Data Science for Economics and Finance : : Methodologies and Applications.
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Place / Publishing House: | Cham : : Springer International Publishing AG,, 2021. ©2021. |
Year of Publication: | 2021 |
Edition: | 1st ed. |
Language: | English |
Online Access: | |
Physical Description: | 1 online resource (357 pages) |
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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.