Tidy Finance with Python
by Christoph Scheuch, Stefan Voigt, Patrick Weiss
1st Edition
Publisher: Chapman & Hall
Book Details
| Print ISBN | 9781032676418 |
| eText ISBN | 9781040048719 |
| Publisher | Chapman & Hall |
| Publishing Year | 2024 |
| Edition | 1st Edition |
| Language | English |
Tidy Finance with Python, 1st Edition, is a textbook that introduces tidy data structures and coding standards for empirical finance research from the ground up. The text demonstrates how to use Python libraries such as pandas, numpy, and plotnine for data handling and financial modeling.
The volume addresses financial data management across open-source and proprietary platforms alongside core methods in empirical asset pricing. Broad coverage spans portfolio sorting techniques, factor replication, and predictive modeling applications.
Each chapter is reproducible, enabling readers to recreate tables, figures, and numerical results with the provided code. Classroom-tested exercises offer structured practice for independent study or university course instruction.
Table of Contents
Chapter 1: Setting Up Your Environment
Chapter 2: Introduction to Tidy Finance
Chapter 3: Accessing and Managing Financial Data
Chapter 4: WRDS, CRSP, and Compustat
Chapter 5: TRACE and FISD
Chapter 6: Other Data Providers
Chapter 7: Beta Estimation
Chapter 8: Univariate Portfolio Sorts
Chapter 9: Size Sorts and p-Hacking
Chapter 10: Value and Bivariate Sorts
Chapter 11: Replicating Fama and French Factors
Chapter 12: Fama-MacBeth Regressions
Chapter 13: Fixed Effects and Clustered Standard Errors
Chapter 14: Difference in Differences
Chapter 15: Factor Selection via Machine Learning
Chapter 16: Option Pricing via Machine Learning
Chapter 17: Parametric Portfolio Policies
Chapter 18: Constrained Optimization and Backtesting
Chapter A: Colophon
Chapter B: Proofs
Chapter C: WRDS Dummy Data
Chapter D: Clean Enhanced TRACE with Python
Chapter E: Cover Image
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