Intelligent Systems and Financial Forecasting - Brossura

Kingdon, Jason

Brossura

ISBN 10: 3540760989 ISBN 13: 9783540760986

Casa editrice: Springer-Verlag, 1997

Riassunto

This book 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 t

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Contenuti

1 From Learning Systems to Financial Modelling.- 1.1 Introduction.- 1.2 Adaptive Systems and Financial Modelling.- 1.2.1 Financial Modelling: The Efficient Markets Hypothesis.- 1.2.2 Learning Systems.- 1.2.3 Technical Issues.- 1.3 Time Series Analysis.- 1.3.1 Fundamentals of Time Series Forecasting and Learning.- 1.4 Brief History of Neural Networks.- 1.4.1 The Development of Neural Net Techniques.- 1.4.2 More Recent Issues.- 1.5 Book Overview.- 1.5.1 Research Objectives.- 1.5.2 Book Structure.- 1.6 Summary.- 2 Adaptive Systems and Financial Modelling.- 2.1 Financial Modelling.- 2.2 The Problems with Financial Modelling.- 2.2.1 Fuzzy Rationality and Uncertainty.- 2.2.2 Efficient Markets and Price Movement.- 2.3 Evidence Against the Efficiency Hypothesis.- 2.4 An Adaptive Systems Approach.- 2.5 Neural Nets and Financial Modelling.- 2.5.1 Comparisons Between Neural Nets and Other Time Series Methods.- 2.6 Genetic Algorithms in Finance.- 2.6.1 The Genetic Algorithm Search Technique.- 2.6.2 Applications of Genetic Algorithms.- 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.- 3.5.1 Architecture.- 3.5.2 Activation Function.- 3.5.3 Learning Rules, Batch and On-Line Training.- 3.6 Network Performance.- 3.6.1 Convergence.- 3.6.2 Network Validation and Generalisation.- 3.6.3 Automated Validation.- 3.7 Summary.- 4 Genetic Algorithms.- 4.1 Using Genetic Algorithms.- 4.2 Search Algorithms.- 4.2.1 The GA Search Process: The Simple GA.- 4.2.2 Schema Analysis.- 4.2.3 Building Blocks Under Review.- 4.3 GA Parameters.- 4.3.1 The Shape of Space.- 4.3.2 Population Encodings.- 4.3.3 Crossover, Selection, Mutation and Populations.- 4.4 A Strategy for GA Search: Transmutation.- 4.4.1 Five New Algorithms: Morphic GAs (MGAs).- 4.5 Summary.- 5 Hypothesising Neural Nets.- 5.1 System Objectives.- 5.2 Hypothesising Neural Network Models.- 5.3 Occam’s Razor and Network Architecture.- 5.3.1 Existing Regulisation and Pruning Methods.- 5.3.2 Why use Occam’s Razor?.- 5.4 Testing Occam’s Razor.- 5.4.1 Generating Time Series.- 5.4.2 Artificial Network Generation (ANG).- 5.4.3 ANG Results.- 5.4.4 Testing Architectures.- 5.5 Strategies using Occam’s Razor.- 5.5.1 Minimally Descriptive Nets.- 5.5.2 Network Model.- 5.5.3 Network Regression Pruning (NRP).- 5.5.4 Results of NRP on ANG Series.- 5.5.5 Interpretation of the Pruning Error Profiles.- 5.5.6 Determining Topologies.- 5.6 Validation.- 5.7 GA-NN Hybrids: Representations.- 5.7.1 Fitness Measures for GA-NN Hybrids.- 5.7.2 Neural Networks and GAs: Fitness Measure for Generalisation.- 5.8 Summary.- 6 Automating Neural Net Time Series Analysis.- 6.1 System Objectives.- 6.2 ANTAS.- 6.2.1 Stage I: Primary Modelling.- 6.2.2 Stage II: Secondary Modelling.- 6.2.3 Stage III: System Modelling.- 6.3 Primary Modelling.- 6.3.1 Automating the use of Neural Nets.- 6.3.2 GA Rule-Based Modelling.- 6.4 Secondary Modelling.- 6.4.1 Generating Secondary Models.- 6.4.2 Model Integration.- 6.4.3 Model Performance Statistics.- 6.5 Validation Modules.- 6.6 Control Flow.- 6.6.1 Neural Net Control.- 6.6.2 GA Control.- 6.7 Summary.- 7 The Data: The Long Gilt Futures Contract.- 7.1 The Long Gilt Futures Contract.- 7.2 The LGFC Data.- 7.2.1 Time Series Construction.- 7.3 Secondary Data.- 7.4 Data Preparation.- 7.4.1 LGFC Data Treatment.- 7.4.2 Using Moving Averages.- 7.5 Data Treatment Modules.- 7.5.1 Moving Average 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.- 8.2.1 NN Hypothesis Modules (Phase I).- 8.2.2 Results for GA-NN Module.- 8.2.3 In-Sample Testing and Validation of the 15–4 Neural Network.- 8.3 GA-RB Module and Combined Validation.- 8.4 Phase II ― Secondary GA-RB Models.- 8.4.1 Secondary Model Control Module.- 8.5 Phase III ― Validation and Simulated Live Trading.- 8.6 Controls: Analysis of ANTAS.- 8.6.1 Choosing a Network Architecture.- 8.6.2 GA Control Tests.- 8.6.3 Second Order Modelling.- 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 Results.- 9.3.1 Morphic Genetic Algorithms using Base Changes.- 9.3.2 Artificial Network Generation.- 9.3.3 Network Regression Pruning.- 9.3.4 ANTAS and the Long Gilt Futures Contract.- 9.3.5 Results.- 9.4 Conclusions.- 9.5 Future Work.- Appendices.- A Test Functions.- B ANTAS Outline Code.- C ANTAS Results.- References.

Dettagli dell'edizione

• Editore: Springer-Verlag
• Data di pubblicazione: 1997
• Lingua: Inglese
• ISBN 10: 3540760989
• ISBN 13: 9783540760986
• Rilegatura: Copertina flessibile
• Numero di pagine: 240
• Contatto del produttore: Springer-Verlag GmbH; customerservice@springernature.com; ; Tiergartenstraße 17; Heidelberg; Baden-Württemberg; 69121; Germania