FE800 Project in Financial
Course Catalog Description
This course is designed for FE students undertaking a research or a project in financial engineering either individually or as a group. The project may be suggested either by faculty members or industry senior managers associated with your internship, as well as any internship that a student may receive through this course. The goal of this course is to involve students in developing research skills, communication skills while keeping their interest in result-oriented techniques. The ability to work on a research-oriented project in a group environment and train their professional presentation and scientific writing skills lead to a competitive graduate who is ready to lead in the work place.
FE 800 Enrollment form
Download the enrollment form, fill it and submit it to the Center for Student Success
The projects covered by this course are typically one of the following three types:
- Research projects in Financial Engineering deal with mathematical, analytical and industry types of problems. The designation indicates emphasis only not to imply that they are separate and distinct; in fact all areas are connected and build on aspects. The classification helps focus on the research question and the importance of the answer with respect to the ultimate user or client. Some examples include:
Analytics projects typically involve Software Design and Implementation. Some examples include:
- Statistical Modeling of Trading Strategies
- Machine learning in finance
- Credit Derivative Arbitrage
- Dynamic Portfolio Allocation Methods
- Optimal Hedging Monte Carlo
- Systemic Risk and Regulatory Research (Basel III, CVA, etc.)
- Stochastic Volatility modeling
- Volatility trading strategies
Industry projects are geared toward solving problems arising in the industry and are generally proposed by our industry partners. Some examples include:
- High Frequency Market and client implementation
- News Analytics
- Rare events automated discovery
- Regulatory (Basel III, CVA) Software Design and Implementation
- UBS projects
- Machine learning Projects from Bank of America
The project chosen and developed throughout the semester must be:
- A project proposed by a faculty member,
- A project proposed by an industry supervisor and approved by the course instructors, or
- A project proposed by a student and approved by the course instructors.
The Web section students are encouraged to attend the class at the same time as the on campus students. If there is a conflict with work, then the students can check the recording of the lectures. Note that we do the oral presentations (weekly updates, phase 1&2 presentations) during class time. The students will be given the opportunity to present through the Zoom. In the occasion that the student can not present during the class time, please contact the instructor and TA beforehand to arrange for the presentation through other means.
The final grade will be assigned according to the scheme in Table 1.
Table 1: Grading Scheme
|Abstract, Schematics, and Literature Review
|Phase One Presentations
|Phase Two Presentations
|Final Written Report Due
|Final Oral Presentation
||TBD after 2020-5-6
|Attendance Throughout semester
Additionally, you will be required to give a status and progress report about the project in a presentation format every week of the semester, unless that class time is reserved for the phase 1 or phase 2 presentations. The weekly updates are not graded but the comments received will help guide you in your development of the project.
Late submissions will strictly not be accepted without prior notice and permission of the instructor. If outside circumstances are affecting your ability to perform in the course, you must contact the professor before you fall behind.