Applications of Computational Intelligence in Data-Driven Trading

"Life on earth is filled with many mysteries, but perhaps the most challenging of these is the nature of Intelligence."
 
- Prof. Terrence J. Sejnowski, Computational Neurobiologist
 
The main objective of this book is to create awareness about both the promises and the formidable challenges that the era of Data-Driven Decision-Making and Machine Learning are confronted with, and especially about how these new developments may influence the future of the financial industry.
 
The subject of Financial Machine Learning has attracted a lot of interest recently, specifically because it represents one of the most challenging problem spaces for the applicability of Machine Learning. The author has used a novel approach to introduce the reader to this topic:
* The first half of the book is a readable and coherent introduction to two modern topics that are not generally considered together: the data-driven paradigm and Computational Intelligence.
* The second half of the book illustrates a set of Case Studies that are contemporarily relevant to quantitative trading practitioners who are dealing with problems such as trade execution optimization, price dynamics forecast, portfolio management, market making, derivatives valuation, risk, and compliance.
 
The main purpose of this book is pedagogical in nature, and it is specifically aimed at defining an adequate level of engineering and scientific clarity when it comes to the usage of the term "Artificial Intelligence," especially as it relates to the financial industry.
 
The message conveyed by this book is one of confidence in the possibilities offered by this new era of Data-Intensive Computation. This message is not grounded on the current hype surrounding the latest technologies, but on a deep analysis of their effectiveness and also on the author's two decades of professional experience as a technologist, quant and academic.

Sommario

About the Author xvii
 
Acknowledgments xix
 
About the Website xxi
 
Introduction xxiii
 
Motivation xxiv
 
Target Audience xxvi
 
Book Structure xxvii
 
1 The Evolution of Trading Paradigms 1
 
1.1 Infrastructure-Related Paradigms in Trading 1
 
1.1.1 Open Outcry Trading 2
 
1.1.2 Advances in Communication Technology 2
 
1.1.3 The Digital Revolution in the Financial Markets 3
 
1.1.4 The High-Frequency Trading Paradigm 5
 
1.1.5 Blockchain and the Decentralization of Markets 6
 
1.2 Decision-Making Paradigms in Trading 7
 
1.2.1 Discretionary Trading 8
 
1.2.2 Systematic Trading 8
 
1.2.3 Algorithmic Trading 9
 
1.3 The New Paradigm of Data-Driven Trading 11
 
References 14
 
2 The Role of Data in Trading and Investing 15
 
2.1 The Data-Driven Decision-Making Paradigm 15
 
2.2 The Data Economy is Fueling the Future 17
 
2.2.1 The Value of Data - Data as an Asset 18
 
2.3 Defining Data and Its Utility 20
 
2.4 The Journey from Data to Intelligence 24
 
2.5 The Utility of Data in Trading and Investing 30
 
2.6 The Alternative Data and Its Use in Trading and Investing 34
 
References 36
 
3 Artificial Intelligence - Between Myth and Reality 39
 
3.1 Introduction 39
 
3.2 The Evolution of AI 41
 
3.2.1 Early History 41
 
3.2.2 The Modern AI Era 43
 
3.2.3 Important Milestones in the Development of AI 44
 
3.2.4 Projections for the Immediate Future 48
 
3.2.5 Meta-Learning - An Exciting New Development 49
 
3.3 The Meaning of AI - A Critical View 51
 
3.4 On the Applicability of AI to Finance 54
 
3.4.1 Data Stationarity 57
 
3.4.2 Data Quality 58
 
3.4.3 Data Dimensionality 59
 
3.5 Perspectives and Future Directions 60
 
References 62
 
4 Computational Intelligence - A Principled Approach for the Era of Data Exploration 63
 
4.1 Introduction to Computational Intelligence 63
 
4.1.1 Defining Intelligence 63
 
4.1.2 What is Computational Intelligence? 64
 
4.1.3 Mapping the Field of Study 66
 
4.1.4 Problems vs. Tools 68
 
4.1.5 Current Challenges 69
 
4.1.6 The Future of Computational Intelligence 70
 
4.1.7 Examples in Finance 71
 
4.2 The PAC Theory 72
 
4.2.1 The Probably Approximately Correct Framework 73
 
4.2.2 Why AI is a Very Lofty Goal to Achieve 75
 
4.2.3 Examples of Ecorithms in Finance 78
 
4.3 Technology Drivers Behind the ML Surge 81
 
4.3.1 Data 82
 
4.3.2 Algorithms 82
 
4.3.3 Hardware Accelerators 82
 
References 84
 
5 How to Apply the Principles of Computational Intelligence in Quantitative Finance 87
 
5.1 The Viability of Computational Intelligence 87
 
5.2 On the Applicability of CI to Quantitative Finance 91
 
5.3 A Brief Introduction to Reinforcement Learning 94
 
5.3.1 Defining the Agent 96
 
5.3.2 Model-Based Markov Decision Process 98
 
5.3.3 Model-Free Reinforcement Learning 101
 
5.4 Conclusions 104
 
References 104
 
6 Case Study 1: Optimizing Trade Execution 107
 
6.1 Introduction to the Problem 107
 
6.1.1 On Limit Orders and Market Microstructure 109
 
6.1.2 Formulation of Base-Line Strategies 111
 
6.1.3 A Reinforcement Learning Formulation for the Optimized Execution Problem 112
 
6.2 Current State-of-the-Art in Optimized Trade Execution 114
 
6.3 Implementation

Info autore

CRIS DOLOC is a leading computational scientist with more than 25 years of experience in quantitative finance. He holds a PhD in Computational Physics and is currently teaching at the University of Chicago in the Financial Mathematics program. Cris is also the founder of FintelligeX, a technology platform designed to promote data-driven education, and he is very passionate about the opportunities that recent developments in Cognitive Computing and Computational Intelligence could bring to the field of Quant education.

Riassunto

"Life on earth is filled with many mysteries, but perhaps the most challenging of these is the nature of Intelligence."

- Prof. Terrence J. Sejnowski, Computational Neurobiologist

The main objective of this book is to create awareness about both the promises and the formidable challenges that the era of Data-Driven Decision-Making and Machine Learning are confronted with, and especially about how these new developments may influence the future of the financial industry.

The subject of Financial Machine Learning has attracted a lot of interest recently, specifically because it represents one of the most challenging problem spaces for the applicability of Machine Learning. The author has used a novel approach to introduce the reader to this topic:
* The first half of the book is a readable and coherent introduction to two modern topics that are not generally considered together: the data-driven paradigm and Computational Intelligence.
* The second half of the book illustrates a set of Case Studies that are contemporarily relevant to quantitative trading practitioners who are dealing with problems such as trade execution optimization, price dynamics forecast, portfolio management, market making, derivatives valuation, risk, and compliance.

The main purpose of this book is pedagogical in nature, and it is specifically aimed at defining an adequate level of engineering and scientific clarity when it comes to the usage of the term "Artificial Intelligence," especially as it relates to the financial industry.

The message conveyed by this book is one of confidence in the possibilities offered by this new era of Data-Intensive Computation. This message is not grounded on the current hype surrounding the latest technologies, but on a deep analysis of their effectiveness and also on the author's two decades of professional experience as a technologist, quant and academic.

Dettagli sul prodotto

• Autori: C Doloc, Cris Doloc
• Editore: Wiley, John and Sons Ltd
• Lingue: Inglese
• Formato: Copertina rigida
• Pubblicazione: 31.01.2020
• EAN: 9781119550501
• ISBN: 978-1-119-55050-1
• Pagine: 304
• Categorie: Scienze sociali, diritto, economia > Economia > Economia aziendale; Finanzwesen, Finance & Investments, Finanz- u. Anlagewesen, Spezialthemen Finanz- u. Anlagewesen, Finance & Investments Special Topics