Tidy Finance with Python

Christoph Scheuch, Stefan Voigt, Patrick Weiss, Christoph Frey (Autoren)

Buch | Hardcover

246 Seiten

2024
Chapman & Hall/CRC (Verlag)
978-1-032-68429-1 (ISBN)

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This textbook shows how to bring theoretical concepts from finance and econometrics to the data. Focusing on coding and data analysis with Python, we show how to conduct research in empirical finance from scratch. We start by introducing the concepts of tidy data and coding principles using pandas, numpy, and plotnine. Code is provided to prepare common open-source and proprietary financial data sources (CRSP, Compustat, Mergent FISD, TRACE) and organize them in a database. We reuse these data in all the subsequent chapters, which we keep as self-contained as possible. The empirical applications range from key concepts of empirical asset pricing (beta estimation, portfolio sorts, performance analysis, Fama-French factors) to modeling and machine learning applications (fixed effects estimation, clustering standard errors, difference-in-difference estimators, ridge regression, Lasso, Elastic net, random forests, neural networks) and portfolio optimization techniques.

Key Features:

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 copying and pasting the code we provide.
A full-fledged introduction to machine learning with scikit-learn based on tidy principles to show how factor selection and option pricing can benefit from Machine Learning methods.
We show how to retrieve and prepare the most important datasets financial economics: CRSP and Compustat, including detailed explanations of the most relevant data characteristics.
Each chapter provides exercises based on established lectures and classes which are designed to help students to dig deeper. The exercises can be used for self-studying or as a source of inspiration for teaching exercises.

Christoph Frey is a Quantitative Researcher and Portfolio Manager at a family office in Hamburg and a Research Fellow at the Centre for Financial Econometrics, Asset Markets and Macroeconomic Policy at Lancaster University. Prior to this, he was the leading quantitative researcher for systematic multi-asset strategies at Berenberg Bank and worked as an Assistant Professor at the Erasmus Universiteit Rotterdam. Christoph published research on Bayesian Econometrics and specializes in financial econometrics and portfolio optimization problems. Christoph Scheuch is the Head of Artificial Intelligence at the social trading platform wikifolio.com. He is responsible for researching, designing, and prototyping of cutting-edge AI-driven products using R and Python. Before his focus on AI, he was responsible for product management and business intelligence at wikifolio.com and an external lecturer at the Vienna University of Economics and Business, where he taught finance students how to manage empirical projects. Stefan Voigt is an Assistant Professor of Finance at the Department of Economics at the University in Copenhagen and a research fellow at the Danish Finance Institute. His research focuses on blockchain technology, high-frequency trading, and financial econometrics. Stefan's research has been published in the leading finance and econometrics journals and he received the Danish Finance Institute Teaching Award 2022 for his courses for students and practitioners on empirical finance based on Tidy Finance. Patrick Weiss is an Assistant Professor of Finance at Reykjavik University and an external lecturer at the Vienna University of Economics and Business. His research activity centers around the intersection of empirical asset pricing and corporate finance, with his research appearing in leading journals in financial economics. Patrick is especially passionate about empirical asset pricing and strives to understand the impact of methodological uncertainty on research outcomes.

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

Erscheinungsdatum	06.07.2024
Reihe/Serie	Chapman & Hall/CRC The Python Series
Zusatzinfo	33 Line drawings, black and white; 33 Illustrations, black and white
Sprache	englisch
Maße	178 x 254 mm
Gewicht	644 g
Themenwelt	Mathematik / Informatik ► Mathematik
	Wirtschaft ► Betriebswirtschaft / Management
	Wirtschaft ► Volkswirtschaftslehre ► Ökonometrie
ISBN-10	1-032-68429-1 / 1032684291
ISBN-13	978-1-032-68429-1 / 9781032684291
Zustand	Neuware