Stefan Voigt – författare
628 kr
Skickas inom 7-10 vardagar
2 226 kr
Skickas inom 5-8 vardagar
1 994 kr
Läs direkt efter köp
1 924 kr
Läs direkt efter köp
2 306 kr
Skickas inom 5-8 vardagar
1 994 kr
Läs direkt efter köp
1 994 kr
Läs direkt efter köp
Design of Constitutions
5 670 kr
Skickas inom 5-8 vardagar
1 177 kr
Läs direkt efter köp
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.
Highlights
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 tidymodels based on tidy principles to show how factor selection and option pricing can benefit from Machine Learning methods Chapter 2 on accessing and managing financial data shows how to retrieve and prepare the most important datasets financial economics: CRSP and Compustat. The chapter also contains 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 exercises1 177 kr
Läs direkt efter köp
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.
Highlights
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 tidymodels based on tidy principles to show how factor selection and option pricing can benefit from Machine Learning methods Chapter 2 on accessing and managing financial data shows how to retrieve and prepare the most important datasets financial economics: CRSP and Compustat. The chapter also contains 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 exercises1 221 kr
Skickas inom 7-10 vardagar
489 kr
Läs direkt efter köp
404 kr
Skickas inom 7-10 vardagar
489 kr
Läs direkt efter köp
2 648 kr
Skickas inom 10-15 vardagar
1 004 kr
Skickas inom 10-15 vardagar
1 004 kr
Skickas inom 10-15 vardagar
2 648 kr
Skickas inom 10-15 vardagar
1 194 kr
Läs direkt efter köp
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.1 194 kr
Läs direkt efter köp
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.1 358 kr
Skickas inom 7-10 vardagar
1 156 kr
Skickas inom 5-8 vardagar
717 kr
Läs direkt efter köp
717 kr
Läs direkt efter köp
391 kr
Skickas inom 7-10 vardagar
473 kr
Läs direkt efter köp
1 112 kr
Skickas inom 10-15 vardagar
1 459 kr
Läs direkt efter köp
Making European Merger Policy More Predictable analyses European Merger Control with regard to its capacity to generate predictability among the concerned parties. Starting from the premise that predictability is of overwhelming importance for the functioning of market economies, Voigt and Schmidt ask to what degree European Merger Control has been predictable over the last couple of years. The authors show both theoretically and empirically that there have been serious shortcomings with regard to the predictability of competition policy. They identify the insufficient recognition of the consequences of globalization on the competitive processes as well as an often inconsistent application of economic theory as the root causes for the lack of predictability. The inconsistent application of economic theory is particularly relevant with regard to potential competition and the evaluation of collective dominance. The authors generate a substantial number of proposals that could help to improve predictability. On this basis, Voigt and Schmidt critically assess the recent reforms of European Merger Control.
1 112 kr
Skickas inom 10-15 vardagar
1 766 kr
Skickas inom 5-8 vardagar