PDF Ebook An Introduction to Analysis of Financial Data with R
PDF Ebook An Introduction to Analysis of Financial Data with R
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An Introduction to Analysis of Financial Data with R
PDF Ebook An Introduction to Analysis of Financial Data with R
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Review
“I found this book highly informative and interesting to read. The proper mix of theory and hands-on programming examples makes it recommended reading for both R programmers interested in finance and financial analysts with a basic programming background. Well written and following a clear and defined logical layout, the author has written a current reference text on using a powerful open-source programming language for typical financial analysis.” (Computing Reviews, 25 March 2014) “All in all, this book is a good and useful introduction to financial time series with many real-world examples. It is suitable for use both as a textbook and for self-study, with exercises provided at the end of each chapter.” (International Statistical Review, 14 June 2013)
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From the Back Cover
A complete set of statistical tools for beginning financial analysts from a leading authority Written by one of the leading experts on the topic, An Introduction to Analysis of Financial Data with R explores basic concepts of visualization of financial data. Through a fundamental balance between theory and applications, the book supplies readers with an accessible approach to financial econometric models and their applications to real-world empirical research. The author supplies a hands-on introduction to the analysis of financial data using the freely available R software package and case studies to illustrate actual implementations of the discussed methods. The book begins with the basics of financial data, discussing their summary statistics and related visualization methods. Subsequent chapters explore basic time series analysis and simple econometric models for business, finance, and economics as well as related topics including: Linear time series analysis, with coverage of exponential smoothing for forecasting and methods for model comparison Different approaches to calculating asset volatility and various volatility models High-frequency financial data and simple models for price changes, trading intensity, and realized volatility Quantitative methods for risk management, including value at risk and conditional value at risk Econometric and statistical methods for risk assessment based on extreme value theory and quantile regression Throughout the book, the visual nature of the topic is showcased through graphical representations in R, and two detailed case studies demonstrate the relevance of statistics in finance. A related website features additional data sets and R scripts so readers can create their own simulations and test their comprehension of the presented techniques. An Introduction to Analysis of Financial Data with R is an excellent book for introductory courses on time series and business statistics at the upper-undergraduate and graduate level. The book is also an excellent resource for researchers and practitioners in the fields of business, finance, and economics who would like to enhance their understanding of financial data and today's financial markets.
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Product details
Hardcover: 416 pages
Publisher: Wiley; 1 edition (October 29, 2012)
Language: English
ISBN-10: 0470890819
ISBN-13: 978-0470890813
Product Dimensions:
6.5 x 1.1 x 9.2 inches
Shipping Weight: 1.5 pounds (View shipping rates and policies)
Average Customer Review:
3.7 out of 5 stars
8 customer reviews
Amazon Best Sellers Rank:
#371,176 in Books (See Top 100 in Books)
(As a disclaimer, I have an advanced degree in signal processing and image analysis.) This appears to be a textbook for an advanced course in finance. I had hoped it was a book for investors who have an interest in programming and writing their own portfolio management software in R. Instead, it focuses on being a book that covers many of the advanced time series analysis methods used in financial modeling. That would be fine if the author had presented more examples of how the methods were applied to financial modeling and how the results were used. It would have been helpful to also have more comparisons of the pros and cons of the different methods.What I found lacking in this book was more description of the application of the methods described. The presentation is: here is a method for analyzing a time series and here is some R code (sometimes). It assumes that the reader already understands the derivation/origin of the method being presented from a signal processing/mathematics standpoint. The text does a poor job of explaining the role of each method in financial modeling. How would one apply a given technique and exactly for what purpose? So, the book seems to be written for that narrow audience who knows the derivation of the methods presented and knows the application as well. Equations are presented with a terse explanation of their parameters.Finally, the book cries out for a good editor. Dr. Tsay does not write clearly, unfortunately, and in many instances the syntax of the book is cumbersome, making the meaning difficult to discern.
This is a really, really dense and technical read, especially for a book that calls itself an "introduction". I'm a graduate student in analytics but have been unable to get anything out of this book because it is so high-level and filled with unreadable equations. I'd only recommend it for those who have a very advanced understanding of statistics and mathematics (calculus and beyond), since that seems to be the assumed baseline for readers.
Everything is explained fully. If you don't have a solid math background, don't bother trying to read it though.
I like it very much!
As the Russians say, "the veteran horse plows a straight but shallow furrow". The book's preface acknowledges an overlap with the earlier "Analysis of financial time series" (AFTS), pointing out the novelty of included examples. I would call "overlap" an understatement, and describe "Introduction to analysis of financial data with R" (IAFD) as a subset of AFTS, illustrated with examples, implemented in R. I may have wished for something fancier than the oh-so-nineties repertoire of ARIMA and GARCH - the marginal exceptions show up in the sections on ACD and EVT - but accept that the book is meant to be a textbook in a master's-level course. (Yet is priced at over $100! This made the choice between four and three stars easy). I am unimpressed with its usage of R: the ARMA/GARCH analyses are quite repetitive, the plotting is basic, data manipulation altogether absent, an early encouraging use of quantmod package does not develop at all. (If you visit the book's web page - and you will have to, to get the scripts that, for example, estimate GARCH flavors; in the book, it's just "source(..)" - you will count nine R packages used: fGarch, MASS, evir, quantreg, quantmod, TSA, fracdiff, fBasics, mnormt). The occasional typos - the amusing "Merry Lynch" has been caught in the errata, but others remain - add to the impression of a path-of-low-resistance approach. Even so, given the author's caliber, you get a useful, quality book. I would suggest taking a look at IAFD and the two books by David Ruppert, to see which one(s) you prefer.
The book is very terse and useful while introducing both, time series theory and R. It uses up to date packages that permit to analyze time series directly connected to updated time series from Internet sites (such as Yahoo!), which is very practical since all the resources are free. Actually, I got this book from a price error (almost 25 USD), which was pretty sweet for me!! Nevertheless, I think it is worth the usual price (< 100 USD). Read it!!
Good book...poorly executed.The author has gone great lengths to include mathematical rigor in the book but the code is staring at me as early as page 13!!Code R Demonstration with quantmod package on page 13 does not work.Someone has to spend a full day trying to figure out what the author left out..it's a painful experience.
Finally found some irregular time series analysis from this book.
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