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Machine Learning and Data Sciences for Financial Markets

Machine Learning and Data Sciences for Financial Markets

Machine Learning and Data Sciences for Financial Markets
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Machine Learning and Data Sciences for Financial Markets Hardback - 2023

by Agostino Capponi, Charles-Albert Lehalle

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Description

Cambridge University Press, 2023. Hardback. Acceptable. Acceptable condition. There is a printing error where pages 459-474 are lower than they should be, and the bottom is cut off. They are connected at the top also and could be separated by cutting along the edge. No content is missing as only the margin is removed from the bottom of each of those pages. There are some scuffs to the edges but otherwise in good condition.
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Details

  • Title Machine Learning and Data Sciences for Financial Markets
  • Author Agostino Capponi, Charles-Albert Lehalle
  • Binding Hardback
  • Condition Used - Acceptable
  • Pages 741
  • Volumes 1
  • Language ENG
  • Publisher Cambridge University Press
  • Publication date 2023
  • Features Index
  • Bookseller's Inventory # 7822
  • 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)
  • Category Mathematics
  • Library of Congress subjects Finance - Data processing, Financial institutions - Data processing
  • Library of Congress Catalogue Number 2023016903
  • Dewey Decimal Code 332.106

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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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