Followup on Books on Finance & R

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Followup on Books on Finance & R

Nelson Wong
Dear all,

OK, as promised, here’s a brief introduction to the reference books on finance & R, which I shared recently. I’ve included the name of the first author only for clarity. Some books have multiple authors, which I’ll not include here for the sake of brevity.

1. An Introduction to Analysis of Financial Data with R. Ruey S. Tsay. Wiley. 2013. 657 Pages.
This book is for those who are new to the deployment of R in analyzing corporate financial data. Ample worked examples of real world financial data are used to reinforce concepts & methodology in this book. Readers should have an intermediate level of financial engineering math & some basic understanding of working in R before diving into this book. It’s quite a comprehensive book & it includes chapters on Financial Data & Their Properties, Linear Models for Time Series, Case Studies in Linear Time Series, Asset Volatility & Volatility Models, Applications of Volatility Models, High Frequency Financial Data, and Value at Risk.

2. Financial Analytics with R. Building a Laptop Laboratory for Data Science. Mark J. Bennett. Cambridge University Press. 2016. 394 Pages.
This is not an introductory text on R & financial data. This book emphasizes on building simulation models to perform financial analytics that will work on a laptop or PC. Therefore, working knowledge of Hadoop, Spark & parallel processing is not required here. This book was developed as materials for a master’s degree course in financial analytics in the Graham School at the University of Chicago & the undergraduate courses in investment in the Tippie College of Business at the University of Iowa. While a finance background is not required here, the author recommends that a course in statistical analysis be taken before reading this book. Some experience with C, C++, Java, C#, Python or Mathlab & especially R will be helpful. Chapters include Analytical Thinking, The R Language for Statistical Computing, Financial Statistics, Financial Securities, Dataset Analytics & Risk Measurement, Time Series Analysis, The Sharpe Ratio, Markowitz Mean-Variance Optimization, Cluster Analysis, Gauging the Market Sentiment, Simulating Trading Strategies, Data Exploration Using Fundamentals, Prediction Using Fundamentals, Binomial Models for Options, and Black-Scholes Model & Option-Implied Volatility.

3. Statistics & Data Analysis for Financial Engineering. David Ruppert. Springer. 2011. 662 pages.
This book was developed by the author while teaching a course in Statistics for Financial Engineering to master’s degree students in financial engineering at Cornell University. This book can be used in a one or two semester course at both master’s & PhD levels. Readers are assumed to already have some good experiences in working with R, as R will be used extensively here but will not be taught. Chapters include an Introduction, Returns, Fixed Income Securities, Exploratory Data Analysis, Modeling Univariate Distributions, Resampling, Multivariate Statistical Models, Copulas, Time Series Models: Basics, Time Series Models: Further Topics, Portfolio Theory, Regression: Basics, Regression: Troubleshooting, Regression: Advanced Topic, Cointegration, The Capital Asset Pricing Model, Factor Models & Principal Components, GARCH Models, Risk Management, Bayesian Data Analysis & MCMC, Nonparametric Regression & Splines, Facts from Probability, Statistics, & Algebra.

4. Financial Risk Modelling and Portfolio Optimization with R. Berhard Pfaff. Second Edition. Wiley. 2016. 437 Pages.
This is a very practical reference book that can be used in both the classroom & trading room. One of the plus points for this book is that all R packages used in this book are listed in the table of contents, instead of being buried inside the texts of each chapter. Packages are not only used to demonstrate data modeling & data visualization, a short synopsis is also provided for the various packages in its application for each scenario. Broken into 3 major parts, chapters include A Brief Course in R, Financial Market Data, Measuring Risks, Modern Portfolio Theory, Suitable Distributions for Returns, Extreme Value Theory, Modelling Volatility, Modelling Dependence, Robust Portfolio Optimization, Diversification Reconsidered, Risk-Optimal Portfolios, Tactical Asset Allocation, and Probabilistic Utility.

5. Automated Trading with R. Quantitative Research and Platform Development Chris Conlan. Apress. 2016. 217 Pages.
This is a short book with a sharp learning curve. It requires a strong background in financial engineering, IT & R programming. Some working knowledge in UNIX, Python & C++ is also recommended. As indicated by its title, this is about building a platform for automated trading with R. This will be a good handbook for a team in such projects. Chapters include Fundamentals of Automated Trading, Networking, Data Preparation, Indicators, Rule Sets, High-Performance Computing, Simulation & Backtesting, Optimization, and Organizing & Automating Scripts.

6. Introduction to R for Quantitative Finance. Gergely Darόczi. Packt. 2013. 165 Pages.
This book is written by a team of Hungarian academicians, many of them from the Corvinus University of Budapest. This is a book for first-timers who want to read an introductory text on R & quantitative finance. Chapters include Time Series Analysis, Portfolio Optimization, Asset Pricing Models, Fixed Income Securities, Estimating the Term Structure of Interest Rates, Derivatives Pricing, Credit Risk Management, Extreme Value Theory, and Financial Networks.

7. Mastering R for Quantitative Finance. Edina Berlinger. Packt. 2015. 362 Pages.
This is another book on R & quantitative finance by another team of academicians from the Corvinus University of Budapest. Building on book #6, the book dives deeper into a wider range of subjects in quantitative finance & R. Chapters include Time Series Analysis, Factor Models, Forecasting Volume, Big Data – Advanced Analytics, FX Derivatives, Interest Rate Derivatives & Models, Exotic Options, Optimal Hedging, Fundamental Analysis, Technical Analysis, Neural Networks & Logoptimal Portfolios, Asset & Liability Management, Capital Adequacy, and Systemic Risks.

Happy reading.


Nelson Wong

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