Data Science for Financial Econometrics
Language: English
Published by Springer, 2021
Series: Book 426 of 538 - Studies in Computational Intelligence
- Softcover
- Used
Condition: Used
US$ 186.70
Quantity: 1 available
Add to basketItem description from seller
Zustand: Hervorragend | Seiten: 644 | Sprache: Englisch | Produktart: Bücher | 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.…
Seller Inventory # 38226623/1
- Title
- Data Science for Financial Econometrics
- Author
- Unbekannt
- Publisher
- Springer
- Publication year
- 2021
- Condition
- Hervorragend
- Binding
- Soft cover
- Language
- English
- ISBN 10
- 3030488551
- ISBN 13
- 9783030488550
- Series
- Book 426 of 538: Studies in Computational Intelligence
- Seller catalogs
- Bücher
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.
"Synopsis" may belong to another edition of this title.
From the Back Cover
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.
"About the title" may belong to another edition of this title.
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