Tidy Finance with R - eBook

**Tidy Finance with R** offers a practical guide to applying finance and econometrics theories to real-world data using R. Focusing on coding and data analysis with the tidyverse suite, the book walks readers through the entire process of empirical finance research. It starts with foundational concepts such as tidy data principles and essential coding practices, providing comprehensive code to prepare and integrate key financial datasets—including CRSP, Compustat, Mergent FISD, and TRACE—into a unified database used throughout the text.

The book covers a wide range of empirical finance topics, including asset pricing methods like beta estimation, portfolio sorts, and performance analysis with Fama-French factors. It also explores advanced modeling and machine learning techniques such as fixed effects estimation, clustering standard errors, difference-in-differences, ridge regression, Lasso, Elastic Net, random forests, neural networks, and portfolio optimization.

Key features include:

- Self-contained chapters that focus on crucial finance applications and methodologies, suitable for research or as references in empirical finance courses.
- Fully reproducible content, enabling readers to replicate all figures, tables, and results by running the provided code.
- A thorough introduction to machine learning using tidy models, demonstrating applications in factor selection and option pricing.
- Detailed guidance on accessing and managing financial data, especially CRSP and Compustat, including explanations of their core characteristics.
- Exercises in each chapter inspired by established lectures to encourage deeper understanding, ideal for self-study or teaching.

**Authors:** Christoph Scheuch, Stefan Voigt, Patrick Weiss
**Format:** PDF
**Publisher:** Chapman & Hall (1st Edition)
**Language:** English
**ISBNs:** 9781000858785, 9781003347538, 9781032389349, 9781000858716, 9781032389332

**Note:** This offering includes only the eBook *Tidy Finance with R*, 1st Edition, in its original PDF format. No access codes are provided.

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