quantitative finance physics phd

How To Get A Quant Job Once You Have A PhD

In this article we are going to discuss an issue that repeatedly crops up via the QuantStart mailbox, namely how to get a quant job once you have a PhD . There's a lot of confusion around this topic because quite a few people who currently work in academia and want to make the shift believe that it is quite straightforward to "walk into" a high-paying financial role. While this may have been true 10-15 years ago, the reality of the current job market is such that quant roles are now highly competitive and candidates need to stand out if they are to get the best jobs.

Firstly we'll discuss what sort of candidates you will be competing against when considering going for interview. Secondly, we'll discuss how to make an honest assessment of your PhD and what you got out of it that might be relevant to quantitative finance roles. Finally, we'll consider whether it is necessary to return to school in order to train up in a quant-specific qualification.

The Competition

I've made it rather clear on QuantStart that the competition for some of the top quantitative trading researcher roles can be extremely tough. In the UK the best roles tend to be filled well upstream of any "front door" interview process. Usually extremely bright academics in mathematics, physics, computer science, economics or mathematical finance are head-hunted for a particular skill set, such as deep expertise on market microstructure, insight into high-frequency trading algorithms, novel stochastic calculus techniques for certain derivatives pricing regimes or extensive statistical machine learning knowledge that applies to datasets used by such funds.

When such quant researcher roles ARE opened up to the public they will often state that they are looking for "only the best and brightest", which in the UK usually means "Top Five" universities (Cambridge, Oxford, Imperial College, LSE and UCL). In the US this will mean high-end Ivy League institutions. The adverts will often state that they want to see evidence of consistent Mathematical Olympiad prizes and an extensive publication list in a relevant field.

While this is certainly true of the top roles, there are plenty of other (very well paying and prestigious) jobs that also need filling. Bear in mind that there are only so many Mathematical Olympiad winners, after all! Thus one should not be disheartened when seeing numerous adverts asking for such qualifications. There are plenty of smaller funds and boutique outfits that do not have the resources to aggressively hunt for the ultimate talent and so will be more than willing to employ bright PhDs who might not necessarily have an Olympiad track record.

Honestly Assess Your PhD

The first task to carry out when applying for quant roles is an honest assessment of your PhD and what you achieved with it . Primarily you need to consider the level of mathematical ability you were able to attain as well as your computational programming skill.

Quant roles in the derivative pricing space, known traditionally as the "quant analyst" or "financial engineer", require a reasonable amount of mathematical sophistication. Specifically, expertise in stochastic calculus, probability and measure theory. These are topics usually taught in an undergraduate mathematics course, but can form a component of taught graduate school PhDs. In addition they require a good understanding of scientific programming usually in C++, Python or MatLab. Since the role of a quant analyst is often to code up an implementation of a particular algorithm from a research paper, under heavy deadlines, it is quite naturally suited to those with PhDs of this type.

Quant roles in the algorithmic trading and quant hedge fund world are almost exclusively going to require novel methods for generating "alpha" (i.e. excess return above a benchmark). Usually this is accomplished via time series analysis and econometrics, but more recently statistical machine learning techniques have been applied, as have methods related to sentiment analysis. Some of the best quant funds make extensive use of even more advanced graduate level mathematics in the realms of algebraic geometry, number theory and information theory. Hence anything highly mathematically, statistically or physically oriented is likely to be of interest to a top quant hedge fund.

As for computer scientists and strong scientific software developers, generally there is always work available for quantitative developer roles. Although you will be competing against those with industry experience in rigourous software engineering. Hence "academic code" of the "20,000 line single-file of Fortran" variety might be a bit of a hindrance! Make sure to brush up on the more modern software development methodologies such as OOP , Agile , etc.

I want to discuss specific PhD fields as well, to give you an idea of where you might consider focusing your efforts based on what you have previously studied:

• Pure Mathematics - The top funds generally hire the pure mathematicians from esoteric realms such as algebraic geometry and information theory. Banks will also take individuals who study stochastic calculus to a high level for their derivatives research teams.
• Mathematical Finance - Portfolio optimisation and derivatives pricing are two common themes studied in mathematical finance PhDs. You will often have collaborated with banks during your PhD, so it is unlikely your job prospects will be slim! If you are struggling, it can be very helpful to contact department heads as they will often have a strong network.
• Theoretical Physics - Funds will be very interested in your ability to model physical phenomena, either through direct or statistical approaches. Some theoretical physics areas are highly mathematical (Cosmology, String Theory, Quantum Field Theory etc) and so the advice given to theoretical physics PhDs is similar to pure mathematicians.
• Computational Physics/Engineering - The main skillset taught here is how to take an algorithm and produce a robust scientific computing implementation, perhaps in a parallelised fashion. This is an extremely useful skill for quant work both in banks and funds, especially for developing infrastructure. Make sure however to brush up on core topics such as statistics and stochastic calculus prior to interview.
• Statistics/Econometrics - Statisticians and theoretical econometricians will be in good demand from technical quant funds, especially in the Commodity Trading Advisor (CTA)/Managed Futures space. The time series modelling will be highly appropriate here.
• Computer Science/Machine Learning - Many funds are now making extensive use of machine learning and optimisation tools, which are the natural domain of the theoretical computer scientist and, more recently, the "data scientist". Familiarity with statistical machine learning and Bayesian methods will be highly attractive.
• Bioinformatics - Bioinformaticians also make extensive use of machine learning tools on "big data" sets. For interview you will want to emphasise your familiarity with such tools and your programming capability. Depending upon your background you may need to brush up on your (pure) mathematics for interview questions.
• Economics/Finance - Economics and Finance PhDs do not always teach you the mathematical maturity necessary for pure quant work, but it really depends on the project. You will need to be honest with yourself about where you lie on the mathematical spectrum. In addition you will need to consider your programming ability.

Heading Back To School

An extremely common question that I receive in the QuantStart mailbag is whether to return to school for finance-specific training subsequent to a PhD.

I've previously documented my views on Masters in Financial Engineering (MFE) programs as related to quantitative trading . In essence I believe that MFEs are not hugely suitable for quantitative trading research work, but they are a good entry point into investment banking quant work.

If your PhD was not heavy on quantitative or programming work, but you have a sufficiently mature mathematical background, then it can make good sense to take a MFE assuming that you can afford to fund the course. A MFE at a top-tier school will provide you with a solid network of other candidates (and thus people who might later help you secure a role), a relatively healthy recruitment position upon graduation and a useful skillset for investment banking derivatives pricing work.

I would advise against returning to school if you have a strong quantitative PhD as you simply won't need the additional qualifications and you should be able to pick up the necessary interview material yourself, albeit with a lot of study.

If you have a PhD in a non-quantitative field and your background is not sufficiently mathematical, then you should definitely consider that you will likely need to return to school if you truly want to work in quantitative finance. In particular you will need to study an undergraduate degree that has a strong quantitative component such as Mathematics or Physics as these two degrees will generally let you transition into other quantitative fields easily.

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Quantitative Finance MS and PhD  

Figure: Implied Volatility Surface

SPECIAL QUALITIES OF STONY BROOK QUANTITATIVE FINANCE PROGRAM

Most of the Applied Mathematics faculty teaching quantitative finance courses have extensive experience building quantitative trading systems on Wall Street. Because of their Wall Street backgrounds, our faculty are able to place many of their QF students in  internships  during the summer and the academic year at hedge funds and major investment companies. Few other QF programs offer internships. There is limited use of adjunct faculty who come to campus one or two evenings a week after work.

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In the world of finance, the name 'Stony Brook' is famous for Renaissance Technologies, which is located a mile from the Stony Brook campus and headed by the former chairman of the Stony Brook Mathematics Department. Renaissance's flagship Medallion Fund has been the best performing hedge fund in the world for the past 20 years. One of the key creative minds at Renaissance, Robert Frey, Stony Brook Applied Mathematics PhD 1986, returned to Stony Brook in 2005 after early retirement at Renaissance to develop a Quantitative Finance program in the Stony Brook Applied Mathematics Department. Frey is chairman of the advisory committee to the University of Chicago's mathematical finance program, the country's best-ranked program in this area. 

The Stony Brook Quantitative Finance program is unique among mathematical sciences departments in its very practical focus on 'alpha generation', Wall Street term for trading strategies for making money. Courses are centered on projects where students use real tick data to analyze and predict the performance of individual stocks and commodities, market indices and derivatives. Also, Stony Brook is one of a small number of quantitative finance programs offering PhD as well as MS training. Our PhDs have taken positions both in Wall Street firms and in university quantitative finance programs. For more information about our quantitative finance courses and faculty, see  QF Courses  and  QF People .

Figure: New York Stock Exchange

Course Requirements for the Quantitative Finance Track

The standard program of study for the M.S. degree specializing in quantitative finance consists of : 

AMS 507   Introduction to Probability AMS 510   Analytical Methods for Applied Mathematics and Statistics AMS 511   Foundations of Quantitative Finance AMS 512   Portfolio Theory AMS 513   Financial Derivatives and Stochastic Calculus AMS 514   Computational Finance AMS 516   Statistical Methods in Finance AMS 517   Quantitative Risk Management AMS 518   Advanced Stochastic Models, Risk Assessment, and Portfolio Optimization AMS 572   Data Analysis

Quantitative Finance Track Electives (students must take at least  2 elective courses  to achieve at least  36  graduate credits along with the required courses):  AMS 515 Case Studies in Machine Learning and Finance  AMS 520 Machine Learning in Quantitative Finance AMS 522 Bayesian Methods in Finance AMS 523 Mathematics of High Frequency Finance AMS 526 Numerical Analysis I  AMS 527 Numerical Analysis II  AMS 528 Numerical Analysis III  AMS 530 Principles of Parallel Computing  AMS 540 Linear Programming AMS 542 Analysis of Algorithms AMS 550 Stochastic Models AMS 553 Simulation and Modeling AMS 560 Big Data Systems, Algorithms and Networks AMS 561 Introduction to Computational and Data Science AMS 562 Introduction to Scientific Programming in C++ AMS 569 Probability Theory I AMS 570 Introduction to Mathematical Statistics AMS 578 Regression Theory AMS 580 Statistical Learning AMS 586  Time Series AMS 595   Fundamentals of Computing AMS 603   Risk Measures for Finance and Data Analysis

Elective courses in the QF program are split in five focus areas. Students can follow one of the following course sequences depending upon their interests.

(A) Typical course sequence:   Modelling and risk management in finance

• First Semester - AMS  507 ,  510 ,  511 , 572 ( or Electives: AMS 520 for those who have already taken an equivalent data analysis course before and have experience with Python)
• Second Semester - AMS  512 ,  513 ,  517 (Electives: AMS 515 , 522 , 523 , 603 )
• Third Semester - AMS  514 ,  516 , 518 (Electives: AMS 553 )

 (B) Typical course sequence:   Machine learning and big data

• First Semester - AMS  507 ,  510 ,  511 , 572 (or Elective AMS 520 for those who have already taken an equivalent data analysis course before and have experience with Python)
• Second Semester - AMS  512 ,  513 ,  517 (Electives: AMS 515 , 560 , 580 )
• Third Semester - AMS  514 ,  516 , 518 (Electives: AMS 586 )

 (C) Typical course sequence:   Statistics and data analytics

• First Semester - AMS  507 ,  510 ,  511 , 572 (or Elective AMS 520 for those who have already taken an equivalent data analysis course before and have experience with Python)
• Second Semester - AMS  512 ,  513 ,  517 (Electives: AMS 515 , 570 , 578 (with pre-requisite 572  )
• Third Semester - AMS  514 ,  516 , 518 (Electives: AMS 553 , 586 ) 

  (D) Typical course sequence:   Stochastic calculus, optimization, and   operation research

• Second Semester - AMS  512 ,  513 ,  517 (Electives: AMS 515 , 542 , 550 , 569 )
• Third Semester - AMS  514 ,  516 , 518 (Electives: AMS 540 , 553 )

(E) Typical course sequence: Computational methods and algorithms

• Second Semester - AMS  512 ,  513 ,  517 (Electives: AMS 515 , 527 , 528 , 561 )
• Third Semester - AMS  514 ,  516 , 518 (Electives: AMS 530 , 562 , 526 (co-requisite or pre-requisite 595 or 561 )

Note 1 : If you have poor programming skills take the following electives (instead of electives recommended in sequences) : AMS 595  Fundamentals of computing (Fall semester) or AMS 561 Introduction to computational and data science (Spring semester). Programming skills are critically important for industrial jobs. Note 2: If a 4th semester becomes necessary, a required course will be needed to continue.

For Ph.D. requirements please click here .

Quantitative Finance Opportunities for Applied Mathematics Graduate Students in Other Tracks Any strong student (3.5+ GPA in first-semester core courses) in another track may enroll in AMS 511 , Foundations in Quantitative Finance.  Selected students, with the permission of the Director of the Center for Quantitative Finance, may take additional quantitative finance courses. Students are eligible to earn an  Advanced Certificate in Quantitative Finance . You must formally apply for the secondary certificate program prior to taking the required courses. Only a maximum of six credits taken prior to enrolling in the certificate program may be used towards the requirements.  The 15-credit advanced certificate requires AMS 511 ,  AMS 512 ,  AMS 513 ,  one additional Quantitative Finance Graduate course  elective, and one additional Applied Mathematics course chosen with an advisor’s approval.

To apply, download the registration form, please click here .

For gainful employment disclosure information for our Quantitative Finance Program, please contact the AMS Department.

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Quants: The Rocket Scientists of Wall Street

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Quantitative analysts are professionals who understand the complex mathematical models that price financial securities and are able to enhance them to generate profits and reduce risk. As financial securities have become increasingly complex, demand has grown steadily for quantitative analysts , often called simply "quants," or even the colloquially affectionate "quant geeks."

Because of the challenging nature of the work—which needs to blend mathematics, finance, and computer skills effectively—quant analysts are in great demand and able to command very high salaries. Here's a look at what they do, where they work, how much they earn, and what knowledge is required, to help you decide whether this may be the career for you.  

Key Takeaways

• Quantitative analysts, or quants, combine their skills in finance, math, and computer software to analyze and predict markets, creating complex models that can be used to price and trade securities.
• They tend to work in investment banks and hedge funds, although insurance companies, commercial banks, and financial software and information providers may also hire them.
• Quants work in major financial centers in the U.S. and in London and Asia, among other places across the globe.
• Firms often look for candidates who have a master's degree or a Ph.D. in a quantitative subject, such as mathematics, economics, finance, or statistics.
• Compensation can be in the low-to-middle six figures.

What Do Quantitative Analysts Do?

Quantitative analysts design and implement complex models that allow financial firms to price and trade securities. They are employed primarily by investment banks and hedge funds, but sometimes also by commercial banks, insurance companies, and management consultancies; in addition to financial software and information providers.

Quants who work directly with traders, providing them with pricing or trading tools, are often referred to as " front-office " quants.

In the " back office ," quants validate the models, conduct research, and create new trading strategies. For banks and insurance companies, the work is focused more on risk management than trading strategies. Front-office positions are typically more stressful and demanding but are better compensated.

The high demand for quants is driven by multiple trends:

• The rapid growth of hedge funds and automated trading systems
• The increasing complexity of both liquid and illiquid securities
• The need to give traders, accountants, and sales reps access to pricing and risk models
• The ongoing search for market-neutral investment strategies 

Quantitative analyst positions are found almost exclusively in major financial centers with trading operations. In the United States, that would be New York and Chicago, and areas where hedge funds tend to cluster, such as Boston, Massachusetts, and Stamford, Connecticut.

Across the Atlantic, London dominates; in Asia, many quants are working in Hong Kong, Singapore, Tokyo, and Sydney, among other regional financial centers.

Despite the heavy concentration in those cities, quants are found all over the world—after all, many global firms analyze and/or trade complex securities, which creates demand for the quant's brainpower and abilities.

But the problem that a quant working in Houston or San Francisco faces is that changing employers most likely would mean changing cities, whereas a quant working in Manhattan should be able to interview for and find a job within a mile or two of their previous one. 

What Do Quants Earn?

Compensation in the field of finance tends to be very high, and quantitative analysis follows this trend. It is not uncommon to find positions with posted salaries of $200,000 or more, and when you add in bonuses, a quant could earn over $300,000 per year.

As with most careers, the key to landing high-paying jobs is a resume filled with experience, including with well-known employers, as well as reliance on recruiting firms and professional networking for opportunities. 

The highest-paid positions are with hedge funds or other trading firms, and part of the compensation depends on the firm's earnings, also known as profit and loss (P&L) .

At the other end of the pay scale, an entry-level quant position may earn only $120,000 to $210,000, but this type of position provides a fast learning curve and plenty of room for future growth in both responsibilities and salary.

Also, some of the lower-paid quant positions likely would be primarily quant developers, which is more of a software development position where the individual is not required to have as much math and financial expertise. An excellent quant developer could certainly earn $250,000, but that's about as high as the compensation package generally would go.

Despite the high pay level, some quants do complain that they are "second-class citizens" on Wall Street and don't earn the multimillion-dollar salaries that top hedge fund managers or investment bankers command. As you can see, financial success is always relative.

The estimated median total pay of a quantitative analyst in the U.S. in 2024. Google is among the highest-paying companies for a quant, offering a median annual pay of $262,000.

Quants Skills and Education

Financial knowledge.

Many financial securities, such as options and convertibles , are easy to understand conceptually but are very difficult to model precisely. Because of this hidden complexity, the skills most valued in a quant are those related to mathematics and computation rather than finance.

It is a quant's ability to structure a complex problem that makes them valuable, not their specific knowledge of a company or market.

A quant should understand the following mathematical concepts:

• Calculus (including differential, integral, and stochastic)
• Linear algebra and differential equations
• Probability and statistics

Key financial topics include:

• Portfolio theory
• Equity and interest rate derivatives, including exotics
• Credit-risk products

Some quants will specialize in specific products, such as commodities, foreign exchange (Forex), or asset-backed securities .

Computer Competency

Software skills are also critical to job performance. C++ is typically used for high-frequency trading applications, and offline statistical analysis would be performed in MATLAB, SAS, S-PLUS, or a similar package.

Pricing knowledge may also be embedded in trading tools created with Java, .NET, or VBA , and are often integrated with Excel. Monte Carlo techniques are essential. A majority of the work is also realized in Python, as scripting-type languages are good for running lots of data and multiple scenarios.

Education and Certifications

Most firms look for at least a master's degree or preferably a Ph.D. in a quantitative subject, such as mathematics, economics, finance, or statistics. Master's degrees in financial engineering or computational finance are also effective entry points for quant careers.

Generally, an MBA is not enough by itself to obtain a quant position, unless the applicant also has a very strong mathematical or computational skill set in addition to some solid experience in the real world. 

While most financial certifications, such as the Chartered Financial Analyst (CFA) designation likely wouldn't add much value to a prospective quant's resume, one that may is the Certificate in Quantitative Finance (CQF) —which you may earn globally via distance learning in a six-month intensive program.

Clearly, you need to have "the right stuff" to be a quantitative analyst. It requires both the intellectual ability to master complex and abstract mathematical domains and a willingness to tackle challenges that can seem insurmountable—all while under considerable pressure—which only a select few can do.

But that also doesn't mean that everyone who has the ability to be a quant should become one. The financial problems that quants face are very abstract and narrow. Unlike fundamental or qualitative analysts , quants don't read annual reports, meet with management, visit operations, prepare roadshows, or talk to shareholders. Most of their time is spent working with computer code and numbers on a screen.

Individuals with strong analytical skills are valuable in many different areas of finance, such as economic and financial analysis, for example. Having to compete against the best and brightest quants every single day may not be the quickest path through the ranks, especially for those with broader skills and interests and a desire to manage.

Another career issue to consider is that many Ph.D. quants who come from academic environments find they miss the research environment. Instead of being able to study a problem for several months, when supporting a trading desk you need to find solutions in days or hours. This usually precludes making any breakthroughs in the field. 

Do Quants Get Paid Well?

Yes, quants tend to command high salaries, in part because they are in demand. Hedge funds and other trading firms generally offer the highest compensation. Entry-level positions may earn only $120,000 to $210,000, but there is usually room for future growth in both responsibilities and salary and the ability to earn upwards of $300,000.

How Hard Is Quant Finance?

It takes advanced-level skills in finance, math, and computer programming to get into quantitative trading, and the competition for a first job can be fierce. Once someone has landed a job, it then requires long working hours, innovation, and comfort with risk to succeed.

Do You Need a Ph.D. to Be a Quant?

Having a Ph.D. in a subject like math, finance, economics, or statistics can be a definite plus for anyone wanting to become a quant. However, a master's degree in computational finance or financial engineering can also be the ticket to a career as a quantitative analyst.

Success in quantitative analysis is largely based on knowledge, talent, merit, and dedication instead of the ability to sell, network, or play politics. The quants who work in the field are there because they can do the job well—an environment that many find remarkably refreshing—and they are justly rewarded for their work.

Bureau of Labor Statistics. " Financial Analysts ."

Glassdoor. " Quantitative Analyst Salaries ."

Bureau of Labor Statistics. " Financial Analysts: Pay ."

Glassdoor. " How Much Does a Junior Quantitative Analyst Make? "

Glassdoor. " How Much Does a Quantitative Analyst Make? "

Bureau of Labor Statistics. " Financial Analysts: How to Become One ."

Certificate in Quantitative Finance. " Who Is It For? "

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quantitative finance PhD Projects, Programmes & Scholarships

Phd mathematical sciences, funded phd programme (students worldwide).

Some or all of the PhD opportunities in this programme have funding attached. Applications for this programme are welcome from suitably qualified candidates worldwide. Funding may only be available to a limited set of nationalities and you should read the full programme details for further information.

China PhD Programme

A Chinese PhD usually takes 3-4 years and often involves following a formal teaching plan (set by your supervisor) as well as carrying out your own original research. Your PhD thesis will be publicly examined in front of a panel of expert. Some international programmes are offered in English, but others will be taught in Mandarin Chinese.

PUEB Doctoral School

Poland phd programme.

A Polish PhD usually takes 3-4 years. Programmes are often highly structured with a set study curriculum including coursework and examinations. You will also complete an original thesis to be submitted for a public defence at the end of your degree. Some programmes are delivered in English.

Fully Funded PhD opportunities in Business, Economics and Finance Sciences

4 year phd programme.

4 Year PhD Programmes are extended PhD opportunities that involve more training and preparation. You will usually complete taught courses in your first year (sometimes equivalent to a Masters in your subject) before choosing and proposing your research project. You will then research and submit your thesis in the normal way.

PhD in Finance at Henley Business School

Business research programme.

Business Research Programmes present a range of research opportunities, shaped by a university’s particular expertise, facilities and resources. You will usually identify a suitable topic for your PhD and propose your own project. Additional training and development opportunities may also be offered as part of your programme.

GIF CDT: Scaling industrial decarbonisation with data and finance

Phd research project.

PhD Research Projects are advertised opportunities to examine a pre-defined topic or answer a stated research question. Some projects may also provide scope for you to propose your own ideas and approaches.

Competition Funded PhD Project (Students Worldwide)

This project is in competition for funding with other projects. Usually the project which receives the best applicant will be successful. Unsuccessful projects may still go ahead as self-funded opportunities. Applications for the project are welcome from all suitably qualified candidates, but potential funding may be restricted to a limited set of nationalities. You should check the project and department details for more information.

Global environmental change and biodiversity risks to macrofinancial and industrial sectors

Funded phd project (european/uk students only).

This project has funding attached for UK and EU students, though the amount may depend on your nationality. Non-EU students may still be able to apply for the project provided they can find separate funding. You should check the project and department details for more information.

PhD in Business, Economics and Social Sciences – 55 doctoral positions

Germany phd programme.

A German PhD usually takes 3-4 years. Traditional programmes focus on independent research, but more structured PhDs involve additional training units (worth 180-240 ECTS credits) as well as placement opportunities. Both options require you to produce a thesis and present it for examination. Many programmes are delivered in English.

Leveraging Business Models for Investment Due Diligence in the Venture Capital

Self-funded phd students only.

This project does not have funding attached. You will need to have your own means of paying fees and living costs and / or seek separate funding from student finance, charities or trusts.

Structural characterizatoin of cell signaling at the membrane interface

Funded phd project (students worldwide).

This project has funding attached, subject to eligibility criteria. Applications for the project are welcome from all suitably qualified candidates, but its funding may be restricted to a limited set of nationalities. You should check the project and department details for more information.

Unraveling nuclear PKA activity in neurons of mouse striatum during behavior

Hijacking mechanism of dengue virus on human plasmin to enhance the permeability of mosquito midgut for infection., effects of urbanisation on insect biodiversity, cryo-em structure and function of membrane proteins involved in human diseases, control of human chromosome condensation in situ, characterization of the species and functional diversity of mangrove sediment fungi and other microbes.

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The Mathematical and Computational Finance Program at Stanford University (“MCF”) is one of the oldest and most established programs of its kind in the world. Starting out in the late 1990’s as an interdisciplinary financial mathematics research group, at a time when “quants” started having a greater impact on finance in particular, the program formally admitted masters students starting in 1999. The current MCF program was relaunched under the auspices of the Institute for Computational and Mathematical Engineering in the Stanford School of Engineering in 2014 to better align with changes in industry and to broaden into areas of financial technology in particular. We are excited to remain at the cutting edge of innovation in finance while carrying on our long tradition of excellence.

The MCF Program is designed to have smaller cohorts of exceptional students with diverse interests and viewpoints, and prepare them for impactful roles in finance. We are characterized by our cutting edge curriculum marrying traditional financial mathematics and core fundamentals, with an innovative technical spirit unique to Stanford with preparation in software engineering, data science and machine learning as well as the hands-on practical coursework which is the hallmark skill-set for leaders in present day finance.

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Quantitative finance for physicists

I am looking for good books to learn quantitative finance. As I have strong background in physics, I would appreciate introductions that do not hesitate to show the equations, but in the same time cover the finance rather comprehensively. Most of what I have seen up till now errs either a) in the direction of explaining elementary probabilistic concepts, or b) towards formal math/statistics, or c) giving just a gist of it.

• finance-mathematics
• statistical-finance

• 3 $\begingroup$ You may also want to check Wilmott’s quantitative finance, he takes a very PDE centric approach, which will be very easy to follow. But please check sample pages before buying! $\endgroup$ –  Magic is in the chain Commented Jun 2, 2020 at 19:15
• $\begingroup$ Did you study measures in calculus curriculum? Did you study measure theoretic probability? $\endgroup$ –  Aksakal almost surely binary Commented Jun 2, 2020 at 23:25
• 2 $\begingroup$ Perhaps Steven Steve 2 volumes of Stochastic calculus in Finance? If your background in dealing with Maths is strong, I think you can start with volume 2 directly, which deals with continuous time $\endgroup$ –  Idonknow Commented Jun 2, 2020 at 23:39
• 1 $\begingroup$ What's your goal? Generally, Baxter & Rennie is a good intro to the Black Scholes world. $\endgroup$ –  LazyCat Commented Jun 3, 2020 at 2:37
• $\begingroup$ @Aksakalalmostsurelybinary I don't have background in measures... but I am quite at ease with Langevin equations, Fokker-Planck and path integrals. $\endgroup$ –  Roger V. Commented Jun 3, 2020 at 4:30

5 Answers 5

Physicists typically know pdes but not stochastic calculus.

I have a masters in physics, so have a reasonable idea of the usual skillsets a physicist will know (at least at undergraduate level), and also then a masters in mathematical finance, so learnt the hard way the bits of maths physicists typically don't know but will need to know for quantitative finance.

Typically physicists are very strong with linear algebra, and PDEs, but the world we work in is largely deterministic (I'll overlook QM for now) and we rarely do much with distributions. If you are happy enough to have a 5-minute overview of Ito calculus and focus on the PDEs that appear in finance and options pricing, then it is possible to take a very PDE centered approach. A great book in this regard is the book The Mathematics of Financial Derivatives (1995) by Wilmott, Howison, and Dewynne.

If you want to know stochastics

If you take the PDE approach then much of quantitative finance will be inaccessible to you, as you can only go a small way before learning Ito calculus is required. A great resource I found for this was Introduction to stochastic calculus with applications by Klebaner . This will give you pretty much all the stochastic calculus skills you will need.

Some more advanced stochastics and control theory

At this point you will be able to go into much of quantitative finance (or at least have the core skills to). However there are some branches where I think you will need a fair chunk more of mathematics, and the biggest is likely control theory (and the HJB equations), for which there are only really graduate books, and the best I can think of is Stochastic Controls: Hamiltonian Systems and HJB Equations by Yong and Zhou .

So far all of this is largely focussed on financial modelling, but from a theory based perspective rather than from an empirical or statistical perspective. Of course a huge number of hedge funds (and investment banks) model financial behaviour through statistical trends, or even just through blackbox machine learning. A great book for time series and statistics is Introduction to Time Series and Forecasting by Brockwell and Davis, and the standard book (amongst several) for statistics and machine learning is The Elements of Statistical Learning by Friedman, Tibshirani, and Hastie .

At this point you can now cover the main items including: options pricing, fixed income, statistical arbitrage, time series modelling, numerical methods, optimal control, etc.

• $\begingroup$ Thank you for the recommendations! In fact, I am rather comfortable with Langevin equations, Fokker-Planck equation and have some inactive background in path integrals. However the mathematical underpinnings seem to be lacking, since measures and Ito calculus seem like things I might have to learn about. $\endgroup$ –  Roger V. Commented Jun 3, 2020 at 10:09
• 1 $\begingroup$ Langevin and Fokker-Planck are still just PDEs and they give you the evolution for distributions, but they are still very disjoint from SDEs, which is really the language of quantitative finance, for which you need to know Ito calculus. The best book for this that I found was Klebaner's. $\endgroup$ –  oliversm Commented Jun 3, 2020 at 10:18

It's not a great book, but Jan Dash. Quantitative Finance and Risk Management: A Physicist's Approach. World Scientific Publishing Company (2004) takes the approach that you might like - not too much formal math, and not too elementary.

Since you didn't study measure theoretic probability, that would be the first thing I recommend. In my opinion that's the main gap that many physicists on math side, because stochastic calculus is not in mainstream physics curriculum.

Whether you first study measure theory in calculus then take on probability, or jump right into measure theoretic probability is up to you. I took the first approach:

• I studied Kolmogorov's classical text in Russian. It's very clearly written, and surprisingly accessible to non mathematicians. I had one of my math professors help me digest the content.
• Then I took PhD course with Billingsley's text " probability and measure ," which covers both subjects at once. I think it's possible in principle to learn both following this book, but I had a feeling that everyone in the class room knew measure theory, sets etc.
• I also took a PhD seminar on continuous stochastic calculus and we used Shreve's text's second volume . Again, it is not impossible to start with this book, but it's written for mathematicians, unless you're a theoretical or math physicist it will not be a comfortable read.

If you want to follow this path then I recommend enrolling/auditing PhD classes on this subjects in a local university.

A completely different approach would be to start from the end, e.g. read Wilmott's three volume book , Hull's "options..." text or Neftci's stochastic calculus text. I've seen people going this route too. It depends on your background and how much time you allocate for this project.

Then you need to study finance itself. That's a whole different ball game. If you have funds and time, then maybe getting MBA or CFA Level 1 exam is the most comprehensive approach.

• $\begingroup$ I would recommend the second approach. $\endgroup$ –  Lisa Ann Commented Jun 3, 2020 at 19:23

As a long practicing plasma physicist who moved into quantdom (now retired), I suggest focusing on stochastic calculus and modeling. How deep you go down the rabbit hole of measure theory will depend on what you do. Simulation will be your friend and help you in many situations. To the excellent suggestions above, I add Paul Glasserman's Monte Carlo Methods in Financial Engineering. Build up a repertoire of solved derivative models as soon as you can. Have Fun, I did. ntgladd

As a former physicist you will certainly enjoy Jean-Philippe Bouchaud’s approach. Pragmatic and empirical with simple models that are sophisticated enough to be useful.

Check out “Theory of Financial Risk and Derivative Pricing: From Statistical Physics to Risk Management” and “Trades, Quotes and Prices: Financial Markets Under the Microscope” in that order.

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2024 Quantitative Finance Summer Program - PhDs (New York)

Application Deadline: Tuesday, October 31, 2023 at 11:55pm ET

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The Quantitative Finance Summer Program is an intensive 10-week program that provides Summer Associates the opportunity to work alongside full-time professionals on impactful, quantitative projects. Summer Associates will work within an assigned team for the entirety of the program. These teams are primarily embedded within the Firm's Institutional Securities Group and provide critical quantitative solutions for the Institutional Equity and Fixed Income Divisions. The multi-faceted program features senior quant teach-in sessions, divisional speaker series, product area training, networking events, and community service. With individual coaching and continuous feedback, the program enables Summer Associates to experience and understand what a long-term career with the Firm entails.    TRAINING PROGRAM  The Summer will kick off with a week-long introductory training program, which will provide an institutional contextualization to the work that Summer Associates will be doing through market-knowledge training, finance workshops, coding and product training. Following the training week, Summer Associates will continue to receive more individualized, on-the-job training as they join their assigned desks and begin their daily work and projects. Summer Associates will have a direct manager, as well as a program mentor, both of whom will act as invaluable resources throughout their time at Morgan Stanley.    RESPONSIBILITIES  Morgan Stanley operates several teams which require experts in statistical analysis, applied mathematics, computer science and computational finance. These teams operate our leading trading platforms, market making operations and derivative structuring, pricing and risk management. The mathematical problems arising in these areas are subtle, complex and require a broad range of technical skills. As a Summer Associate, you will leverage the technical expertise you have been grooming in your academic studies, and apply it to extremely applied problems. Many of the applied problems and processes that you will work on are still unsolved and are yet to be optimized.    QUALIFICATIONS/ SKILLS/ REQUIREMENTS  • You are pursuing a PhD in Financial Engineering, Mathematics, Financial Math, Physics, Statistics, Engineering, Quantitative Finance, Computer Science, or other related quantitative field.  • You are completing your degree in December 2024 or Spring 2025  • You have excellent programming skills in C++, Java, Matlab, Python, R or Scala.  • You have strong mathematical academic training.  • You have a keen interest in financial markets.  • You have the drive and desire to work in an intense team-oriented environment.  • You have excellent decision-making abilities.  • You have strong communication skills. 

Expected base pay rate(s) for the role will be $175,000 per year at the commencement of employment. However, base pay is only part of the total compensation package, which, depending on the position, may also include discretionary bonuses and other Morgan Stanley sponsored benefit programs. 

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Physics + finance = career opportunities.

A joint venture between UNC’s Department of Physics and Astronomy and UNC Kenan-Flagler, the new major combines physics classes – such as mechanics and electromagnetism – with business and math courses – such as applied derivatives and differential equations.

Finance professor Alexander Arapoglou and physics professor Reyco Henning are collaborating to offer the new major.

“Physics is an intellectually interesting subject,” said Arapoglou. “This major creates an opportunity for students to prepare themselves for future employment in such areas as quantitative finance, risk analysis and management, and portfolio management.”

The numerical methods developed for solving problems in physics have direct applications in finance. Top financial institutions regularly employ students with math and physics backgrounds – known as “quants.”

There is strong demand for people who can understand complex mathematical models and solve hard quantitative business problems, according to Arapoglou. “This is a great time to enter the finance job market.”

A physics major lends itself well to a career in finance because students learn quantitative analysis and how to handle real world data and uncertainties, Henning said. “In finance, a lot of times there are hard problems for which there is no text book or class. In physics, students learn to handle real-world data and uncertainties.”

Henning said that many of his physicist colleagues now work in finance, and when Arapoglou (MBA ’80) worked at J.P. Morgan his colleagues often had PhDs in physics. Finance professors Jacob Sagi and Greg Brown studied physics before they embarked their academic careers – Brown worked for the Board of Governors of the Federal Reserve System.

Many analytical models used in financial engineering are derived from mathematical models used in physics, including the Nobel-Prize-winning Black-Scholes options pricing model, which is based on the heat equation in physics. Economists Fischer Black and Myron Scholes both studied physics prior to making their mark in finance.

The program was specifically designed as a BA rather than a BS to give students the freedom to choose the classes that interested them, Henning said. “It gives you flexibility. You can fill up your BA with as much math and science as you want or you can fill it up with business classes. This makes it appealing to a wide audience.”

The major is available for current and incoming students with no separate application. Henning and Arapoglou encourage students interested in combining math and problem solving with business to consider the degree.

“If you’re a student who likes to do quantitative problems and you like business, this is the degree for you,” said Henning.

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Quantitative Finance for Physicists: An Introduction (Academic Press Advanced Finance) 1st Edition

With more and more physicists and physics students exploring the possibility of utilizing their advanced math skills for a career in the finance industry, this much-needed book quickly introduces them to fundamental and advanced finance principles and methods.

Quantitative Finance for Physicists provides a short, straightforward introduction for those who already have a background in physics. Find out how fractals, scaling, chaos, and other physics concepts are useful in analyzing financial time series. Learn about key topics in quantitative finance such as option pricing, portfolio management, and risk measurement. This book provides the basic knowledge in finance required to enable readers with physics backgrounds to move successfully into the financial industry.

• Short, self-contained book for physicists to master basic concepts and quantitative methods of finance
• Growing field―many physicists are moving into finance positions because of the high-level math required
• Draws on the author's own experience as a physicist who moved into a financial analyst position
• ISBN-10 012088464X
• ISBN-13 978-0120884643
• Edition 1st
• Publisher Academic Press
• Publication date December 28, 2004
• Part of series Academic Press Advanced Finance
• Language English
• Dimensions 6.28 x 0.68 x 9.2 inches
• Print length 184 pages
• See all details

Editorial Reviews

"… Schmidt's book is the most pedagogical among the few good econophysics books to have appeared in the last years. I am going to use it whenever teaching econophysics to young researchers.... A very positive contribution, giving the new generation of scientists a balanced, interdisciplinary, yet soundly professional background in this fascinating and promising field." -- Sorin Solomon, Professor at the Racah Institute of Physics, Hebrew University of Jerusalem and Director of the Multi-Agent Systems Division at the Institute for Scientific Interchange, Torino "…What amazes me most in this nicely crafted presentation of hot topics in econometrics, mathematical finance, econophysics, and agent-based modeling is how the selection of topics is well-informed and how these pour out smoothly. I will recommend this book to my own financial economics students as an up-to-date, quick reference companion to classes and the lab." -- Sergio Da Silva, Department of Economics, Federal University of Santa Catarina, Brazil

From the Back Cover

Business/Finance ANATOLY B. SCHMIDT Quantitative Finance for Physicists An Introduction

"…What amazes me most in this nicely crafted presentation of hot topics in econometrics, mathematical finance, econophysics, and agent-based modeling is how the selection of topics is well-informed and how these pour out smoothly. I will recommend this book to my own financial economics students as an up-to-date, quick reference companion to classes and the lab." ― Sergio Da Silva, Department of Economics, Federal University of Santa Catarina, Brazil

"… Schmidt's book is the most pedagogical among the few good econophysics books to have appeared in the last years. I am going to use it whenever teaching econophysics to young researchers.... A very positive contribution, giving the new generation of scientists a balanced, interdisciplinary, yet soundly professional background in this fascinating and promising field." ― Sorin Solomon, Professor at the Racah Institute of Physics, Hebrew University of Jerusalem and Director of the Multi-Agent Systems Division at the Institute for Scientific Interchange, Torino

Anatoly B. Schmidt is a Financial Data Analyst. He holds a Ph.D. in Physics from Latvian University and has more than forty publications in biophysics, statistical physics, and econophysics.

About the Author

analyst since 1997.

Product details

• Publisher ‏ : ‎ Academic Press; 1st edition (December 28, 2004)
• Language ‏ : ‎ English
• Hardcover ‏ : ‎ 184 pages
• ISBN-10 ‏ : ‎ 012088464X
• ISBN-13 ‏ : ‎ 978-0120884643
• Item Weight ‏ : ‎ 14.4 ounces
• Dimensions ‏ : ‎ 6.28 x 0.68 x 9.2 inches
• #911 in Business Mathematics
• #1,631 in Econometrics & Statistics
• #2,902 in Business Finance

About the author

Anatoly b. schmidt.

Anatoly (Alec) Schmidt holds PhD in physics from Latvian University. He has been a quantitative analyst in the financial industry since 1997. Alec also teaches at the Financial Engineering program of Stevens Institute of Technology. He has published three books: Statistical Thermodynamics of Classical Plasmas (Energoatomizdat, Moscow, 1991), Quantitative Finance for Physicists: An Introduction (Elsevier, 2004), and Financial Markets and Trading: An Introduction to Market Microstructure and Trading Strategies (Wiley, 2011), and multiple academic papers on statistical physics, biophysics, econophysics, agent-based modeling of financial markets, market microstructure, and algorithmic trading.

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• Career Advice

Theoretical Physics PhD going into Quant. Finance

• Thread starter Dr. Ricky
• Start date 12/6/12

Hello All, I'm a recent PhD in theoretical physics (in fact, got it last year) and now I'm working as a post-doc researcher. As the title suggests I'm planning to break into the finance world. I just want to know what the odds are for me and that's why I'm posting here. This is my first post as you can see but I wish I had discovered this forum earlier. Without further ado let me briefly describe what pertinent skills and knowledge I have: - Seven years of quantitative research experience (undergrad + grad school and postdoc). - Intuitive understanding of stochastic calculus and its application to finance. - Numerical calculation and simulations with C++ - Probability theory, pricing and hedging withing the Black-Scholes framework. So, I told my boss the idea of leaving physics recently, and he gave me a project that has implications to finance (I have a lot of respect for him since). Part of the project involves writing a Markov Chain Monte Carlo algorithm to estimate the distribution of the return of an asset with arbitrage modeled as quantum fluctuations. This has been shown to produce the "fat tail" distribution produced in real markets. I have already written most of the code in two weeks using C++ giving expected results. So my question is, how do I maximize my chances from here?  

Andy Nguyen

Hey Ricky, Welcome to QN. The trickiest part is to find direction as finance is a changing field. Whatever you read 2 years ago may no longer apply. You will always facing the question, why finance and what you want to do in finance. Until then, it's hard to know the next steps. Take a look at this QuantNet Guide https://www.quantnet.com/threads/how-to-get-a-quant-job-advice-from-wall-street-executives.4537/  

Ah! Thanks for the reply. I guess I left out what I wanted to to do in Finance. I have my eyes on risk management, quantitative research in a hedge fund, algorithmic trading and FX. This answers the 'What finance' question. From what I understand so far these areas involve a fair amount of number crunching and programming, which I love. And this answers the 'why finance'. Given this, how should I proceed? I'm working through "Stochastic Processes" by Ross right now. I would recommend it to everyone. A lot of challenging problems!  

For risk management, read everything that Ken Abbott posts on here. https://www.quantnet.com/tags/risk+management/  

Apply - Hoping to get interview in current market - Beating other Ivy league or Oxbridge folks to get offer There are two books I feel useful 1. Quant Job Interview Questions & Answers 2. Heard on the Street I have been applying Quant jobs for 3 months now, what I found is in the current situation, it is even difficult to get interviews in the first place; So be well prepared for the 'Quants questions' & be sure you can answer them well in those interviews; cos those 2 or 3 interviews are possibly all the interviews you could get this year.  

zxilver: Thank you sir. I just ordered 'Heard on the Street', it was recommended by another person as well.  

Dr. Ricky said: Hello All, I'm a recent PhD in theoretical physics (in fact, got it last year) and now I'm working as a post-doc researcher. As the title suggests I'm planning to break into the finance world. I just want to know what the odds are for me and that's why I'm posting here. This is my first post as you can see but I wish I had discovered this forum earlier. Without further ado let me briefly describe what pertinent skills and knowledge I have: - Seven years of quantitative research experience (undergrad + grad school and postdoc). - Intuitive understanding of stochastic calculus and its application to finance. - Numerical calculation and simulations with C++ - Probability theory, pricing and hedging withing the Black-Scholes framework. So, I told my boss the idea of leaving physics recently, and he gave me a project that has implications to finance (I have a lot of respect for him since). Part of the project involves writing a Markov Chain Monte Carlo algorithm to estimate the distribution of the return of an asset with arbitrage modeled as quantum fluctuations. This has been shown to produce the "fat tail" distribution produced in real markets. I have already written most of the code in two weeks using C++ giving expected results. So my question is, how do I maximize my chances from here? Click to expand...

Thanks albo! I'd definitely look at the resumes of PhD quants. But what is the trend if I may ask? And the future in my field of theoretical physics isn't looking too good. Funding is being cut already everywhere.  

Daniel Duffy

C++ author, trainer.

Dr. Ricky said: Ah! Thanks for the reply. I guess I left out what I wanted to to do in Finance. I have my eyes on risk management, quantitative research in a hedge fund, algorithmic trading and FX. This answers the 'What finance' question. From what I understand so far these areas involve a fair amount of number crunching and programming, which I love. And this answers the 'why finance'. Given this, how should I proceed? I'm working through "Stochastic Processes" by Ross right now. I would recommend it to everyone. A lot of challenging problems! Click to expand...

nudge. Any followup on how Dr. Ricky turned out?? I'm in a very similar situation as him (phd theoretical astrophysics -> risk)  

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PhD Physics Student Seeking Quant Advice

• Thread starter oreliphan
• Start date Feb 7, 2010
• Tags Phd Physics
• Feb 7, 2010
• Oxygen tweaking may be key to accelerator optimization
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oreliphan said: I want to go into investment banking, and become a quant. Can you please give some advice on what I can start doing?

What ideal things should I do so that I can have a greater edge to break into the finance industry?

I read a little about the different fields, but the things I'm looking for are interesting, challenging, and hectic work. (and lucrative pay, though I know this is something that shouldn't be admitted). I would love to work directly on Wall St.

So, please, if you can, offer me some advice based on your own experience to get me that edge! (I know it's tough to get in, but I'll do what it takes!)

Also, do you like your job? Is it stressful, long, etc? Cons, Pros? (psst: for you, twofish-quant!)

oreliphan said: Do you find that you have time for a social (read: love, family) life?

And, should I expect to work on Wall St (move to NYC) for better chances at landing a job?

If I do end up working on Wall St., is it possible to put in a few years work and then will it be easier move to a more desirable location?

Does prestige of your PhD University matter?

twofish-quant said: The big thing is to learn C++. Most quant positions are glorified coding positions, and even if you don't get a heavy coding position, C++ is the standard language. If you have done much coding in your Ph.D., this is what banks are looking for, but if you haven't try to get as much as you can. The first thing to remember is that banks are looking specifically for numerical modellers. So keep your math skills sharp.

• Feb 8, 2010

comp_math said: What about knowledge on finance, for e.g., things contained in the Hull book?

For good measure, are there any C++ books you like?  

This is some good stuff written here. Thanks a mill twofish-quant.  

oreliphan said: For good measure, are there any C++ books you like?

Twofish-quant, can you tell me a little bit about the future finance career options of a PhD physicist quant who started out on Wall St.? I'm looking at some job postings at QauntFinanceJobs.com, and there are some hedge funds in New York looking to hire Quants with a salary of $450k. Is that plausible? Here's the link to the job posting: http://www.quantfinancejobs.com/jobdetails.asp?dbid=&guid=&JobID=7745 Is that not the same "quant" we're talking about? What's the deal? What's the career ladder like, basically?  

While we're on the subject of quantitative finance and other related jobs, can I get into that field with a PhD in computer science? MS in computer science? EDIT: I do know that masters programs in computational finance exist (Carnegie Mellon), but how is that different than entering the field with an advanced computer science degree or a physics PhD?  

• Feb 9, 2010

oreliphan said: I'm looking at some job postings at QauntFinanceJobs.com, and there are some hedge funds in New York looking to hire Quants with a salary of $450k. Is that plausible?

Is that not the same "quant" we're talking about? What's the deal?

What's the career ladder like, basically?

For interested readers, here is a very interesting discussion I found by twofish-quant in these forums discussing the same topic: https://www.physicsforums.com/showthread.php?t=354287  

twofish-quant said: It's plausible but unlikely. You can get $450K if you happen to have a few years of experience, if you are in a prime position in a hot field. But I wouldn't count on it. My guess is that with three to five years of experience, a physics Ph.D. would have a 1 in 10 chance of making $450K. On the one hand, you shouldn't count on making that much money, but on the other hand, having a 1 in 10 chance of making $450K and a 8 in 10 chance of making $180K isn't that bad. Part of the reason I'm doing what I'm doing is that I figure that I have maybe a 1 in 50 chance of making $1 million/year sometime in my life. Now 1 in 50 isn't great odds, but I can't think of anything else that I could be doing that realistically gives me 1 in 50 of making $1 million/year. Something that does happen is the window case effect. A recruiter is going to put their best job in the showroom window so that you walk in the store. You probably can't get the diamond necklace that they show out in front, but they might have something else for you. One thing that you have to be very, very careful about is to realize that getting rich is largely a matter of "dumb luck." If you think to yourself, I'm smart so I'll be number one and get that $450K, well it doesn't work that way. For example, this year people doing bond products made a ton of money because you had a sharp interest rate yield curve. Next year, someone else will get the jackpot. Even if the market totally self-destructs, someone will win big. So whether you get the really big money is luck not skill. "Quant" is like "webmaster" it was a job that existed a while ago, but because the industry has grown a lot, people now tend to specialize. One curious thing is that people really don't have fixed job titles. Hard to say. The industry is so new that people are just making things up as they go along. One thing that people that enter the field have to think about is long term sustainability. Personally I think that I'm doing things that are wealth-creating and socially useful. There are intelligent people that disagree. This matters because since I think that I'm creating real wealth, there will be enough money to keep me in business. Now if Paul Krugman is right and we are just faking stuff, then it's all a bubble that's going to fall apart. I think Paul Krugman is pretty smart, he might be right, but ultimately I have to make my decisions based on what I think is going on. This matters, because I look around me and figure out that with the salaries that people are making, this is sustainable. If everyone were making $2 million/year then it wouldn't be since we are just not creating that much wealth, and so everything is going to blow apart. Also part of the reason I'm careful not to get scammed by Wall Street is that I got scammed by academia before.

98whbf said: How did you get scammed?

Personally, I think it's a great thing if people study science and engineering, and I do think that in the long run having more people study science and engineering will help the economy. But please *DO NOT LIE TO ME*. And especially *DO NOT LIE TO ME TO FOR YOU OWN PERSONAL BENEFIT*. The weird thing about all of these studies is that they always end with some stirring call for more funding for universities. What's basically happening is the moral equivalent of a Ponzi scam or Nigerian advance fee scam. Actually it's worse than a Ponzi scam. When someone sends you a Nigerian spam e-mail or sell you a bad used car, they don't claim to care about you. This ain't some guy in the alley lying to you, it's the damn National Science Foundation and the damn National Academy of Sciences. There is this wonderful quote by Michael Nesmith. "It's like finding your grandmother stealing your stereo. You're happy to get your stereo back, but it's sad to find out your grandmother is a thief."  

Also the fact that I've been lied to makes me very careful about saying anything that would turn into a lie to other people. For example, physics Ph.D.'s on Wall Street make decent money right now, but if tomorrow I found out that there were tens of thousands of people getting their physics Ph.D. in the hopes of making tons of money on Wall Street, I'd cash out and find something else to do. So I'm *not* encouraging people to get their physics Ph.D. to make big money, and in fact if that's the reason you are getting your Ph.D. you are in the game for the wrong reasons, and you probably won't make big money either, since you have tons of people trying to "get rich quick" you've formed a market bubble that is going to pop.  

• Feb 10, 2010

Twofish, in your experience, what are the chances of a PhD Physicist being able to work a hedge fund? Is this the 1 in 50 chance you were mentioning earlier? How about doing high frequency trading? Are these qualifications tougher than what you mentioned in the previous posts about becoming a "quant"? I did some searching around, and found out about Renaissance Technologies, which seems to hire mostly Math and Science PhDs. (Not that what they do is high frequency trading - they seem very confidential about their methods). They also seem to try and get people before they get on Wall St., so it seems like something one could apply for right out of grad school. Seems extra competitive though. Can you tell us what your opinion is on high-frequency trading?  

Does Wall St still just look for physics PhDs? In the mid-90s only physics/maths people knew this stuff, now it seems that every university in the world is offering courses on derivatives, options pricing and Black-Scholes. The local CS dept even has a final year course on writing derivative pricing models. So physics PhDs are good IQ/$ ratio compared to MBAs but it seems that this isn't arcane knowledge anymore.  

now it seems that every university in the world is offering courses on derivatives, options pricing and Black-Scholes.

oreliphan said: Twofish, in your experience, what are the chances of a PhD Physicist being able to work a hedge fund?

Is this the 1 in 50 chance you were mentioning earlier?

How about doing high frequency trading?

I did some searching around, and found out about Renaissance Technologies, which seems to hire mostly Math and Science PhDs. (Not that what they do is high frequency trading - they seem very confidential about their methods). They also seem to try and get people before they get on Wall St., so it seems like something one could apply for right out of grad school. Seems extra competitive though.

Can you tell us what your opinion is on high-frequency trading?

Is it true that many of the super high pay quant jobs are related to algorithmic trading, more towards computer science/networks? This seems like a very small and specialized area, i.e. hard.  

comp_math said: Is it true that many of the super high pay quant jobs are related to algorithmic trading, more towards computer science/networks?

This seems like a very small and specialized area, i.e. hard.

• Feb 15, 2010

what if one just obtained a BS in math+physics, and is now debating between a career in mechanical engineering vs a quant/actuary? would it be feasible to obtain a MS in ME, then work for a few years, and if he doesn't like the work, he can just apply and obtain quant positions? or would that be a total waste of time to get the MS ME, and just get a MFE or some MS that's related to financial mathematics? if that person got offered a full-time position in programming with his BS degrees, should he take them, or instead just apply to grad school programs ASAP? is it typical for quants to work 60 hrs/week, and make salaries in the 150k+ range? how are the salaries different for quants who have a phD in physics vs those with a BS physics + MFE or MS in some other field?  

Thank you twofish-quant for your explanations, they are very helpful. If you don't mind, I would like to ask you some personal doubts: I am doing a PhD in theoretical physics, and when I finish I am unsure if I will go for postdoc and misery, so I was considering finance as a possible "escape". I am not doing much programming, just numerical stuff with mathematica and matlab. I don't think I can get to learn C++ for my PhD work, but I could start learning it at some point. Does this restrict a lot my future possibilities of finding a job as a quant, or basic knowledge of the language would suffice as long as I have sufficient mathematical skills? Another question I have is about the future of physics PhD hiring in finance. Some universities are offering quantitative finance M.Sc., so I guess it will become more and more difficult to get to work as a quant with just a PhD in particle physics. Am I right or they are still hiring? Finally, I am doing my PhD in an European university and I am not from the US. Does that play against finding a quant job in NY? Thank you very much!  

ferm said: I am not doing much programming, just numerical stuff with mathematica and matlab. I don't think I can get to learn C++ for my PhD work, but I could start learning it at some point. Does this restrict a lot my future possibilities of finding a job as a quant, or basic knowledge of the language would suffice as long as I have sufficient mathematical skills?

Another question I have is about the future of physics PhD hiring in finance. Some universities are offering quantitative finance M.Sc., so I guess it will become more and more difficult to get to work as a quant with just a PhD in particle physics. Am I right or they are still hiring?

Finally, I am doing my PhD in an European university and I am not from the US. Does that play against finding a quant job in NY?

twofish-quant said: I don't think very highly of MFE degrees, because they usually train people with a specific set of skills, and those skills become obsolete very quickly. Personally, if I were going after a masters degree, I'd avoid the MFE and go after a CS, applied math, statistics, finance, or MBA degree.

• Feb 16, 2010

twofish-quant said: Something that I'm very firm on is that I will not work people that I do not trust, and I will not do jobs which I don't believe to be socially useful.

creepypasta13 said: what about MS in engineering?

gatorphys said: You should be able to transition over with an engineering degree, but you should really try to figure out what you actually want to do. You mentioned engineer vs quant vs actuary, all of which are completely different fields, with completely different career paths. So I would first figure out what you want to do, and then start thinking about grad schools.

creepypasta13 said: how does one figure that out? by working in industry? i already got my BS degrees

• Feb 17, 2010

ferm said: Could you please elaborate a little on which kind of socially useful works can be done in finance and why you think they are useful?

• Feb 22, 2010

twofish-quant said: If you look back to the 1960's, there has always been this mythic shortage of scientists. What really gets me upset is *** But Education Secretary Arne Duncan says a surplus of STEM graduates is a problem he'd like to have. ***

• Feb 23, 2010

Twofish-quant's advice is excellent, and I'd like to add a few thoughts from the perspective of a trader-quant for what they are worth. There are many different roles for physicists in finance. In this sense, wall street is similar to academia. Your individual experience, career development, and pursuits are highly dependent on the specific group in which you work. Though there is mobility between firms, to a variable degree within a firm, and to a rapidly diminishing degree between niches, the initial step you take likely will determine the course of you career. So make it carefully. There are several types of firms and roles for quants. Large firms such as the major investment banks are good starting points. They have the pluses and minuses of a postdoc at a top tier university. They look great on the resume and have many exciting areas to work in and high quality people to learn from, but you probably won't end up there permanently. Many people start at these firms on the "sell-side", learn a lot, and then move to some sort of "buy-side" firm -- either the prop branch of an IB or a hedge fund. Hedge funds can be great places to work, though I tend not to recommend them as a first job. Often the starting compensation is higher, and the environment can be cozier for a quiet academic. Generally, your boss is your entire world there -- so choose carefully if you follow this route. Also, they have traditionally been considered career destinations rather than entry points. Be mindful of whether the hedge fund is large and well known. Only a few of the biggest, best will help your resume. Some common roles for physicists are listed below. Bear in mind that preparation is difficult without knowing your ultimate path. Programming experience in C++ and/or Java is important, as is knowledge of at least one scripting language (Python, Perl, etc). Knowing your way around a *nix environment can't hurt either. Be careful how you portray yourself, however. Unless you want to become a programmer, avoid pegging yourself that way. (1) Developer/Programmer: If you enjoy programming, this can be a great route. Large institutions can be challenging for the starting physicist, and long term career growth tends to be along the managerial route. My personal opinion is that small firms are much better in this regard, even if the risks are higher, because you probably don't really know what you want to do. Despite copious claims to the contrary, my observation has been that it is very hard to move from I.T. or other purely programming roles into research or trading positions. If you are targetting such roles, expect to be grilled hard. Re-reading Stroustrup's book on C++ isn't a bad idea. Some additional topics people tend to ask about: STL, Boost, Template Metaprogramming, various simple containers and algorithms. (2) Statistical Arbitrage: If you want to play with statistics and write programs to beat the markets this is a good area. It combines lots of programming with statistical inference and computer theory. This can be an excellent field for an experimental particle physicist such as yourself. The data issues and statistical analysis are quite similar. If you're lucky, a role in statistical arbitrage can combine research and trading. (3) Options theory/interest rate modeling (MBS, etc): This uses path integrals, Monte carlo analysis, etc to integrate over paths. There are elements of stochastic analysis, etc, as well. If you like heavy duty math and aren't too worried about statistical validity, this is a very interesting field. I would suggest that you not apply through HR (firmwide or I.T.) at most large firms. As in academia, you are much better off contacting a specific group that is of interest. Ask for an informational interview with the head of the group. If you make a good impression, they either will consider you for their group or pass you along to colleagues. Despite the apparent competition in this job market, there is a matching inefficiency and good candidates are scarce. Almost all my hires have been referred to me. My best suggestion is that you allocate a few months to shamelessly networking, determine the specific opportunities available to you and whether they are of interest, and make a decision by a fixed date. Assume that you will remain in your first job for at least 3 years and ignore any promises about your career path. It's great if they transpire, but things change quickly on wall street and you can't count on them. Also, don't worry about the money once you start. The first few years are for learning and building a reputation; think of them as a postdoc. All of the quants I have know who stuck it out for 8+ years were happy financially (though as twofish-quant points out, it is relative). The one thing you cannot afford to have on Wall Street is an ego. If you compare yourself to the next guy, then you will always be unhappy. The worst career blunders I have seen have arisen from that sort of thinking. Otherwise, the $$$ can be quite nice. On the practical points: Hours: This depends on your goal. If you want to take the fast track (or have a good shot at it), you will have to work very long hours. This should be fun, though. If not, the slow track is fine. You will do well financially and can find the balance you desire. If you can afford to, I would suggest committing yourself 100% the first few years and then things should ease up a bit (and you'll have a sense of what you want). Social life: Same as anywhere. Others are in the same boat, and the city abounds with ways to accommodate young people with money who want to meet. Starting a family can be rough if both of you are working. After a couple of years, when you know your way around the city, you would have an easier time. I've known many people who've done this and are quite happy with their lives. Location: NY, London, Tokyo. There are some hedge funds in Chicago, Greenwich/Stamford, and California, but most likely you will spend your career in one of those 3 cities. There are worse fates as these things go. Money: You will be poor the first year. My standard of living during my first year on Wall street was lower than as a grad student. After a year or two most people seem to do ok. Bear in mind that pay can be high on wall street, but there is no job stability. That's the tradeoff. This is something to consider if you one day wish to raise a family. Internship: This can help, and may give you a sense of whether a firm, group, or field will work for you. If you are well received, there is a chance they may try to buy you out of finishing grad school. Approach this as you would a job -- target individual groups if you can. Pros: Money. Can be challenging and exciting. There is a satisfaction to being in the middle of the action. Cons: It's not physics. I hope this helps. Best of luck. Cheers, Osmosis  

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We accept either GMAT or GRE score reports for applications to the MSMFT program. BU’s GMAT codes vary by program, so please see the GMAT Code chart below for your degree. BU’s GRE code is 3087.GMAT and GRE test scores are valid for 5 years and you should upload an unofficial copy with your online application. You can register for the GMAT exam at  mba.com , and the GRE at  ets.org . You may also wish to familiarize yourself with these tests by viewing free GMAT  sample questions  and  practice tests , as well as free GRE  preparation materials , though we do not endorse any particular prep source and encourage you to do your own research.

The GRE Math Subject test is optional.

The Boston University Questrom School of Business Graduate Admissions Committee conducts a rigorous and holistic review of each application. Students admitted to our graduate degree programs must provide proof of an earned bachelor’s degree or equivalent international credential. Boston University does not require official transcripts with the submission of an application. Should a student decide to enroll at Boston University Questrom School of Business, official transcripts must be submitted and verified prior to the point of matriculation.

Additional Application Information

Video Essay

We know that you are more than what’s on paper. During the online application process, all MS in Mathematical Finance & Financial Technology (MSMFT)  candidates are asked to submit three video essays, which allow us to get to know you on a more personal level.

Within the “Documents” section of the “Program Materials” portion of the application, you will find your personalized link to the video essay website. Clicking  this link  will bring you to the Kira Talent website, our partner for the video essay process. When you register, you must use the same email address that you used to create your admission application account.

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If you have additional information to bring to the attention of the Admissions Committee, you may address it in an optional essay of no more than 250 words. Acceptable topics for the optional essay include, but are not limited to, obstacles in your life that you’ve had to overcome, gaps in post-college work experience, choice of recommenders, and concerns about academic/test performance. If you plan to retake any admissions tests (GMAT or GRE; TOEFL, IELTS or PTE), please provide the date of your scheduled test here. To upload an optional essay, please scroll to the “Essay” section and click “Add Document.”

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Prerequisites

Our admission process is wholistic but one cannot succeed without needed quantitative skills. You are expected to have deep exposure to undergraduate mathematics, probability theory, statistics, and programming. You will be asked to highlight your relevant coursework within the online application. In addition, prior exposure to economics and finance will be a crucial complement of your quantitative skills.

At the minimum, we expect applicants to be familiar with:

• Computer Programming
• Multivariate Calculus (including derivatives and integrals)
• Linear Algebra
• Probability Theory

GRE and GMAT Tests

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We will consider your application to MS in Mathematical Finance & Financial Technology program without a GMAT or GRE score if you have a strong overall academic record. Relevant full-time work experience may also be considered when evaluating your application.  PLEASE NOTE: Candidates who submit competitive GMAT/GRE scores will receive preferential consideration for admission and merit-based scholarships.  The decision to consider your application without a GMAT or GRE score is made at the discretion of the admissions committee.  If you do not submit a test score, the admissions committee may ask for you to submit a GMAT or GRE score for your application to be considered.    In the event we need this additional information, we will inform you once your application has been initially reviewed.

GMAT CODE MS in Mathematical Finance & Financial Technology: P42-5K-30

GRE CODE MS in Mathematical Finance & Financial Technology: 3087

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Candidates who have earned or will have completed at least two years of a bachelor’s or master’s degree at an institution in which the language of instruction was solely based in English are eligible for an automatic TOEFL/IELTS/PTE waiver. Candidates must present evidence of language of instruction with the application for admission. Appropriate documentation includes a transcript coversheet, an official statement from the university, or a link to the institution’s website that specifies that the mode of instruction is English. Additionally, candidates who have or will have worked for at least two years in a full-time, post-bachelors degree position in the US are eligible for an automatic waiver. If you do not meet this criteria but would like to be considered for a waiver, you can submit a written request highlighting your experience with and proficiency in the English language to [email protected] prior to the submission of your application . The admissions committee will consider these requests on a case-by-case basis. If you submit test results with your application, please be advised that tests are valid for two years.

We are currently accepting Duolingo scores in addition to TOEFL and IELTS.

To access the Duolingo test, visit  https://englishtest.duolingo.com/applicants . Select Boston University Graduate & Professional Programs as a score recipient, and email  [email protected]  with your name and the date you have taken the Duolingo test.

• TOEFL: BU’s TOEFL code is 3087. Minimum Scores of 600 on the PBT or 95 on the iBT are recommended.
• IELTS: Competitive applicants typically score a minimum of 6.5 on each band.

PTE: As more candidates submit the PTE, we are developing updated standards for our requirements. As new information becomes available, it will be posted here.

• Duolingo: a minimum score of 120 is recommended.

Undergraduate Degrees

We require that applicants hold a four-year bachelor’s degree from an accredited US institution or its international equivalent. However, a three-year degree equivalent to a four-year degree in the US will be accepted in the admissions process. Diplomas, the Higher National Diploma (H.N.D.), certificates, memberships or associate memberships in some professional organizations, and some bachelor’s and technical degrees are not considered equivalent to a U.S. bachelor’s degree to gain admission to the Graduate School. For example, if you earned a three-year degree in India, your degree will qualify you to be considered for admission. If you are uncertain whether your undergraduate degree meets our qualifications, please contact  [email protected]  before starting the application process.

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Permanent residents should submit a copy (front and back) of their permanent resident card.

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The following tuition, fees, and other expenses have been established at Boston University Questrom School of Business for the 2024-2025 academic year. These numbers are expected to be updated to reflect the 2025-2026 academic year in late March of 2025.

Graduate Financial Aid Office

Our team is here to help you finance your education. From scholarships and loans to benefits and eligibility, we can help you explore your financing options and find the plan that works for you.

Our MSMFT Admissions Ambassadors are around to chat via email, on the phone, or even virtually through Zoom to answer your questions about what being an MSMFT student at the Questrom School of Business is really like.

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If you would like to speak with an MSMFT Admissions Ambassador, please complete our Ambassador Contact Request Form so we can ensure we connect you to the appropriate person.

James Burrill

MS in Mathematical Finance & Financial Technology

Tan Mengjia

Kaiyu Jordan Wen

Jiyao (Owen) Ma

Frequently asked questions, application process.

What type of applicant does BU look for? The admission committee looks for a few primary factors when they review an application:

• A record of academic excellence in quantitative coursework — so we can be assured that you will thrive in our dynamic and rigorous program.
• At least initial programming coursework and a commitment to prepping prior to the program.
• Prior experience via internship and/or post-bachelors employment is helpful. You can find employment ranges in our  class profile .
• Commitment to ethics — to ensure that you will maintain the high level of integrity expected of all students in the Questrom School of Business
• Evidence of student and community involvement — to indicate the value you will bring to future classmates

Learn more about our  strategies and initiatives.

We are committed to enrolling qualitatively excellent incoming classes representing global, ethnic, and demographic diversity. Our policy encourages prospective students to consider our management programs regardless of race, color, religious creed, gender, national or ethnic origin, sexual orientation, age, disability, eligible veteran status, or any other category protected by law.

How do I begin an application? We ask all interested applicants to fill out our  online application  and follow our  admission procedures .

Where do I send my official application components (i.e official transcripts)? You should first upload your documents with your application or email them to  [email protected] . If you are admitted, you will be required to submit official documents. Official documents can be mailed to:

Boston University Questrom School of Business Graduate Admissions and Financial Aid Office 595 Commonwealth Avenue Boston, Massachusetts 02215

How long will I have to wait for my admission decision? If your application is submitted in its entirety by the required deadline, we aim to release the decision within 6-8 weeks. This precludes our doctoral programs.

Requirements

What tests are required for admission? Applicants to the MS in Mathematical Finance& Financial Technology program may either submit a GMAT score or a GRE score. We accept students with a range of scores. Having a score that exceeds the average does not guarantee admission, nor does a score below the average necessarily mean that you will not be accepted. To see the averages of our current classes, please visit the  class profile  pages.

Are official documents, such as transcripts and GMAT or GRE scores, required? To expedite the admissions process, please upload unofficial documents when submitting your application. Candidates who enroll in the program are required to submit official copies of all documents.

Do I need a TOEFL/IELTS/PTE score? Candidates who have earned or will have completed at least two years of a Bachelor’s or Master’s degree at an institution in which the language of instruction was solely based in English are eligible for an automatic TOEFL/IELTS/PTE waiver. Candidates must present evidence of language of instruction with the application for admission (through their transcript/accompanying documents or a link to the institution’s website). Additionally, candidates who have or will have worked for at least two years in a full-time, post-bachelor’s degree position in the US are eligible for an automatic waiver.

If you do not meet these criteria but would like to be considered for a waiver, you can submit a written request highlighting your experience with and proficiency in the English language to [email protected] before submitting your application. The admissions committee will consider these requests on a case-by-case basis.

If you submit test results with your application, please be advised that tests are valid for 2 years.

We are currently accepting Duolingo scores in addition to TOEFL and IELTS. To access the Duolingo test, go to  https://englishtest.duolingo.com/applicants . Select Boston University Graduate & Professional Programs as a score recipient, and email  [email protected]  with your name and the date you have taken the Duolingo test. –

If you are submitting test results with your application, please be advised that tests are valid for 2 years. 

To access the Duolingo test, go to  https://englishtest.duolingo.com/applicants .  Select  Boston University Graduate & Professional Programs  as a score recipient, and email  [email protected]  with your name and the date you have taken the Duolingo test.

What is the minimum TOEFL/IELTS/PTE scores you accept?

TOEFL: Minimum scores of 600 on the PBT or 90 on the IBT are recommended. BU’s TOEFL code is 3087, and the department code is 02.

IELTS: A student’s overall score must be a minimum of 6.5. PTE: As more candidates submit the PTE, we are developing updated standards for our requirements. As new information becomes available, it will be posted here.

Duolingo: A minimum score of 120 is recommended.

Do I need to interview? No, formal interviews are not required for the MS in Mathematical Finance & Financial Technology Program.  Instead, applicants are required to complete the three video essay questions as part of their applications. Please use the hyperlink found in the “Documents” section of the “Program Materials” portion of the application to complete the video essay process.

Do I need a four-year degree? We require that applicants hold a four-year Bachelor’s degree from an accredited US institution or its international equivalent. If you have earned a three-year degree that is the equivalent of a four-year degree in the US, it will be accepted in the admissions process.

Diplomas, the Higher National Diploma (H.N.D.), certificates, memberships or associate memberships in some professional organizations, and some bachelor’s and technical degrees are not considered equivalent to a U.S. bachelor’s degree for the purpose of gaining admission to the Graduate School.

For example, if you earned a three-year degree in India, your degree will qualify you to be considered for admission. Regardless of program length, if you are uncertain whether your undergraduate degree meets our qualifications, please contact [email protected] before starting the application process.

Do I need to complete all essays? All candidates to our MSMFT program need to respond to three video essay questions as part of their applications. Please use the hyperlink found in the “Documents” section of the “Program Materials” portion of the application to complete the video essay process.

Program & Transfer

Is this program STEM designated?

The MS in Mathematical Finance & Financial Technology program at the Questrom School of Business is currently designated by US Department of Homeland Security (DHS) as a STEM-eligible degree program. International students in F-1 student status may be able to apply for a 24-month extension of their 12-month Optional Practical Training (OPT) employment authorization. More information about  STEM OPT eligibility  is available from the BU International Students and Scholars Office (ISSO).

If my undergraduate degree is not in mathematics, can I apply to the program?  Yes, if you have successfully completed the  prerequisite courses  and are able to satisfy the University’s other requirements for admission.

Can this program be completed part-time?  No, students may only enroll in the program on a full-time basis.

Does the specialized training provided in the Mathematical Finance & Financial Technology program limit the career options of its graduates?  While the program covers the main aspects of mathematical finance and aims to prepare students for careers primarily in the financial services industry, it also provides training in the general areas of optimization, computing and statistics. Graduates of the MF program enter into a number of fields upon graduation. Some of these career paths include:

• Financial Advisory and Investment
• Fixed Income Securities and Derivative
• Products Management
• Algorithmic Trading and Risk Management
• Proprietary Trading and Asset Management
• Financial Product Design and Implementation

Is work experience required in order to apply to the Master’s program?  No, the program was designed to meet the educational needs of both recent college graduates and practitioners. In fact, those individuals who have earned academic degrees quite recently are well-positioned to succeed in this highly-rigorous, quantitative program.

Financial Aid & Scholarships

How do I apply for Scholarships & Financial Aid?  Admitted candidates to the MS in Mathematical Finance & Financial Technology are considered for merit-based scholarships. You do not need to submit any additional information to be eligible for a merit-based award. Notification about scholarship is included in the acceptance packet. The Federal Direct Stafford Loan is available to U.S. citizens and U.S. permanent residents. Learn more about our  financial aid offerings .

Do you offer financial aid to international students?  Yes, all applicants to the MS in Mathematical Finance& Financial Technology program are automatically considered for merit-based scholarships provided that their applications are submitted by the admission deadlines. A separate application is not required. In addition, international students can finance their graduate education with private education loans provided they apply with a creditworthy U.S. citizen or permanent resident with a U.S.-based address.

Still Have Questions?

The Graduate Admissions and Financial Aid team is here to support you during the admission process. Please feel free to email us at  [email protected]  or to call us at (617) 353-2670.

Upcoming MSMFT Admissions Events

Gmac: the mba & master’s tour for women in business.

Wednesday, September 11

GMAC: The Master’s Tour – Boston

Tuesday, October 8

GMAC: The MBA & Master’s Tour North America

Wednesday, November 20

Ready to Apply?

Ready to apply? Follow the link to learn more about the application process. Once you’ve submitted your materials, we’ll start the review process. We’re happy to answer your questions along the way.

Application Deadlines

• Round 1: December 1, 2023
• Round 2: February 9, 2024
• Round 3: March 29, 2024

Rolling admissions will be on a space available basis after the 3/29/24 deadline

Postdoctoral Research Position (f/m/d) in Single-Molecule Biophysics

The Max Planck Institute for the Science of Light (MPL) conducts research that covers a wide range of topics, including nonlinear optics, quantum optics, nanophotonics, photonic crystal fibers, optomechanics, quantum technologies, biophysics, and links between physics and medicine.

The research group of Dr. Katja Zieske invites applications for a  Postdoctoral Research Position (f/m/d) in Single-Molecule Biophysics .

Location : Max Planck Institute for the Science of Light, Erlangen, Germany Application deadline : July 26, 2024 Start date : Preferably September 2024 Duration : 2 years Salary : Based in E13 TVöD Bund Working language : English

The research group of Dr. Zieske is an independent research group at the Max Planck Institute for the Science of Light. We are an interdisciplinary team at the intersection of membrane biophysics, microfluidics and synthetic biology. Our research focuses on understanding the biophysical properties and assembly mechanisms of living soft matter, pushing the boundaries of scientific knowledge.

Lab environment

Our team consists of interdisciplinary researchers from diverse backgrounds. We foster a supportive environment that encourages scientific growth. Our lab features an optical laboratory for assembling custom instruments and technologies for membrane biophysics and protein isolation. Postdoctoral researchers in our team leverage a diverse set of lipid model membranes, microfluidic technologies, and protein expertise. Additionally, we have access to shared state-of-the-art facilities, such as an in-house cleanroom and an optical imaging center.

Zieske group Clean room facility Optical imaging center

Position overview

We are seeking a highly motivated postdoctoral researcher with expertise in quantitative microscopy or single-molecule measurements. The successful candidate will conduct cutting-edge interdisciplinary research, investigating the physical properties of protein patterns at lipid membranes at the single-molecule level. Responsibilities include assembling an optical setup for single-molecule tracking, performing data analysis, and interpreting results.

Key responsibilities

• Build and optimize an optical setup for single molecule tracking
• Investigate the biophysical properties of protein pattern on lipid membranes using single-molecule measurements
• Analyze and interpret experimental data
• Collaborate with team members on interdisciplinary projects addressing the assembly and control of protein patterns at model membranes
• Present research findings in group meetings and at scientific meetings
• Train graduate and undergraduate students as needed

Qualification of PhD candidate

• PhD in Physics, Optical technologies, Biophysics, or a related field
• Strong interested in single-molecule measurements of proteins at lipid model membrane interfaces
• Assembly of optical setups
• Single molecule tracking or optical trapping / manipulation / imaging of biological objects
• Membrane biophysics
• Biophysical research

Application process

To apply, please submit the following materials:

• Letter of motivation
• Curriculum vitae (CV)
• List of publications
• One-page summary of PhD project
• Transcript of records (Bachelor’s and Master studies)
• Contact details of your PhD advisor and a second academic reference familiar with your PhD project

Submit your application by July 26, 2024 via our online application tool . The Max-Planck Society is committed to increasing the number of individuals with disabilities in its workforce and therefore encourages applications from such qualified individuals. Furthermore, the Max Planck Society seeks to increase the number of women in those areas where they are underrepresented and therefore explicitly encourages women to apply.

Join us at the Max Planck Institute for the Science of Light and contribute to groundbreaking research in single-molecule biophysics at lipid membranes!

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Thoughts on Quant Finance after PhD?

Currently double majoring in Finance & Physics in undergrad. Should finish in about 3 years, then I plan to pursue my PhD in Physics. I would love to do research/teaching as my career, but I would like to do so because I want to, not because I need a salary. Thinking of having quant finance as my backup plan if I receive my PhD and am not at the point where I could survive without that salary. Could work in quant for about 10 years until I “retire” to teaching in university.

Basically, I want to do research/teach because it’s my passion, and depending on the money from that salary would detract from my passion for it.

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Bank of America

Global quantitative strategies summer analyst / associate – 2025 – hong kong.

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What we’re looking for:

• Penultimate year Bachelor’s and Masters’ student (for Analyst) or PHD student (for Associate)
• Outstanding academic achievement
• Mathematics, Engineering, Physics or Computer Science background required
• Strong programming skills required (preferably in an Object-oriented language such as Python, Java, C#, C++)
• Experience using Excel/VBA
• Database skills and exposure to KDB/q advantageous
• A commercial mindset and an interest in financial markets and quantitative finance
• Strong quantitative and analytical skills
• Strong verbal and written communication skills
• Creative thinking and problem-solving skills
• Fluency in English is essential
• The ability to work well independently and in teams

The Quantitative Strategies Group (QSG) is responsible for the models and analytics used in the Sales and Trading divisions within Global Markets. The group designs, builds and maintains industrial strength tools for analyzing, pricing and risk managing financial products across all asset classes traded by Bank of America. The group works closely with trading, structuring, risk management and technology to provide analytics which can quickly and accurately quantify the firm’s risks. This involves developing models and algorithms able to accurately calculate the value and risk of the financial products we offer our clients.

What you’ll do:

• Build quantitative models for pricing and risk management
• Work closely with technology to implement and maintain models and algorithms
• Apply new technologies to solve business problems more efficiently
• Work closely with trading, sales and technology to optimize client experience, trading revenues, and trading volumes
• Drive new initiatives and products in the electronic trading space.

Placement within QSG is determined after the hiring process depending on the business needs and the interests/skills of candidate. The various teams within Equity and FICC QSG include:

• Flow Derivatives Strats
• Exotic Derivatives Strats
• Electronic & Algorithmic Trading Strats
• Index Financing Strats
• Delta One Strats
• Rates Strats

Possible Responsibilities (vary with placement):

• Development of new products/models
• Risk/PL analysis
• Development of quantitative tools
• Investigation of hedging strategies and hedging cost
• Electronic trading analysis (e.g. portfolio optimization, market microstructure, transaction costs)

Program Overview

The Summer program is a 10-week program designed to provide Summer Analysts/ Associates with a unique opportunity to gain an exposure to life at Bank of America and also make significant contributions to the team. The Summer Program begins with an orientation and induction to the firm. Summer Analysts/ Associates are given a true associate experience, as assignments mirror full-time responsibility and include goal-setting and a formal review process.

• Access to the bank’s learning hub containing a variety of learning resources, ranging from banking fundamentals to communication skills
• Structured and on-the-job training
• Networking and social opportunities
• Speaker series with senior management across all lines of business
• Corporate Social Responsibility project/learning
• Involvement in Diversity and Inclusion training/events
• Consideration for full-time employment upon graduation

At Bank of America, we are guided by a common purpose to help make financial lives better through the power of every connection. Responsible Growth is how we run our company and how we deliver for our clients, teammates, communities and shareholders every day.

One of the keys to driving Responsible Growth is being a great place to work for our teammates around the world. We’re devoted to being a diverse and inclusive workplace for everyone. We hire individuals with a broad range of backgrounds and experiences and invest heavily in our teammates and their families by offering competitive benefits to support their physical, emotional, and financial well-being.

Bank of America believes both in the importance of working together and offering flexibility to our employees. We use a multi-faceted approach for flexibility, depending on the various roles in our organization.

Working at Bank of America will give you a great career with opportunities to learn, grow and make an impact, along with the power to make a difference. Join us!

Connecting Asia Pacific to the World

Our Asia Pacific team is spread across 19 cities in 12 markets. We are focused on connecting Asia to the world and the world to Asia, using our global expertise to ensure success is shared between us, our clients and our communities. Our regional footprint covers 12 currencies, more than a dozen languages and five time zones, placing us firmly among the region’s leading financial services companies.