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Neural Networks and the Financial Markets : Predicting, Combining and Portfolio Optimisation - Jimmy Shadbolt

Neural Networks and the Financial Markets

Predicting, Combining and Portfolio Optimisation

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Published: 6th August 2002
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This is abook about the methods developed byour research team,over a period of 10years, for predicting financial market returns. Thework began in late 1991,at a time when one ofus (Jimmy Shadbolt) had just completed a rewrite of the software used at Econostat by the economics team for medium-term trend prediction of economic indica­ tors.Looking for anewproject,itwassuggestedthatwelook atnon-linear modelling of financial markets, and that a good place to start might be with neural networks. One small caveat should be added before we start: we use the terms "prediction" and "prediction model" throughout the book, although, with only such a small amount of information being extracted about future performance, can we really claim to be building predictors at all? Some might saythat the future ofmarkets, especially one month ahead, is too dim to perceive. We think we can claim to "predict" for two reasons. Firstlywedoindeedpredictafewper cent offuturevalues ofcertainassets in terms ofpast values ofcertainindicators, asshown by our trackrecord. Secondly, we use standard and in-house prediction methods that are purely quantitative. Weallow no subjective viewto alter what the models tell us. Thus weare doing prediction, even if the problem isvery hard. So while we could throughout the book talk about "getting a better view of the future" or some such euphemism, we would not be correctly describing what it isweare actually doing. Weare indeed getting abetter view of the future, by using prediction methods.

List of Contributors
Introduction to Prediction in the Financial Markets
Introduction to the Financial Markets
Univariate and Multivariate Time Series Predictions
Evidence of Predictability in Financial Markets
Bond Pricing and the Yield Curve
Data Selection
Theory of Prediction Modelling: General Form of Models of Financial Markets
Overfitting, Generalisation and Regularisation
The Bootstrap, Bagging and Ensembles
Linear Models
Input Selection
Theory of Specific Prediction Models: Neural Networks
Learning Trading Strategies for Imperfect Markets
Dynamical Systems Perspective and Embedding
Vector Machines
Bayesian Methods and Evidence
Prediction Model Applications: Yield Curve Modelling
Predicting Bonds Using the Linear Relevance Vector Machine
Artificial Neural Networks
Adaptive Lag Networks
Network Integration
Cointegration
Joint Optimisation in Statistical Arbitrage Trading
Univariate Modelling
Combining Models
Optimising and Beyond: Portfolio Optimisation
Multi-Agent Modelling
Finance Prediction Modelling: Summary and Future Avenues
References
Subject Index
Table of Contents provided by Publisher. All Rights Reserved.

ISBN: 9781852335311
ISBN-10: 1852335319
Series: Perspectives in Neural Computing
Audience: Professional
Format: Paperback
Language: English
Number Of Pages: 273
Published: 6th August 2002
Publisher: Springer London Ltd
Country of Publication: GB
Dimensions (cm): 23.5 x 15.5  x 1.52
Weight (kg): 0.9