Neural Networks and the Financial Markets : Predicting, Combining and Portfolio Optimisation /
This volume looks at financial prediction from a broad range of perspectives. It covers: - the economic arguments - the practicalities of the markets - how predictions are used - how predictions are made - how predictions are turned into something usable (asset locations) It combines a discussion of...
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| Format: | eBook |
| Language: | English |
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London :
Springer London,
2002.
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| Series: | Perspectives in neural computing.
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| Online Access: | Connect to the full text of this electronic book |
Table of Contents:
- List of Contributors
- Part I. 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
- Part II. Theory of Prediction Modelling: General Form of Models of Financial Markets. Overfitting, Generalisation and Regularisation. The Bootstrap, Bagging and Ensembles. Linear Models. Input Selection
- Part III. Theory of Specific Prediction Models: Neural Networks. Learning Trading Strategies for Imperfect Markets. Dynamical Systems Perspective and Embedding. Vector Machines. Bayesian Methods and Evidence
- Part IV. 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
- Part V. Optimising and Beyond: Portfolio Optimisation. Multi-Agent Modelling. Finance Prediction Modelling: Summary and Future Avenues
- References
- Subject Index.