Overview
- 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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Table of contents (14 chapters)
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Front Matter
Keywords
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.
Editors and Affiliations
About the editors
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 CompositeIndicators 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.
Bibliographic Information
Book Title: Data Science for Economics and Finance
Book Subtitle: Methodologies and Applications
Editors: Sergio Consoli, Diego Reforgiato Recupero, Michaela Saisana
DOI: https://doi.org/10.1007/978-3-030-66891-4
Publisher: Springer Cham
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s) 2021
Hardcover ISBN: 978-3-030-66890-7Published: 10 June 2021
Softcover ISBN: 978-3-030-66893-8Published: 10 June 2021
eBook ISBN: 978-3-030-66891-4Published: 09 June 2021
Edition Number: 1
Number of Pages: XIV, 355
Number of Illustrations: 12 b/w illustrations, 44 illustrations in colour
Topics: Data Mining and Knowledge Discovery, Machine Learning, Business Information Systems, Big Data/Analytics, Computer Appl. in Administrative Data Processing, Information Storage and Retrieval