AI for Finance

Description

Finance students and practitioners may ask: can machines learn everything? Could AI help me? Computing students or practitioners may ask: which of my skills could contribute to finance? Where in finance should I pay attention? This book aims to answer these questions. No prior knowledge is expected in AI or finance. Including original research, the book explains the impact of ignoring computation in classical economics; examines the relationship between computing and finance and points out potential misunderstandings between economists and computer scientists; and introduces Directional Change and explains how this can be used. To finance students and practitioners, this book will explain the promise of AI, as well as its limitations. It will cover knowledge representation, modelling, simulation and machine learning, explaining the principles of how they work. To computing students and practitioners, this book will introduce the financial applications in which AI has made an impact. This includes algorithmic trading, forecasting, risk analysis portfolio optimization and other less well-known areas in finance. Trading depth for readability, AI for Finance will help readers decide whether to invest more time into the subject. Cover Endorsement Page Half Title Series Page Title Page Copyright Page Table of Contents Dedication Acknowledgements Preface Introduction Chapter 1 AI–Finance Synergy 1.1 Speed Matters 1.2 The Race Is on Seeking, Not Running 1.3 Pattern Recognition 1.4 Data Mining 1.5 Forecasting 1.6 Concluding Summary: Synergy between AI and Finance Notes Chapter 2 Machine Learning Knows No Boundaries? 2.1 AlphaGo: The Success 2.2 General AI: The Rose Garden 2.3 Complication: The Reality 2.4 Combinatorial Explosion, the Curse of Computation 2.5 A Missing Ingredient in Classical Economics 2.6 Neither Can Live While the Other Survives 2.7 Summary: Powerful but not Magical Notes Chapter 3 Machine Learning in Finance 3.1 Machine Learning for Forecasting 3.2 Supervised Learning 3.3 Know Your Data 3.4 A Glimpse of Game Theory 3.5 “Unsupervised Learning” for Bargaining 3.6 Summary: Machine Learning Is a Game Changer Note Chapter 4 Modelling, Simulation and Machine Learning 4.1 Modelling 4.2 Modelling: Imperfect but Useful 4.3 Simulation: Beyond Mathematical Analysis 4.4 Case Study: Risk Analysis 4.5 Adding Machine Learning to Modelling and Simulation 4.6 Mechanism Design 4.7 Conclusion: Model–Simulate–Learn, a Powerful Combination Notes Chapter 5 Portfolio Optimization 5.1 Maximizing Profit, Minimizing Risk 5.2 The Markowitz Model for Portfolio Optimization 5.3 Constrained Optimization 5.4 Two-Objective Optimization 5.5 The Reality Is Much More Complex 5.6 Economics vs Computer Science 5.7 Summary Notes Chapter 6 Financial Data: Beyond Time Series 6.1 What Is Time Exactly? 6.2 Event-Based Time Representation 6.3 Measuring Market Volatility under DC 6.4 Two Eyes Are Better Than One 6.5 Striking Discoveries under DC 6.6 Research in DC 6.7 Conclusion: New Representation, New Frontier Notes Chapter 7 Over the Horizon 7.1 Algorithmic Trading Drones 7.2 High-Frequency Finance 7.3 Blockchain 7.4 Information Extraction from News 7.5 Finance as a Hard Science Notes Bibliographical Remarks Bibliography Index