Tidy finance python di scheuch christoph (38 risultati)
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Da: GreatBookPrices, Columbia, MD, U.S.A.GreatBookPrices
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Tidy Finance with Python (Chapman & Hall/CRC The Python Series)
Scheuch, Christoph; Voigt, Stefan; Weiss, Patrick; Frey, Christoph
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Da: California Books, Miami, FL, U.S.A.California Books
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Tidy Finance with Python (Chapman & Hall/CRC The Python Series)
Christoph Frey, Christoph Scheuch, Stefan Voigt, Patrick Weiss
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Da: Chiron Media, Wallingford, Regno UnitoChiron Media
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Tidy Finance with Python (Chapman & Hall/CRC The Python Series)
Scheuch, Christoph; Voigt, Stefan; Weiss, Patrick; Frey, Christoph
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Da: Ria Christie Collections, Uxbridge, Regno UnitoRia Christie Collections
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Tidy Finance with Python (Chapman & Hall/CRC The Python Series)
Scheuch, Christoph; Voigt, Stefan; Weiss, Patrick; Frey, Christoph
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Da: Majestic Books, Hounslow, Regno UnitoMajestic Books
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Da: Rarewaves USA, OSWEGO, IL, U.S.A.Rarewaves USA
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EUR 109,61
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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, nu…mpy, 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.
Tidy Finance with Python (Chapman & Hall/CRC The Python Series)
Scheuch, Christoph; Voigt, Stefan; Weiss, Patrick; Frey, Christoph
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Da: Books Puddle, New York, NY, U.S.A.Books Puddle
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EUR 106,57
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Condizione: New. 1st edition NO-PA16APR2015-KAP.
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Da: THE SAINT BOOKSTORE, Southport, Regno UnitoTHE SAINT BOOKSTORE
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Paperback / softback. Condizione: New. New copy - Usually dispatched within 4 working days.
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Da: Rarewaves.com USA, London, LONDO, Regno UnitoRarewaves.com USA
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EUR 118,16
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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, nu…mpy, 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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Da: Revaluation Books, Exeter, Regno UnitoRevaluation Books
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EUR 128,04
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Paperback. Condizione: Brand New. 272 pages. 10.00x7.00x10.00 inches. In Stock.
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Da: moluna, Greven, Germaniamoluna
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Condizione: 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.
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Da: Rarewaves USA United, OSWEGO, IL, U.S.A.Rarewaves USA United
Contatta il venditoreVenditore con 5 stelleCondizione: Nuovo
EUR 111,78
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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, nu…mpy, 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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Da: Rarewaves.com UK, London, Regno UnitoRarewaves.com UK
Contatta il venditoreVenditore con 5 stelleCondizione: Nuovo
EUR 110,75
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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, nu…mpy, 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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Da: GreatBookPrices, Columbia, MD, U.S.A.GreatBookPrices
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EUR 234,62
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Da: GreatBookPricesUK, Woodford Green, Regno UnitoGreatBookPricesUK
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Da: GreatBookPrices, Columbia, MD, U.S.A.GreatBookPrices
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EUR 243,76
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Condizione: New.
Tidy Finance with Python (Chapman & Hall/CRC The Python Series)
Scheuch, Christoph; Voigt, Stefan; Weiss, Patrick; Frey, Christoph
- Rilegato
Da: California Books, Miami, FL, U.S.A.California Books
Contatta il venditoreVenditore con 4 stelleCondizione: Nuovo
EUR 249,68
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Da: GreatBookPricesUK, Woodford Green, Regno UnitoGreatBookPricesUK
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EUR 234,06
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Condizione: As New. Unread book in perfect condition.
Tidy Finance with Python (Chapman & Hall/CRC The Python Series)
Scheuch, Christoph; Voigt, Stefan; Weiss, Patrick; Frey, Christoph
- Rilegato
Da: Majestic Books, Hounslow, Regno UnitoMajestic Books
Contatta il venditoreVenditore con 4 stelleCondizione: Nuovo
EUR 248,73
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Tidy Finance with Python (Chapman & Hall/CRC The Python Series)
Scheuch, Christoph; Voigt, Stefan; Weiss, Patrick; Frey, Christoph
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Da: Ria Christie Collections, Uxbridge, Regno UnitoRia Christie Collections
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EUR 250,04
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Tidy Finance with Python (Chapman & Hall/CRC The Python Series)
Scheuch, Christoph; Voigt, Stefan; Weiss, Patrick; Frey, Christoph
- Rilegato
Da: Books Puddle, New York, NY, U.S.A.Books Puddle
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Condizione: New. 1st edition NO-PA16APR2015-KAP.
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Da: Revaluation Books, Exeter, Regno UnitoRevaluation Books
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Hardcover. Condizione: Brand New. 272 pages. 10.00x7.00x10.00 inches. In Stock.
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Da: Basi6 International, Irving, TX, U.S.A.Basi6 International
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Condizione: Brand New. New. US edition. Print on demand title. Delivery takes 20-25 days.
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Da: Revaluation Books, Exeter, Regno UnitoRevaluation Books
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EUR 108,20
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Paperback. Condizione: Brand New. 272 pages. 10.00x7.00x10.00 inches. In Stock. This item is printed on demand.
Tidy Finance with Python (Chapman & Hall/CRC The Python Series)
Scheuch, Christoph; Voigt, Stefan; Weiss, Patrick; Frey, Christoph
- Brossura
- Print on Demand
Da: Biblios, frankfurt am main, HESSE, GermaniaBiblios
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EUR 108,87
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