Table Of ContentPALGRAVE STUDIES IN RISK AND INSURANCE
Alternative Data
and Artificial
Intelligence
Techniques
Applications in Investment
and Risk Management
Qingquan Tony Zhang · Beibei Li ·
Danxia Xie
Palgrave Studies in Risk and Insurance
Series Editor
Maurizio POMPELLA, Università di Siena, Siena, Italy
Palgrave Studies in Risk and Insurance explores leading research in the
fields of risk and insurance, alternative risk transfer and structured finance
and insurance. It reflects on the convergence of insurance and financial
markets over the last two decades. It offers insight into the areas of
risk theory and insurance economics, and offers a special focus on cross-
diagonal and market risk transfers. The series covers a range of related
topics, including financial and pure risk securitization, structured finance
and innovation, the engineering of insurance policies and risk transfer
between banks and insurance companies. The collection represents some
of the most up to date and innovative content currently available in risk
and insurance.
· ·
Qingquan Tony Zhang Beibei Li Danxia Xie
Alternative Data
and Artificial
Intelligence
Techniques
Applications in Investment and Risk Management
Qingquan Tony Zhang Beibei Li
University of Illinois Carnegie Mellon University
Urbana-Champaign Pittsburgh, PA, USA
Palatine, IL, USA
Danxia Xie
Tsinghua University
Beijing, China
ISSN 2523-8221 ISSN 2523-823X (electronic)
Palgrave Studies in Risk and Insurance
ISBN 978-3-031-11611-7 ISBN 978-3-031-11612-4 (eBook)
https://doi.org/10.1007/978-3-031-11612-4
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Preface
In recent years, with the advancement of the Artificial Intelligence and
the Internet of Things (IOTs), the power of AI-based technology and
non-financial data have become too critical to be ignored in invest-
ment management. Leading financial institutions including hedge funds,
mutual funds, and investment banks rely heavily on the business infor-
mation reflected in the non-financial data to make both strategic and
managerial decisions. Fresh knowledge can be created from these new
datasets.
By talking with dozens of academic scholars and financial professionals,
we came to the conclusion that a book with a structure that focuses on
non-financial data (a.k.a. alternative data) may bring value to the finan-
cial industry and bolster research in the field with practical applications.
We didn’t set out to write a comprehensive book. The plan was simply
to organize state-of-art research and our discoveries. However, it didn’t
quite turn out that way.
The financial technology is disrupting the traditional financial industry
from almost every perspective. There are new technologies, new develop-
ments in theories, and completely new business models and applications.
So much is new that we decided that not just write fundamental introduc-
tion but include case studies as well. We intend to change what financial
analysts do in the industry. Our goal is to help you think alternatively by
introducing the concepts and applications of the alternative data, machine
learning, and AI.
v
vi PREFACE
This book is intended for people who are just starting to learn about
quantitative finance as well as for those who want to advance their basic
knowledge in financial data analytics, business administration, and risk
management to a higher level. In this book, we conduct an overview
of the major trends in equity portfolio and risk management. The book
also covers a variety of topics faced by financial institutions that you are
likely to encounter when working in the field: machine learning and
AI in portfolio management, alternative data, textural analysis, smart
Beta and risk factors based on IoTs, Environmental, Social Responsibility
and Corporate Governance (ESG), Sentiment Factors, fraud and decep-
tion detection, algorithm trading, and data visualization. Students, junior
analysts, and young professionals may find the book valuable to help them
to ace the interviews in portfolio management and equity research.
Part I includes Chapters 1 and 2. These chapters review recent develop-
ments in quantitative portfolio and risk management and the future trends
in global financial asset management. These two chapters are intended for
the professionals and students who just starting out in portfolio and risk
management. Those who have some experience may still find it beneficial
to refresh some of their knowledge.
Part II includes Chapters 3 through 5.Chapter 3 mainly introduces the
machine learning techniques and their applications in the financial field.
Chapters 4 and 5 will introduce the concepts of alternative data, their
respective sources, and how they have been utilized in different countries
in the world.
Part III includes Chapters 6 through 9. Each chapter in this section
covers a factor or smart Beta in financial management that is intended
for people who want to broaden their knowledge and understanding of
alternative data by learning multiple popular investment related factors on
Wall Street. Professionals and researchers may be enlightened to construct
other factors through skimming these chapters.
Part IV includes Chapters 10 through 13. As both researchers and
professionals, we have come to the full realization of the importance of
learning through example plays in the strategic utilization of alternative
data and AI in financial management and risk management. Therefore, we
share some of the examples of how machine learning and alternative data
can be integrated to analyze some of the most interesting topics in the
field such as algorithmic trading, SPAC, and ESG. Even though we offer
limited practical examples and applications, our purpose remains to help
you develop your own ways of thinking and developing new applications
PREFACE vii
based on alternative data and AI. We realize that there may be better
methodologies and theories to analyze some of the topics presented in
this part.
Part V includes Chapters 14 through 15. Each chapter covers a
useful technique in financial management, data visualization, and cloud-
based databases. This part is intended for people who want to get some
hands-on experience in financial programming. Quantitative analysts and
researchers may also find some interesting tricks in visualizing their data.
We owe special thanks to Maurizio Pompella, the series editor who
gave us many useful ideas and comments on earlier drafts. Will Lin
Cong at Cornell University gave us the idea of putting contents into
a handbook. David Wozniak and David Hubbard have provided many
useful suggestions. In addition, over the years, many students and
professors from University of Illinois, Urbana Champaign, Tsinghua
University, Carnegie Mellon University, and other universities, including
Jinglei Huang, Huahao Lu, Xinzhu Qu, Xiuping Wu, Yating Ran, Jia
Wang, Weiyi Zhao, Jinyan Zhang, Leiyu Zhao, etc., have contributed
significantly to this book.
Palatine, USA Qingquan Tony Zhang
Pittsburgh, USA Beibei Li
Beijing, China Danxia Xie
August 2022
Contents
Part I Portfolio and Risk Management Overview
1 An Introduction to Quantitative Portfolio
Management and Risk Management 3
1.1 Introduction 3
1.2 Types of Portfolio Management 6
1.3 The Classic Asset and Derivatives 7
1.4 Traditional and Modern Approaches 10
1.5 Tools for Measuring Portfolio Returns 11
1.6 Variance on Return in a Portfolio 12
1.7 Conclusions 13
References 14
2 The Major Trends in Global Financial Asset
Management 15
2.1 Introduction 15
2.2 Global Asset Management Today 16
2.3 Development Trends in the Asset Management
Industry 21
References 28
ix
x CONTENTS
Part II Machine Learning and Alternative Data
Overview
3 Machine Learning and AI in Financial Portfolio
Management 33
3.1 Overview 33
3.2 Analysis of Machine Learning Application 42
3.3 Comparison of Machine Learning Algorithms 59
3.4 Select the Best Model 63
3.5 Application of Machine Learning in Financial Field 65
3.6 Problem Analysis of Machine Learning 68
3.7 Future Perspectives 73
4 Introduction of Alternative Data in Finance 75
4.1 Alternative Data Overview 75
4.2 Sources of Alternative Data 78
4.3 Criteria for Evaluating Alternative Datasets 81
4.4 Working with Alternative Data 83
References 88
5 Alternative Data Utilization from a Country
Perspective 89
5.1 The United States 89
5.2 China 95
5.3 Europe 100
5.4 Asia (Except China) 104
References 106
Part III Factors Applications in Financial Management
6 Smart Beta and Risk Factors Based on Textural Data
and Machine Learning 111
6.1 Introduction 111
6.2 Textural Analysis Technologies 112
6.3 Natural Language Processing 112
6.4 Machine Learning/Deep Learning (ML/DL) 113
6.5 Factors for Finance Built on Textural Dataset
Analysis 116
6.6 Conclusion 124
References 125
