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 Analytics and Visualization
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.
Research
As an engineering discipline based out of a business school, the Ph.D. program in Financial Engineering is unique for its emphasis on preparing students to become diligent researchers who bring a problem-solving perspective to the emerging challenges associated with finance.
Before graduating from the program, students become specialists in one or more of these areas through independent research and collaborative work with faculty, who provide one-on-one guidance to doctoral candidates. Each student is required to publish a minimum of two conference papers and one journal paper before completing the program; many exceed this requirement.
The state-of-the-art tools in the Hanlon Financial Systems Center — from Bloomberg and Mezzanine to WRDS and Gurobi — prepare students to employ technology in conducting the kind of research that's in greatest demand at finance companies seeking an edge in an increasingly competitive market. Combined with the skills they receive from faculty, many of whom have had successful careers in industry, students complete the program ready to become the kind of thought leaders able to drive innovative solutions for the industry or bring scientific perspectives to complex problems.
The doctoral program is built around six areas of research expertise of School of Business faculty. Students who complete the program will be prepared to lead corporate research efforts in these areas:
Asset pricing and behavioral finance - Researchers here analyze technology’s impact on asset pricing, including the deployment of social network indicators to forecast pricing trends and success drivers in developing new derivative products.
Portfolio optimization - Faculty studying portfolio optimization consider how new technologies can help investors create value while creating realistic assessments of risk.
Systemic risk - Financial markets and risks are systemic — events in one sector of the finance world are quickly felt in other sectors, crossing old boundaries with complex consequences that are difficult to predict. Research in this area explores the new regulatory, risk and technology management perspectives required to ensure successful outcomes in a global systemic framework.
Mathematical finance - Researchers here use quantitative methods to examine mathematical and numerical models and their applications in finance to better study concepts like pricing and value.
Financial analytics and innovation - Working with researchers in this area, you’ll use massive data sets — from market prices to text messages — in discovering and extracting meaningful signals from data. This helps professionals in the industry improve decision making through the development of new metrics.