Neural Networks in the Capital Markets

Neural Networks in the Capital Markets

3.33 (6 ratings by Goodreads)

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Description

This study offers comprehensive coverage of neural network applications in the capital markets, combining the viewpoints of those involved in finance with neural network engineers. The author takes a pragmatic view, treating neural networks as computational equivalents to well-understood, non-parametric inference methods in decision science. He makes comparisons with established techniques where appropriate. The book is based on original papers which represent new and significant research, development and applications in finance and investment.
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Product details

  • Hardback | 350 pages
  • 158.5 x 236.5 x 31mm | 730.3g
  • John Wiley & Sons Ltd
  • Chichester, United Kingdom
  • English
  • 0471943649
  • 9780471943648

Table of contents

PART ONE: NEURAL NETWORKS: Introduction; Design Considerations; Methods for Optimal Network Design; Data Modelling Considerations; Testing Strategies and Metrics; PART TWO: EQUITY APPLICATIONS: Modelling Stock Returns in the Framework of APT: A Comparative Study with Regression Models; Testing the Efficient Markets Hypothesis with Gradient Descent Algorithms; Neural Networks as an Alternative Market Model; PART THREE: FOREIGN EXCHANGE APPLICATIONS: The Foreign Exchange Markets; Nonlinear Modelling of the US$/DM Exchange Rate; Managing Exchange Rate Trading Strategies; Financial Market Applications of Learning from Hints; Machine Learning for Foreign Exchange Trading; Indicator Selection; PART FOUR: BOND APPLICATIONS: Criteria for Performance in Gilt Futures Pricing; Bond Rating with Neural Networks; PART FIVE: MACRO-ECONOMIC FORECASTING APPLICATIONS: Bankruptcy Prediction: a Comparison of Discriminant Analysis with Neural Networks; Predicting Corporate Mergers Using Backpropagation Networks; Self-Organizing Neural Networks: the Financial State of Spanish Companies.
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Rating details

6 ratings
3.33 out of 5 stars
5 17% (1)
4 17% (1)
3 50% (3)
2 17% (1)
1 0% (0)
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