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Cham : Springer International Publishing AG, 2021

1 online resource (357 pages)

ISBN 9783030668914 (electronic bk.)

ISBN 9783030668907

Print version: Consoli, Sergio Data Science for Economics and Finance Cham : Springer International Publishing AG,c2021 ISBN 9783030668907

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.

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.

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.

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 Naive 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.

001895829

express

(Au-PeEL)EBL6640078

(MiAaPQ)EBC6640078

(OCoLC)1257416604