There’s a new source in town for those who want to learn R and it’s a good, old-fashioned book called Financial Analytics with R: Building a Laptop Laboratory for Data Science. Written by Mark Bennett and Dirk Hugen, it hits the shelves in the U.K. in September and the U.S. in November. Though designed as a graduate-level textbook, it is a highly appropriate read for practitioners in financial analysis who are new to R, or who want to improve their understanding and use of R. Be warned, however, that a sufficient background in university-level math, statistics, and computer science is needed.
Co-author Bennett is a lecturer at the University of Chicago and a senior data scientist at a major investment bank. We met him at an event in Chicago in April and were struck by his ability to understand both the need for better analysis in the financial industry, especially post-2008, and the need to prepare students to be better analysts in their careers. Bennett straddles academia and business, and therefore understands the long game, so when he requested that our R experts review his book, we agreed.
The goal of Financial Analytics with R is to arm individuals with the robust capabilities of R and to do so within the context of financial markets. The “laptop laboratory” analogy employed throughout the book refers to using software to run simulations that mimic financial markets and to use them to test models and drive decisions. Readers will use their own laptop laboratories to master some of the most important applied models used in finance today, all within the context of R.
We like this book because it is a one-stop-shop for everything you need to know to use R for financial analysis. The book meaningfully combines an education on R with relevant problem-solving in financial analysis. Financial Analytics with R is thorough and contextualized with examples from extreme financial events in recent times such as the housing crisis and the Euro crisis. The code samples are relevant – think functions to compute the Sharpe ratio or to implement Bayesian reasoning – and answer many of the questions you might have while trying them out. This is a book that will make you a better practitioner/student/analyst/entrepreneur – whatever your goals may be.
If you’re well versed in financial analysis, some of the early chapters may feel familiar. Those with the right skills who are eager to get to the meat of simulating trades should skip to Chapter 11 (with a pit stop at Chapter 2, which introduces you to R). But the introductory chapters are foundationally important if you need to brush up on things like security classes, analysis factors, data preparation, or risk management.
Similarly, you can start from 0 with respect to your R experience. The book will guide you through installation, verification, editor selection, and the basics of the composition and syntax of the language. We would argue that chapter 2 in this book will get you up to speed with R faster than the documentation on CRAN (sorry, CRAN!) because it is more focused for the financial analyst.
All in all, we’ll be adding Financial Analytics with R to our Quandl bookshelf. At $85, this textbook is certainly not cheap, but it’s a wise investment for anyone seriously interested in using R for financial analysis.
Disclaimer: Quandl has not been paid to write this review or promote this book.