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.

A group of students in a row at computers in the Hanlon Lab.
Courses at the doctoral level challenge Financial Engineering students to embrace technology-driven solutions to new research opportunities.

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. 

To complete the Ph.D., a student must earn at least 84 credits beyond the bachelor's level. A maximum of 30 credits is awarded for a master's degree.

Required courses

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.

Area-specific courses

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.

Quantitative methods

  • 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 

Domain tools

  • 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

Dissertation

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.