Building Quantitative Finance Applications with R

Building Quantitative Finance Applications with R

Building Quantitative Finance Applications with R, is possible due to two driving forces: First, developing computer programs using the interactive R language is now easily within reach of the financial analyst. Every effort is made here to keep the R programming elementary.

First, Building Quantitative Finance Applications with R is written to assist financial analysts to quickly learn to express their ideas in the R computer language. Second, quantitative finance is now widely applied in a variety of career fields. Quantitative financial analysts are now needed in a variety of different arenas. The ability for these analysts to express their own ideas in R is a highly valued skill.

Finance is a social science. Thus, as ideas about how certain financial products should be valued and managed evolve, the actual value and risk properties also change. Therefore, there will never be a “theory of everything” in finance. More likely, we should be surprised if there ever appears a “theory of anything” that endures for very long.

Because of the dynamic nature of financial markets, financial analysts need to be able to rapidly adapt their valuation and risk management models to changing times. Rather than rely on faulty communication between analysts and professional programmers, financial analysts can express their ideas in prototype R code. This ability dramatically reduces errors and allows financial analysts greater precision in expressing their ideas.

Building Quantitative Finance Applications with R is written for college students and entry-level financial analysts. No prior knowledge of programming is assumed. As with any language, having access to multiple sources when learning technical material is highly recommended. Therefore, it is assumed that you have access to several introductory R books or similar web-based materials.

There are several other books linking quantitative finance with computer programming. The approach taken here is distinctly different. Rather than present state-of-the-art programming techniques, we use only elementary R. Rarely do financial analysts want to become professional programmers. Rather, they want to rapidly learn how to express their unique analytical ideas in a form that the computer can run. Therefore, we focus on the minimal set of computer programming tools necessary to perform this task.

The expected publication date is early 2021.

The following materials are provided for those participating in the Introduction to R Tutorial sessions offered in Fall 2020. If possible, download the materials for Sessions 1 and 2. The material provided in Session 2 will be used for the remainder of our time together. I will attempt to video just the training portion of our time together. If successful, I intend to make them available here some time later for those who cannot attend and those who wish to revised a particular topic.

Session 1: R Illustrated (8/26/20)
Installing R    Module 9.5 PDF    Module 9.5 Zip   
Chapter 1    Ch 1 PPT    Ch 1 PPT Full    Video 1   

Session 2: R Basics (9/2/20)
Book Appendix R    Appendix Zip File    Video 2   

Session 3: R Variables and Vocabulary (9/9/20)
Video 3   

Session 4: R Functions (9/16/20)
Video 4    Assignment 1 Zip File    PV Assignment Video

Session 5: R File Management (9/23/20)
Video 5   

Session 6: R Plots (9/30/20)
Video 6   

Session 7: R Calendar Challenges (10/7/20)
Video 7   

Session 8: R Markdown (10/14/20)
Video 8   

Session 9: R Simulation (Extra: Module 3.3)
Session 9 Zip File    Video 9   

Session 10: R Embedded Parameters (Extra: Module 3.6)
Session 10 Zip File    Video 10   

Robert E. Brooks
Robert E. Brooks
Wallace D. Malone, Jr. Professor of Financial Management

My research interests include financial derivatives, enterprise risk management, performance attribution, options, futures, swaps, and financial philosophy.