best phd programs quantitative finance

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2024 QuantNet Ranking of Best Financial Engineering Programs

The QuantNet ranking of Financial Engineering and Quantitative Finance master's programs in the US offers comprehensive insights into placement and admission statistics from the nation's premier programs, making it the ultimate guide for prospective applicants in selecting and enrolling in the top master’s programs in quantitative finance.

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Wharton’s PhD program in Finance provides students with a solid foundation in the theoretical and empirical tools of modern finance, drawing heavily on the discipline of economics.

The department prepares students for careers in research and teaching at the world’s leading academic institutions, focusing on Asset Pricing and Portfolio Management, Corporate Finance, International Finance, Financial Institutions and Macroeconomics.

Wharton’s Finance faculty, widely recognized as the finest in the world, has been at the forefront of several areas of research. For example, members of the faculty have led modern innovations in theories of portfolio choice and savings behavior, which have significantly impacted the asset pricing techniques used by researchers, practitioners, and policymakers. Another example is the contribution by faculty members to the analysis of financial institutions and markets, which is fundamental to our understanding of the trade-offs between economic systems and their implications for financial fragility and crises.

Faculty research, both empirical and theoretical, includes such areas as:

• Structure of financial markets
• Formation and behavior of financial asset prices
• Banking and monetary systems
• Corporate control and capital structure
• Saving and capital formation
• International financial markets

Candidates with undergraduate training in economics, mathematics, engineering, statistics, and other quantitative disciplines have an ideal background for doctoral studies in this field.

Effective 2023, The Wharton Finance PhD Program is now STEM certified.

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Which program is right for you?

Through intellectual rigor and experiential learning, this full-time, two-year MBA program develops leaders who make a difference in the world.

A rigorous, hands-on program that prepares adaptive problem solvers for premier finance careers.

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Earn your MBA and SM in engineering with this transformative two-year program.

Combine an international MBA with a deep dive into management science. A special opportunity for partner and affiliate schools only.

A doctoral program that produces outstanding scholars who are leading in their fields of research.

Bring a business perspective to your technical and quantitative expertise with a bachelor’s degree in management, business analytics, or finance.

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PhD Program in Finance

2023-24 curriculum outline.

The MIT Sloan Finance Group offers a doctoral program specialization in Finance for students interested in research careers in academic finance. The requirements of the program may be loosely divided into five categories: coursework, the Finance Seminar, the general examination, the research paper, and the dissertation. Attendance at the weekly Finance Seminar is mandatory in the second year and beyond and is encouraged in the first year.  During the first two years, students are engaged primarily in coursework, taking both required and elective courses in preparation for their general examination at the end of the second year.  Students are required to complete a research paper by the end of their fifth semester, present it in front of the faculty committee and receive a passing grade.  After that, students are required to find a formal thesis advisor and form a thesis committee by the end of their eighth semester. The Thesis Committee should consist of at least one tenured faculty from the MIT Sloan Finance Group.

Required Courses

The following set of required courses is designed to furnish each student with a sound and well-rounded understanding of the theoretical and empirical foundations of finance, as well as the tools necessary to make original contributions in each of these areas. Finance PhD courses (15.470, 15.471, 15.472, 15.473, 15.474) in which the student does not receive a grade of B or higher must be retaken.

First Year - Summer

Math Camp begins on the second Monday in August. 

First Year - Fall Semester

14.121/14.122 Micro Theory I/II

14.451/14.452 Macro Theory I/II ( strongly recommended)

14.380/14.381 — Statistics/Applied Econometrics

15.470 — Asset Pricing

First Year - Spring Semester

14.123/14.124 Micro Theory III/IV

14.453/14.454 Macro Theory III/IV (strongly recommended)

14.382 – Econometrics

15.471 – Corporate Finance

Second Year - Fall Semester

15.472 — Advanced Asset Pricing

  14.384 — Time-Series Analysis or  14.385 — Nonlinear Econometric Analysis  (Enrolled students receive a one-semester waiver from attending the Finance Seminar due to a scheduling conflict)

15.475 — Current Research in Financial Economics

Second Year - Spring Semester

15.473 — Advanced Corporate Finance

 15.474 — Current Topics in Finance (strongly encouraged to take multiple times)

15.475 — Current Research in Financial Economics

Recommended Elective Courses

Beyond these required courses, students are expected to enroll in elective courses determined by their primary area of interest. There are two informal “tracks” in Financial Economics: Corporate Finance and Asset Pricing. Recommended electives are designed to deepen the student's grasp of material that will be central to the writing of his/her dissertation. Students also have the opportunity to take courses at Harvard University. There is no formal requirement to select one track or another, and students are free to take any of the electives.

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The 10 Best PhD Programs in Finance

In essence, finance is the study of economics and the claims on resources. The best PhD programs in finance help you develop professionally so you can make difficult decisions around fund allocation, financial planning, and corporate financial management. This qualification will also equip you for a career in teaching or research at top universities.

Which of the 10 best finance PhDs is best for you?

Read on to learn everything you need to know.

Table of Contents

Why Get a Doctorate in Finance?

According to the Bureau of Labor Statistics (BLS), finance managerial professionals have an average salary of $131,710  per year, and jobs are estimated to grow by 17%  from 2020 to 2030. This is much more than the average across all occupations. With a PhD in finance, you may work as a finance manager or even become a CEO of a large corporation.

Jobs and Salaries for Doctors of Finance

After earning a PhD in finance, you can find well-paid jobs as a professor or in various corporate finance roles.

Here are some of the most common finance professions with the average annual salaries for each:

• Financial Manager ( $96,255 )
• Financial Analyst ( $63,295 )
• Finance Professor ( $73,776 )
• Chief Financial Officer ( $140,694 )
• Investment Analyst ( $67,730 )

Read More:   The Highest Paying PhD Programs

What’s the average cost of a phd program in finance.

The tuition for a PhD in finance can vary depending on the university, with public institutions generally being much more affordable than private ones.

Across all schools, the average tuition is around $30,000 per year.

However, on top of this, you need to factor in other expenses, which could add up to another $30,000 a year. Some top universities offer full funding, including tuition and a stipend for all students who are successfully admitted to the program.

Read Next: The Average Cost of a Master’s Degree in Finance

Top finance phd programs and schools, stanford university, graduate school of business.

PhD in Finance

Stanford University is one of the most prestigious business schools in the world. Its PhD in finance programs has an emphasis on theoretical modeling and empirical testing of financial and economic principles.

• Courses include: Financial markets, empirical asset pricing, macroeconomics, and financial markets.
• Duration: 5 years
• Tuition : Full funding
• Financial aid: Research & teaching assistantship, grants, outside employment, and outside support.
• Delivery: On-campus
• Acceptance rate: 5%
• Location: Stanford, California

The University of Pennsylvania, The Wharton School

The University of Pennsylvania’s renowned Wharton School of Business is home to faculty who are well-known in the field of business research. The school boasts a low student-faculty ratio in an atmosphere that allows you to work with faculty members as peers. This doctor of finance program emphasizes subjects like asset pricing, corporate finance, and portfolio management. This helps students become experts in research and teaching in these areas.

• Courses include: Topics in asset pricing, financial economics, and international finance.
• Credits: 18 courses
• Financial aid: Fellowships, grants, student employment, health insurance, stipend, and loans.
• Acceptance rate: 9%
• Location: Philadelphia, Pennsylvania

The University of Chicago, Booth School of Business

Booth School of Business is a major center for finance education because its faculty includes Eugene F. Fama, Nobel laureate and the father of modern empirical finance. This finance doctoral degree has an option for a joint PhD in collaboration with the university’s economics department.

• Courses: Financial economics, financial markets in the macroeconomy, and behavioral finance.
• Tuition : Refer tuition page
• Financial aid: Grants, stipends, health insurance, scholarships, fellowships, teaching assistantships, research assistantships, and loans.
• Acceptance rate: 7%
• Location: Chicago, Illinois

The University of Illinois at Urbana-Champaign, Gies College of Business

The University of Illinois at Urbana Champaign is one of the best places for studying and conducting research in finance. Its finance research faculty was ranked #4  in the UTD Top 100 Business School Research Rankings between 2016-2019. In this PhD in finance program, students can take the qualifying examination at the end of the first year and, if successful. They’ll be able to start their research project earlier and complete the degree sooner.

• Courses include: Empirical analysis in finance, corporate finance, and statistics & probability.
• Duration: 4-5 years
• Financial aid: Full tuition waiver, stipends, scholarships, grants, student employment, and loans.
• Acceptance rate: 63%
• Location: Champaign, Illinois

Massachusetts Institute of Technology, Sloan School of Management

The Sloan School is one of the top research centers in the world, which aims to transform students into experts who can handle real-world problems in a wide range of spheres, from business and healthcare to climate change. This PhD program in finance gives students the flexibility to choose between a wide range of electives and even study some courses at Harvard.

• Courses include: Current research in financial economics, statistics/applied econometrics, and corporate finance.
• Duration: 6 years
• Financial aid: Full tuition, stipend, teaching assistantships, research assistantships, health insurance, fellowships, scholarships, and loans.
• Location: Cambridge, Massachusetts

Northwestern University, Kellogg School of Management

The Kellogg School of Management allows students to conduct independent research under the supervision of faculty who’ve made significant contributions to the field and have earned numerous prestigious awards. This doctorate of finance program’s admission process has a dual application option. You can also apply to the Economics PhD simultaneously, so if you are not selected for the finance program, you may be considered for economics.

• Courses include: Econometrics, corporate finance, and asset pricing.
• Duration: 5.5 years
• Financial aid: Tuition scholarship, stipends, health insurance, moving allowance, and subsidies.
• Location: Evanston, Illinois

The University of California Berkeley, Haas School of Business

The Haas School of Business in Berkeley is an innovative institution that questions the status quo, takes intelligent risks, and accepts sensible failures in its path to progress. This finance PhD program offers students opportunities to learn about cutting-edge research from faculty from around the world.

• Courses include: Corporate finance theory, stochastic calculus, and applications of psychology & economics.
• Tuition : Refer cost page
• Financial aid: Fellowships, grants, tuition allowance, stipends, teaching assistantships, and research assistantships.
• Acceptance rate: 17%
• Location: Berkeley, California

The University of Texas at San Antonio, Alvarez College of Business

The Alvarez College of Business is one of the forty largest business schools in the USA. It follows a comprehensive and practical approach to education that allows students to apply the knowledge they gain directly in the workplace. This PhD in finance encourages students to do collaborative research with the faculty, which helps them publish their own academic papers before they even complete the program.

• Courses include: Corporate finance, international financial markets, and microeconomic theory.
• Credits: 84 (post-bachelors)
• Financial aid: Scholarships, grants, work-study, teaching assistantships, research assistantships, research fellowships, and loans.
• Acceptance rate: 84%
• Location: San Antonio, Texas

Liberty University, School of Business

Doctor of Business Administration (DBA) in Finance

Liberty University is a non-profit institution among the top five online schools in the USA and has been offering fixed tuition fees for the past seven years. This is one of the best PhD in Finance programs you can do completely online. It aims to prepare students to address issues in business finance through research, best practices, and relevant literature.

• Courses: Managerial Finance, Investments & Derivatives, Business Valuation, etc.
• Credits: 60
• Duration: 3 years average
• Tuition : $595 per credit
• Financial aid: Grants, scholarships, work-study, veteran benefits, and loans.
• Delivery: Online
• Acceptance rate: 50%
• Location: Lynchburg, Virginia

Northcentral University

PhD in Business Administration (PhD-BA) – Finance Management

Northcentral University was founded with the objective of offering flexible, fully-online programs to working professionals around the world. This doctorate degree in finance online is flexible and allows you to design your own schedule. You will also get one-on-one personal mentoring from qualified faculty.

• Courses include: Business financial systems, business statistics, and business leadership & strategy.
• Duration: 84 months average
• Tuition: $1,105 per credit
• Financial aid: Grants, scholarships, and military scholarships.
• Acceptance rate: NA
• Location: Scottsdale, Arizona

Things To Consider When Choosing a Finance PhD Program

The right PhD program for you is a very personal decision and will depend on several individual factors.

However, these general questions will help you to make the right choice:

• Is the university properly accredited?
• Does the university conduct innovative and cutting-edge research?
• Are there renowned faculty members who you’ll want to work with?
• Do they offer subjects or specializations that match your career goals?
• What is the school’s placement history?
• What are the tuition fees, costs, and options for scholarships and financial aid?
• Does the program offer online study options?

It’s also important to consider if you want to pursue a career in academia or work in organizations as a senior finance professional. A PhD degree will generally set you up for a career in research or academia, while a DBA is more suited to a career in business or government.

Preparing for a Finance Doctorate Program

It’s important to start preparing early if you want to be selected for one of the best finance PhD programs.

These handy tips can help you put your best foot forward:

• Research the requirements of the best universities offering PhD in finance degrees, including pre-requisite subjects and qualifying grades. Keep these in mind when completing your bachelor’s or master’s degree.
• Understand your strengths and weaknesses in relation to the program’s requirements. Work on your weaknesses and continue to hone relevant skills.
• Read extensively in the field and keep up-to-date on regional and global developments.
• Join communities of finance professionals to build your network and be exposed to the latest knowledge in the discipline.

Skills You Gain from Earning a PhD in Finance

The most important skills you learn as a doctor of finance include:

• Communication skills, including writing and presentation skills
• Data analytical skills
• Economics and accounting skills
• Critical thinking skills
• Mathematical skills
• Analytical software skills
• Management and leadership skills
• Problem-solving skills

PhD Programs in Finance FAQs

How long does a phd in finance take.

PhD programs in finance usually take between three and eight years to complete.

Is It Worth Getting a PhD in Finance?

A PhD in Finance is a qualification that’s in high demand today. It is a terminal degree and can help you get top-level jobs with lucrative salaries in corporate or large organizations.

How Much Can You Make With a PhD in Finance?

With a finance doctorate, you can expect to earn a salary anywhere from around $45,000 to $150,000, depending on your experience, role, and the organization you work for. According to the BLS, the average salary for finance PhD holders is $131,710 .

What Do You Need To Get a PhD in Finance?

The admissions requirements vary depending on the program, but you’ll typically need a bachelor’s or master’s degree in finance. The programs can take three to eight years of coursework and research.

To apply, you’ll usually need to submit:

• Application
• Academic resume
• Academic transcripts
• Recommendation letters
• GRE or GMAT score
• Personal essay

Final Thoughts

With a doctorate in finance, you can build a rewarding career in academia, research, or the business sector. Like any doctorate, these programs ask for dedication and hard work. By planning early, you’ll set yourself up to pursue one of the best PhD programs in finance.

For more on how to build your career in the field, take a look at our guides to the best master’s degree in finance , the highest paying PhDs , and fully-funded PhD programs .

Lisa Marlin

Lisa is a full-time writer specializing in career advice, further education, and personal development. She works from all over the world, and when not writing you'll find her hiking, practicing yoga, or enjoying a glass of Malbec.

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PhD | Finance

The Ph.D. in Finance

Stern’s Ph.D. program in finance trains scholars to conduct research at the leading edge of financial economics. The faculty represents one of the largest finance research groups in the world that has been ranked consistently as the leading publisher of academic research in top finance journals. Comprised of more than 40 researchers, including a Nobel-prize-winning economist, our faculty are active in all areas of finance—asset pricing, corporate finance, derivatives, market microstructure, and behavioral finance—with both theoretical and empirical focus, and with emerging specialization in the areas of financial intermediation, crises, and macro-finance. As a result of this unusual breadth, students have access to expertise in almost any topic that they might wish to explore.

Explore Finance

Discover our other fields of study.

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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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, greg buchak, antonio coppola, peter m. demarzo, 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, financial inclusion, economic development, and inequality: evidence from brazil, dissecting mechanisms of financial crises: intermediation and sentiment, financial innovation in the 21st century: evidence from u.s. patents, recent insights by stanford business, why the “venture mindset” is not just for tech investors, how to: reject pitches like a venture capitalist, when the export-import bank closed up, u.s. companies saw global sales plummet.

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Press Release

NYU Tandon financial engineering program ranked among the best by QuantNet

BROOKLYN, New York, Wednesday, December 18, 2019 – The 2020 QuantNet Ranking of Best Financial Engineering Programs placed the New York University Tandon School of Engineering at Number 9 in the nation — a seven-position improvement within the last five years.

The school’s ranking reflects worldwide demand by financial institutions and insurance firms for machine learning expertise, which NYU Tandon’s Department of Finance and Risk Engineering embraced early and comprehensively: Its master’s degree program now offers a dozen courses covering artificial intelligence — more than any other institution.

The school also offers the largest selection of financial engineering courses of any kind, made possible because NYU Tandon’s program has the largest enrollment — 272 students studying in four focus areas — and small class sizes. Coursework includes sophisticated modeling and information technology that shape today’s financial industry, spanning corporate finance and financial markets, computational finance, technology and algorithmic finance, and risk finance.

Eight-six percent of NYU Tandon’s financial engineering students are employed within three months of graduation, with nearly three-quarters of those placements within the United States — an unusually high percentage, given that 59% of engineering degrees bestowed in the United States are to international students. The department offers career training and networking events, and it appointed a dedicated career placement officer whose background includes managing Morgan Stanley’s quantitative finance campus recruitment.

The department chair, Peter Carr , is ranked second in the world by Google Scholar in Quantitative Finance citations. The department faculty also includes acclaimed thought leader Nassim Nicholas Taleb , ranked fourth, along with other leading researchers and practitioners.

NYU Tandon also ranked near the top of TFE Times listing earlier this year, which placed it fourth in the nation. Its financial engineering program is one of the oldest, and was the first to be certified by the International Association of Financial Engineers.

QuantNet scores reflect career placement, program selectivity, and employer and peer-school assessment. QuantNet.com is the largest online community of master’s of financial engineering applicants, with more than 50,000 community members, newsletter subscribers, and social networking followers since its start in 2006. Candidates visit QuantNet for information and advice on financial engineering and quantitative finance graduate programs. Its rankings of top programs and forum are tools for prospective applicants all over the world.

About the New York University Tandon School of Engineering

The NYU Tandon School of Engineering dates to 1854, the founding date for both the New York University School of Civil Engineering and Architecture and the Brooklyn Collegiate and Polytechnic Institute (widely known as Brooklyn Poly). A January 2014 merger created a comprehensive school of education and research in engineering and applied sciences, rooted in a tradition of invention and entrepreneurship and dedicated to furthering technology in service to society. In addition to its main location in Brooklyn, NYU Tandon collaborates with other schools within NYU, one of the country’s foremost private research universities, and is closely connected to engineering programs at NYU Abu Dhabi and NYU Shanghai. It operates Future Labs focused on start-up businesses in Brooklyn and an award-winning online graduate program. For more information, visit engineering.nyu.edu .

More to Read

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Boston University Academics

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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 credits, depending on whether the courses are 3 or 4 credits each). Students’ course choices must be approved by the Mathematical Finance Director prior to registration each semester. The following is a typical program of courses.

• GRS EC 701 Microeconomic Theory
• GRS MA 711 Real Analysis
• GRS MA 779 Probability Theory I
• QST FE 918 Doctoral Seminar in Finance
• GRS EC 703 Advanced Microeconomic Theory
• GRS MA 776 Partial Differential Equations
• GRS MA 781 Probability Theory 2
• QST FE 920 Advanced Capital Market Theory
• GRS EC 702 Macroeconomic Theory
• GRS MA 783 Advanced Stochastic Processes
• QST MF 850 Advanced Computational Methods
• QST MF 922 Advanced Mathematical Finance
• GRS EC 704 Advanced Microeconomic Theory
• GRS 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 semester 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-credit course, for each subsequent regular semester 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 semester to correct their status. Prior to the start of the semester, 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 seven 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 semester 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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• 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 Graduate Programs in America

1-25 of 68 results

MIT Sloan School of Management

• Cambridge, MA ·
• Massachusetts Institute of Technology ·
• Graduate School
• · Rating 4.9 out of 5   10 reviews

Massachusetts Institute of Technology ,

Graduate School ,

CAMBRIDGE, MA ,

10 Niche users give it an average review of 4.9 stars.

Featured Review: Master's Student says I am an incoming MBA student to MIT Sloan. The application and enrollment process has been incredibly informative. I’ve been able to network with numerous current students and have worked on mapping... .

Read 10 reviews.

Princeton University

• Princeton, NJ ·
• · Rating 4.33 out of 5   3 reviews

PRINCETON, NJ ,

3 Niche users give it an average review of 4.3 stars.

Featured Review: Master's Student says The best part of the Princeton University mechanical engineering graduate degree is the excellent faculty that teach the courses. They are incredibly knowledgeable and also very willing to help... .

Read 3 reviews.

Owen Graduate School of Management

• Nashville, TN ·
• Vanderbilt University ·
• · Rating 4.4 out of 5   5 reviews

Vanderbilt University ,

NASHVILLE, TN ,

5 Niche users give it an average review of 4.4 stars.

Featured Review: Master's Student says I attend my first semester in the fall of 2024. My experience so far has been amazing. I cannot wait until I start my experience. .

Read 5 reviews.

Illinois Institute of Technology

• Graduate School ·
• CHICAGO, IL
• · Rating 4.38 out of 5   39

Lehigh University

• BETHLEHEM, PA
• · Rating 4.45 out of 5   20

Sacred Heart University

• FAIRFIELD, CT
• · Rating 4.4 out of 5   45

Olin Business School

• St. Louis, MO ·
• Washington University in St. Louis ·
• · Rating 4 out of 5   7 reviews

Washington University in St. Louis ,

ST. LOUIS, MO ,

7 Niche users give it an average review of 4 stars.

Featured Review: Master's Student says The enrolment process was an absolute pleasure. Being able to sit in on classes and interact with faculty and students was a fantastic opportunity. It gave me a real feel for the academic dynamic and... .

Read 7 reviews.

Chicago Booth School of Business

• Chicago, IL ·
• University of Chicago ·
• · Rating 4.86 out of 5   14 reviews

University of Chicago ,

CHICAGO, IL ,

14 Niche users give it an average review of 4.9 stars.

Featured Review: Master's Student says My academic experience at the Chicago Booth Executive MBA program has been exceptionally enriching. The program's rigorous curriculum, combined with interactive class discussions and real-world case... .

Read 14 reviews.

Marshall School of Business

• Los Angeles, CA ·
• University of Southern California ·
• · Rating 4.71 out of 5   17 reviews

University of Southern California ,

LOS ANGELES, CA ,

17 Niche users give it an average review of 4.7 stars.

Featured Review: Current Master's student says Marshall is a fantastic business program. So far, my academic experience has been nothing short of fantastic. I have never enjoyed school before, but almost every class I have learned new,... Marshall has clubs for almost every industry, so depending on your industry of interest, Marshall has clubs to prepare you for interviews and get you great exposure, and they're a great way to... Marshall is still relatively underrepresented in minority categories like women and people of color, but from my understanding, they are working to improve those numbers. .

Read 17 reviews.

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Whiting School of Engineering

• Baltimore, MD ·
• Johns Hopkins University ·
• · Rating 4.83 out of 5   12 reviews

Johns Hopkins University ,

BALTIMORE, MD ,

12 Niche users give it an average review of 4.8 stars.

Featured Review: Master's Student says Just starting now but the onboarding process is great! The student orientation was very helpful and the school has an amazing network. .

Read 12 reviews.

The Graduate School of Arts & Sciences - University of Virginia

• Charlottesville, VA ·
• University of Virginia ·
• · Rating 4 out of 5   1 review

University of Virginia ,

CHARLOTTESVILLE, VA ,

1 Niche users give it an average review of 4 stars.

Featured Review: Alum says Very good in some areas, excellent in other areas, many academic choices available in all areas of study .

Read 1 reviews.

College of Sciences - Georgia Institute of Technology

• Atlanta, GA ·
• Georgia Institute of Technology ·
• · Rating 5 out of 5   1 review

Georgia Institute of Technology ,

ATLANTA, GA ,

1 Niche users give it an average review of 5 stars.

Featured Review: Other says Georgia Tech is among the top colleges you can attend. My journey there had its highs and lows, but looking back, every experience contributed significantly to my growth. A piece of advice I'd offer... .

Haas School of Business

• Berkeley, CA ·
• University of California - Berkeley ·
• · Rating 5 out of 5   3 reviews

Blue checkmark.

University of California - Berkeley ,

BERKELEY, CA ,

3 Niche users give it an average review of 5 stars.

Featured Review: Master's Student says I am a new EWMBA admit at Haas and so far the experience has been fantastic! It's a close-knit community of intellectual, humble, and talented working professionals. The entire team of program directors, admissions officers, etc. are SO willing to help! .

Tandon School of Engineering

• Brooklyn, NY ·
• New York University ·
• · Rating 4.73 out of 5   15 reviews

New York University ,

BROOKLYN, NY ,

15 Niche users give it an average review of 4.7 stars.

Featured Review: Master's Student says As a bioinformatics master's student at the NYU Tandon School of Engineering, I've had the opportunity to explore the fascinating intersection of biology and computer science. The program has... The faculty at Tandon are experienced and supportive, and I've had the chance to collaborate with fellow students on exciting research projects. The interdisciplinary nature of bioinformatics has... Overall, my experience at NYU Tandon has been enriching, and I look forward to applying the knowledge and skills acquired during my master's program to contribute meaningfully to the field of... .

Read 15 reviews.

Leonard N. Stern School of Business

• New York, NY ·
• · Rating 4.82 out of 5   28 reviews

NEW YORK, NY ,

28 Niche users give it an average review of 4.8 stars.

Featured Review: Master's Student says As a part-time MBA candidate at NYU Stern, I'm deeply impressed by the program's blend of academic rigor and practical application. The faculty, industry leaders in their own right, provide... .

Read 28 reviews.

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Carroll School of Management

• Chestnut Hill, MA ·
• Boston College ·
• · Rating 5 out of 5   2 reviews

Boston College ,

CHESTNUT HILL, MA ,

2 Niche users give it an average review of 5 stars.

Featured Review: Master's Student says I am currently enrolled in the part time, professional evening MBA program. So far it has brought me to communicate with like minded individuals and the professors truly want to see you succeed so... .

Read 2 reviews.

College of Natural Sciences

• Austin, TX ·
• University of Texas - Austin ·

University of Texas - Austin ,

AUSTIN, TX ,

Questrom School of Business

• Boston, MA ·
• Boston University ·
• · Rating 4.29 out of 5   7 reviews

Boston University ,

BOSTON, MA ,

7 Niche users give it an average review of 4.3 stars.

Featured Review: Master's Student says Boston University's MBA program transformed me. Faculty, experts in their fields, nurtured my learning. A diverse student body broadened my perspective. Experiential learning honed skills and... .

College of Liberal Arts & Sciences - University of Illinois

• Urbana, IL ·
• University of Illinois Urbana-Champaign ·

University of Illinois Urbana-Champaign ,

URBANA, IL ,

Lally School of Management

• Rensselaer Polytechnic Institute ·

Rensselaer Polytechnic Institute ,

College of Arts and Sciences - University of Miami

• Coral Gables, FL ·
• University of Miami ·
• · Rating 4.67 out of 5   6 reviews

University of Miami ,

CORAL GABLES, FL ,

6 Niche users give it an average review of 4.7 stars.

Featured Review: Master's Student says I am in graduate school and needed something online but also wanted something that was going to challenge me and provide me with a step further than my undergrad school provided. I compared many MPA... .

Read 6 reviews.

Dedman College of Humanities and Sciences

• Dallas, TX ·
• Southern Methodist University ·

Southern Methodist University ,

DALLAS, TX ,

Tulane University School of Science and Engineering

• New Orleans, LA ·
• Tulane University ·

Tulane University ,

NEW ORLEANS, LA ,

Featured Review: Master's Student says I am currently in the materials science and engineering 4+1 program which will allow me to earn my MS in one year instead of two. There are some really amazing professors here despite the department... .

Babson College

• Babson Park, MA ·
• · Rating 4.54 out of 5   13 reviews

BABSON PARK, MA ,

13 Niche users give it an average review of 4.5 stars.

Featured Review: Master's Student says So far my academic experience has been good at Babson college. The instructors are available and willing to go out of their way to answer any questions you have and make sure that you are comfortable... .

Read 13 reviews.

College of Science and Engineering - University of Minnesota Twin Cities

• Minneapolis, MN ·
• University of Minnesota Twin Cities ·
• · Rating 4 out of 5   2 reviews

University of Minnesota Twin Cities ,

MINNEAPOLIS, MN ,

2 Niche users give it an average review of 4 stars.

Featured Review: Doctoral Student says I started graduate school at UMN during the height of the COVID pandemic. Despite not being able to go to classes in person or get into the lab, the UMN community has been very inclusive and I have... .

The Ohio State University College of Arts and Sciences

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2022 Quantitative Finance Diversity Graduate Mentorship Program – Morgan Stanley

MORGAN STANLEY

2022 Quantitative Finance Diversity Graduate Mentorship Program

At Morgan Stanley, Diversity and Inclusion helps our clients benefit from the widest range of insights, experiences and perspectives. With a strong commitment to a culture of inclusion, we strive to attract and retain top candidates from all backgrounds through a focus on recruitment, career development and individual advancement

Our Graduate Diversity Mentorship Program is designed to help diverse PhD & Master students gain a better understanding of the financial services industry and how your degree could apply to a career within Quantitative Finance.

Selected candidates will be paired with a mentor within the Morgan Stanley Quantitative Finance division and will then be invited to attend programming in the fall. Event sessions will include speed networking, job shadowing, mock interviews, and/or programming tutorials! Don’t miss out on this exciting opportunity for mentorship and networking!

To learn more and apply, please visit:

2022 Quantitative Finance Diversity Graduate Mentorship Program – Morgan Stanley Campus

Application Requirements

• Applicants must be pursuing a Masters or PhD in a field such as Engineering, Mathematics, Physics, Statistics or related Quantitative field
• You identify as Black and/or Hispanic student
• You are graduating in December 2023 or later

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Best Schools for Quantitative Finance (BA/BS, MA/MS, MBA, PhD)

Selecting the right program in quantitative finance involves considering factors such as faculty expertise, research opportunities, industry connections, and geographic location.

Prospective students should look for programs that align with their career goals and offer strong placement records in relevant finance positions.

It’s advisable to review course offerings, talk to alumni, and consider the mathematical and computational intensity of each program to ensure the best fit for one’s career aspirations.

We’ll go through some of the most notable programs for quantitative finance below.

Best Schools for Quantitative Finance at Bachelor’s Level

• Massachusetts Institute of Technology (MIT)
• Harvard University (Harvard College)
• University of California, Berkeley
• New York University (NYU)
• Carnegie Mellon University
• University of Chicago
• Stanford University
• Princeton University
• Columbia University
• University of Pennsylvania (Wharton)
• University of Michigan
• University of California, Los Angeles (UCLA)
• London School of Economics and Political Science (LSE)
• University of Warwick
• University of Toronto
• University of Waterloo
• National University of Singapore (NUS)
• University of Sydney
• University of Hong Kong

Choosing the right school for a Bachelor’s in Quantitative Finance is important for foundational knowledge and skill-building.

The Massachusetts Institute of Technology (MIT) stands out for the rigor of its Bachelor of Science in Mathematics with a specialization in Finance.

Harvard has a Quantitative Finance track through its Statistics program.

University of California, Berkeley also offers a Bachelor of Arts in Statistics with a concentration in Quantitative Finance.

European institutions also excel in this field.

The London School of Economics and Political Science (LSE) provides a strong Bachelor of Science in Financial Mathematics and Statistics.

ETH Zurich in Switzerland is known for its interdisciplinary Bachelor of Science in Computational Science and Engineering (CSE), which has a strong quantitative finance component.

Best Institutions for Master’s Degrees in Quantitative Finance

Best Schools for Quantitative Finance (MA/MS)

• University of Oxford
• Imperial College London
• Tsinghua University
• University of Illinois at Urbana-Champaign

When considering a Master’s, Princeton University’s Master of Finance program often leads the list with its mathematics-focused curriculum.

Columbia University’s Master in Financial Engineering (MSFE) is another top choice, offering a blend of applied mathematics, economics, and computer science.

For those looking for options outside the United States, the University of Oxford’s Master of Science in Mathematical and Computational Finance gives students a rigorous grounding in quantitative finance theory and practice.

In Asia, the National University of Singapore’s Master in Quantitative Finance is known for its strong analytical focus and ties to industry.

Top MBA Programs with Quantitative Finance Specializations

• University of Chicago Booth School of Business
• The Wharton School at the University of Pennsylvania
• New York University (NYU) Stern School of Business
• Columbia Business School
• Stanford Graduate School of Business
• Massachusetts Institute of Technology (MIT) Sloan School of Management
• London Business School
• University of California, Berkeley Haas School of Business
• Northwestern University Kellogg School of Management
• Harvard Business School
• University of Cambridge Judge Business School
• Hong Kong University of Science and Technology Business School
• University of Oxford Saïd Business School
• University of Toronto Rotman School of Management
• University of Michigan Ross School of Business
• University of California, Los Angeles (UCLA) Anderson School of Management
• National University of Singapore (NUS) Business School

MBA programs with a quantitative finance specialization blend business leadership with technical finance skills.

The University of Chicago Booth School of Business offers an MBA with a concentration in Analytic Finance.

The Wharton School at the University of Pennsylvania is also renowned for its MBA program, where students can take a major in Quantitative Finance.

For those looking to study in Europe, INSEAD’s MBA program offers a robust finance track with quantitative elements.

The Hong Kong University of Science and Technology Business School is a premier choice in Asia, known for its global perspective and quantitative approach to finance in its MBA curriculum.

Leading PhD Programs in Quantitative Finance

• Northwestern University
• University of Pennsylvania
• Yale University
• Harvard University
• Swiss Finance Institute / University of Zurich
• University of Cambridge
• University of British Columbia
• University of Melbourne

PhD programs in Quantitative Finance are designed for deep academic research and advanced theory development .

Stanford University’s PhD program in Finance is distinguished by its strong quantitative and behavioral finance research.

The University of Chicago offers a PhD in Financial Economics that integrates economics and finance for a thorough understanding of finance.

In Europe there are various programs.

The Swiss Finance Institute PhD Program, run in partnership with leading Swiss universities, including the University of Zurich, is tailored for students aiming to pursue academic careers or high-level research positions in finance.

The UK’s Imperial College London also offers a PhD in Finance, emphasizing a quantitative and practical approach to financial market research.

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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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UCLA Anderson Master of Financial Engineering

Join a top 10-ranked mfe program*, leverage your skill with numbers.

*UCLA Anderson MFE is Ranked #3 in the U.S. by QS World University Rankings

Stay up to date on the UCLA Anderson MFE Program

A few numbers to get you started:, expand your career readiness through these exclusive offerings:.

Leading-edge elective courses , including classes focused on cryptocurrency, data analytics and machine learning

The Applied Finance Project, a course where students solve a problem for a company or organization with faculty advisors’ guidance

The Master of Financial Engineering Advisory Board, an esteemed group of industry leaders who help steer the program’s curriculum, guest lecture for courses and support career services’ efforts.

Events, programs and competitions in collaboration with UCLA Anderson’s Fink Center for Finance , a highly regarded organization in the field.

Hear From Our Graduates

Key Strengths of the MFE Program

Business School Advantage

UCLA Anderson offers exceptional academic preparation, a cooperative and congenial student culture, and access to a thriving business community, as well as support services for scholastic and career advancement. Our MFE program affords a dynamic curriculum that combines theory, analytical skills and current business practice. UCLA Anderson MFEs join one of the world’s largest and most renowned alumni networks, with more than 36,000 members in over 100 countries.

Learn From Renowned Finance Thought Leaders

Our trailblazing faculty are respected professors and researchers, as well as revered leaders in the world of finance. They will teach you in a curriculum that merges quantitative finance theories with up-to-the-minute business practices, you can more effectively apply what you learn. Outside of our curriculum, our Financial Institutions Seminar brings industry leaders to you, giving you a window into the trends and dynamics that drive their businesses.

Learn More About Our Academics

Real-World Projects

Beyond the classroom, UCLA Anderson MFE students apply their knowledge to real-world settings through a summer internship and our Applied Finance Project (AFP). The hands-on AFP pairs student teams with real client projects, providing valuable exposure to potential employers and broadening their professional networks.

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Graeme S. Baker

Irina Bogacheva

Irina Bogacheva is Director of Research at Millburn Ridgefield and Adjunct Professor within the Department of Mathematics.

Previously, she held senior research positions with Goldman Sachs, Deutsche Asset Management, QS Investors, and Franklin Templeton. Irina’s professional experience includes research and implementation of systematic global macro strategies, strategic and dynamic asset allocation, and active factor equity strategies. She holds Diploma in Mathematics from Moscow State University, Master’s in Economics from New Economic School (Moscow), and an MBA in Analytic Finance from the University of Chicago Booth School of Business, where she also completed Ph.D. coursework in Finance.

Alberto Botter

Adjunct Professor, Department of Mathematics MS, Columbia University Quantitative Methods in Investment Management

Alberto Botter is a Managing Director within the Portfolio Management department at AQR Capital Management. In this role, he oversees the construction, optimization, and management of AQR’s Equities Long-Short and Tax-Managed portfolios. Prior to AQR, Mr. Botter was a quant in the Wealth Strategies Group at Morgan Stanley. Alberto earned a B.S. and an M.S. in economics from the University of Bologna and an M.A. in Mathematics of Finance from Columbia University.

[email protected]

Luca Capriotti

Adjunct Professor, Department of Mathematics Ph.D., International School for Advanced Studies Credit Models, Computational Finance, and Machine Learning

Luca is the Global Head of Quantitative Strategies (QS) Credit at Credit Suisse, where he has worked since 2004. Previous to this role, he was the global head of QS for Credit and Structured Notes; he was the EMEA head and the US head of QS Global Credit Products; he worked in Commodities in New York and London, and he was part of the cross-asset modeling R&D group of QS in the London office.

Luca is also Visiting Professor at the Department of Mathematics at University College London, and an Adjunct Professor at Columbia University. His current research interests are in Credit Models, Computational Finance, and Machine Learning, with a focus on efficient numerical techniques for Derivatives Pricing and Risk Management, and applications of Adjoint Algorithmic Differentiation (AAD), which he has pioneered in Finance and Physics, and for which he holds a US Patent. Luca has published over 70 scientific papers, with the top 3 papers collecting to date over 1000 citations (h factor 27, i10 factor 49).

Prior to working in Finance, Luca was a researcher at the Kavli Institute for Theoretical Physics, Santa Barbara, California, working in High-Temperature Superconductivity and Quantum Monte Carlo methods for Condensed Matter systems. He has been awarded the Director’s fellowship at Los Alamos National Laboratory, and the Wigner Fellowship at Oak Ridge National Laboratory.

Luca holds an M.S. cum laude in Physics from the University of Florence, and an M.Phil. and a Ph.D. cum laude in Condensed Matter Theory, from the International School for Advanced Studies, Trieste.

[email protected]

Alexei Chekhlov

Capital Markets and Investments, Math Methods in Financial Price Analysis

Alexei Chekhlov is an Adjunct Assistant Professor within the Department of Mathematics. He has previously taught graduate courses such as “Mathematical Methods in Financial Price Analysis” and “Capital Markets and Investments.” Additionally, Chekhlov serves as the Head of Research and Partner at Systematic Alpha Management, LLC, and he previously worked as a research associate at Princeton University, where he conducted research on the theory of fluid turbulence. He has published repeatedly on fluid mechanics, the kinetic theory of gases, turbulence, and within the fields of applied mathematics and quantitative finance. Chekhlov earned his Ph.D. in Applied and Computational Mathematics from Princeton University.

[email protected]

Paul-Guillaume Fournié

MSc, Ecole Polytechnique, 2015 MSc, Corps des Mines, 2018

Interest Rates Models

Paul-Guillaume Fournié has been working as an Options Rates Quant at BNP Paribas’ New York office since 2019, heading the team locally since 2022. Covering both Vanilla and Exotic options, his most recent focus has been on the creation and adaptation of interest rates models to SOFR. He holds MSc in Mathematics and Physics from Ecole Polytechnique and MSc in Economics and Public Policy from Corps des Mines. Before switching to finance he occupied several positions in leading French industrial and telecommunications companies and served in France’s Ministry of the Economy.

[email protected]

Tat Sang Fung

Adjunct Professor, Department of Mathematics PhD, Columbia University, 1996 Numerical Methods in Finance and Risk Model Methodologies

Tat Sang Fung is an Adjunct Professor at Columbia University, and he currently teaches “Numerical Methods in Finances,” a graduate course required for students in the Mathematics of Finance program. His areas of expertise include Quantitative Finance and Risk Management Methodology, and Mathematics. Additionally, Fung serves as the Global Head of Risk Model Methodology at Jefferies and has held other senior positions in the finance industry at Finch Lead Inc. and Finastra. Fung earned his Ph.D. in Mathematics from Columbia University.

[email protected] | 212-854-5880 | Website

Julien guyon

Adjunct Professor, Department of Mathematics PhD, École des ponts ParisTech, 2006 Nonlinear Option Pricing

Julien Guyon is a professor of Applied Mathematics at Ecole des Ponts ParisTech, one of the oldest and one of the most prestigious French Grandes Ecoles, where he holds the BNP Paribas Chair Futures of Quantitative Finance. Before joining Ecole des Ponts, Julien worked in the financial industry for 16 years, first in the Global Markets Quantitative Research team at Societe Generale in Paris (2006-2012), then as a senior quantitative analyst in the Quantitative Research group at Bloomberg L.P., New York (2012-2022). Julien was also an adjunct professor in the Department of Mathematics at Columbia University and at the Courant Institute of Mathematical Sciences, NYU, from 2015 to 2022; and previously at Universite Paris Diderot and Ecole des Ponts ParisTech. Julien serves as an Associate Editor of Finance & Stochastics, SIAM Journal on Financial Mathematics, Quantitative Finance, and Journal of Dynamics and Games. He is also a Louis Bachelier Fellow.

Julien co-authored the book Nonlinear Option Pricing (Chapman & Hall, 2014) with Pierre Henry-Labordere. He has published more than 20 articles in peer-reviewed journals (including Finance and Stochastics, SIAM Journal on Financial Mathematics, Quantitative Finance, Risk, Journal of Computational Finance, Annals of Applied Probability, Stochastic Processes and their Applications) and is a regular speaker at international conferences, both academic and professional. His main research interests include volatility and correlation modeling, option pricing, optimal transport, and numerical probabilistic methods.

A big soccer fan, Julien has also published articles on fairness in sports both in academic journals and in top-tier newspapers including The New York Times, The Times, Le Monde, and El Pais. Some of his suggestions for draws and tournament design have been adopted by FIFA and UEFA, including a new, fairer draw method for the FIFA World Cup; a fairer format for the 2026 FIFA World Cup (in progress); a new knockout bracket for the UEFA Euro; and an optimized schedule of the UEFA Champions League. His paper “Risk of collusion: Will groups of 3 ruin the FIFA World Cup?” won the 2nd prize at the 2021 MIT Sloan Sports Analytics Conference, the biggest sports analytics event in the world.

Ioannis Karatzas

Ioannis Karatzas is the Eugene Higgins Professor of Applied Probability in Columbia’s Department of Mathematics, whose research interests include Probability and Mathematical Statistics, Random Processes, Stochastic Analysis, Optimization, and Mathematical Economics and Finance. He has served as the managing editor for the book series Applications of Mathematics and on numerous editorial boards such as “Applied Mathematics & Optimization,” “Stochastics,” the “SIAM Journal on Mathematical Analysis,” and the “SIAM Journal on Control & Optimization.” His book with Steven Shreve, Brownian Motion and Stochastic Calculus, first published in 1987, is the standard reference within the field of Stochastic Analysis. Karatzas earned his Ph.D. from Columbia University and helped build and establish this Mathematics of Finance Master’s program.

[email protected] | Website

David X. Li

Adjunct Professor, Department of Mathematics PhD, University of Waterloo, 1995 Credit analytics, Risk Management, FinTech

David X. Li currently teaches at Shanghai Advanced Institute of Finance, Shanghai Jiaotong University. Previously, he held senior positions at various leading financial institutions for more than two decades in the areas of new product development, risk management, asset/liability management and investment analytics.

David has a PhD degree in statistics from the University of Waterloo, Master’s degrees in economics, finance and actuarial science, and a bachelor’s degree in mathematics. Dr. Li was one of the early practitioners in credit derivatives. His work of using copula functions for credit portfolio modeling has been widely cited by academic researchers, broadly used by practitioners for credit portfolio trading, credit risk management and credit rating, and well covered by media such as Wall Street Journal, Financial Times, Nikkei, CBC News.

[email protected]

Bryan Liang

Adjunct Professor, Department of Mathematics PhD, University of Michigan, 2002 Non-Linear Option Pricing

Bryan Liang is an Adjunct Assistant Professor in the Department of Mathematics, specializing in Derivatives Modelling. Additionally, Liang has extensive experience as a quantitative analyst in the finance industry, currently working as a Quantitative Researcher in the Quantitative Financial Research Group for Bloomberg. Liang earned his Ph.D. in Mathematics from the University of Michigan.

Amal Moussa

Adjunct Professor, Department of Mathematics PhD, Columbia University, 2011 Modeling and Trading Derivatives

Dr. Amal Moussa is a Managing Director at Goldman Sachs, where she leads the Single Stocks Exotic Derivatives Trading desk. Prior to that, Amal held senior-level positions in equity derivatives trading at other leading financial institutions such as J.P. Morgan, UBS, and Citigroup. In addition to her work in Markets, Amal is an Adjunct Professor at Columbia University, where she teaches a graduate course on Modeling and Trading Derivatives in the Mathematics of Finance Masters program.

Amal has a Ph.D. in Statistics, obtained with distinction, from Columbia University. Her thesis “Contagion and Systemic Risk in Financial Networks” shed light on the importance of the network structure in identifying systemic financial institutions and formulating regulatory policies and has been cited by several scholars and industry professionals, including former Federal Reserve president Janet Yellen. She was also awarded the Minghui Yu Teaching Award at Columbia University. Prior to her Ph.D., Amal graduated with a Masters in Mathematical Finance from Sorbonne University (former Paris VI) and a Grande Ecole engineering degree from Télécom Paris.

Amal is a board member of Teach for Lebanon, an NGO working to ensure that all children in Lebanon have access to education regardless of socioeconomic background, and she is an active member of the Women in Trading network at Goldman Sachs.

[email protected]

Adjunct Assistant Professor, Department of Mathematics PhD, Columbia University, 1996 Programming for Quantitative & Computational Finance

Ka Yi Ng is an Adjunct Assistant Professor in the Department of Mathematics with extensive quantitative experience in FinTech. Currently, Ng works at Calypso Technology Inc. and serves as an advisor at Finch Lead Inc. Ng previously worked at Wall Street Systems and ION. At Columbia, her interests include Derivatives and Structured Products Development, and Machine Learning. Ng earned her Ph.D. in Mathematics from Columbia University.

[email protected] | Website

Lars Tyge Nielsen

Director of the Mathematics of Finance MA program PhD, Harvard University, 1985

Lars Tyge Nielsen is the Director of the Mathematics of Finance MA (MAFN) program. He was a Chaired Professor, Associate Dean, and Director of the PhD program at INSEAD, the international business school in Fontainebleau, France and Singapore and now Abu Dhabi. He has held academic appointments at Nankai University, China (Chair Professor), NYU, UT Austin, Copenhagen Business School and the University of Copenhagen. Dr. Nielsen spent 13 years on Wall Street as an executive at various institutions including Morgan Stanley and Goldman Sachs.

[email protected] | 212-854-4306 | Website

Colm O’Cinneide

Adjunct Professor, Department of Mathematics PhD, University of Kentucky, 1983 Multi-Asset Portfolio Management

Colm O’Cinneide is an adjunct professor in the Department of Mathematics. He has worked in quantitative asset allocation and portfolio construction roles for the past 20 years at Deutsche Asset Management, QS Investors, and Franklin Templeton Investments, where he is currently an SVP. He was a partner at QS investors. Prior to this, he worked in academia from 1982 to 2000 and held tenured positions in Mathematical Sciences (Statistics) at the University of Arkansas and Industrial Engineering (Operations Research) at Purdue University. He has 40+ refereed publications related to probability, statistics, numerical analysis, and finance, with 1300+ citations and a track record of National Science Foundation funding. He has a PhD in Statistics from the University of Kentucky.

[email protected]

Inna Okounkova

Editor-in-Chief of the Journal of Investment Consulting, Wealth Management eJournal, and Wealth Management Editor’s Choice eJournal; formerly Director of Investment Strategy at AB Bernstein; prior to that head of Strategic Asset Allocation Portfolio Management and partner at QS Investors, LLC and head of Strategic Asset Allocation Portfolio Management at Deutsche Asset Management; Past President and Director of the Honorary Board of the Society of Quantitative Analysts; MS in Mathematical Economics and ABD from Moscow State University; MBA from the University of Chicago (1999).

[email protected]

Rosanna Pezzo-Brizio

Rosanna Pezzo-Brizio is an Adjunct Professor in the Department of Mathematics, specializing in Fixed Income Portfolio Management. Pezzo-Brizio has a vast array of professional, senior experience, working at Goldman Sachs, Greenwich Capital Markets, Intesa Sanpaolo. Currently, she is the Director of the Investment Consulting Group at New York Life Investments. Pezzo-Brizio holds a Ph.D. in Mathematics of Finance from the University of Brescia. Additionally, she graduated from Columbia University’s Mathematics of Finance program in 1998 as one of the program’s first classes.

[email protected]

Franz Rembart

Adjunct Assistant Professor, Department of Statistics PhD, University of Oxford, 2016 Statistical Inference / Time-Series Modelling

[email protected]

Gordon Ritter

PhD, Harvard University Machine Learning for Finance

Professor Ritter is founder and CIO of Ritter Alpha LP, a registered investment adviser running systematic absolute-return trading strategies across multiple asset classes and geographical regions. Before Ritter Alpha, he was a senior portfolio manager at GSA Capital and a Vice President in the Statistical Arbitrage Group at Highbridge Capital Management (HCM). Gordon completed his PhD in mathematical physics at Harvard University and his Bachelors’ degree with honors in Mathematics from the University of Chicago. While at Harvard, he published several papers in the areas of quantum field theory, differential geometry, quantum computation and abstract algebra. His current research is on portfolio optimization and statistical machine learning. Notable publications include “Optimal turnover, liquidity, and autocorrelation,” with @Bastien Baldacci of @ Université Paris Dauphine – PSL and @Jerome Benveniste of @New York University, Risk, 2022, and “Machine learning for trading,” Risk, 2017. In recognition of the latter publication, Professor Ritter was named Buy-Side Quant of the Year in 2019.

[email protected]

Renzo Silva

Adjunct Assistant Professor, Department of Mathematics MBA, Columbia Business School Machine Learning for Finance

Renzo Silva is an Adjunct Assistant Professor in the Department of Mathematics. In addition to his teaching position, Mr. Silva has extensive professional experience in the Financial Technology industry, and he currently serves as a Software Engineering Manager at Google. Previous experiences include Software Development Manager at Amazon, CTO at P1 Capital, and Managing Director at the New York Stock Exchange. Mr. Silva is a graduate of Columbia University’s Mathematics of Finance MA program, and he also holds an MBA in Finance and Economics from Columbia Business School. His research interests include Artificial Intelligence and Machine Learning, Optimization, Simulation, and Quantum Computing.

[email protected]

Mikhail Smirnov

Mikhail Smirnov is a Senior Lecturer in Discipline in the Department of Mathematics and was Director of the Mathematics of Finance program in 1998-2012. His research interests include Quantitative Portfolio Management, Quantitative Investment Strategies, and Risk Measurement. He holds a Ph.D. from Princeton University.

[email protected] | 212-854-6955 | Website

Harvey J. Stein

Dr. Harvey J. Stein left Bloomberg in March 2022 after a distinguished 28 3/4 year career where he built quant and engineering teams and introduced a variety of innovations in option pricing, computation and risk analysis. Dr. Stein is well known in the industry, having published and lectured on credit risk modeling, financial regulation, interest rate and FX modeling, CVA calculations, mortgage-backed security valuation, COVID-19 data analysis, and other subjects. Dr. Stein is on the board of directors of the IAQF, a board member of the Rutgers University Mathematical Finance program, an adjunct professor at Columbia University, and an organizer of the IAQF/Thalesians financial seminar series. He’s also worked as a quant researcher on the Bloomberg for President campaign. He received his BA in mathematics from WPI in 1982 and his PhD in mathematics from UC Berkeley in 1991. He recently started a new position as Senior VP, Quant Research at Two Sigma.

[email protected]

Statistical Interference/ Time-Series Modeling

Yisha Yao is an Assistant Professor with the Department of Statistics. Her research interests span a wide range of modern high-dimensional statistics, including but not limited to nonparametric methods, statistical inferences, mixture models, FDR control, iterative algorithms, tensor analysis, and applications in biomedical sciences. She obtained her PhD in statistics from Rutgers University (New Brunswick) in 2021, and then spent two years at Yale University as a postdoctoral researcher in biostatistics.

[email protected]

Adjunct Professor, Department of Mathematics AB/SM, Harvard University, 1998 Hedge Funds Strategies and Risk

Eric Yeh is an Adjunct Professor in the Department of Mathematics, specializing in Quantitative Investment Strategies. Yeh’s vast professional experience in the Finance industry includes senior positions at Morgan Stanley, Deutsche Bank, Tower Research Capital, and AllianceBernstein. Currently, he is President of Vermillion Leaf Capital LLC and an advisor to multiple investment managers, including the $100M hedge fund he previously co-founded. Yeh holds an AB in Mathematics and an SM in Computer Science from Harvard University.

[email protected]

The Best Quantitative Finance Degrees

Discover the perfect educational program to break into quantitative finance..

Are you a prospective student looking to break into quantitative finance? Not sure which school to apply to and what major to pursue? Curious about what education will help you land a job as a quant?

In this article, we’ll break down a plethora of options you can choose when pursuing a bachelor's or master's degree in quantitative finance . While the ideas that we present in this article are tailored towards the traditional pathway of pursuing a degree from an accredited university, it’s important to remember that a lot of the learning you will need to become a quant will take place outside the classroom. While traditional degrees are great for learning foundational mathematics, statistics, finance, and programming concepts, a lot of the more advanced and modern methods in this field will be beyond the scope of any class you take. If you’re interested in learning the best resources for becoming a quant, checkout out our article here .

Preface: The Quant Trinity

Before we outline the various education tracks available, it’s important to keep in mind that depending on the type of quant you would like to become, the education you pursue may vary. Broadly, there are three primary categories of quants: Quantitative Researchers, Quantitative Traders, and Quantitative Developers. While all three types of quants share some overlapping skill sets, there are a few stark differences between the three.

The role of a quantitative researcher is to develop proprietary trading strategies using statistical and machine learning based models. Oftentimes, quantitative researchers come from backgrounds in advanced statistics, mathematics, or econometrics.

Quantitative traders actively monitor the firm’s trades and send orders to exchanges. At some firms, quant traders also work as portfolio managers and help the firm with risk allocation. Oftentimes a degree in mathematics or finance is sufficient for these types of jobs.

Finally, quantitative developers are primarily responsible for the various programming tasks that take place in a firm. This could range from implementing models that were designed by quantitative researchers to developing back-testing engines and ad-hoc scripts. Quantitative developers need to have a solid foundation in software development, and therefore a degree in computer science and statistics is often the most valuable.

Now that we’ve covered the slight differences that exist in the educational backgrounds of different types of quants, we will next share with you what are the best programs that offer this form of education.

Undergraduate Degrees in Quantitative Finance

Finding an undergraduate degree that specializes purely in quantitative finance can be cumbersome. In fact, only a few select universities in the United States cater to this specific program. However, there are many interdisciplinary programs that offer a combination of statistics, computer science, and finance that could easily function as good alternatives. Pursuing a double major, or dual degree, in any of the aforementioned subjects may serve as a viable alternative if your school doesn't offer a quantitative finance program.

Below, we will highlight some of the universities and programs that specialize in quantitative finance as well as some alternatives that offer similar coursework. The schools we mention below aren’t in any particular order, but all provide high-caliber programs.

Carnegie Mellon University - Bachelor of Science in Computational Finance

• Program Website: CMU Quant Finance
• Tuition: ~ $60,000 / year (as of 2022)
• Acceptance Rate: 14%

Carnegie Mellon offers an undergraduate program in computational finance that focuses on helping students develop a deep knowledge of mathematics, probability, statistics, and their applications to finance.

This program is particularly interesting because it blends coursework from three different schools at CMU: The Mellon College of Science, the Heinz College of Public Policy and Management, and the Tepper School of Business. The coursework for this major involves “Introduction to Mathematical Finance”, “Modern Regression”, and “Principles of Computing”. Furthermore, CMU offers some amazing electives such as “Mathematics of Fixed Income Markets” and “Advanced Methods for Data Analysis”

While the major itself at CMU is great, you get much more than the education when attending this university. In fact, some of the top quantitative finance firms in the country all recruit from this school. Companies like Citadel, DRW, and Two Sigma all hire CMU computational finance students so your odds of landing a quant job are definitely increased by attending this university.

Stevens Institute of Technology - Quantitative Finance

• Program Website: Stevens Institute of Technology Quant Finance
• Tuition: ~ $57,000 / year (as of 2022)
• Acceptance Rate: 40%

Stevens Institute of Technology offers a great bachelor's program in quantitative finance . The program places a heavy emphasis on business, math, finance, and computer science with curricula such as “Statistics for Quantitative Finance”, “Advanced Time Series Analytics and Machine Learning”, and “Financial Market Microstructure and Trading”.

Stevens Institute of Technology places a heavy emphasis on career opportunities post-graduation by helping students obtain certifications and internships. Many top quant firms recruit out of SIT including BlackRock, Goldman Sachs, Deutsche Bank, and Citigroup.

Princeton University - Operations Research & Financial Engineering

• Program Website: Princeton Quant Finance
• Tuition: $56,000 / year (as of 2022)
• Acceptance Rate: 5.5%

Princeton University offers a Bachelor in Operations Research & Financial Engineering (ORFE). This program is highly technical involving academic coursework in statistics, probability, stochastic processes, and optimization. It also features many certificate opportunities in areas such as “Statistics and Machine Learning”, “Finance”, and “Applied and Computational Mathematics”.

Princeton also offers many research opportunities in quantitative finance. The Financial Econometrics Laboratory specializes in building statistical methods for risk management and data analytics modeling.

Overall, the ORFE program at Princeton is great if you are interested in diving deep into mathematical modeling and want a strong foundation to bring with you when pursuing graduate school.

Baruch College - Bachelor of Science in Financial Mathematics

• Program website: Baruch Quant Finance
• Tuition: $19,000 / year (as of 2022)
• Acceptance Rate: 51%

Baruch College offers a Bachelor of Science in Financial Mathematics that focuses on computational tools and techniques needed to become a quant. The program features courses such as “Introduction to Probability” and “Data Analysis and Simulation for Financial Engineers”.

One of the nice parts about the program at Baruch is that it is quite affordable. Similar programs at other top-tier universities are often double the price of the degree offered at Baruch which makes it a great option if you’re looking for a more price-friendly alternative.

Minors in Quantitative Finance

While it is often difficult to find schools that offer an undergraduate program in quantitative finance, there are many schools that offer greater statistics, finance, and computer science majors that can be complemented by a minor in quantitative finance. Here are a few schools that offer minors in quantitative finance.

Rice University

• Minor in Financial Computation and Modeling
• Program website: Rice Minor in Quant Finance
• Acceptance rate: 9%

University of Washington

• Minor in Computational Finance
• Program website: University of Washington Minor in Quant Finance
• Acceptance rate: 53%

University of Southern California

• Minor in Mathematical Finance
• Program website: USC Minor in Quant Finance
• Acceptance rate: 13%

University of Virginia

• Quantitative Finance Track
• Program website: UVA Minor in Quant Finance
• Acceptance rate: 22%

Masters Programs in Quantitative Finance

If you’re looking to pursue higher education, in the form of a Master’s degree in quantitative finance , there are many great programs that you may want to consider. Here are a few schools that have highly-ranked programs.

Princeton University - Master in Finance

• Tuition: ~ $112,000

Princeton’s Master in Finance has consistently ranked amongst the best programs to learn quantitative finance. This shouldn’t be a surprise based on the success its students have had in finding career opportunities post-graduation. One of the great aspects of this program is that they provide you with one-on-one career counseling to help you find jobs and internships, while also hosting many workshops and speaker series to help you with your career search.

Baruch College - Master of Science in Financial Engineering

• Program Website: Baruch Quant Finance

Baruch College’s Master of Financial Engineering is one of the most esteemed master’s degrees available in quantitative finance. The program offers a cutting-edge, flexible, and streamlined curriculum featuring a wide variety of different electives to choose from. Furthermore, the program has had an amazing track record of placing students, with a 100% employment placement and 100% internship placement rate in 2022.

University of California Berkeley - Master of Financial Engineering

• Program Website: UC Berkeley Quant Finance

UC Berkeley’s Master of Financial Engineering is one of the top programs in the country. The school features a rich curriculum in statistics, mathematics and computer science. One neat aspect of this program is that it also offers a part-time option for those looking to pursue a Master while also working in industry.

Imperial College London - Master of Science in Mathematics and Finance

• Program Website: ICL Quant Finance

If you’re looking for an astute program outside of the United States, this is the college for you. ICL offers excellent training in modern mathematics and its applications to various financial concepts. The school also places a heavy emphasis on research in the quantitative modeling of financial markets.

Carnegie Mellon - Master of Science in Computational Finance

• Program Website: Carnegie Mellon University
• Tuition: ~ $92,534 / year (as of 2022)
• Acceptance Rate: 22%

The MSCF program at Carnegie Mellon offers students a unique opportunity to blend coursework in finance with computer science and statistics. One of the many highlights of this program is that 98% of graduates received full-time job offers within three months of their graduation. Furthermore, the mean salary for these graduates was $117,513.

It’s important to recognize that while we have listed a few degree programs that offer specialized programs in quantitative finance, there are many other majors that you can pursue to learn similar material. For example, a blend of applied statistics and computer science could be nearly equivalent to a specialized major in computational finance.

Thanks for reading this article. If you enjoyed it, feel free to check out more of our content on our quant blog . Also, if you’re looking for a job or internship as a quant check out OpenQuant for the best quant jobs .

100 Best universities for Finance in Russia

Updated: February 29, 2024

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• Mathematics

Below is a list of best universities in Russia ranked based on their research performance in Finance. A graph of 234K citations received by 54.9K academic papers made by 128 universities in Russia was used to calculate publications' ratings, which then were adjusted for release dates and added to final scores.

We don't distinguish between undergraduate and graduate programs nor do we adjust for current majors offered. You can find information about granted degrees on a university page but always double-check with the university website.

1. Moscow State University

For Finance

2. National Research University Higher School of Economics

3. St. Petersburg State University

4. Finance Academy under the Government of the Russian Federation

5. Russian Presidential Academy of National Economy and Public Administration

6. RUDN University

7. Kazan Federal University

8. National Research Nuclear University MEPI

9. Ural Federal University

10. Moscow State Institute of International Relations

11. Plekhanov Russian University of Economics

12. Peter the Great St.Petersburg Polytechnic University

13. Southern Federal University

14. Novosibirsk State University

15. Tomsk State University

16. Moscow Institute of Physics and Technology

17. New Economic School

18. Saratov State University

19. State University of Management

20. ITMO University

21. South Ural State University

22. Moscow Aviation Institute

23. Voronezh State University

24. Lobachevsky State University of Nizhni Novgorod

25. Tomsk Polytechnic University

26. Bauman Moscow State Technical University

27. N.R.U. Moscow Power Engineering Institute

28. Chelyabinsk State University

29. Samara National Research University

30. Far Eastern Federal University

31. St. Petersburg State University of Economics

32. Siberian Federal University

33. Novgorod State University

34. baikal state university.

35. Moscow State Pedagogical University

36. Ulyanovsk State University

37. Volgograd State University

38. Novosibirsk State Technical University

39. Bashkir State University

40. Ufa State Aviation Technical University

41. Moscow Medical Academy

42. Saint Petersburg State Electrotechnical University

43. North Caucasus Federal University

44. Kuban State University

45. European University at St. Petersburg

46. National University of Science and Technology "MISIS"

47. Yaroslavl State University

48. Nizhny Novgorod State Technical University

49. Moscow State University of Railway Engineering

50. Samara State Technical University

51. Belgorod State University

52. St. Petersburg State University of Architecture and Civil Engineering

53. University of Tyumen

54. Kemerovo State University

55. Altai State University

56. St. Petersburg State University of Aerospace Instrumentation

57. Tambov State University

58. Saint-Petersburg Mining University

59. Perm State Technical University

60. Tomsk State Pedagogical University

61. Irkutsk State University

62. Leningrad State University

63. Moscow Polytech

64. Perm State University

65. Kazan State Technological University

66. Tomsk State University of Control Systems and Radioelectronics

67. kuban state agricultural university.

68. Tver State University

69. Siberian State Aerospace University

70. Russian State University for the Humanities

71. South-Russian State University of Economics and Service

72. Omsk State University

73. Mendeleev University of Chemical Technology of Russia

74. Belgorod State Technological University

75. Kalashnikov Izhevsk State Technical University

76. North-Eastern Federal University

77. South-Western State University

78. Penza State University

79. Russian State University of Oil and Gas

80. orenburg state university.

81. Moscow State Technological University "Stankin"

82. Kazan National Research Technical University named after A.N. Tupolev - KAI

83. Ogarev Mordovia State University

84. Chuvash State University

85. chechen state university.

86. Ufa State Petroleum Technological University

87. Immanuel Kant Baltic Federal University

88. Orel State University

89. Petrozavodsk State University

90. Amur State University

91. Syktyvkar State University

92. magnitogorsk state technical university.

93. Kuzbass State Technical University

94. Saint Petersburg State Institute of Technology

95. Kuban State University of Technology

96. Astrakhan State Technical University

97. Saratov State Technical University

98. Tula State University

99. Sevastopol State University

100. Kaliningrad State Technical University

The best cities to study Finance in Russia based on the number of universities and their ranks are Moscow , Saint Petersburg , Kazan , and Yekaterinburg .

Economics subfields in Russia

• Best Scholarship Websites of 2023
• Peterson's 
• College Board
• Scholarship Owl
• Our Experts for the Best Personal Loans
• Our Experts' Advice for Choosing the Best Scholarship Websites

Best Scholarship Websites of 2024

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Even the best student loans and other financial aid items don't always provide enough to cover all the costs of college. Scholarships are a great way to bridge the gap, with no obligation to repay the money. Finding scholarships you qualify for can be difficult. But there are several great scholarship websites that can help you do it.

Keep in mind, however, that many colleges deduct private scholarships from the amount of gift aid they provide, reducing how much "free money" you can get from the school. That would effectively negate the work you did in seeking out and applying for private scholarships. Therefore, you should consider looking at schools that offer merit-based scholarships or provide generous packages to students with financial need.

Scholarships.com

Collegeboard, scholarshipowl, compare the best scholarship websites.

We've rounded up the best scholarship websites for students looking to save on the cost of college, with an emphasis on those that provide a wide breadth of opportunities. Most of the sites are free to use. Some offer basic features at no cost, with an option to pay for additional functionality. 

Peterson's 

Our experts for the best scholarship websites.

We consulted scholarship and financial experts to give their insights into finding the best scholarship websites — and when to avoid them altogether.

• Christopher Rim, founder and CEO at Command Education
• Jennifer Ledwith, owner at Scholar Ready
• Neeta Vallab, founder at MeritMore
• Ryan Wangman, former loans reporter at Personal Finance Insider

Best Scholarship Websites FAQs

There isn't a singular website that's the best to apply for scholarships. Instead, consider scholarships.com, Fastweb, Bold.org, Peterson's, College Board, Scholly, GoingMerry, Scholarship Owl, and ProFellow.

Do your due diligence when looking to apply for scholarships you find on aggregation websites. Confirm that the scholarship's sponsor is legitimate by looking at reviews of the company, and check its Better Business Bureau grade, if available. 

Most private scholarships won't completely cover the cost of your education. You're better off looking for merit scholarships from individual colleges that cover your costs completely or filling out the Free Application for Federal Student Aid (FAFSA)  to see if you're eligible for a need-based full ride. 

Generally speaking, you shouldn't pay someone to search for scholarships for you, though there are exceptions to every rule. Many scholarship websites are free to use. They only "cost" the time you spend searching on them. 

The timing on when you should start filling out applications for scholarships varies depending on the specific scholarship you're applying for. Some scholarships are even available to high school students, so it's never too early to start your search.

The GPA requirement varies from scholarship to scholarship, but the lowest GPA is usually around 2.0. In general, you'll have a better chance of success with a higher GPA. 

Some of the most common scholarship types include academic scholarships, athletic scholarships, identity-based scholarships, and first-generation student scholarships.

Our Experts' Advice for Choosing the Best Scholarship Websites

What are the advantages of private scholarships versus scholarships or grants from schools.

Neeta Vallab: 

"Honestly, there are very limited advantages to using private scholarships over grants directly from schools. Unlike private scholarships, most grants from schools do not require a separate application and are renewable for four years. Additionally, many colleges will deduct the value of a private scholarship from any award they give you. This is called 'displacement.' And because this practice is so prevalent, it often leaves students with no net financial benefit, despite the effort they invested in submitting an application."

Jennifer Ledwith:

"Unlike scholarships from schools, private scholarships can be used at multiple universities. Furthermore, unlike grants from schools, private scholarships don't have to follow federal or institutional financial aid methodology. Consequently, students can receive private scholarships without regard to other financial aid awards. Furthermore, some private scholarships send the money directly to the student, allowing greater flexibility in using the funds."

Should you always consider scholarships before loans? Are there any scenarios you should pick a loan?

Christopher Rim: 

"No, you should never pick a loan over a scholarship! You always have to pay back loans. Students should bear in mind that they may have their scholarship rescinded if they don't meet the terms and conditions. They should be absolutely sure to read the terms of their scholarship thoroughly to understand the possible consequences if they fail to meet the set conditions."

Ryan Wangman:

"You'll need to repay the money you borrow from any lender, so it's never a good idea to choose a loan over a scholarship, which is money you don't have to pay back. There aren't any scenarios I can think of where it is better to pick a loan than a scholarship. 

How do you know what scholarships you're qualified for?

Rim : 

"Many websites will allow students to filter through their databases to find scholarships they are eligible for. Others ask students to create a profile and then match them based on demographic information. Once students find the scholarships on databases, they should be sure to cross check eligibility requirements on the sponsoring organization's website."

Vallab : 

"Private scholarships will usually have a qualifications section in their description. Some colleges may publish a scholarship grid showing the GPA and test scores required for certain levels of eligibility. Not all merit scholarship information is published however."

What's the best way to track what scholarships you've applied for and are waiting to hear back from?

"Students should keep a spreadsheet to track their scholarship application status."

"Like Jennifer mentioned, a spreadsheet is usually the best way to track application status. Make sure you note how far along you are in each process, as well as any awards you have received — both private and from the schools you've been accepted to.

Editorial Note: Any opinions, analyses, reviews, or recommendations expressed in this article are the author’s alone, and have not been reviewed, approved, or otherwise endorsed by any card issuer. Read our editorial standards .

Please note: While the offers mentioned above are accurate at the time of publication, they're subject to change at any time and may have changed, or may no longer be available.

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• Main content

Wall Street Journal Ranks UVA Among Leaders in Finance, Consulting, Tech Jobs

May 29, 2024 • By Jane Kelly, [email protected] Jane Kelly, [email protected]

• Jane Kelly, [email protected]

(Rotunda photo, University Communications; illustration by John DiJulio, University Communications)

The Wall Street Journal found the University of Virginia is exceptional when it comes to positioning undergraduates for high-paying jobs in finance, consulting and technology.

In its springtime ranking, the business publication  ranked UVA No. 3 for finance, No. 5 for consulting and No. 13 in technology , when compared to other public schools.

“The rankings – which include the top 20 public and private universities for launching graduates into high-earning jobs – aim to answer: If the chosen career and the number of years in the field are the same, what effect does the undergraduate school somebody went to have on their salary?” the paper wrote. “The effect can be huge.”

Related Story

New Rankings Place Darden Atop All Public MBA Programs in the U.S.

Of the top public schools for finance, UVA ranked just after the University of Michigan and the University of California, Berkeley. The ranking estimates UVA graduates in the finance industry have an average annual salary of $136,816, which is $34,985 more than the median industry salary. That $34,985 extra is what the Wall Street Journal called the “salary premium,” or extra earnings based on having a UVA degree. The premium for Michigan graduates is $38,191 and $35,643 for Berkeley alumni.

In the category of consulting, UVA’s No. 5 spot follows schools including the U.S. Military Academy and the Georgia Institute of Technology. For UVA alumni, the salary premium was estimated at $14,274, bringing the average UVA consulting salary to $112,770.

For UVA technology graduates, the Wall Street Journal estimated they earn an average annual salary of $136,557, which includes a salary premium $14,465. That pay bump means UVA is ranked No. 13 among public schools for technology salaries.

Media Contact

University News Senior Associate Office of University Communications

[email protected] (434) 243-9935

Article Information

May 31, 2024

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Analysis-Tax Hikes to Give Russia Fiscal Headroom to Further Increase Spending

FILE PHOTO: Russian President Vladimir Putin chairs a meeting with officials and heads of the country's military industrial sector outside Moscow, Russia, May 25, 2024. Sputnik/Vyacheslav Prokofyev/Pool via REUTERS/File Photo

By Darya Korsunskaya and Alexander Marrow

LONDON (Reuters) - Russia's proposed tax hikes for companies and the wealthy, which may add an extra $30 billion to next year's budget revenues, will allow Moscow to further ratchet up spending, including on its war in Ukraine, without compromising fiscal stability.

The finance ministry insists the tax hikes, proposed on Tuesday, are fair and target Russia's wealthiest. It says tax changes should solve national problems, reduce inequality and help develop Russia's regions.

The changes will cover President Vladimir Putin's more than 11.5 trillion roubles ($128 billion) in infrastructure and social spending pledges for his new six-year term, analysts say, without requiring a reworking of Russia's budget rule.

That would leave funds left over for Moscow to pump additional money into the huge resources it is devoting to defence and security for what it calls its "special military operation" in Ukraine. Overall budget spending is planned at 36.6 trillion roubles in 2024, more than double what it was in 2019.

"The overall appetite of the finance ministry is calculated in trillions of roubles," said Sergei Aleksashenko, a former deputy governor of the Bank of Russia who has criticised Russia's war in Ukraine and been designated a 'foreign agent' by Russia's justice ministry.

"Yes, war is not a cheap thing."

The finance ministry estimates that new taxes - extra progressive income tax rates, a 5% corporation tax hike and new mineral extraction taxes - will bring in an additional 2.6 trillion roubles in 2025, a 3.6% increase in budget revenues according to Rosbank analysts, and 17 trillion roubles by 2030.

"The expected revenues of 2.6 trillion roubles in 2025 and about 3 trillion roubles per year in 2026-2030, at first glance, completely cover additional budget expenditures," said Dmitry Polevoy, investment director at Astra Asset Management.

The tax adjustments carry no risks for budget fulfilment, said Yevegeny Kogan, professor at Russia's Higher School of Economics.

"Moreover, the finance ministry will be able to increase spending next year provided the economy does not stagnate," Kogan said.

FISCALLY SOUND

Crucially, the proposed amendments to income and corporation tax target non-oil and gas revenues, meaning no changes to Russia's budget rule or impact on the National Wealth Fund (NWF).

"Accordingly, there will be more money in the budget that can be spent with fewer restrictions," said Expert RA Chief Economist Anton Tabakh.

Under its budget rule, Russia sells foreign currency from its NWF to make up for any shortfall in revenue from oil and gas exports, or makes purchases in the event of a surplus.

The tax increases are the "most balanced" way to meet the budget's emerging need for additional funds since 2022, especially considering that the NWF has been largely exhausted, said Sofya Donets, Tinkoff Investments' chief economist.

The NWF's liquid assets have dropped to $56.4 billion, from $112.7 billion before Russia's invasion of Ukraine.

FAIR DISTRIBUTION?

Finance Minister Anton Siluanov's buzzword since announcing the proposals has been "fairness", with the increases in income tax affecting just 3.2% of the workforce, or 2 million people.

All earners in Russia pay a flat rate of 13% income tax. In 2021, earnings over 5 million roubles were taxed at 15%. Now, the finance ministry is proposing a 15% rate for annual incomes from 2.4 to 5 million roubles, with a progressive scale up to 22% for earnings exceeding 50 million roubles.

But Tabakh said wage growth and inflation could see 5-10% of the workforce paying more tax in the coming years.

Furthermore, investment income - on deposits, dividends and the sale of securities - will be taxed at just 13-15%.

"Taxation will be more beneficial for investment income than salaries ... which does not quite correspond to a more equitable distribution of the tax burden," said Finam analyst Olga Belenkaya.

Tabakh noted that income from labour was set to be taxed more heavily than from capital.

"In fact, this means increasing the burden on the working middle class," Tabakh said.

Business will shoulder the majority of the increase, with the rise in corporation tax to 25% from 20% to yield 1.6 trillion roubles in 2025.

That change will leave companies with fewer resources, Belenkaya said, and impinge on investment activity, especially when high interest rates at 16% limit borrowing potential.

In a blow to SMEs, companies with annual revenue above 60 million roubles will lose their VAT exemption.

But there were trade-offs, too. Last year's one-off excess profits tax will not be revived, and Siluanov promised to abolish exchange-rate-linked export duties in return for higher mineral extraction taxes.

"Whether (these tax changes are) enough for the next six years in case of shortfalls in planned tax revenues or additional expenditure, is not yet clear," Belenkaya said.

($1 = 89.7425 roubles)

(Reporting by Darya Korsunskaya and Alexander Marrow; Editing by Susan Fenton)

Copyright 2024 Thomson Reuters .

Tags: Ukraine , Russia , Europe

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