Intelligent Systems and Financial Forecasting

Häftad, Engelska, 1997

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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.

Produktinformation

Utgivningsdatum1997-04-28
Mått155 x 235 x 14 mm
Vikt371 g
FormatHäftad
SpråkEngelska
SeriePerspectives in Neural Computing
Antal sidor227
FörlagSpringer-Verlag Berlin and Heidelberg GmbH & Co. KG
ISBN9783540760986

Tillhör följande kategorier

Finansiering inom Ekonomi och ledarskap
Systemvetenskap och AI inom Data och it
Offentliga sektorn inom Ekonomi och ledarskap
Redovisning inom Ekonomi och ledarskap

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 Occam’s Razor and Network Architecture.- 5.4 Testing Occam’s Razor.- 5.5 Strategies using Occam’s 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 LiveTrading.- 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 Results.- 9.4 Conclusions.- 9.5 Future Work.- Appendices.- A Test Functions.- B ANTAS Outline Code.- C ANTAS Results.- References.