**Tidy Finance with Python** is a comprehensive guide that bridges theoretical finance and econometrics with practical data analysis using Python. Focusing on coding and empirical research, the book starts by introducing foundational concepts like tidy data and programming principles with pandas, numpy, and plotnine. It provides code to prepare and organize financial datasets from both open-source and proprietary sources (e.g., CRSP, Compustat, Mergent FISD, TRACE) into a unified database used throughout the text.
The book covers essential asset pricing topics such as beta estimation, portfolio sorting, performance evaluation, and Fama-French factors. It also explores advanced modeling and machine learning methods, including fixed effects, clustering standard errors, difference-in-differences, ridge regression, Lasso, Elastic Net, random forests, neural networks, and portfolio optimization.
**Key Features:**
- Self-contained chapters that present practical finance applications and methodologies, ideal for research or as course references.
- Fully reproducible figures, tables, and results through provided Python code.
- A detailed introduction to machine learning with scikit-learn, emphasizing tidy data and applications like factor selection and option pricing.
- Step-by-step guidance on accessing and preparing major financial datasets, with clear explanations of their key features.
- Exercises designed to complement lectures and promote deeper learning, suitable for self-study and teaching.
**eBook Details:**
- **Authors:** Christoph Scheuch, Stefan Voigt, Patrick Weiss, Christoph Frey
- **Format:** PDF
- **Publisher:** Chapman & Hall (1st edition)
- **Language:** English
- **ISBNs:** 9781040048719, 9781032684291, 9781032684307, 9781040048610
**Note:** This sale includes only the eBook in original PDF format; no access codes are provided.
https://textbooks.dad/product/tidy-finance-with-python-ebook/?fsp_sid=1160
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