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.
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.
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.
Inhaltsangabe
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
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
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