Data Science for Economics and Finance: Methodologies and Applications
Sergio / Reforgiato Recupero Consoli
Résumé
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.
Foreword.- Preface..- Data Science technologies in Economics and Finance: a gentle walk-in, by Luca Barbaglia, Sergio Consoli, Sebastiano Manzan, Diego Reforgiato Recupero, Michaela Saisana, and Luca Tiozzo Pezzoli.- Supervised Learning for the Prediction of Firm Dynamics , by Falco Bargagli-Stoffi, Jan Niederreiter, and Massimo Riccaboni.- Opening the black box: Machine learning interpretability and inference tools with an application to economic forecasting , by Andreas Joseph, Marcus Buckmann, and Helena Robertson.- Machine learning for financial stability , by Lucia Alessi and Roberto Savona.- Sharpening the Accuracy of Credit Scoring Models with Machine Learning Algorithms , by Massimo Guidolin and Manuela Pedio.- Classifying counterparty sector in EMIR data , by Francesca Daniela Lenoci and Elisa Letizia.- Massive data analytics for macroeconomic nowcasting , by Peng Cheng, Laurent Ferrara, Alice Froidevaux, and Thanh-Long Huynh.- New data sources for Central Banks , by Corinna Ghirelli, Samuel Hurtado, Javier J Perez, and Alberto Urtasun.- Sentiment Analysis of Financial News: Mechanics and Statistics , by Argimiro Arratia, Gustavo Avalos, Alejandra Cabana, Ariel Duarte Lopez, and Marti Renedo-Mirambell.- Semi-supervised text mining for monitoring the news about the ESG performance of companies , by Samuel Borms, Kris Boudt, Frederiek Van Holle, and Joeri Willems.- Extraction and Representation of Financial Entities from Text , by Tim Repke and Ralf Krestel.- Quantifying News Narratives to Predict Movements In Market Risk , by Thomas Dierckx, Jesse Davis, and Wim Schoutens .- Do the Hype of the Benefits from Using New Data Science Tools Extend to Forecasting Extremely Volatile Assets? , by Steven Lehrer, Tian Xie, and Guanxi Yi.- Network Analysis for Economics and Finance. An application to firm ownership , by Janina Engel, Michela Nardo, and Michela Rancan.
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.
Caractéristiques techniques
| PAPIER | |
| Éditeur(s) | Springer |
| Auteur(s) | Sergio / Reforgiato Recupero Consoli |
| Parution | 19/04/2021 |
| Nb. de pages | 265 |
| EAN13 | 9783030668938 |
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