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

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Alternative Data and Artificial Intelligence Techniques: Applications in Investment and Risk ManagementPalielināt
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This book introduces a state-of-art approach in evaluating portfolio management and risk based on artificial intelligence and alternative data. The book covers a textual analysis of news and social media, information extraction from GPS and IoTs data, and risk predictions based on small transaction data, etc. The book summarizes and introduces the advancement in each area and highlights the machine learning and deep learning techniques utilized to achieve the goals. As a complement, it also illustrates examples on how to leverage the python package to visualize and analyze the alternative datasets, and will be of interest to academics, researchers, and students of risk evaluation, risk management, data, AI, and financial innovation.

Part I Portfolio and Risk Management Overview
1 An Introduction to Quantitative Portfolio Management and Risk Management
3(12)
1.1 Introduction
3(3)
1.2 Types of Portfolio Management
6(1)
1.3 The Classic Asset and Derivatives
7(3)
1.4 Traditional and Modern Approaches
10(1)
1.5 Tools for Measuring Portfolio Returns
11(1)
1.6 Variance on Return in a Portfolio
12(1)
1.7 Conclusions
13(2)
References
14(1)
2 The Major Trends in Global Financial Asset Management
15(18)
2.1 Introduction
15(1)
2.2 Global Asset Management Today
16(5)
2.3 Development Trends in the Asset Management Industry
21(12)
References
28(5)
Part II Machine Learning and Alternative Data Overview
3 Machine Learning and AI in Financial Portfolio Management
33(42)
3.1 Overview
33(9)
3.2 Analysis of Machine Learning Application
42(17)
3.3 Comparison of Machine Learning Algorithms
59(4)
3.4 Select the Best Model
63(2)
3.5 Application of Machine Learning in Financial Field
65(3)
3.6 Problem Analysis of Machine Learning
68(5)
3.7 Future Perspectives
73(2)
4 Introduction of Alternative Data in Finance
75(14)
4.1 Alternative Data Overview
75(3)
4.2 Sources of Alternative Data
78(3)
4.3 Criteria for Evaluating Alternative Datasets
81(2)
4.4 Working with Alternative Data
83(6)
References
88(1)
5 Alternative Data Utilization from a Country Perspective
89(22)
5.1 The United States
89(6)
5.2 China
95(5)
5.3 Europe
100(4)
5.4 Asia (Except China)
104(7)
References
106(5)
Part III Factors Applications in Financial Management
6 Smart Beta and Risk Factors Based on Textural Data and Machine Learning
111(18)
6.1 Introduction
111(1)
6.2 Textural Analysis Technologies
112(1)
6.3 Natural Language Processing
112(1)
6.4 Machine Learning/Deep Learning (ML/DL)
113(3)
6.5 Factors for Finance Built on Textural Dataset Analysis
116(8)
6.6 Conclusion
124(5)
References
125(4)
7 Smart Beta and Risk Factors Based on IoTs
129(12)
7.1 Introduction
129(2)
7.2 A Risk Assessment Model Based on IoT and AIoT
131(3)
7.3 Applications of IoT and AIoT in Finance
134(7)
References
138(3)
8 Environmental, Social Responsibility, and Corporate Governance (ESG) Factors of Corporations
141(26)
8.1 Introduction of Environmental, Social, and Governance (ESG)
141(4)
8.2 ESG in the Eyes of Investors
145(6)
8.3 The Influence of ESG on Firm Risk
151(3)
8.4 The Influence of ESG on Firm Performance and Firm Value
154(9)
8.5 Is ESG a Risk Factor?
163(1)
8.6 The Digital Economy and ESG
164(3)
References
165(2)
9 Sentiment Factors in Finance
167(18)
9.1 What Is Sentiment Factor?
167(2)
9.2 Investor Sentiment and Behavioral Finance
169(5)
9.3 Sentiment's Market Influence
174(3)
9.4 Sentiment Factor Constructions and Sentiment Analysis
177(8)
References
180(5)
Part IV Case Studies of Machine Learnings and Alternative Data
10 Fraud and Deception Detection: Text-Based Data Analytics
185(14)
10.1 Copycat Detection
186(3)
10.2 Fraudulent Reviews
189(10)
References
197(2)
11 Machine Learning Technique in Trading: A Case Study in the EURUSD Market
199(18)
11.1 Introduction to Foreign Exchange Markets
199(2)
11.2 Characteristics of Foreign Exchange Markets
201(1)
11.3 Euro Dollar Exchange Rate (EURUSD)
202(1)
11.4 Fundamental Factors Affecting the Foreign Exchange Rate
202(2)
11.5 Data and Trading Strategy Overview
204(1)
11.6 Supervised Machine Learning Techniques
205(5)
11.7 Trading Strategy
210(3)
11.8 Conclusion
213(4)
References
215(2)
12 Analyzing the Special Purpose Acquisition Corporation (SPAC) with ESG Factors
217(34)
12.1 Brief Introduction to SPACs
217(6)
12.2 The Role of SPACs
223(8)
12.3 Analysis of the Impact of Founder Factors on the Revenue of SPACs
231(17)
12.4 Conclusions
248(3)
References
249(2)
13 ESG Impacts on Corporation's Fundamental: Studies from the Healthcare Industry
251(28)
13.1 Introduction
251(1)
13.2 Data and Methodology
252(3)
13.3 Empirical Model and Results
255(10)
13.4 Investment Strategy on ESG Factors
265(4)
13.5 Conclusion
269(10)
Appendix 1 List of Companies Used in the Research
269(1)
Appendix 2 Quantile ESG Score for Every Quarter
270(1)
Appendix 3 Results of the Return of Strategy and Control Group
271(1)
Appendix 4 Cumulative Return of Strategy and Control Group
272(1)
Appendix 5 Quarterly Return of 3 Strategies and Control Group
273(1)
Appendix 6 Cumulative Return of 3 Strategies and Control Group
274(1)
References
274(5)
Part V Techniques in Data Visualization and Database
14 Data Visualization
279(32)
14.1 Data Visualization Fundamentals
279(2)
14.2 Introduction to Python Visualization Tools
281(15)
14.3 Data Distribution Chart
296(9)
14.4 Financial Data Case Analysis
305(4)
14.5 Summary
309(2)
References
310(1)
15 Interacting with a MongoDB Database from a Python Function in AWS Lambda
311(18)
15.1 MongoDB
311(5)
15.2 Python
316(5)
15.3 AWS
321(8)
References
326(3)
Index 329
Qingquan Tony Zhang is an Adjunct Professor at the University of Illinois at Champaign, R.C. Evan Fellow, Gies Business School, focusing on finance, quantitative investment and entrepreneurship. He is President of the Chicago chapter of the Chinese American Association for Trading and Investment, who has long worked in FinTech, including artificial intelligence and big data. 





Beibei Li is an Associate Professor of IT & Management and Anna Loomis McCandless Chair at Carnegie Mellon University. Dr. Li has extensive experience at leveraging large-scale observational data analytics and experimental analysis with a strong focus on modeling individual user behavior across online, offline, and mobile channels for decision support. 





Danxia Xie is an Associate Professor in Economics at Tsinghua University, China. Dr. Xies teaching and research focuses on digital economy, finance, law and economics, and macroeconomics. Dr. Xie has also worked at Peterson Institute for International Economics, a top think tank at Washington, DC.
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