{"product_id":"applications-of-computational-intelligence-in-datadriven-trading-9781119550501","title":"Applications of Computational Intelligence in","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e\u003ci\u003eLife on earth is filled with many mysteries, but perhaps the most challenging of these is the nature of Intelligence.\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e Prof. Terrence J. Sejnowski, Computational Neurobiologist\u003c\/p\u003e \u003cp\u003eThe 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.\u003c\/p\u003e \u003cp\u003eThe subject of \u003ci\u003eFinancial Machine Learning\u003c\/i\u003e has attracted a lot of interest recently, specifically because it represents one of the most challenging \u003ci\u003eproblem spaces\u003c\/i\u003e for the applicability of Machine Learning. The author has used a novel approach to introduce the reader to this topic:\u003c\/p\u003e \u003cul\u003e \u003cli\u003eThe first half of the book is a readable and coherent introduction to two modern topics that are not generally considered together: the \u003ci\u003edata-driven \u003c\/i\u003eparadigm and \u003ci\u003eComputational Inte\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003c\/i\u003e\u003cp\u003eAbout the Author xvii\u003c\/p\u003e \u003cp\u003eAcknowledgments xix\u003c\/p\u003e \u003cp\u003eAbout the Website xxi\u003c\/p\u003e \u003cp\u003eIntroduction xxiii\u003c\/p\u003e \u003cp\u003eMotivation xxiv\u003c\/p\u003e \u003cp\u003eTarget Audience xxvi\u003c\/p\u003e \u003cp\u003eBook Structure xxvii\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 The Evolution of Trading Paradigms 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 Infrastructure-Related Paradigms in Trading 1\u003c\/p\u003e \u003cp\u003e1.1.1 Open Outcry Trading 2\u003c\/p\u003e \u003cp\u003e1.1.2 Advances in Communication Technology 2\u003c\/p\u003e \u003cp\u003e1.1.3 The Digital Revolution in the Financial Markets 3\u003c\/p\u003e \u003cp\u003e1.1.4 The High-Frequency Trading Paradigm 5\u003c\/p\u003e \u003cp\u003e1.1.5 Blockchain and the Decentralization of Markets 6\u003c\/p\u003e \u003cp\u003e1.2 Decision-Making Paradigms in Trading 7\u003c\/p\u003e \u003cp\u003e1.2.1 Discretionary Trading 8\u003c\/p\u003e \u003cp\u003e1.2.2 Systematic Trading 8\u003c\/p\u003e \u003cp\u003e1.2.3 Algorithmic Trading 9\u003c\/p\u003e \u003cp\u003e1.3 The New Paradigm of Data-Driven Trading 11\u003c\/p\u003e \u003cp\u003eReferences 14\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 The Role of Data in Trading and Investing 15\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 The Data-Driven Decision-Making Paradigm 15\u003c\/p\u003e \u003cp\u003e2.2 The Data Economy is Fueling the Future 17\u003c\/p\u003e \u003cp\u003e2.2.1 The Value of Data – Data as an Asset 18\u003c\/p\u003e \u003cp\u003e2.3 Defining Data and Its Utility 20\u003c\/p\u003e \u003cp\u003e2.4 The Journey from Data to Intelligence 24\u003c\/p\u003e \u003cp\u003e2.5 The Utility of Data in Trading and Investing 30\u003c\/p\u003e \u003cp\u003e2.6 The Alternative Data and Its Use in Trading and Investing 34\u003c\/p\u003e \u003cp\u003eReferences 36\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Artificial Intelligence – Between Myth and Reality 39\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction 39\u003c\/p\u003e \u003cp\u003e3.2 The Evolution of AI 41\u003c\/p\u003e \u003cp\u003e3.2.1 Early History 41\u003c\/p\u003e \u003cp\u003e3.2.2 The Modern AI Era 43\u003c\/p\u003e \u003cp\u003e3.2.3 Important Milestones in the Development of AI 44\u003c\/p\u003e \u003cp\u003e3.2.4 Projections for the Immediate Future 48\u003c\/p\u003e \u003cp\u003e3.2.5 Meta-Learning – An Exciting New Development 49\u003c\/p\u003e \u003cp\u003e3.3 The Meaning of AI – A Critical View 51\u003c\/p\u003e \u003cp\u003e3.4 On the Applicability of AI to Finance 54\u003c\/p\u003e \u003cp\u003e3.4.1 Data Stationarity 57\u003c\/p\u003e \u003cp\u003e3.4.2 Data Quality 58\u003c\/p\u003e \u003cp\u003e3.4.3 Data Dimensionality 59\u003c\/p\u003e \u003cp\u003e3.5 Perspectives and Future Directions 60\u003c\/p\u003e \u003cp\u003eReferences 62\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Computational Intelligence – A Principled Approach for the Era of Data Exploration 63\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction to Computational Intelligence 63\u003c\/p\u003e \u003cp\u003e4.1.1 Defining Intelligence 63\u003c\/p\u003e \u003cp\u003e4.1.2 What is Computational Intelligence? 64\u003c\/p\u003e \u003cp\u003e4.1.3 Mapping the Field of Study 66\u003c\/p\u003e \u003cp\u003e4.1.4 Problems vs. Tools 68\u003c\/p\u003e \u003cp\u003e4.1.5 Current Challenges 69\u003c\/p\u003e \u003cp\u003e4.1.6 The Future of Computational Intelligence 70\u003c\/p\u003e \u003cp\u003e4.1.7 Examples in Finance 71\u003c\/p\u003e \u003cp\u003e4.2 The PAC Theory 72\u003c\/p\u003e \u003cp\u003e4.2.1 The Probably Approximately Correct Framework 73\u003c\/p\u003e \u003cp\u003e4.2.2 Why AI is a Very Lofty Goal to Achieve 75\u003c\/p\u003e \u003cp\u003e4.2.3 Examples of Ecorithms in Finance 78\u003c\/p\u003e \u003cp\u003e4.3 Technology Drivers Behind the ML Surge 81\u003c\/p\u003e \u003cp\u003e4.3.1 Data 82\u003c\/p\u003e \u003cp\u003e4.3.2 Algorithms 82\u003c\/p\u003e \u003cp\u003e4.3.3 Hardware Accelerators 82\u003c\/p\u003e \u003cp\u003eReferences 84\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 How to Apply the Principles of Computational Intelligence in Quantitative Finance 87\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 The Viability of Computational Intelligence 87\u003c\/p\u003e \u003cp\u003e5.2 On the Applicability of CI to Quantitative Finance 91\u003c\/p\u003e \u003cp\u003e5.3 A Brief Introduction to Reinforcement Learning 94\u003c\/p\u003e \u003cp\u003e5.3.1 Defining the Agent 96\u003c\/p\u003e \u003cp\u003e5.3.2 Model-Based Markov Decision Process 98\u003c\/p\u003e \u003cp\u003e5.3.3 Model-Free Reinforcement Learning 101\u003c\/p\u003e \u003cp\u003e5.4 Conclusions 104\u003c\/p\u003e \u003cp\u003eReferences 104\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Case Study 1: Optimizing Trade Execution 107\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction to the Problem 107\u003c\/p\u003e \u003cp\u003e6.1.1 On Limit Orders and Market Microstructure 109\u003c\/p\u003e \u003cp\u003e6.1.2 Formulation of Base-Line Strategies 111\u003c\/p\u003e \u003cp\u003e6.1.3 A Reinforcement Learning Formulation for the Optimized Execution Problem 112\u003c\/p\u003e \u003cp\u003e6.2 Current State-of-the-Art in Optimized Trade Execution 114\u003c\/p\u003e \u003cp\u003e6.3 Implementation Methodology 116\u003c\/p\u003e \u003cp\u003e6.3.1 Simulating the Interaction with the Market Microstructure 116\u003c\/p\u003e \u003cp\u003e6.3.2 Using Dynamic Programming to Optimize Trade Execution 118\u003c\/p\u003e \u003cp\u003e6.3.3 Using Reinforcement Learning to Optimize Trade Execution 119\u003c\/p\u003e \u003cp\u003e6.4 Empirical Results 122\u003c\/p\u003e \u003cp\u003e6.4.1 Application to Equities 122\u003c\/p\u003e \u003cp\u003e6.4.2 Using Private Variables Only 123\u003c\/p\u003e \u003cp\u003e6.4.3 Using Both Private and Market Variables 123\u003c\/p\u003e \u003cp\u003e6.4.4 Application to Futures 124\u003c\/p\u003e \u003cp\u003e6.4.5 Another Example 126\u003c\/p\u003e \u003cp\u003e6.5 Conclusions and Future Directions 127\u003c\/p\u003e \u003cp\u003e6.5.1 Further Research 127\u003c\/p\u003e \u003cp\u003eReferences 128\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Case Study 2: The Dynamics of the Limit Order Book 129\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction to the Problem 129\u003c\/p\u003e \u003cp\u003e7.1.1 The New Era of Prediction 130\u003c\/p\u003e \u003cp\u003e7.1.2 New Challenges 131\u003c\/p\u003e \u003cp\u003e7.1.3 High-Frequency Data 132\u003c\/p\u003e \u003cp\u003e7.2 Current State-of-the-Art in the Prediction of Directional Price Movement in the LOB 133\u003c\/p\u003e \u003cp\u003e7.2.1 The Contrarians 136\u003c\/p\u003e \u003cp\u003e7.3 Using Support Vector Machines and Random Forest Classifiers for Directional Price Forecast 138\u003c\/p\u003e \u003cp\u003e7.3.1 Empirical Results 139\u003c\/p\u003e \u003cp\u003e7.4 Studying the Dynamics of the LOB with Reinforcement Learning 141\u003c\/p\u003e \u003cp\u003e7.4.1 Empirical Results 142\u003c\/p\u003e \u003cp\u003e7.4.2 Conclusions 144\u003c\/p\u003e \u003cp\u003e7.5 Studying the Dynamics of the LOB with Deep Neural Networks 145\u003c\/p\u003e \u003cp\u003e7.5.1 Results 148\u003c\/p\u003e \u003cp\u003e7.6 Studying the Dynamics of the Limit Order Book with Long Short-Term Memory Networks 149\u003c\/p\u003e \u003cp\u003e7.6.1 Empirical Results 152\u003c\/p\u003e \u003cp\u003e7.6.2 Conclusions 153\u003c\/p\u003e \u003cp\u003e7.7 Studying the Dynamics of the LOB with Convolutional Neural Networks 153\u003c\/p\u003e \u003cp\u003e7.7.1 Empirical Results 155\u003c\/p\u003e \u003cp\u003e7.7.2 Conclusions 156\u003c\/p\u003e \u003cp\u003eReferences 157\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Case Study 3: Applying Machine Learning to Portfolio Management 159\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction to the Problem 159\u003c\/p\u003e \u003cp\u003e8.1.1 The Problem of Portfolio Diversification 160\u003c\/p\u003e \u003cp\u003e8.2 Current State-of-the-Art in Portfolio Modeling 161\u003c\/p\u003e \u003cp\u003e8.2.1 The Classic Approach 161\u003c\/p\u003e \u003cp\u003e8.2.2 The ML Approach 162\u003c\/p\u003e \u003cp\u003e8.3 A Deep Portfolio Approach to Portfolio Optimization 163\u003c\/p\u003e \u003cp\u003e8.3.1 Autoencoders 164\u003c\/p\u003e \u003cp\u003e8.3.2 Methodology – The Four-Step Algorithm 166\u003c\/p\u003e \u003cp\u003e8.3.3 Results 167\u003c\/p\u003e \u003cp\u003e8.4 A Q-Learning Approach to the Problem of Portfolio Optimization 167\u003c\/p\u003e \u003cp\u003e8.4.1 Problem Statement 168\u003c\/p\u003e \u003cp\u003e8.4.2 Methodology 169\u003c\/p\u003e \u003cp\u003e8.4.3 The Deep Q-Learning Algorithm 169\u003c\/p\u003e \u003cp\u003e8.4.4 Results 170\u003c\/p\u003e \u003cp\u003e8.5 A Deep Reinforcement Learning Approach to Portfolio Management 170\u003c\/p\u003e \u003cp\u003e8.5.1 Methodology 170\u003c\/p\u003e \u003cp\u003e8.5.2 Data 171\u003c\/p\u003e \u003cp\u003e8.5.3 The RL Setting: Agent, Environment, and\u003c\/p\u003e \u003cp\u003ePolicy 172\u003c\/p\u003e \u003cp\u003e8.5.4 The CNN Implementation 172\u003c\/p\u003e \u003cp\u003e8.5.5 The RNN and LSTM Implementations 172\u003c\/p\u003e \u003cp\u003e8.5.6 Results 173\u003c\/p\u003e \u003cp\u003eReferences 174\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Case Study 4: Applying Machine Learning to Market Making 175\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction to the Problem 175\u003c\/p\u003e \u003cp\u003e9.2 Current State-of-the-Art in Market Making 177\u003c\/p\u003e \u003cp\u003e9.3 Applications of Temporal-Difference RL in Market Making 180\u003c\/p\u003e \u003cp\u003e9.3.1 Methodology 180\u003c\/p\u003e \u003cp\u003e9.3.2 The Simulator 181\u003c\/p\u003e \u003cp\u003e9.3.3 Market Making Agent Specification 182\u003c\/p\u003e \u003cp\u003e9.3.4 Empirical Results 185\u003c\/p\u003e \u003cp\u003e9.4 Market Making in High-Frequency Trading Using RL 189\u003c\/p\u003e \u003cp\u003e9.4.1 Methodology 190\u003c\/p\u003e \u003cp\u003e9.4.2 Experimental Setting 191\u003c\/p\u003e \u003cp\u003e9.4.3 Results and Conclusions 192\u003c\/p\u003e \u003cp\u003e9.5 Other Research Studies 192\u003c\/p\u003e \u003cp\u003eReferences 193\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Case Study 5: Applications of Machine Learning to Derivatives Valuation 197\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction to the Problem 197\u003c\/p\u003e \u003cp\u003e10.1.1 Problem Statement and Research Questions 199\u003c\/p\u003e \u003cp\u003e10.2 Current State-of-the-Art in Derivatives Valuation by Applying ML 200\u003c\/p\u003e \u003cp\u003e10.2.1 The Beginnings: 1992–2004 201\u003c\/p\u003e \u003cp\u003e10.2.2 The Last Decade 202\u003c\/p\u003e \u003cp\u003e10.3 Using Deep Learning for Valuation of Derivatives 204\u003c\/p\u003e \u003cp\u003e10.3.1 Implementation Methodology 205\u003c\/p\u003e \u003cp\u003e10.3.2 Empirical Results 207\u003c\/p\u003e \u003cp\u003e10.3.3 Conclusions and Future Directions 208\u003c\/p\u003e \u003cp\u003e10.3.4 Other Research Studies 208\u003c\/p\u003e \u003cp\u003e10.4 Using RL for Valuation of Derivatives 210\u003c\/p\u003e \u003cp\u003e10.4.1 Using a Simple Markov Decision Process 210\u003c\/p\u003e \u003cp\u003e10.4.2 The Q-Learning Black-Scholes Model (QLBS) 212\u003c\/p\u003e \u003cp\u003eReferences 214\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Case Study 6: Using Machine Learning for Risk Management and Compliance 217\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e11.1 Introduction to the Problem 217\u003c\/p\u003e \u003cp\u003e11.1.1 Challenges 218\u003c\/p\u003e \u003cp\u003e11.1.2 The Problem 219\u003c\/p\u003e \u003cp\u003e11.2 Current State-of-the-Art for Applications of ML to Risk Management and Compliance 219\u003c\/p\u003e \u003cp\u003e11.2.1 Credit Risk 219\u003c\/p\u003e \u003cp\u003e11.2.2 Market Risk 220\u003c\/p\u003e \u003cp\u003e11.2.3 Operational Risk 221\u003c\/p\u003e \u003cp\u003e11.2.4 Regulatory Compliance Risk and RegTech 222\u003c\/p\u003e \u003cp\u003e11.2.5 Current Challenges and Future Directions 223\u003c\/p\u003e \u003cp\u003e11.3 Machine Learning in Credit Risk Modeling 224\u003c\/p\u003e \u003cp\u003e11.3.1 Data 225\u003c\/p\u003e \u003cp\u003e11.3.2 Models 225\u003c\/p\u003e \u003cp\u003e11.3.3 Results 226\u003c\/p\u003e \u003cp\u003e11.4 Using Deep Learning for Credit Scoring 227\u003c\/p\u003e \u003cp\u003e11.4.1 Introduction 227\u003c\/p\u003e \u003cp\u003e11.4.2 Deep Belief Networks and Restricted Boltzmann Machines 228\u003c\/p\u003e \u003cp\u003e11.4.3 Empirical Results 230\u003c\/p\u003e \u003cp\u003e11.5 Using ML in Operational Risk and Market Surveillance 230\u003c\/p\u003e \u003cp\u003e11.5.1 Introduction 230\u003c\/p\u003e \u003cp\u003e11.5.2 An ML Approach to Market Surveillance 232\u003c\/p\u003e \u003cp\u003e11.5.3 Conclusions 233\u003c\/p\u003e \u003cp\u003eReferences 233\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Conclusions and Future Directions 237\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e12.1 Concluding Remarks 237\u003c\/p\u003e \u003cp\u003e12.2 The Paradigm Shift 239\u003c\/p\u003e \u003cp\u003e12.2.1 Mathematical Models vs. Data Inference 240\u003c\/p\u003e \u003cp\u003e12.3 De-Noising the AI Hype 243\u003c\/p\u003e \u003cp\u003e12.3.1 Why Intellectual Honesty Should Not Be Abandoned 244\u003c\/p\u003e \u003cp\u003e12.4 An Emerging Engineering Discipline 245\u003c\/p\u003e \u003cp\u003e12.4.1 The Problem 246\u003c\/p\u003e \u003cp\u003e12.4.2 The Market 246\u003c\/p\u003e \u003cp\u003e12.4.3 A Possible Solution 246\u003c\/p\u003e \u003cp\u003e12.5 Future Directions 247\u003c\/p\u003e \u003cp\u003eReferences 248\u003c\/p\u003e \u003cp\u003eIndex 249\u003c\/p\u003e\n\u003c\/li\u003e\n\u003c\/ul\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":49407080595799,"sku":"9781119550501","price":45.12,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781119550501.jpg?v=1730498106","url":"https:\/\/bookcurl.com\/products\/applications-of-computational-intelligence-in-datadriven-trading-9781119550501","provider":"Book Curl","version":"1.0","type":"link"}