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 that hedge funds and investment professionals use to maximize return and reduce risk in equity portfolios.
Computational Investing, Part I
Georgia Institute of Technology via Coursera
This course may be unavailable.
Overview
Syllabus
Portfolio Management and Market Mechanics
In this module, you will understand the course content from a portfolio manager's viewpoint, the incentives for portfolio managers, types of hedge fund, and how to assess fund performance; Also, you will gain insight into market orders, the basic infrastructure of an exchange, and computational components of a hedge fund.
Company Worth, Capital Assets Pricing Model and QSTK Software Overview
In this module, you will learn how to value a company, and an overview of the theory Capital Assets Pricing Model (CAPM), its assumptions, implications and how you can apply it in fund management. Finally, you will learn to install QSTK Software.
Manipulating Data in Python and QSTK
In this module, you will learn how to work with financial data, create a portfolio and optimize a portfolio using Python with Numpy library as well as QSTK and the Pandas library.
Efficient Markets Hypothesis and Event Studies, Portfolio Optimization and the Efficient Frontier
In this module, you will learn about information may affect equity prices and company value, understand efficient market hypothesis and how event studies work; Also, you will learn about the inputs and outputs of a portfolio optimizer, correlation and covariance, Mean Variance Optimization, and the Efficient Frontier.
Digging into Data
We will go into more detail in this module about how to read an event study. We will also talk about the differences between actual and adjusted historical price data, and how to detect and fix wrong data.
The Fundamental Law, CAPM for Portfolios
In this module, you will learn the fundamental law of active portfolio management. We will recap CAPM, and extend it for portfolios. Finally, we're going to look at ways that we can leverage the capital assets pricing model to manage, maybe even reduce market risk.
Information Feeds and Technical Analysis
In this module, we will dive deeper into a few examples of information feeds, and learn about technical analysis, and look at a few example technical indicators. Finally, we are going to learn about Bollinger Bands.
Jensen's Alpha, Back Testing and Machine Learning
In this module, we're going to learn about another measure of a fund performance called Jensen's Alpha, and dig deeper into back testing. We will also take a sneak peek at machine learning.
In this module, you will understand the course content from a portfolio manager's viewpoint, the incentives for portfolio managers, types of hedge fund, and how to assess fund performance; Also, you will gain insight into market orders, the basic infrastructure of an exchange, and computational components of a hedge fund.
Company Worth, Capital Assets Pricing Model and QSTK Software Overview
In this module, you will learn how to value a company, and an overview of the theory Capital Assets Pricing Model (CAPM), its assumptions, implications and how you can apply it in fund management. Finally, you will learn to install QSTK Software.
Manipulating Data in Python and QSTK
In this module, you will learn how to work with financial data, create a portfolio and optimize a portfolio using Python with Numpy library as well as QSTK and the Pandas library.
Efficient Markets Hypothesis and Event Studies, Portfolio Optimization and the Efficient Frontier
In this module, you will learn about information may affect equity prices and company value, understand efficient market hypothesis and how event studies work; Also, you will learn about the inputs and outputs of a portfolio optimizer, correlation and covariance, Mean Variance Optimization, and the Efficient Frontier.
Digging into Data
We will go into more detail in this module about how to read an event study. We will also talk about the differences between actual and adjusted historical price data, and how to detect and fix wrong data.
The Fundamental Law, CAPM for Portfolios
In this module, you will learn the fundamental law of active portfolio management. We will recap CAPM, and extend it for portfolios. Finally, we're going to look at ways that we can leverage the capital assets pricing model to manage, maybe even reduce market risk.
Information Feeds and Technical Analysis
In this module, we will dive deeper into a few examples of information feeds, and learn about technical analysis, and look at a few example technical indicators. Finally, we are going to learn about Bollinger Bands.
Jensen's Alpha, Back Testing and Machine Learning
In this module, we're going to learn about another measure of a fund performance called Jensen's Alpha, and dig deeper into back testing. We will also take a sneak peek at machine learning.
Taught by
Tucker Balch
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Reviews
2.7 rating, based on 33 Class Central reviews
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I'm the instructor.
Most of these reviews relate to the first offering of the course. We spend a lot of effort revising the course in response to student feedback. I think if you take a look at our recent survey results you'll see that our efforts were successful: http://wp.me/p11WgN-hW
Best,
Tucker -
As other posters have said, the first iteration of the course was definitely premature. Professor Balch was pretty much just winging it the whole time and you had to wait a bit for course materials to come out. If you're not a patient, laid-back per…
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Prior experience: Python and statistics. The course started out really good (even if I played the videos with 2x speed, since the instructor speaks really slow and explains well enough to be able to understand everything at the first time), and the…
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I took the class in the Fall of 2013 and while I did learn a few things, overall, I would have to call the class a disappointment. The lectures were poorly organized. Dr. Balch should have started off with all the basics one should know about readin…
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Hello,
I'm the instructor of this course. Some of the reviews here are not consistent with the responses we've received from students who enrolled. I invite students who are interested to take a look at those survey responses here:
http://wp.me/p11WgN-hW
Best regards,
Tucker -
I think Tucker's heart is in the right place, but the course at this point is a mess. Too much time was spent dealing with Linux and getting QSTK loaded. I am starting Introduction to Computational Finance and Financial Econometrics by Eric Zivot wh…
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I took this course in Fall 2012. It was a pragmatic and engaging course, and it did not shy away from introducing complex financial concepts. At first I felt that we went at a slow pace but when reflecting back I am proud of what I have learned. Th…
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What can I say....I agree with every statement in the preceding reviews. I think Tucker is a great guy (to have a beer with) and has his heart in the right place. His teaching style is lacking and the course was not ready for deployment. Hard to get…
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I was simply shocked by how bad this course is. The guy clearly does not know the subject and tries to re-interpret what little he understood from introductory books on investments. It is evident from any five-minute segment from any of his lectures…
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Great course for people starting to study about electronic exchange markets. Professor Balch has an excellent performance in front of the camera telling us the essential matter, so the video lessons are really short which is very good for people engaged in other projects. Practical homework is welcome, as we developed several cool phyton programs. Some tweaks should be made concerning forum issues clarification. Thanks Tucker!
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Unfortunately, this course is just not ready at the present time (Dec 2012). Lectures are late, and are very short. They have very little information in them. The programming interface is very difficult to install, an image with the software preinstalled would work better. Instructor seems like a nice person, but the current iteration needs a lot of improvement in order to recommend.
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I also agree with most of the previous reviews of this class. I was able to get the software running on Windows, thanks only to a very helpful forum post from another student who provided step-by-step instructions. The concept is excellent, but unfortunately the execution has been exceedingly poor, especially compared with the other excellent Coursera courses that I've taken.
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As stated above, great concept which was poorly executed. The foundation for an excellent course is in place, but there needs to be greater emphases on lecture content and follow through in putting together a series of examples and homework problems which highlight a lecture point. Failing that students can use the QuantSoftware Toolkit website to do independent study.
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Hopefully things will improve in next iteration but at present its not worth the time to take this class. Whats sad is that lectures are too short with very less content (although it is interesting if you are new to finance/trading) [BTW I am comparing this course against 3 other MOOC I have taken.]
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Great course if you have a technical background. If you don't know python programming it'll be a bit hard. But if you know python its a good introduction to computational investing. The one thing I'll remove from the course is the videos answering questions from the people taking the course.
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I took this class last year and agree with negative reviews here. It was a very frustrating experience. Instructor totally lacked commitment to the course.
I was waiting for the 2nd offering on Feb 22, it did not start! Looks like the same mess is being repeated again. unbelievable! -
I likewise was very disappointed in this course. I'm sure Tucker knows his stuff, but it seems like other priorities in his life (i.e., teaching live, running a business) have left little time for him to focus on this class.
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Great insight into how the stock market works, and you build a market simulator with buy and sell orders based on real data. Anyone who is computer literate and investing in stock markets would benefit from the class.
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Course is not very well organised at the moment (deadlines are changed, lectures get publish behind schedule, etc.). If you are familiar with Python, the exercises will be easy; if not - good luck!
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I enjoyed this course a lot. It is targeted at folks with a computing background. Covers the key points of Modern Portfolio Theory, and shows how to implement them in python.
