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Data science for economics and finan...

Consoli, Sergio.

Data science for economics and financemethodologies and applications /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Data science for economics and financeedited by Sergio Consoli, Diego Reforgiato Recupero, Michaela Saisana.
Reminder of title:	methodologies and applications /
other author:	Consoli, Sergio.
Published:	Cham :Springer International Publishing :2021.
Description:	xiv, 355 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	EconomicsData processing.
Online resource:	https://doi.org/10.1007/978-3-030-66891-4
ISBN:	9783030668914$q(electronic bk.)

Data science for economics and financemethodologies and applications /
Data science for economics and financemethodologies and applications /[electronic resource] :edited by Sergio Consoli, Diego Reforgiato Recupero, Michaela Saisana. - Cham :Springer International Publishing :2021. - xiv, 355 p. :ill., digital ;24 cm.

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.

Open access.

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.

ISBN: 9783030668914$q(electronic bk.)

Standard No.: 10.1007/978-3-030-66891-4doiSubjects--Topical Terms:

208545
Economics
--Data processing.

LC Class. No.: HB143.5 / .D38 2021

Dewey Class. No.: 330.0285

Data science for economics and financemethodologies and applications /
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245 0 0 $a Data science for economics and finance $h [electronic resource] : $b methodologies and applications / $c edited by Sergio Consoli, Diego Reforgiato Recupero, Michaela Saisana.
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300 $a xiv, 355 p. : $b ill., digital ; $c 24 cm.
505 0 $a 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.
506 $a Open access.
520 $a 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.
650 0 $a Economics $x Data processing. $3 208545
650 0 $a Economic forecasting $x Data processing. $3 897568
650 1 4 $a Data Mining and Knowledge Discovery. $3 275288
650 2 4 $a Machine Learning. $3 833608
650 2 4 $a Business Information Systems. $3 274346
650 2 4 $a Big Data/Analytics. $3 742047
650 2 4 $a Computer Appl. in Administrative Data Processing. $3 275283
650 2 4 $a Information Storage and Retrieval. $3 274190
700 1 $a Consoli, Sergio. $3 835016
700 1 $a Reforgiato Recupero, Diego. $3 824572
700 1 $a Saisana, Michaela. $3 897567
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