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Machine Learning Finance Theory by Dixon Matthew (30 results)
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Hardcover. Condition: Good. Connecting readers with great books since 1972! Used textbooks may not include companion materials such as access codes, etc. May have some wear or writing/highlighting. We ship orders daily and Customer Service is our top priority!
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Hardcover. Condition: Acceptable. Connecting readers with great books since 1972. Used textbooks may not include companion materials such as access codes, etc. May have condition issues including wear and notes/highlighting. We ship orders daily and Customer Service is our top priority!
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Machine Learning in Finance : From Theory to Practice
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
Seller rating 5 out of 5 stars
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Machine Learning in Finance: From Theory to Practice
Seller: Ria Christie Collections, Uxbridge, United Kingdom
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Machine Learning in Finance : From Theory to Practice
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
Seller rating 5 out of 5 stars
Condition: As New. Unread book in perfect condition.
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Condition: As New. Unread book in perfect condition.
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Machine Learning in Finance: From Theory to Practice
Seller: Ria Christie Collections, Uxbridge, United Kingdom
Seller rating 5 out of 5 stars
Condition: New. In.
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Machine Learning in Finance : From Theory to Practice
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
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Machine Learning in Finance : From Theory to Practice
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
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Paperback. Condition: Brand New. 573 pages. 9.25x6.10x1.17 inches. In Stock.
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Taschenbuch. Condition: Neu. Machine Learning in Finance | From Theory to Practice | Matthew F. Dixon (u. a.) | Taschenbuch | xxv | Englisch | 2021 | Springer | EAN 9783030410704 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
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Condition: New.
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Machine Learning in Finance : From Theory to Practice
Published by Springer International Publishing, Springer International Publishing, 2021
ISBN 10: 3030410706 ISBN 13: 9783030410704
Language: English
Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book introduces machine learning methods in finance. It presents a unified treatment of machine learning and various statistical and computational disciplines in quantitative finance, such as financial econometrics and discrete time stochastic control, with an emphasis on how theory and hypothesis tests inform the choice of algorithm for financial data modeling and decision making. With the trend towards increasing computational resources and larger datasets, machine learning has grown into an important skillset for the finance industry. This book is written for advanced graduate students and academics in financial econometrics, mathematical finance and applied statistics, in addition to quants and data scientists in the field of quantitative finance.Machine Learning in Finance: From Theory to Practiceis divided into three parts, each part covering theory and applications. The first presents supervised learning for cross-sectional data from both a Bayesianand frequentist perspective. The more advanced material places a firm emphasis on neural networks, including deep learning, as well as Gaussian processes, with examples in investment management and derivative modeling. The second part presents supervised learning for time series data, arguably the most common data type used in finance with examples in trading, stochastic volatility and fixed income modeling. Finally, the third part presents reinforcement learning and its applications in trading, investment and wealth management. Python code examples are provided to support the readers' understanding of the methodologies and applications. The book also includes more than 80 mathematical and programming exercises, with worked solutions available to instructors. As a bridge to research in this emergent field, the final chapter presents the frontiers of machine learning in finance from a researcher's perspective, highlighting how many well-known concepts in statistical physics are likelyto emerge as important methodologies for machine learning in finance.
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Machine Learning in Finance : From Theory to Practice
Published by Springer International Publishing, 2020
ISBN 10: 3030410676 ISBN 13: 9783030410674
Language: English
Buch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book introduces machine learning methods in finance. It presents a unified treatment of machine learning and various statistical and computational disciplines in quantitative finance, such as financial econometrics and discrete time stochastic control, with an emphasis on how theory and hypothesis tests inform the choice of algorithm for financial data modeling and decision making. With the trend towards increasing computational resources and larger datasets, machine learning has grown into an important skillset for the finance industry. This book is written for advanced graduate students and academics in financial econometrics, mathematical finance and applied statistics, in addition to quants and data scientists in the field of quantitative finance.Machine Learning in Finance: From Theory to Practiceis divided into three parts, each part covering theory and applications. The first presents supervised learning for cross-sectional data from both a Bayesianand frequentist perspective. The more advanced material places a firm emphasis on neural networks, including deep learning, as well as Gaussian processes, with examples in investment management and derivative modeling. The second part presents supervised learning for time series data, arguably the most common data type used in finance with examples in trading, stochastic volatility and fixed income modeling. Finally, the third part presents reinforcement learning and its applications in trading, investment and wealth management. Python code examples are provided to support the readers' understanding of the methodologies and applications. The book also includes more than 80 mathematical and programming exercises, with worked solutions available to instructors. As a bridge to research in this emergent field, the final chapter presents the frontiers of machine learning in finance from a researcher's perspective, highlighting how many well-known concepts in statistical physics are likelyto emerge as important methodologies for machine learning in finance.
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Hardcover. Condition: Brand New. 548 pages. 9.50x6.50x1.25 inches. In Stock.
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Hardcover. Condition: New. New. book.
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Paperback. Condition: Brand New. 573 pages. 9.25x6.10x1.17 inches. In Stock. This item is printed on demand.
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Hardcover. Condition: Brand New. 548 pages. 9.50x6.50x1.25 inches. In Stock. This item is printed on demand.
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Condition: New. Print on Demand.
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Condition: New. PRINT ON DEMAND.
