FE571 Quantitative Hedge Fund Strategies

Course Catalog Description


Hedge funds are among the most influential participants in the financial markets. These private investment pools have many unique features and vary significantly from one another. Common hedge fund strategies include those that are equity-oriented, macro-focused, and arbitrage strategies. This course provides an introduction to different hedge fund strategies, their economic intuition, and practical guidance for implementing quantitative strategies using financial engineering toolsets such as the R programming language. Topics such as the lifecycle of developing a sound trading strategy, from conceptualization to implementation and validation/back-testing will be introduced.


Professor Email Office Course Date/Time
Anshul K. Sharma
asharm70@stevens.edu By Appointment (Email) Tuesday 6:30 – 9:00 PM

More Information

Course Objectives

This course aims to introduce students to different hedge fund strategies and the application of financial engineering techniques in the pursuit of non-traditional returns. It also helps students to understand the technical requirements of developing a quantitative trading program. This course is designed for graduate students in the Financial Engineering program at the School of Systems and Enterprises.

By the end of the course, students will be able to:

  1. Understand the theoretical and practical features of different hedge fund strategies
  2. Understand the structural organization of hedge funds and their emphasis on alpha generation, risk and, portfolio management
  3. Leverage financial engineering techniques to achieve non-traditional returns
  4. Develop a quantitative trading program, seeing the project from idea generation to testing to implementation and participating in the investment competition at the end of the course.

Graduate Student Code of Academic Integrity

All Stevens graduate students promise to be fully truthful and avoid dishonesty, fraud, misrepresentation, and deceit of any type in relation to their academic work. A student’s submission of work for academic credit indicates that the work is the student's own. All outside assistance must be acknowledged. Any student who violates this code or who knowingly assists another student in violating this code shall be subject to discipline. All graduate students are bound to the Graduate Student Code of Academic Integrity by enrollment in graduate coursework at Stevens. It is the responsibility of each graduate student to understand and adhere to the Graduate Student Code of Academic Integrity. More information including types of violations, the process for handling perceived violations, and types of sanctions can be found here.

Inclusivity Statement

Stevens Institute of Technology believes that diversity and inclusiveness are essential to excellence in education and innovation. Our community represents a rich variety of backgrounds, experiences, demographics, and perspectives and Stevens is committed to fostering a learning environment where every individual is respected and engaged. To facilitate a dynamic and inclusive educational experience, we ask all members of the community to:

  • Be open to the perspectives of others
  • Appreciate the uniqueness of their colleagues
  • Take advantage of the opportunity to learn from each other
  • Exchange experiences, values, and beliefs
  • Communicate in a respectful manner
  • Be aware of individuals who are marginalized and involve them
  • Keep confidential discussions private


A background in econometrics, probability and statistics, with a basic level of R programming, is required. FE515 (Introduction to R) is recommended.

Course Resources


    Key References
  • [1]Lasse H. Pedersen, Efficiently Inefficient: How Smart Money Invests and Market Prices Are Determined, Princeton University Press (2015).
    Optional References
  • [2] Jack D. Schwager, Hedge Fund Market Wizards, Wiley Finance Series (2012).


Grading Procedures

    Class Participation (20%): Attendance will be taken. Attendance is taken each week. Regular attendance and active participation will be a portion of the course grade assessment.

    Midterm Exam (40%): There will be one examination covering the core materials of the course. The exam is open-book and must be completed independently. Students are not allowed to work or discuss with other students.

    Course Project (40%): This course features a multi-week project that requires students to design and implement a trading strategy based on course materials and discussion. Team should be no more than 4 students per group. The course project is further assessed based on the performance of the strategy (50%) and delivery of the presentation (50%).

Lecture Outline

Week Date Topic(s)
Week 1 1/24 L1: Course Introduction / HF Introduction
Week 2 1/31 L2: Performance & Alpha
Week 3 2/7 L3: Alpha & Backtesting
Week 4 2/14 L4: Portfolio Risk & Trading
Week 5 2/21 L5: Fundamental Equity Strategies
Week 6 2/28 L6: Quantitative Equity Strategies
Week 7 3/7 L7: tatistical Arbitrage Strategies
Week 9 3/21 L8: Macro Strategies
Week 10 3/28 L9: Managed Futures Strategies
Week 11 4/4 L10: Event Driven Strategies & Course Project
Week 12 4/11 L11: Fixed Income Arbitrage Strategies & Midterm
Week 13 4/18 L12: Fixed Income Arbitrage Strategies (cont’d)
Week 14 4/25 L13: Convertible Arbitrage Strategies