As R is more and more popular in the industry as well as in the academics for analyzing financial data. For people unfamiliar with R, this post suggests some books for learning financial data analysis using R. From our teaching and learning R experience, the fast way to learn R is to start with the topics you have been familiar with. Thus, the book list below suits people with some background in finance but are not R user. These books below will provide useful guidance for your R learning journey. Try to read and compare these books to find what really fits you.
Table: R book list for learning financial data analysis
|Book Cover||Extracted summary|
|Book Title: Automated Trading with R |
Quantitative Research and Platform Development
Author: Christopher Conlan
This book has full source code and step-by-step
explanation for plug-and-play trading platform.
Platform can be used in brokerage-level simulation
or production before reading every chapter
|Book Title: Analyzing Financial Data and |
Implementing Financial Models Using R
Author: Clifford Ang
This book Teaches students how to use R to analyze
financial data and implement financial models from
start (e.g., obtaining data) to finish (e.g.,
generating output expected for a particular analysis)
using real-world data
|Book Title: Modeling Financial Time Series with S-PLUS® |
Author: Eric Zivot and Jiahui Wang
This book represents an integration of theory, methods
, and examples using the S-PLUS statistical modeling
language and the S+FinMetrics module to facilitate the
practice of financial econometrics. This is the first
book to show the power of S-PLUS for the analysis of
time series data.
|Book Title: Time Series Analysis With Applications in R |
Author: Jonathan D.Cryer and Kung-Sik Chan
This book presents an accessible approach to understanding
time series models and their applications. The new edition
devotes two chapters to the frequency domain
and three to time series regression models, models for
heteroscedasticity, and threshold models.
|Book Title: Statistics and Data Analysis for Financial Engineering |
Author: David Ruppert and David S. Matteson
This book contains an ideal blend of innovative
research and practical applications, tackles
relevant investor problems, and provides a
multi-disciplined approach, solving problems
from both fundamental and non-traditional methods
|Book Title: Financial Analytics with R |
Author: David Ruppert and David S. Matteson
This book give examples using financial markets and
economic data to illustrate important concepts.
R Labs with real-data exercises give students practice
in data analysis.
|Book Title: R in Finance and Economics |
Author: Abhay Kumar Singh and David E Allen
This book provides an introduction to the statistical software
R and its application with an empirical approach in finance
and economics. It is specifically targeted towards undergraduate
and graduate students. It provides beginner-level introduction
to R using RStudio and reproducible research examples.
|Book Title: An Introduction to Analysis of Financial Data with R |
Author: Ruey S. Tsay
This book 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.
|Book Title: Statistical Analysis of Financial Data in R |
Author: René Carmona
Although there are many books on mathematical finance, few deal
with the statistical aspects of modern data analysis as applied
to financial problems. This textbook fills this gap by addressing
some of the most challenging issues facing financial engineers. It
shows how modern statistical techniques can be used in
the solutions of concrete financial problems.
|Book Title: Mastering R for Quantitative Finance |
Author: Edina Berlinger et al.
This book is organized as a step-by-step practical guide to
using R. Starting with time series analysis, you will also
learn how to forecast the volume for VWAP Trading.
Among other topics, the book covers FX derivatives,
interest rate derivatives, and optimal hedging.
|Book Title: Multivariate Time Series Analysis |
Author: Ruey S. Tsay
This book is the much anticipated sequel coming from one of
the most influential and prominent experts on the topic of time
series. Through a fundamental balance of theory and methodology,
the book supplies readers with a comprehensible approach to
financial econometric models and their applications to real-world
|Book Title: Option Pricing and Estimation of Financial Models with R |
Author: Stefano M. Iacus
This book presents inference and simulation of stochastic process
in the field of model calibration for financial times series
modelled by continuous time processes and numerical option pricing.
It also introduces the bases of probability theory and goes on to
explain how to model financial times series with continuous models.
|Book Title: Quantitative Trading with R |
Author: Georgakopoulos, H.
This book offers a winning strategy for devising
expertly-crafted and workable trading models using
the R open source programming language, providing
readers with a step-by-step approach to understanding
complex quantitative finance problems and building
functional computer code.
|Book Title: Computational Finance |
Author: Argimiro Arratia
This book teaches you how to use the statistical tools
and methods available in the free software R, for
processing and analyzing real financial data
|Book Title: Numerical Methods and Optimization in Finance |
Author: Manfred Gilli et al.
This book describes computational finance tools.
It covers fundamental numerical analysis and
computational techniques, such as option pricing,
and gives special attention to simulation
|Book Title: Tools for Computational Finance |
Author: Seydel, Rüdiger
This book covers on an introductory level the very
important issue of computational aspects of
|Book Title: Financial Risk Forecasting |
Author: Jon Danielsson
This book is a complete introduction to practical
quantitative risk management, with a focus on market
risk. It brings together the three key disciplines
of finance, statistics and modeling (programming)
|Book Title: Financial Risk Modelling and Portfolio |
Optimization with R, 2nd Edition
Author: Bernhard Pfaff
This book is a great collection of many R finance
package introductions. It will be especially useful
for the experienced financial data analysts. It also
provides a plethora of R code examples
|Book Title: Forecasting: principles and practice |
Author: Rob J Hyndman and George Athanasopoulos
This textbook provides a comprehensive introduction to
forecasting methods and presents enough information about
each method for readers to use them sensibly.
Notice that the information above is directly collected from the publisher website and we just summarize it for you. Further details about these books can be assessed by clicking the links to the book publisher. If you would like to get a quick review of financial data analysis using R, see our recent presentation here.
Finally, since more and more books are published these years to address using R in financial data analysis, the book list above might not be comprehensive. You are very welcome to leave the comments below to tell us what we missed. We will try to add them to the list ASAP!
Page last updated on 10 Nov. 2016.