Vendeur : Big River Books, Powder Springs, GA, Etats-Unis
EUR 51,63
Quantité disponible : 1 disponible(s)
Ajouter au panierEtat : good. This book is in good condition. The cover has minor creases or bends. The binding is tight and pages are intact. Some pages may have writing or highlighting.
EUR 81,30
Quantité disponible : Plus de 20 disponibles
Ajouter au panierEtat : New.
EUR 84,11
Quantité disponible : Plus de 20 disponibles
Ajouter au panierEtat : As New. Unread book in perfect condition.
Vendeur : Books Puddle, New York, NY, Etats-Unis
EUR 86,75
Quantité disponible : 4 disponible(s)
Ajouter au panierEtat : New. 1st edition NO-PA16APR2015-KAP.
EUR 71,56
Quantité disponible : Plus de 20 disponibles
Ajouter au panierEtat : New.
Vendeur : California Books, Miami, FL, Etats-Unis
EUR 92,03
Quantité disponible : Plus de 20 disponibles
Ajouter au panierEtat : New.
Vendeur : Majestic Books, Hounslow, Royaume-Uni
EUR 86,07
Quantité disponible : 3 disponible(s)
Ajouter au panierEtat : New.
Vendeur : Ria Christie Collections, Uxbridge, Royaume-Uni
EUR 79,95
Quantité disponible : Plus de 20 disponibles
Ajouter au panierEtat : New. In.
Edité par Chapman and Hall/CRC 2024-07-15, 2024
ISBN 10 : 1032676418 ISBN 13 : 9781032676418
Langue: anglais
Vendeur : Chiron Media, Wallingford, Royaume-Uni
EUR 76,88
Quantité disponible : 4 disponible(s)
Ajouter au panierPaperback. Etat : New.
EUR 84,97
Quantité disponible : Plus de 20 disponibles
Ajouter au panierEtat : As New. Unread book in perfect condition.
EUR 94,47
Quantité disponible : 2 disponible(s)
Ajouter au panierPaperback. Etat : Brand New. 272 pages. 10.00x7.00x10.00 inches. In Stock.
Edité par Taylor and Francis Ltd, GB, 2024
ISBN 10 : 1032676418 ISBN 13 : 9781032676418
Langue: anglais
Vendeur : Rarewaves USA, OSWEGO, IL, Etats-Unis
EUR 108,66
Quantité disponible : 2 disponible(s)
Ajouter au panierPaperback. Etat : New. 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.
Edité par Taylor and Francis Ltd, GB, 2024
ISBN 10 : 1032676418 ISBN 13 : 9781032676418
Langue: anglais
Vendeur : Rarewaves.com USA, London, LONDO, Royaume-Uni
EUR 119,47
Quantité disponible : 2 disponible(s)
Ajouter au panierPaperback. Etat : New. 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.
EUR 120,13
Quantité disponible : 2 disponible(s)
Ajouter au panierPaperback. Etat : Brand New. 272 pages. 10.00x7.00x10.00 inches. In Stock.
EUR 87,56
Quantité disponible : 4 disponible(s)
Ajouter au panierEtat : New. Christoph Frey is a Quantitative Researcher and Portfolio Manager at a family office in Hamburg and a Research Fellow at the Centre for Financial Econometrics, Asset Markets and Macroeconomic Policy at Lancaster University. Prior to this, he was t.
Edité par Taylor and Francis Ltd, GB, 2024
ISBN 10 : 1032676418 ISBN 13 : 9781032676418
Langue: anglais
Vendeur : Rarewaves USA United, OSWEGO, IL, Etats-Unis
EUR 110,11
Quantité disponible : 2 disponible(s)
Ajouter au panierPaperback. Etat : New. 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.
Edité par Taylor and Francis Ltd, GB, 2024
ISBN 10 : 1032676418 ISBN 13 : 9781032676418
Langue: anglais
Vendeur : Rarewaves.com UK, London, Royaume-Uni
EUR 107,87
Quantité disponible : 2 disponible(s)
Ajouter au panierPaperback. Etat : New. 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.
EUR 221,58
Quantité disponible : Plus de 20 disponibles
Ajouter au panierEtat : New.
Vendeur : Majestic Books, Hounslow, Royaume-Uni
EUR 216,31
Quantité disponible : 3 disponible(s)
Ajouter au panierEtat : New.
Vendeur : Books Puddle, New York, NY, Etats-Unis
EUR 227,20
Quantité disponible : 3 disponible(s)
Ajouter au panierEtat : New. 1st edition NO-PA16APR2015-KAP.
EUR 232,93
Quantité disponible : Plus de 20 disponibles
Ajouter au panierEtat : As New. Unread book in perfect condition.
Vendeur : Ria Christie Collections, Uxbridge, Royaume-Uni
EUR 222,70
Quantité disponible : Plus de 20 disponibles
Ajouter au panierEtat : New. In.
EUR 222,43
Quantité disponible : Plus de 20 disponibles
Ajouter au panierEtat : New.
EUR 238,84
Quantité disponible : Plus de 20 disponibles
Ajouter au panierEtat : As New. Unread book in perfect condition.
Vendeur : Biblios, Frankfurt am main, HESSE, Allemagne
EUR 238,47
Quantité disponible : 3 disponible(s)
Ajouter au panierEtat : New.
EUR 305
Quantité disponible : 2 disponible(s)
Ajouter au panierHardcover. Etat : Brand New. 272 pages. 10.00x7.00x10.00 inches. In Stock.
Vendeur : Biblios, Frankfurt am main, HESSE, Allemagne
EUR 90,35
Quantité disponible : 4 disponible(s)
Ajouter au panierEtat : New. PRINT ON DEMAND.
Vendeur : moluna, Greven, Allemagne
EUR 184,46
Quantité disponible : Plus de 20 disponibles
Ajouter au panierEtat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Christoph Frey is a Quantitative Researcher and Portfolio Manager at a family office in Hamburg and a Research Fellow at the Centre for Financial Econometrics, Asset Markets and Macroeconomic Policy at Lancaster University. Prior to this, he was t.