NEW YORK, October 6th, 2015 – QuantNet today released its 4th rankings, selecting the Best 30 Master Programs in Financial Engineering, Mathematical Finance, Quantitative Finance in North America.
QuantNet surveyed programs administrators, hiring managers, corporate recruiters, and finance professionals to get the information used in the 2015 rankings.
In additional to the top 30 rankings, this year we rank the top 10 programs with the best employment outcomes and the 3 most improved programs.
C++ Programing for Financial Engineering Online Certificate. Approved for 15 CPE credit hours by GARP
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- Junior quants – campus recruiting
- Mid-level quants – imported from offshore locations
- Organizations are flatter – the senior ranks have been thinned, there’s less middle management, and everyone is expected to do more. You still need hands-on people to do the work, and that’s where the demand is. There are very few executive director level hires being made, and for every ED opening, there are a lot of good people vying for the role.
- There’s less compensation differentiation. Firms used to maintain significant compensation disparity between top talent and mediocre contributors. Wage compression has made such differentiation difficult. Feeling screwed on comp? Look around you, everyone else is feeling the same way. One senior executive noted to me that the front-office was still able to differentiate their top 10%, but IT and other support groups have lost that ability to a large degree.
- Less management means that individual contributors need to be experts. The folks who are doing well in the current market are able to differentiate themselves with their technical skills or subject matter expertise in those areas with high demand.
- It used to be that prior financial knowledge was highly valued experience. However, in the current market, financial experience is highly discounted. Given the arc of the industry in the past decade, there is currently an oversupply of people in New York with financial experience.
- A Primer For The Mathematics Of Financial Engineering, Second Edition - Dan Stefanica
- 150 Most Frequently Asked Questions on Quant Interviews - Dan Stefanica, Rados Radoicic, Tai-Ho Wang
- Fifty Challenging Problems in Probability with Solutions - Frederick Mosteller
- Solutions Manual - A Primer For The Mathematics Of Financial Engineering, Second Edition - Dan Stefanica
- The Complete Guide to Capital Markets for Quantitative Professionals - Alex Kuznetsov
- A Practical Guide To Quantitative Finance Interviews - Xinfeng Zhou
- Quant Job Interview Questions and Answers (Second edition) - Mark Joshi
- Liar's Poker: Rising Through the Wreckage on Wall Street - Michael Lewis
- When Genius Failed: The Rise and Fall of Long-Term Capital Management - Roger Lowenstein
- C++ Primer Plus (6th Edition) (Developer's Library) - Stephen Prata
- My Life as a Quant: Reflections on Physics and Finance - Emanuel Derman
- Heard on the Street: Quantitative Questions from Wall Street Job Interviews - Timothy Crack
- Frequently Asked Questions in Quantitative Finance - Paul Wilmott
- How I Became a Quant: Insights from 25 of Wall Street's Elite - Richard R. Lindsey, Barry Schachter
- Cracking the Coding Interview: 150 Programming Questions and Solutions - Gayle Laakmann McDowell
- Stochastic Calculus for Finance I: The Binomial Asset Pricing Model - Steven Shreve
- Stochastic Calculus for Finance II: Continuous-Time Models (Springer Finance) - Steven Shreve
- A Linear Algebra Primer for Financial Engineering: Covariance Matrices, Eigenvectors, OLS, and more - Dan Stefanica
- Financial Options: From Theory to Practice - Stephen Figlewski, William Silber
- Matlab, Third Edition: A Practical Introduction to Programming and Problem Solving - Stormy Attaway
- Very smart
- Quantitative thinkers
- Good at figuring things out with minimal guidance
- Education in advanced math (stochastic calculus, statistics, probability, etc.)
- Good software development skills
- Good data analysis skills
- The funds that participate in algo/HF trading generally look for the following:
- PhD in a quantitative field
- Experience with high performance computing
- Excellent software development skills – the ability to turn quantitative analysis into systems
- Macro funds look for a very different profile. They use third party products for trade processing, risk, regulatory reporting, and other needs. The main challenges involve system integration and the ability to make adjustments on the fly to accommodate new trade types and new requirements. Typically these funds stress:
- Excellent software development skills (C# front to back is the norm)
- Excellent communication skills – nobody has time to explain things twice in a fast-paced trading environment
- Good instincts and the ability to work independently – you need to be able to understand how things are done at the firm, and do it w/o being managed
- Experience with a variety of financial products, trade booking, order management, and risk
- Top notch developers with significant experience in high-performance computing
- C++ used almost exclusively
- R and KDB+ skills are often desired
- Knowledge of machine learning and data mining
- MS or PhD in a quantitative field
- Fixed Income
- Top notch quantitative skills – generally a PhD in a quantitative field is required
- Knowledge of machine learning and data mining
- Knowledge of fixed income products – cash and derivatives
- Software development skills
- Stop focusing upon HFT positions—there aren’t that many jobs out there, and many of the people who get those jobs find that it’s VERY hard to make money.
- Consider positions in model review, audit, and price verification. Those areas are growing rapidly.
- Check the job ads at the regulatory agencies (FRB, SEC, OCC, CFTC, and FINRA). Many people get their start at these organizations.
- Think about jobs outside of banking. Corporate treasuries need quants, too, as do data/ media companies.
- Know the industry. Be able to identify the top firms in each sector in which you interview (hedge funds, banks, insurance companies, etc.) Read the industry press. Know the regulatory landscape.
- 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.
- Don't spout off about all the big-name academics you know. Everyone else knows them, too.
- Have a good reason for wanting to be in finance. Wanting to make lots of money isn't one of them. Be convincing or you'll be tagged as a gold digger.
- Dress the part. Show up for your interview in business attire. Wall Street isn't Silicon Valley.
- 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.
- Don't pad your résumé. 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.
- Have an opinion. Show that you've thought about the issues facing the industry. Keep on top of current events.
- 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 flubbed response doesn't ruin an interview.
- Don't talk salary. The market is reasonably efficient. If you try to negotiate too hard, you will run into difficulty.
- Stop worrying about GPA. It probably won't matter that much unless it's really low.
- Don't brag too much about your programming expertise unless you're interviewing for a programming job, whiles there's an overlap, most quants aren't programmers and most programmers aren't quants.
- Get them interested in your career
Many students admitted to Bendheim have some sort of front-office experience- either full-time or as an intern. Either way, Princeton generally gets a 90-100% placement rate every year. Having our placement rate drop to 75% would be nearly as big a disaster as having our main building burn to the ground overnight. Since Princeton only gets 30-40 students per year, even one student missing an offer changes our placement rate 2-3%, and I'm sure the admissions committee spends a lot of time making sure that the final list of admits can all land jobs.
We take a lot of bright people without work experience. Many of them probably could have landed a Front-Office offer at a bank, or a Finance/Econ PhD admit at a top ten school, without our help.
Therefore, part of your application needs to convince Princeton that you are already capable of landing a good job at a bank, hedge fund, PE firm, or prop shop, and that a full-time admission to the program would still benefit your career further. Talk about your career goals, be realistic about how your current background would play into a potential job upon graduation, and basically set a tone that you WANT Princeton and would really LIKE Princeton, but you do not NEED Princeton.
- Get them interested in your academics and research
Every year or two, Princeton MFin sends a few students on to get PhDs, and the graduate school that we live in happens to largely be research-oriented. We don't have a Medical School, Law School, or Business School. We only have a few Master's programs, and many of them are more about preparing people for academia than industry. To top it all off, you will be living with mostly PhDs and grabbing beer with them in the graduate dorms. We are a research-oriented campus and these essays are being read by research-oriented professors. Many MFin students complete a research project their second year, and some eventually wind up getting published in a significant (not necessarily Big Three) finance journal.
So, if you have worked on published research, or you have some research idea that someone with a Finance PhD agrees is interesting, it can't hurt to mention that in your essay. It's also important to mention anything challenging you've done academically, particularly if it involves math, statistics, or finance.
Please note that research isn't required. I had some research ideas but no formal research experience, and it's certainly less helpful than a front office internship or full-time experience. But I think it helps to mention something academically interesting in your personal statement.
Most full-time students came in with at least something close to the traditional engineering math sequence. That typically means Calc I-III (Multivariable), Differential Equations, Linear Algebra, and Probability. Some came in with much more; a few came in with a little less. This is a fairly quantitative program at its foundation- with core courses that build on linear algebra and calculus-based probability, so it's difficult to see us admitting someone who did the bare minimum math requirements for a typical undergrad finance or liberal arts degree.
- Get them interested in you as a person
The one common trait among Princeton MFin students is that they all have something really interesting about them that is completely unrelated to finance. One of our students worked for NASA and to my understanding was on track to be part of their astronaut program before the space shuttle was cancelled. Another runs marathons and recently came in 1st in a 10K hosted by Princeton Theological Seminary. Another runs his own successful startup (in addition to working as a financial engineer). Another is an amazing soccer player.
I'm not sure hang gliding helped me get in, but it certainly didn't hurt. (It's also a lot easier to be at the top of the stack in a certain interesting area that few people do than it is to be an excellent marathon runner.) And I was sure to mention that in my essay and my resume. If you're not essentially being hired by the university to do research (such as with a funded PhD program), Princeton wants you contributing to campus in some other way.
Therefore, the goal here is to show that you're the very best applicant in a certain area related to finance (such as quant development), as well as the best applicant at (or near the top of the stack in) two or three other completely orthogonal areas to finance. Best runner? Best soccer player? Best wingsuit skydiver? Best cave diver?
This is sort of what I'm talking about with the later filtering looking more like undergraduate admissions or an MBA program. Princeton usually winds up with many more exceptionally qualified candidates than it has spots to fill, but a campus filled with people who can only talk about work and academics- and don't have interesting stories to tell and interesting things to get friends involved in- is going to be boring. So if you can talk about going two hours into an underwater cave in Florida on a rebreather, and send students on campus off to your tech diving instructor for a PADI Tec 50 course, you're really adding something to campus and ultimately making Princeton a more interesting place to attend grad school.
To be clear, this shouldn't be the core of your essay and your application- the core should be about academics, career, and perhaps research. You should probably devote most of your personal statement to those parts. But it's good to work this in somewhere. Like the MBA programs, we want interesting people on our campus and in our program. And it's possible that this is what helps you make the final cut.
- They really know you well.
- They have worked with you in a relevant subject.
- They have observed your outstanding ability and accomplishments.
- Baruch College Team B (Financial Engineering)
- LUIS - LUISS Guido Carli University of Rome
- University of Chicago Team A (Math Finance)
- Baruch College Team A (Financial Engineering)
- University of Waterloo Team A
- Evaluate the Position: The actual position is the most important part of the offer. In this new economy, where jobs tend to have a shorter tenure, each position becomes the stepping-stone to the next position. So if you have a career path in mind, each job positions you for the next step in your career. Is this opportunity the right one for you at this time? Would this position align you with your intended career path? Do you have the necessary skills to do this job well, and at the same time does this job provide you with growth opportunities for your continued professional evolution?
- Is there Proper Chemistry? The number one ingredient that determines job satisfaction is the relationship between the employee and the direct manager. Poor relationships with managers are the main reason people leave companies. How would you define your new manager’s leadership and communication style? How would you classify the manager's expectations – reasonable, aggressive or unreasonable? Did you get a chance to meet your new teammates or that dedicated client to whom you are supposed to provide outstanding customer service? What do you like best about your new manager? What troubles you about the manager? I realize it is hard to make these judgments after only a few interviews, but remember it is a two-way street. They had the same amount of time with you, and they also need to go through this evaluation process.
- The Compensation: The third thing I look at is the actual compensation. Is the base salary consistent with your current market value? If you are being compensated by income other than a base salary (performance bonus, commission, etc.), then you need to look at the total compensation structure for the position. How are the health, dental and retirement benefits? What is the vacation policy? Are there education and training reimbursement opportunities for you to take advantage of so you can keep your competitive edge? Are there perks that are unique to this company? I had a client who had two equal offers and finally took the position that had the best company perks.
- Evaluate the Company: Is this the right company for you? Is it in the right industry at the right size? Is this company financially stable or in growth mode or targeted for a take over? What is the reputation of the company or its relative position in the marketplace? How would you describe its corporate culture or value system? Just reading a corporate vision/mission statement could be misleading. It is best to find answers directly from your social network. What is the company's employer's proposition? Why do people like to work there and choose to stay there? Does this company provide professional growth opportunities and an employee friendly culture in which to work?
- Consider the Geography and the Environment: The last thing I tend to look at is the physical environment of the company. If your position is working remotely, then this becomes a non-issue. However, if you physically need to show up every day, then the location may become important. Consider the impact of the daily commute. Is there public transportation or do you need to drive? Is the location of the company near your network, or would the location isolate you from your networking contacts? How would you evaluate the neighborhood? I have had clients who turned down a position due to the neighborhood in which they had to work. Does the company provide flex-time or telecommuting opportunities? I had other clients who turned down positions because they didn’t feel comfortable in the building. One client received an offer from a hospital, but found the administrative building old, worn down and musty. He decided that he didn’t want to work there.
An Interesting Development of an Offshore Strategy
I was talking to the head of fixed income Strategy at a major investment bank last week. He described their current hiring pattern as follows:
I thought that latter was really interesting. What a smart move to let talent bubble up through the lower cost regions, then import that talent, a known commodity, to the head office. The strategy provides great career growth for the individuals involved, is less expensive for the firm, and less risky than an outside hire. This strategy has obvious implications for quants in NYC who are considering a lateral move. To be sure, this is one observation, not necessarily an industry trend.
CDS Market is Dead
I wasn’t paying attention, but the CDS market has died. Once the synthetic CDO market disappeared, it turned out there was little demand for CDS. Apparently only financial names continue to trade with any regularity. No wonder I’m seeing a lot of resumes from people who were working on CDS booking systems.
It’s well known that Data Analytics is booming, and the IT field is proving attractive to many quants who are not seeing career growth in finance.
One senior quant manager recently told me that in recent years, it’s fairly typical for entry level quants to work a couple of years on the trading desk, get disillusioned, and move to the tech industry to exploit their skills in Data Analytics. Note: NYC area saw $4.2 billion in venture funding in 2014, roughly tied with Boston, though well behind San Fran/San Jose.
As always, you can reach me at firstname.lastname@example.org. LinkedIn profile: www.linkedin.com/in/peterwagner123
QuantNet has been compiling the annual list of best-selling quant books our members purchase every year since 2010 (see the best-selling book lists of 2010, 2011,2012, 2013. The following list is 20 best-selling quant books of 2014 (both hard-copy and ebooks), compiled from aggregate, anonymously collected data, provided by Amazon with QuantNet tags.
With great pleasure, we'd like to announce that the Global Association of Risk Professionals (GARP) has approved our "C++ Programming for Financial Engineering" online certificate for 15 GARP CPE credit hours.
These 15 CPE credits will be only available to those who completed our C++ certificate in 2014 and later. If you are a Certified FRM®, ERP®, please record this activity in your Credit Tracker at www.garp.org/cpe. Contact CPE@garp.com with questions.
Since 2011, many of our members have taken the course as an important tool to achieve their career goal, be it to get in a top quant program or move to a more quantitative job. The feedback has been overwhelmingly positive. We will continue to work hard to provide the course at the highest level of quality.
An interesting article on NYTimes about the changing landscape of business schools where it's increasingly important to teach "skills like A/B testing, rapid prototyping and data-driven decision making, the bread and butter of Silicon Valley."
According to the article, the study body at MBA programs is changing as well. At Harvard Business School, "a third or more of the 900 students there have experience as programmers, and far more of them have undergraduate degrees in the so-called STEM disciplines — science, technology, engineering or mathematics".
How these changes at the top M.B.A programs will trickle down to science, business department across the country remain to be seen.
“But the major business issue, especially for entrepreneurs, is often that problems are not known, need to be discovered or defined in a new way,” Mr. Pass said. “You need a more integrated, broader view of things.”
The M.B.A. program, he said, is trying to nurture people with those wider horizons, technical know-how and quick business reflexes — “a new pivot on graduate education,” as he put it.
Now, this is the kind of training I may pay big bucks for.
From their press release
A group of students at Montana State University are in their first semester of a new financial engineering program that will feature professors from two colleges.
The new program, approved by the Montana Board of Regents last year, will allow MSU undergraduate students to major or minor in financial engineering with a curriculum of courses spanning the Department of Agricultural Economics and Economics in the College of Agriculture and the Department of Mechanical and Industrial Engineering in the College of Engineering.
MSU will be one of the only universities in the West to offer students a degree in this increasingly important discipline, said Wendy Stock, professor and head of the Department of Agricultural Economics and Economics.
While the program will largely focus on the kind of computer modeling and theory used to predict market behavior and manage risk in the context of the financial sector, Watts said students would also come away with skills applicable to important Montana industries like agriculture, mining and timber. National statistics put average salary range at $74,000 to 115,000, according to the website PayScale.
“We feel really good about the launch of this program,” Watts said. “We’ve had calls from as far away as Florida and we’ve had a lot of interest from students who are already enrolled at MSU.”
If you’re someone with a mathematical bent, for whom ‘challenging’ maths questions are elementary and differential equations are fun, there is – allegedly – one place where you’ll feel especially fulfilled in banking: Goldman Sachs’ ‘strats’ Group. All banks have mathematical wizards, but the warlocks in Goldman’s strats team are reportedly higher paid, happier, and more empowered than those elsewhere.
“The strats team here is broader in scope than quant teams at other banks,” one strats vice president at Goldman in London tells us, speaking off the record as he is not authorised to talk to the press. “As well as quants, we have quant developers, technologists, structurers and salespeople.” Goldman’s strats team is at the forefront of the firm’s algorithmic and electronic trading business, he adds – making it key to the future of the firm.
Goldman itself describes its strats team as a, ‘a world leader in developing quantitative and technological techniques to solve complex business problems.’ Members of the team are divided into three groups: ‘desk strats’ who sit with traders and develop derivative pricing models and trading algorithms, ‘sales strats’ who support the sales team with quantitative research for client questions, and ‘core strats’ who work on the firm’s technology architecture using Goldman’s own proprietary coding language, ‘Slang.’ As we reported last week, Goldman’s already powerful strats group is said to be gaining influence internally now that the new CIO, Marty Chavez (a former strat himself) manages both strats and technology for the firm. The former CIO, Steve Scopellite, merely had technology in his purview.
One London-based recruiter who hires for Goldman’s strats group says members of the strats team get paid far more than technologists and quants at other firms. “I’ve seen top programmers with excellent understanding of specific derivatives receiving offers of £350k,” he claims.”Goldman strats get ridiculous amounts of money compared to most tech people.”
Goldman was unable to comment within the time frame of this article, but doesn’t typically discuss pay anyway. What does seem certain, however, is that positions in Goldman’s strats team – however sought after – aren’t always easy to fill. Strats jobs are often advertised for months on the firm’s own website. For example, Goldman has been advertising for an associate-level member of its IBD strats team since January 2014 and for a member of its fixed income and commodities strats team since March.
“Goldman is extremely fussy about it hires into the strats group,” says the head of the technology practice at one search firm, speaking anonymously as Goldman is a client. This fussiness is combined with a need to keep feeding the beast.”Goldman has a rolling requirement for recruiting a lot of people into strats,” says another quantitative finance recruiter, speaking equally anonymously. “It’s a huge area of their business – about eight times the size of other banks’ quant groups,” he adds.
Needless to say, members of Goldman’s strats team need to be talented mathematicians. According to Goldman’s own recruiters, they also need to be have, ‘an ability to code and to pick up a new coding language.’ “90% of the desk strats in London have a PhD in maths from places like Imperial, the London School of Economics and Cambridge,” claims one recruiter. Among Goldman’s core strats, a first class degree in computer science from a top university is said to be more common – along with expertise in everything from C++ to C#, Cocoa (Touch), Objective C and Java.
Interviews, interviews and interviews
As with all roles at Goldman Sachs, getting a job in the strats team involves multiple interviews. Anecdotally, however, strats roles involve even more interviews than most.
Recruiters say the process starts with a telephone interview. This can last anything from 45 minutes to an hour and a half. If the initial telephone interview goes well, it will be followed by loads of other interviews – some of which may also be conducted via phone. “They might want you to meet anything from 15 to 20 people,” says another London quant recruiter, speaking off the record as he works with Goldman. “The reporting lines within the strats team can be blurred, so there are a lot of people who will want to sign the hire off,” he adds.
Strats interview questions
Strats interviews at Goldman Sachs are notorious for including challenging interview questions. “They will go through you CV and test you on everything you claim to know about,” says one of the quant recruiters we spoke to. Stochastic calculus, partial differential equations, Monte Carlo Simulations and questions involving sorting algorithms are all common. The initial telephone interview is likely to cover the basics of computer science, along with questions on multithreaded programming and deadlock issues, he adds.
Goldman strats interviews are also famous for their brainteasers. Previous questions, listed here, have reportedly included those below. If you want a strats role at Goldman Sachs, you’ll need to leap a lot of hurdles.
-There’s a horse race with 25 horses and 5 lanes. What’s the minimum number of races you need to find the fastest three horses?
-There are 100 prisoners in a cell and you are the warden and you have one bullet. How can you prevent all of them from escaping?
-You toss a fair coin 400 times. What’s the probability that you get at least 220 heads? Round your answer to the nearest per cent.
-We are at a junction. P is the probability that at least one car will pass in the next 20 minutes. What’s the probability that no car will pass in the next five minutes?
-10 people of different ages sit around a table. What’s the probability that they sit in an age-ascending order?
-A two year bond yields 6%. A one year bond yields 4%. What’s the implied rate for a bond that starts one year from now?
Rise of the quants at Goldman Sachs
Since writing for this blog in January about the HFT/algo job market, I’ve received many inquiries from students asking about the “requirements” for quant jobs on Wall Street. “Do I need a PhD?” is a frequent question. Each time I receive one of these inquiries, I struggle with the answer. My instinct is no. But when I look at who is working in these jobs, I do see a predominance of PhD’s in the top positions. PhD’s in mathematics, physics, operations research, EE, etc. are common in the quant community. So it’s tempting to tell students that a PhD is helpful, but it feels like the wrong answer. In my gut I know that the people getting these jobs are not getting offers because they have extra letters after their name. The people in these positions are there because they have proved over their academic and professional lives that they are:
But the above is a generic list of attributes for hiring into just about any job. So what is it that makes someone hirable as a quant? The list isn’t long:
Okay, now combine the two lists, and you have the list of qualifications for a quant.
So, back to the question of whether to get a PhD. Should I get a PhD?, asks one student who is angling for a career in quantitative finance. Will it help me? Is it necessary? No, it’s definitely not necessary. Will it help? Empirically, it seems to help. But does it? I’ve finally come to clarity on the subject with the help of a conversation today with the director of a quant group supporting credit trading for a major investment bank. Of the two lists above, the important qualifications are on the first list. This list has nothing to do with your education. Your success in any field depends on the first list. The 2nd list consists of skills, skills that can come from your education or experience. They are enabling skills, but they are not dictators of success. All career success comes from differentiating oneself with respect to the elements on the first list. You can get a PhD, spend the money and the time, but if you don’t differentiate yourself in the fundamental elements of success, the PhD won’t help.
So why are there so many PhD’s in quantitative roles, anyway? I think the answer is pretty obvious. Very smart people with quantitative instincts are drawn to the PhD path. Later they find that they are well suited to a career in finance. They satisfy both lists and hence are successful in quantitative roles in finance. Almost without exception, these are individuals who pursued a PhD based on their interests and passions (EE, Physics, Applied Math, etc.), not people who pursued a PhD as a means to a job in finance. QED: A PhD is not a requirement for a career as a quant in finance.
I feel this article isn’t complete without addressing the MFE degree. The MFE provides students with the fundamental skills utilized in quantitative jobs. If you can afford it, it’s an easy way to satisfy List 2. However, it’s by no means a ticket to success in quantitative finance. I’ll explore the MFE further in my next post, “The MFE, Is it a Contra-indicator?”
As always, you can reach me at email@example.com. LinkedIn profile: www.linkedin.com/in/peterwagner123 (I keep a listing of active quant roles here).
At the end of last year I sent a State-of-the-Talent-Market email to colleagues. In that mail I wrote:
@Ken Abbott, a frequent contributor to QuantNet, replied to my mail:
Subsequently, he asked if I would elaborate on the job market in algo and HFT for this blog.
The simple answer is yes, there are jobs in Algo/HFT, and some of them are paying very well. However, that jaw dropping salary that you heard from a friend is only going to the very best individuals with rarified talent.
I have a large hedge fund client that is a pioneer in algo trading. I recently presented a candidate for a quant role at the firm, and the first thing the recruiter asked me about him was, “Does he have any medals?” “Medals?” I asked. “Yes, from math Olympiads.” As we talked further, it became clear that winning a medal at your country’s math Olympiad was okay, but really they’d prefer someone who had done well at the International Math Olympiad… Rarified indeed.
As with most things, there’s a spectrum of jobs in this area. The above speaks to the jobs that are responsible for the high salaries and subsequent buzz around algo/HF trading. Here’s what I actually see in the market:
Buy Side - Hedge Funds
- Larger, well capitalized funds are looking for unique talent and may pay extremely well
- Smaller funds want this talent too, but they can only pay by promising a reward based on returns. The majority of these funds struggle to make money, are very volatile, and the rewards tend to be marginal.
I worked with an individual who worked at five funds over a period of ten years. I asked him to explain his work history, since hiring companies would need to understand why he moved around so much. His story, which I’ve seen numerous times, was Fund A failed, so I moved to Fund B, then B failed, so I moved to Fund C, which failed, etc. His earnings over the period weren’t any better (and probably were worse) than if he had taken a good quantitative developer job at an investment bank.
What they look for when they hire
- Best of the best talent. You do not need a Stanford or MIT degree, but you’ll need to impress a lot of people who interview you with your sheer intellect (a top school helps for entry level jobs, as these firms recruit heavily from top schools). An A level talent for Goldman Sachs may be a B+ player for a top hedge fund. These funds are relatively small and can afford to be selective. They usually hire opportunistically when they see the level of talent they want.
Sell Side - Banks
With current regulations, banks can’t invest their own money in algo trading. However, they do employ algo/HFT quants and developers to conduct business on behalf of their clients and as market makers in fixed income markets.
Most algo dev/quant roles are on the equity side, as firms seek an execution advantage in equity markets which are now almost entirely electronic. Complex algorithms are deployed to assess market microstructure across a myriad of exchanges. I see roles in this area with some regularity. Generally these are dev/quant roles with an emphasis on excellent C++ skills and a strong background in a quantitative field.
Fixed income markets have slowly moved to electronic exchanges over the course of the past two decades, but the migration has accelerated due to recent regulation that requires the majority of fixed income derivative contracts to be traded on exchanges.
Algo roles in fixed income are concentrated on automating the process of making markets on electronic exchanges. For example, quoting a credit default swap involves a complex assessment of the market including analysis of the underlying equity where there is far more liquidity. Each fixed income instrument requires a different type of analysis. This is a new field, and trading desks are looking for outstanding individuals to research and implement strategies. These jobs pay well, but there aren’t a lot of them.
What they look for when they hire
The above observations are generalizations. Every company has its own way of operating, and needs differ from firm to firm. And of course every individual offers a different blend of experience and abilities. If you’d like to discuss any of the above topics or determine if your background qualifies you for a specific role, I’d be happy to talk to you. You can reach me at firstname.lastname@example.org.
LinkedIn profile: www.linkedin.com/in/peterwagner123 (I keep a listing of active quant roles here)
Peter Wagner has a masters in computer science and spent 20 years developing trading and risk systems for major investment banks. He formed Affinity Resource Group in 2011 to apply his experience in the field to help firms find talented IT and quantitative professionals.
Teri Geske is a lecturer and Executive in Residence at the UCLA Anderson MFE Program. She share with QuantNet a few years ago about "Top 10 Things Practitioners Really Want from Financial Engineers". She recently wrote an article on GARP to reflect on what she has observed about the financial engineering programs the last few years. Here is her 5 recommendations for improving quantitative finance programs.
1. Agree on a Name.
Despite its growing acceptance in the financial community, there is still no consensus about what these degrees are called. Does it matter? I believe so. Some schools call the degree a "Master of Financial Engineering" and they call their graduates "Financial Engineers"; others offer a "Master of Computational Finance" or a "Master of Quantitative Finance," and use various acronyms to refer to their graduates. This inconsistent labeling should be fixed, as it would help to improve recognition of, and professional respect for, the degree-holders.
Just as law school graduates unambiguously have a law degree and B-school students earn MBAs, graduates with a master's degree in financial engineering would benefit from having a widely recognized credential. Interestingly, the International Association of Financial Engineers (IAFE) recently changed its name to the International Association for Quantitative Finance (IAQF) -- perhaps suggesting that the term "engineering" has a negative connotation, equating "to engineer" with "to manipulate."
My personal view is that "engineering" in its best sense is what students in these programs are taught to do: to use concepts from mathematics and physics to build models (with financial data as raw materials), and to test the models, adjust them and put them to use in the real world.
For the remainder of this article, I will refer to the degree as an MFE, and holders of these degrees as financial engineers, leaving it to others to make a final decision on an official name.
2. Remember the Laws of Supply and Demand.
I believe financial engineering is a career path with staying power, but not one with unlimited growth potential. If more and more universities add MFE programs, the number of graduates supplied by MFE programs may soon exceed the number of graduates the industry can absorb.
The truth is, with an annual tuition of $50,000 or more, these programs are a potential source of profits for their universities. Indeed, they are an appealing prospect for B-schools, as well as math, engineering and computer science departments that are facing budget constraints and pressure to control undergraduate tuition.
Existing programs are tempted to increase class sizes, because the marginal cost of an additional student is virtually nil up to a certain point. But not only is there a finite demand for MFE graduates, there is also a finite supply of qualified applicants. If supply outstrips demand, some MFE Programs -- especially new ones with little name recognition -- will have to reach lower into the applicant pool, diluting the quality and reputation of those programs.
My advice to MFE program directors and prospective applicants and employers: choose quality over quantity; the employers that hire your students certainly will.
3. Don't Dumb It Down.
MFE programs can face pressure from students to focus on what is "hot" at the moment. Recently, I have noticed an increase in the number of applicants interested in an MFE because they believe it will give them quick access to a job with a firm that does statistical arbitrage ("stat arb") trading. So, they focus on whether a program offers a stat arb "training class," without much concern for the rest of the curriculum.
There's nothing wrong with stat arb; indeed, there are compelling arguments to be made that finding and exploiting these statistical anomalies helps to make financial markets more efficient. However, if all a potential applicant wants from an MFE program is a "how to" manual for what he or she thinks is a surefire way to beat the market, I suggest looking elsewhere. There are many books available on this subject, not to mention YouTube videos. (To would-be stat arb traders: before you rush to view those YouTube videos, remember that no trader is going to reveal a profitable strategy to you, and don't forget to account for transaction costs before you conclude you've found a real moneymaker.)
Will some MFE graduates go on to make a lot of money from trading? Sure. But to MFE program directors, I suggest that while we need to be responsive to students' interests, MFE programs shouldn't cater to the headline-grabbing topic at the expense of providing solid training in rigorous theories and practices, because today's hot topic will be replaced by another, and our alumni will not know what to do when it does. The experience of attending a distinguished university with top-notch academic scholars never loses its relevance.
4. Expand Your Horizons.
A recent Wall Street Journal article noted that elite MBA programs (Harvard, Stanford, etc.) are sending more of their new graduates to jobs within tech firms than to finance jobs, for the first time in, well, ever. There are various reasons cited, a number of which would not be applicable to MFE programs and students, but what struck me is the idea is that many of the skills taught in a rigorous MFE program can be extremely useful to tech-related and other businesses.
One obvious example: MFE programs teach (or at least they should teach) econometric analysis, which Wikipedia defines as "the application of mathematics, statistical methods and, more recently, computer science, to economic data." This is strikingly similar to the Wikipedia definition of "Predictive Analytics" (a buzz-term associated with "Big Data"): "Predictive analytics encompasses a variety of techniques, from statistics, modeling, machine learning and data mining, that analyze current and historical facts to make predictions about future, or otherwise unknown, events."
Clearly, financial engineering students are taught skills that are applicable beyond banks and investment management, and MFE programs should explore this opportunity. To satisfy the best employers in these industries, MFE programs need to attract students with creative problem-solving abilities as well as top-notch math skills.
I often tell students that financial engineering can be frustrating to people who need the predictable outcomes offered by chemistry or biology, because, most of the time, quantitative finance jobs involve tackling problems or analyzing data without much of a roadmap. This takes a certain tolerance for ambiguity and an ability to articulate and debate the merits of different ideas.
MFE admissions committees should look for students who can not only solve partial differential equations (PDEs) but who have genuine intellectual curiosity along with those strong math skills; they will make the best financial engineers, and will be marketable to companies outside of the financial services industry.
5. Embrace Change.
OK, it's a cliché, but it definitely applies here. Randomness and change are defining traits of financial markets and of the models designed to capture their characteristics. Just as financial models and investment strategies tend to have a limited half-life (they need to be modified, updated and improved over time to retain their usefulness), MFE programs must also constantly evolve.
The global financial crisis hit not long after the MFE degree had just started to gain recognition (certainly a 3+-sigma event in the lifetime of an academic discipline). Among the consequences of the crisis: a marked reduction in the risk-taking ability and profitability of the bank trading desks that had been the biggest employers of financial engineering talent pre-crisis, as well as increased scrutiny of, and costs associated with, trading derivatives.
The upshot is that there is simply less need to design the types of exotic structures that attracted certain students to MFE programs in the first place. Does this mean MFE programs are now less useful than they were before? I don't think so -- in fact, I believe overall the financial industry will require more people with quantitative analytical skills, not fewer.
Quants will be the go-to people who will measure risks in complex global markets, identify opportunities and create solutions for investors, for corporations and for the banks that serve them. (MBAs, with few exceptions, do not have the skills for this kind of work.) However, there will be less demand for financial engineers to create convoluted, multi-layered derivatives-based products designed to exploit regulatory gaps or investor naiveté.
Strong MFE programs will adapt. They should continue to teach the academic theories that are the foundation of quantitative finance, but should also seek input from the industry, including their own alumni, about how to improve. Moreover, they should involve practitioners to give students a real-world view, to teach them the "vocabulary" of the industry and to emphasize those applications that are most relevant to the changing marketplace for financial engineering talent.
Teri Geske is a lecturer and Executive in Residence at the UCLA Anderson MFE Program, which she joined in 2009. She oversees the MFE Applied Finance Project, delivers weekly lectures on current topics in financial markets and advises prospective and current students about career options. She also does consulting work on investment and risk analytics and teaches Corporate Finance at Mount St. Mary's College. Prior to joining the MFE Program, she worked for more than 20 years in the financial services industry.
This article appears in QuantNet 2013-2014 International Guide to Programs in Financial Engineering.
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.
About the author
Kenneth Abbott is a Managing Director at Morgan Stanley, where he is the Chief Operating Officer 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.
QuantNet is proud to announce the release of our second publication
QuantNet 2013-2014 International Guide to Financial Engineering
Our inaugural edition has been an extremely useful companion guide to the field of financial engineering for many students. It has been downloaded over 10,000 times since its debut. The 2013-2014 Guide has many new and updated contents, among them 2013-14 ranking of quantitative finance programs, career advice from top Wall Street executive, etc.
"Big Data" is an important topic these days, not only in finance but also other industries where quantitative skills are in high demand. We have it covered in this guide. You will learn what it means, how to prepare and reinvent yourself for this growing job market.
To share/download the Guide, use quantnet.com/guide
NEW YORK, September 24th, 2013 – QuantNet today released its 3rd bi-annual rankings, selecting the Best 25 Master Programs in Financial Engineering, Mathematical Finance, Quantitative Finance in North America.
QuantNet surveyed programs administrators, hiring managers, corporate recruiters, and finance professionals to get the information used in the 2013-14 rankings.
The degree will cost $6,600 compared to $45,000 for traditional on-campus degree. It will be offered via Udacity in January. The school hopes to attract 10,000 paying students annually.
Over the past two months, I have seen several threads asking for advice on applying to Princeton Bendheim's MFin program, what the school is looking for, and many other questions. People on the forums have pointed to me because I go to school there, and they think I have some clue as to how the admissions process works. I have done my best to answer their questions with what little additional insight that being on campus gives me.
Lately, people have asked me to do a more complete write-up. I figured that since it is August and people are starting to think about grad school applications, this may be a good time for a post on general advice for applying to Princeton's MFin program, especially since there seems to be a dearth of information on applying to competitive MFin/MFE programs in general.
All of the opinions expressed in here are my own and not the school's and probably not also the majority of students'. I don’t want this to turn into some sort of fiasco, so it’s important to remember that this is just a post by IlliniProgrammer talking about what he thinks could be helpful for getting into the program. This could be as disconnected from reality as IP’s view that everyone needs to be thrifty. (I honestly don’t think either is disconnected from reality, which is why the analogy works.)
ABOUT THE PROCESS
Every year, somewhere between 700 and 1000 smart and successful people submit applications to the Bendheim Center. The admissions process, which involves Bendheim Center faculty members, staff and the Graduate School, has to get this number down to 30-50 admits. The size of each class varies from year to year but is generally smaller than other programs, which is intentional. I’ve heard from the staff that the admissions committee bases the class size on a variety of factors, including the job outlook for prospective students, classroom space, and room in the graduate dorms.
Roughly the top 100-150 applicants will be extended an offer to interview with Princeton. You'll typically get asked questions about your academic accomplishments, career and research interests and what you did at your prior job or internships. There are rarely technical questions although there is always an opportunity to showcase your ability to explain a complicated technical issue in simple terms. While everyone is offered the opportunity to conduct their interview in-person on-campus, many students opt to skype in. In any event, the interview is very important. According to the staff, most people who are admitted to the program have a very strong interview and it can be a deciding factor in the admissions process (all other qualifications and scores being equal). However, not everyone who gives a great interview is accepted.
According to the staff, the admissions committee is made up of the program director, a number of Bendheim faculty members, the corporate relations director, and Deans of the Graduate School. Princeton asks for a personal statement and three letters of reference. They also ask for GRE or GMAT scores and a resume. An optional additional essay is available, but most admitted students I have spoken with did not submit one (although some did.)
HOW TO MAKE A STRONG APPLICATION
Princeton's Master in Finance program has a fairly competitive application process. This isn't a top four Econ PhD where you have 500 applicants and 4 admits, but they do have to whittle 800 very accomplished applicants who are largely from Wall Street or Wall Street-bound- down to 30-50 admits. So by one measure, this makes the process more competitive than the most selective business schools.
My educated guess is that the admissions process starts like one for an MFE program, that there may be a bit of a research program component to it, and that it ends like the admission process for an MBA program or a top tier undergrad.
For some background, I'd like to believe that Princeton thinks of its MFin graduates as being financial engineers and strategists who can also go head to head with M7 MBAs (at least within finance) on the soft skills- and for the most part, I believe that is the case for the students I've seen on campus. We are the closest thing Princeton has to an MBA program, and Wall Street wants people who can understand and solve complex quantitative problems, communicate effectively in plain English, and help lead and manage the bank. I think we have the right combination of quantitative ability and personal skills to help lead in finance- and the program may be trying to look for that in its admits. So I have a feeling the admissions process is going to move from an MFE-like filtering process at the beginning to a more MBA-like or competitive undergrad-like admissions approach at the end.
There are a lot of great programs out there that banks should look at for hiring Associates and Quants; I'm just glad we're on that list and that we're a little less expensive than some of the MBA alternatives.
THE PERSONAL STATEMENT & RESUME
Princeton asks for a 1000 word essay as part of the admissions process. It's not an MBA essay. We don't give you a theme, and there's not a lot of advice and commentary out there on what to write. The MBA and grad school admissions consultants will tell you that to get into a top MFE program, you need a top GRE or GMAT score and good math grades, but that only helps the program whittle things down to probably the top ~20%-50% of applicants. So getting down to the top 5% requires more work.
If you can, try to find a theme to start the essay with and to tie back into every once in a while. If your life is all about water or all about changes or ideas and you can keep going back to that theme in an interesting way that isn't corny, this could be a good way to structure your essay. It would show you're a great writer as well as a great financial engineer. Your goal is to get- and keep- the reader's attention while also selling them on how interesting you are as an applicant. To that end- I suggest three goals for any good application:
IF YOU ARE APPLYING NEXT YEAR (FOR A 2015 ADMISSION), YOU ARE PROBABLY AT A COMPETITIVE ADVANTAGE
You should always be looking for opportunities to make yourself a more interesting person and expand your horizons- and it's best for this stuff to happen naturally- but the issue is even more pressing if you're thinking of applying to grad school soon. So if you're not applying to an MBA or MFin until next year, the stuff you do now is going to be more natural and more genuine- and look that way in the admissions process- than doing something four months before you submit applications.
Try to be creative- your goal is to have something interesting and attention-getting that no other applicant has. Certainly for an MBA program and perhaps for an MFin program, it is going to be tough to be the best soccer player or (American) football player in the applicant pool. But we need people who can play water polo (actually we have a lot of intramural sports). We need people who are into some of the crazier sports out there. We need people who've volunteered in interesting places. If you've already worked on Wall Street, all of this makes it harder to cut you in the final rounds. It is probably more difficult to reject someone who's volunteered as a tour guide at the Chicago Art Institute than someone who has worked at a volunteer mentoring/tutoring organization largely populated by MBA-bound individuals. Both are very successful, accomplished people- one has something which is a little more interesting than the other.
So the social and the personal dividends- and the stories you collect- should be enough to make you want to seek out opportunities to be more interesting- but if that hasn't been enough, it might be enough if I told you it could help with B-school or MFE admissions.
As mentioned above, your career is more core to your application than any interesting hobbies you may have. If you have at least 18 months before it’s time to apply, that may also be enough time to try and land a better job. Princeton is probably going to evaluate people as potential admits based on their work experience if they are more than a year or two out of school. Obviously the program is going to try to find smart, hard-working people wherever it can, but certain jobs probably make you easier to find than others.
IF AT FIRST YOU DON'T SUCCEED, TRY, TRY, AGAIN
I didn't get into Princeton Bendheim on my first try.
My first time, I was a fairly competitive candidate. I worked in Credit Analytics at a major bank, which wasn't indisputably front-office but certainly wasn't back-office. I rode motorcycles and occasionally raced them on the track at the time- I also did some waterskiing. I got an on-campus interview from them, but not an admission. I was accepted several other places, but at about the same time, I got an internal transfer offer to Options Sales and Trading, which I took.
Getting rejected felt bad. It was cold comfort to know I was in the company of the 95% of the other students who applied and it reinforced my view that after a point, the final cut gets pretty random. But it did make me determined to prove that the rejection didn't matter, and that (at least for the next 21 months) staying in industry was better for me than getting an MFin there.
Two years later, I applied again. I enjoyed where I was working, but wanted a change of pace and felt school was the best way to do it. My circumstances had materially improved this time- I was clearly in a Front Office group, I was a USHPA rated hang glider pilot, which I practically had stamped all over my application, and my sense was that it was a fairly narrow miss last time.
I really don't know what happened, but reapplying with a materially stronger application two years later earned me a fresh, second look, and it also could have validated the arguments of the people who were fighting for me the first time (somebody probably had to have supported me in order for me to get an interview). Princeton has a relatively small final round- about 100 students, and I think there's a strong possibility people will remember you if you came close before and reapply within a few years. (This may also be true with MBA programs and MFE programs)
So, if you get rejected, don't feel bad. It's probably more random than you think. And if it was a reasonably close decision (you'll know this if you got an interview), and you come back a year or two later with a materially stronger application, you actually have some of the best chances out of anyone (except the Polytechnique stochastic calculus ninja) at an admission. You already have a few people there rooting for you, you've now provided validation to their original argument, and this is the admissions committee's chance to make the right decision this time.
There are a lot of great MFE/MFin programs out there. NYU (which I believe had a placement at DE Shaw this summer- rare for people with finance backgrounds), CMU, Cornell, Stanford, Columbia, Berkeley, and Princeton are all on that list. If you're good at math, enjoy finance, and aren't sure about an M7 MBA, I'd encourage you to apply here or to some of the other MFE programs mentioned. There are also likely several other schools that I left out which deserve to be on that list.
The admissions process at any top MFE program is going to be a bit random at the very end, and if you're a reasonably successful person, you shouldn't take acceptance or rejection as some sort of validation or lack thereof. (If it makes you mildly annoyed and gives you something to prove, more power to you.) CMU may reject you and Princeton may admit you, or it may be the other way around. Instances like these seem to show it really is a dice roll and that it's really best for hiring managers to take which school you're from with a bit of a grain of salt.
The students you'll meet here are very smart, very hard-working, and somehow pretty humble despite that. They also know how to have a good time when they aren't running proofs on covariance matrices, solving an interest rate model or building a multithreaded pricing application. The graduate dorms literally look like a castle, have a bar in the basement, and we have an awesome Halloween party. So if you are great at math, enjoy amazing parties, but you also enjoy getting something fundamentally academic out of $80K (not $120K) worth of graduate tuition, Princeton might be a better place for you than a top tier MBA. I think the ideal student for a program like Princeton is the Wall Street trader, quant, researcher- or some other worker with a fairly technical job- who is on the fence about an MBA or MFE program- or even a Finance PhD program, but knows he wants to stay in finance. We're basically the quants and strategists with the polish, leadership, and occasionally connections of the M7 MBA- just with $40K less debt.
My observations and advice above only capture a small, incomplete, and biased snapshot of the information that professors and other students hold. Moreover, most people associated with the program and its admissions process can figure out exactly who an MFin student with the username "IlliniProgrammer" is and just how clueless he is about what really goes on. So a lot of what I've written above is a mildly educated guess that's only a little better than what the average applicant can surmise. Still, it fills in a few gaps in the FAQ and gives you a place to start thinking about your personal statement.
So please take this with a grain of salt. Obviously if anything I've said conflicts with what you hear from our staff, our professors, or what other MFin students are pretty sure of, or certainly anything you see on our website (below), I'd defer to that.
Thanks and good luck if you decide to apply!
Originally posted at http://www.wallstreetoasis.com/forums/advice-for-princeton-mfin-applicants
A recent NYT article sheds light on how the trading landscape has been changing on Wall Street, due to technological advances as well as regulatory reforms such as the Dodd-Frank financial legislation.
The article makes clear that technology has been and will play a HUGE part in the industry as an increasing volume of trading occurs over automated exchanges as required by laws or other factors.
"The increased use of automated platforms means that more programmers are needed, but fewer employees over all."
The trading desks at Credit Suisse are demonstrations of how changes have transformed the type of trading and traders needed for the job.
"The traders here are mostly educated in math or physics, often outside the United States, and their desks are piled high with textbooks like the “R Graphs Cookbook,” for working with obscure computer programming languages."
“Our best traders spend a lot of their time pounding away writing code,” said Ryan Sheftel, head of the bank’s automated Treasury bond trading, pointing at one of his young employees. “He is doing thousands of trades, but doesn’t need to be manually involved anymore. The code he wrote is making the trading decisions.”
Banks are being pushed to move their trading onto automated platforms as a consequence of the international bank changes known as Basel III. These require banks to hold big cushions of capital to protect themselves in case trading positions lose value, with bonds requiring particularly large and expensive cushions.
This has resulted in banks shrinking the inventories of bonds they used to have on hand in case a customer wanted, say, a million dollars’ worth of 10-year General Motors bonds. Now, those same customers have to look more broadly to find the same quantity, potentially bypassing Wall Street all together.
Another regulation forcing fixed-income desks to change quickly is a part of the Dodd-Frank financial legislation that is set to push derivatives like interest rate swaps onto exchangelike platforms later this year. Banks have lobbied vigorously against it, arguing that the electronic infrastructure is not ready for the complexities of the multitrillion-dollar market.
If there is one single point I want to drive home about, it is the future of trading will mean programming. That means there is no way you can find top dream jobs in this industry, be it traders, quants, without knowing some kind of programming languages very well.
Note: Tim Grant was mentioned in the article. He was the first person we interviewed on QuantNet.
Source: Bond Trading Loses Some Swagger Amid Upheaval
This article appears in QuantNet 2013-2014 International Guide to Programs in Financial Engineering.
When applying to a master’s program, it is not possible to specify the exact requirements necessary to be accepted, because there is flexibility in the process. An application that is weaker in one area might be accepted because of strengths in other areas.
For 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 application submission. The purpose of this article is to help prospective applicants to improve the principal components of their applications.
Your undergraduate and graduate course work is shown in your transcripts. You do not need to have majored in mathematics since many successful applicants have an undergraduate degree in economics, engineering, physics, statistics, or some other quantitative field. Neither is it necessary to have graduated from a top-rated university. Many offers are made to fine 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 calculus-based probability. If you do not have these, you should be aware that you will stand a much better chance if you take courses (either online or at a local college) to remedy that deficiency.
This is the minimum but most applicants have more than that. Some helpful courses include ordinary (ODE) and partial differential equations (PDE), and other subjects such as real variables, and other courses that show evidence of interest and ability in mathematics. Additional courses in programming, finance, and economics will also help your application.
Also, 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, even if they are all relevant to a career in finance.
All these are not necessarily requirements of the faculty but rather a practical reflection 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 course work in relevant subjects, then those will obviously have an advantage in the admissions process.
Most applicants have very good grades for all their undergraduate coursework, mostly A and B, especially in the quantitative subjects. An occasional C in a subject not related to mathematical finance such as painting or music will not hurt the application. But the hard fact is that you are competing with applicants who have no C grades in any quantitative subject. Similar comments apply to graduate coursework for applicants who have a master’s degree.
If you think the program’s faculty might have difficulty understanding your transcript (for example, if your courses are called Mathematics I, Mathematics II, etc., rather than Calculus, Linear Algebra, etc.), it can be helpful to provide more detail about 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, and you think the program’s faculty might have difficulty understanding what topics the course covered, provide a cover sheet that lists the topics for each course (e.g., probability, linear algebra, differential equations, etc.), and the grade you received, books used, any additional materials covered, and so on. This will be more helpful to an appraiser than just knowing that you received an A in, for example, Engineering Mathematics.
Statement of Purpose
The Statement of Purpose should be a simple essay that describes why you came to choose this field and what you hope to do with your master’s degree after completing the program. It should be clearly written and have a logical construction. It is not necessary to re-state your course work and grades because these are shown on your transcripts. Some applicants lavish praise on the program and the faculty or the university. This will not hurt nor help your application; it just is a waste of space.
Although some international students might not yet have perfect command of the English language, make sure your ideas come through, because that is what is important. Ask a friend who is a native English speaker to proofread the essay.
Helpful Tip: Customize your essay for each program that you apply to, and make sure you answer the questions specific to each university. Many students use a general format for each program, which may not include the detail the reviewer is seeking.
There is no specific requirement for the sources or content of letters of reference. Ideally, your references should be from your undergraduate teachers who demonstrate that:
People 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 quantitative context such as your professors in mathematics courses. For graduate applicants and others who have been out of school for some time, it is important to have at least one reference from an individual who currently knows you well.
Helpful Tip: Your references should not come from friends, neighbors, relatives, or anyone who has not supervised you in a professional capacity.
GRE Quant Score
The GRE Quant score is a measure of the quantitative ability of a candidate. Clearly, students need to have quantitative ability if they are to benefit 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 before. Because of this, it is more convenient to discuss this topic in terms of the percentile quant score. At many top programs, it’s usual for more than 50% of applicant pool to score 94th percentile or above on the Quant GRE. This is by no means an absolute indicator since candidates 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 percentile score of greater than 94% in order to stand a chance, but you must remember that your application will be compared with others who do have a high score. If your GRE score is low, you will stand a better chance if you take the GRE a second time and try for a higher score.
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, from projects or work experience, of programming ability, 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 demonstrate such skills. If your computing skills are weak, you should take at least one programming course. Competence in C++ or Java or MATLAB is definitely 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 reviewer a better idea of your skills.
Many schools have students from non-English speaking countries and the faculty understands very well that a student with weak English language capability usually learns quickly after moving to the United States. However, once again, no matter how capable you are, you will be competing with other international candidates who do have very good English language skills. It is expected that many applicants to top programs to score above 100 on the TOEFL. Although this is not a requirement, especially if your application is very strong in other areas, the fact is that if your score is less than 100, you are at a competitive disadvantage. It might be worthwhile to improve you English skills and then take the TOEFL a second time to try for a higher score.
This should be a clear record of your education and accomplishments; however, the educational and work experience you present should be supported by the transcripts and reference letters.
Helpful Tip: Use a professional format and standard fonts to make sure that the final document has a professional appearance. Have your resume reviewed by someone with experience with these tasks.
It is a good idea to participate in a professional organization such as IAFE or SQA. Also, join a website such as QuantNet. This will show that you have taken the time to learn something about the industry and understand that this is something that you really want to do.
I hope that these guidelines help you to ensure that your application does fully reflect your abilities and your accomplishments so that you obtain the highest ranking possible in a very competitive admissions process.
About the author
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.
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. Univertisty of Toronto
5. BI Norwegian Business School
Results from other MFE/Math Financial programs
12. University of Chicago (Math Finance)
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)
Results from last year competition http://ritc.rotman.utoronto.ca/results12.asp
The top 5
Here are a list of proposed activities you can do to keep you sane. The list hopes to help you strengthen your profile and ALSO build transferable skills in case you don't get into the program you hope for.
10) Do your homework / take non-preprogram classes. The problem with specialized preprogram courses is their narrowness in focus. So if you want to learn C++, learn C++. If you wanna learn statistics, learn statistics. Don't bother with the dumb down finance version. MFE will do that for you when you're in the program.
9) Read. Forget about the technical stuff. Read vaults guide on financial careers. Read WSJ. What's going on in the world? How is a bank / hedge fund structured? Why are banks firing and where are they hiring? What are the industry trend? What are the recruitment channels / schedule?
8) Start a project. Nothing says passion more than action. if you know finance, compile a database of companies, track their capital structure, and find where financial instruments can be useful for asset/liability management. If you know compsci, make an app to visualize monte carlo simulation. If you know econ, propose an indicator and explore its prediction / trading value.
7) Explore alternative careers. Actuary, trading software development, project management all expose you to the technical aspect of finance and they all pay pretty well.
6) Study the CFA. This is probably controversial. CFA doesn't really help you get a job. It does, however, teach you the portfolio management objectives of different market participants (banks, hedge funds, insurance company, etc) and how these competing goals translate to market demand and supply.
5) Stop obsessing with quantnet, global derivatives, or whatever MFE theme site you waste countless hours on. If you want a better idea of your profile, go to school websites and go through their student profiles. NYU even has some of the resumes out. Think what they can write about you. If they can write something similar / better with your achievements / credentials, then you have a good shot. If not, then maybe you should try doing some of the "selling points" current students have done.
4) Find a hobby. Get a pet. Do yoga. Find a girlfriend. Whatever it takes your mind off this MFE crap. You submitted your application and there's nothing left to do. Admissions will call / email you if they need you. So go take the time to find yourself. Enjoy the little things in life. You might never see them again when you start spending all your days in front of computer doing simulation and coding and missing piano recitation of your daughter's.
3) Start a trading account. Nothing teaches you more about yourself when you lose your lunch money over a stupid mistake. If you don't have the money, use paper money and go to updown.com or a dozen of other virtual trading site. Track all your transactions and record down your rationale. Why did you buy it? Was there a headline? Did you grow up next to a factory? No education makes you more attractive as a potential trader than a list of transactions with positive pnl.
2) Apply to non-MFE programs. Seriously, most respectable programs all have single digits admissions rate. Even if their decisions ARE independent, 5 apps * $100 per app = $500 in applications fee yet still short of 50% chance getting anywhere. Is there other ways getting the same job through other grad programs (compsci, MBA, statistics?) In fact, I argue that non-MFE programs are BETTER because they give you backup plans which MFEs don't have. Even if you do get a 100k job after graduation, what if you hate it?
1) Call your mother. Yes, that's right. Spend time with your family. Once admitted, you'll be drowned in work and start living in a world your parents won't know a thing about ("delta? Like the airline?") Worst yet, all they will know is that 1) banks are firing, and they worry about you, and 2) you'll be one of the jackasses who brought on the financial crisis.
Refine your resume, subscribe to a job posting listserv, apply to jobs, and go through some interviews. Mark Joshi's book has a very good intro on what quants really do and it's a fun workbook. You'll learn so much more about yourself and the industry than listening to all the marketing pitches flying around.
After conducting an exhaustive job-search campaign, you finally received an offer –congratulations! There are many factors to consider when examining this new offer. Evaluating a job offer is very subjective, but people often focus on the salary and disregard other key areas. Here is a five-step process that I developed to help my clients fully evaluate new job opportunities and determine if this is the right fit for them.
As I mentioned earlier, evaluating a job offer is very subjective. I have had clients who rearranged the order of importance, placing more weight on some issues rather than others. However, all of my clients appreciated an approach that allowed them to evaluate an offer from several perspectives and not base their decision solely on money. Remember, careful evaluations lead to better choices.
About the author
Damon Montal, Executive Career Management Consultant at The Ayers Group, has over 25 years of experience in sales, recruiting, and career management. He has successfully developed and conducted hundreds of career training seminars, presentations, and workshops intended for candidates of all levels. At the same time, Damon is a career strategist for the Maths in Finance program at Courant Institute, New York University.
In addition to academia, Damon has been active in several professional organizations. He is a Certified Mediator with NY State Unified Court System and conducts Alternate Dispute Resolutions at small-claims court. He has held board positions at his ToastMasters chapter and ACPNY.
Damon was formally recognized by NYS Senator Thomas P Morahan, NYS Assemblywoman Ellen Jaffe, and The County of Rockland - for Outstanding Service to the Community. In 2007, Kelly Services recognized his ability for global excellence by presenting him with the company’s most prestigious honor, The William Russell Kelly Award.
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