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Intelligent Systems and Financial Forecasting

  • Book
  • © 1997

Overview

Authors:
  1. Jason Kingdon

    1. Searchspace Limited, London, UK

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

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Table of contents (9 chapters)

  1. Front Matter

    Pages i-xii
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  2. From Learning Systems to Financial Modelling

    • Jason Kingdon
    Pages 1-17

  3. Adaptive Systems and Financial Modelling

    • Jason Kingdon
    Pages 19-35

  4. Feed-Forward Neural Network Modelling

    • Jason Kingdon
    Pages 37-53

  5. Genetic Algorithms

    • Jason Kingdon
    Pages 55-80

  6. Hypothesising Neural Nets

    • Jason Kingdon
    Pages 81-106

  7. Automating Neural Net Time Series Analysis

    • Jason Kingdon
    Pages 107-123

  8. The Data: The Long Gilt Futures Contract

    • Jason Kingdon
    Pages 125-136

  9. Experimental Results

    • Jason Kingdon
    Pages 137-161

  10. Summary, Conclusions and Future Work

    • Jason Kingdon
    Pages 163-177

  11. Back Matter

    Pages 179-227
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Keywords

About this book

A fundamental objective of Artificial Intelligence (AI) is the creation of in­ telligent computer programs. In more modest terms AI is simply con­ cerned with expanding the repertoire of computer applications into new domains and to new levels of efficiency. The motivation for this effort comes from many sources. At a practical level there is always a demand for achieving things in more efficient ways. Equally, there is the technical challenge of building programs that allow a machine to do something a machine has never done before. Both of these desires are contained within AI and both provide the inspirational force behind its development. In terms of satisfying both of these desires there can be no better example than machine learning. Machines that can learn have an in-built effi­ ciency. The same software can be applied in many applications and in many circumstances. The machine can adapt its behaviour so as to meet the demands of new, or changing, environments without the need for costly re-programming. In addition, a machine that can learn can be ap­ plied in new domains with the genuine potential for innovation. In this sense a machine that can learn can be applied in areas where little is known about possible causal relationships, and even in circumstances where causal relationships are judged not to exist. This last aspect is of major significance when considering machine learning as applied to fi­ nancial forecasting.

Authors and Affiliations

  • Searchspace Limited, London, UK

    Jason Kingdon

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