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

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

Machine Learning and Data Sciences for Financial Markets: A Guide to
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Machine Learning and Data Sciences for Financial Markets: A Guide to Contemporary Practices Hardback - 2023

by Agostino Capponi

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Cambridge University Press, 2023. First Edition. Hardcover. Like New. 7x1x10. 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.
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Details

  • Title Machine Learning and Data Sciences for Financial Markets: A Guide to Contemporary Practices
  • Author Agostino Capponi
  • Binding Hardback
  • Edition First Edition
  • Condition New
  • Pages 741
  • Volumes 1
  • Language ENG
  • Publisher Cambridge University Press
  • Publication date 2023
  • Features Index
  • Bookseller's Inventory # 205686
  • ISBN 9781316516195 / 1316516199
  • Weight 3.5 lbs (1.59 kg)
  • Dimensions 10.08 x 7.17 x 1.57 in (25.60 x 18.21 x 3.99 cm)
  • Size 7x1x10
  • Category Mathematics
  • Library of Congress subjects Finance - Data processing, Financial institutions - Data processing
  • Library of Congress Catalogue Number 2023016903
  • Dewey Decimal Code 332.106
  • Quantity available 1

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From the publisher

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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