Neural Networks and the Financial Markets

Predicting, Combining and Portfolio Optimisation
1. Auflage. 2002
ISBN: 978-1-85233-531-1
Verlag: Springer

Buch, Englisch, 273 Seiten, Paperback, Format (B × H): 155 mm x 235 mm, Gewicht: 441 g

Reihe: Perspectives in Neural Computing

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.

Zielgruppe

Research

Autoren/Hrsg.

Shadbolt, Jimmy

Taylor, John G.

Fachgebiete

• Wirtschaftswissenschaften Finanzsektor & Finanzdienstleistungen Unternehmensfinanzierung
• Wirtschaftswissenschaften Betriebswirtschaft Wirtschaftsmathematik und -statistik
• Wirtschaftswissenschaften Finanzsektor & Finanzdienstleistungen Anlagen & Wertpapiere
• Wirtschaftswissenschaften Volkswirtschaftslehre Volkswirtschaftslehre Allgemein Ökonometrie
• Technische Wissenschaften Elektronik | Nachrichtentechnik Elektronik Robotik
• Wirtschaftswissenschaften Finanzsektor & Finanzdienstleistungen Bankwirtschaft
• Wirtschaftswissenschaften Betriebswirtschaft Unternehmensfinanzen Finanzierung, Investition, Leasing
• Mathematik | Informatik EDV | Informatik Technische Informatik Systemverwaltung & Management
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Wissensbasierte Systeme, Expertensysteme
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Fuzzy-Systeme

Inhaltsverzeichnis

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