Statistical Quantitative Methods in Finance: From Theory to Quantitative Portfolio Management - Softcover
Synopsis
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 Learn
- Understand the fundamentals of linear regression and its applications in financial data analysis and prediction
- Apply generalized linear models for handling various types of data distributions and enhancing model flexibility
- Gain insights into regime switching models to capture different market conditions and improve financial forecasting
- Benchmark machine learning models against traditional statistical methods to ensure robustness and reliability in financial applications
Who This Book Is For
Data scientists, machine learning engineers, finance professionals, and software engineers
"synopsis" may belong to another edition of this title.
About the Author
Samit Ahlawat is a portfolio manager at QSpark Investment, specializing in US equity and derivative trading. He has extensive experience in quantitative asset management and market risk management, having previously worked at JP Morgan Chase and Bank of America. His research interests include artificial intelligence, risk management, and algorithmic trading strategies. Samit holds a master’s degree in numerical computation from the University of Illinois, Urbana-Champaign.
From the Back Cover
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 Learn
- Understand the fundamentals of linear regression and its applications in financial data analysis and prediction
- Apply generalized linear models for handling various types of data distributions and enhancing model flexibility
- Gain insights into regime switching models to capture different market conditions and improve financial forecasting
- Benchmark machine learning models against traditional statistical methods to ensure robustness and reliability in financial applications
"About this title" may belong to another edition of this title.
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Paperback. Condition: new. Paperback. 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 Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Seller Inventory # 9798868809613
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Statistical Quantitative Methods in Finance: From Theory to Quantitative Portfolio Management (eng)
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Taschenbuch. Condition: 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) 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 500 pp. Englisch. Seller Inventory # 9798868809613
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Taschenbuch. Condition: 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 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 engineersLibri GmbH, Europaallee 1, 36244 Bad Hersfeld 312 pp. Englisch. Seller Inventory # 9798868809613
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Taschenbuch. Condition: 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. Seller Inventory # 9798868809613
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