Tidy Finance with Python (Hardcover, 1)

Preface

Author Biographies

Part 1: Getting Started

1. Setting Up Your Environment

2. Introduction to Tidy Finance

Part 2: Financial Data

3. Accessing and Managing Financial Data

4. WRDS, CRSP, and Compustat

5. TRACE and FISD

6. Other Data Providers

Part 3: Asset Pricing

7. Beta Estimation

8. Univariate Portfolio Sorts

9. Size Sorts and p-Hacking

10. Value and Bivariate Sorts

11. Replicating Fama and French Factors

12. Fama-MacBeth Regressions

Part 4: Modeling and Machine Learning

13. Fixed Effects and Clustered Standard Errors

14. Difference in Differences

15. Factor Selection via Machine Learning

16. Option Pricing via Machine Learning

Part 5: Portfolio Optimization

17. Parametric Portfolio Policies

18. Constrained Optimization and Backtesting

Appendices

A. Colophon

B. Proofs

C. WRDS Dummy Data

D. Clean Enhanced TRACE with Python

E. Cover Image

Bibliography

Index