Название: Reproducible Finance with R: Code Flows and Shiny Apps for Portfolio Analysis Автор: Jonathan K. Regenstein Jr. Издательство: Chapman and Hall/CRC Серия: The R Series ISBN: 1138484032 Год: 2018 Страниц: 249 Язык: английский Формат: pdf (true) Размер: 54.7 MB
This book has two practical motivations and one philosophic motiavtion. The two practical movitations are: (1) to introduce R to finance professionals, or people who want to become finance professionals, who wish to move beyond Excel for their quantitative work and (2) to introduce various finance coding paradigms to R coders. The book seeks to be a resource for R coders interested in finance, or financiers who are interested in R or quantitative work generally.
The philosophical motivation for both audiences is to demonstrate and emphasize reproducible finance with good R code. The reason the first word in the title of this book is not ‘financial’, or ‘quantitative’, or ‘algorithmic’ is that the driving force behind this book is clean, neat, readable, reusable and reproducible R code for finance. We will prioritize code that is understandable over code that is theoretically brilliant.
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 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.
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