Tidy Finance with R
Research output: Book/Report › Book › Research › peer-review
Standard
Tidy Finance with R. / Voigt, Stefan; Scheuch, Christoph; Weiss, Patrick.
Taylor & Francis, 2023. 268 p.Research output: Book/Report › Book › Research › peer-review
Harvard
APA
Vancouver
Author
Bibtex
}
RIS
TY - BOOK
T1 - Tidy Finance with R
AU - Voigt, Stefan
AU - Scheuch, Christoph
AU - Weiss, Patrick
PY - 2023
Y1 - 2023
N2 - 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. 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.
AB - 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. 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.
U2 - 10.1201/b23237
DO - 10.1201/b23237
M3 - Book
SN - 9781032389349
SN - 9781032389332
BT - Tidy Finance with R
PB - Taylor & Francis
ER -
ID: 336461246