phd quantitative finance

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.

The Center for Quantitative Finance has a distinguished advisory board consisting of senior Wall Street executives and leading academics in quantitative finance, including Robert Merton who received the Nobel Prize in Economics for laying the foundations for modern quantitative finance.

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 around 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 .

Course Requirements for the Quantitative Finance Track   (students admitted PRIOR to Fall 2015)

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

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 Quantitative Finance AMS 519  Internship in Quantitative Finance AMS 522  Bayesian Methods in Finance  AMS 523  Mathematics of High Frequency Finance AMS 550  Stochastic Models AMS 553  Simulation and Modeling AMS 572  Data Analysis AMS 578  Regression Theory AMS 586  Time Series AMS 595  Fundamentals of Computing (1 credit) AMS, FIN, ECO or CS course approved by the AMS Graduate Program Director as well as the Graduate Program Director of the Corresponding Department 

Typical Course Sequence for Quantitative Finance Research Track First Semester: AMS  507 ,  510 ,  511 ,  513 Second Semester: AMS  512 ,  517 , FIN 539, elective Third Semester: AMS  514 ,  516 ,  518 , elective

Course Requirements for the Quantitative Finance Track   (students admitted Fall 2015 and thereafter) Required (core) courses for the Quantitative Finance Track:

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 FIN 539 Investment Analysis

Quantitative Finance Track Electives (students must take at least  1  elective course  to achieve at least  36  graduate credits along with the required courses):   AMS 515  Case Studies in Quantitative Finance AMS 522  Bayesian Methods in Finance  AMS 523  Mathematics of High Frequency Finance AMS 600  Socially Responsible Investing AMS 601  Risk Management and Business Risk Control in BRIC Countries One course in Statistics (AMS 570-586) One course in Operations Research (AMS 540-556)

Typical Course Sequence for Quantitative Finance Research Track First Semester: AMS  507 ,  510 ,  511 ,  572   Second Semester: AMS  512 ,  513 ,  517 , FIN 539 Third Semester: AMS  514 ,  516 ,  518 Fourth Semester: Elective(s)

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 and 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. Please note that credits used toward your primary program may not be used toward the certificate program. The 15-credit advanced certificate requires AMS 511, 512, 513, one additional QF elective, and one additional Applied Mathematics course chosen with an advisor’s approval. To apply down load the registration form here:

http://www.grad.sunysb.edu/pdf/forms/New_Forms/Permission%20to%20Enroll%20in%20a% 20Secondary%20Program%20-%20Certificates%20Only.pdf

Gainful employment disclosure information for our Quantitative Finance Program: http://www.stonybrook.edu/finaid/ge/quan_finance_ge.html

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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• PhD in Mathematical Finance

The PhD in Mathematical Finance is for students seeking careers in research and academia. Doctoral candidates will have a strong affinity for quantitative reasoning and the ability to connect advanced mathematical theories with real-world phenomena. They will have an interest in the creation of complex models and financial instruments as well as a passion for in-depth analysis.

Learning Outcomes

The PhD curriculum has the following learning goals. Students will:

• Demonstrate advanced knowledge of literature, theory, and methods in their field.
• Be prepared to teach at the undergraduate, master’s, and/or doctoral level in a business school or mathematics department.
• Produce original research of quality appropriate for publication in scholarly journals.

After matriculation into the PhD program, a candidate for the degree must register for and satisfactorily complete a minimum of 16 graduate-level courses at Boston University. More courses may be needed, depending on departmental requirements.

PhD in Mathematical Finance Curriculum

The curriculum for the PhD in Mathematical Finance is tailored to each incoming student, based on their academic background. Students will begin the program with a full course load to build a solid foundation in not only math and finance but also the interplay between them in the financial world. As technology plays an increasingly larger role in financial models, computer programming is also a part of the core coursework.

Once a foundation has been established, students work toward a dissertation. Working closely with a faculty advisor in a mutual area of interest, students will embark on in-depth research. It is also expected that doctoral students will perform teaching assistant duties, which may include lectures to master’s-level classes.

Course Requirements

The minimum course requirement is 16 courses (between 48 and 64 units, depending on whether the courses are 3 or 4 units each). Students’ course choices must be approved by the Mathematical Finance Director prior to registration each term. The following is a typical program of courses.

• CAS EC 701 Microeconomic Theory
• CAS MA 711 Real Analysis
• CAS MA 779 Probability Theory I
• QST FE 918 Doctoral Seminar in Finance
• CAS EC 703 Advanced Microeconomic Theory
• CAS MA 776 Partial Differential Equations
• CAS MA 781 Probability Theory 2
• QST FE 920 Advanced Capital Market Theory
• CAS EC 702 Macroeconomic Theory
• CAS MA 783 Advanced Stochastic Processes
• QST MF 850 Advanced Computational Methods
• QST MF 922 Advanced Mathematical Finance
• CAS EC 704 Advanced Microeconomic Theory
• CAS MA 751 Statistical Machine Learning
• QST MF 810 FinTech Programming
• QST MF 921 Topics in Dynamic Asset Pricing

Additional Requirements

Qualifying examination.

Students must appear for a qualifying examination after completion of all coursework to demonstrate that they have:

• acquired advanced knowledge of literature and theory in their area of specialization;
• acquired advanced knowledge of research techniques; and
• developed adequate ability to craft a research proposal.

Guidelines for the examination are available from the departments. Students who do not pass either the written and/or oral comprehensive examination upon first try will be given a second opportunity to pass the exam. Should the student fail a second time, the student’s case will be reviewed by the Mathematical Finance Program Development Committee (MF PDC), which will determine if the student will be withdrawn from the PhD program. In addition, the PhD fellowship (if applicable) of any student who does not pass either the written and/or oral comprehensive examination after two attempts will be suspended the term after the exam was attempted.

Dissertation

Following successful completion of the qualifying examination, the student will develop a research proposal for the dissertation. The final phase of the doctoral program is the completion of an approved dissertation. The dissertation must be based on an original investigation that makes a substantive contribution to knowledge and demonstrates capacity for independent, scholarly research.

Doctoral candidates must register as continuing students for DS 999 Dissertation, a 2-unit course, for each subsequent regular term until all requirements for the degree have been completed. PhD students graduating in September are required to register for Dissertation in Summer Session II preceding graduation.

Academic Standards

Time limit for degree completion.

After matriculation into the PhD program, a candidate for the degree must meet certain milestones within specified time periods (as noted in the table below) and complete all degree requirements within six years of the date of first registration. Those who fail to meet the milestones within the specified time, or who do not complete all requirements within six years, will be reviewed by the PhD PDC and may be dismissed from the program. A Leave of Absence does not extend the six-year time limit for degree completion.

Performance Review

The Mathematical Finance Program Development Committee will review the progress of each doctoral candidate. Students must maintain a 3.30 cumulative grade point average in all courses to remain in good academic standing. Students who are not in good academic standing will be allowed one term to correct their status. Prior to the start of the term, the student must submit a letter to the Faculty Director (who will forward it to the PDC) explaining why the student has fallen short of the CGPA requirement and how the student plans to correct the situation. Failure to increase the CGPA to acceptable levels may result in probation or withdrawal from the program, at the discretion of the PhD Program Development Committee (PDC).

Graduation Application

Students must submit a graduation application at least five months before the date they expect to complete degree requirements. It is the student’s responsibility to initiate the process for graduation. The application is available online and should be submitted through the Specialty Master’s & PhD Center website for graduation in January, May, or August.

If graduation must be postponed beyond the term for which the application is submitted, students should contact the Specialty Master’s & PhD Center to defer the date. If students wish to postpone their graduation date past the six-year time limit for completion, they must formally petition the PhD Program Development Committee (PDC) for an extension. The petition, which must include the reason(s) for the extension as well as a detailed timetable for completion, is subject to departmental and PDC approval.

PhD degree requirements are complete only when copies of the dissertation have been certified as meeting the standards of Questrom School of Business and have been accepted by Mugar Memorial Library.

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We have 37 quantitative finance PhD Projects, Programmes & Scholarships

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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.

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.

A critical political economy of money, finance and finacialization

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.

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.

GIF CDT: Scaling industrial decarbonisation with data and finance

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.

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.

Various projects in theoretical ecology and modelling

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.

PhD Studentship (3 years): Understanding Financial Wellbeing in broader inclusive context: conceptualisation, measurement and interventions

Phd studentship in organizational psychology, ukri centre for doctoral training.

UKRI Centres for Doctoral Training conduct research and training in priority topics related to Artificial Intelligence. They are funded by the UK Government through UK Research and Innovation. Students may receive additional training and development opportunities as part of their programme.

An investigation of Quantum Cognition in Financial Decision Making

Leveraging business models for investment due diligence in the venture capital, structural characterizatoin of cell signaling at the membrane interface, 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.

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The field of finance covers the economics of claims on resources. Financial economists study the valuation of these claims, the markets in which they are traded, and their use by individuals, corporations, and the society at large.

At Stanford GSB, finance faculty and doctoral students study a wide spectrum of financial topics, including the pricing and valuation of assets, the behavior of financial markets, and the structure and financial decision-making of firms and financial intermediaries.

Investigation of issues arising in these areas is pursued both through the development of theoretical models and through the empirical testing of those models. The PhD Program is designed to give students a good understanding of the methods used in theoretical modeling and empirical testing.

Preparation and Qualifications

All students are required to have, or to obtain during their first year, mathematical skills at the level of one year of calculus and one course each in linear algebra and matrix theory, theory of probability, and statistical inference.

Students are expected to have familiarity with programming and data analysis using tools and software such as MATLAB, Stata, R, Python, or Julia, or to correct any deficiencies before enrolling at Stanford.

The PhD program in finance involves a great deal of very hard work, and there is keen competition for admission. For both these reasons, the faculty is selective in offering admission. Prospective applicants must have an aptitude for quantitative work and be at ease in handling formal models. A strong background in economics and college-level mathematics is desirable.

It is particularly important to realize that a PhD in finance is not a higher-level MBA, but an advanced, academically oriented degree in financial economics, with a reflective and analytical, rather than operational, viewpoint.

Faculty in Finance

Anat r. admati, juliane begenau, jonathan b. berk, michael blank, greg buchak, antonio coppola, darrell duffie, steven grenadier, benjamin hébert, arvind krishnamurthy, hanno lustig, matteo maggiori, paul pfleiderer, joshua d. rauh, claudia robles-garcia, ilya a. strebulaev, vikrant vig, jeffrey zwiebel, emeriti faculty, robert l. joss, george g.c. parker, myron s. scholes, william f. sharpe, kenneth j. singleton, james c. van horne, recent publications in finance, monetary tightening and u.s. bank fragility in 2023: mark-to-market losses and uninsured depositor runs, trading stocks builds financial confidence and compresses the gender gap, expectations and the neutrality of interest rates, recent insights by stanford business, the surprising economic upside to money in u.s. politics, your summer 2024 podcast playlist, why the “venture mindset” is not just for tech investors.

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• Mathematical and Computational Finance @ Oxford
• Study with us

DPhil (PhD) studies in Mathematical Finance @ Oxford

The Mathematical and Computational Finance Group (MCFG) at Oxford is one of the largest and most dynamic research environments in mathematical finance in the world.

We combine core mathematical expertise with interdisciplinary approach. We foster lively interactions between researchers coming from different backgrounds and a truly impressive seminar programme, all this within one of the world's top universities, singular through its tradition and unique environment.

If you are passionate about mathematics and research and want to pursue a DPhil in Financial Mathematics, Oxford simply offers one of the best and most exciting places to do it!

 Research Topic and Supervisor Allocation

We welcome students with their own particular ideas of research topic as well as students with a broad interest in the field of Mathematical Finance. You have an opportunity to tell us about your research passions, and indicate potential supervisors, in your application form. This will be followed up during the interview.

In light of this, if you are offered a place, an appropriate supervisor will be proposed prior to your arrival in Oxford. However, there can be some flexibility over this once you arrive.  Keeping with the Oxford tradition, we offer our students independence and respect as early researchers, and always aim to match students with the most appropriate supervisors.

Outstanding students with a strong background in analysis, probability and data science are welcome to apply for our DPhil program. Each year we receive a large number of excellent applications. The selection process is extremely competitive and we can only admit a handful of candidates each year.

In order to apply for DPhil studies in Mathematical & Computational Finance, please indicate your interest in Mathematical and Computational Finance on your application form. Selected applicants will be invited for an interview -- either in person or by video call.

For general information on DPhil please consult our  Doctor of Philosophy (DPhil) admissions pages .

For the CDT Mathematics of Random Systems please consult our  the CDT website .

Or please contact  @email .

Funding for DPhil students is available from a variety of sources. Please note that some funding opportunities have deadlines: it is advised to apply before the deadline in order to maximise your chances of receiving funding.

Funding is also available through the  Centre for Doctoral Training in Mathematics of Random Systems . To apply for this program please How to Apply .

Email:  @email Phone:  +44 (0)1865 615234

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DPhil Graduates

• Quantitative Finance Specialization
• Academic Programs
• Management Science and Analytics (Ph.D.)

The Quantitative Finance specialization in the Ph.D. in Management Science and Analytics program is excellent preparation for either academic careers or for students who want to apply the theoretical, analytical, and quantitative rigor of management science to careers in finance.

Dissertation research in this area may include a wide range of topics such as risk modeling, financial time series analysis, and investment analysis.

Required courses for the Quantitative Finance specialization (three credits per course):

• MSC 621—Corporate Finance
• MSC 623—Investments
• MSC 631—Theory of Finance I
• MSC 633—Theory of Finance II
• MSF 545/MSC 613—Structured Fixed Income Portfolios
• MSF 546/MSC 614—Quantitative Investment Strategies

View the curriculum for the Ph.D. in Management Science (MSC) program and MSC course descriptions .

Career Opportunities

Industry and Research

The specialization in Quantitative Finance prepares students for a wide range of careers in finance, particularly in areas such as investment and commercial banking, trading, and risk management. This background also opens career opportunities across industries in business functions focused on finance, financial modeling, economics, and risk compliance.

Chicago’s position as a global center for finance and fintech, as well as the home to the world’s largest markets in financial derivatives, make it a prime location for internships, networking, and job opportunities for Stuart students in quantitative finance.

Our graduates are ready to step into roles such as:

• Senior quantitative analyst or quantitative analytics manager-economic modeling
• Quantitative developer, senior quantitative modeler, or quantitative risk modeler
• Research data scientist, senior quantitative researcher, or quantitative researcher-asset management
• Portfolio risk analyst, senior quantitative risk analyst, or exotic rates quantitative analyst
• Equity derivatives quantitative strategist or quantitative portfolio strategist
• Senior quantitative markets analyst or machine learning analyst

Students interested in academic careers are supported by strong mentoring relationships with our faculty, opportunities to co-author papers published in prestigious scholarly journals, and help in securing adjunct positions to develop their teaching skills.

As a result, our graduates have launched teaching and research careers as finance faculty members at colleges and universities in the United States and around the world, such as:

• Carnegie Mellon University
• Beijing Normal University
• Lewis University
• Brooklyn College - City University of New York
• Benedictine University
• Northeastern Illinois University
• East China Normal University
• Saint Michael’s College (Vermont)

Learn more...

Financial Mathematics

Career paths in quantitative finance, quantitative research and analysis.

Professionals in this area use statistical and quantitative methods to analyze and predict the markets, and apply programming tools to produce robust investment strategies. Their work revolves around creating mathematical models that are used to assess and manage financial systems, potential risk, and timing of trades. 

Necessary Skills : a strong command of programming languages, such as Python, C#, and SQL, as well as statistical analysis tools, such as R, Matlab, and SAS. Some roles will also require knowledge of machine learning and natural language processing techniques.  Good understanding of a variety of mathematical and statistical models used in finance. 

Sample of Employer Partners in this area :

Portfolio Management

Portfolio managers engage in portfolio construction, monitoring asset exposures and allocations, managing client requests, tax management, monitoring pre-trade client guideline compliance and exception resolution. They initiate trades, and monitor the portfolios on an ongoing basis.  They also develop a deep understanding of investment products and operational policies and procedures. With career progression, they can manage a team of analysts and researchers.

Necessary Skills : in addition to effective communication skills and knowledge of asset classes, professionals in this area also require strong quantitative and mathematical modeling, coding, and analytical thinking skills. This role often prefer a financial analyst certification, like the Chartered Financial Analyst (CFA), and previous experience.  Most portfolio managers will start their careers as portfolio analysts.

Programming and Software Development

Quantitative engineers or quantitative developers work in the FinTech space. They are responsible for designing, developing, testing and deploying sophisticated software solutions to facilitate the work of various financial institutions.

Necessary Skills : excellent coding skills in Python, C++, and Java, and knowledge in probability, linear regression and time series data analysis.  In addition, interest in financial markets and knowledge of various financial products give quant developers a distinct advantage, since they work on a variety of projects with teams across an organization.

Risk Management

Professionals in this area empower the decision making process for investments and trades by providing risk analysis, and developing/enhancing risk model frameworks across various markets and assets. They use various techniques, including "value at risk" (VaR), Monte Carlo simulation, and linear regression-based statistical models, to measure the potential of loss on an investment profile. They also run stress tests to gauge the effectiveness of their models. 

Necessary Skills : strong skills in communication and detail orientation, quantitative and financial modeling skills, programming abilities using tools like VBA, Python, R, and SAS, as well as knowledge of various statistical and volatility models.

Traders analyze market data, such as price and volume, and use mathematical and statistical models to identify and execute trading decisions that may involve hundreds of thousands of shares and securities.

A trader develops a strategy and applies the model to historical market data so that it can be back-tested and optimized. If the strategy yields profit, it is then applied onto real-time markets to implement an automated trading process. Quantitative trading techniques also include high-frequency trading, algorithmic trading and statistical arbitrage.

Necessary Skills : a strong background in programming skills in Python, C++, SQL, R, and/ or Java. Ability to navigate price indexes, such as SPX and VIX. It also requires the knowledge of statistical analysis, numerical linear algebra, and machine learning processes. In addition, traders must possess the ability to thrive under pressure, maintain focus despite long hours, withstand an often competitive/intense environment, and respond well to failure.

Data Science and Analytics

As financial institutions further integrate the practice of collecting and analyzing data to gauge profit, loss, and client satisfaction, data science continues to be the fastest growing area of quantitative finance.

Professionals in this area work on data mining, gathering data sets, and deriving insights from these data sets. Data Scientists work in many data driven companies, such as investment banks, asset management firms, and technology companies. Their roles typically focus on risk management and predictive analytics.  Data Scientists are increasingly using machine learning, clustering algorithms, and artificial intelligence to identify unusual data patterns.

Necessary Skills : command of programming languages used in statistical modeling, such as Python and R, ability to work with large sets of financial data, and strong quantitative analysis skills. Time Series Analysis is also key to analyzing financial data. Machine learning and AI re also areas of growing importance in this field. 

Other Career Paths

Why Study for a Mathematical Finance PhD?

I was emailed by a reader recently asking about mathematical finance PhD programs and the benefits of such a course. If you are considering gaining a PhD in mathematical finance, this article will be of interest to you.

If you are currently near the end of your undergraduate studies or are returning to study after some time in industry, you might consider starting a PhD in mathematical finance. This is an alternative to undertaking a Masters in Financial Engineering (MFE), which is another route into a quantitative role. This article will discuss exactly what you will be studying and what you are likely to get out of a PhD program. Clearly there will be differences between studying in the US, UK or elsewhere. I personally went to grad school in the UK, but I will discuss both UK and US programs.

Mathematical finance PhD programs exist because the techniques within the derivatives pricing industry are becoming more mathematical and rigourous with each passing year. In order to develop new exotic derivatives instruments, as well as price and hedge them, the financial industry has turned to academia. This has lead to the formation of mathematical finance research groups - academics who specialise in derivatives pricing models, risk analysis and quantitative trading.

Graduate school, for those unfamiliar with it, is a very different experience to undergraduate. The idea of grad school is to teach you how to effectively research a concept without any guidance and use that research as a basis for developing your own models. Grad school really consists of a transition from the "spoon fed" undergraduate lecture system to independent study and presentation of material. The taught component of grad school is smaller and the thesis component is far larger. In the US, it is not uncommon to have two years of taught courses before embarking on a thesis (and thus finding a supervisor). In the UK, a PhD program is generally 3-4 years long with either a year of taught courses, or none, and then 3 years of research.

A good mathematical finance PhD program will make extensive use of your undergraduate knowledge and put you through graduate level courses on stochastic analysis, statistical theory and financial engineering. It will also allow you to take courses on general finance, particularly on corporate finance and derivative securities. When you finish the program you will have gained a broad knowledge in most areas of mathematical finance, while specialising in one particular area for your thesis. This "broad and deep" level of knowledge is the hallmark of a good PhD program.

Mathematical Finance research groups study a wide variety of topics. Some of the more common areas include:

• Derivative Securities Pricing/Hedging: The technical term for this is "financial engineering", as "quantitative analysis" now encompasses a wide variety of financial areas. Some of the latest research topics include sophisticated models of options including stochastic volatility models, jump-diffusion models, asymptotic methods as well as investment strategies.
• Stochastic Calculus/Analysis: This is more of a theoretical area, where the basic motivation stems from the need to solve stochastic differential equations. Research groups may look at path-dependent PDEs, functional Ito calculus, measure theory and probability theory.
• Fixed Income Modeling: Research in this area centres on effectively modelling interest rates - such as multi-factor models, multi-curve term structure models as well as interest rate derivatives such as swaptions.
• Numerical Methods: Although not always strictly related to mathematical finance, there is a vast amount of university research carried out to try and develop more effective means of solving equations numerically (i.e. on the computer!). Recent developments include GPU-based Monte Carlo solvers, more efficient matrix solvers as well as Finite Differences on GPUs. These groups will almost certainly possess substantial programming expertise.
• Market Microstructure/High-Frequency Modeling: This type of research is extremely applied and highly valued by funds engaged in this activity. You will find many academics consulting, if not contracting, for specialised hedge funds. Research areas include creating limit order market models, high frequency data statistical modelling, market stability analysis and volatility analysis.
• Credit Risk: Credit risk was a huge concern in the 2007-2008 financial crisis and many research groups are engaged in determining such "counterparty risks". Credit derivatives are still a huge business and so a lot of research goes into collateralisation of securities as well as pricing of exotic credit derivatives.

These are only a fraction of the total areas that are studied within mathematical finance. The best place to find out more about research topics is to visit the websites of all the universities which have a mathematical finance research group, which is typically found within the mathematics, statistics or economics faculty.

The benefits of undertaking a PhD program are numerous:

• Employment Prospects: A PhD program sets you apart from candidates who only possess an undergraduate or Masters level ability. By successfully defending a thesis, you have shown independence in your research ability, a skill highly valued by numerate employers. Many funds (and to a lesser extent, banks) will only hire PhD level candidates for their mathematical finance positions, so in a pragmatic sense it is often a necessary "rubber stamp". In investment banks, this is not the case so much anymore, as programming ability is generally prized more. However, in funds, it is still often a requirement. Upon being hired you will likely be at "associate" level rather than "analyst" level, which is common of undergraduates. Your starting salary will reflect this too.
• Knowledge: You will spend a large amount of time becoming familiar with many aspects of mathematical finance and derivatives theory. This will give you a holistic view into the industry and a more transferable skill set than an undergraduate degree as you progress up the career ladder. In addition, you will have a great deal of time to learn how to program models effectively (without the day-to-day pressure to get something implemented any way possible!), so by the time you're employed, you will be "ahead of the game" and will know best practices. This aspect is down to you, however!
• Intellectual Prospects: You are far more likely to gain a position at a fund after completing a PhD than without one. Funds are often better environments to work in. There is usually less stress and a more relaxed "collegiate" environment. Compare this to working on a noisy trading floor, where research might be harder to carry out and be perceived as less important.

I would highly recommend a mathematical finance PhD, so long as you are extremely sure that a career in quantitative finance is for you. If you are still unsure of your potential career options, then a more general mathematics, physics or engineering PhD might be a better choice.

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2024 QuantNet Ranking of Best Financial Engineering Programs

The QuantNet ranking of Financial Engineering, Mathematical, and Quantitative Finance master's programs in the US offers detailed insights into placement and admission statistics from the nation's top programs. It serves as the ultimate guide for prospective applicants, helping them choose and enroll in the best master’s programs in quantitative finance.

Baruch College

Princeton university, carnegie mellon university, university of california, berkeley, columbia university, university of chicago, cornell university, nyu courant, massachusetts institute of technology.

NYU Tandon School of Engineering

Georgia institute of technology, north carolina state university, university of california, los angeles, johns hopkins university, university of washington, rutgers university, university of illinois urbana champaign, stevens institute of technology, university of minnesota, boston university.

Fordham University

Uc san diego.

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New York University

University of illinois urbana-champaign.

University of California, San Diego

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Graduate Programs

Thank you for your interest in the Graduate Program in Economics at the University of Washington. We offer a program leading to the Doctor of Philosophy degree in economics. The PhD program is for students interested in pursuing advanced study and doing original research in economics. This program develops professional economists for a variety of careers in teaching, in government, in industry, or with international agencies in the United States and abroad.

Quan Wen Professor, Graduate Program Director

Overview of the Doctoral Program's three phases .

Econ Graduate Student Resources

• Graduate Guide for Students (PDF) A detailed description of the current requirements for the MA and PhD degrees.
• Supervisory Committee Form (PDF) Students may either collect signatures or emails from their prospective committee members. Emails should be printed out and stapled to the back of the committee form.
• Supervisory Committee Change Request   Students need to collect signatures and submit this form for any committee changes
• Econ Grad Job Placement Profiles of some of our recent graduates who have been placed in both academic and non-academic positions.
• How We Allocate TAs (PDF) A description of the process by which students are ranked for funding purposes.
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PhD Financial Mathematics / Overview

Year of entry: 2024

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The standard academic entry requirement for this PhD is an upper second-class (2:1) honours degree in a discipline directly relevant to the PhD (or international equivalent) OR any upper-second class (2:1) honours degree and a Master’s degree at merit in a discipline directly relevant to the PhD (or international equivalent).

Other combinations of qualifications and research or work experience may also be considered. Please contact the admissions team to check.

Full entry requirements

Apply online

In your application you’ll need to include:

• The name of this programme
• Your research project title (i.e. the advertised project name or proposed project name) or area of research
• Your proposed supervisor’s name
• If you already have funding or you wish to be considered for any of the available funding
• A supporting statement (see 'Advice to Applicants for what to include)
• Details of your previous university level study
• Names and contact details of your two referees.

Programme options

Programme description

Opportunities for PhD research are available in a range of Financial Mathematics research topics or Mathemathics research areas . For more information, please see advice on choosing a project or find out more about specific projects . Please contact the relevant individual members of staff for information about a specific project, or get in touch with the  Postgraduate Admissions Tutor .

Students may enter our graduate programme in Mathematical Finance by initially taking our taught M.Sc. course over 1 year. This, subject to satisfactory progress, can lead to admission to the PhD programme.

For entry in the academic year beginning September 2024, the tuition fees are as follows:

• PhD (full-time) UK students (per annum): Band A £4,786; Band B £7,000; Band C £10,000; Band D £14,500; Band E £24,500 International, including EU, students (per annum): Band A £28,000; Band B £30,000; Band C £35,500; Band D £43,000; Band E £57,000
• PhD (part-time) UK students (per annum): Band A £2393; Band B £3,500; Band C £5,000; Band D £7,250; Band E 12,250 International, including EU, students (per annum): Band A £14,000; Band B £15,000; Band C £17,750; Band D £21,500; Band E £28,500

Further information for EU students can be found on our dedicated EU page.

The programme fee will vary depending on the cost of running the project. Fees quoted are fully inclusive and, therefore, you will not be required to pay any additional bench fees or administration costs.

All fees for entry will be subject to yearly review and incremental rises per annum are also likely over the duration of the course for Home students (fees are typically fixed for International students, for the course duration at the year of entry). For general fees information please visit the postgraduate fees page .

Always contact the Admissions team if you are unsure which fees apply to your project.

Scholarships/sponsorships

There are a range of scholarships, studentships and awards at university, faculty and department level to support both UK and overseas postgraduate researchers.

To be considered for many of our scholarships, you’ll need to be nominated by your proposed supervisor. Therefore, we’d highly recommend you discuss potential sources of funding with your supervisor first, so they can advise on your suitability and make sure you meet nomination deadlines.

For more information about our scholarships, visit our funding page or use our funding database to search for scholarships, studentships and awards you may be eligible for.

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Our internationally-renowned expertise across the School of Natural Sciences informs research led teaching with strong collaboration across disciplines, unlocking new and exciting fields and translating science into reality.  Our multidisciplinary learning and research activities advance the boundaries of science for the wider benefit of society, inspiring students to promote positive change through educating future leaders in the true fundamentals of science. Find out more about Science and Engineering at Manchester .

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Finance Group

Finance is the study of markets for real and financial assets. The practical implications of modern financial theory are widely recognized and implemented by Wall Street and corporations. The PhD program provides students with an understanding of the theory on which the field is based and the tools they need to conduct theoretical and applied research.

Once required coursework in microeconomics and macroeconomics theory is completed, students are free to develop their programs of study and research with the guidance of faculty members. Often, faculty offer students an opportunity to participate in and expand on faculty research interests. Finance Faculty

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Example Thesis Topics

Thesis Committee: Monika Piazzesi (co-primary) [email protected] Martin Schneider (co-primary) [email protected] Melanie Morten [email protected] This paper studies how frictions in debt and equity markets affect wealth inequality in Eurozone countries. Using micro data on households and firms, I document that in more unequal countries, there are more privately held firms, and ownership of publicly traded firms is more concentrated. I develop a dynamic general equilibrium model in which entrepreneurs have the option to run a private firm and issue debt, or go public and also issue outside equity. Both forms of external finance are subject to country-specific frictions. With more access to debt, entrepreneurs can run larger firms and are wealthier. Similar to debt, outside equity allows entrepreneurs to increase investment in their firm, but it also reduces their risk exposure, which lowers savings and wealth holdings. When parameters are chosen to match the facts I document on firm ownership and financing, the model successfully predicts differences in wealth concentration across countries. (with ) (with ) (with Monika Piazzesi and Martin Schneider) ( ) (with Arun Chandrashekhar and Melanie Morten) Undergraduate Students Doctoral Students Master’s Students Engineering Master’s Students Faculty & Staff Parents & Families Asian / Pacific Islander Black/African American First Generation/Low Income Hispanic/Latinx International Native American/Indigenous Neurodiverse Student Athletes Students with Disabilities Undocumented What is a Career Community? Business, Finance & Consulting Data, Technology & Engineering Discovery & Exploration Education, Government, Nonprofit & Policy Energy, Environment & Sustainability Entertainment, Media & Arts Healthcare & Biomedical Sciences Innovation, Entrepreneurship & Design Know Yourself Explore Options Focus & Prepare Take Action Evaluate & Refine Featured Opportunities Career Readiness Resources Personalize Your Hub For Employers Wells Fargo 2025 summer internship quantitative analytics program – early careers phd. Share This: Share 2025 Summer Internship Quantitative Analytics Program – Early Careers PhD on Facebook Share 2025 Summer Internship Quantitative Analytics Program – Early Careers PhD on LinkedIn Share 2025 Summer Internship Quantitative Analytics Program – Early Careers PhD on X About this role: Wells Fargo is seeking talent to join the Summer Internship Quantitative Analytics Program (PhD) – Early Careers.  Learn more about the career areas and lines of business at wellsfargojobs.com   Program Overview  The internship program is a ten-week overview of Wells Fargo, and prepares you to develop, implement, calibrate, or validate quantitative models. The program is designed to provide you a blend of technical, business, and professional developmental opportunities that incorporates real-world experiences and prepares you for a career at Wells Fargo.  Based on individual achievement, those who excel in the internship program may receive an offer for full-time employment.  There are two tracks:    The Capital Markets track deals with mathematical models for pricing, hedging and risking complex financial instruments. Wells Fargo trading portfolios include products in all traded asset classes such as credit, commodity, Equity, FX, Rate, Mortgages, and Asset-Backed Finance.   The Risk Analytics & Decision Science track deals with statistical, econometric, and machine learning/AI models for a variety of applications, including loss and revenue forecasting, credit decisions, financial crimes, fair lending, operational risks, and analysis of unstructured data such as text and audio.    Tracks will be designated and assigned to the candidate upon reviewing application.  Placement group includes:   Artificial Intelligence Machine Learning Model Development Center  Traded Products Model Development   Risk Modeling Group – Forecast Model Development  Risk Modeling Group – Decision Model Development  Market and Counterparty Risk Analytics   Mortgage Model Development Center  Model Risk Management   Consumer Model Development Center  In this role, you will: Perform mathematical model development and validation under the direction of experienced team members using Python, R, C++, SAS, SQL or other programming languages  Be part of a team that provides solutions to business needs and improves risk management processes  Communicate solutions to stakeholders through verbal presentations and written documentation  Stay up to speed on industry challenges and new and innovative modeling techniques used across Wells Fargo to solve business problems or enhance business capabilities  What you will experience:   Spend your Intern Induction Week at an offsite location  Structured and engaging onboarding experience   Speaker series with Wells Fargo senior leaders  Professional development opportunities  Networking and engaging with peers  On-the-job experiences contributing to strategic business goals  Ideal candidate for this role: Ability to communicate concepts and ideas, both verbally and written, and explain technical material to a non-technical audience   For the Capital Markets Track : Experience and demonstrated knowledge in mathematical and numerical methods including Monte Carlo methods, differential equations, linear algebra, applied probability, and statistics   For the Risk Analytics & Decision Science Track : Experience and demonstrated first-hand knowledge in a number of these areas: data analysis, statistical modeling, machine learning/AI models, data management, and computing  Energetic self-starter who proactively takes initiative, remains curious and has a genuine interest in learning and growth. Program Dates: June – August,2025 (10 weeks, 40 hours per week)  Program Location:  Charlotte, NC  Required Qualifications: 6+ months of work experience, or equivalent demonstrated through one or a combination of the following: work experience, training, military experience, education Desired Qualifications: Currently pursuing a PhD degree with an expected graduation date between December 2025 – or later  Currently pursuing a PhD degree in with emphasis on Statistics, Mathematics, Computer Science, Economics, Physics, Quantitative Finance, Operations Research, Data Science, Engineering or related quantitative field or a related quantitative field   Excellent computer programing skills and use of statistical software packages such as Python, R, SAS, SQL, Spark, Java, and C++   Excellent verbal, written, and interpersonal communication skills  Involvement in extracurricular enrichment activities through one or more of the following: volunteerism, student organization involvement, study abroad program(s), leadership position(s), non-profit involvement  Intermediate Microsoft Office (Word, Excel, Outlook, and PowerPoint) skills  #earlycareers How to Apply:  The 2025 Summer Internship Quantitative Analytics Development Program (PhD) will be accepting applications through the Wells Fargo Career website.   To search and apply for open positions in this program, please go to https://www.wellsfargo.com/careers/ and click on “Search Jobs”.   Search by Job ID #: R-384891 or by keywords, “2025 Summer Internship Quantitative Analytics Development Program – PhD” and follow the instructions to complete your application.  We Value Diversity   At Wells Fargo, we believe in diversity and inclusion in the workplace; accordingly, we welcome applications for employment from all qualified candidates, regardless of race, color, gender, national origin, religion, age, sexual orientation, gender identity, gender expression, genetic information, individuals with disabilities, pregnancy, marital status, status as a protected veteran or any other status protected by applicable law.  Featured Events Undergraduate Students Graduate Students Career Center Policies Recruiting & Policy Guide Employer Resources Faculty and Staff Applied Science & Technology (B.A.) Biomedical Engineering (B.S., M.S., Ph.D.) Civil & Environmental Engineering (B.S., M.S., Ph.D.) Computer Engineering (B.S., M.S., Ph.D.) Computer Science (B.S., M.S., Ph.D.) Cybersecurity (M.S.) Data Analytics (M.S.) Electrical Engineering (B.S., M.S., Ph.D.) Engineering Management (M.S. Ph.D.) Mechanical & Aerospace Engineering (B.S., M.S., Ph.D.) Regulatory Biomedical Engineering (M.Eng.) Systems Engineering (B.S., M.S., Ph.D.) Telecommunications Engineering (M.S.) Featured Jobs & Internships Resume & Cover Letter Preparation Job/Internship Search Interview Preparation Funding & Scholarships International Student Resources PRAXIS Application Meet the Team Our Mission Wells Fargo 2025 quantitative analytics development program – capital markets (phd). Share This: Share 2025 Quantitative Analytics Development Program – Capital Markets (PhD) on Facebook Share 2025 Quantitative Analytics Development Program – Capital Markets (PhD) on LinkedIn Share 2025 Quantitative Analytics Development Program – Capital Markets (PhD) on X About this role: Wells Fargo is seeking talent to join the Quantitative Analytics Program. Learn more about the career areas and lines of business at wellsfargojobs.com   Program Overview   The Quantitative Analytics Program provides you with an overview of Wells Fargo and prepares you to develop, implement, calibrate, or validate quantitative models. It is designed to give you a blend of technical, business, and professional developmental opportunities that incorporates real-world experiences and prepares you for a career at Wells Fargo.  Upon completion of two six-month rotations, you will join a team within your program track.  The Capital Markets track deals with mathematical models for pricing, hedging and risking complex financial instruments. Wells Fargo trading portfolios include products in all traded asset classes such as credit, commodity, Equity, FX, Rate, Mortgages, and Asset-Backed Finance.    Placement group includes:    Traded Products Model Development   Market and Counterparty Risk Analytics   Mortgage Model Development Center   Model Risk Management   Consumer Model Development Center   In this role you will:   Be part of a team that provides solutions to business needs and improves risk management processes   Communicate solutions to stakeholders through presentations and written documentation   Stay up to speed on industry challenges and new and innovative modeling techniques used across Wells Fargo to solve business problems or enhance business capabilities.     Ideal candidate for this role:   Excellent computer programing skills and use of statistical software packages such as Python, R, SAS, SQL, Java, Spark, and C++   Ability to communicate concepts and ideas, both verbally and written, and explain technical material to a non-technical audience.   Energetic self-starter who proactively takes initiative, remains curious and has a genuine interest in learning and growth.  Program Start Date:  July 2025    Program Location:   Charlotte, NC   Program Length: 24 months (Year 1: training and 2 rotations; Year 2: permanent placement)   Required Qualifications: 2+ years of Quantitative Analytics experience, or equivalent demonstrated through one or a combination of the following: work experience, training, military experience, education Master’s degree or higher in statistics, mathematics, physics, engineering, computer science, economics, or quantitative discipline Desired Qualifications: ​Currently pursuing PhD degree with an expected graduation date in December 2024-June 2025 OR graduated from a PhD program after May 1, 2022 and is currently completing a postdoc with emphasis in Mathematics, Operations Research, Physics, Engineering, Computer Science, Statistics, Quantitative Finance or related quantitative field.    Perform mathematical model development and validation under the direction of experienced team members using Python, R, C++, SAS, SQL or other programming languages   Experience and demonstrated knowledge in mathematical and numerical methods including Monte Carlo methods, differential equations, linear algebra, applied probability, and statistics    Excellent verbal, written, and interpersonal communication skills  Ability to work effectively, as well as independently, in a collaborative, change driven environment  Intermediate Microsoft Office (Word, Excel, Outlook, and PowerPoint) skills  Involvement in extracurricular enrichment activities through one or more of the following: volunteerism, student organization involvement, study abroad program(s), leadership position(s), non-profit involvement   #earlycareers  How to Apply:  The 2025 Quantitative Analytics Development Program will be accepting applications through the Wells Fargo Career website.   To search and apply for open positions in this program, please go to https://www.wellsfargo.com/careers/ and click on “Search Jobs”.   Search by Job ID #: R-3865666 or by keywords, “2025 Quantitative Analytics Development Program – Capital Markets – PhD” and follow the instructions to complete your application.  We Value Diversity   At Wells Fargo, we believe in diversity and inclusion in the workplace; accordingly, we welcome applications for employment from all qualified candidates, regardless of race, color, gender, national origin, religion, age, sexual orientation, gender identity, gender expression, genetic information, individuals with disabilities, pregnancy, marital status, status as a protected veteran or any other status protected by applicable law.  W. Scott Amey Career Services Center 800 22nd St. NW Suite 2730 Washington, DC 20052 Phone: (202) 994-4205 seascareers@gwu.edu Jillian Grennan Emory University Innovator Diversity Pilots Initiative Elements in Law, Economics, and Politics [email protected] +1(202)265-6111 Academic interests: Finance, Law, Innovation, and Culture  Professor Grennan is a financial economist whose research provides actionable insights for practitioners navigating corporate value creation and emerging technologies.  Professor Grennan’s primary research focuses on how corporations create value, especially intangible (e.g., intellectual property).  In her pioneering research, Professor Grennan employs innovative computational techniques to quantify aspects of sustainability, such as corporate culture and the efficacy of diversity, equity, and inclusion (DEI) initiatives.  She utilizes machine learning (ML) and natural language processing (NLP) techniques to measure these often difficult-to-measure factors and empirically examines their impact on financial value. Additionally, Professor Grennan's expertise extends to emerging financial technologies (FinTechs) catalyzed by artificial intelligence (AI) and blockchain advancements.  Her research in this domain investigates the effects of AI on high-skilled work, the relationship between market efficiency and AI signals, the dynamics of competition between incumbent corporations and FinTechs, and the evolving governance, tax, and regulatory needs brought about by FinTechs, digital assets, and decentralized autonomous organizations (DAOs).   For more details, please see Jill's CV .   Professor Grennan received her Ph.D. from The Wharton School at the University of Pennsylvania, an M.S. from Georgetown University, and a B.A. from Wellesley College.  Grennan was previously on the faculty at Duke University, the University of California (Berkeley), and Santa Clara University, and before academia, she worked at the U.S. Federal Reserve Board of Governors, the World Trade Organization, and KPMG. RESEARCH PAPERS CULTURE, GOVERNANCE, AND CORPORATE FINANCE RESEARCH Graham, J., J. Grennan, C. Harvey, and S. Rajgopal, 2022. "Corporate Culture: Evidence from the Field" Journal of Financial Economics 146 ,  552 – 593. [ JFE ], [ SSRN ], [ Data & Code ] Grennan, J., 2019, "Dividend Payments as a Response to Peer Influence."  Journal of Financial Economics  131, 549 – 570. [ JFE ], [ SSRN ], [ slides ] Eldar, O., and J. Grennan, 2024, "Common Venture Capital Investors and Startup Growth" Review of Financial Studies 37, 549-590.   [ RFS ],   [ SSRN ] Eldar, O., and J. Grennan, 2021, "Common Ownership and Entrepreneurship." AEA Papers & Proceedings  111, 582-586. [ P&P ], [ SSRN ], [ Data & Code ] Gorton, G., J. Grennan, and A. Zentefis, 2022. "Corporate Culture" Annual Review of Financial Economics 14, 535-561. [ ARFE ], [ SSRN ], [ slides ], [ Summary ] Grennan, J., and K. Li, 2023. "Corporate Culture: A Review and Directions for Future Research" In G. Hilary and D. McLean (Eds.) , Handbook of Financial Decision Making . [ SSRN ], [ Handbook ] Graham, J., J. Grennan, C. Harvey, and S. Rajgopal, 2023. "What Do Financial Executives Say About Corporate Culture and Strategy?" Management and Business Review , forthcoming. [ SSRN ], [MBR] Grennan, J., M. Lowry, 2024, "Common Venture Capital Investors" In: Cumming, D., Hammer, B. (eds) The Palgrave Encyclopedia of Private Equity. Palgrave Macmillan. [ SSRN ], [Handbook]  Grennan, J., 2016, "Balancing Governance and Culture to Create Sustainable Value."  Governance Studies, The Initiative on 21st Century Capitalism, No. 27, 1-13, Brookings Institution . [ Brookings ] Graham, J., J. Grennan, C. Harvey, and S. Rajgopal, 2022. "Corporate Culture: The Interview Evidence" Journal of Applied Corporate Finance 34 , 22– 41. [ SSRN ], [ JACF ] Graham, J., J. Grennan, C. Harvey, and S. Rajgopal, 2023. "Corporate culture in a new era: Views from the C-suite" Journal of Applied Corporate Finance 35 , 1-20 . [ SSRN ], [ JACF ] Grennan, J., and J. Pacelli 2023. "Click-by-click: Using Alternative Data to Make a Business Case for Culture" California Management Review Insights [ CMRI ], [ SSRN ] Grennan, J., and R. Michaely, 2022 "The Deleveraging of U.S. Firms and the Role of Institutional Investors" (R&R Review of Finance ) [ SSRN ] Cai, W., J. Grennan, and L. Qiu, 2024, "Do Diverse Directors Influence DEI Outcomes?" [ SSRN ] Grennan, J. 2022, "A Corporate Culture Channel: How Increased Shareholder Governance Reduces Firm Value" [ SSRN ] FINTECH, AI, BLOCKCHAIN AND INTELLECTUAL PROPERTY RESEARCH Grennan, J., and R. Michaely, 2021, "FinTechs and the Market for Financial Analysis" Journal of Financial and Quantitative Analysis , 56(6) 1877– 1907. (Lead Article) [ JFQA ], [ SSRN ], [ slides ], [ Video ]  Appel, I. and Grennan, J., 2023 "Control of Decentralized Autonomous Organizations" AEA Papers & Proceedings  113. [ P&P ], [ SSRN ], [ Data & Code ] Grennan, J., 2022, "Social Change through Financial Innovation: Evidence from Donor-advised Funds" Review of Corporate Finance Studies 11, 694 – 735. [ RCFS ], [ SSRN ], [ Data & Code ]  Abrams, D., U. Akcigit, and J. Grennan, 2023 "Patent Value and Citations: Creative Destruction or Strategic Disruption?" (R&R RAND Journal of Economics ) [ SSRN ] Grennan, J., and R. Michaely, 2022, "Artificial Intelligence and High-Skilled Work: Evidence from Analysts" [ SSRN ]  Beale et al., 2023, "Common-sense Recommendations for the Application of Tax Law to Digital Assets" [ SSRN ]  Appel, I. and Grennan, J., 2023 "Decentralized Governance and Digital Asset Prices" [ SSRN ] Grennan, J., 2022 "FinTech Regulation in the United States: Past, Present, and Future" (R&R Elements in Law, Economics, and Politics) [ SSRN ] Chien, C. and J. Grennan, 2024, "Unpacking the Innovator-Inventor Gap: Evidence from Engineers" [ SSRN ] Chien, C. and J. Grennan, J. 2024, "Closing the Innovator-Inventor Gap: Evidence from Proactive (Opt-Out) Outreach" [ AEA registered RCT ], [ Pre-Analysis Plan ] Chien, C., J. Grennan, J. Sandvik, 2024, "Small-Scale Mentoring, Large-Scale Impact: Evidence from a Superstar Firm" [in-progress] Chien, C. and J. Grennan, J. 2024, "Does Poor Management and Ineffective Culture Increase the Gender Gap in Patent Applications?" [in-progress] Grennan, J. and D. Rock, 2023, "Regulating Emerging Technology: Evidence from Artificial Intelligence and Digital Assets" [in-progress] Grennan, J., and D. Musto, 2018, "Who Benefits from Bond Market Modernization?" [ SSRN ] Grennan, J., 2023, "Embracing Sustainability and Inclusivity: A Roadmap for Thriving in Web3" [in-progress] Grennan, J., C. Makridis, and M. Zator, 2022, "AI-augmented Culture and Leadership" [ AEA registered RCT ] Grennan, J. 2022, "Communicating Culture Consistently: Evidence from Banks" [ SSRN ] Grennan, J. and R. Michaely 2023, "Values-Based Investing: Evidence from Debiased Data" [in-progress] Grennan, J. 2024, "Decomposing the Value of Corporate Culture" [in-progress] MAGAZINES & OP-EDS My research has been featured in major news outlets like the Wall Street Journal, Forbes, and Fortune.  For instance, my research is highlighted in " Should you follow an activist into a stock? " You can also discover my book reviews and opinion pieces.  For instance, I reviewed Driverless Finance   by Hilary J. Allen. Connecting business leaders to new academic ideas is a passion of mine.  Check out these blog forums where I express my views on culture, governance, and more. Posts on the Harvard Law School Corporate Governance Blog: [ 1 ], [ 2 ], [ 3 ], [ 4 ], [ 5 ], [ 6 ], [ 7 ] Posts on the Columbia Law School BlueSky Blog: [ 1 ], [ 2 ] Posts on the Duke University School of Law FinReg Blog: [ 1 ] Join me for discussions of culture, business, and technology: Culture Gap podcast produced by THRUUE The Manage Your Message  podcast produced by Jim Karrh  Every Other Wednesday videocast with Mike Kerr, Jeff Tobe, and Sunjay Nath  EXLEARN Talks: Culture and Gorwth , videocast with Tier1 Performance  CORPORATE CULTURE Corporate culture is an important driver of business value, and policymakers often blame dysfunctional cultures for egregious actions at firms like Wells Fargo, VW, Toshiba, Uber, and Pinterest.  Building on a broader literature on corporate institutions, this course examines antecedents and consequences of corporate culture through the lens of an informal institution.  We will learn how differences in espoused and lived cultural values are associated with various business outcomes.  Then, we will study specific cases to recognize when, how, and why aspirational ideals may not be met.  Finally, we will examine how leaders can foster a culture that meets the evolving regulatory and stakeholder expectations surrounding ethics and compliance. [ Syllabus ], [ Full course slides ], [ PhD lecture ] ECONOMICS OF DIGITAL ASSETS The scale of digital asset activity has increased significantly in recent years.  Although the interconnections with the traditional financial and economic system are relatively limited, they could grow rapidly.  This course introduces students to the emerging technologies associated with decentralized systems, beginning with the principles of blockchain technology and smart contracts.  Students will gain a comprehensive understanding of how these technologies underpin the growing area of decentralized finance (DeFi) and how it contrasts sharply with traditional finance.  The course highlights decentralized systems' unique advantages and challenges through applications like DEXs, stablecoins, DAOs, and oracles.  The course will end with an introduction to the governance and regulatory frameworks in the blockchain ecosystem, assessing their effectiveness and potential vulnerabilities.  By engaging with these topics, students will understand the economic principles within decentralized systems and be able to evaluate their implications for life in the digital age. [BA] SUSTAINABLE INVESTMENT  This course trains students on sustainable investment principles and methodologies and to become managers and investors of sustainable/responsible/ESG investment portfolios.  The Sustainable Investment Fund was launched with generous gifts from Haas alumni, and this flagship course allows MBA students, as Fund Principals, to manage a $4.5 million fund dedicated to delivering both strong financial returns and positive social impact. I was awarded Haas's "Club 6" Teaching Award for this course.  [ MBA ] FINANCIAL ECONOMICS OF SUSTAINABILITY This course explores how market participants respond to and incorporate sustainability criteria such as environmental, social, and governance (ESG) considerations into their decision-making.  This course aims to give students a solid foundation in the financial economics of sustainability and to critically examine the strategies used in capital markets to achieve sustainability goals.  We will consider the economic risks and opportunities that fall under the sustainability lens and their role in driving long-term value creation.  For instance, we will explore various ways financial institutions have modeled climate risks and analyze how communities most impacted by injustice leverage investment strategies to pursue social justice.  Throughout the course, we will examine how big data and artificial intelligence are being used to help tackle sustainability challenges.  [BA] ESG PHD ELECTIVE In 2020, I helped design an ESG reading group for graduate students at Duke.  Topics include (i) the purpose of business and stakeholders, (ii) sustainable, impact, and ESG investing, (iii) ESG and traditional asset pricing models, (iv) ESG with a focus on “S”, (v) the economics of discrimination, (vi) ESG with a focus on “G”, (vii) causality and identification in ESG, (viii) ESG and innovation, and (ix) the real effects of ESG. Link to the reading list: [ PhD ]. CORPORATE FINANCE I've been honored to teach Corporate Finance across a number of programs: MS, MBA, EMBA, PhD, and JD.  I always say that maximizing firm value is a team sport, so I'm happy to partner with students where they are in their careers and help them to learn about how investment, financing, and payout decisions are made.  I was awarded Fuqua's Excellence in Teaching Award for this course.  Links to recent syllabi for the PhD and MBA courses: [ PhD ], [ MBA ], [ JD ].   BUSINESS LAW Decisions to trade and produce require trust: trust that consumers, firms, workers, financial institutions, and asset owners will do as they promise and that violations of such promises will be unacceptable in the marketplace.  Business law provides these guarantees and the boundaries within which certain promises can be made and enforced.  This course provides a broad overview of the legal system and the primary substantive areas of law relevant to business decisions and transactions. [ BA ] INSIDE THE BOARDROOM This course examines the relationships between executives and the boards of directors charged with overseeing them.  While boards are legally bound to represent the interests of equity investors, when carrying out this duty they are often called on to respond to the needs of numerous other stakeholders and society at large.  With mistakes instantly transmitted via social media, the reputational stakes are high.  Via case studies, we examine what determines the best corporate governance practices. [MBA]  CASE STUDIES Grennan, J. and L. Tyson, “Just Climate: A New Investment Model,” 2024. [ HBSP ]  Grennan, J., A. Lefort, and J. Pacelli, “Social Media Background Screening at Fama Technologies” Harvard Business School Case, 2023. [ HBSP ] Grennan, J., A. Lefort, and J. Pacelli, “Social Media Background Screening at Fama Technologies (B)” Harvard Business School Case, 2023. [ HBSP ] Musto, D. and J. Popadak, “The Relationship between the Board and Stockholders: Air Products Takeover Bid for Airgas,” The Wharton School Case 76, 2014. THE WHARTON SCHOOL, UNIVERSITY OF PENNSYLVANIA Dissertation title: "Social Forces in Corporate Finance" Dissertation committee: David Musto, Mark Duggan (co-chairs), Michael Roberts and Todd Sinai GEORGETOWN UNIVERSITY Completed a Master's degree in Mathematics and Statistics while working full-time in Washington, D.C. WELLESLEY COLLEGE Double majored in Economics and Classical Civilizations IE Virtual Asset Regulation Lab for "Decentralized Digital Asset Regulation: Exploring the Dynamics of Protective and Enabling Approaches" Ripple's University Blockchain Research Initiative Grant for "Scaling Decentralized Autonomous Organizations" Convergence Research (CORE) Institute Fellowship for "Tackling Climate-Induced Challenges with AI" Leavey Center Grant for Sustainability Research Ripple's University Blockchain Initiative Grant for "Control of Decentralized Autonomous Organizations" HKU-SCF FinTech Academy Grant for "The FinTech Workforce" Center for Growth Markets Grant Institute for Humane Studies Grant for "Inclusivity in the Metaverse" INQUIRE Europe Research Grant for "ESG Integration Across Funds and Debiasing Data" Paris-Dauphine FinTech Research Award for "ESG Integration Across Funds and Debiasing Data" IBSI-Haas Lab for Sustainable Financial Services and Innovation for "FinTech Regulation" Best Paper in Corporate Governance awarded by IRRC for "Corporate Culture: Evidence from the Field" Duke Intellectual Community Planning Grant for "Big Data and Social Interactions" Thomas Edison Innovation Award, George Mason University Fuqua's Junior Faculty Recognition Research Grant Best Paper in Corporate Governance awarded by IRRC for "A Corporate Culture Channel: How Increased Shareholder Governance Reduces Firm Value" KPMG's Global Valuation Research Grant for "Decomposing the Value of Corporate Culture" Duke's Center for Financial Excellence Research Grant AEA's CSWEP Cement Fellow WFA's Cubist Systematic Strategies Award for Outstanding Research for "A Corporate Culture Channel: How Increased Shareholder Governance Reduces Firm Value" Best Finance PhD Dissertation Award, Olin Business School for "A Corporate Culture Channel: How Increased Shareholder Governance Reduces Firm Value" Mack Institute for Innovation Management Research Grant for "Patent Value and Citations: Creative Destruction or Strategic Disruption?" ANNOUNCEMENTS UPCOMING CONFERENCES I'm helping to organize the 2nd Innovator Diversity Pilots Conference .  I've previously organized a symposium on Web3 Financing and Inclusivity . Check out all the insights.  INNOVATOR DIVERSITY PILOTS INITIATIVE I'm a Principal for the Innovator Diversity Pilots Initiative . We help organizations unlock value in their engineering, product, and data science teams. RESEARCH ASSISTANTS  For students interested in getting involved with academic research, especially in the areas of culture, sustainability, and emerging technology, please email me to show your interest.   Quantitative Research Internship, PhD (Summer 2025 -Shanghai) Ready to accelerate your growth in one of the most fascinating and dynamic industries? Our Research Summer Internship program will give you real insights into how data and research is used to improve global financial markets. Expand your knowledge of the financial markets and solve challenging problems that could impact the way we trade. Plus, if you’ve excelled over the summer and shown us your potential, you could receive an offer to join us as a graduate quantitative researcher. With Optiver’s internship program, your work improving the market starts today. Who we are: Optiver is a global market maker founded in Amsterdam, with offices in London, Chicago, Austin, New York, Sydney, Shanghai, Hong Kong, Singapore, Taipei and Mumbai. Established in 1986, today we are a leading liquidity provider, with close to 2,000 employees in offices around the world, united in our commitment to improve the market through competitive pricing, execution and risk management. By providing liquidity on multiple exchanges across the world in various financial instruments we participate in the safeguarding of healthy and efficient markets. We provide liquidity to financial markets using our own capital, at our own risk, trading a wide range of products: listed derivatives, cash equities, ETFs, bonds and foreign currencies. Since its establishment in 2012, our Shanghai office is a rapidly growing participant in the Chinese markets, trading exchange-listed futures, options and equities in China mainland. Our vision is to become the trusted partner in the development of Chinese financial markets. With the culture of a start-up but the backing of a 35+ year-old trading firm, the Optiver Shanghai office is truly unique. Everyone who joins us will help shape the future of our company and its global impact. Get ready: we are only just beginning.  What you’ll do: As a Quantitative Research Intern, you’ll work with our researchers and traders on real-life research projects, that directly impact the way we trade. Our quantitative researchers are responsible for the accuracy of our core pricing models. They work closely with our traders to analyse and improve all facets of our trading strategies. As part of the internship, you’ll get to: Perform extensive analysis in order to implement new algorithms that support and improve our existing models. Develop risk management and portfolio optimisation tools to improve our execution algorithm. Work with petabytes of low latency, high-frequency market data sets. Collaborate with our developers to test and drive changes to our trading system, that will improve our ability to make successful trades. Keep up to date on the latest development of new models and technologies No previous experience in trading or financial markets? You bring the passion and we'll have the training to support you along the way   Who you are: PhD student, who will graduate during or after 2026. Major in a highly quantitative field. Strong knowledge of probability and statistics, experience in machine learning and time-series analysis is a big plus. Programming experience in any language (C, C++, Python, JAVA, etc.), ideally with a preference towards Python. Ability to carry a project on your own in a structured way within a short timeframe. Experience in working with large datasets. Both a self-motivated contributor and a team player, with an entrepreneurial attitude and hunger for success. Interest in the trading/quantitative finance industry. What you’ll get: The chance to work alongside diverse and intelligent peers in a rewarding environment. Competitive remuneration, including an attractive bonus structure and additional leave entitlements. Training, mentorship and personal development opportunities. Daily breakfast, lunch and snacks. Gym membership, sports and leisure activities, plus weekly in-house chair massages. Regular social events, clubs and Friday afternoon drinks. Diversity, Equity and Inclusion statement As an intentionally flat organisation, we believe that great ideas and impact can come from everyone. We are passionate about empowering individuals and creating diverse teams that thrive. Every person at Optiver should feel included, valued and respected, because we believe our best work is done together. Our commitment to diversity and inclusion is hardwired through every stage of our hiring process. We encourage applications from candidates from any and all backgrounds, and we welcome requests for reasonable adjustments during the process to ensure that you can best demonstrate your abilities. HOW TO APPLY If you’re interested in taking your career to the next level and work on one of the most exciting trading floors in China mainland, apply now via the form below. Applications are open until  8 October 2024 . While we love how bilingual our teams are, be sure to submit the below application materials in English: Academic transcripts, including Bachelors and Masters and PhD if any For any other inquiries, please email [email protected] . PRIVACY DISCLAIMER Optiver 重视个人信息的保护。请您在提供个人信息给我们之前，认真阅读Optiver China Privacy Notice, 了解我们如何收集及处理您的个人信息。 Personal information protection is of utmost importance to Optiver. Before you provide any personal information to us, we strongly urge you to read Optiver China Privacy Notice for acknowledging how we collect and process your personal information. Related vacancies Positional options trader. At Optiver, we seek to continuously improve our approach to pricing, execution and risk management. We believe that the rigour with which we approach these problems is a source of competitive edge. We have an appetite to diversify and improve and are always looking for great new talent to bring fresh ideas and perspectives to […] Graduate & Intern – Expression of Interest   Our formal applications for our 2024 Internship and Graduate positions are closed. However, we’d love to stay in touch for 2025 internship and graduate opportunities! Register below to be the first to know when our 2025 roles open. We'll keep you updated on upcoming opportunities and events.   ___________________________________________________________________________________________________________________   WHO WE ARE: At Optiver, […] Performance Researcher WHO WE ARE: At Optiver, trading is our business, and we believe that to be competitive, we need to adapt, innovate, and continuously improve. Our strategy research team combines data and a systematic approach to identify opportunities in the market. Then, we iterate on these ideas to maximise the performance of our strategies. This is […] Experienced Equity Options Trader At Optiver, we seek to continuously improve our approach to pricing, execution and risk management. We believe that the rigor with which we approach these problems is a source of competitive edge. We have an appetite to diversify and improve and are always looking for great new talent to bring fresh ideas and perspectives to […] Senior Statarb PM / QR WHO WE ARE   Optiver is a global market maker with offices in Amsterdam, London, Chicago, Austin, Sydney, Shanghai, Hong Kong, Singapore and Taipei. Founded in 1986, today we are a leading liquidity provider, with close to 2,000 employees in offices around the world, united in our commitment to improve the market through competitive pricing, […] Experienced Futures Trader/Researcher Optiver is seeking an Experienced Futures Trader/Researcher to join our High Frequency Trading Team. Our HFT team is comprised of Researchers and Software Engineers who focus on designing, improving and executing trading strategies using machine learning. Our Quantitative Researchers specifically focus on conducting alpha/signal/features research, improving those machine-learning models, and refining trading algorithms. Should you […] Quantitative Research Intern, Bachelor’s or Master’s Degree As a Quantitative Research Intern, you will work side-by-side with our Research Team of mathematicians, scientists and technologists, to develop and enhance the models that drive Optiver’s trading. You will tackle a practical research challenge that has real-world impact and directly influences Optiver’s trading decisions. In our business, where the markets are always evolving, you […] Quantitative Research Internship, Bachelor’s or Master’s Degree (Summer 2025 -Shanghai) Ready to accelerate your growth in one of the most fascinating and dynamic industries? Our Research Summer Internship program will give you real insights into how data and research is used to improve global financial markets. Expand your knowledge of the financial markets and solve challenging problems that could impact the way we trade. Plus, if you’ve […] 179 IMAGES Quantitative Finance by T. 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FIN 7808, Corporate Finance (Fall) 4 cr. 3. FIN 7809, Investments (Spring) 4 cr. Mathematics Dept. 4. MAP 6467, Stochastic Differential Equations and Filtering Theory I (Fall) 3 cr. 5. DPhil (PhD) studies in Mathematical Finance @ Oxford The Mathematical and Computational Finance Group (MCFG) at Oxford is one of the largest and most dynamic research environments in mathematical finance in the world. We combine core mathematical expertise with interdisciplinary approach. We foster lively interactions between researchers coming from different backgrounds and a truly impressive ... PhD Program MIT Sloan PhD Program graduates lead in their fields and are teaching and producing research at the world's most prestigious universities. Rigorous, discipline-based research is the hallmark of the MIT Sloan PhD Program. The program is committed to educating scholars who will lead in their fields of research—those with outstanding ... 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They are responsible for designing, developing, testing and deploying sophisticated software solutions to facilitate the work of various financial institutions. : excellent coding skills in Python, C++, and Java, and knowledge in probability, linear regression and time ... Why Study for a Mathematical Finance PhD? A good mathematical finance PhD program will make extensive use of your undergraduate knowledge and put you through graduate level courses on stochastic analysis, statistical theory and financial engineering. It will also allow you to take courses on general finance, particularly on corporate finance and derivative securities. Quantitative Finance Ph.D. Fellowship Apply now for Bloomberg's Quantitative Finance Ph.D. Fellowship program. Applications for the 2024-2025 academic year are due by Friday, September 27, 2024. Finance Academics. 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PhD Financial Mathematics Students may enter our graduate programme in Mathematical Finance by initially taking our taught M.Sc. course over 1 year. This, subject to satisfactory progress, can lead to admission to the PhD programme. ... PhD (full-time) UK students (per annum): Band A £4,786; Band B £7,000; Band C £10,000; Band D £14,500; Band E £24,500 ... Finance Group Finance Group. Finance is the study of markets for real and financial assets. The practical implications of modern financial theory are widely recognized and implemented by Wall Street and corporations. The PhD program provides students with an understanding of the theory on which the field is based and the tools they need to conduct ... Alessandra Peter PhD Candidate. Stanford University Department of Economics 579 Serra Mall Stanford, CA 94305 [email protected]. Curriculum Vitae: Fields: Macroeconomics, Finance, Development Expected Graduation Date: June, 2019: Thesis Committee: ... 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