Neural networks and the financial markets : predicting, combining and portfolio optimisation
著者
書誌事項
Neural networks and the financial markets : predicting, combining and portfolio optimisation
（Perspectives in neural computing）
Springer, c2002
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注記
Bibliography: p. 261-268
Includes index
内容説明・目次
内容説明
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.
目次
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.
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