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Machine Learning and Data Sciences for Financial Markets: A Guide to Contemporary Practices - Hardcover
Inhaltsangabe
Leveraging the research efforts of more than sixty experts in the area, this book reviews cutting-edge practices in machine learning for financial markets. Instead of seeing machine learning as a new field, the authors explore the connection between knowledge developed by quantitative finance over the past forty years and techniques generated by the current revolution driven by data sciences and artificial intelligence. The text is structured around three main areas: 'Interactions with investors and asset owners,' which covers robo-advisors and price formation; 'Risk intermediation,' which discusses derivative hedging, portfolio construction, and machine learning for dynamic optimization; and 'Connections with the real economy,' which explores nowcasting, alternative data, and ethics of algorithms. Accessible to a wide audience, this invaluable resource will allow practitioners to include machine learning driven techniques in their day-to-day quantitative practices, while students will build intuition and come to appreciate the technical tools and motivation for the theory.
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Über die Autorinnen und Autoren
Agostino Capponi is Associate Professor in the Department of Industrial Engineering and Operations Research at Columbia University. He conducts research in financial technology and market microstructure. His work has been recognized with the NSF CAREER Award, and a JP Morgan AI Research award. Capponi is a co-editor of Management Science and Mathematics and Financial Economics. He is a Council member of the Bachelier Financial Society, and recently served as Chair of the SIAM-FME and INFORMS Finance.
Charles-Albert Lehalle is Global Head of Quantitative R&D at Abu Dhabi Investment Authority and Visiting Professor at Imperial College London. He has a Ph.D. in machine learning, was previously Head of Data Analytics at CFM, and held different Global Head positions at Crédit Agricole CIB. Recognized as an expert in market microstructure, Lehalle is often invited to present to regulators and policy-makers.
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Machine Learning and Data Sciences for Financial Markets: A Guide to Contemporary Practices
Verlag:
Cambridge University Press, 2023
ISBN 10: 1316516199
ISBN 13: 9781316516195
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Hardcover
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hardcover. Zustand: Good. Cover and edges may have some wear. Mild to Moderate creasing / bending to covers and pages. Good reading copy. Artikel-Nr. mon0003652541
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Machine Learning and Data Sciences for Financial Markets: A Guide to Contemporary Practices
Verlag:
Cambridge University Press, 2023
ISBN 10: 1316516199
ISBN 13: 9781316516195
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Machine Learning and Data Sciences for Financial Markets: A Guide to Contemporary Practices
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Machine Learning and Data Sciences for Financial Markets: A Guide to Contemporary Practices
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MACHINE LEARNING & DATA SCIENCES FOR F
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Machine Learning and Data Sciences for Financial Markets: A Guide to Contemporary Practices
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Machine Learning and Data Sciences for Financial Markets: A Guide to Contemporary Practices
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Zustand: New. InhaltsverzeichnisInteracting with Investors and Asset Owners: Part I. Robo-advisors and Automated Recommendation: 1. Introduction to Part I. Robo-advising as a technological platform for optimization and recommendations 2. New frontier. Artikel-Nr. 813000650
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Machine Learning and Data Sciences for Financial Markets: A Guide to Contemporary Practices
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Machine Learning and Data Sciences for Financial Markets : A Guide to Contemporary Practices
Verlag:
Cambridge University Press Jun 2023, 2023
ISBN 10: 1316516199
ISBN 13: 9781316516195
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Hardcover
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Buch. Zustand: Neu. Neuware - 'Leveraging the research efforts of more than 60 experts in the area, this book reviews cutting-edge practices in machine learning for financial markets. Instead of seeing machine learning as a new field, the authors explore the connection between knowledge developed in quantitative finance over the past 40 years and modern techniques generated by the current revolution in data sciences and artificial intelligence. The text is structured around three main areas: 'Interacting with investors and asset owners,' which covers robo-advisors and price formation; 'Towards better risk intermediation,' which discusses derivative hedging, portfolio construction, and machine learning for dynamic optimization; and 'Connections with the real economy,' which explores nowcasting, alternative data, and ethics of algorithms. Accessible to a wide audience, this invaluable resource will allow practitioners to include machine learning driven techniques in their day-to-day quantitative practices, while students will build intuition and come to appreciate the technical tools and motivation behind the theory'--. Artikel-Nr. 9781316516195
Anzahl: 2 verfügbar
Beispielbild für diese ISBN
Machine Learning and Data Sciences for Financial Markets: A Guide to Contemporary Practices
Anbieter: Revaluation Books, Exeter, Vereinigtes Königreich
Verkäuferbewertung 5 von 5 Sternen
Hardcover. Zustand: Brand New. 741 pages. 10.24x7.24x1.54 inches. In Stock. Artikel-Nr. x-1316516199
Anzahl: 2 verfügbar