Quantitative Finance, MSQF
The Master of Science in Quantitative Finance (MSQF) is a rigorous degree designed to prepare students for analytically intensive careers in finance. Combining advanced quantitative methods, economic intuition, and applied financial skills, the MSQF equips graduates with the skills needed to navigate the challenges of modern financial markets.
Open to current University of Pennsylvania undergraduates across all schools, the program follows a structured submatriculation format. Students apply in their third year, begin MSQF coursework during their fourth year while completing their undergraduate degree, and then continue into a dedicated post-graduation fifth year focused entirely on advanced MSQF study.
The degree and major requirements displayed are intended as a guide for students entering in the Fall of 2026 and later. Students should consult with their academic program regarding final certifications and requirements for graduation.
Curriculum
| Code | Title | Course Units |
|---|---|---|
| Pre-requisite Foundations (2 CUs) – Required pre-admission or by the end of 4th year 1 | ||
| Corporate Finance | ||
| Accounting and Financial Reporting | ||
| Core Requirements (6 CUs) 3 | 6 | |
| Investment Management 2 | ||
| Financial Derivatives | ||
| Fixed Income Securities | ||
| Data Science for Finance | ||
| Foundations of Asset Pricing | ||
| Applied Research Practicum | ||
| Electives (4 CUs) | 4 | |
Students must choose at least 3 of the below: | ||
| Forensic Analytics | ||
| Financial Reporting and Business Analysis | ||
| Financial Disclosure Analytics | ||
| Climate and Financial Markets | ||
| Risk Management | ||
| Valuation | ||
| International Financial Markets and Cryptocurrencies | ||
| Global Valuation and Risk Analysis | ||
| Capital Markets | ||
| Behavioral Finance | ||
| Introduction to Empirical Methods in Finance | ||
| Mathematical Modeling and its Application in Finance | ||
| Introduction to Python for Data Science | ||
| Introduction to Optimization | ||
| Stochastic Models | ||
| Stochastic Processes ll | ||
| Dynamic Programming and Stochastic Models | ||
| Real Estate Investment: Analysis and Financing | ||
| Real Estate Data Analytics | ||
| Probability | ||
| Advanced Statistical Inference I | ||
| Advanced Statistical Inference II | ||
| Applied Econometrics I | ||
| Applied Econometrics II | ||
| Stochastic Processes | ||
| Forecasting Methods for Management | ||
| Bayesian Methods and Computation | ||
| Applied Bayesian Modeling | ||
| Modern Data Mining | ||
| Advanced Statistical Computing | ||
| Convex Optimization for Statistics and Data Science | ||
| Causal Inference | ||
Students may choose up to 1 of the below: | ||
| Fundamentals of Linear Algebra and Optimization | ||
| Applied Machine Learning | ||
| Machine Learning | ||
| Big Data Analytics | ||
| Programming Languages and Techniques | ||
| Introduction to Software Development | ||
| Data-driven Modeling and Probabilistic Scientific Computing | ||
| Statistics for Data Science | ||
| Data Mining: Learning from Massive Datasets | ||
| Computational Linear Algebra | ||
| Mathematics of Finance | ||
| Probability Theory | ||
| Stochastic Processes | ||
| Total Graduate Level Coursework | 10 | |
- 1
Prior to admission, students must have completed MATH 2400 Calculus Part III or ESE 2030 Linear Algebra with Applications to Engineering and AI.
- 2
Students must take FNCE 5050 Investment Management by the end of their fourth year.
- 3
Students who took any of FNCE 2050, FNCE 2170 and FNCE 2250 prior to their fourth years and received at least a B- grade can waive the requirement to take FNCE 5050, FNCE 5170 and FNCE 5250, respectively, toward the MSQF.
Waiving a course does not reduce the number CUs taken for the MSQF degree. Any student waiving one of these courses will have to replace it with an elective to get to 10 CUs of graduate level coursework.
- 4
All coursework counting toward the MSQF degree must be completed at Penn. Transfer, study abroad, ‘credit away’ courses are not permitted.
| Fourth Year | ||
|---|---|---|
| Pre-requisite Foundations - ACCT 1010 & FNCE 1000 1 | ||
| FNCE 5050 | Investment Management 2 | 1 |
| FNCE 5170 & FNCE 5250 or FNCE 5250 | Financial Derivatives 2 or Fixed Income Securities | 1 |
| Course Units | 2.00 | |
| Summer | Course Units | |
| Summer Internship | ||
| Course Units | 0.00 | |
| Fifth Year | ||
| Fall | ||
| FNCE 5170 & FNCE 5250 or FNCE 5250 | Financial Derivatives or Fixed Income Securities | 1 |
| FNCE 5370 | Data Science for Finance | 1 |
| FNCE 5570 | Foundations of Asset Pricing | 1 |
| Electives to bring student schedule to at least 4 CUs for the term | 1 | |
| Course Units | 4.00 | |
| Spring | ||
| FNCE 5900 | Applied Research Practicum | 1 |
| Electives to bring student schedule to at least 4 CUs for the term and at least 10 graduate level CUs for the program | 3 | |
| Course Units | 4.00 | |
| Total Course Units | 10.00 | |
- 1
Pre-admission or by the end of the Fourth Year
- 2
Students waiving out of FNCE 5050, FNCE 5170, and/or FNCE 5250 can replace these courses with an approved MSQF elective
Undergraduate courses must be at least 50% of the student's course load per university policy.