ISBN: 9781316516195 - machine learning and data sciences for financial markets: a guide to contemporary practices (27 risultati)

Hardcover. Condizione: Very Good. No Jacket. May have limited writing in cover pages. Pages are unmarked. ~ ThriftBooks: Read More, Spend Less.

hardcover. Condizione: Good. Cover and edges may have some wear. Mild to Moderate creasing / bending to covers and pages. Good reading copy.

Hardcover. Condizione: Like New. First Edition. A firm, square hardback, bright and clean with strong joints and sharp corners, just showing a few very mild bumps and rubs. Hence a non-text page is stamped 'damaged'. Despite such this book is crisp, fresh and tight with no defects. Thus a nearly new copy that looks and feels unread now offered for sale at a very reasonable price.…

Condizione: As New. Unread book in perfect condition.

Condizione: New.

hardcover. Condizione: new.

Hardcover. Condizione: new. New Copy. Customer Service Guaranteed.

Condizione: New.

Hardcover. Condizione: New. Brand new book, sourced directly from publisher. Dispatch time is 24-48 hours from our warehouse. Book will be sent in robust, secure packaging to ensure it reaches you securely.

Condizione: New. 2023. Hardcover. . . . . .

Condizione: New.

Condizione: New.

Condizione: New. In English.

Condizione: As New. Unread book in perfect condition.

Condizione: New. 2023. Hardcover. . . . . . Books ship from the US and Ireland.

Hardback. Condizione: New. 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.…

Condizione: 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.

Condizione: Gut. Zustand: Gut | Seiten: 741 | Sprache: Englisch | Produktart: Bücher | Keine Beschreibung verfügbar.

Buch. Condizione: Neu. Neuware - 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.…

Buch. Condizione: Neu. Machine Learning and Data Sciences for Financial Markets | A Guide to Contemporary Practices | Agostino Capponi (u. a.) | Buch | Gebunden | Englisch | 2023 | Cambridge University Press | EAN 9781316516195 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu.…

Hardcover. Condizione: Brand New. 741 pages. 10.24x7.24x1.54 inches. In Stock.

Hardback. Condizione: New. 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.…

Hardcover. Condizione: gut. 2023. Machine Learning and Data Sciences for Financial Markets In englischer Sprache. pages.

Condizione: New. Print on Demand.

Condizione: New. PRINT ON DEMAND.

Hardcover. Condizione: Brand New. 741 pages. 10.24x7.24x1.54 inches. In Stock. This item is printed on demand.

Hardcover. Condizione: new. Hardcover. 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. Written by more than sixty experts in the area, this book reviews cutting-edge practices in machine learning for financial markets, and explores connections with data science and more traditional approaches. This is an invaluable resource for researchers and graduate students in financial engineering, as well as practitioners in the sector. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…