Overview
- Presents recent findings and ideas on applying data science techniques to economic phenomena – and, in particular, financial phenomena
- Inspires practitioners to learn how to apply various data science techniques to economic problems and encourages researchers to further improve the existing techniques and to come up with new data science-based techniques for economics
- Written by respected experts in the field
Part of the book series: Studies in Computational Intelligence (SCI, volume 898)
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Table of contents (42 chapters)
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Front Matter
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Theoretical Research
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Front Matter
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Practical Applications
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Front Matter
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Keywords
About this book
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.
Editors and Affiliations
Bibliographic Information
Book Title: Data Science for Financial Econometrics
Editors: Nguyen Ngoc Thach, Vladik Kreinovich, Nguyen Duc Trung
Series Title: Studies in Computational Intelligence
DOI: https://doi.org/10.1007/978-3-030-48853-6
Publisher: Springer Cham
eBook Packages: Intelligent Technologies and Robotics, Intelligent Technologies and Robotics (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021
Hardcover ISBN: 978-3-030-48852-9Published: 14 November 2020
Softcover ISBN: 978-3-030-48855-0Published: 14 November 2021
eBook ISBN: 978-3-030-48853-6Published: 13 November 2020
Series ISSN: 1860-949X
Series E-ISSN: 1860-9503
Edition Number: 1
Number of Pages: X, 633
Number of Illustrations: 20 b/w illustrations, 71 illustrations in colour
Topics: Data Engineering, Computational Intelligence, Economic Theory/Quantitative Economics/Mathematical Methods