Tidy finance with R
Author(s)
Bibliographic Information
Tidy finance with R
（The R series）（A Chapman & Hall book）
CRC Press, 2023
- : pbk
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Note
Includes bibliographical references (p. 235-245) and index
Description and Table of Contents
Description
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.
Each chapter is reproducible in the sense that the reader can replicate every single figure, table, or number by simply copy-pasting the code we provide.
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.
Chapter 2 on accessing & managing financial data shows how to retrieve and prepare the most important datasets in the field of financial economics: CRSP and Compustat. The chapter also contains detailed explanations of the most important data characteristics.
Each chapter provides exercises that are based on established lectures and exercise classes and which are designed to help students to dig deeper. The exercises can be used for self-studying or as source of inspiration for teaching exercises.
Table of Contents
1. Introduction to Tidy Finance 2. Accessing & Managing Financial Data 3. WRDS, CRSP, and Compustat 4. TRACE and FISD 5. Other Data Providers 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 12. Fixed Effects and Clustered Standard Errors 13. Difference in Differences 14. Factor Selection via Machine Learning 15. Option Pricing via Machine Learning 16. Parametric Portfolio Policies 17. Constrained Optimization and Backtesting Appendix A. Cover Design Appendix B. Clean Enhanced TRACE with R
by "Nielsen BookData"