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Neural Networks and the Financial Markets

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Cover of 'Neural Networks and the Financial Markets'

Table of Contents

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    Book Overview
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    Chapter 1 Introduction to the Financial Markets
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    Chapter 2 Univariate and Multivariate Time Series Predictions
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    Chapter 3 Evidence of Predictability in Financial Markets
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    Chapter 4 Bond Pricing and the Yield Curve
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    Chapter 5 Data Selection
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    Chapter 6 General Form of Models of Financial Markets
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    Chapter 7 Overfitting, Generalisation and Regularisation
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    Chapter 8 The Bootstrap, Bagging and Ensembles
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    Chapter 9 Linear Models
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    Chapter 10 Input Selection
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    Chapter 11 Neural Networks
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    Chapter 12 Learning Trading Strategies for Imperfect Markets
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    Chapter 13 Dynamical Systems Perspective and Embedding
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    Chapter 14 Vector Machines
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    Chapter 15 Bayesian Methods and Evidence
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    Chapter 16 Yield Curve Modelling
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    Chapter 17 Predicting Bonds Using the Linear Relevance Vector Machine
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    Chapter 18 Artificial Neural Network
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    Chapter 19 Adaptive Lag Networks
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    Chapter 20 Network Integration
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    Chapter 21 Cointegration
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    Chapter 22 Joint Optimisation in Statistical Arbitrage Trading
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    Chapter 23 Univariate Modelling
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    Chapter 24 Combining Models
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    Chapter 25 Portfolio Optimisation
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    Chapter 26 Multi-Agent Modelling
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    Chapter 27 Financial Prediction Modelling: Summary and Future Avenues
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Citations

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Title
Neural Networks and the Financial Markets
Published by
Springer-Verlag London, January 2002
DOI 10.1007/978-1-4471-0151-2
ISBNs
978-1-4471-0151-2, 978-1-85233-531-1
Authors

Jimmy Shadbolt

Editors

Shadbolt, Jimmy, Taylor, John G.

X Demographics

X Demographics

The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 11 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 11 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 3 27%
Lecturer > Senior Lecturer 1 9%
Student > Bachelor 1 9%
Lecturer 1 9%
Student > Master 1 9%
Other 1 9%
Unknown 3 27%
Readers by discipline Count As %
Computer Science 3 27%
Business, Management and Accounting 2 18%
Economics, Econometrics and Finance 1 9%
Physics and Astronomy 1 9%
Earth and Planetary Sciences 1 9%
Other 0 0%
Unknown 3 27%