BIBLIO is the largest independent book marketplace in the world, with over 100 million books.

Skip to content

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
Stock photo: cover may vary

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

by Agostino Capponi

Add to wish list
  • New
New

Description

new.
Ask the seller a question Add to wish list
A$319.56
A$5.87 Delivery within USA
Standard delivery: 2 to 14 days
More delivery options
Ships from GreatBookPrices (Maryland, United States)
The BIBLIO Guarantee

Details

  • Title Machine Learning and Data Sciences for Financial Markets: A Guide to Contemporary Practices
  • Author Agostino Capponi
  • Binding Hardback
  • Condition New
  • Pages 741
  • Volumes 1
  • Language ENG
  • Publisher Cambridge University Press
  • Publication date 2023-06-01
  • Features Index
  • Bookseller's Inventory # 45647455-n
  • 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
  • Quantity available 5

About GreatBookPrices Maryland, United States

Biblio member since 2024

Since 1991, we have worked every day to serve our customers with state-of-the-art technology and world class service. We are dedicated to providing customers around the world with the widest selection of books, DVDs, and CDs at the absolute lowest price.

Terms of Sale: 30 day return guarantee, with full refund including original shipping costs for up to 30 days after delivery if an item arrives misdescribed or damaged.

Browse books from GreatBookPrices

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

Write a review for this book

There are no reviews for this book yet. Be the first to write a review!

Your review will be anonymous unless you login or create an account first.

Overall rating
Remember: You’re rating the book as a work, not the seller or the condition of the specific copy you purchased!

Important Terms and Guidelines

  • Please focus on the book’s content and context. Also, add any personal comments as to how you enjoyed the book. Substantiate your likes and dislikes. You may make comparisons to other books.
  • Reviews must be at least 140 characters in length.
  • Please do not reveal critical plot elements.
  • This is not a help line. Contact customer support if you need help.
Your review must not include:
  • Obscenities, discriminatory language, or other insulting language not suitable for public domain
  • Advertisements, “spam” content, or references to other products, offers or websites.
  • Email addresses, URLs, phone numbers, physical addresses or other contact information.
  • Overly critical comments about other reviews or reviewers
  • Time-sensitive material (i.e. promotional tours, seminars, lectures, etc.)
  • Availability, price, or alternative ordering/shipping information

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.

Please wait a moment while we load the requested page.

tracking-