
about placements, who comes
on campus for recruiting, and
can help you get in contact
with alumni who can give you
a rsthand account of how the
program helped them.
4) How is the
program ranked?
A program’s ranking is
important and where the
master in nancial engineering
has a distinct advantage over
other specialized master’s
degrees. QuantNet provides a
comprehensive and important
continued on page 30
“Flexible programs give you the opportunity to
more effectively target a narrower
field of employment, and the upside is
you will be better prepared.”
“Programs without a dedicated person
rely on the overall career center,
which can sometimes work,
but oftentimes does not.”
QUANTNET | 2013-2014 GUIDE | quantnet.com30
How to Identify a Master in Finance Program Worth Attending continued from page 29
biannual ranking of U.S.-based
MFE programs that gives
students a quick and easy way
to size up programs. Currently
only the Financial Times ranks the
master’s in nance degree, and
it is more relevant for European
master’s programs (and Asian
programs to a lesser extent).
As these degree programs
mature, more publications will
rank them and increase the
transparency, but until then you
will have to use the options that
are available.
Looking at a program’s ranking
gives students an idea of the
reputation and performance of
each school and which ones they
should 1) focus on, and 2) which
ones you will be competitive at
when applying. Both of these
factors are important to consider
so you don’t waste time or
money.
A SPECIAL NOTE FOR
INTERNATIONAL STUDENTS
International students should
pay special attention to
placements and career services.
The economy in the U.S., as well
as globally, is still recovering,
and sponsoring graduates
is an added cost that many
rms prefer not to shoulder.
Financial engineering tends
to be more forgiving than a
general master’s in nance, but
it is still something you should
consider. I recommend casting a
wide net and focusing on larger
employers, as they are both
used to sponsoring students and
nancially able to.
Choosing a graduate program
is always going to be a hard
decision, but by factoring in
these things to look for, the
decision should become a little
easier. While this article does
not cover every issue potential
students must consider, I believe
this should help clarify the
primary choices you should
make. Make the decision that
you feel most comfortable with,
and once you decide on the
course of action, commit to it.
It might take some time and
effort, but if you remain focused
and work hard you will achieve
your goals.
“Looking at a program’s ranking
gives students an idea of the reputation
and performance of each school and which ones
they should 1) focus on, and 2) which ones you
will be competitive at when applying.”
Anthony DeAngelis is a 2010 graduate of the Villanova Master in Finance program and
owner of MSFHQ.com, a site dedicated to the Master of Finance degree. He previously
worked for The Bank of New York and HSBC and is currently working in xed income.
QuantNet’s 2013-2014 International Guide to Programs in Financial Engineering | quantnet.com 31
How to Pick an
MFE Program
By Aaron Brown
rom Amsterdam
to Zurich you can
attend nancial
engineering
masters programs
in all the nancial
centers in the world, or places
off the nancial beaten path
such as Bethlehem, Coimbra,
Potchefstroom, or Stillwater.
You can be taught by some of
the great names in academic
quantitative nance—such
as Carol Alexander, Marco
Avellaneda, Emanuel Derman,
Darrell Dufe, John Hull, Robert
Jarrow, Mark Rubinstein, Philipp
Schönbucher, and Steven
Shreve (leaving out many just as
distinguished)—or by professors
who may be as competent,
but whose names will not
resonate with as many potential
employers. You can pay $20,000
to $80,000, and no doubt more
or less, and spend one to two
years or, in some cases, attend
part-time.
There are nancial engineering
programs with stellar
international reputations,
enviable starting salaries and
top placement statistics; with
graduates placed highly in most
global nancial institutions,
but you can also opt for less
famous programs that may be
cheaper, easier to get into, more
convenient, or that offer special
features that interest you. There
are also programs as prestigious
as the top nancial engineering
masters programs that offer
similar master’s degrees such
as computational nance,
mathematics in nance, and
nancial mathematics. There
are lesser-known programs
associated with great schools,
and top programs associated
with less well-known schools.
Then there are bad programs.
There really are, and some of
them are at schools with good
reputations. I won’t name them.
I’m not shy, but I don’t want to
taint their graduates. Anyway,
it’s something you should nd
out for yourself. If you don’t do
the due diligence to eliminate
the bad programs, you won’t
have the information you need
to make a decision.
A bad program is a cynical
attempt to get tuition dollars
from students who are good at
math and desperate for jobs, and
deliver in return lectures from
people who have nothing better
to do. Some clues that a program
is bad are:
d Few students have nancial
experience or successful career
experience.
d Enrollment uctuates with
demand for nancial quants.
d No distinguishing approach,
just a bunch of standard
courses.
d Instructors with no
publications or experience in
quantitative nance.
d Few courses that truly
combine math and nance,
just pure courses in math,
statistics, computers, and other
quantitative elds, plus basic
nance courses.
d Dated content.
d Exclusive emphasis on
lecture and multiple choice or
numerical answer exams.
continued on page 32
QUANTNET | 2013-2014 GUIDE | quantnet.com32
How to Pick an MFE Program continued from page 31
Beyond these signs, you may
be able to tell by the people you
meet. Do the administrators,
faculty, and students strike you
as a group of successful people
working together to create an
exciting future? Or do they seem
to be unsuccessful people trying
to use each other for personal
benet?
Once you eliminate the bad
programs, the next question is
whether to try for a top program
or select a lower-ranked one
that may have other advantages.
My general advice is to go for
the highest-ranked program that
will accept you, even if it is less
convenient, more expensive, and
less suited to your particular
situation. That’s not universal
advice. There are times to
overrule it. But make sure you
have clear, strong reasons if
you do. Top programs have top
faculty, top students, and top
alumni networks. The best ones
also have unique approaches.
Along with the brand name
advantage, those are powerful
tailwinds to a career. Lots of
people succeed without them,
but why make things harder
than necessary?
For some students, that is the
end of the process. Once they
eliminate the bad programs,
they nd there is only one
acceptable alternative, or one
obviously superior choice. But
for many students, especially
those who are willing and able
to relocate anywhere in the
U.S., there are going to be
several suitable programs of
roughly equal reputation and
quality, at any level from rst-
tier to third-tier.
The next thing I would look
at is the level of mathematics
required. You have to dig
deeply to nd this out. There
are, believe it or not, nancial
engineering programs in which
a professor cannot work a
simple calculus example and
be condent that the class is
following. Everyone passes a
calculus exam of course, but
that’s quite different from being
able to use calculus or other
mathematics in a classroom
setting (not to mention a real-
world setting). I think you will
usually do best to choose the
program with the highest level
of mathematics that you can
handle—and if that level is
below calculus you should nd
another eld. I’m talking about
the actual mathematics used
in classrooms, discussions, and
cases; not the course or exam
requirements.
The other factors like faculty
reputation and experience,
placement statistics, rankings by
independent parties, and
continued on page 33
“Top programs have top faculty, top students, and top alumni networks.
The best ones also have unique approaches. Along with the brand name
advantage, those are powerful tailwinds to a career.”
“I think you will usually do best
to choose the program with the highest level
of mathematics that you can handle—
and if that level is below calculus
you should find another field.“
QuantNet’s 2013-2014 International Guide to Programs in Financial Engineering | quantnet.com 33
How to Pick an MFE Program continued from page 32
admission metrics I lump
together as general quality.
They are too highly correlated
to make decisions by weighting
one versus another, you’re
overtting if you try (if you
don’t know what that means,
nd another eld). If you are
accepted by two programs of
the same overall quality and
mathematics level, I think your
choice is likely to come down to
idiosyncratic personal factors
rather than any systematic
advice I can give you.
If everything above is not
sufcient to make a choice,
consider whether you are
sufciently decisive for nancial
engineering. After you get the
degree, you will have to make
much harder choices with less
information at higher stakes
if you want to use it. If you
can make a choice,
congratulations, and I wish
you the best. I look forward
to the benets your nancial
innovations will bring to the
world, making my eventual
retirement secure and happy.
“If everything above is not sufficient to make a choice,
consider whether you are sufficiently decisive for financial engineering.
After you get the degree, you will have to make much harder choices
with less information at higher stakes if you want to use it.”
Aaron Brown is risk manager at AQR Capital Management and the current
Global Association of Risk Professionals Risk Manager of the Year. He is the
author of Red-Blooded Risk (Wiley, 2012), The Poker Face of Wall Street (Wiley,
2006, selected one of the 10 best books of 2006 by Business Week) and
A World of Chance (with Reuven and Gabrielle Brenner, Cambridge University
Press, 2008). In his 31 year Wall Street career he has been a trader, portfolio
manager, head of mortgage securities and risk manager for institutions
including Citigroup and Morgan Stanley. He also served a stint as a nance professor
and was one of the top professional poker players in the world during the 1970s and
80s. He holds degrees in Applied Mathematics from Harvard and Finance and Statistics
from the University of Chicago.
QuantNet’s 2013-2014 International Guide to Programs in Financial Engineering | quantnet.com 34
RANK UNIVERSITY PROGRAM TUITION SCORE
1Carnegie Mellon University
Pittsburgh, PA
Computational Finance $77,100 100
2Columbia University
New York, NY
Financial Engineering $56,808 99
2Princeton University
Princeton, NJ
Master in Finance $84,140 99
4Baruch College,
City University of New York
New York, NY
Financial Engineering $35,040
$24,315
(resident)
98
4University of California, Berkeley
Berkeley, CA
Financial Engineering $58,895 98
6New York University
New York, NY
Mathematics in Finance $62,000 97
7Columbia University
New York, NY
Mathematics of Finance $54,720 92
8Massachusetts Institute
of Technology
Cambridge, MA
Master of Finance $74,900 90
9Cornell University
Ithaca, NY
Master of Engineering,
Financial Engineering
concentration
$69,000 89
10 Georgia Institute of Technology
Atlanta, GA
Quantitative and
Computational Finance
$54,144
$20,772
(resident)
82
10 University of California,
Los Angeles
Los Angeles, CA
Financial Engineering $55,600 82
12 Rutgers University
New Brunswick, NJ
Mathematical Finance $47,250
$28,539
(resident)
79
2013-14 QUANTNET RANKING
Master of Financial Engineering Programs
The 2013-14 QuantNet ranking is the most comprehensive ranking to date of master programs in
Financial Engineering (MFE) and Mathematical Finance in North America. QuantNet surveyed program
administrators, hiring managers, and quantitative nance professionals from nancial institutions
around the world for statistics reecting student selectivity and graduate employment.
continued on page 35
QUANTNET | 2013-2014 GUIDE | quantnet.com35
RANK UNIVERSITY PROGRAM TUITION SCORE
12 University of Toronto
Toronto, Canada
Mathematical Finance CAD 42,000 79
14 Boston University
Boston, MA
Mathematical Finance $65,955 78
14 University of Chicago
Chicago, IL
Financial Mathematics $51,012 78
16 NYU-Poly
Brooklyn, NY
Financial Engineering $44,979 75
16 Rutgers University
Newark, NJ
Quantitative Finance $58,317
$35,445
(resident)
75
18 Fordham University
New York, NY
Quantitative Finance $50,875 74
19 Johns Hopkins University
Baltimore, MD
Financial Mathematics $60,000 73
20 University of Illinois
Urbana, IL
Financial Engineering $51,000
$25,500
(resident)
70
20 University of Michigan
Ann Arbor, MI
Financial Engineering $63,184
$33,671
(resident)
70
20 University of Minnesota
Minneapolis, MN
Financial Mathematics $40,470
$32,870
(resident)
70
20 University of Washington
Seattle, WA
Computational Finance &
Risk Management $37,800 70
24 Claremont Graduate University
Claremont, CA
Financial Engineering $81,120 66
25 Illinois Institute of Technology
Chicago, IL
Mathematical Finance $49,104 62
U.S. Master of Financial Engineering Programs continued from page 34
QuantNet’s 2013-2014 International Guide to Programs in Financial Engineering | quantnet.com 36
INTERNATIONAL LIST
Education Programs in Financial Engineering
and Quantitative Finance
This list comprises an international list of programs in Financial Engineering and Quantitative Finance.
Organized by geographic region, the list details the programs offered; whether each is full-time,
part-time, or both, the degrees offered, and the location. Each university’s name links directly to the
program website, where you can nd additional information on the admission process and deadlines,
contact information, faculty, pricing, and program length.
NORTHEAST
Baruch College
New York, NY
Financial Engineering
(FT/PT)
Master
Boston University
Boston, MA
Mathematical Finance
(FT/PT)
Master, PhD
Carnegie Mellon University
Pittsburgh, PA
Computational Finance
(FT/PT)
Master, PhD
Columbia University
New York, NY
Financial Engineering
(FT)
Master
Columbia University
New York, NY
Mathematics of Finance
(FT/PT)
Master
Cornell University
Ithaca, NY
Financial Engineering
(FT)
Master
Fordham University
New York, NY
Quantitative Finance
(FT/PT)
Master
George Washington University
Washington, D.C.
Finance
(FT/PT)
Master
Hofstra University
Hempstead, NY
Quantitative Finance
(FT/PT)
Master
Johns Hopkins University
Washington, D.C.
Financial Mathematics
(FT/PT)
Master
Massachusetts Institute
of Technology
Cambridge, MA
Finance
(FT)
Master
New York University
New York, NY
Mathematics in Finance
(FT)
Master
NYU-Poly University
Brooklyn, NY
Financial Engineering
(FT/PT)
Master
continued on page 37
QUANTNET | 2013-2014 GUIDE | quantnet.com37
Education Programs in Financial Engineering and Quantitative Finance continued from page 36
NORTHEAST continued
Princeton University
Princeton, NJ
Finance
(FT)
Master
Rensselaer Polytechnic Institute
Troy, NY
Financial Engineering and Risk Analytics
(FT)
Master
Rutgers University
Piscataway, NJ
Mathematical Finance
(FT/PT)
Master
Rutgers University
Newark, NJ
Quantitative Finance
(FT/PT)
Master
Rutgers University
Piscataway, NJ
Financial Statistics & Risk Management
(FT/PT)
Master
Stevens Institute of Technology
Hoboken, NJ
Financial Engineering
(FT/PT)
Master
Stony Brook University
Stony Brook, NY
Applied Mathematics,
Quantitative Finance Track
(FT)
Master, PhD
continued on page 38
www.msfe.illinois.edu
Applications Open December 1st
Why Illinois?
Joint venture between the
College of Engineering &
College of Business
Comprehensive curriculum
Required real world financial “Practicum”
Illinois develops FE Theory
Illinois develops FE Practice
In Short:
QuantNet’s 2013-2014 International Guide to Programs in Financial Engineering | quantnet.com 38
Education Programs in Financial Engineering and Quantitative Finance continued from page 37
NORTHEAST continued
Temple University
Philadelphia, PA
Financial Engineering
(FT/PT)
Master
University at Buffalo
Buffalo, NY
Finance with Financial Engineering track
(FT)
Master
University of Connecticut
Storrs, CT
Applied Financial Mathematics
(FT)
Master
Worcester Polytechnic Institute
Worcester, MA
Financial Mathematics
(FT/PT)
Master
MIDWEST
Ball State University
Muncie, IN
Financial Mathematics
(FT)
Bachelor
DePaul University
Chicago, IL
Computational Finance
(FT/PT)
Master
Illinois Institute of Technology
Chicago, IL
Mathematical Finance
(FT/PT)
Master
Purdue University
West Lafayette, IN
Computational Finance
(FT)
Master, PhD
University of Chicago
Chicago, IL
Financial Mathematics
(FT/PT)
Master
University of Cincinnati
Cincinnati, OH
Financial Mathematics
(FT/PT)
Master
University of Dayton
Westerville, OH
Financial Mathematics
(FT/PT)
Master
University of Illinois
at Urbana-Champaign
Urbana, IL
Financial Engineering
(FT/PT)
Master
University of Michigan
Ann Arbor, MI
Financial Engineering
(FT)
Master
University of Minnesota
Minneapolis, MN
Financial Mathematics
(FT/PT)
Master
University of Notre Dame
Notre Dame, IN
Computational Finance
(FT)
Master
SOUTH
Asbury College
Wilmore, KY
Financial Mathematics
(FT/PT)
Bachelor
Florida State University
Tallahassee, FL
Financial Mathematics
(FT/PT)
Master
continued on page 39
QUANTNET | 2013-2014 GUIDE | quantnet.com39
Education Programs in Financial Engineering and Quantitative Finance continued from page 38
SOUTH continued
Georgia Institute of Technology
Atlanta, GA
Quantitative and Computational Finance
(FT/PT)
Master
Georgia State University
Atlanta, GA
Mathematical Risk Management
(FT/PT)
Master
Louisiana State University
Baton Rouge, LA
Mathematical Finance
(FT)
Master
North Carolina State University
Raleigh, NC
Financial Mathematics
(FT)
Master
UNC Charlotte
Charlotte, NC
Mathematical Finance
(FT/PT)
Master
WEST
Claremont Graduate University
Claremont, CA
Financial Engineering
(FT/PT)
Master
University of California at Berkeley
Berkeley, CA
Financial Engineering
(FT)
Master
University of California
at Los Angeles
Los Angeles, CA
Financial Engineering
(FT)
Master
University of Hawaii
Honolulu, HI
Financial Engineering
(FT)
Master
University of Southern California
Los Angeles, CA
Mathematical Finance
(FT)
Master
University of Southern California
Los Angeles, CA
Financial Engineering
(FT)
Master
University of Washington
Seattle, WA
Computational Finance and Risk
Management
(FT/PT)
Master, Certicate
CANADA
HEC Montreal
Montreal
Financial Engineering
(FT/PT)
Master
McMaster University
Hamilton, Ontario
Financial Mathematics
(FT)
Master
Universite du Quebec
a Montreal-Ecole
Quebec
MSc Applied Finance
(FT/PT)
Master
CANADA continued
continued on page 40
QuantNet’s 2013-2014 International Guide to Programs in Financial Engineering | quantnet.com 40
Education Programs in Financial Engineering and Quantitative Finance continued from page 39
continued on page 41
University Laval
Quebec
Financial Engineering
(FT/PT)
Master
University of Toronto
Toronto, Ontario
Mathematical Finance
(FT)
Master
University of Waterloo
Waterloo
Quantitative Finance
(FT)
Master
UNITED KINGDOM & EUROPE
Alcala University
Madrid, Spain
Quantitative Finance
(FT)
Master
Bar Ilan University
Ramat Gan, Israel
Financial Mathematics
(FT)
Master
Birkbeck University
London, England
Financial Engineering
(FT/PT)
Master
Birkbeck University
London, England
Mathematical Finance
(FT)
PhD
Birkbeck University
London, England
Finance/Finance and Commodities
(FT/PT)
Master
Bocconi University
Milan, Italy
Quantitative Finance and
Risk Management
(FT)
Master
Bogazici University
Istanbul, Turkey
Financial Engineering Master
Dublin City University
Dublin, Ireland
Financial and Industrial Mathematics
(FT)
Master
Ecole Polytechnique Federale
de Lausanne
Ecublens, Switzerland
Financial Engineering
(FT)
Master
EISTI
Cergy-Pontoise, France
Quantitative Finance and Risk
Management Master
Erasmus Universiteit Rotterdam
Rotterdam, Netherlands
Quantitative Finance
(FT)
Master
ETH Zurich and University of Zurich
Zurich, Switzerland
Quantitative Finance
(FT/PT)
Master
HECTOR School of Engineering &
Management
Rotterdam, Netherlands
Financial Engineering
(PT)
Master
ICMA Centre
Rotterdam, Netherlands
Financial Engineering
(FT)
Master
QUANTNET | 2013-2014 GUIDE | quantnet.com41
Education Programs in Financial Engineering and Quantitative Finance continued from page 40
continued on page 42
UK & EUROPE continued
Imperial College of London
London, England
Risk Management and Financial
Engineering
(FT)
Master
International University of Monaco
Monte Carlo, Monaco
Financial Engineering
(FT)
Master
London School of Economics and
Political Science
London, England
Financial Mathematics
(FT)
Master
Mälardalen University
Eskilstuna, Sweden
Financial Engineering
(FT)
Master
Reykjavik University
Reykjavik, Iceland
Financial Engineering
(FT)
Master
The Karol Adamiecki Academy
of Economics
Katowice, Poland
Quantitative Asset and Risk
Management
Master
Tilburg University
Tilburg, Netherlands
Quantitative Finance and
Actuarial Sciences
(FT)
Master
Universidad Nacional de Educación
a Distancia
Madrid, Spain
Stock Markets and Financial Derivatives
(FT)
Master
University College London
London, England
Financial Mathematics
(FT)
Master
University of Birmingham
Birmingham, England
Mathematical Finance
(FT/PT)
Master
University of Cambridge
Cambridge, England
Finance with Financial
Engineering Specialization
(FT)
MPhil
University of Edinburgh
Edinburgh, Scotland
Financial Mathematics
(FT)
Master
University of Glasgow
Glasgow, Scottland
Quantitative Finance
(FT)
Master
University of Konstanz
Constance, Germany
Mathematical Finance
(FT/PT)
Master
University of Leicester
Leicester, England
Financial Mathematics
and Computation
(FT/PT)
Master
QuantNet’s 2013-2014 International Guide to Programs in Financial Engineering | quantnet.com 42
Education Programs in Financial Engineering and Quantitative Finance continued from page 41
UK & EUROPE continued
University of Reading
Reading, England
Financial Engineering
(FT/PT)
Master
University of Warwick
Coventry, England
Financial Mathematics
(FT)
Master
University of York
York, England
Mathematical Finance
(FT/PT)
Master
University of York
York, England
Financial Engineering
(FT)
Master
Wits University
Johannesburg, South Africa
Mathematics of Finance
(FT)
Master, PhD
WU Vienna University
Vienna, Austria
Quantitative Finance
(FT)
Master
PACIFIC RIM
City University of Hong Kong
Kowloon, Hong Kong
Financial Engineering
(FT/PT)
Master
Hong Kong University of Science
and Technology
Kowloon, Hong Kong
Financial Mathematics and Statistics
(FT/PT)
Master
Nanyang Technological University
Singapore
Financial Engineering
(FT/PT)
Master
National University of Singapore
Singapore
Quantitative Finance
(FT/PT)
Master
National University of Singapore
Singapore
Financial Engineering
(FT/PT)
Master
Singapore Management University
Singapore
Quantitative Finance
(FT)
Master
The University of Hong Kong
Hong Kong
Finance with Financial Engineering
Track and Risk Management Track
(FT/PT)
Master
The University of Melbourne
Melbourne, Australia
Applied Finance
(FT/PT)
Master
University of Colombo
Colombo, Sri Lanka
Financial Mathematics
(FT)
Master
University of New South Wales
Sydney, Australia
Financial Mathematics
(FT/PT)
Master
University of Technology Sydney
Sydney, Australia
Quantitative Finance
(PT)
Master
QUANTNET | 2013-2014 GUIDE | quantnet.com43
he quant scene on
both sides of the
Atlantic ourished
in the 10 years
leading up to the
credit crisis of 2008. In London,
this was hugely due to a boom
in asset securitization and
credit derivatives activity. New
job opportunities popped up
and, according to academics in
the UK, an increasing amount
of applicants to master’s in
nancial engineering (MFE)
showed how inherent quants
were becoming to the nancial
services industry. But like all
elds in this industry the quant
one is not exactly what it used
to be.
John Crosby, a quantitative
analyst at Grizzly Bear Capital
in London, says: “There’s
denitely less demand for
people with derivatives pricing
skills now given all the losses
and downsizing in these
markets. It’s not that maths
are no longer important. It’s
just that economics is already
becoming a much bigger focus
in the nance world. It’s about
seeing the bigger picture and
understanding economic risks.
This more than anything else
will be shaping quant roles
moving forward. Banks and
other nancial institutions will
consider it more important than
a PhD in maths or physics.
Crosby, who is a visiting
Professor of Finance for the
Centre for Economic and
Financial Studies in the
Department of Economics at
Glasgow University, as well as
an invited lecturer for the M.Sc.
Mathematical Finance course
in the Mathematical Institute at
Oxford University, believes the
impact on MFE programs is yet
to come. “There are hundreds
of MFE programs around the
world now and considering what
has happened since the crisis
it will be difcult to see how
some of them don’t struggle
moving forward, he adds. He
says interest in top-ranked
MFE programs might become
stronger while the bottom-half
nd it more difcult to attract as
many applicants. “MFE programs
are denitely differentiated
by the reputations of their
respective universities, he
asserts.
James Sefton,
a professor of
economics at
Imperial College
London and a
senior quantitative analyst at
UBS in London, agrees that the
reputation of Imperial College’s
MSc in risk management and
nancial engineering is helped
by the fact that Imperial College
is consistently ranked in the top
10 universities in the world.
“Imperial’s MSc in risk
management and nancial
engineering prepares students
for a wide range of quant jobs—
whether it is risk management,
continued on page 44
Quant’s Next Top Model
By Rachael Horsewood
As repercussions from the crisis continue to reshape the world of financial services, one question for those people
thinking of pursuing a career in quantitative finance is where and why should they go for a masters in financial
engineering (MFE).
—Rachael Horsewood compares notes from London
QuantNet’s 2013-2014 International Guide to Programs in Financial Engineering | quantnet.com 44
Quant’s Next Top Model continued from page 43
developing pricing models,
high frequency nance
and algorithmic trading, or
quantitative investment, which
is the area that I work in at UBS.
The maths is obviously more
intense for those interested
in pricing models, whereas
those pursuing quantitative
investment might broaden their
focus with other subjects such
as computer programming
and database management,
he explains. Like most MFE
programs in the UK, Imperial’s
MSc in risk management and
nancial engineering, lasts
for one year (beginning in
September). It takes about 100
students each year, which is
a bit more compared to other
programs in the UK.
“We have a great mix of practical
and theoretical teaching styles—
for example, I’m not the only
professor who also works in the
industry. Our faculty is made
up of a lot different experiences
and areas of expertise. This
breadth can help prepare the
students for the job market. It
can give them an edge when
they are interviewing and giving
presentations, Sefton adds.
Michael Dempster, a professor
of nance and management
science emeritus at the
University of Cambridge, agrees
that economics has become
more of a lure for people
considering MFEs. He says the
prevalence of systemic risks
in recent years is no doubt
leading people to try to model
macroeconomic effects more.
“Financial quants have to
take some responsibility for
the demise of the derivatives
business. Most did not
understand the parameters
for these products. This is why
economics has become a more
major focus, he explains.
“Our MPhil in Finance is a post-
grad degree that specializes in
nancial engineering. It has a
broad range of disciplines and
is not purely quantitative. The
curriculum incorporates other
subjects such as, economics,
and lectures are provided
by professors from other
departments too, not just from
the Judge Business School, says
Dempster, who is a founder of
the University of Cambridge’s
Centre for Financial Research.
He adds that applicants to
Cambridge’s MPhil in Finance
(one of the rst MFEs established
in the UK) still come from all
over the world, not only the UK
and Europe.
“We also have
built strong ties in
China, having set
up a system for
the Agricultural
Bank of China
and the Industrial
and Commercial
Bank of China. We have
provided executive education
for other Chinese banks too,
mainly by training middle
managers earmarked for top
management, Dempster notes.
But what do
employers think
about MFEs?
Ed Fishwick,
managing director
and co-head of
BlackRock risk and quantitative
analysis group in London, says:
“If I were to speak before a
group of new graduates today
I would say that they need
strong technical skills, strong
communications skills, and a
keen interest in the nancial
markets. That might seem
obvious but there was a time
when you could have a PhD in
quantum gravity and come in
not knowing a thing about a
continued on page 45
“Financial quants have to take some responsibility for the demise of
the derivatives business. Most did not understand the parameters for
these products. This is why economics has become a more major focus.”
Michael Dempster, Professor of Finance and Management Science Emeritus, University of Cambridge
QUANTNET | 2013-2014 GUIDE | quantnet.com45
Quant’s Next Top Model continued from page 44
market but yet still get hired as a
quant. You need to bring more
to the table now. You need to
understand the markets and the
people who work in them and
that is the case no matter where
you are based or what speciality
you enter into, he explains.
“We denitely require
quantitative knowledge, but
it can come from a degree in
various subjects whether it’s
economics, maths, statistics,
engineering, or science. I look
out my ofce window right now
and see an array of educational
backgrounds. The one skill
we look for regardless of their
educational achievements is
communications. They were
hired because they could prove
what they know. They were hired
because they have passion about
what they do, Fishwick replies.
Loic Fery, founder and chief
executive of Chenavari Financial
Group in London, adds: “We
like our quants to have a good
understanding of business.
Their interaction is not a
one-way exchange, as both
risk managers and portfolio
managers bring their experience
and comprehension to the table.
Quants are not only here to
support but also to suggest and
create.
“Quants need to have a very
good level of IT knowledge. We
work more and more with many
different types of systems, and
you must be able to understand
how they work—how to x and
improve them. Quants need to
have a better understanding
of the market too. You need to
understand the data you work
with, as well as the practical
assumptions and risks involved.
Market data plays an important
role. You cannot rely on any
data and must extract relevant
samples of data. Pricing models
is the basic, you cannot go
without them. You need to
continued on page 46
Did You
Know?
QuantNet’s Events page lists
upcoming conferences, seminars,
workshops, and meetings
for financial engineers.
Each event lists the date and
location, along with a link to its
complete information so you can see
what’s going on in your area
and across the industry.
Sign up or log in
to create an event
on QuantNet.
QuantNet’s 2013-2014 International Guide to Programs in Financial Engineering | quantnet.com 46
Quant’s Next Top Model continued from page 45
be able to understand them
perfectly, he explains.
At a bachelor level, you rarely
would have studied and had
time to acquire a mathematical
and nancial background. The
science background is nearly
compulsory, the nance one
not always. We mainly look at
people who have studied at
French engineering schools—
Grandes Ecoles, which means
‘great schools’ in French—or
someone with a bachelor in
either maths or science and a
master of nance. An MFE is
important if the student has a
good level in programming and
maths. Desire is equally
important. We have interviewed
many students and professionals
who cannot answer basic maths
questions (for example, dene
the variance, correlation) or
simple IT questions (what is
oriented object language), even
though their CV says that they
are good at it, says Fery, who
has an MsC in nance and
entrepreneurship from the HEC
School of Management in Paris.
Indeed, the attractiveness of
MFE programs varies across
Europe considering all of
the language and cultural
differences. Jan Kallsen, a
professor of mathematical
nance at the University of Kiel
in Germany, says most of his
students come from Germany
and go onto work for German
or Swiss rms. “Most students
in Germany leave universities
with a master’s degree, no
matter what the subject is,
not a bachelor’s degree. Thus,
comparing a master’s degree
to work experience does not
really apply to the German job
market, he explains.
“Some of our courses are
taught in English (i.e., those
with an overlap to a degree
in ‘quantitative economics’).
We think our program is an
alternative to a master’s in
mathematics in the sense it
is for people who know that
they would like to specialize in
nancial mathematics. Another
attraction of our program is
that it is free. We do not charge
tuition even though the course
program is similar to very
expensive international ones,
adds Kallsen.
Indeed, one of the biggest
questions for MFE candidates
is job placement, especially in
Europe given the euro crisis.
“Many of our MPhil grads
go on to do their PhDs. Our
relationship with them is
personal and long-standing.
I write a lot of references, but
often a contact in the City or
on Wall Street will call
and ask whether I
see a student with
a certain skillset
or ambition, and
I recommend
them in that sort
of way. Most of
the jobs they get
are risk-related,
and denitely more
executive or front-ofce, says
Dempster.
“These grads go to all types
of nancial institutions,
money managers, technology
companies, and even insurers.
The insurance industry has
become a lot more like banking.
Insurance companies have to
price products and of course
those in Europe have to adopt
similar risk management
principles under Solvency II,
which comes into effect next
year. Most actuaries today are
continued on page 47
“We have interviewed many students and professionals who
cannot answer basic maths questions (for example, define the
variance, correlation) or simple IT questions (what is oriented object
language), even though their CV says that they are good at it.”
Loic Fery, Founder and Chief Executive of Chenavari Financial Group in London
QUANTNET | 2013-2014 GUIDE | quantnet.com47
Quant’s Next Top Model continued from page 46
not that different from nancial
quants since they essentially
have to use a lot of the same
techniques that bankers do,
he adds.
Sefton says: A lot of our grads
get work at hedge funds and
other money managers. Most
end up in London or the U.S.
because that is where the bulk
of nancial quant jobs are, but
we have seen them go all over
the world. The quant industry is
global.
Sefton also says it has become
more important for students to
develop views of their own and
to show that they are genuinely
interested in this industry.
“Computing, economics, and
data management are all
useful subjects but it is a very
competitive job market and
being able to show your passion
for it is what’s really going to
help you, he explains.
Crosby says work visas are
usually not a problem in the
UK, especially if the applicant
studied there. “In most cases,
if a rm really wants you they
won’t have a problem sorting it
out for you. I haven’t seen any
problems with the many foreign
people who I know and have
worked with, he explains.
He also emphasises the
benet of networking. “It is
really helpful not just for job
opportunities but also for
exchanging ideas. There are
a lot of events worth going to
in London. Most cost money
but there are always free
opportunities through trade
associations and university
alumni groups. The annual ICBI
conference, which was held in
Barcelona this year, is one of the
biggest quant gatherings in the
world, he adds.
Sefton says quant investment
requires a lot more dedication
than it did before the crisis. “The
glory days are over and investors
are no longer willing to trust the
strategy almost unconditionally.
They want to understand
the approach in more detail
and have an idea of why your
approach is innovative. Risk
control is obviously more vital
than ever before so quant
investors are focusing a lot on
this now too, Sefton adds.
Human resources sources at
banks say that most of the
job prospects in London right
now are risk-related. Fishwick
explains why quants remain
one of the most integral parts
of risk management. “When I
entered into this industry 28
years ago, quantitative nance
was a fringe activity and quants
themselves were a tiny minority
of the nancial services
world. Now quants
are everywhere
and many of the
processes, products,
and functioning of
nancial markets
involve the use
of quantitative
techniques. This is
now a very important part of the
whole industry. In fact, they are
intrinsic and that is not going to
change. All of the fundamental
changes in technology, computer
power and data are not going
away. It’s all here to stay, he
says.
“That said, the crisis and
events since have shown us
how important it is to strike
a balance between judgment,
experience, and analysis. While
the nature of the job has not
changed much in recent years,
there is more competition for
continued on page 48
“Computing, economics, and data management are all
useful subjects but it is a very competitive job market and being able
to show your passion for it is what’s really going to help you.”
James Sefton, Professor of Economics, Imperial College London
QUANTNET | 2013-2014 GUIDE | quantnet.com48
Quant’s Next Top Model continued from page 47
fewer roles. The nancial crisis
reduced demand in the job
market as a whole, Fishwick
adds.
Overall the job picture for
quants, at least in London, is
still quite institution-specic.
For example, sources say that
smaller institutions are less
likely to have distinct front and
middle-ofce roles; instead
quants are more likely to work
across different areas. “For
the desk quant, which in my
view probably represents 95%
of quants (the other 5% being
research quants), you must
understand the needs of the
business and provide adequate
solutions for them. These
quants work on measure
whether it is measuring the
current risk of the book or
the potential risk to add. This
requires a good background in
maths, nancial maths, IT, as
well as a good comprehension
of the nancial market. The
maths, nancial maths, and IT
skills are mainly acquired at
university. As for understanding
the business, you learn it on the
desk with the people already
working in the business. This is
true for most jobs in nance,
explains Fery.
“To sum it up, what matters
is not your degree, but your
knowledge. The degree gets you
the interview because it means
you have the basics in the elds
we are interested in. But you
must be able to prove that you
master what you studied and
experienced, Fery concludes.
Researching
MFE Programs?
The QuantNet Resources is a
robust resource for prospective
students interested in
master’s programs in financial
engineering and quantitative
finance. Each program’s
page contains information
and additional links for:
History of the program
Admission requirements
and deadlines
Program type, duration,
and tuition
Application and placement
statistics
Student reviews
QuantNet’s 2013-2014 International Guide to Programs in Financial Engineering | quantnet.com 49
Understanding
the Quantitative
Finance Industry
in Asia
he practice of
quantitative
nance used to be
the prerogative of
global trading hubs
such as New York or London.
When major investment banks,
hedge funds, or proprietary
trading rms were expanding to
Asia, they tended to send senior
executives from New York or
London to selected Asian cities
to head quant teams, and staff
the team with local junior hires
—traditionally smart graduates
fresh from college. The quant
teams in Asia would look to
deploy mathematical models
developed and implemented in
the U.S. or Europe to the Asian
market. In other words, the
Western world was the center
of innovation in quantitative
nance and nance in general,
while Asia was passively
adopting the products and
models developed in the West.
However, the situation has
changed drastically. The past
decade has seen the global
focus shifting toward the
East, with the Asian market
rapidly gaining liquidity,
complexity, sophistication, and
independence. As the market
matures and with regional
institutional investors playing
increasingly dominant roles,
nancial institutions have
adjusted their stafng strategy
and are now looking to hire
local talents with practical Asian
market knowledge, experience,
and contacts for the senior roles.
Airlifting experts from the West
is no longer seen as a viable
way to form quant teams—the
Asian market needs locally
groomed talents with a good
understanding of the domestic
market and regional economy in
order to perform effectively.
Differences Between the
U.S. and Europe
So what are the differences
between a quant role in the
U.S. or Europe vs. Asia? The
key distinguishing factor is
the breadth of products and
currencies coverage. Quant
teams in the U.S. or Europe
are highly specialized. These
include exotic products teams
responsible for highly structured
deals, ow teams covering
liquid securities and vanilla
derivatives, high-frequency
quant trading teams covering
electronic market making
and trading, and short-term
interest rate teams covering
repo and money market, to
name a few. On the contrary, an
Asian quant team will need to
function independently while
covering all the scopes described
above. One will need to be able
to model exotic deals, and at
the same time be capable of
dealing with highly liquid ow
products such as futures. As
an example, an Asian quant
might spend a typical working
day determining the volga and
vanna of a particular exotic
deal using a two-factor model
in the morning, discussing the
wrong way risk and funding
implications with the trading
desk and corporate treasury in
continued on page 50
By Chyng Wen Tee and
Christopher Ting
QUANTNET | 2013-2014 GUIDE | quantnet.com50
Understanding the Quantitative Finance Industry in Asia continued from page 49
the afternoon, and preparing the
pricing platform and database
for a when-issue government
bond that will start auctioning
the next day in the
evening.
The breadth
of currency
coverage is also
signicantly wider
in Asia. A quant
team in New York
will be covering
USD and CAD,
whereas a team in London will
be devoted to EUR and GBP. On
top of these, there are Latin
American and emerging market
subdivisions, formed with
quants having complementary
skill sets and sitting alongside
designated teams described
above to cover specic markets.
A single Asian quant team, in
contrast, will need to cover at
least 12 currencies, ranging
from highly liquid (e.g., JPY or
AUD) to the less liquid ones (e.g.,
VND). In fact, any standard-
size trading desk in Asia will
typically have exposure and
trading activities in AUD,
CNH, CNY, HKD, IDR, INR, JPY,
KRW, MYR, NZD, SGD, and
TWD. Each of these currencies
has their own conventions
and market preferences, and
yet the Asian market on the
whole is closely interlinked
and possesses distinct
regional avors. The ability to
multitask, to keep up-to-date
with market development, and
to compartmentalize one’s
knowledge so that one can
switch seamlessly between
ongoing projects is vital for
effective performance.
There are also the added
challenges of managed
currencies, transaction
restrictions and government
regulations. These give rise to
the need to distinguish markets
between onshore and offshore,
deliverable and non-deliverable.
How do we account for onshore
and offshore CNY markets, and
how are these two related to
CNH? How are these markets, in
principle of the same currency,
interrelated, and how should
the modeling approach be
formulated? These are the
challenges facing an Asian
quant. Solutions are very often
derived from the rst principle,
as the standard assumptions
made in conventional
quantitative nance modeling
are not necessarily valid.
Quants will need to liaise
with the legal department,
corporate treasury, and trading
teams to keep up-to-date with
the latest developments in
governing policies and adapt
their modeling
approach accordingly.
Locally trained
quants with a good
understanding of the
domestic economies
will possess
the competitive
advantage to tackle
the problem more
effectively.
Master of Quantitative
Finance Programs: A Case
Study of Singapore
In tandem with the growing
demand of quants in Asia,
many Asian universities
have launched master degree
programs to equip students with
the necessary knowledge and
skills in applying mathematical
models and in computing. In
Singapore, for example, there
are at least ve programs
located in this tiny city state
where two large sovereign
wealth funds, GIC and Temasek
Holdings, are incorporated.
One of the earliest to launch the
Master of Financial Engineering
(MFE) program in Singapore
was Nanyang Technological
University (NTU). The NTU MFE
program is offered under her
Nanyang Business School,
continued on page 51
A single Asian quant team, in contrast
[to quants in other parts of the world],
will need to cover at least 12 currencies,
ranging from highly liquid
(e.g., JPY or AUD) to the less liquid
ones (e.g., VND).“
QuantNet’s 2013-2014 International Guide to Programs in Financial Engineering | quantnet.com 51
Understanding the Quantitative Finance Industry in Asia continued from page 50
and draws upon the faculty
members in the schools of
engineering to teach the more
mathematically demanding
courses and programming. The
NTU MFE program includes a
seven-week term at Carnegie
Mellon University (CMU). Upon
successful completion of the
seven-week term, students
are awarded a certicate in
Computational Finance from
CMU.
At the National University of
Singapore (NUS), the Master of
Science in Quantitative Finance
(MQF) program is offered by the
Department of Mathematics
with the cooperation of the
Department of Economics and
the Department of Statistics
& Applied Probability. The
university-level Saw Centre
for Quantitative Finance is
entrusted with providing the
necessary support to manage
the program. In addition, the
Risk Management Institute
afliated to NUS runs a separate
MFE program.
The University of Chicago’s
Singapore campus offers the
Master of Science in Financial
Mathematics. Curriculum and
coursework are identical to
the main Chicago campus.
Simultaneously, students in
Singapore and at the Stamford
campus “electronically attend”
lectures as they are presented
live at the Chicago campus
via real-time interactive video
conferencing.
Beginning September 2012,
the Lee Kong Chian School
of Business at the Singapore
Management University (SMU)
begins to offer the Master of
Science in Quantitative Finance
jointly with Cass Business
School at City University
London. Students of this joint
three-semester MQF program
spend the four-month second
semester at Cass Business
School, where they study the
same ve core modules together
with their fellow students
of Cass. Upon successful
completion of the program, SMU
students are awarded a degree
scroll jointly endorsed by the
two universities.
One of the reasons that ve
similar programs are able to
co-exist in Singapore is that
many of the students are from
overseas: China, India, Malaysia,
Indonesia, and other countries
in the region. It is also worth
mentioning that the Monetary
Authority of Singapore (MAS),
the central bank of the city state,
is actively grooming a critical
mass of specialists in targeted
elds such as risk management,
quantitative nance, nancial
engineering, and actuarial
science. MAS holds the policy
view that these specialized
skills are necessary
to support the
long-term growth
of Singapore’s
nancial services
sector.
Besides Singapore,
universities in Hong Kong,
Australia, Taiwan, and Korea
also offer specialized master’s
programs in response to the
trending demand in Asia for
quants. The simple reason is
that there is still ample room
for growth in Asia, especially
in the areas of derivatives
trading, risk management,
and quantitative hedge fund
investments. Many Asian
banks are acutely aware of the
importance of risk management,
and they are strategically
positioning themselves to adopt
the industry best practices,
even beyond what the Basel
committee has recommended.
continued on page 52
“One of the reasons that five similar programs are able to co-exist
in Singapore is that many of the students are from overseas: China,
India, Malaysia, Indonesia, and other countries in the region.”
QUANTNET | 2013-2014 GUIDE | quantnet.com52
Understanding the Quantitative Finance Industry in Asia continued from page 51
What Is the Value of a
Master Degree in Quantitative
Finance?
A quantitative nance degree
in Asia will not only grant
you access to the major
investment banks, hedge
funds, and proprietary rms
with Asia presence, but you
will also be sought after by
Asian institutions, which
are vigorously building their
institutional sales and trading
teams to compete locally and
globally. In addition, you will nd
employment at sovereign wealth
funds, asset management
groups, commercial banks,
central banks, and government
subsidiaries responsible
for regulations and market
monitoring. The Asian market
on the whole is upbeat,
sanguine, and lled with vitality.
It continues to evolve and to
grow in importance in a vibrant
economic environment.
So what sort of qualities are
Asian employers looking for?
Standard quantitative nance
training and the quintessential
quant traits aside, employers
are also looking for individuals
displaying the aptitude to
multitask, an avid interest
in the nancial market, and
good communication skills.
Unlike traditional quant
roles, having a keen interest
in the mathematical side of
nance is not sufcient. As
the Asian market continues
to develop, new products are
continually being introduced,
and government policy and
regulation continue to play
a crucial role in shaping
the market. Convince the
interviewer that you can
multitask by effectively handling
an array of projects, that
you are up-to-date with the
general trend of Asian market
development, and that you are
an effective communicator and
can be relied upon to conduct
or support businesses in more
than a dozen Asian countries,
and you will get the opportunity
to apply your mathematical
skills to a breadth of products
in one of the most exciting and
rewarding markets for decades
to come.
Chyng Wen Tee is Assistant Professor of Quantitative Finance at Singapore Management
University. Prior to joining the quantitative nance faculty, Tee spent three years as a
quantitative analyst at the exotic interest rate trading desk at Morgan Stanley, London,
and another three years as a desk strategist at the macro trading desk at Goldman
Sachs, Hong Kong. He has a PhD from the University of Cambridge.
Christopher Ting is Associate Professor of Quantitative Finance and head of the
quantitative nance faculty at Singapore Management University. He is also the
academic director of the MSc in the Quantitative Finance Program. He has worked in
the industry as a proprietary trader, and he teaches quantitative trading strategies in
the Program. His research interests include high-frequency market microstructure,
derivatives, and statistical arbitrage. He has a bachelor degree and a master’s degree
from the University of Tokyo, and a PhD from the National University of Singapore.
QuantNet’s 2013-2014 International Guide to Programs in Financial Engineering | quantnet.com 53
or the most part,
the standard of the
applicants is very
high. However, in
some cases, it is
apparent that a
capable applicant would have
fared better with more careful
preparation. There are things
that candidates could do to
improve their chances of
admission, especially if they
give some thought to this well
before the submission deadline.
The purpose of this article is
to help prospective applicants
to improve their submissions.
Although my experience has
been with the NYU program,
I suspect that the admission
criteria for other prestigious
programs would not differ much.
1. Coursework: Your undergrad-
uate and graduate coursework is
shown in your transcripts. You
do not need to have majored
in mathematics since many
successful applicants have an
undergraduate degree in eco-
nomics, engineering, physics,
statistics, or some other quanti-
tative eld. Nor is it necessary to
have graduated from a top-rated
university. Many offers are made
to ne applicants from less well-
known schools.
However, a strong mathematics
background is needed. At the
very least, an applicant should
have a course in calculus
(including multi-variable
calculus), linear algebra, and
probability. If you do not have
these, you should be aware that
you will stand a much better
chance if you take courses to
remedy that deciency.
continued on page 54
Making Sure Your MFE
Application Stands Out
The Mathematics in Finance masters program at the Courant Institute of NYU has
long been recognized as an outstanding program, not just in the United States, but
also internationally. As such, it attracts applicants from Asia, Europe, and North and South America. One feature is
the program’s success in helping students to find internships during the summer before their third semester. This
is due to the strong relations that the University has had for many years with the major Wall Street firms. The ad-
missions process is very competitive and the caliber of the applicants is very high. The class size is limited, and, as
a result, less than 10% of applicants actually receive an offer. For three years, I have helped the faculty of the Cou-
rant Institute screen the applications. During this process, I have read more than 1,500 applications and have been
able to observe how they fared in the admissions process.
By Bill Stanley
“. . . there is flexibility in the process.
An application that is weaker in one area
might be accepted because of strengths
in other areas.“
QUANTNET | 2013-2014 GUIDE | quantnet.com54
Making Sure Your MFE Application Stands Out continued from page 53
Most applicants, however, have
more than the minimum back-
ground described above. Some
helpful courses include ordinary
and partial differential equa-
tions, and subjects such as real
variables, complex variables,
and others that show evidence
of interest and ability in math-
ematics. Courses in business,
nance, and economics will also
help your application.
You will have a better chance
of admission if you have a depth
of knowledge in one quantita-
tive area rather than a super-
cial knowledge of several areas,
even if they are all relevant to a
career in nance.
All these are not necessarily
requirements of the faculty but
rather a practical reection of
the competitive nature of the
process. No matter how strong
your application may be, if there
are others which are exactly
equal to yours but with more
and stronger coursework in rel-
evant subjects, then those will
obviously have an advantage in
the admissions process.
Most of the applicants have very
good grades for all their under-
graduate coursework, mostly A
and B grades, especially in the
quantitative subjects. An occa-
sional C in a subject not related
to mathematical nance such as
painting or music will not hurt
the application. But the hard fact
is that that you are competing
with applicants who have no C
grades. Similar comments ap-
ply to graduate coursework for
applicants who have a master’s
degree.
If you think the program’s fac-
ulty might have difculty under-
standing your transcript (for ex-
ample, if your courses are called
Mathematics I, Mathematics II,
etc., rather than Calculus, Linear
Algebra, etc.), it can be helpful
to provide information such as
the topics that were covered and
textbooks that were used.
HELPFUL TIP: If you have taken
a course with a generic name,
such as Mathematics III or
Computers I, provide a cover
sheet that explains the courses
in detail (the subjects covered,
for example, probability, linear
algebra, differential equations,
etc., grade, books used, materi-
als covered, and so on). This
will be more helpful to an ap-
praiser than just knowing that
you received an A in Engineering
Mathematics.
2. Statement of Purpose: The
Statement of Purpose should
be a simple document that
describes why you came to
choose this eld. It should be
clearly written and have a
logical construction. Although
some international students
might not yet have perfect
command of the English
language, nevertheless their
ideas will come through, which
is what is most important here.
It is not necessary to state again
your coursework and grades be-
cause these are shown on your
transcripts. Some applicants, in
their statement, lavish praise on
the program and the faculty or
the University. This may not
continued on page 56
MFE Candidate
Must-Haves
Mostly A and B grades
Strong background in math
Focus on one or more quantitative
areas
Computer programming course-
work and/or work experience
GRE Quant Score of >94%
“You will have a better
chance of admission if you
have a depth of knowledge in
one quantitative area rather
than a superficial knowledge
of several areas . . . “
QuantNet’s MFE Application Tracker is used by hundreds of MFE applicants every year
to evaluate their chance of admission to the top programs. It now tracks 68 master’s pro-
grams in financial engineering and quantitative finance worldwide.
By adding profile data to the Tracker, applicants can get a detailed comparison of their
applications against others who have applied and been accepted to the same programs.
The Tracker tool collects the following information:
/ Grade point average / Interview date
/ GRE scores / Acceptance/rejection status and date notified
/ Application submission date / Notes from the applicant (if any)
/ If accepted, the applicant’s decision
QUANTNET | 2013-2014 GUIDE | quantnet.com56
Making Sure Your MFE Application Stands Out continued from page 54
hurt your application but it will
certainly not help—it is just a
waste of space.
HELPFUL TIP: Customize your essay
for each program that you apply
to. Many students use a general
format for each program, regard-
less of the specic questions.
Ask a friend who is a native
English speaker to proofread the
essay.
The objective of the statement
is to explain clearly and logically
how and why you chose this
eld and what you hope to do
with your master’s degree after
completing the program.
3. References: There is no spe-
cic requirement for the sources
or content of letters of reference.
Ideally, your references should
be from your undergraduate in-
structors who demonstrate that:
d They really know you well.
d They have worked with you
in a relevant subject.
d They have observed
outstanding ability and accom-
plishments.
Recommenders who know you
through projects or advanced
classes are better choices than
those who know you only
through basic courses. At least
one reference should be from
someone who knows you in a
highly
quantita-
tive con-
text. For
graduate
appli-
cants
and oth-
ers who
have been out of school for some
time, it is important to have at
least one reference from an indi-
vidual who currently knows you
well.
4. GRE Quant Score: The GRE
Quant Score is a measure of the
quantitative ability of a candi-
date. Clearly, students need to
have quantitative ability if they
are to benet from a master’s
program in mathematics. The
GRE quant exam has recently
changed to a different structure
based on a maximum score of
170 instead of 800 as before.
Because of this, it is more con-
venient to discuss this topic in
terms of the percentile quant
score. The experience at NYU is
that approximately 60% of the
applicants to this program are
at or above the 94th percentile
of candidates who take the GRE
quant exam. This is by no means
an absolute indicator since can-
didates with lower scores have
been accepted in the past when
they have presented strongly
along other dimensions. So you
do not need to have a percen-
tile score of greater than 94%
in order to
stand a
chance, but
you must
remember
that your
applica-
tion will be
compared
to those with very high scores.
If your GRE score is low, it is rec-
ommended that you take the
GRE a second time to improve
your score.
5. Computer Science: You will
need some familiarity with
computers and programming in
order to complete this program.
Most applicants have at least
one course in computer science,
but many have more than that.
In addition, it is helpful to show
evidence of programming
ability from projects or work
experience, particularly of object
-oriented programming. Lack of
experience in programming will
not exclude an application, but
once again, it will be competing
with many others that do dem-
onstrate such skills. If your com-
puting skills are weak,
continued on page 57
“Most applicants have at
least one course in computer
science, but many have more
than that.”
QuantNet’s 2013-2014 International Guide to Programs in Financial Engineering | quantnet.com 57
Making Sure Your MFE Application Stands Out continued from page 56
you should take at least one pro-
gramming course. Competence
in C++ or Java or MATLAB is de-
nitely an advantage.
HELPFUL TIP: Be sure to describe
your programming background.
Some applicants don’t bother
to mention their technical
skills. You can use your essay or
resume to elaborate on which
languages you are familiar with
and the kind of projects you
have done in these languages.
This will give the reader a better
idea of your skills.
6. English language: NYU has
many students from non-English
speaking countries and the fac-
ulty understands very well that
a student who arrives with weak
English language capability usu-
ally learns quickly after moving
to the United States. However,
once again, no matter how capa-
ble you are, you will be compet-
ing with other international can-
didates who do have very good
English language skills. Our ex-
perience at NYU is that of those
students who take the TOEFL,
more than 60% have a score of
100 or more. Although this is not
a requirement, especially if your
application is very strong in oth-
er areas, the fact is that if your
score is less than 100, you are at
a competitive disadvantage. It
might be worthwhile to improve
your English skills and then take
the TOEFL a second time to try
for a higher score.
7. Resume: This should be a
clear record of your education
and accomplishments. However,
the educational and work expe-
rience should be supported by
the transcripts and reference
letters.
HELPFUL TIP: Use a professional
format and standard fonts to
make sure that the nal docu-
ment has a good appearance.
8. Memberships: It is a good
idea to participate in a profes-
sional organization such as IAQF,
SQA, etc., and you should join a
website such as QuantNet. This
will show that you have taken
the time to learn something
about the industry and under-
stand that this is something that
you really want to do.
I hope that these guidelines
help ensure that your applica-
tion fully reects your abilities
and accomplishments. The latter
will help you obtain the high-
est ranking possible in a very
competitive admissions process.
BEST OF LUCK!
Bill Stanley earned his M.A. in mathematics at the University of Oxford in England. On
moving to the U.S., he completed an M.S. in Operations Research at New York University
(NYU), while working for JPMorgan Chase. Later, he worked for Citigroup where his role
was to document various kinds of derivative securities. Bill is also a C.P.A. in New York
State. For the last three years, he has appraised more than 1,500 applications for the MS
program of Mathematics in Finance for NYU’s Courant Institute.
TESTIMONIALS
The course is very comprehensive in its curriculum and provides students
with insight into numerou=s valuable C++ libraries and an introduction into
programming for financial applications. The support of the TA and other
students was invaluable.
“It’s a balanced mixture of theoretical approach and
practical implementation. The curriculum was designed
with many hand-on exercises which will help students to
familiarize themselves with the actual code in action.
Also the coherency between chapters makes the whole
understanding process firmer.”
It has been a wonderful experience. The learning is no less rigorous than
the classroom environment. Anyone who wishes to embark on a financial
engineering career who has yet to learn the programming skills should definitely
come check out this program.
“The course was phenomenal and obviously one-of-a-kind.
A lot of material was covered. I had absolutely great support
from my TA.”
This course teaches from the most basic aspect of C++ to the usage of
industrial-grade codes. Fantastic forum and TA, plus useful materials.
For example, lecture videos can be downloaded and use on off-line devices.
Highly recommended.
For more information about the C++ online
certificate, click here.
This 16-week C++ Programming for
Financial Engineering online seminar
is a joint project by the Baruch MFE
program, Dr. Daniel Duffy, and
QuantNet. The content was
developed by best-selling author Dr.
Daniel Duffy and the course is
delivered entirely online by QuantNet.
The Baruch MFE Program provides
a teaching assistant to each student
and grants a Certificate of Comple-
tion upon passing the final exam.
AUDIENCE: This certificate is
designed for people interested in
pursuing graduate studies in
financial engineering and covers
essential C++ topics with applica-
tions to finance. Approximately half
of the students who successfully
completed the seminar prior to July
2013 are now enrolled in financial
engineering graduate programs.
TOPICS COVERED:
Basic C/C++ Language and Syntax
Object-Oriented Programming (OOP)
in C++
Inheritance and Polymorphism
Generic Programming in C++ and
Standard Template Library (STL)
An Introduction to Boost C++ Libraries
Applications in Computational
Finance: Black Scholes pricing and
Greeks, Monte Carlo methods, finite
difference methods (Euler, Crank-Nicolson),
lattice methods, exact methods (Barone-
Adesi-Whaley, bonds, swaps, swaptions)
FORMAT: The C++ online seminar
consists of 10 levels, each with
video lectures, reading materials,
programming homework, and a quiz.
Each student is assigned a personal
teaching assistant (an alum of the
Baruch MFE Program). A dedicated
forum is available for discussing
homework problems.
QuantNet’s 2013-2014 International Guide to Programs in Financial Engineering | quantnet.com 59
Graduate Schools
Want Thoughtful,
Well-Rounded Students
By Gwen
Stanczak
he decision to
pursue a degree
in nancial
engineering is an
important step
in advancing your career.
Congratulations! Whether you
are seeking to switch careers
or advance within the nancial
services industry, the skills
you will acquire are vital to
mastering the strategic and
analytical demands of the
industry.
Applying to graduate school
is a big decision—as well as
a signicant investment of
time and resources—and the
emotional and rational aspects
can inuence candidates’
applications and interviews.
Those students who prepared
in advance have the advantage
of conveying a clear and
compelling idea of what is
motivating their application
. . . which is not only important
for admission counselors but
a worthy exercise for students
themselves!
The process of researching,
applying to, and interviewing
at graduate schools does not
need to be a daunting task. Just
as there is no “one best” school,
there is no singular formula
for gaining admittance into
the university of your choice.
Often, the most challenging
aspect of the admission process
is the personal due diligence
prospective students must
conduct. Ask yourself the
important questions regarding
your priorities and expectations.
What is most important to you?
How do you weigh the pros and
cons of each school? What are
you seeking from the career
services staff? Do you expect
networking opportunities from a
well-connected alumni network?
You should expect the process
to be a helpful back-and-forth
between you and the school(s)
into which you’re seeking
admittance.
The Balancing Act
The admissions process
is holistic. Ultimately, the
admissions staff is seeking
candidates with the potential
for success academically as well
as professionally. Academically,
nancial engineering programs
are seeking candidates with
strong quantitative backgrounds
who have shown strong
performance in a depth of
mathematics courses including
calculus, linear algebra,
differential equations, and
a calculus-based probability
course. Many quantitative
nance programs have differing
degrees of programming
requirements, but often are
looking for candidates to have
some introductory programming
experience. Check with each
individual program to determine
the requirements set forth. If
you are lacking a prerequisite
course for your program of
study, it doesn’t mean that
you are out of consideration.
However, a strong foundation
is the key to academic success,
so you should consider taking
any prerequisite course(s) that
continued on page 60
QUANTNET | 2013-2014 GUIDE | quantnet.com60
Looking for Thoughtful, Well-Rounded Students continued from page 59
you are missing in advance of
applying in order to strengthen
your candidacy.
From a career perspective, while
having relevant experience
is preferred, it’s not always
required. Those lacking
experience or those who are
switching careers should start
taking steps to understand the
eld of quantitative nance.
For some it might be the daily
reading of The Wall Street Journal
or informational interviews with
those in the eld. Stay abreast of
current market trends as well as
changes occurring within and
among companies, and you’ll
strengthen your career goals
and trajectory.
Finally, any manager and
executive will tell you that
the less quantitative skills—
communication, presentation,
team leadership, and
interpersonal skills—are also
important indicators of success.
Admissions counselors and
recruiters alike expect that
nancial engineering graduates
have the ability to communicate
effectively both orally and in
writing in English.
The Essay: Clarity Is Worth a
Thousand Words (or Less)
It’s not surprising that many
nancial engineering candidates
are less than enthused about
tackling the essay accompanying
their application. After all, GRE
and GMAT scores speak for your
qualications, right? Wrong.
It’s important that you convey
your aspirations in a way that
helps admissions counselors
familiarize themselves with you
. . . outside quant.
Be sure to answer the question
you’re being asked. There’s no
need to infer and unnecessarily
read between the lines. Often
candidates will try to be wordy
and elaborate in the essay, and
they ultimately lose site of the
question the program is truly
seeking to answer. Be clear,
concise, and direct and avoid
any misinterpretation.
Evaluate your career goals.
Take time to read and talk with
others in the industry, including
students and alumni of the
programs to which you are
applying so that you can present
a clear career plan. Also, take
the time to understand the
programs. No two are alike, and,
in fact, many are very different.
Take those strong quant
skills and use them for your
own benet by assessing the
differences among programs.
It is your responsibility to
understand each program’s
unique differences and which
differences best support your
career goals. Within your essay,
show the admission counselor
that you understand the
program and are familiar with
its mission and distinctions
. . . and how it aligns with your
goals. As admissions counselors,
we are trained to spot pat
answers that appear
to be “one size ts
all” essays. Don’t
fall into the trap of
thinking one essay
will meet the needs
of all applications.
Proofread! There is no excuse
for typos and obvious grammar
errors. Employers expect strong
verbal skills, and schools are no
different. Use your computer’s
spell check function and be
sure to proofread your essays.
I always advise candidates to
pass along essays to friends and
family and ask them to guess
what question preceded the
essay question. If they guess
correctly, you’ve done a terric
job of providing a clear, direct
response . . . and they may also
spot the errant typo!
continued on page 61
“. . . any manager and executive will tell you that the less
quantitative skills—communication, presentation, team leadership,
and interpersonal skills—are also important indicators of success.”
QuantNet’s 2013-2014 International Guide to Programs in Financial Engineering | quantnet.com 61
Looking for Thoughtful, Well-Rounded Students continued from page 60
Don’t fall prey to the temptation
of presenting yourself as
something you’re not. Programs
are seeking authenticity—
admissions counselors want to
know who you are . . . not who
you think you are. Be sure to
write an essay that is honest
and don’t second-guess yourself.
Allow your personality to come
through, and tell the story of
who you are, your interests,
and goals within the eld of
quantitative nance.
Making the Most of the
Application Process
There are a few aspects of the
application process in which
candidates make common
errors. Because you’ve been
thinking about grad school for
quite some time, it’s easy to
assume that admissions reps
understand what’s on your
mind. But, not so! There are
many thousands of applicants
each year, men and women
who have varying motivations,
backgrounds, and expectations.
Just as there is no one “typical”
nancial engineering program,
there is no one “average”
student.
The tips below will assist
in avoiding some common
application mistakes:
d Treat the admissions
interview as if it were a
job interview. The level of
professionalism you lend
to gaining admittance into
graduate school is no less than
the standard you would lend
to a new job. Be sure to arrive
on time, even a bit early. Dress
appropriately in business attire.
Allow the meeting to proceed
as a dialogue (with two people),
not a monologue (featuring only
your comments).
continued on page 62
2013-14
Ranking of
Financial Engineering
programs
The 2013-2014 Quantnet ranking is the most comprehensive ranking
to date of master programs in Financial Engineering (MFE),
Mathematical Finance in North America. QuantNet surveyed program
administrators, hiring managers, and quantitative finance professionals
from financial institutions around the world for statistics reflecting student
selectivity and graduate employment.
Click here for the Ranking list.
QUANTNET | 2013-2014 GUIDE | quantnet.com62
Looking for Thoughtful, Well-Rounded Students continued from page 61
d Be prepared. Understand
your goals and be ready to
communicate how your aims
for a graduate school education
support your career plan.
Rather than say, “I want to get
a job in nance, share your
knowledge of the industry and
how the school in which you are
interviewing will advance your
career objectives.
d Often, there is a lot to be
learned via the questions that
are asked, so be sure to ask
questions of the interviewer as
well. School websites may not
have 100% of the information
you are seeking, so make a list of
questions that were not obvious
from your secondary research.
d Your letter of
recommendation is a key tool
that helps to tell the story of
who you are. Applicants often
wonder about whom they
should ask to write a letter of
recommendation. Remember
that title doesn’t matter as
much as content and familiarity.
When selecting recommenders,
choose someone who knows
you well and can provide a
robust recommendation letter
with examples of your technical
and/or professional qualities
that will make you a good t
for the program. It is not, for
example, advantageous to you
to send along a letter signed by
a CEO who only shares general
information that lacks specicity
and detail.
d Take the time to talk with
your recommender in advance of
applying to assure that he or she
understands the program(s) to
which you are applying and your
goals associated with a nancial
engineering degree. This will
help your recommender write a
genuine letter that is thorough,
thoughtful, and highlighted by
examples of your analytical
skills as well as your potential
for career success.
Most importantly, take the time
to prepare that is commensurate
with the effort you will ultimately
put forth in grad school. The
admissions process is an ideal
opportunity for self-reection.
Schools won’t need to be
convinced that you’re a larger-
than-life leader in the making,
but rather, admissions ofcers
are genuinely seeking to
understand your goals and
interests in the eld of
quantitative nance . . . so
don’t make them work hard
to appreciate your story! The
strongest application is the one
that is supported by preparation,
so invest in yourself and enjoy
the return on investment.
Gwen Stanczak is the Director of Admissions for the Carnegie Mellon University MSCF
Program. Gwen holds a dual BA in Psychology and Spanish from Kutztown University
of Pennsylvania and a Master in Public Management from Carnegie Mellon. Gwen
is available to discuss all aspects of the application process with prospective MSCF
students and to assist admitted students in the transition into the MSCF program.
QuantNet’s 2013-2014 International Guide to Programs in Financial Engineering | quantnet.com 63
A Primer for the Mathematics of
Financial Engineering
AUTHOR: Dan Stefanica
PAGES: 352
Format: Paperback
WHY YOU SHOULD READ IT: Dan Stefanica’s book
is used by many prospective MFE applicants to
refresh mathematical concepts and understand
the quantitative models used in nancial
engineering. It contains 175 exercises, many of
these being frequently asked interview questions.
The book also includes pseudo-code allowing
readers to implement numerical methods in a
programming language of their choice.
Options, Futures, and Other Derivatives
AUTHOR: John C. Hull
PAGES: 864
FORMAT: Hardcover
WHY YOU SHOULD READ IT: This book is
considered “the Bible” for nance practitioners
and a popular textbook for business students,
particularly those with a limited background
in math. The book covers the major nancial
products, their practical uses, and valuations.
An Introduction to the Mathematics
of Financial Derivatives
AUTHOR: Salih N. Neftci
PAGES: 527
FORMAT: Hardcover, Paperback, Kindle
WHY YOU SHOULD READ IT: This book provides
an excellent treatment of the mathematics
underlying the pricing of derivatives. Aimed
at professionals and students in PhD or MBA
programs, Neftci provides clear explanations of
complex nancial products from a practitioner’s
point of view.
Frequently Asked Questions in
Quantitative Finance
AUTHOR: Paul Wilmott
PAGES: 624
FORMAT: Paperback, Kindle
WHY YOU SHOULD READ IT: The book provides a
broad overview and FAQ of main topics that every
nancial engineering master student should know
in order to prepare for their internship and job
interviews. Wilmott discusses quantitative nance
theory as well as everyday practice, including how
to solve popular models, equations, and formulae.
See more recommended reading at
QUANTNET MASTER READING
LIST FOR QUANTS.
READING LIST: Books for Applicants and Students
of Financial Engineering Programs
This Reading List recommends books to help prepare students who will apply for or begin their
MFE study soon. The books cover many topics MFE students should know when they interview for
quantitative nance internships during their rst semester.
QUANTNET | 2013-2014 GUIDE | quantnet.com64
SECTION 3:
Getting a Job
QuantNet’s 2013-2014 International Guide to Programs in Financial Engineering | quantnet.com 65
By Ellen Reeves
Job Search Strategies &
Interview Techniques
Questions and Answers
Can I Wear My Nose Ring to the Interview? is based on the real job-hunting questions my students and clients have
asked me over the years. In preparing students for the quant job hunt at Baruch’s Master’s in Financial Engineering
program in New York and the Master of Science in Computational Finance and Risk Management program at the
University of Washington, I’ve been asked the following questions:
How do I approach
looking for a job?
Stop Looking for a Job and
Look for a Person
The mantra of my book is the
strategy I advise for all job-
seekers: Stop looking for a job
and start looking for a person.
The right person will lead you to
the right job or opportunity.
Applying blindly is less effective
than targeting specic jobs
and companies and then using
alumni and other community
contacts and social media to
get inside the company, to have
informational interviews, and
get your name and resume
circulated from inside. It takes
work, but you can usually nd
someone to connect you. Use
LinkedIn and Facebook to post
a note asking if anyone knows
ANYONE at a company you’re
interested in or where you’re
applying, and ignore geography;
for example, someone in the
West Coast ofce of a company
is likely to have some contacts
in their East Coast ofce.
The 80-20 Rule
Stop sending your resume
hurtling into the black void
of cyberspace. It’s depressing,
and statistically, only 20% of
jobs are lled by over-
the-transom
applications.
Eighty percent
of all jobs are
lled by personal
referral, and
80% of all jobs
(although not the
same 80%; I know I can’t pull
the statistical wool over quant
eyes) are never even advertised;
they exist in what we call the
Hidden Job Market. I’m not a
quant, but even my basic math
tells me that it’s better to spend
100% of your time on 80% of the
opportunities. But you have to
know what you’re looking for,
understand the requirements
of the jobs you want to apply
for, and be able to spin your
past experience to the needs
of the employer. Recruiters
and hiring managers will tell
you that authenticity wins out
every time; an interviewer can
sense if you really want a job or
not. Don’t apply for a job just
because you can get it. Apply
for it because you want it. Self-
condence is key; it’s a lot easier
to feel condent if you know
you’re as prepared as you could
be and that you really want the
job at hand.
continued on page 66
“Recruiters and hiring managers
will tell you that authenticity wins out
every time; an interviewer can sense
if you really want a job or not.”
QUANTNET | 2013-2014 GUIDE | quantnet.com66
Job Search Strategies & Interview Techniques continued from page 65
Tap into the
Hidden Job Market
To tap into the Hidden Job
Market, you’ve got to get out
there and start talking to people.
Hone your “elevator pitch” and
let people know who you are,
what you can do for them, and
what you’re looking for. Employers
have two concerns: making
money and saving money. Show
them how you can do this for
them and you’re golden.
The Rule of 3
It’s important to break what
could be an overwhelming job
search or career transition into
manageable, bite-sized tasks.
Use what I call “The Rule of 3.
Vow to reach out by phone or
email to three people a day to
jumpstart your job search. Ask
each person for another lead to a
job or informational interview. If
you did this every day for a year,
even taking the weekends off,
you’d speak with almost 1,000
people—and this is obviously
more than you need. But you can
handle three contacts a day: one
in the morning, one at lunch,
one in the evening, right? If this
seems overwhelming, aim for
three a week.
Start today. Make a list of the
people to whom you can reach
out easily. Begin with family
and friends who will be most
receptive and let the circle
widen from there. Make sure
you have an up-to-date resume
and LinkedIn prole, and a
business card with your name
and contact information
(just name, phone, and email
address).
How can I stand out
at a career fair or
recruiting event?
Career fairs and recruiting
events can feel like insane, giant
meat markets. You are not going
to have just one interview; you
may have a dozen impromptu
interviews! Just as you’d prepare
for a regular interview, prepare
for the fair. Try to nd out in
advance which companies
will be represented and who
is representing the company.
Research the companies and
representatives as much as you
can. Get a good night’s sleep
and come having eaten a good
breakfast or lunch. Remember
these tips:
d Dress neatly and presentably
with some memorable, but
not outlandish, detail: a good
tie, great scarf, bright color, or
interesting piece of jewelry.
d Be extremely knowledgeable
about the company and ask
intelligent questions. Ask to set
up an informational interview
at a later date because you’re
so committed to the company
whether the jobs listed (if they
have specic openings) pan
out or not. Be well-prepared
for what could be a real, on-
the-spot interview! Be ready
with your 30-second pitch and
concrete, brief examples to
convince the recruiter what you
can do for the company. Your
delivery must be enthusiastic
and energetic but authentic.
d Make sure you know with
whom you’re speaking so you
can spin your pitch accordingly.
Is this an HR person? The person
for whom you’d be working? Be
sure to get his/her card or title
so you can thank the person and
follow up.
continued on page 67
“Set easy goals:
go to one lecture or event each week
and introduce yourself
to three people.”
QuantNet’s 2013-2014 International Guide to Programs in Financial Engineering | quantnet.com 67
Job Search Strategies & Interview Techniques continued from page 66
d Have an excellent resume
and cover letter tailored to
each company’s needs and to a
specic job description if there
is one (i.e., nd out what might
be available online ahead of
time). Target the companies
you’d really like to work for.
Have a business card ready.
d Do not take up all the
recruiter’s time. If the
representative is alone and
can’t leave the booth, offer a
favor: “May I get you a glass of
water?” If you are engaged in a
great conversation but there’s
a line of people, say “I’d like
to talk more, but I know other
people are waiting. Could we
exchange cards and set up a
phone meeting or another time
to talk?” And if you see shy
colleagues or classmates waiting
on the periphery, say, “May I
introduce my classmate John?”
I’m sure you know the
quant” types here are
mostly Asian males,
introverted types. The
language barrier is
one reason they don’t
network as much as
they should. How would
you advise they break
the ice and better sell
themselves?
It’s a vicious circle, because the
less you interact with people,
the less you feel you can,
because your language skills and
condence aren’t developing if
you’re not talking as much as
possible with native speakers. A
few things to think about:
d Improve your language skills,
both passively (listening to the
radio, watching TV, and movies)
and actively by getting involved
in an activity you like or having
lunch with native speakers.
d I ask my more outgoing
students to take shyer students
under their wing, and I tell
the shy ones to ask the more
outgoing ones for help.
d Set easy goals: go to one
lecture or event each week and
introduce yourself to three
people. You have to get used to
saying politely “May I join you?”
but prepare yourself for a not-
always friendly response. Don’t
take it personally. You may go
through uncomfortable periods,
but if you stick with it, you’ll be
amazed at how your condence
level and language will improve.
d If you know someone who
has conquered these barriers,
ask about his/her story.
Do you have advice
about a LinkedIn
profile?
The LinkedIn prole is an
uploaded version of your
resume. You can add a
professional photo if you choose,
but remember that if you do,
you may be giving employers
access to information it’s
illegal for them to ask about,
including race, age, and gender.
Recommendations should be
current—recommendations
that are not from this year are
too out of date. Have at least
one from this year to offset
older ones. Tailor the headers
of categories to what you’re
looking for and the keywords
you read in job listings. As you
would for a resume, avoid self-
assessment and adjectives
that are subjective: “excellent
communication skills” or “strong
programming skills. Instead,
use neutral, unassailable,
objective language showing
what you have done with that
skill: “extensive experience with
C++” or better yet, a line about
a project you did using it. BE
SPECIFIC!
continued on page 68
“Do not take upall the
recruiter’s time . . . say,
‘I’d like to talk more. . . .
Could we exchange cards and
set up a phone meeting or
another time to talk?’”
QUANTNET | 2013-2014 GUIDE | quantnet.com68
Job Search Strategies & Interview Techniques continued from page 67
In my experience and from
asking clients and students,
LinkedIn is more useful for
nding leads and making
connections than being an
actual source of jobs (apart from
headhunters scanning them).
How do I talk about my
previous experience
and/or a career change?
Highlight Your Transferable
Skills for the Employer
Learn to talk about past
experience in terms of the
language of your target
job. Make analogies for the
employer. Do the work and
make the connections for him
or her by doing enough research
and informational interviewing
before the actual interview
so that you know what the
company is looking for and how
your past experience may be
valuable. Learning how to talk
about your transferable skills is
the name of the game.
When You’re Job-hunting,
Nothing is Bad: X was
Good; Y is Better
Even if the reality is that you
hated your previous work,
speak only positively about
it: Although I learned so
much working on my PhD in
chemistry, the more I learned
about quantitative nance, the
more I realized how well my
background would serve me
and how interesting the eld is.
Then give a concrete, specic
anecdote to illustrate your point.
How important is
appearance?
Project the Best
Professional You Possible
As Mark Twain says, you never
get a second chance to make
a rst impression. This is your
only chance to look professional
with advance notice; what you
are saying in an interview with
how you look and dress is: This
is the best professional me I can
be. Everything is an act of self-
presentation and everything is
a signal to the employer about
what kind of work you will do
for him or her, whether you can
follow directions and whether
you understand the conventions
of the industry. A loose button,
wrinkled clothes, sweaty palms,
ragged nails and cuticles,
unkempt hair, body odor, or bad
breath means you haven’t taken
care of things you could have
taken care of, that you aren’t
meticulous and attentive to
detail.
What to Wear to
the Interview
Get your clothes tailored; have
someone look you over before
you leave for the interview;
check yourself in the restroom
before the interview starts.
Err on the side of dressing
conservatively.
FOR MEN: A dark suit, preferably
navy or charcoal gray. The risk
of the black suit is looking like
an undertaker, so be careful.
Choose a conservative, matching
tie and a solid white shirt. Your
shoes should be well-polished,
lace-up black shoes. Make sure
your belt matches your shoes
(plain, professional belt buckle)
and that the socks match pants.
Socks should be solid color and
match each other. You must
be clean-shaven with a good
haircut and well-groomed nails
and eyebrows. No visible jewelry
besides a professional watch.
Use no/very little cologne or
aftershave.
HERE’S ADVICE FROM A WALL
STREET MANAGER: Think Brooks
Brothers if your budget permits
it; otherwise, consider Jos. A.
Bank and Land’s End. The latter
two have business wear that is
reasonable in price and popular
with Wall Street people.
continued on page 69
“Learning how to talk about
your transferable skills
is the name of the game.”
QuantNet’s 2013-2014 International Guide to Programs in Financial Engineering | quantnet.com 69
Job Search Strategies & Interview Techniques continued from page 68
FOR WOMEN, an equivalent
conservative suit or pants suit
or professional-looking dress—
not evening or casual day-time
wear—with properly tting
and concealed undergarments.
Wear nude stockings, minimal
jewelry and makeup, shoes with
an appropriate heel, natural
nail color, and hair groomed
and not constantly in your face.
You don’t need to look like a
man, but you need to look like a
professional business woman.
As classes start and
students are distracted
by their academic
responsibilities, how
can we stay prepared for
the interview process?
Go into the semester with a job-
hunting plan and goal in place.
The community of your school
is so important, and I always
tell my students that they—and
the faculty and staff—are each
other’s best resources when it
comes to interviewing if the
spirit is collaborative and not
competitive.
d Sit down with your class
schedule, block out times
for study, eating, sleeping,
exercise, family, social and work
responsibilities, and schedule
coffee, lunch, or a drink with
people who can enlighten you
about what they do.
d Block out time every week to
read industry periodicals and
attend recruiting sessions and
special lectures where you can
introduce yourself to people in
the industry.
d Find out where everyone
worked this summer or had
internships. If there are 25
students in a class, this means
you’ve got 25 (well, 50) feet
in the door already, plus the
families and friends of those
fellow students.
d Set up exploratory interviews
with and through classmates,
faculty, former colleagues and
connections through LinkedIn
to nd out what kind of job you
may want.
d Choose a capstone or
independent project if your
school has one to test out an
area and see if you like it.
d Take time every week to
browse QuantNet, LinkedIn, and
other sites of interest.
d If you’ve already dened
companies or areas in which
you’d like to work, focus on
networking there. I recommend
choosing the top three, then
focusing on the rst one until
you’ve exhausted all your
resources and connections, then
move on to number two.
d Plan your interview
wardrobe.
d Practice your elevator pitch.
How do I prepare for the
first interview? Second
interview?
d Review your resume.
d Make sure you have
rehearsed a brief anecdote
highlighting your contributions
and achievements for everything
on it.
d Do some mock interviewing
with colleagues or college or
graduate school career services,
and rehearse answers to both
basic and tough questions:
“Why do you want to work
here? Why should we hire you?
What are your strengths and
weaknesses?” and so on.
continued on page 70
“Go into the semester
with a job-hunting plan and goal in place.”
QuantNet’s 2013-2014 International Guide to Programs in Financial Engineering | quantnet.com 70
Job Search Strategies & Interview Techniques continued from page 69
d If the company will tell you,
nd out with whom you’ll be
interviewing; look up LinkedIn
proles, and set a Google alert
for the interviewers and for the
company or department.
d Research the company and
department and do as much
informational interviewing as
you can around the position.
The more you know about how
the company works, from those
who work there or from clients
or competitors, the better.
d Prepare a list of questions
you want to ask and points
you want to bring up in the
interview.
d At a rst interview, ask “If I
were hired, what would be my
rst tasks?” If you have a second
interview, come prepared with a
plan for how to handle some of
the tasks or issues they’ve said
you’d be responsible for: “I took
the liberty of thinking about X.
d Demonstrate that you really
want the job and can hit the
ground running. Ask “I know
what the stated job description
says, but can you tell me more
about what has worked and not
worked for you in the past?”and
then spin your answer with
anecdotes to address their
interests and concerns.
For more on topics including
what to do in the waiting room,
getting rid of anxiety, handling
difcult or illegal questions, see
“Getting Through the Interview”
in Can I Wear My Nose Ring to
the Interview?
What do I do after the
interview? Should I
waste time sending a
thank-you note?
Follow Up
After the Interview
WASTE YOUR TIME? To me, the
thank-you note is an essential
part of the interview. Email a
note right away (this way, it
can be easily circulated; don’t
send the exact same note to
everyone!) and follow up with
a neatly handwritten note. This
gives you a chance to reiterate
how much you want the job, to
note anything you left out, and
to show that you are someone
who follows up. There ARE no
jobs that don’t require some
kind of follow-up, and the thank
you note is key.
The note stresses why you want
the job (framed as what you
can do for the employer), and
why you’re the right person
to do the job. And even if you
don’t want the job, why not
stand out as someone polite and
professional? You’ve made a
professional connection through
the interview—maintain it! If,
after the interview, you know
you wouldn’t take the job if it
were offered to you, withdraw
your candidacy politely. (“Dear
Ms. So-and-So: I so much
enjoyed our interview today.
Thank you for taking the time
to meet with me. Given that
the job entails x, y, and z as you
described today, I am writing
to withdraw my candidacy; I
am looking for a position that
involves more . . . . If you have or
know of another such opening
in another department, I’d be
very interested. Best, . . . ”)
continued on page 71
“The note stresses why you want the job (framed as what you can do for the employer),
and why you’re the right person to do the job.].”
QUANTNET | 2013-2014 GUIDE | quantnet.com71
Job Search Strategies & Interview Techniques continued from page 70
Ask for the Job
If You Want It
Use yourself as a litmus test for
what’s annoying. You don’t want
to be perceived as a stalker, but
you must follow up. Employers
say that the reason many people
don’t get jobs they’ve applied
for is that they don’t follow up
after an interview. They never
say they actually WANT the
job. They don’t send a thank-
you note; they just wait for the
judgment to be handed down.
NO! The successful job search is
actually the intersection of two
searches—yours for the right
job, and the employers’ for the
right employee.
Ask What the Hiring
Timeline Is
At the end of the interview, nd
out what the hiring timeline is
and how you should follow up.
You are not going to be denied
the job because you ask this
question: “What’s your hiring
timeline? If I don’t hear from
you, may I check back in two
weeks? Would you prefer to be
contacted by phone or email?”
I am a non-native
English speaker
and worried about
interviews, particularly
phone interviews—or
conversely, I am a
native English speaker
but can’t understand
the accent of my
interviewer. Any tips?
Don’t Pretend You
Understand If You Don’t
Everyone has an accent. It’s
important never to pretend you
understand what someone is
saying if you don’t.
d Be direct with the
interviewer. At the beginning of
the interview, say, As you can
probably tell from my accent,
I’m not a native English speaker,
and I can function perfectly well
in English, but I hope you won’t
mind if I ask you to speak slowly
or to repeat things. This is
especially important for phone
and video or Skype interviews.
d Improving your accent:
Listen to National Public Radio
(NPR). If you are interested in
working on your language skills
and accent—and this goes for
native speakers, too—listen
to NPR hosts (not the guests!),
most of whom speak clear and
standard English.
Tackle One Language
Issue at a Time
Don’t try to tackle everything at
once; choose one issue to work
on, for example the “th” sound
in “the. Practice saying words
with “th. Exaggerate the sound
by 1) Making sure your tongue
licks the bottom of your top
teeth, 2) Placing your forenger
in front of your mouth, and
3) Making sure your tongue
touches your nger as you make
the “th” sound.
People can’t pronounce
my name. Should I
use a nickname, a
transliteration, or a
classically “American”
name?
This is an individual decision
about your own identity; a
compromise may be to use a
transliteration of your real name
with a nickname, and on your
resume list the nickname in
parentheses, that is, Yingxue
(Jay) Cheng if that’s comfortable
for you.
Make it Easy for the
Interviewer
Interviewers don’t like to
feel incompetent, and if they
can’t pronounce your name
or tell whether you are male
or female—even though it’s
not your fault at all—they may
literally move on to the next
resume, so make it as easy as
possible for them.
continued on page 72
“Everyone has an accent . . .
never pretend you
understand what someone is
saying if you don’t.”
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Job Search Strategies & Interview Techniques continued from page 71
Offer Gender Markers
The eld is male dominated; if
you are a woman, offer a gender
marker on your resume if your
name doesn’t indicate gender
to an English-speaker: (Member,
Women in Business, Baruch College).
Offer Pronunciation Aids
When you meet the interviewer,
state your name clearly and
offer some help with the
pronunciation: “Hi, I’m Samita,
like Anita” or “My name is Xin—
like shin” (and point to it).
I sometimes feel I am
at a disadvantage since
I am from another
country. How do I
deal with cultural
differences?
Your Cultural Advantage
To the contrary! You have an
advantage. You speak your
language and probably others, as
well as English; you have insider
knowledge of another culture or
cultures including how business
is done, how the economy of that
country functions—embrace it
and use what you know! You can
easily learn about and work on
questions of etiquette.
Get American Friends to
Work with You
With friends, practice things
like greeting an employer.
For example, when greeting
employers or colleagues in
America, you must look people
in the eye and have a rm
handshake.
If you are used to bowing and/or
looking down, or covering your
mouth when you speak, you
must practice NOT doing these
things.
Learn American
Dining Etiquette
You must learn which utensils
are used for which foods. At
dinner one night, I saw one of
my highly talented students
with a PhD butter her roll with
a fork. She was about to have
10 interviews at Citibank, and
I knew this could jeopardize
her candidacy. While you might
think something like this
has nothing to do with one’s
capacity to do the job, it’s not
about that: it’s about showing
that you understand cultural
conventions and can work
within a new culture.
If you’re interviewed over a
meal or invited for a meal or
drink after an interview, you
have to make sure not to eat
with your mouth open, not to
slurp or make noise while you
eat, not to order the messiest
or most expensive items on the
menu, and so on. These are the
small things that can sink you
despite a great interview, and
it’s likely that no one will tell
you. Etiquette resources like
the classic Emily Post or Miss
Manners books can help!
I tend to be shy. How
do I talk to people and
maintain my network?
How do I follow up
with people, build and
maintain my network?
Don’t Be a Wallflower
If you’re offered the chance to
go to a recruiting event, cocktail
party, or any kind of professional
development event, go! Set a
goal for yourself of meeting and
talking to at least three new
people. Ride the coat tails of
your more outgoing colleagues
or classmates; ask if they (or
the hosts and organizers) will
introduce you to people. If
someone offers a card, follow up
with a note and a brief reminder
of how you met and what you
talked about.
continued on page 73
“(Dining etiquette) is about showing that you understand
cultural conventions and can work within a new culture.”
QUANTNET | 2013-2014 GUIDE | quantnet.com73
Job Search Strategies & Interview Techniques continued from page 72
Don’t Forget About
Your References
Remember that your references
are a key part of your network.
Check in with them periodically,
ask them for leads, thank
them, and follow up. Seek new
references as necessary.
How do I negotiate
salary and benefits?
You’ve Got to Negotiate
You never have more leverage
than between the time you’ve
been offered the job and the
time you accept it. People are
uncomfortable negotiating for
themselves. So if you’re going
to feel sick, which would make
you feel sicker? Negotiating and
getting a better package, or not
negotiating and nding out that
someone else was hired at the
same level but with a higher
salary and better benets simply
because he/she negotiated?
Remember that it’s the
employer’s job to offer you as
little as possible and see what’s
the lowest you will accept, and
it’s your job to advocate for the
best package you can get.
d Stay positive; assume you
can reach a compromise. Say
“I’m really excited about this job.
I know we can make this work.
d Ask “Is there any exibility
there?”
d Try not to put all your cards
on the table at once. The lowest
number you state may be your
salary, so be careful.
d Negotiate based on what you
bring to the position, not what
you think you’re worth or what
you “need. Your lifestyle choices
or debt are not the employer’s
problem.
d Go in armed with a sense of
the salary range not only for this
industry but for the company
and geographical location.
d Find out what the employer
values most. Think beyond
salary—benets, vacation, and
perks may be negotiable.
Above All: Feel Valuable,
Not Vulnerable
Take control of your job hunt
with these tips. I want you to
feel valuable, not vulnerable.
There’s no reason why you
shouldn’t have the job you
want—even in this economy.
I didn’t get an offer for
a job I really wanted.
I feel depressed and
rejected. What do I do?
When someone doesn’t get an
offer, I say: Congratulations! A
bullet dodged you. This means
it wasn’t the right job for you,
for whatever reason. Because if
it were, you would have gotten
it. You were spared for the right
job. Here are a few things to
consider, excerpted from my
book Can I Wear My Nose Ring to
the Interview?
d If you feel you had a good
rapport with the interviewer, it
doesn’t hurt to ask for feedback.
“I’m very disappointed.
I’m wondering if, at your
convenience, you might be
able to offer suggestions for
improving my candidacy.
continued on page 74
“. . . it’s the employer’s job to offer you as little as possible
and see what’s the lowest you will accept, and it’s your job to advocate
for the best package you can get.”
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Job Search Strategies & Interview Techniques continued from page 73
One candidate found she was
competing with a pool of MBA
candidates; she was also told
that during her interview, her
“headlining” skills were weak.
They expected her to be able to
offer a brief, focused summary
of her resume orally, tying her
skills and experience to the
stated job requirements.
d Were you perceived as
overqualied? Time to rethink
your resume or the jobs for
which you’re applying.
d Take steps to remedy any
obstacles that might be standing
in your way. If you think you’re
lacking experience, go out and
get some. Intern, volunteer, or
temp in the eld.
If the interviews went well and
you get the sense that you were
a top candidate, communicate
how much you enjoyed the
interview process and the
people; express that you’re more
determined than ever to nd the
right position at the company,
and ask if they’d be willing to
keep your resume on le. Keep
in touch. Another position
might become available in a few
months or even weeks.
Good luck!
Career and workplace advisor Ellen Gordon Reeves, QuantNet career
columnist, is the author of the Business Week bestseller, Can I Wear My
Nose Ring to the Interview? A Crash Course in Finding, Landing, and Keeping
Your First Real Job, featured in media including CNN, CBS, ABC, FOX, NPR,
and Money Magazine. She teaches communications, interviewing, job-
hunting, and self-presentation skills in programs including Baruch’s
Masters in Financial Engineering and the University of Washington
Masters in Computational Finance, preparing students for the job
market. Read her career advice column at
https://www.quantnet.com/threads/ask-ellen-job-hunting-and-career-development-advice.10689/.
Contact her at caniwearmynosering@gmail.com to sign up for Extreme Professional Makeover: Boot Camp
for the 2014 Quant Job/Internship Hunt (December, NYC), to inquire about bringing the boot camp to your
campus, or to bring her DIYPD (Do-It-Yourself Professional Development): Making Your Workplace Work for You
or DIYPD: Making the Most of Your Internship workshops to your rm or organization.
“If you think you’re lacking experience,
go out and get some. Intern, volunteer,
or temp in the field.”
QUANTNET | 2013-2014 GUIDE | quantnet.com75
Finance Industry
Dictates Changes to
Job Market
d Stop focusing upon HFT
positions—there aren’t that
many jobs out there, and many
of the people who get those jobs
nd that it’s VERY hard to make
money.
d Consider positions in
model review, audit, and price
verication. Those areas are
growing rapidly.
d Check the job ads at the
regulatory agencies (FRB, SEC,
OCC, CFTC, and FINRA). Many
people get their start at these
organizations.
d Think about jobs outside of
banking. Corporate treasuries
need quants, too, as do data/
media companies.
d Know the industry. Be
able to identify the top rms
in each sector in which you
interview (hedge funds, banks,
insurance companies, etc.) Read
the industry press. Know the
regulatory landscape.
d Know the company. Read
their annual report. Know their
position in the industry and
their strengths and weaknesses.
Read all recent news articles
about them.
d Don’t spout off about all the
big-name academics you know.
Everyone else knows them, too.
d Have a good reason for
wanting to be in nance.
Wanting to make lots of money
isn’t one of them. Be convincing
or you’ll be tagged as a gold
digger.
d Dress the part. Show up for
your interview in business attire.
Wall Street isn’t Silicon Valley.
continued on page 76
The job market for quants has changed inexorably. The “particle finance” trend of the last 20 years is on the
wane. While funds will still be able to trade on a prop basis, banks’ ability to do so has been severely restricted.
Some may see this as a pendulum, but most agree that the aggressive trading styles seen in regulated financial
institutions will never be seen again.
Does that mean that there are no more jobs for quants? Certainly not. It does mean, however, that the nature of
the job market will be different. The growing number of quant finance programs also suggests that there will be
much more competition for these jobs.
The following suggestions may be helpful in the job hunt.
By Ken Abbott
QuantNet’s 2013-2014 International Guide to Programs in Financial Engineering | quantnet.com 76
Finance Industry Dictates Changes to Job Market continued from page 75
d Speak clearly. One of the
biggest challenges facing many
quants is being articulate.
Most senior executives, while
intelligent, aren’t quants. Be able
to express complex concepts in
simple terms.
d Don’t pad your resume. If
you make a major omission or
misstate something, there’s a
good chance you’ll be discovered
and dismissed. Be prepared to
discuss any topic you mention
in your vitae. The quickest way
to get dinged is to come off as a
faker.
d Have an opinion. Show that
you’ve thought about the issues
facing the industry. Keep on top
of current events.
d Don’t get thrown off by a
tough question. Pressure is part
of the business. Do the best you
can. If you simply don’t know
the answer, say so. Don’t try to
fake it. One ubbed response
doesn’t ruin an interview.
d Don’t talk salary. The market
is reasonably efcient. If you try
to negotiate too hard, you will
run into difculty.
d Stop worrying about your
GPA. It probably won’t matter
that much unless it’s really low.
d Don’t brag too much about
your programming expertise
unless you’re interviewing
for a programming job. While
there’s an overlap, most quants
aren’t programmers and most
programmers aren’t quants.
Kenneth Abbott is a Managing Director at Morgan Stanley, where he is the Chief Operating
Ofcer for Firm Risk Management. In addition, he also supervises the risk management of the
Investment Management businesses. He is also responsible for legal entity risk management
for Morgan Stanley’s US swap dealers and and sits on the investment and valuation committees
for the Morgan Stanley Private Equity and Infrastructure funds. Previously, he ran market risk
management for Bank of America’s Investment Bank. He has over 30 years banking experience,
including 14 years at Bankers Trust as an analyst, trader, and risk manager. Ken has a B.A. from
Harvard in Economics, an M.A. from NYU in Economics and an M.S. from NYU/Stern in Statistics
and Operations Research. He is an adjunct faculty member at NYU, Baruch, and Claremont and
sits on the Board of Trustees for the Global Association of Risk Professionals (GARP) and the NJ
Scholars Program.
Ever wonder howyour
compensationcompares with other
MFE graduates with a similar job
and experience profile?
Ever wonder about the base/bonus
when you move to thebuy side?
Go to quantnet.com/salary to see the results of the QuantNet Salary Survey, a constantly updating tool
that allows you to anonymously compare your base salary and bonus against others. Using QuantNet’s
filters, you can create a more precise comparison based on your education level, location, job type,
employer type and size, years of work experience after MFE and other factors.
QuantNet’s 2013-2014 International Guide to Programs in Financial Engineering | quantnet.com 78
Brainteasers
1. You and other Santas are attending a
Santas-only Christmas party where every
Santa knows exactly 22 other Santas. Among
the 22 Santas, each Santa knows none know
each other. But any two Santas who do not
know each other have exactly six mutual
friends present. How many Santas are at the
party?
2. There are two sealed envelopes: one has $50,
and another has $100. You get to choose one
at random and keep the money. How much
would you be willing to pay for this?
3. Assume now that you have $50M and $100M.
How much would you be willing to pay this
time?
4. You and a friend are playing a game where a
random number X between 1 to 20 is chosen.
One of you will pick a number and then the
other will pick a different number. The one
who guesses the closer number wins X dollars.
Should you choose to go rst? What number
should you choose?
5. There are 60 blue ribbons in a box such that
all 120 ends are hanging out and you cannot
see which ends belong to which ribbons. You
randomly join all 120 ends together into
pretty bows and dump out the box. Depending
on chance you will form anywhere from 1 to 60
loops. How many loops would you expect?
Programming
1. Write one line of code to swap the contents
two variables without using a temp variable.
2. Write a program to print 1-100 and backward
without using loops.
3. Write a function to compute factorial using
recursion.
4. Do the same for Fibonacci numbers.
5. Write a class, in an object oriented program-
ming language, which performs arithmetic
operations between arbitrarily large numbers.
6. With an integer represented as a string, write
a function that represents this number with
the thousands separated by commas.
Finance
1. How do you price swap rate? How do you price
swaption? What is Macaulay, modied,
effective, dollar duration?
2. Which bond has higher duration: the ve-year
coupon bond or the ve-year zero-coupon bond?
3. If the cost of money (prevailing interest rate)
rises from 4% to 5%, does that affect a ve-year
zero coupon bond’s duration?
4. What is the value of an ATM European call with
time to maturity as innity? What is the value
of American ATM with time to maturity innity?
5. Consider a one-period binomial tree with r = 0,
u = 10, and d = 5. Write the equations you would
write to get a risk-neutral world. Calculate the
price of the option. What’s wrong with the
price you get?
Questions Asked at Quant
Interviews These are sample questions similar to those you might be asked during an
interview. For more questions and solutions, visit the Quant Interviews section.
QUANTNET | 2013-2014 GUIDE | quantnet.com79
Firms that Employ Graduates with MFE Degrees
These rms hire graduates with master’s of Financial Engineering degrees. While this list is by no
means exhaustive, it does show the international reach of the eld, as well as a place to begin your
job search. Click on the company names (below) for site links.
HEDGE FUNDS
AQR Capital Management
Aviva Investors
Bogle Investment
Management
Bridgewater
Citadel Group
Clinton Group
DEShaw
DFG Investment Advisers
First Quadrant
GBAR
Glenwood Capital
Investment
Houlihan Lokey Howard
& Zukin
Innum Capital
Management
Investec
Knight Capital Group
M&G Investments
Mitsubishi UFJ Securities
International
Numeric Investors
Nuveen Investment
Optiver
QVT Financial
SIG Susquehanna
State Street
Tower Research Capital
Two Sigma Investments
INVESTMENT BANKS
Bank of America
Merrill Lynch
Barclays Capital
BNP Paribas
Citigroup
Credit Suisse
Deutsche Bank
Goldman Sachs
J.P.Morgan
Morgan Stanley
Nomura
Societe Generale
UBS
COMMERCIAL BANKS
Commerzbank
HSBC
ING
Royal Bank of Canada
Royal Bank of Scotland
Standard Chartered Bank
Wells Fargo
PROP TRADING
Chicago Trading Company
Chopper Trading
DRW Trading Group
GETCO
Jane Street
Jump Trading
Nico Trading
QuantRes
RSJ
Spot Trading
TransMarket Group
Walleye Trading
Wolverine Trading
FINANCIAL SERVICES
Bloomberg Financials
FINCAD
Gloucester Research
Interactive Brokers
Numerix
Opera Solutions
Quanti Solutions
Financial Technologies
Thomson Reuters
continued on page 80