Sergio Consoli (editor) - Data Science for Economics and Finance: Methodologies and Applications

This book is an open access publication.

Open Access This book is licensed under the terms of the Creative Commons Attribution 4.0 International 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.

This Springer imprint is published by the registered company Springer Nature Switzerland AG

The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

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 worlds 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 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. 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 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.

Economic and fiscal policies conceived by international organizations, governments, 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.