Below you will find a short version of my reading list of books and academic articles related to investment systems, data analysis and machine learning. I include these because they influenced my approach to investing. This can give you a sense of how I approach investing in general, and how I go about developing my investment strategies. Some of these are highly technical so don’t take this as a recommendation for bedtime reading! Unless of course you have a strong interest in the statistical theories and data analysis foundation of my work. 🙂
Investment Strategies and Systems
- The Ivy Portfolio: How to Invest Like the Top Endowments and Avoid Bear Markets, by Mebane T. Faber and Eric W. Richardson, John Wiley & Sons, 2009. This is one of my favorite book because it is sensible and approachable. The authors analyze how Yale and Harvard invest their endowment funds, and then propose a simple, Do-It-Yourself way to emulate the pros using ETFs. This is a must-read if you are serious about ETF investing. It is not very technical and can be read by anyone with a casual understanding of investing. So if you want to read one book, this is the one to start with.
- Trading Systems: A New Approach to System Development and Portfolio Optimisation, by Urban Jaekle and Emilio Tomasini, Harriman House Ltd, 2013. This book is for serious developers of automated investment and trading systems. The authors have worked for European hedge funds and understand the risk / return equation very well. An excellent read for the technically minded. Not for beginners.
Data Science Books
- Practical Data Science with R, by Nina Zumel and John Mount, Manning Publications, 2014. A good, practical book that describes the process of doing data science projects using the R statistical analysis programming language.
- Machine Learning with R, by Brett Lantz, Pakt Publishing, 2013. A nice overview of how to develop use various machine learning algorithms within the framework of R.
- The Art of R Programming: A Tour of Statistical Software Design, by Norman Matloff, No Starch Press, 2011. A very good overview of the basic constructs of R, taught in a tutorial manner. A good reference.
Machine Learning and Statistics
- Elements of Statistical Learning. The hands-down top reference on machine learning and data analysis theory, written by the inventors of many modern computer statistical methods, including big data analysis. Free pdf version available here. Not for beginners.
- An Introduction to Statistical Learning (with applications in R). An excellent, approachable book on the fundamental elements of statistical / machine learning. Free pdf available here.
Selected Technical Papers
- Tidy Data, by Hadley Wickham. A nice paper outlining how to make a tidy, easy to analyze data set. A must-read for any practical data scientist. Free pdf available here.