Alternative Data and Artificial Intelligence Techniques: Applications in Investment and Risk Management

Preface
Contents
About the Authors
List of Figures
List of Tables
Part I Portfolio and Risk Management Overview
1 An Introduction to Quantitative Portfolio Management and Risk Management
1.1 Introduction
1.2 Types of Portfolio Management
1.3 The Classic Asset and Derivatives
1.3.1 Classic Assets Classes in Portfolio Management
1.3.2 Derivatives in Portfolio Management
1.4 Traditional and Modern Approaches
1.4.1 Traditional Approaches to Portfolio Management
1.4.2 Modern Approaches to Portfolio Management
1.5 Tools for Measuring Portfolio Returns
1.6 Variance on Return in a Portfolio
1.7 Conclusions
References
2 The Major Trends in Global Financial Asset Management
2.1 Introduction
2.2 Global Asset Management Today
2.2.1 The United States
2.2.2 Europe
2.2.3 China
2.3 Development Trends in the Asset Management Industry
2.3.1 ESG Investing
2.3.2 Blockchain
2.3.3 Robo-Advisors
References
Part II Machine Learning and Alternative Data Overview
3 Machine Learning and AI in Financial Portfolio Management
3.1 Overview
3.1.1 Basic Introduction to Machine Learning
3.1.2 Overview of the Application of Machine Learning in Financial Portfolio Management
3.1.2.1 Mobile Payments
3.1.2.2 P2P
3.1.2.3 Big Data Analysis
3.1.2.4 Digital Currency and Data Blockchain Technology
3.1.2.5 Intelligent Trading and Finance
3.1.3 Implementation Conditions for Machine Learning
3.1.3.1 Big Data
3.1.3.2 Techniques
3.1.3.3 Humans
3.1.4 Introduction to Article Structure
3.2 Analysis of Machine Learning Application
3.2.1 Supervised Learning
3.2.1.1 Classification
3.2.1.2 Logistic Regression
3.2.1.3 Support Vector Machines (SVMs)
3.2.1.4 Decision Trees
3.2.1.5 Random Forest
3.2.1.6 Hidden Markov Models (HMMs)
3.2.1.7 Regression
3.2.1.8 Nonparametric Regression: Loess and K-Nearest Neighbor
3.2.1.9 Dynamical Systems: Kalman Filtering
3.2.1.10 Extreme Gradient Boosting
3.2.2 Unsupervised Learning
3.2.2.1 Clustering
3.2.2.2 K-Means
3.2.2.3 Birch
3.2.2.4 Ward’s Method
3.2.2.5 Factor Analysis Through PCA
3.2.3 Deep Learning
3.2.3.1 Multilayer Perceptron (MLP)
3.2.3.2 Time-Series Analysis: Long Short-Term Memory (LSTM)
3.2.3.3 Convolutional Neural Networks (CNNs)
3.2.3.4 Restricted Boltzmann Machines (RBM)
3.2.4 Reinforcement Learning
3.2.5 Active Learning
3.3 Comparison of Machine Learning Algorithms
3.3.1 Comparison of Supervised Learning Algorithms—Regression
3.3.2 Comparison of Supervised Learning Algorithms—Classification
3.3.3 Comparison of Unsupervised Learning Algorithms—Clustering
3.4 Select the Best Model
3.4.1 Variance-Bias Trade-Off Theory
3.4.2 Model Complexity
3.5 Application of Machine Learning in Financial Field
3.6 Problem Analysis of Machine Learning
3.7 Future Perspectives
4 Introduction of Alternative Data in Finance
4.1 Alternative Data Overview
4.1.1 Trends
4.1.2 Risk
4.2 Sources of Alternative Data
4.2.1 Data Generated by Personal Activities
4.2.2 Data Generated by Business Activities
4.2.3 Data Obtained by High-Tech Monitoring
4.3 Criteria for Evaluating Alternative Datasets
4.4 Working with Alternative Data
4.4.1 Application Example: Google
4.4.2 Application Example: Peloton
4.4.2.1 Advantage Analysis Using Alternative Data
4.4.3 Application Example: WeWork
References
5 Alternative Data Utilization from a Country Perspective
5.1 The United States
5.1.1 Microsoft
5.1.2 BondCliQ
5.1.3 EventVestor
5.1.4 Verbatim
5.1.5 Thinknum
5.1.6 IBM
5.1.7 Oracle
5.1.8 Google
5.1.9 Amazon
5.1.10 HP Vertica
5.1.11 Intel
5.1.12 Teradata
5.2 China
5.2.1 CDP (Cloud Data Platform)
5.2.2 Huawei
5.2.3 Alibaba Group
5.2.4 Super Pair Technology
5.2.5 Choice
5.2.6 SmarTag
5.2.7 Jove Bird
5.2.8 IUAP Yonyoucloud
5.2.9 Sugon
5.3 Europe
5.3.1 PatentSight
5.3.2 Peekd
5.3.3 Unacast
5.3.4 Owlin
5.3.5 EPFR
5.3.6 Huq Industries (Geo-Location)
5.4 Asia (Except China)
5.4.1 Nikkei
5.4.2 Qmit
5.4.3 Datapulse
5.4.4 Liases Foras
5.4.5 Propstack
References
Part III Factors Applications in Financial Management
6 Smart Beta and Risk Factors Based on Textural Data and Machine Learning
6.1 Introduction
6.2 Textural Analysis Technologies
6.3 Natural Language Processing
6.4 Machine Learning/Deep Learning (ML/DL)
6.5 Factors for Finance Built on Textural Dataset Analysis
6.5.1 Readability
6.5.2 Tone and Sentiment Factors
6.5.2.1 Tone Factors of Disclosures from Listed Company
6.5.2.2 Tone Factors of Media
6.5.2.3 Sentimental Factors of Investor Community and Social Media
6.5.2.4 Sentimental Factors of Employee Ratings
6.5.2.5 Sentiment Factors of Customer Ratings
6.5.3 Similarity Factors
6.5.4 Semantic Factors
6.5.4.1 Semantic Factors of Disclosures from Listed Companies
6.5.4.2 Semantic Factors of Regulatory Comments
6.5.4.3 Semantic Factors of Alternative Data
6.5.5 Uncertainty Factors
6.5.6 Accuracy Factors
6.5.7 Popularity Factors
6.5.7.1 Popularity Factors of Firm Disclosure
6.5.7.2 Popularity Factors of Market Trends
6.6 Conclusion
References
7 Smart Beta and Risk Factors Based on IoTs
7.1 Introduction
7.2 A Risk Assessment Model Based on IoT and AIoT
7.2.1 Analytic Hierarchy Process
7.2.2 Artificial Immune Models
7.2.3 The Cloud Transformation Model
7.3 Applications of IoT and AIoT in Finance
7.3.1 Malware Capture
7.3.2 In the Field of Credit
7.3.3 In the Insurance Field
7.3.4 In Operations Monitoring
References
8 Environmental, Social Responsibility, and Corporate Governance (ESG) Factors of Corporations
8.1 Introduction of Environmental, Social, and Governance (ESG)
8.1.1 The Development of ESG
8.1.2 The Definition of ESG
8.2 ESG in the Eyes of Investors
8.2.1 Investors Positive About ESG Investing
8.2.2 Investors Negative About ESG Investing
8.2.3 Regional Differences in Investor Perspectives on ESG Investing
8.2.4 E, S, G in the Eyes of Investors Respectively
8.3 The Influence of ESG on Firm Risk
8.3.1 Systematic Risks
8.3.2 Idiosyncratic Risks
8.4 The Influence of ESG on Firm Performance and Firm Value
8.4.1 ESG Ratings
8.4.2 Why Does ESG Matter? Starting from the DCF Model
8.4.3 The Influence of ESG on Discounted Rate: Firm Risk
8.4.3.1 ESG Reduces the company’s Discount Rate by Reducing Systematic Risk
8.4.3.2 ESG Reduces the company’s Discount Rate by Reducing Firm Idiosyncratic Risk
8.4.4 The Influence of ESG on Discounted Rate: Cost of Capital
8.4.5 Other Channels
8.5 Is ESG a Risk Factor?
8.6 The Digital Economy and ESG
References
9 Sentiment Factors in Finance
9.1 What Is Sentiment Factor?
9.1.1 An Introduction to Sentiment Analysis
9.1.2 Literature Review on Sentiment Analysis
9.1.3 An Example of Sentiment Affecting the Market
9.2 Investor Sentiment and Behavioral Finance
9.2.1 Limits to Arbitrage
9.2.2 Psychology
9.2.3 Herding Behavior
9.3 Sentiment’s Market Influence
9.3.1 Sentiment in the Stock Market
9.3.2 Sentiment in the Cryptocurrency Market
9.4 Sentiment Factor Constructions and Sentiment Analysis
9.4.1 Sentiment Metrics
9.4.2 Sentiment Analysis Methods
References
Part IV Case Studies of Machine Learnings and Alternative Data
10 Fraud and Deception Detection: Text-Based Data Analytics
10.1 Copycat Detection
10.1.1 Estimation Results
10.1.1.1 The Aggregate Effect of Copycats on the Demand of the Original
10.1.1.2 How the Quality and the Imitation Type of the Copycats Affect the Original App’s Demand
10.2 Fraudulent Reviews
10.2.1 How to Identify False Comments Through Text Information
10.2.2 How to Deal with Potentially False Comments
10.2.2.1 Support Vector Machine
10.2.2.2 Naive Bayes
10.2.2.3 Random Forest
10.2.2.4 Adaptive Boost
References
11 Machine Learning Technique in Trading: A Case Study in the EURUSD Market
11.1 Introduction to Foreign Exchange Markets
11.2 Characteristics of Foreign Exchange Markets
11.2.1 24 Hours Trading × 5 Days a Week
11.2.2 Market Transparency
11.2.3 Highly Leveraged Market
11.2.4 Higher Liquidity
11.3 Euro Dollar Exchange Rate (EURUSD)
11.4 Fundamental Factors Affecting the Foreign Exchange Rate
11.5 Data and Trading Strategy Overview
11.5.1 Data Used for Modeling a Trading Strategy in the EURUSD Market
11.5.2 Benchmark Strategies
11.6 Supervised Machine Learning Techniques
11.6.1 Random Forest
11.6.2 Support Vector Machines
11.6.3 K-Nearest Neighbors
11.7 Trading Strategy
11.7.1 The Overall Performance of All the Techniques
11.7.1.1 Cumulative Returns for the Test Period
11.7.2 Performance During the 2008 Crisis
11.8 Conclusion
References
12 Analyzing the Special Purpose Acquisition Corporation (SPAC) with ESG Factors
12.1 Brief Introduction to SPACs
12.1.1 The History and Background of SPACs
12.1.2 The Operating Mechanism of SPACs
12.1.3 The Characteristics of SPACs
12.2 The Role of SPACs
12.2.1 The Importance of Founders
12.2.2 The Development Status of SPACs
12.2.3 Regulatory Norms and Post-Merger Performance
12.2.3.1 Post-Merger Performance
12.2.3.2 Regulatory Norms
12.3 Analysis of the Impact of Founder Factors on the Revenue of SPACs
12.3.1 Data Source and Description
12.3.2 Data Processing
12.3.2.1 Technical Difficulties: Parsing HTML Files
12.3.2.2 Gain Educational Experience
12.3.3 Data Analysis
12.3.3.1 Descriptive Analysis
12.3.3.2 Correlation Analysis
12.3.4 Data Visualization
12.4 Conclusions
References
13 ESG Impacts on Corporation’s Fundamental: Studies from the Healthcare Industry
13.1 Introduction
13.2 Data and Methodology
13.2.1 Financial Data
13.2.2 ESG Data
13.2.3 Transforming the ESG Data
13.2.4 Formulating the ESG Score
13.2.5 Company Selection
13.2.6 Variables and Summary Statistics
13.3 Empirical Model and Results
13.3.1 Has the Pharmaceutical Industry Gained Excess Economic Benefits from the Epidemic?
13.3.2 Do ESG Factors Affect Firms’ Economic Performance on the ROA/ROE Level?
13.4 Investment Strategy on ESG Factors
13.4.1 Do ESG Scores Generate Excess Returns?
13.4.2 Sensitivity Testing
13.5 Conclusion
Appendix 1: List of Companies Used in the Research
Appendix 2: Quantile ESG Score for Every Quarter
Appendix 3: Results of the Return of Strategy and Control Group
Appendix 4: Cumulative Return of Strategy and Control Group
Appendix 5: Quarterly Return of 3 Strategies and Control Group
Appendix 6: Cumulative Return of 3 Strategies and Control Group
References
Part V Techniques in Data Visualization and Database
14 Data Visualization
14.1 Data Visualization Fundamentals
14.1.1 Background of Data Visualization
14.1.2 Research Status
14.2 Introduction to Python Visualization Tools
14.2.1 Matplotlib
14.2.1.1 Introduction
14.2.1.2 Basic Application
14.2.2 Seaborn
14.2.2.1 Introduction
14.2.2.2 Basic Application
14.2.3 Plotly
14.2.3.1 Introduction
14.2.3.2 Basic Application
14.2.4 Pyecharts
14.2.4.1 Introduction
14.2.4.2 Basic Application
14.3 Data Distribution Chart
14.3.1 Statistical Histogram
14.3.2 Boxplot
14.3.3 Scatter Chart and Line Chart
14.3.4 Column Distribution Chart
14.3.5 Violin Chart
14.4 Financial Data Case Analysis
14.4.1 Data Sources
14.4.2 Financial Chart
14.5 Summary
References
15 Interacting with a MongoDB Database from a Python Function in AWS Lambda
15.1 MongoDB
15.1.1 Introduction to MongoDB
15.1.2 MongoDB Pricing
15.1.3 Creating a MongoDB Database
15.1.3.1 Tone Factors Based on Disclosures from Listed Companies
15.2 Python
15.2.1 Prepare a Python Environment
15.2.2 Simple Example Code to Test Locally
15.2.3 Modified Code for AWS
15.2.4 Downloading Python Dependencies
15.3 AWS
15.3.1 Introduction to AWS
15.3.2 Introduction to AWS Lambda
15.3.3 AWS Pricing
15.3.4 AWS IAM Accounts vs. AWS Root Accounts
15.3.5 Creating an IAM User
15.3.6 Creating an AWS Lambda Function
15.3.7 Adding Triggers to an AWS Lambda Function
References
Index