Unlike many doctoral programs, the Ph.D. in Financial Engineering at the School of Business is specifically tailored to students interested in pursuing careers in industry upon graduation. While a Stevens doctoral degree in Financial Engineering is also suitable to those who wish to pursue academic careers, the unique bent of the courses in this program ensures students can compete for jobs at the highest levels in the finance industry, where their research will help companies create and refine new products and develop strategies to ensure competitive advantage in a quickly evolving industry.
While coursework is extremely challenging, students benefit from a supportive environment, working closely with both faculty mentors and fellow doctoral candidates. They also attend research colloquia that give fresh insight into the financial engineering challenges faced in industry and the tools and methods researchers are using to move the industry forward.
The other defining feature of the curriculum is its heavy emphasis on new technologies available through the Hanlon Financial Systems Center. Classes and lab courses go beyond basics in Bloomberg, OneTick, Thomson Reuters and more to ensure mastery of financial data sets and the ability to visualize real-time data to make better choices.
Every doctoral student at Stevens completes PRV 961 Ph.D. Signature Course as part of their studies. In addition, Ph.D. students in the Financial Engineering program complete either MGT 719 Research Methods or SYS 710 Research Methodologies.
Working with their advisor, students choose from among the following courses to tailor their studies to their particular area of research interest. Additional courses may be substituted in with approval from the Ph.D. committee.
FE 641 Advanced Multivariate Statistics
FE 646 Optimization Models and Methods in Finance
MA 611 Probability
MA 612 Mathematical Statistics
MA 623 Stochastic Processes
MA 629 Convex Analysis and Optimization
MA 630 Numerical Models of Optimization
MA 653 Numerical Solutions of Partial Differential Equations
MA 655 Optimal Control Theory
MA 661 Dynamic Programming and Stochastic Optimal Control
MA 662 Stochastic Programming
FE 710 Applied Stochastic Differential Equations
FE 720 Volatility Surface: Risk and Models
FE 635 Financial Enterprise Risk Engineering
FE 655 Systemic Risk and Financial Regulation
FE 622 Simulation Methods in Comp. Finance and Economics
FE 670 Algorithmic Trading Strategies
FE 672 Modern Market Structure and HFT Strategies
CS 541 Artificial Intelligence
CS 559 Machine Learning: Fundamentals and Applications
CS 590 Algorithms
CS 600 Advanced Algorithm Design and Implementation
BIA 658 Social Network Analysis
BIA 810 Cognitive Computing
Domain-specific research topics
Each course in the FE 801 series offers a deep dive into a particular area of financial engineering research. Students must complete one course from the below.
Advanced Topics in Portfolio Optimization
Advanced Topics in Market Microstructure and Algorithmic Trading
Advanced Topics in Financial Risk Modeling
Advanced Topics in Systemic Risk Modeling
Following completion of all written exams and coursework, students are required to write and defend a dissertation in a selected area of concentration. It is expected that doctoral dissertations will make significant contributions to the creation of knowledge and the development of theory and practice in a selected area.