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 |
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Physical Description: | 1 online resource (357 pages) |
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Consoli, Sergio. Data Science for Economics and Finance : Methodologies and Applications. 1st ed. Cham : Springer International Publishing AG, 2021. ©2021. 1 online resource (357 pages) text txt rdacontent computer c rdamedia online resource cr rdacarrier 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. Description based on publisher supplied metadata and other sources. Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2024. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries. Electronic books. Reforgiato Recupero, Diego. Saisana, Michaela. Print version: Consoli, Sergio Data Science for Economics and Finance Cham : Springer International Publishing AG,c2021 9783030668907 ProQuest (Firm) https://ebookcentral.proquest.com/lib/oeawat/detail.action?docID=6640078 Click to View |
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English |
format |
eBook |
author |
Consoli, Sergio. |
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Consoli, Sergio. Data Science for Economics and Finance : Methodologies and Applications. 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. |
author_facet |
Consoli, Sergio. Reforgiato Recupero, Diego. Saisana, Michaela. |
author_variant |
s c sc |
author2 |
Reforgiato Recupero, Diego. Saisana, Michaela. |
author2_variant |
r d r rd rdr m s ms |
author2_role |
TeilnehmendeR TeilnehmendeR |
author_sort |
Consoli, Sergio. |
title |
Data Science for Economics and Finance : Methodologies and Applications. |
title_sub |
Methodologies and Applications. |
title_full |
Data Science for Economics and Finance : Methodologies and Applications. |
title_fullStr |
Data Science for Economics and Finance : Methodologies and Applications. |
title_full_unstemmed |
Data Science for Economics and Finance : Methodologies and Applications. |
title_auth |
Data Science for Economics and Finance : Methodologies and Applications. |
title_new |
Data Science for Economics and Finance : |
title_sort |
data science for economics and finance : methodologies and applications. |
publisher |
Springer International Publishing AG, |
publishDate |
2021 |
physical |
1 online resource (357 pages) |
edition |
1st ed. |
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. |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>09909nam a22004453i 4500</leader><controlfield tag="001">5006640078</controlfield><controlfield tag="003">MiAaPQ</controlfield><controlfield tag="005">20240229073842.0</controlfield><controlfield tag="006">m o d | </controlfield><controlfield tag="007">cr cnu||||||||</controlfield><controlfield tag="008">240229s2021 xx o ||||0 eng d</controlfield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9783030668914</subfield><subfield code="q">(electronic bk.)</subfield></datafield><datafield tag="020" ind1=" " ind2=" "><subfield code="z">9783030668907</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(MiAaPQ)5006640078</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(Au-PeEL)EBL6640078</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(OCoLC)1257416604</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">MiAaPQ</subfield><subfield code="b">eng</subfield><subfield code="e">rda</subfield><subfield code="e">pn</subfield><subfield code="c">MiAaPQ</subfield><subfield code="d">MiAaPQ</subfield></datafield><datafield tag="050" ind1=" " ind2="4"><subfield code="a">QA76.9.D343</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Consoli, Sergio.</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Data Science for Economics and Finance :</subfield><subfield code="b">Methodologies and Applications.</subfield></datafield><datafield tag="250" ind1=" " ind2=" "><subfield code="a">1st ed.</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">Cham :</subfield><subfield code="b">Springer International Publishing AG,</subfield><subfield code="c">2021.</subfield></datafield><datafield tag="264" ind1=" " ind2="4"><subfield code="c">©2021.</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">1 online resource (357 pages)</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">computer</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">online resource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="505" ind1="0" ind2=" "><subfield code="a">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.</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">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.</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">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.</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">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.</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">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.</subfield></datafield><datafield tag="588" ind1=" " ind2=" "><subfield code="a">Description based on publisher supplied metadata and other sources.</subfield></datafield><datafield tag="590" ind1=" " ind2=" "><subfield code="a">Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2024. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries. </subfield></datafield><datafield tag="655" ind1=" " ind2="4"><subfield code="a">Electronic books.</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Reforgiato Recupero, Diego.</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Saisana, Michaela.</subfield></datafield><datafield tag="776" ind1="0" ind2="8"><subfield code="i">Print version:</subfield><subfield code="a">Consoli, Sergio</subfield><subfield code="t">Data Science for Economics and Finance</subfield><subfield code="d">Cham : Springer International Publishing AG,c2021</subfield><subfield code="z">9783030668907</subfield></datafield><datafield tag="797" ind1="2" ind2=" "><subfield code="a">ProQuest (Firm)</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://ebookcentral.proquest.com/lib/oeawat/detail.action?docID=6640078</subfield><subfield code="z">Click to View</subfield></datafield></record></collection> |