Abstract

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.

Highlights

  • 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]

  • In this chapter we have introduced the topic of data science applied to economic and financial modeling

  • These technologies can handle, analyze, and exploit the set of very diverse, interlinked, and complex data that already exist in the economic universe to improve models and forecasting quality, in terms of guarantee on the trustworthiness of information, a focus on generating actionable advice, and improving the interactivity of data processing and analytics

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Summary

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]. Data science technologies have been successfully applied in many different domains (e.g., healthcare [15], predictive maintenance [16], and supply chain management [39], among others), their potentials have been little explored in economics and finance In this context, devising efficient forecasting and nowcasting models is essential for designing suitable monetary and fiscal policies, and their accuracy is relevant during times of economic turmoil. Key economic indicators, on which they rely upon during their decision-making process, are produced at low frequency and released with considerable lags—for instance, around 45 days for the Gross Domestic Product (GDP) in Europe—and are often subject to revisions that could be substantial With such an incomplete set of information, economists can only approximately gauge the actual, the future, and even the very recent past economic conditions, making the nowcasting and forecasting of the economy extremely challenging tasks. Policy-makers are confronted with a twofold problem: timeliness in the evaluation of the economy as well as prompt impact assessment of external shocks

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