Data Science for Financial Econometrics (Studies in Computational Intelligence, 898)

ISBN 10: 3030488527 / ISBN 13: 9783030488529

Verlag: Springer, 2020

Beschreibung

New.

Bestandsnummer des Verkäufers D8F0-0-M-3030488527-6

Bibliografische Details

• Titel: Data Science for Financial Econometrics (Studies in Computational Intelligence, 898)
• Verlag: Springer
• Veröffentlichungsdatum: 2020
• Sprache: Englisch
• ISBN-10: 3030488527
• ISBN-13: 9783030488529
• Bindung: Hardcover
• Zustand: New
• Buchtyp: book

Über diesen Titel

Inhaltsangabe

This book offers an overview of state-of-the-art econometric techniques, with a special emphasis on financial econometrics. There is a major need for such techniques, since the traditional way of designing mathematical models - based on researchers' insights - can no longer keep pace with the ever-increasing data flow. To catch up, many application areas have begun relying on data science, i.e., on techniques for extracting models from data, such as data mining, machine learning, and innovative statistics. In terms of capitalizing on data science, many application areas are way ahead of economics. To close this gap, the book provides examples of how data science techniques can be used in economics. Corresponding techniques range from almost traditional statistics to promising novel ideas such as quantum econometrics. Given its scope, the book will appeal to students and researchers interested in state-of-the-art developments, and to practitioners interested in using data science techniques.  

Von der hinteren Coverseite

This book offers an overview of state-of-the-art econometric techniques, with a special emphasis on financial econometrics. There is a major need for such techniques, since the traditional way of designing mathematical models based on researchers insights can no longer keep pace with the ever-increasing data flow. To catch up, many application areas have begun relying on data science, i.e., on techniques for extracting models from data, such as data mining, machine learning, and innovative statistics. In terms of capitalizing on data science, many application areas are way ahead of economics. To close this gap, the book provides examples of how data science techniques can be used in economics. Corresponding techniques range from almost traditional statistics to promising novel ideas such as quantum econometrics. Given its scope, the book will appeal to students and researchers interested in state-of-the-art developments, and to practitioners interested in using data science techniques.