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Neural Networks and the Financial Markets

Predicting, Combining and Portfolio Optimisation

  • Book
  • © 2002

Overview

Editors:
  1. Jimmy Shadbolt

    1. Econostat New Quant Limited, Berkshire, UK

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    You can also search for this editor in PubMed   Google Scholar

  2. John G. Taylor

    1. Centre for Neural Networks, Department of Mathematics, King’s College, London, UK

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    You can also search for this editor in PubMed   Google Scholar

  • Provides the most up-to-date overview of information processing techniques as applied to cutting-edge financial problems
  • Includes supplementary material: sn.pub/extras

Part of the book series: Perspectives in Neural Computing (PERSPECT.NEURAL)

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Table of contents (27 chapters)

  1. Front Matter

    Pages i-xiii
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  2. Introduction to Prediction in the Financial Markets

    1. Front Matter

      Pages 1-1
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    2. Introduction to the Financial Markets

      • John G. Taylor
      Pages 3-9

    3. Univariate and Multivariate Time Series Predictions

      • John G. Taylor
      Pages 11-22

    4. Evidence of Predictability in Financial Markets

      • Neville Towers
      Pages 23-33

    5. Bond Pricing and the Yield Curve

      • Sebastian Larsson
      Pages 35-40

    6. Data Selection

      • Jimmy Shadbolt
      Pages 41-45

  3. Theory of Prediction Modelling

    1. Front Matter

      Pages 47-47
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    2. General Form of Models of Financial Markets

      • John G. Taylor
      Pages 49-53

    3. Overfitting, Generalisation and Regularisation

      • Jimmy Shadbolt
      Pages 55-59

    4. The Bootstrap, Bagging and Ensembles

      • Jimmy Shadbolt
      Pages 61-67

    5. Linear Models

      • Neville Towers
      Pages 69-76

    6. Input Selection

      • Jimmy Shadbolt, John G. Taylor
      Pages 77-83

  4. Theory of Specific Prediction Models

    1. Front Matter

      Pages 85-85
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    2. Neural Networks

      • John G. Taylor
      Pages 87-93

    3. Learning Trading Strategies for Imperfect Markets

      • Neville Towers, A. N. Burgess
      Pages 95-108

    4. Dynamical Systems Perspective and Embedding

      • Neep Hazarika
      Pages 109-115

    5. Vector Machines

      • Neep Hazarika, John G. Taylor
      Pages 117-121

    6. Bayesian Methods and Evidence

      • Sebastian Larsson
      Pages 123-130

  5. Prediction Model Applications

    1. Front Matter

      Pages 131-132
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Keywords

Editors and Affiliations

  • Econostat New Quant Limited, Berkshire, UK

    Jimmy Shadbolt

  • Centre for Neural Networks, Department of Mathematics, King’s College, London, UK

    John G. Taylor

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