Christoph Scheuch, Stefan Voigt, Patrick Weiss

Tidy Finance with R (eBook, ePUB)

• Format: ePub

Produktbeschreibung

This textbook shows how to bring theoretical concepts from finance and econometrics to the data. Focusing on coding and data analysis with R, we show how to conduct research in empirical finance from scratch. We start by introducing the concepts of tidy data and coding principles using the tidyverse family of R packages.

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Produktdetails

• Verlag: Taylor & Francis eBooks
• Seitenzahl: 268
• Erscheinungstermin: 5. April 2023
• Englisch
• ISBN-13: 9781000858785
• Artikelnr.: 67317418

Autorenporträt

Christoph Scheuch is the Director of Product at the social trading platform wikifolio.com. He is responsible for product planning, execution, and monitoring and manages a team of data scientists to analyze user behavior and develop data-driven products. Christoph is also an external lecturer at the Vienna University of Economics and Business where he teaches finance students how to manage empirical projects.

Stefan Voigt is Assistant Professor of Finance at the Department of Economics at the University of 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. He teaches parts of this book in his courses on empirical finance for students and practitioners.

Patrick Weiss is a postdoctoral researcher at the Vienna University of Economics and Business and an external lecturer at Reykjavík University. His research activity centers around the intersection of empirical asset pricing and corporate finance. Patrick is especially passionate about empirical asset pricing and has published research in a top journal in financial economics.

Inhaltsangabe

1. Introduction to Tidy Finance 2. Accessing & Managing Financial Data 3.
WRDS, CRSP, and Compustat 4. TRACE and FISD 5. Other Data Providers 6. Beta
Estimation 7. Univariate Portfolio Sorts 8. Size Sorts and P-Hacking 9.
Value and Bivariate Sorts 10. Replicating Fama and French Factors 11.
Fama-MacBeth Regressions 12. Fixed Effects and Clustered Standard Errors
13. Difference in Differences 14. Factor Selection via Machine Learning 15.
Option Pricing via Machine Learning 16. Parametric Portfolio Policies 17.
Constrained Optimization and Backtesting Appendix A. Cover Design Appendix
B. Clean Enhanced TRACE with R