This post is to share with you the recent publication of the book: "Data Science for Economics and Finance: Methodologies and Applications", by Sergio Consoli, Diego Reforgiato Recupero, and Michaela Saisana.
The use of data science and artificial intelligence for economics and finance is providing benefits for scientists, professionals and policy-makers by improving the available data analysis methodologies for economic forecasting and therefore making our societies better prepared for the challenges of tomorrow.
This book is a good example of how combining expertise from the European Commission, universities in the U.S. and Europe, financial and economic institutions, and multilateral organizations, can bring forward a shared vision on the benefits of data science applied to economics and finance; from the research point of view to the evaluation of policies on the other hand. It showcases how data science is reshaping the business sector. It includes examples of novel big data sources and some successful applications on the use of advanced machine learning, natural language processing, networks analysis, and time series analysis and forecasting, among others, in the economic and financial sectors. At the same time, the book is making an appeal for further adoption of these novel applications in the field of economics and finance so that they can reach their full potential and support policy-makers and the related stakeholders in the transformational recovery of our societies.
The book is entirely published as Gold OA to reach a large audience. Here are the links:
https://www.springer.com/gp/book/9783030668907
https://link.springer.com/book/10.1007%2F978-3-030-66891-4
This book follows up another previously published Springer volume titled: “Data Science for Healthcare: Methodologies and Applications”, which was co-edited by Sergio Consoli, Diego Reforgiato Recupero, and Milan Petkovic, that tackles the healthcare domain under different data analysis angles.
Considering the number of recent initiatives that are now pushing towards the use of data analysis within the economic field, we are pursuing with the present book at highlighting successful applications of data science and artificial intelligence into the economic and financial sectors.
We believe the topics dealt by the book to be extremely relevant nowadays within the scientific community, and that the book would be an interesting read for the related audience to let them be acquainted with the latest advancements on these subjects.
Contents
Data Science Technologies in Economics and Finance: A Gentle
Walk-In............................................................................ 1
Luca Barbaglia, Sergio Consoli, Sebastiano Manzan, Diego Reforgiato
Recupero, Michaela Saisana, and Luca Tiozzo Pezzoli
Supervised Learning for the Prediction of Firm Dynamics ................. 19
Falco J. Bargagli-Stoffi, Jan Niederreiter, and Massimo Riccaboni
Opening the Black Box: Machine Learning Interpretability and
Inference Tools with an Application to Economic Forecasting.............. 43
Marcus Buckmann, Andreas Joseph, and Helena Robertson
Machine Learning for Financial Stability ..................................... 65
Lucia Alessi and Roberto Savona
Sharpening the Accuracy of Credit Scoring Models with Machine
Learning Algorithms............................................................. 89
Massimo Guidolin and Manuela Pedio
Classifying Counterparty Sector in EMIR Data.............................. 117
Francesca D. Lenoci and Elisa Letizia
Massive Data Analytics for Macroeconomic Nowcasting .................... 145
Peng Cheng, Laurent Ferrara, Alice Froidevaux, and Thanh-Long Huynh
New Data Sources for Central Banks .......................................... 169
Corinna Ghirelli, Samuel Hurtado, Javier J. Pérez, and Alberto Urtasun
Sentiment Analysis of Financial News: Mechanics and Statistics .......... 195
Argimiro Arratia, Gustavo Avalos, Alejandra Cabaña, Ariel Duarte-López,
and Martí Renedo-Mirambell
Semi-supervised Text Mining for Monitoring the News About the
ESG Performance of Companies ............................................... 217
Samuel Borms, Kris Boudt, Frederiek Van Holle, and Joeri Willems
xiii
xiv Contents
Extraction and Representation of Financial Entities from Text ............ 241
Tim Repke and Ralf Krestel
Quantifying News Narratives to Predict Movements in Market Risk...... 265
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?............................ 287
Steven F. Lehrer, Tian Xie, and Guanxi Yi
Network Analysis for Economics and Finance: An application to
Firm Ownership .................................................................. 331
Janina Engel, Michela Nardo, and Michela Rancan
Abstract
This chapter is an introduction to the use of data science technologies in the fields of economics and finance. The recent explosion in computation and information technology in the past decade has made available vast amounts of data in various domains, which has been referred to as Big Data. In economics and finance, in particular, tapping into these data brings research and business closer together, as data generated in ordinary economic activity can be used towards effective and personalized models. In this context, the recent use of data science technologies for economics and finance provides mutual benefits to both scientists and professionals, improving forecasting and nowcasting for several kinds of applications. This chapter introduces the subject through underlying technical challenges such as data handling and protection, modeling, integration, and interpretation. It also outlines some of the common issues in economic modeling with data science technologies and surveys the relevant big data management and analytics solutions, motivating the use of data science methods in economics and finance.
1 Introduction
The rapid advances in information and communications technology experienced in the last two decades have produced an explosive growth in the amount of information collected, leading to the new era of big data [31]. According to [26], approximately three billion bytes of data are produced every day from sensors, mobile devices, online transactions, and social networks, with 90% of the data in
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