Description
This textbook lifts the curtain on reproducible finance and shows how to apply theoretical concepts from finance and econometrics by providing a fully transparent R code base. Focusing on coding and data analysis with R, we illustrate how students, researchers, data scientists, and professionals can conduct research in empirical finance from scratch. We start with a beginner-friendly introduction to the tidyverse family of R packages around which our approach revolves. We then show how to access and prepare common open-source (e.g., French data library, macroeconomic data) and proprietary financial data sources (such as CRSP, Compustat, Mergent FISD, and TRACE). We present data management principles using an SQLite database, which constitutes the basis for the applications presented in the subsequent chapters. The empirical applications range from key concepts of empirical asset pricing (like beta estimation, portfolio sorts, performance analysis, and Fama-French factors) to modeling and machine learning applications (such as fixed effects estimation, clustering standard errors, difference-in-difference estimators, ridge regressions, Lasso, Elastic nets, random forests, and neural networks) and portfolio optimization techniques.| Period | 23 Jun 2022 |
|---|---|
| Event title | useR! 2022 |
| Event type | Unknown |
| Degree of Recognition | International |