Computational Investing, Part I
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Why do the prices of some companies’ stocks seem to move up and down together while others move separately? What does portfolio “diversification” really mean and how important is it? What should the price of a stock be? How can we discover and exploit the relationships between equity prices automatically? We’ll examine these questions, and others, from a computational point of view. You will learn many of the principles and algorithms hedge funds and investment professionals use to maximize return and reduce risk in equity portfolios.
We start with a tour of the mathematics and statistics that underlie equity price changes, and the relationships between different groups of equities. We’ll review the most important economic theories of investing and how to create programs that take advantage of them. We’ll look at the data needed to do this, and how to manipulate it effectively. Take a look at the course syllabus here
.Important note: This is a project oriented course involving Python programming in a Unix environment.
Be sure this course is right for you!
This course is intended for folks who have a strong programming background, but who are new to finance and investing. Check out the two links below to see if the course is a good match.
- Take a look at the course syllabus here.
- Take a look at what other students thought of the course here.
You can enroll in the course in several ways:
- Regular enrollment. In this track you are expected to watch the videos and complete the assignments.
- Signature track: This is a brand new option offered by Coursera. More information below.
Outcomes for regular and signature tracks
At the end of the course you will have created a working market simulator that you can use to test your own investing strategies. You will understand the basic principles of Modern Portfolio Theory and Active Portfolio Management.
On average you can expect to spend up to 8 to 12 hours per week on programming.
Please take a look at the course syllabus here.