Introduction

This course investigates methods implemented in multiple quantitative trading strategies with emphasis on automated trading and quantitative finance based approaches to enhance the trade-decision making mechanism. The course provides a comprehensive view of the algorithmic trading paradigm and some of the key quantitative finance foundations of these trading strategies. Topics explore markets, financial modeling and its pitfalls, factor model based strategies, portfolio optimization strategies, and order execution strategies. The data mining and machine learning based trading strategies are also introduced, and these strategies include, but not limited to, weak classifier method, boosting, neural network and genetic programming algorithmic emerging methods

Course Description

TThis course investigates methods implemented in multiple quantitative trading strategies with emphasis on automated trading and quantitative finance based approaches to enhance the trade-decision making mechanism. The course provides a comprehensive view of the algorithmic trading paradigm and some of the key quantitative finance foundations of these trading strategies. Topics explore markets, financial modeling and its pitfalls, factor model based strategies, portfolio optimization strategies, and order execution strategies. The data mining and machine learning based trading strategies are also introduced, and these strategies include, but not limited to, weak classifier method, boosting, neural network and genetic programming algorithmic emerging methods.

Textbook

[Required:] Frank J. Fabozzi, Sergio M. Focardi, and Petter N. Kolm, Quantitative Equity Investing: Techniques and Strategies (Wiley, 2010).

[Optional:] Barry Johnson, Algorithmic Trading & DMA, 4Myeloma Press London, 2010.

Grading Policies

Assignments - 20% Class Challenges - 20% Midterm exam - 30% Final project - 30% Total Grade - 100%

Exams: Two Exams. (Mid-term) EXAM I: March 23 - (Thursday). (Final Presentation) EXAM II: May. 18 - (Thursday).

Exam Honor Policy: You are not allowed to discuss any of the exam questions with one another or to show any of your solutions. The work must be done independently and pledged.

Homework: There will be 4 homework assignments (approximately every 2-3 weeks).

Homework Honor Policy: You are allowed to discuss the problems between yourselves, but once you begin writing up your solution, you must do so independently, and cannot show one another any parts of your written solutions. The HW is to be pledged (that it adheres to this).

Class Challenges: There will be several in-class challenges that students will form groups to work on small projects. It is expected that groups will finish the given task by the end of the class session. These challenges enhance understanding of course materials and advance progress for the final project.

Final Project: You need to form a project team with 2-3 people at most. You will pick a topic related to the course content, and a one-page project proposal needs to be submitted right after the midterm. If you do it right, this can be an immensely satisfying experience. You will turn in this project - I don't want the computer output, but descriptions of the results IN YOUR OWN WORDS - 3 single-spaced pages, including plots, at most. We will talk more about this as the semester proceeds. You will each give a brief presentation on your project to the class, during the last week - Attendance is MANDATORY at the presentations.

Attendance will be taken randomly (e.g., 6-7 times during the semester) and will determine "which direction" the resulting grade will “fall”, for those grades, which are borderline (e.g., between B+ or A-).