Description of Intelligent Systems and Financial Forecasting
This text examines the design of an automated system for financial time series forecasting. It explores the level of automation which can be achieved by a system for modelling a given financial time series with the minimum of human intervention. It aims to help the reader understand the issues involved in setting neural network, or genetic algorithm parameters, and to develop methods to deal with the problems they raise in a practical manner.
Contents of Intelligent Systems and Financial Forecasting
1 From Learning Systems to Financial Modelling 1.1 Introduction 1.2 Adaptive Systems and Financial Modelling 1.3 Time Series Analysis 1.4 Brief History of Neural Networks 1.5 Book Overview 1.6 Summary 2 Adaptive Systems and Financial Modelling 2.1 Financial Modelling 2.2 The Problems with Financial Modelling 2.3 Evidence Against the Efficiency Hypothesis 2.4 An Adaptive Systems Approach 2.5 Neural Nets and Financial Modelling 2.6 Genetic Algorithms in Finance 2.7 Summary 3 Feed-Forward Neural Network Modelling 3.1 Neural Net Search 3.2 MLP Training: The Model 3.3 MLP: Model Parameters 3.4 The Data 3.5 MLP: Training Parameters 3.6 Network Performance 3.7 Summary 4 Genetic Algorithms 4.1 Using Genetic Algorithms 4.2 Search Algorithms 4.3 GA Parameters 4.4 A Strategy for GA Search: Transmutation 4.5 Summary 5 Hypothesising Neural Nets 5.1 System Objectives 5.2 Hypothesising Neural Network Models 5.3 OccamUs Razor and Network Architecture 5.4 Testing OccamUs Razor 5.5 Strategies using OccamUs Razor 5.6 Validation 5.7 GA-NN Hybrids: Representations 5.8 Summary 6 Automating Neural Net Time Series Analysis 6.1 System Objectives 6.2 ANTAS 6.3 Primary Modelling 6.4 Secondary Modelling 6.5 Validation Modules 6.6 Control Flow 6.7 Summary 7 The Data: The Long Gilt Futures Contract 7.1 The Long Gilt Futures Contract 7.2 The LGFC Data 7.3 Secondary Data 7.4 Data Preparation 7.5 Data Treatment Modules 7.6 Efficient Market Hypothesis and the LGFC 7.7 Summary 8 Experimental Results 8.1 Experimental Design 8.2 Phase I - Primary Models 8.3 GA-RB Module and Combined Validation 8.4 Phase II - Secondary GA-RB Models 8.5 Phase III - Validation and Simulated Live Trading 8.6 Controls: Analysis of ANTAS 8.7 ANTAS: Conclusions 8.8 Summary 9 Summary, Conclusions and Future Work 9.1 Motivations 9.2 Objectives: Neural Networks and Learning 9.3 Book Outline and Result 9.4 Conclusions 9.5 Future Work Appendices A Test Functions B ANTAS Outline Code C ANTAS Results References Index
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