Tidy Finance with R

<p>This textbook shows how to bring theoretical concepts from finance and econometrics to the data. Focusing on coding and data analysis with R we show how to conduct research in empirical finance from scratch. We start by introducing the concepts of tidy data and coding principles using the tidyverse family of R packages. Code is provided to prepare common open-source and proprietary financial data sources (CRSP Compustat Mergent FISD TRACE) and organize them in a database. We reuse these data in all the subsequent chapters which we keep as self-contained as possible. The empirical applications range from key concepts of empirical asset pricing (beta estimation portfolio sorts performance analysis Fama-French factors) to modeling and machine learning applications (fixed effects estimation clustering standard errors difference-in-difference estimators ridge regression Lasso Elastic net random forests neural networks) and portfolio optimization techniques.</p><p>Highlights</p><ol> <li>Self-contained chapters on the most important applications and methodologies in finance which can easily be used for the reader’s research or as a reference for courses on empirical finance</li> <li>Each chapter is reproducible in the sense that the reader can replicate every single figure table or number by simply copying and pasting the code we provide</li> <li>A full-fledged introduction to machine learning with tidymodels based on tidy principles to show how factor selection and option pricing can benefit from Machine Learning methods</li> <li>Chapter 2 on accessing and managing financial data shows how to retrieve and prepare the most important datasets financial economics: CRSP and Compustat. The chapter also contains detailed explanations of the most relevant data characteristics</li> <li>Each chapter provides exercises based on established lectures and classes which are designed to help students to dig deeper. The exercises can be used for self-studying or as a source of inspiration for teaching exercises</li> </ol>