You focus on a lecture in which I am responding to student questions 3 weeks into the course. Here is some context:

Engagement is one of the key challenges in teaching a MOOC. It’s much tougher than in person teaching. In order to build that engagement I invited the students to post questions in the course forum and to vote for the questions they were most interested in. I promised to answer the 10 questions with the most votes.

The question with the most votes by far was “Do you manage your own money using computational investment techniques?”

This is not a topic I planned to address in the syllabus. However, the question is fair enough, and I felt it deserved an answer. You raised some questions about the details of the strategy I described, and I’ll address those further down. But the point here is that this was a response to questions from the students.

With regard to goals for this course: The course is not intended to provide comprehensive coverage of quantitative techniques. It’s intended to offer an introduction to the most important topics (CAPM, EMH, risk/reward, survivor bias) and to provide some hands-on experience with historical data. The goal is to spark interest with the hope that some students will carry that forward to deeper study. I think that is pretty clear from the course description materials. I do not recommend or suggest that anybody rush out and start managing a hedge fund on the basis of this course.

Also, the course is not meant to be a replacement for the course I teach in person at Georgia Tech. The content represents only about 1/3 of the course I teach at GT. We do not provide course credit for completing this course.

You criticized the recommended reading “All about Hedge Funds” by Jaeger. Remember that one goal is to make the subject accessible, and Jaeger’s book provides a readable introduction to many of the details of the industry. You didn’t mention my other recommendation, “Active Portfolio Management” by Grinold and Kahn. This is a substantial tome viewed by many as a standard reference for portfolio management. I think it would have been fair to mention both.

You go on to comment on the presentation of a strategy I trade. And you make some good points.

Let me first be more specific about what is depicted. The chart and analysis are a back test of a strategy simulated since January 2011. The back test simulates a $20M initial investment at 2X leverage. The strategy has been traded live with a more modest sum over the last 4 months. Return over that period is 2.7% (without leverage). We plan to lever up soon.

With regard to slippage: You are correct that in practice this “cost” is built into the results. The slippage value reported in the chart is an estimate provided by the simulation.

Best regards,

Tucker Balch

From reading *your* article Blach course seems like it an exciting course that use examples that draws you in.

this is not a finance course.

this is a computer programming course with a minor focus on financial applications.

there are no disclaimers about the risks involved in implementing any of the strategies or ideas he talks about. the concepts related to finance and the financial markets are merely glossed over as unimportant details. in one lecture he even goes so far as to say that markets are efficient because of high frequency traders.

the course really feels like it’s being taught by someone who had a lot of success with machine learning algorithms and thinks he’s absolutely conquered the world of finance, and he’s here to teach you how to get rich quick with these techniques!

as someone who has studied finance in college and grad school and has been involved with the industry for several years, the way these topics are being approached in this course is an absolute travesty. i was worried a few weeks ago about the nonchalant style in which the materials were being offered. i’m glad i wasn’t the only one who thought it was problematic.

The class is not as finished and polished as An Introduction to Interactive Programming in Python, also at Coursera, but it’s the first run of Balch’s class.

As an aside, Balch says he is a former fighter pilot, which makes me think from the get-go that he has a different risk profile than me.

*He could be getting leverage via a leveraged ETF/Mutual Fund without margin costs. It could also be leveraged via the use of options.

*Slippage could refer to the underlying model’s price and the actual price received when the trade was executed.

*$20 million may be small once you take into consideration operation costs. Based on my limited observations, it seems the cusp of solid returns lie somewhere north of $20 to $30 million AUM (and again, this is just from my limited observations).

This all being said, some things I would also ask Professor Balch:

*While the returns may be low risk now, are they low risk always (i.e. does the underlying model adapt to different market regimes) This is akin to cherry-picking a time period to run a model off of.

*Beta/Sharpe/Correlation are all useful measures for normally distributed returns, but unfortunately the model’s returns are likely not normally distributed. If you want to see how this works, take historical market returns and square them; compare the correlation between the returns and the squared returns. It’ll be close to zero, but it is hard to say there isn’t market risk in the model.

*Given the above, how well does a non-linear model, such as local regression (LOESS), fit the data (and thus indicate market risk)?

Sorry for the lengthy post; just thought it may be useful to contribute some more points on both sides.

He should either be rich enough to have retired, or have started his own hedge fund.