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Statistical Quantitative Methods in Finance

- From Theory to Quantitative Portfolio Management
Af: Samit Ahlawat Engelsk Paperback

Statistical Quantitative Methods in Finance

- From Theory to Quantitative Portfolio Management
Af: Samit Ahlawat Engelsk Paperback
Tjek vores konkurrenters priser
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 applications Who This Book Is ForData scientists, machine learning engineers, finance professionals, and software engineers
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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 applications Who This Book Is ForData scientists, machine learning engineers, finance professionals, and software engineers
Produktdetaljer
Sprog: Engelsk
Sider: 295
ISBN-13: 9798868809613
Indbinding: Paperback
Udgave:
ISBN-10: 8868809613
Kategori: Machine learning
Udg. Dato: 23 jan 2025
Længde: 0mm
Bredde: 155mm
Højde: 235mm
Forlag: Springer-Verlag Berlin and Heidelberg GmbH & Co. KG
Oplagsdato: 23 jan 2025
Forfatter(e): Samit Ahlawat
Forfatter(e) Samit Ahlawat


Kategori Machine learning


ISBN-13 9798868809613


Sprog Engelsk


Indbinding Paperback


Sider 295


Udgave


Længde 0mm


Bredde 155mm


Højde 235mm


Udg. Dato 23 jan 2025


Oplagsdato 23 jan 2025


Forlag Springer-Verlag Berlin and Heidelberg GmbH & Co. KG