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Data Science for Economics and Finance: Methodologies and Applications by Sergio
US $61.92
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Item specifics
- Condition
- ISBN-13
- 9783030668938
- Book Title
- Data Science for Economics and Finance
- ISBN
- 9783030668938
- Subject Area
- Mathematics, Computers
- Publication Name
- Data Science for Economics and Finance : Methodologies and Applications
- Publisher
- Springer International Publishing A&G
- Item Length
- 9.3 in
- Subject
- Probability & Statistics / General, Intelligence (Ai) & Semantics, Databases / Data Mining
- Publication Year
- 2021
- Type
- Textbook
- Format
- Trade Paperback
- Language
- English
- Item Weight
- 20.1 Oz
- Item Width
- 6.1 in
- Number of Pages
- Xiv, 355 Pages
About this product
Product Identifiers
Publisher
Springer International Publishing A&G
ISBN-10
3030668932
ISBN-13
9783030668938
eBay Product ID (ePID)
6050422563
Product Key Features
Number of Pages
Xiv, 355 Pages
Publication Name
Data Science for Economics and Finance : Methodologies and Applications
Language
English
Subject
Probability & Statistics / General, Intelligence (Ai) & Semantics, Databases / Data Mining
Publication Year
2021
Type
Textbook
Subject Area
Mathematics, Computers
Format
Trade Paperback
Dimensions
Item Weight
20.1 Oz
Item Length
9.3 in
Item Width
6.1 in
Additional Product Features
Number of Volumes
1 vol.
Illustrated
Yes
Table Of Content
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.
Synopsis
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.
LC Classification Number
QA76.9.D343
Item description from the seller
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