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.
“Stevens faculty are incredibly supportive. My advisor takes care of his students like family; he's always making opportunities for his students.”
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:
- Algorithmic and high-frequency trading. Technology and the availability of high-frequency data in finance have forced traders to think differently about how to build effective strategies in digital markets. Research here is centered around execution quality of trading strategies, as well as how financial indicators can be combined and selected in generating effective trading rules.
- 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.