Tidy Finance with R

Publikation: Bog/antologi/afhandling/rapport › Bog › Forskning › fagfællebedømt

Standard

Tidy Finance with R. / Voigt, Stefan; Scheuch, Christoph; Weiss, Patrick.

Taylor & Francis, 2023. 268 s.

Publikation: Bog/antologi/afhandling/rapport › Bog › Forskning › fagfællebedømt

Harvard

Voigt, S, Scheuch, C & Weiss, P 2023, Tidy Finance with R. Taylor & Francis. https://doi.org/10.1201/b23237

APA

Voigt, S., Scheuch, C., & Weiss, P. (2023). Tidy Finance with R. Taylor & Francis. https://doi.org/10.1201/b23237

Vancouver

Voigt S, Scheuch C, Weiss P. Tidy Finance with R. Taylor & Francis, 2023. 268 s. https://doi.org/10.1201/b23237

Author

Voigt, Stefan ; Scheuch, Christoph ; Weiss, Patrick. / Tidy Finance with R. Taylor & Francis, 2023. 268 s.

Bibtex

@book{44436a6f15144ba784d649459c47db6b,
title = "Tidy Finance with R",
abstract = "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.",
author = "Stefan Voigt and Christoph Scheuch and Patrick Weiss",
year = "2023",
doi = "10.1201/b23237",
language = "English",
isbn = "9781032389349",
publisher = "Taylor & Francis",
address = "United States",

}

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