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An introduction to analysis of financial data with R
Tsay R., Wiley Publishing, Hoboken, NJ, 2013. 416 pp. Type: Book (978-0-470890-81-3)
Date Reviewed: Mar 25 2014

This book fits a promising niche in books related to financial analysis and the R programming language. Many books addressing the analysis of financial data either focus extensively on the underlying mathematics or use some platform/vendor-specific analysis and programming software, which makes it difficult for readers without access to commercial or proprietary software to follow. What makes the book highly relevant to a broad audience is its reliance on R, an open-source programming language.

With analytics and big data becoming a current major business driver, the R programming language has become an extremely powerful competitor among the existing programming languages. Because of its rich support in contributed libraries, among which the packages related to financial data are good examples of ease of use and supporting functionalities, the book is appealing to many reader categories, including those interested in risk management, stock pricing, volatility measurement, and time series prediction.

Structured in seven chapters, the author starts with a rapid introduction to the R programming language and the Quantmod R library, and continues with practical applications for generating well-known distribution functions. The modeling of financial time series data (autoregressive (AR) models, autoregressive-moving-average (ARMA) models, and random walks) is covered in the second chapter. The third chapter gets hands-on, and the previously described models are instantiated for analyzing several economic indicators: crude oil prices, weekly gasoline price, and temperature anomalies are analyzed via R and public data sources.

Volatility is a crucial concept in finance and thus is explored in two chapters (chapters 4 and 5). The fourth chapter describes two popular models (autoregressive conditional heteroskedasticity (ARCH) and generalized ARCH (GARCH)) and the fifth chapter shows how these models can be practically applied to portfolio management.

With more than 80 percent of financial transactions done using high-frequency computerized trading, it’s natural that the author dedicates an entire chapter (6) to this topic. Finally, the last chapter (7) considers the estimation of value at risk (VaR) and the measurement of risk in financial data. The approach followed in this last chapter is in line with the previous chapters. The mathematical bases are precisely introduced, followed by practical and illustrative examples in R and thorough discussions on some selected test cases.

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.

More reviews about this item: Amazon

Reviewer:  Radu State Review #: CR142104 (1406-0396)
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