Curriculum PhD Financial Engineering

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


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. 

Finance displays at Hanlon Center at Stevens School of BusinessResearch

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. 

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