Data Science for Economics and Finance, 1st ed. 2021
Methodologies and Applications

Language: English

52.74 €

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Data Science for Economics and Finance
Publication date:
355 p. · 15.5x23.5 cm · Hardback

42.19 €

In Print (Delivery period: 15 days).

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Data Science for Economics and Finance
Publication date:
355 p. · 15.5x23.5 cm · Paperback

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.


Data Science Technologies in Economics and Finance: A Gentle Walk-In.- Supervised Learning for the Prediction of Firm Dynamics.- Opening the Black Box: Machine Learning Interpretability and Inference Tools with an Application to Economic Forecasting.- Machine Learning for Financial Stability.- Sharpening the Accuracy of Credit Scoring Models with Machine Learning Algorithms.- Classifying Counterparty Sector in EMIR Data.- Massive Data Analytics for Macroeconomic Nowcasting.- New Data Sources for Central Banks.- Sentiment Analysis of Financial News: Mechanics and Statistics.- Semi-supervised Text Mining for Monitoring the News About the ESG Performance of Companies.- Extraction and Representation of Financial Entities from Text.- Quantifying News Narratives to Predict Movements in Market Risk.- Do the Hype of the Benefits from Using New Data Science Tools Extend to Forecasting Extremely Volatile Assets?.- Network Analysis for Economics and Finance: An application to Firm Ownership.

​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 Composit
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