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Lingua: Inglese
Editore: H N H International Limited, 2023
ISBN 10: 1032389346 ISBN 13: 9781032389349
Da: Majestic Books, Hounslow, Regno Unito
EUR 90,61
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Da: Majestic Books, Hounslow, Regno Unito
EUR 91,11
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Lingua: Inglese
Editore: Chapman and Hall/CRC 2024-07-15, 2024
ISBN 10: 1032676418 ISBN 13: 9781032676418
Da: Chiron Media, Wallingford, Regno Unito
EUR 82,94
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Aggiungi al carrelloPaperback. Condizione: New.
Lingua: Inglese
Editore: H N H International Limited, 2023
ISBN 10: 1032389346 ISBN 13: 9781032389349
Da: Books Puddle, New York, NY, U.S.A.
Condizione: New.
Da: Ria Christie Collections, Uxbridge, Regno Unito
EUR 88,65
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EUR 85,34
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Lingua: Inglese
Editore: Taylor and Francis Ltd, GB, 2024
ISBN 10: 1032676418 ISBN 13: 9781032676418
Da: Rarewaves USA, OSWEGO, IL, U.S.A.
Paperback. Condizione: 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.
Da: Books Puddle, New York, NY, U.S.A.
Condizione: New. 1st edition NO-PA16APR2015-KAP.
EUR 85,86
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Aggiungi al carrelloPaperback / softback. Condizione: New. New copy - Usually dispatched within 4 working days.
Lingua: Inglese
Editore: Taylor and Francis Ltd, GB, 2024
ISBN 10: 1032676418 ISBN 13: 9781032676418
Da: Rarewaves.com USA, London, LONDO, Regno Unito
EUR 110,82
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Aggiungi al carrelloPaperback. Condizione: 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.
Da: Ria Christie Collections, Uxbridge, Regno Unito
EUR 99,02
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EUR 124,85
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Aggiungi al carrelloPaperback. Condizione: Brand New. 272 pages. 9.19x6.13x0.63 inches. In Stock.
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Aggiungi al carrelloCondizione: 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.
Lingua: Inglese
Editore: Taylor and Francis Ltd, GB, 2024
ISBN 10: 1032676418 ISBN 13: 9781032676418
Da: Rarewaves USA United, OSWEGO, IL, U.S.A.
Paperback. Condizione: 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.
Lingua: Inglese
Editore: Taylor and Francis Ltd, GB, 2024
ISBN 10: 1032676418 ISBN 13: 9781032676418
Da: Rarewaves.com UK, London, Regno Unito
EUR 104,59
Quantità: 2 disponibili
Aggiungi al carrelloPaperback. Condizione: 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.
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