Machine Learning and Data Sciences for Financial Markets: A Guide to Contemporary Practices

Agostino Capponi

ISBN 10: 1316516199 / ISBN 13: 9781316516195

Editore: Cambridge University Press, 2023

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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.

Codice articolo 205686

Dati bibliografici

• Titolo: Machine Learning and Data Sciences for Financial Markets: A Guide to Contemporary Practices
• Editore: Cambridge University Press
• Data di pubblicazione: 2023
• Lingua: Inglese
• ISBN 10: 1316516199
• ISBN 13: 9781316516195
• Rilegatura: Hardcover
• Edizione: First Edition.
• Condizione: Like New
• Peso: 67 once
• Cataloghi dei venditori:
  • Science, Mathematics & Technology

Descrizione articolo

Riassunto

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.