Data Science for Economics and Finance : : Methodologies and Applications.

• Author: Consoli, Sergio.
• TeilnehmendeR: Reforgiato Recupero, Diego.; Saisana, Michaela.
• Place / Publishing House: Cham : : Springer International Publishing AG,, 2021.; ©2021.
• Year of Publication: 2021
• Edition: 1st ed.
• Language: English
• Physical Description: 1 online resource (357 pages)

Table of Contents

• Intro
• Foreword
• Preface
• How This Book Is Organized
• Target Audience
• Acknowledgments
• Contents
• Data Science Technologies in Economics and Finance: A Gentle Walk-In
• 1 Introduction
• 2 Technical Challenges
• 2.1 Stewardship and Protection
• 2.2 Data Quantity and Ground Truth
• 2.3 Data Quality and Provenance
• 2.4 Data Integration and Sharing
• 2.5 Data Management and Infrastructures
• 3 Data Analytics Methods
• 3.1 Deep Machine Learning
• 3.2 Semantic Web Technologies
• 4 Conclusions
• References
• Supervised Learning for the Prediction of Firm Dynamics
• 1 Introduction
• 2 Supervised Machine Learning
• 3 SL Prediction of Firm Dynamics
• 3.1 Entrepreneurship and Innovation
• 3.2 Firm Performance and Growth
• 3.3 Financial Distress and Firm Bankruptcy
• 4 Final Discussion
• References
• Opening the Black Box: Machine Learning Interpretability and Inference Tools with an Application to Economic Forecasting
• 1 Introduction
• 2 Data and Experimental Setup
• 2.1 Data
• 2.2 Models
• 2.3 Experimental Procedure
• 3 Forecasting Performance
• 3.1 Baseline Setting
• 3.2 Robustness Checks
• 4 Model Interpretability
• 4.1 Methodology
• 4.1.1 Permutation Importance
• 4.1.2 Shapley Values and Regressions
• 4.2 Results
• 4.2.1 Feature Importance
• 4.2.2 Shapley Regressions
• 5 Conclusion
• References
• Machine Learning for Financial Stability
• 1 Introduction
• 2 Overview of Machine Learning Approaches
• 3 Tree Ensembles
• 3.1 Decision Trees
• 3.2 Random Forest
• 3.3 Tree Boosting
• 3.4 CRAGGING
• 4 Regularization, Shrinkage, and Sparsity
• 4.1 Regularization
• 4.2 Bayesian Learning
• 5 Critical Discussion on Machine Learning as a Tool for Financial Stability Policy
• 6 Literature Overview
• 6.1 Decision Trees for Financial Stability
• 6.2 Sparse Models for Financial Stability.
• 6.3 Unsupervised Learning for Financial Stability
• 7 Conclusions
• References
• Sharpening the Accuracy of Credit Scoring Models with Machine Learning Algorithms
• 1 Introduction
• 2 Preliminaries and Linear Methods for Classification
• 2.1 Logistic Regression
• 2.2 Linear Discriminant Analysis
• 2.3 Naïve Bayes
• 3 Nonlinear Methods for Classification
• 3.1 Decision Trees
• 3.2 Neural Networks
• 3.3 Support Vector Machines
• 3.4 k-Nearest Neighbor
• 3.5 Genetic Algorithms
• 3.6 Ensemble Methods
• 4 Comparison of Classifiers in Credit Scoring Applications
• 4.1 Comparison of Individual Classifiers
• 4.2 Comparison of Ensemble Classifiers
• 4.3 One-Class Classification Methods
• 5 Conclusion
• References
• Classifying Counterparty Sector in EMIR Data
• 1 Introduction
• 2 Reporting Under EMIR
• 3 Methodology
• 3.1 First Step: The Selection of Data Sources
• 3.2 Second Step: Data Harmonisation
• 3.3 Third Step: The Classification
• 3.3.1 Classifying Commercial and Investment Banks
• 3.3.2 Classifying Investment Funds
• 3.4 Description of the Algorithm
• 4 Results
• 5 Applications
• 5.1 Case Study I: Use of Derivatives by EA Investment Funds
• 5.2 Case Study II: The Role of Commercial and Investment Banks
• 5.3 Case Study III: The Role of G16 Dealers in the EA Sovereign CDS Market
• 5.4 Case Study IV: The Use of Derivatives by EA Insurance Companies
• References
• Massive Data Analytics for Macroeconomic Nowcasting
• 1 Introduction
• 2 Review of the Recent Literature
• 2.1 Various Types of Massive Data
• 2.2 Econometric Methods to Deal with Massive Datasets
• 3 Example of Macroeconomic Applications Using Massive Alternative Data
• 3.1 A Real-Time Proxy for Exports and Imports
• 3.1.1 International Trade
• 3.1.2 Localization Data
• 3.1.3 QuantCube International Trade Index: The Case of China.
• 3.2 A Real-Time Proxy for Consumption
• 3.2.1 Private Consumption
• 3.2.2 Alternative Data Sources
• 3.2.3 QuantCube Chinese Tourism Index
• 3.3 A Real-Time Proxy for Activity Level
• 3.3.1 Satellite Images
• 3.3.2 Pre-processing and Modeling
• 3.3.3 QuantCube Activity Level Index
• 4 High-Frequency GDP Nowcasting
• 4.1 Nowcasting US GDP
• 4.2 Nowcasting Chinese GDP
• 5 Applications in Finance
• 6 Conclusions
• References
• New Data Sources for Central Banks
• 1 Introduction
• 2 New Data Sources for Central Banks
• 3 Successful Case Studies
• 3.1 Newspaper Data: Measuring Uncertainty
• 3.1.1 Economic Policy Uncertainty in Spain
• 3.1.2 Economic Policy Uncertainty in Latin America
• 3.2 The Narrative About the Economy as a Shadow Forecast: An Analysis Using the Bank of Spain Quarterly Reports
• 3.3 Forecasting with New Data Sources
• 3.3.1 A Supervised Method
• 3.3.2 An Unsupervised Method
• 3.3.3 Google Forecast Trends of Private Consumption
• 4 Conclusions
• References
• Sentiment Analysis of Financial News: Mechanics and Statistics
• 1 Introduction
• 1.1 Brief Background on Sentiment Analysis in Finance
• 2 Mechanics of Textual Sentiment Analysis
• 3 Statistics of Sentiment Indicators
• 3.1 Stylized Facts
• 3.2 Statistical Tests and Models
• 3.2.1 Independence
• 3.2.2 Stationarity
• 3.2.3 Causality
• 3.2.4 Variable Selection
• 4 Empirical Analysis
• 5 Software
• References
• Semi-supervised Text Mining for Monitoring the News About the ESG Performance of Companies
• 1 Introduction
• 2 Methodology to Create Text-Based Indicators
• 2.1 From Text to Numerical Data
• 2.1.1 Keywords Generation
• 2.1.2 Database Querying
• 2.1.3 News Filtering
• 2.1.4 Indicators Construction
• 2.2 Validation and Decision Making
• 3 Monitoring the News About Company ESG Performance
• 3.1 Motivation and Applications.
• 3.1.1 Text-Based ESG Scoring as a Risk Management Tool
• 3.1.2 Text-Based ESG Scoring as an Investment Tool
• 3.2 Pipeline Tailored to the Creation of News-Based ESG Indices
• 3.2.1 Word Embeddings and Keywords Definition
• 3.2.2 Company Selection and Corpus Creation
• 3.2.3 Aggregation into Indices
• 3.2.4 Validation
• 3.3 Stock and Sector Screening
• 3.3.1 Aggregate Portfolio Performance Analysis
• 3.3.2 Additional Analysis
• 4 Conclusion
• References
• Extraction and Representation of Financial Entities from Text
• 1 Introduction
• 2 Extracting Knowledge Graphs from Text
• 2.1 Named Entity Recognition (NER)
• 2.2 Named Entity Linking (NEL)
• 2.3 Relationship Extraction (RELEX)
• 3 Refining the Knowledge Graph
• 4 Analyzing the Knowledge Graph
• 5 Exploring the Knowledge Graph
• 6 Semantic Exploration Using Visualizations
• 7 Conclusion
• References
• Quantifying News Narratives to Predict Movements in Market Risk
• 1 Introduction
• 2 Preliminaries
• 2.1 Topic Modeling
• 2.1.1 Latent Dirichlet Analysis
• 2.1.2 Paragraph Vector
• 2.1.3 Gaussian Mixture Models
• 2.2 Gradient Boosted Trees
• 2.3 Market Risk and the CBOE Volatility Index (VIX)
• 3 Methodology
• 3.1 News Data Acquisition and Preparation
• 3.2 Narrative Extraction and Topic Modeling
• 3.2.1 Approach 1: Narrative Extraction Using Latent Dirichlet Analysis
• 3.2.2 Approach 2: Narrative Extraction Using Vector Embedding and Gaussian Mixture Models
• 3.3 Predicting Movements in Market Risk with Machine Learning
• 3.4 Evaluation on Time Series
• 4 Experimental Results and Discussion
• 4.1 Feature Setups and Predictive Performance
• 4.2 The Effect of Different Prediction Horizons
• 5 Conclusion
• References
• Do the Hype of the Benefits from Using New Data Science Tools Extend to Forecasting Extremely Volatile Assets?
• 1 Introduction.
• 2 What Is Bitcoin?
• 3 Bitcoin Data and HAR-Type Strategies to Forecast Volatility
• 4 Machine Learning Strategy to Forecast Volatility
• 5 Social Media Data
• 6 Empirical Exercise
• 7 Robustness Check
• 7.1 Different Window Lengths
• 7.2 Different Sample Periods
• 7.3 Different Tuning Parameters
• 7.4 Incorporating Mainstream Assets as Extra Covariates
• 8 Conclusion
• Appendix: Data Resampling Techniques
• References
• Network Analysis for Economics and Finance: An application to Firm Ownership
• 1 Introduction
• 2 Network Analysis in the Literature
• 3 Network Analysis
• 4 Network Analysis: An Application to Firm Ownership
• 4.1 Data
• 4.2 Network Construction
• 4.3 Network Statistics
• 4.4 Bow-Tie Structure
• 5 Conclusion
• Appendix
• References.