Tidy finance With R

Table of Contents:- Part I: Getting Started 1. Introduction to Tidy Finance Part II: Financial Data 2. Accessing & Managing Financial Data 3. WRDS, CRSP, and Compustat 4. TRACE and FISD 5. Other Data Providers Part III: Asset Pricing 6. Beta Estimation 7. Univariate Portfolio Sorts 8. Size Sorts and P-Hacking 9. Value and Bivariate Sorts 10. Replicating Fama and French Factors 11. Fama-MacBeth Regressions Part IV: Modeling & Machine Learning 12. Fixed Effects and Clustered Standard Errors 13. Difference in Differences 14. Factor Selection via Machine Learning 15. Option Pricing via Machine Learning Part V: Portfolio Optimization 16. Parametric Portfolio Policies 17. Constrained Optimization and Backtesting

This book shows how to bring theoretical concepts from finance and econometrics to the data. It focusing on coding and data analysis with R, show how to conduct research in empirical finance from scratch. The book 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. 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.