Data Science for Economics and Finance : Methodologies and Applications / / edited by Sergio Consoli, Diego Reforgiato Recupero, Michaela Saisana.
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 succ...
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| Place / Publishing House: | Cham : : Springer International Publishing :, Imprint: Springer,, 2021. |
| Year of Publication: | 2021 |
| Edition: | 1st ed. 2021. |
| Language: | English |
| Physical Description: | 1 online resource (XIV, 355 p. 56 illus., 44 illus. in color.) |
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Table of Contents:
- 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.