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Data Science for Economics and Finance

Methodologies and Applications

Editors: Consoli, Sergio, Reforgiato Recupero, Diego, Saisana, Michaela (Eds.)

  • Covers the use of data science technologies, including advanced machine learning, Semantic Web technologies, social media analysis, and time series forecasting for applications in economics and finance
  • Shows successful applications of advanced data science solutions to extract knowledge from data in order to improve economic forecasting models
  • Primarily targets data scientists and business analysts exploiting data science technologies, and research students in disciplines and courses related to economics and finance
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eBook  
  • ISBN 978-3-030-66891-4
  • This book is an open access book, you can download it for free on link.springer.com
Hardcover $59.99
price for USA in USD
  • Customers within the U.S. and Canada please contact Customer Service at +1-800-777-4643, Latin America please contact us at +1-212-460-1500 (24 hours a day, 7 days a week). Pre-ordered printed titles are excluded from promotions.
  • Due: July 11, 2021
  • ISBN 978-3-030-66890-7
  • Free shipping for individuals worldwide
  • Institutional customers should get in touch with their account manager
  • Covid-19 shipping restrictions
Softcover $49.99
price for USA in USD
  • Customers within the U.S. and Canada please contact Customer Service at +1-800-777-4643, Latin America please contact us at +1-212-460-1500 (24 hours a day, 7 days a week). Pre-ordered printed titles are excluded from promotions.
  • Due: July 11, 2021
  • ISBN 978-3-030-66893-8
  • Free shipping for individuals worldwide
  • Institutional customers should get in touch with their account manager
  • Covid-19 shipping restrictions
About this book

This open access book covers the use of data science, including advanced machine learning, big data analytics, Semantic Web technologies, natural language processing, social media analysis, time series analysis, among others, for applications in economics and finance. In addition, it shows some successful applications of advanced data science solutions used to extract new knowledge from data in order to improve economic forecasting models.

The book starts with an introduction on the use of data science technologies in economics and finance and is followed by thirteen chapters showing success stories of the application of specific data science methodologies, touching on particular topics related to novel big data sources and technologies for economic analysis (e.g. social media and news); big data models leveraging on supervised/unsupervised (deep) machine learning; natural language processing to build economic and financial indicators; and forecasting and nowcasting of economic variables through time series analysis.  

This book is relevant to all stakeholders involved in digital and data-intensive research in economics and finance, helping them to understand the main opportunities and challenges, become familiar with the latest methodological findings, and learn how to use and evaluate the performances of novel tools and frameworks. It primarily targets data scientists and business analysts exploiting data science technologies, and it will also be a useful resource to research students in disciplines and courses related to these topics. Overall, readers will learn modern and effective data science solutions to create tangible innovations for economic and financial applications.


About the authors

Sergio Consoli is a Scientific Project Officer at the European Commission, Joint Research Centre, Italy, working on the project "Big Data and Forecasting of Economic Developments" aiming at exploring novel big data sources and methodologies to provide better economic forecasting. Formerly Sergio was a Senior Scientist within the Data Science department at Philips Research, a Computer Engineering Officer at the Italian Presidency of the Council of Ministers, and a Junior Researcher at the National Research Council of Italy. Sergio's education and scientific experience fall in the areas of data science, operations research, artificial intelligence, knowledge engineering, and machine learning. He is author of several research publications in peer-reviewed international journals, granted patents, edited books, and leading conferences in these fields.  
Diego Reforgiato Recupero is an Associate Professor at the Department of Mathematics and Computer Science of the University of Cagliari, Italy, where he is also a member of the Technical Commission for Patents and Spin-offs. His interests span from Semantic Web, graph theory, and smart grid optimization to sentiment analysis, data mining, big data, natural language processing, and human-robot interaction. He is the author of several research publications in peer-reviewed international journals, edited books, and leading conferences in these fields. He is Director of the Laboratory of Human Robot Interaction and Co-Director of the Laboratory of Artificial Intelligence and Big Data. He is also affiliated with the National Research Council of Italy (CNR) where he is a member of the Semantic Technology Laboratory and passionate  about bringing the research output to the market. 
Michaela Saisana is Head of the Monitoring, Indicators and Impact Evaluation Unit and she also leads the European Commission's Competence Centre on Composite Indicators and Scoreboards (COIN) at the Joint Research Centre in Italy. She has been working in the JRC since 1998, where she obtained a prize as “Best Young Scientist of the Year” in 2004 and together with her team the “JRC Policy Impact Award” for the Social Scoreboard of the European Pillar of Social Rights in 2018. Specializing on process optimization and spatial statistics, she is actively involved in promoting a sound development and responsible use of performance monitoring tools which feed into EU policy formulation and legislation in a wide range of fields.

Table of contents (14 chapters)

Table of contents (14 chapters)
  • Data Science Technologies in Economics and Finance: A Gentle Walk-In

    Pages 1-17

    Barbaglia, Luca (et al.)

  • Supervised Learning for the Prediction of Firm Dynamics

    Pages 19-41

    Bargagli-Stoffi, Falco J. (et al.)

  • Opening the Black Box: Machine Learning Interpretability and Inference Tools with an Application to Economic Forecasting

    Pages 43-63

    Buckmann, Marcus (et al.)

  • Machine Learning for Financial Stability

    Pages 65-87

    Alessi, Lucia (et al.)

  • Sharpening the Accuracy of Credit Scoring Models with Machine Learning Algorithms

    Pages 89-115

    Guidolin, Massimo (et al.)

Buy this book

eBook  
  • ISBN 978-3-030-66891-4
  • This book is an open access book, you can download it for free on link.springer.com
Hardcover $59.99
price for USA in USD
  • Customers within the U.S. and Canada please contact Customer Service at +1-800-777-4643, Latin America please contact us at +1-212-460-1500 (24 hours a day, 7 days a week). Pre-ordered printed titles are excluded from promotions.
  • Due: July 11, 2021
  • ISBN 978-3-030-66890-7
  • Free shipping for individuals worldwide
  • Institutional customers should get in touch with their account manager
  • Covid-19 shipping restrictions
Softcover $49.99
price for USA in USD
  • Customers within the U.S. and Canada please contact Customer Service at +1-800-777-4643, Latin America please contact us at +1-212-460-1500 (24 hours a day, 7 days a week). Pre-ordered printed titles are excluded from promotions.
  • Due: July 11, 2021
  • ISBN 978-3-030-66893-8
  • Free shipping for individuals worldwide
  • Institutional customers should get in touch with their account manager
  • Covid-19 shipping restrictions
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Bibliographic Information

Bibliographic Information
Book Title
Data Science for Economics and Finance
Book Subtitle
Methodologies and Applications
Editors
  • Sergio Consoli
  • Diego Reforgiato Recupero
  • Michaela Saisana
Copyright
2021
Publisher
Springer International Publishing
Copyright Holder
The Editor(s) (if applicable) and The Author(s)
eBook ISBN
978-3-030-66891-4
DOI
10.1007/978-3-030-66891-4
Hardcover ISBN
978-3-030-66890-7
Softcover ISBN
978-3-030-66893-8
Edition Number
1
Number of Pages
XIV, 355
Number of Illustrations
12 b/w illustrations, 44 illustrations in colour
Topics