Data Science for Economics and Finance

1. Front Matter

   Pages i-xiv

   PDF

2. Data Science Technologies in Economics and Finance: A Gentle Walk-In

   • Luca Barbaglia, Sergio Consoli, Sebastiano Manzan, Diego Reforgiato Recupero, Michaela Saisana, Luca Tiozzo Pezzoli

   Pages 1-17Open Access

3. Supervised Learning for the Prediction of Firm Dynamics

   • Falco J. Bargagli-Stoffi, Jan Niederreiter, Massimo Riccaboni

   Pages 19-41Open Access

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

   • Marcus Buckmann, Andreas Joseph, Helena Robertson

   Pages 43-63Open Access

5. Machine Learning for Financial Stability

   • Lucia Alessi, Roberto Savona

   Pages 65-87Open Access

6. Sharpening the Accuracy of Credit Scoring Models with Machine Learning Algorithms

   • Massimo Guidolin, Manuela Pedio

   Pages 89-115Open Access

7. Classifying Counterparty Sector in EMIR Data

   • Francesca D. Lenoci, Elisa Letizia

   Pages 117-143Open Access

8. Massive Data Analytics for Macroeconomic Nowcasting

   • Peng Cheng, Laurent Ferrara, Alice Froidevaux, Thanh-Long Huynh

   Pages 145-167Open Access

9. New Data Sources for Central Banks

   • Corinna Ghirelli, Samuel Hurtado, Javier J. Pérez, Alberto Urtasun

   Pages 169-194Open Access

10. Sentiment Analysis of Financial News: Mechanics and Statistics

    • Argimiro Arratia, Gustavo Avalos, Alejandra Cabaña, Ariel Duarte-López, Martí Renedo-Mirambell

    Pages 195-216Open Access

11. Semi-supervised Text Mining for Monitoring the News About the ESG Performance of Companies

    • Samuel Borms, Kris Boudt, Frederiek Van Holle, Joeri Willems

    Pages 217-239Open Access

12. Extraction and Representation of Financial Entities from Text

    • Tim Repke, Ralf Krestel

    Pages 241-263Open Access

13. Quantifying News Narratives to Predict Movements in Market Risk

    • Thomas Dierckx, Jesse Davis, Wim Schoutens

    Pages 265-285Open Access

14. Do the Hype of the Benefits from Using New Data Science Tools Extend to Forecasting Extremely Volatile Assets?

    • Steven F. Lehrer, Tian Xie, Guanxi Yi

    Pages 287-330Open Access

15. Network Analysis for Economics and Finance: An Application to Firm Ownership

    • Janina Engel, Michela Nardo, Michela Rancan

    Pages 331-355Open Access

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.

• Open Access
• Data Mining
• Big Data
• Data Analytics
• Decision Support Systems
• Machine Learning
• Semantics and Reasoning

• European Commission, Joint Research Center, Ispra (VA), Italy

  Sergio Consoli, Michaela Saisana

• Department of Mathematics and Computer Science, University of Cagliari, Cagliari, Italy

  Diego Reforgiato Recupero

​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.