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Index
Cover
Table of Contents
About the Author
Acknowledgments
About the Website
Introduction
MOTIVATION
TARGET AUDIENCE
BOOK STRUCTURE
CHAPTER 1: The Evolution of Trading Paradigms
1.1 INFRASTRUCTURE-RELATED PARADIGMS IN TRADING
1.2 DECISION-MAKING PARADIGMS IN TRADING
1.3 THE NEW PARADIGM OF DATA-DRIVEN TRADING
REFERENCES
CHAPTER 2: The Role of Data in Trading and Investing
2.1 THE DATA-DRIVEN DECISION-MAKING PARADIGM
2.2 THE DATA ECONOMY IS FUELING THE FUTURE
2.3 DEFINING DATA AND ITS UTILITY
2.4 THE JOURNEY FROM DATA TO INTELLIGENCE
2.5 THE UTILITY OF DATA IN TRADING AND INVESTING
2.6 THE ALTERNATIVE DATA AND ITS USE IN TRADING AND INVESTING
REFERENCES
CHAPTER 3: Artificial Intelligence – Between Myth and Reality
3.1 INTRODUCTION
3.2 THE EVOLUTION OF AI
3.3 THE MEANING OF AI – A CRITICAL VIEW
3.4 ON THE APPLICABILITY OF AI TO FINANCE
3.5 PERSPECTIVES AND FUTURE DIRECTIONS
REFERENCES
CHAPTER 4: Computational Intelligence – A Principled Approach for the Era of Data Exploration
4.1 INTRODUCTION TO COMPUTATIONAL INTELLIGENCE
4.2 THE PAC THEORY
4.3 TECHNOLOGY DRIVERS BEHIND THE ML SURGE
REFERENCES
CHAPTER 5: How to Apply the Principles of Computational Intelligence in Quantitative Finance
5.1 THE VIABILITY OF COMPUTATIONAL INTELLIGENCE
5.2 ON THE APPLICABILITY OF CI TO QUANTITATIVE FINANCE
5.3 A BRIEF INTRODUCTION TO REINFORCEMENT LEARNING
5.4 CONCLUSIONS
REFERENCES
CHAPTER 6: Case Study 1: Optimizing Trade Execution
6.1 INTRODUCTION TO THE PROBLEM
6.2 CURRENT STATE-OF-THE-ART IN OPTIMIZED TRADE EXECUTION
6.3 IMPLEMENTATION METHODOLOGY
6.4 EMPIRICAL RESULTS
6.5 CONCLUSIONS AND FUTURE DIRECTIONS
REFERENCES
CHAPTER 7: Case Study 2: The Dynamics of the Limit Order Book
7.1 INTRODUCTION TO THE PROBLEM
7.2 CURRENT STATE-OF-THE-ART IN THE PREDICTION OF DIRECTIONAL PRICE MOVEMENT IN THE LOB
7.3 USING SUPPORT VECTOR MACHINES AND RANDOM FOREST CLASSIFIERS FOR DIRECTIONAL PRICE FORECAST
7.4 STUDYING THE DYNAMICS OF THE LOB WITH REINFORCEMENT LEARNING
7.5 STUDYING THE DYNAMICS OF THE LOB WITH DEEP NEURAL NETWORKS
7.6 STUDYING THE DYNAMICS OF THE LIMIT ORDER BOOK WITH LONG SHORT-TERM MEMORY NETWORKS
7.7 STUDYING THE DYNAMICS OF THE LOB WITH CONVOLUTIONAL NEURAL NETWORKS
REFERENCES
CHAPTER 8: Case Study 3: Applying Machine Learning to Portfolio Management
8.1 INTRODUCTION TO THE PROBLEM
8.2 CURRENT STATE-OF-THE-ART IN PORTFOLIO MODELING
8.3 A DEEP PORTFOLIO APPROACH TO PORTFOLIO OPTIMIZATION
8.4 A Q-LEARNING APPROACH TO THE PROBLEM OF PORTFOLIO OPTIMIZATION
8.5 A DEEP REINFORCEMENT LEARNING APPROACH TO PORTFOLIO MANAGEMENT
REFERENCES
CHAPTER 9: Case Study 4: Applying Machine Learning to Market Making
9.1 INTRODUCTION TO THE PROBLEM
9.2 CURRENT STATE-OF-THE-ART IN MARKET MAKING
9.3 APPLICATIONS OF TEMPORAL-DIFFERENCE RL IN MARKET MAKING
9.4 MARKET MAKING IN HIGH-FREQUENCY TRADING USING RL
9.5 OTHER RESEARCH STUDIES
REFERENCES
CHAPTER 10: Case Study 5: Applications of Machine Learning to Derivatives Valuation
10.1 INTRODUCTION TO THE PROBLEM
10.2 CURRENT STATE-OF-THE-ART IN DERIVATIVES VALUATION BY APPLYING ML
10.3 USING DEEP LEARNING FOR VALUATION OF DERIVATIVES
10.4 USING RL FOR VALUATION OF DERIVATIVES
REFERENCES
CHAPTER 11: Case Study 6: Using Machine Learning for Risk Management and Compliance
11.1 INTRODUCTION TO THE PROBLEM
11.2 CURRENT STATE-OF-THE-ART FOR APPLICATIONS OF ML TO RISK MANAGEMENT AND COMPLIANCE
11.3 MACHINE LEARNING IN CREDIT RISK MODELING
11.4 USING DEEP LEARNING FOR CREDIT SCORING
11.5 USING ML IN OPERATIONAL RISK AND MARKET SURVEILLANCE
REFERENCES
CHAPTER 12: Conclusions and Future Directions
12.1 CONCLUDING REMARKS
12.2 THE PARADIGM SHIFT
12.3 DE-NOISING THE AI HYPE
12.4 AN EMERGING ENGINEERING DISCIPLINE
12.5 FUTURE DIRECTIONS
REFERENCES
Index
End User License Agreement
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