Close

Financial Engineering Concentration and Cornell Financial Engineering Manhattan (CFEM)

"Cornell MFE is an extremely rigorous program that taught me a variety of practical skills, which I used immediately in the industry." - Devashish Kumar, MFE Class of 2010

"The Financial Data Science Coursework was just the right amount, such that I learned useful techniques and models." - Shaojie He, MFE Class of 2016

 

2018 Quant Finance Forum "Impact of AI and New Technologies on Asset Management"

On November 13th, join the Cornell community and industry experts as we discuss the transformative impact of AI on asset management.

Moderated Panel & Reception: Detailed Agenda (PDF)

 

New! Check out our latest brochure highlighting Cornell MFE over the past 10 years: CFEM Alumni Journal (2008-2018) >> (PDF, 2.3 MB)

 

Formally recognized as the Master in Engineering with Financial Concentration in the School of Operations Research and Information Engineering, Cornell MFE is a career-oriented and application-focused degree that takes you beyond textbook quantitative finance. With its flexible curriculum that encourages the study of data science, optimization, analytics, and computing, in addition to a broad range of courses in finance, the program has a rich history of providing the relevant and practical coursework in line with the demands of the financial industry.

With Cornell MFE, your career is guaranteed to begin in the classroom (APPLY HERE).

PowerPoint%20CFEM%20Timeline%20-%20500x281.jpg

Pictured: History of Cornell MFE (2008-2018)

 

Cornell MFE consists of (3) semesters (Fall-Spring-Fall) and allows for a summer internship after the first year. All students begin their studies on our scenic Ithaca campus, and they will complete their studies at Cornell Financial Engineering Manhattan in the heart of New York City.

For the price of one program, students have a diversity of settings to experience the full range of Cornell University.

CFEM-Three-Semesters-500x180_426149.jpg

Pictured: Cornell MFE's Three-Semester Model

 

Structured to offer a flexible curriculum, Cornell MFE allows students to focus on a career track of their choice. Some of the most popular career tracks include:

  • Trading
  • Quantitative Portfolio Management
  • Financial Data Science/Fintech
  • Financial Risk Management

MFE-Chart-with-Curriculum-500x322_426149.png

Pictured: Breakdown of Cornell MFE Coursework

 

To complete the ORIE Core Requirements, students must take a certain minimum number of credit hours from three modeling and data science modules (see chart above). While the selection of qualifying courses in each module is broad, each course is strategically hand-picked across various departments to offer students the knowledge most sought after in the field of quantitative finance. In addition, students must take certain credit-hours from the Financial Applications module for completion of the financial engineering concentration (MFE). Financial Applications Module includes courses from the Johnson Graduate School of Management and Cornell Financial Engineering Manhattan (CFEM).

CFEM, established in 2007 as a satellite New York City campus for Cornell MFEs, serves to connect our students with alumni and other practitioners working in the field of quantitative finance. 

Most of the CFEM coursework is taught by practitioners. Our practitioner lecturers work in the same field as the courses they teach. CFEM courses change year to year in response to the fast-paced needs of the financial industry. For specific qualifying courses, please see the ORIE MEng Handbook (pages 6-7 for ORIE Core and pages 11-12 for Financial Applications). 

The Financial Data Science Certificate (FDSC) is integrated in the curriculum and is designed specifically for students who are interested in deepening their knowledge of machine learning and data science applications. FDSC coursework equips students with the knowledge that brings immediate value to an organization. Upon completion, students will have solid backgrounds in each of the following:

Theory: Understand the value-added and potential uses of data science in finance  

Data: Collect/scrape data and create data environment  

Application: Apply algorithms and extract insight

 

Follow us on Facebook and LinkedIn