Review of Coursera’s Computational Investing Part I

I got to know of Dr Tucker Balch‘s MOOC (Computational Investing Part 1) from Lin Xinshan. This is a my personal review of the recently concluded 4th offering of the course.

This is the first MOOC that I’ve actually completed (out of the many that I’ve enrolled on a whim). I think it’s a combination of:

  • My strong interest in the subject matter
  • The excellent pacing of the course materials and assignments
  • The timely release of the course textbook: What Hedge Funds Really Do

The course consists of  video lectures, graded assignments and a forum on Piazza. The video lectures mostly mirror the content of the textbook and I found the videos rather tedious actually.

The textbook does a good job of providing a palatable introduction to quantitative portfolio management. It includes the rationale behind constructing  a portfolio with CAPM and the various measures of evaluating its performance (e.g against risk free Treasuries or a broad market index like the S&P 500). Each chapter also includes a short but interesting biography of prominent traders who were able to achieve alpha (market beating returns).

I would have liked the book to discuss the quantitative aspects of measuring portfolio performance in more detail though:

  • It mentions the Sharpe (risk as both upward/downward deviations in returns) and Sortino (considers downside deviations only) ratios but focuses on the former for the entire book. Why would I use the Sharpe ratio instead of the Sortino ratio when I’m primarily interested in downside risk?
  • There is a chapter dedicated to “Event Studies” which measure the effects of events on equity prices for a period of days before and after the event. The authors suggest testing out candidate events in a simulator and backtesting it for fitness.  I take issue with the method as described in the book as it is ripe for curve fitting. I expected at least referencing methods on mitigating the risk of overfitting event rules to past data such as segregating data into In Sample and Out Of Sample data and performing Walk Forward testing on the OOS data.
  • To be fair, the authors do caution against data mining fallacies in another chapter and hints at the segregation of In Sample and Out Of Sample data for use in backtesting and validation respectively but this point is simply not made obvious enough for my liking.
  • Lastly, I would have appreciated a list of resources curated by the authors where readers can obtain deeper discussion of the main points covered by the book.

The real gem for me though is the QuantSoftware Toolkit (QSTK). The QSTK allows anyone to quickly get to grips with the tasks of analysing and visualising portfolio performance (much like how Rails provides a quickstart for web development).  I especially like the fact that It provides the components of SPX adjusted for survivorship bias.

This library is written in Python and makes extensive use of Pandas, NumPy and Matplotlib. It was an enjoyable experience picking up Python and exploring the Pandas/NumPy/Matplotlib APIs. I can safely say that I’ve gained a newfound appreciation for how I can use these powerful libraries to supplement my own quantitative analysis.

I would highly recommend this course to anyone who wishes to get a good introduction on constructing and evaluating asset portfolios with quantitative methods.

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