FE571 Quantitative Hedge Fund Strategies



Course Catalog Description

Introduction

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.


Instructors

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

Prerequisites

A background in probability and statistics, with a basic level of R programming, is required. FE570 (Market Microstructure and Trading Strategies) is required. FE515 (Introduction to R) is recommended.


Course Resources

Textbook

    Key References
  • [1]Lasse H. Pedersen, Efficiently Inefficient: How Smart Money Invests and Market Prices Are Determined, Princeton University Press (2015).
  • [2] Jack D. Schwager, Hedge Fund Market Wizards, Wiley Finance Series (2012).
    Optional References
  • [3] Filippo Stefanini, Investment Strategies of Hedge Funds, Wiley Finance Series (2006).
  • [4] Andrew W. Lo, Hedge Funds: An Analytic Perspective, Princeton University Press (2008).

Grading

Grading Procedures

    Homework (20%): Collaboration on homework is permitted but you must write up your own findings. 15% of the assignment grade will be deducted for late submission (up to three days).

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

  • Attendance (5%): Attendance will be taken. Active participation is encouraged.
  • News Topic Presentation (5%): Students are highly encouraged to subscribe to the Wall Street Journal. Students are expected to lead an informal discussion on a major financial news story or a topic related to the hedge fund industry.
  • Course Reading Report (10%): The required reading, Hedge Fund Market Wizards, is written by Jack D. Schwager. Students are expected to read the book in its entirety and write a 2-page summary report.

    Midterm Exam (30%): There will be one examination covering the core materials of the course. The exam is closed-book and must be completed independently. Students are not allowed to work or discuss with other students during exams. The use of a calculator is permitted.

    Course Project (30%): This course features a multi-week project that requires students to design and implement a trading strategy based on course materials and discussion. The team shall be no more than 2 students per group. At the end of the course, there will be an investment competition with specific financial objectives that the project work will be tested and evaluated under real trading environments. The course project constitutes 30% of the course grade, which is further assessed based on the delivery of the strategy presentation (50%), and performance in the investment competition (50%).


Lecture Outline

Week Date Topic(s) Milestones
Week 1 1/18 L1: Course Introduction
  • An Overview of Financial Markets
  • Week 2 1/25 L2: An Overview of Hedge Fund Strategies
  • Equity, Macro and Arbitrage hedge fund strategies, Market efficiency vs. inefficiency
  • Week 3 2/1 L3: Equity & Alpha
  • Alpha, Beta, Residual, Introduction to CAPM
  • HW #1 Assigned
    Week 4 2/8 L4: Algorithmic Trading Strategies
  • Quantitative Equity Investing, momentum, stat arbitrage
  • HW #1 Due
    Week 5 2/15 L5: Backtesting Techniques
  • Data mining, in-sample, out-of-sample, regressions
  • HW #2 Assigned
    Week 6 2/22 NO CLASS
    Week 7 3/1 L6: Performance Measurement
  • Information ratio, high water marks, attribution
  • HW #2 Due
    Week 8 3/8 L7: Portfolio Construction
  • Portfolio Optimization, measuring risk, drawdown
  • HW #3 Assigned
    Week 9 3/15 NO CLASS – SPRING BREAK
    Week 10 3/22 L8: Financial Statement Analysis & Equity Valuation
  • Balance sheet, income statement, cash flow, ratios, DDM
  • HW #3 Due
    Week 11 3/29 L9: Options Pricing
  • Black-Scholes Pricing Model
  • Week 12 4/5 MIDTERM EXAM
    Week 13 4/12 L10: Macro Strategies & Investment Project Overview
  • Global macro managed futures
  • HW#4 Assigned
    Week 14 4/19 L11: Arbitrage Strategies
  • Fixed-income arbitrage, convertible arbitrage, event-driven
  • HW#4 Due
    Week 15 4/26 L12: Sentiment Modeling & Trading Strategy
  • Incorporating news/data into trading strategies
  • Week 16 5/3 PROJECT PRESENTATION
  • Economic Intuition, Methodology, Backtest Results and, Expected Outcomes
  • Strategy Presentation