I have recently reviewed couple of books.
The first of which is actually a give-away if you promise to review it. Global Asset Allocation: A Survey of the World’s Top Asset Allocation Strategies
The review:
Relatively short book which reviews different perspectives to portfolio construction. For example how is the philosophy of Warren Buffet performed against a simple 60/40 portfolio. There are a bunch of those, and the accompanied results. So you get two things. The first is an exposition of the portfolio, it is nice to read how the big players budget their money among the different asset classes, even if only approximately. The second is what seems to be a serious comparison over a long history. Lastly, touching upon the price which at the time of writing is comparable to a double espresso, an easy 4/5. Recommended.
The second is: Mastering R for Quantitative Finance
The review:
The book is useful in that the chapters cover many relevant topics in quantitative finance. With 13 chapters you get the popular; for example Time Series Analysis or Factor Models, where a lot of readable material is free and abundant online, but also topics which are “off the beaten track” ; for example exotic options, FX derivatives and asset liability management. The book is well written with explanations that are mostly easy to follow. You also get the very valuable code to play around with. these 3 characteristics: compilation of many subjects (some are not readily accessible elsewhere with such clarity), easy to follow and access to code, makes this book a good value for money. The title of the book is somewhat exaggerated though, as you would probably not master R by reading (or rereading) it.
What else to expect when you buy? The book is compact, which is a plus for some, but it also means you will not get the “full blown” rigorous introduction and analysis for each chapter. The author does not conceal that: “you need to be on an intermediate level in Quantitative Finance, and you also need to have a reasonable knowledge in R”, this is exactly because of the conciseness of the book which may frustrate an absolute beginner. The reader is often encouraged to pursue further using references provided in the end of each chapter. Related to that, the code uses mostly built-in packages which means two things: code is not easily adaptable (for example if you need to add parameters to the functions), and it is not easy to understand exactly what is going on “behind the scenes” (as you get the name of the function, not the function itself so cannot exactly follow the steps in estimation..). In R most functions are publicly available, I recommend here to add an explanation as to how to access function’s code for the interested reader. All in all, practical and useful.
