Introduction to R for Quantitative Finance

I really enjoyed this book. After working through the problems several times I realized how powerful R was and how many tools that are at my disposal. I think the author gives a great spread of very common financial calculations with repeatable examples. I am not am professional in any way but really felt it was good for someone who had a basic knowledge of quantitative finance and was looking to see how to apply that in R.Overall I think i got a great deal out of this book and it gives me much more confidence in working with R. Espeically for people just getting into either quant finance or working in RUsing the kindle version, i did have some annoying problems with the code. Because of 2 things. First off there are + signs on every line which is how its outputted in R, which you must remove. Also the kindle cut/paste function always gives the copyright and location. Which you have to delete out of every time you cut and paste. Its not really the authors fault just a quirk of the kindle cut and paste system. I did not subtract a star for this because of the kindle interface, however i can see where someone else would.

This book claims to be a book on R in finance. It provides very little information on either R or finance.I'll use one example to illustrate the faults of this book. The authors give an example of cointegration and hedging of airplane fuel with options on heating oil. The authors state that they assume that you have a background in finance. Fine. I've done this kind of hedging, but it was a year ago. Having a brief discussion of the relevant equations would be useful. This would provide a context for the R code.As far as the R code goes, all the authors really give are some function calls without much in the way of context. You would get almost as much reading the R on-line documentation.This book is of no use to anyone who knows something about modern quantitative finance and it's of no use when it comes to learning to use R for finance. I short, the book is of no use at all.Packt Publishing seems to specialize in short books that are at either poorly written or at a ridiculously introductory level. I also bought their Machine Learning with R. This could be retitled Machine Learning with R for High School students (and not High School students who are taking AP Calculus or Computer Science).

More for academics, not for use in actual investing.The portfolio optimization chapter is good.The text is refreshingly straightforward.On the other hand, not all of the the code is available for download.

Good book.

The price seduces you and you click on buy. And start trying to come over your loss 30 minutes later. Lesson : Any R book which adds value beyond what is freely available on the internet is going to cost you upwards of USD 25. Sad, but true.

Is better to read the different web forums that this book. Is just a simple very light introduction to R...

As there are currently no R book which specifically cover quantitative finance in broader terms, the book is interesting for all finance guys who want to quickly understand implementation in R.The book is intended to be an introduction to R for readers with some knowledge in finance. It is basically a collection of self-contained essays on some well-known concepts of finance like portfolio optimization, term structure of interest rates and derivatives pricing. These concepts are then implemented in R. To really understand what is going on, a fairly advanced understanding of finance theory and econometrics (master level at least) and a working knowledge or R are required, although the authors claim that no prior R knowledge is needed. There is no R intro included and the code is not easy to follow through without prior knowledge (e.g. the apply functionality is utilized and for R novices this is a complicated animal since it is unique to R and is one of the hottest topics in all help forums). The R code is usually sound, however, there are parts where it could be simplified and there are parts of the code that are not fully explained.A drawback of the book is that it is very short and sometimes the text lacks technical precision or uses unconventional approaches (examples: if the conditions of the CAPM are satisfied, all securities will be on the SML while the text states that they “should” or “the APT states that, in equilibrium, no arbitrage opportunities can exist” – the APT is not an equilibrium model, absence of arbitrage is a necessary but not a sufficient condition for the existence of an equilibrium). At a later stage, it is said that the CAPM is an equilibrium model, while APT is a statistical model (it is in fact a no-arbitrage model). The literature references are not optimal, e.g. the original papers of Markowitz are not cited in the literature references on portfolio theory.The book also contains some relevant typos such as “keynote” duration instead of key rate duration.The R code is generally commented well, however in some cases the code is complicated and could be simplified substantially. For example, the code on p. 79 is certainly unreadable for somebody with no prior knowledge in R. Some important aspects of the code are not explained.All in all, the book does a reasonable job to show how some key concepts of finance can be implemented in R. The book, however, is neither an introduction to R nor an introduction to finance. The book would benefit from a more stringent presentation of finance theory and a simplification of the code chunks.

It seems no thought was put down before writing this book. The content covered is the most basic and of lowest quality. Codes doesn't fully work. In fact I have noticed a similar story for other books from this publisher.

excellent

I admit, it is not easy to write an introduction text covering two subjects like quantitative finance and a programming language like R. It is quite easy to disappoint readers on one of the two aspects. However, this book disappoints on both.At the back cover, the publisher states that some basic knowledge of finance theory is assumed. Working for more than 25 years in the area of fixed income and commodity research, I have some knowledge of the subjects. However, I found that the authors assume too much knowledge from the reader, especially as the book covers 9 different areas and one can not be an expert in all those fields. Thus, some more detailed explanations of the basics would have been advantageous. Furthermore, some aspects of quantitative finance are not covered. For example in the chapter on the term structure of interest rate, there are some standard models like the Vasicek or the Cox-Ingersoll-Ross (CIR) factor models. It would have been a plus to include an example of estimating the parameters of these models by state space models in R.I do most of my quantitative work with a commercial software which I regard as very good. However, also this software has some limitations. Thus, I looked for an additional software and saw in the literature that more and more examples are provided in R. Another advantage of R is that it is available as free of charge open source. But also this comes at a cost. Beside the core components, which are installed with R at the computer, there are further packages required to perform some basic tasks. A plethora of packages are available and as one would suppose with open source software, some packages are providing features already being included in other ones. Thus an introduction to R with special examples from quantitative finance could really fill a gap.What should an introduction to a software provide? First, it should familiarize the reader with the basic aspects of the software. R is an object oriented language and everything is stored in an object. There are various concepts like lists, vectors and matrices or data.frames. None of the essentials for a novice in R are explained. A chapter with an introductions to the basics of R is missing for the absolute beginner.In quantitative analysis, we work with numeric data. For some applications, the data has to be in a chronological order as a time series. The first task is to get this data into R. The various chapters contain commands to get data into R. However, this is not really helpful if the reader wanting to apply the examples to own data. I expect from an introduction that it covers importing data into R, especially as there are differences between time series objects and data.frame objects. Furthermore, it would be advantageous to get the pros and cons of the various packages for importing data either from online sources like Yahoo or the Federal Reserve (FRED). In addition, a bit more detailed explaining of transforming data in a data.frame into a time series object like zoo or xts would also be helpful and could save the reader many hours of searching for a solution.Each of the nine chapters contain very brief explanations, But for learning to work with R, this is by far not enough. A bit more in-depth documentation what is done and why would be helpful for the novice reader.As already mentioned, there are several packages, which could be applied for the same task. The authors just mention the alternative and use one package. However, for an introduction, I expect more. I would like to learn what are the differences of the various packages, what are the advantages or disadvantages compared with other packages.It is positive that the publisher provides a zip-file with the code for the 9 chapters and also the data used in examples if it was not provided in the packages used or retrieved from an online source. For some chapters, I could not replicate the examples due to errata, which i submitted to the publisher.All in all, I is a good idea to provide an introduction to R for a special area like quantitative finance. However, the booklet has covered the 9 chapters on only 138 pages. This is not enough for a good introduction.

El autor aclara que no se necesita saber R, pero eso no quiere decir que el libro vaya a empezar dando una introducción, se aprende sobre la lectura. no esperes encontrar un capitulo de introducción a R.

Nueve autores exponen con claridad y brevedad los temas que desarrollan, y los ilustran con el programa R. Buen resumen.