Sergio Consoli Diego Reforgiato Recupero Michaela Saisana Editors Data Science for Economics and Finance Methodologies and Applications Data Science for Economics and Finance Sergio Consoli • Diego Reforgiato Recupero • Michaela Saisana Editors Data Science for Economics and Finance Methodologies and Applications Editors Sergio Consoli Diego Reforgiato Recupero European Commission Department of Mathematics and Computer Joint Research Centre Science Ispra (VA), Italy University of Cagliari Cagliari, Italy Michaela Saisana European Commission Joint Research Centre Ispra (VA), Italy ISBN 978-3-030-66890-7 ISBN 978-3-030-66891-4 (eBook) https://doi. This book is an open access publication. Open Access This book is licensed under the terms of the Creative Commons Attribution 4.0 Inter- national License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made. The images or other third party material in this book are included in the book’s Creative Commons license, unless indicated otherwise in a credit line to the material.
If material is not included in the book’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication.
Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG. The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland Foreword To help repair the economic and social damage wrought by the coronavirus pandemic, a transformational recovery is needed.
The social and economic situation in the world was already shaken by the fall of 2019, when one fourth of the world’s developed nations were suffering from social unrest, and in more than half the threat of populism was as real as it has ever been. The coronavirus accelerated those trends and I expect the aftermath to be in much worse shape. The urgency to reform our societies is going to be at its highest. Artificial intelligence and data science will be key enablers of such transformation.
They have the potential to revolutionize our way of life and create new opportunities. 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 USA and Europe, financial and economic insti- tutions, 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. 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 a 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. We are not just repairing the damage to our economies and societies, the aim is to build better for the next generation. The problems are inherently interdisciplinary and global, hence they require international cooperation and the investment in collaborative work.
We better learn what each other is doing, and we better learn v vi Foreword the tools and language that each discipline brings to the table, and we better start now. This book is a good place to kick off. Society of Sloan Fellows Professor of Management Roberto Rigobon Professor, Applied Economics Massachusetts Institute of Technology Cambridge, MA, USA Preface Economic and fiscal policies conceived by international organizations, govern- ments, and central banks heavily depend on economic forecasts, in particular during times of economic and societal turmoil like the one we have recently experienced with the coronavirus spreading worldwide. The accuracy of economic forecasting and nowcasting models is however still problematic since modern economies are subject to numerous shocks that make the forecasting and nowcasting tasks extremely hard, both in the short and medium-long runs.
In this context, the use of recent Data Science technologies for improving forecasting and nowcasting for several types of economic and financial applications has high potential. The vast amount of data available in current times, referred to as the Big Data era, opens a huge amount of opportunities to economists and scientists, with a condition that data are opportunately handled, processed, linked, and analyzed. From forecasting economic indexes with little observations and only a few variables, we now have millions of observations and hundreds of variables. Questions that previously could only be answered with a delay of several months or even years can now be addressed nearly in real time.
Big data, related analysis performed through (Deep) Machine Learning technologies, and the availability of more and more performing hardware (Cloud Computing infrastructures, GPUs, etc.) can integrate and augment the information carried out by publicly available aggregated variables produced by national and international statistical agencies. By lowering the level of granularity, Data Science technologies can uncover economic relationships that are often not evident when variables are in an aggregated form over many products, individuals, or time periods. Strictly linked to that, the evolution of ICT has contributed to the development of several decision-making instruments that help investors in taking decisions. This evolution also brought about the development of FinTech, a newly coined abbreviation for Financial Technology, whose aim is to leverage cutting-edge technologies to compete with traditional financial methods for the delivery of financial services.
This book is inspired by the desire for stimulating the adoption of Data Science solutions for Economics and Finance, giving a comprehensive picture on the use of Data Science as a new scientific and technological paradigm for boosting these vii viii Preface sectors. As a result, the book explores a wide spectrum of essential aspects of Data Science, spanning from its main concepts, evolution, technical challenges, and infrastructures to its role and vast opportunities it offers in the economic and financial areas. In addition, the book shows some successful applications on advanced Data Science solutions used to extract new knowledge from data in order to improve economic forecasting and nowcasting models. The theme of the book is at the frontier of economic research in academia, statistical agencies, and central banks.
Also, in the last couple of years, several master’s programs in Data Science and Economics have appeared in top European and international institutions and universities. Therefore, 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. The book follows up a recently published Springer volume titled: “Data Science for Healthcare: Methodologies and Applications,” which was co-edited by Dr. Sergio Consoli, Prof.
Diego Reforgiato Recupero, and Prof. Milan Petkovic, that tackles the healthcare domain under different data analysis angles. How This Book Is Organized The book covers the use of Data Science, including Advanced Machine Learning, Big Data Analytics, Semantic Web technologies, Natural Language Processing, Social Media Analysis, and Time Series Analysis, among others, for applications in Economics and Finance. Particular care on model interpretability is also highlighted.
This book is ideal for some educational sessions to be used in international organizations, research institutions, and enterprises. The book starts with an intro- duction on the use of Data Science technologies in Economics and Finance and is followed by 13 chapters showing successful stories on the application of the specific Data Science technologies into these sectors, touching in particular topics related to: novel big data sources and technologies for economic analysis (e., Social Media and News); Big Data models leveraging on supervised/unsupervised (Deep) Machine Learning; Natural Language Processing to build economic and financial indicators (e., Sentiment Analysis, Information Retrieval, Knowledge Engineering); Forecasting and Nowcasting of economic variables (e., Time Series Analysis and Robo-Trading). Target Audience The book is relevant to all the stakeholders involved in digital and data-intensive research in Economics and Finance, helping them to understand the main oppor- tunities and challenges, become familiar with the latest methodological findings in Preface ix (Deep) Machine Learning, and learn how to use and evaluate the performances of novel Data Science and Artificial Intelligence tools and frameworks. This book is primarily intended for data scientists, business analytics managers, policy-makers, analysts, educators, and practitioners involved in Data Science technologies for Economics and Finance.
It can also be a useful resource to research students in disciplines and courses related to these topics. Interested readers will be able to learn modern and effective Data Science solutions to create tangible innovations for Economics and Finance. Prior knowledge on the basic concepts behind Data Science, Economics, and Finance is recommended to potential readers in order to have a smooth understanding of this book. Ispra (VA), Italy Sergio Consoli Cagliari, Italy Diego Reforgiato Recupero Ispra (VA), Italy Michaela Saisana Acknowledgments We are grateful to Ralf Gerstner and his entire team from Springer for having strongly supported us throughout the publication process.
Furthermore, special thanks to the Scientific Committee members for their efforts to carefully revise their assigned chapter (each chapter has been reviewed by three or four of them), thus leading us to largely improve the quality of the book. They are, in alphabetical order: Arianna Agosto, Daniela Alderuccio, Luca Alfieri, David Ardia, Argimiro Arratia, Andres Azqueta-Gavaldon, Luca Barbaglia, Keven Bluteau, Ludovico Boratto, Ilaria Bordino, Kris Boudt, Michael Bräuning, Francesca Cabiddu, Cem Cakmakli, Ludovic Calès, Francesca Cam- polongo, Annalina Caputo, Alberto Caruso, Michele Catalano, Thomas Cook, Jacopo De Stefani, Wouter Duivesteijn, Svitlana Galeshchuk, Massimo Guidolin, Sumru Guler-Altug, Francesco Gullo, Stephen Hansen, Dragi Kocev, Nicolas Kourtellis, Athanasios Lapatinas, Matteo Manca, Sebastiano Manzan, Elona Marku, Rossana Merola, Claudio Morana, Vincenzo Moscato, Kei Nakagawa, Andrea Pagano, Manuela Pedio, Filippo Pericoli, Luca Tiozzo Pezzoli, Antonio Picariello, Giovanni Ponti, Riccardo Puglisi, Mubashir Qasim, Ju Qiu, Luca Rossini, Armando Rungi, Antonio Jesus Sanchez-Fuentes, Olivier Scaillet, Wim Schoutens, Gustavo Schwenkler, Tatevik Sekhposyan, Simon Smith, Paul Soto, Giancarlo Sperlì, Ali Caner Türkmen, Eryk Walczak, Reinhard Weisser, Nicolas Woloszko, Yucheong Yeung, and Wang Yiru. A particular mention to Antonio Picariello, estimated colleague and friend, who suddenly passed away at the time of this writing and cannot see this book published. Ispra (VA), Italy Sergio Consoli Cagliari, Italy Diego Reforgiato Recupero Ispra (VA), Italy Michaela Saisana xi 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. 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.