Reproducible Finance with R: Code Flows and Shiny Apps for Portfolio Analysis(Chapman & Hall/CRC The R Series)

Reproducible Finance with R: Code Flows and Shiny Apps for Portfolio Analysis is a unique introduction to data science for investment management that explores the three major R/finance coding paradigms, emphasizes data visualization, and explains how to build a cohesive suite of functioning Shiny applications. The full source code, asset price data and live Shiny applications are available at reproduciblefinance.com. The ideal reader works in finance or wants to work in finance and has a desire to learn R code and Shiny through simple, yet practical real-world examples. The book begins with the first step in data science: importing and wrangling data, which in the investment context means importing asset prices, converting to returns, and constructing a portfolio. The next section covers risk and tackles descriptive statistics such as standard deviation, skewness, kurtosis, and their rolling histories. The third section focuses on portfolio theory, analyzing the Sharpe Ratio, CAPM, and Fama French models. The book concludes with applications for finding individual asset contribution to risk and for running Monte Carlo simulations. For each of these tasks, the three major coding paradigms are explored and the work is wrapped into interactive Shiny dashboards.

Table of Contents:
Chapter 1 Introduction Returns Chapter 2 Asset Prices to Returns Converting Daily Prices to Monthly Returns in the xts world Converting Daily Prices to Monthly Returns in the tidyverse Converting Daily Prices to Monthly Returns in the tidyquant world Converting Daily Prices to Monthly Returns with tibbletime Visualizing Asset Returns in the xts world Visualizing Asset Returns in the tidyverse Chapter 3 Building a Portfolio Portfolio Returns in the xts world Portfolio Returns in the tidyverse Portfolio Returns in the tidyquant world Visualizing Portfolio Returns in the xts world Visualizing Portfolio Returns in the tidyverse Shiny App Portfolio Returns Concluding Returns Risk Chapter 4 Standard Deviation Standard Deviation in the xts world Standard Devation in the tidyverse Standard Deviation in the tidyquant world Visualizing Standard Deviation Rolling Standard Deviation Rolling Standard Deviation in the xts world Rolling Standard Deviation in the tidyverse Rolling Standard Devation with the tidyverse and tibbletime Rolling Standard Deviation in the tidyquant world Visualizing Rolling Standard Deviation in the xts world Visualizing Rolling Standard Deviation in the tidyverse Shiny App Standard Deviation Chapter 5 Skewness Skewness in the xts world Skewness in the tidyverse Visualizing Skewness Rolling Skewness in the xts world Rolling Skewness in the tidyverse with tibbletime Rolling Skewness in the tidyquant world Visualizing Rolling Skewness Chapter 6 Kurtosis Kurtosis in the xts world Kurtosis in the tidyverse Visualizing Kurtosis Rolling Kurtosis in the xts world Rolling Kurtosis in the tidyverse with tibbletime Rolling Kurtosis in the tidyquant world Visualizing Rolling Kurtosis Shiny App Skewness and Kurtosis Concluding Risk Portfolio Theory Chapter 7 Sharpe Ratio Sharpe Ratio in the xts world Sharpe Ratio in the tidyverse Shape Ratio in the tidyquant world Visualizing Sharpe Ratio Rolling Sharpe Ratio in the xts World Rolling Sharpe Ratio with the tidyverse and tibbletime Rolling Sharpe Ratio with tidyquant Visualizing the Rolling Sharpe Ratio Shiny App Sharpe Ratio Chapter 8 CAPM CAPM and Market Returns Calculating CAPM Beta Calculating CAPM Beta in the xts world Contents v Calculating CAPM Beta in the tidyverse Calculating CAPM Beta in the tidyquant world Visualizing CAPM with ggplot Augmenting Our Data Visualizing CAPM with highcharter Shiny App CAPM Chapter 9 Fama French Importing and Wrangling Fama French Visualizing Fama French with ggplot Rolling Fama French with the tidyverse and tibbletime Visualizing Rolling Fama French Shiny App Fama French Concluding Portfolio Theory Practice and Applications Chapter 10 Component Contribution to Standard Deviation Component Contribution Step-by-Step Component Contribution with a Custom Function Visualizing Component Contribution Rolling Component Contribution to Volatility Visualizing Rolling Component Contribution to Volatility Shiny App Component Contribution Chapter 11 Monte Carlo Simulation Simulating Growth of a Dollar Several Simulation Functions Running Multiple Simulations Visualizing Simulation Results Visualizing with highcharter Shiny App Monte Carlo Concluding Practice Applications

About the Author :
Jonathan K. Regenstein, Jr. is the Director of Financial Services at RStudio. He studied international relations at Harvard and law at NYU, worked at JP Morgan, and did graduate work in political economy at Emory.

Review :
"There are two major selling points from my perspective. First, Shiny web applications are a new technology that is in high demand. It enables users to communicate data science (including financial analytics) to managers and executives. I believe this alone is a big benefit that separates this book from others. The second is that (he) takes a modern approach to using three different frameworks: xts, tidyverse, and tidyquant/tibbletime. This is refreshing because it shows that there are multiple ways to accomplish the same tasks, and it exposes the user to options that they otherwise might not have considered. Because of these two aspects, I believe that the market is for financial analysts that are seeking to learn these tools. The typical reader will have some knowledge of R (not a complete beginner) and will be hungry to use Shiny in their organization…I enjoyed reading it. I found the prose approachable and not overly technical or formal." ~Matt Dancho, Founder, Business Science, LLC "There are two major selling points from my perspective. First, Shiny web applications are a new technology that is in high demand. It enables users to communicate data science (including financial analytics) to managers and executives. I believe this alone is a big benefit that separates this book from others. The second is that (he) takes a modern approach to using three different frameworks: xts, tidyverse, and tidyquant/tibbletime. This is refreshing because it shows that there are multiple ways to accomplish the same tasks, and it exposes the user to options that they otherwise might not have considered. Because of these two aspects, I believe that the market is for financial analysts that are seeking to learn these tools. The typical reader will have some knowledge of R (not a complete beginner) and will be hungry to use Shiny in their organization…I enjoyed reading it. I found the prose approachable and not overly technical or formal." ~Matt Dancho, Founder, Business Science, LLC