predicting, combining, and portfolio optimisation /
First Statement of Responsibility
Jimmy Shadbolt and John G. Taylor (eds.)
.PUBLICATION, DISTRIBUTION, ETC
Place of Publication, Distribution, etc.
London :
Name of Publisher, Distributor, etc.
Springer,
Date of Publication, Distribution, etc.
2002
PHYSICAL DESCRIPTION
Specific Material Designation and Extent of Item
xiii, 273 pages :
Other Physical Details
illustrations ;
Dimensions
24 cm
SERIES
Series Title
Perspectives in neural computing
INTERNAL BIBLIOGRAPHIES/INDEXES NOTE
Text of Note
Includes bibliographical references (pages 261-268) and index
CONTENTS NOTE
Text of Note
Pt. 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 -- Pt. 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 -- Pt. 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 -- Pt. 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 -- Pt. V. Optimising and Beyond -- 25. Portfolio Optimisation -- 26. Multi-Agent Modelling -- 27. Financial Prediction Modelling: Summary and Future Avenues