9798868809613 - statistical quantitative methods in finance: from theory to quantitative portfolio management di ahlawat, samit (5 risultati)
- Brossura
- Print on Demand
Da: Brook Bookstore On Demand, Napoli, NA, ItaliaBrook Bookstore On Demand
Contatta il venditoreVenditore con 5 stelleCondizione: Nuovo
EUR 39,22
EUR 5,50 spedizioneSpedito da Italia a U.S.A.Quantità: Più di 20 disponibili
Condizione: new. Questo è un articolo print on demand.
- Brossura
- Print on Demand
Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, GermaniaBuchWeltWeit Ludwig Meier e.K.
Contatta il venditoreVenditore con 5 stelleCondizione: Nuovo
EUR 48,14
EUR 23,00 spedizioneSpedito da Germania a U.S.A.Quantità: 2 disponibili
Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Statistical quantitative methods are vital for financial valuation models and benchmarking machine learning models in finance.This book explores the theoretical foundations of statistical models, from ordinary least squares (OLS) t…o the generalized method of moments (GMM) used in econometrics. It enriches your understanding through practical examples drawn from applied finance, demonstrating the real-world applications of these concepts. Additionally, the book delves into non-linear methods and Bayesian approaches, which are becoming increasingly popular among practitioners thanks to advancements in computational resources. By mastering these topics, you will be equipped to build foundational models crucial for applied data science, a skill highly sought after by software engineering and asset management firms. The book also offers valuable insights into quantitative portfolio management, showcasing how traditional data science tools can be enhanced with machine learning models. These enhancements are illustrated through real-world examples from finance and econometrics, accompanied by Python code. This practical approach ensures that you can apply what you learn, gaining proficiency in the statsmodels library and becoming adept at designing, implementing, and calibrating your models.By understanding and applying these statistical models, you enhance your data science skills and effectively tackle financial challenges.What You Will LearnUnderstand the fundamentals of linear regression and its applications in financial data analysis and predictionApply generalized linear models for handling various types of data distributions and enhancing model flexibilityGain insights into regime switching models to capture different market conditions and improve financial forecastingBenchmark machine learning models against traditional statistical methods to ensure robustness and reliability in financial applicationsWho This Book Is ForData scientists, machine learning engineers, finance professionals, and software engineers 500 pp. Englisch.
- Brossura
- Print on Demand
Da: moluna, Greven, Germaniamoluna
Contatta il venditoreVenditore con 5 stelleCondizione: Nuovo
EUR 40,39
EUR 48,99 spedizioneSpedito da Germania a U.S.A.Quantità: Più di 20 disponibili
Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt.
- Brossura
- Print on Demand
Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germaniabuchversandmimpf2000
Contatta il venditoreVenditore con 5 stelleCondizione: Nuovo
EUR 48,14
EUR 60,00 spedizioneSpedito da Germania a U.S.A.Quantità: 1 disponibili
Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Statistical quantitative methods are vital for financial valuation models and benchmarking machine learning models in finance.This book explores the theoretical foundations of statistical models, from ordinary least squares (OLS) to th…e generalized method of moments (GMM) used in econometrics. It enriches your understanding through practical examples drawn from applied finance, demonstrating the real-world applications of these concepts. Additionally, the book delves into non-linear methods and Bayesian approaches, which are becoming increasingly popular among practitioners thanks to advancements in computational resources. By mastering these topics, you will be equipped to build foundational models crucial for applied data science, a skill highly sought after by software engineering and asset management firms. The book also offers valuable insights into quantitative portfolio management, showcasing how traditional data science tools can be enhanced with machine learning models. These enhancements are illustrated through real-world examples from finance and econometrics, accompanied by Python code. This practical approach ensures that you can apply what you learn, gaining proficiency in the statsmodels library and becoming adept at designing, implementing, and calibrating your models.By understanding and applying these statistical models, you enhance your data science skills and effectively tackle financial challenges.What You Will LearnUnderstand the fundamentals of linear regression and its applications in financial data analysis and predictionApply generalized linear models for handling various types of data distributions and enhancing model flexibilityGain insights into regime switching models to capture different market conditions and improve financial forecastingBenchmark machine learning models against traditional statistical methods to ensure robustness and reliability in financial applicationsWho This Book Is ForData scientists, machine learning engineers, finance professionals, and software engineersLibri GmbH, Europaallee 1, 36244 Bad Hersfeld 312 pp. Englisch.
- Brossura
- Print on Demand
Da: AHA-BUCH GmbH, Einbeck, GermaniaAHA-BUCH GmbH
Contatta il venditoreVenditore con 5 stelleCondizione: Nuovo
EUR 48,14
EUR 62,38 spedizioneSpedito da Germania a U.S.A.Quantità: 1 disponibili
Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Statistical quantitative methods are vital for financial valuation models and benchmarking machine learning models in finance.This book explores the theoretical foundations of statistical models, from ordinary least squares (OLS) to the… generalized method of moments (GMM) used in econometrics. It enriches your understanding through practical examples drawn from applied finance, demonstrating the real-world applications of these concepts. Additionally, the book delves into non-linear methods and Bayesian approaches, which are becoming increasingly popular among practitioners thanks to advancements in computational resources. By mastering these topics, you will be equipped to build foundational models crucial for applied data science, a skill highly sought after by software engineering and asset management firms. The book also offers valuable insights into quantitative portfolio management, showcasing how traditional data science tools can be enhanced with machine learning models. These enhancements are illustrated through real-world examples from finance and econometrics, accompanied by Python code. This practical approach ensures that you can apply what you learn, gaining proficiency in the statsmodels library and becoming adept at designing, implementing, and calibrating your models.By understanding and applying these statistical models, you enhance your data science skills and effectively tackle financial challenges.What You Will LearnUnderstand the fundamentals of linear regression and its applications in financial data analysis and predictionApply generalized linear models for handling various types of data distributions and enhancing model flexibilityGain insights into regime switching models to capture different market conditions and improve financial forecastingBenchmark machine learning models against traditional statistical methods to ensure robustness and reliability in financial applicationsWho This Book Is ForData scientists, machine learning engineers, finance professionals, and software engineers.
