Tidy Finance with Python
◆Taylor & Francis セール開催中!:2024年12月22日(日)ご注文分まで
※上記表示の販売価格は割引適用後の価格です 出版済み 3-5週間でお届けいたします。 Series: Chapman & Hall/CRC The Python Series Author: Christoph Scheuch, Stefan Voigt, Patrick Weiss, Christoph Frey Publisher: Taylor & Francis ISBN: 9781032684291 Cover: HARDCOVER Date: 2024年07月 DESCRIPTION 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. TABLE OF CONTENTS 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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