Ebook: 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 standard theory with state-of-the-art material on a wide range of information processing techniques as applied to cutting-edge financial problems. All the techniques are demonstrated with real examples using actual market data, and show that it is possible to extract information from very noisy, sparse data sets.
Aimed primarily at researchers in financial prediction, time series analysis and information processing, this book will also be of interest to quantitative fund managers and other professionals involved in financial prediction.

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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 standard theory with state-of-the-art material on a wide range of information processing techniques as applied to cutting-edge financial problems. All the techniques are demonstrated with real examples using actual market data, and show that it is possible to extract information from very noisy, sparse data sets.
Aimed primarily at researchers in financial prediction, time series analysis and information processing, this book will also be of interest to quantitative fund managers and other professionals involved in financial prediction.
Content:
Front Matter....Pages i-xiii
Front Matter....Pages 1-1
Introduction to the Financial Markets....Pages 3-9
Univariate and Multivariate Time Series Predictions....Pages 11-22
Evidence of Predictability in Financial Markets....Pages 23-33
Bond Pricing and the Yield Curve....Pages 35-40
Data Selection....Pages 41-45
Front Matter....Pages 47-47
General Form of Models of Financial Markets....Pages 49-53
Overfitting, Generalisation and Regularisation....Pages 55-59
The Bootstrap, Bagging and Ensembles....Pages 61-67
Linear Models....Pages 69-76
Input Selection....Pages 77-83
Front Matter....Pages 85-85
Neural Networks....Pages 87-93
Learning Trading Strategies for Imperfect Markets....Pages 95-108
Dynamical Systems Perspective and Embedding....Pages 109-115
Vector Machines....Pages 117-121
Bayesian Methods and Evidence....Pages 123-130
Front Matter....Pages 131-132
Yield Curve Modelling....Pages 133-143
Predicting Bonds Using the Linear Relevance Vector Machine....Pages 145-155
Artificial Neural Network....Pages 157-166
Adaptive Lag Networks....Pages 167-174
Network Integration....Pages 175-179
Front Matter....Pages 131-132
Cointegration....Pages 181-191
Joint Optimisation in Statistical Arbitrage Trading....Pages 193-201
Univariate Modelling....Pages 203-210
Combining Models....Pages 211-217
Front Matter....Pages 219-219
Portfolio Optimisation....Pages 221-246
Multi-Agent Modelling....Pages 247-252
Financial Prediction Modelling: Summary and Future Avenues....Pages 253-258
Back Matter....Pages 259-273

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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 standard theory with state-of-the-art material on a wide range of information processing techniques as applied to cutting-edge financial problems. All the techniques are demonstrated with real examples using actual market data, and show that it is possible to extract information from very noisy, sparse data sets.
Aimed primarily at researchers in financial prediction, time series analysis and information processing, this book will also be of interest to quantitative fund managers and other professionals involved in financial prediction.
Content:
Front Matter....Pages i-xiii
Front Matter....Pages 1-1
Introduction to the Financial Markets....Pages 3-9
Univariate and Multivariate Time Series Predictions....Pages 11-22
Evidence of Predictability in Financial Markets....Pages 23-33
Bond Pricing and the Yield Curve....Pages 35-40
Data Selection....Pages 41-45
Front Matter....Pages 47-47
General Form of Models of Financial Markets....Pages 49-53
Overfitting, Generalisation and Regularisation....Pages 55-59
The Bootstrap, Bagging and Ensembles....Pages 61-67
Linear Models....Pages 69-76
Input Selection....Pages 77-83
Front Matter....Pages 85-85
Neural Networks....Pages 87-93
Learning Trading Strategies for Imperfect Markets....Pages 95-108
Dynamical Systems Perspective and Embedding....Pages 109-115
Vector Machines....Pages 117-121
Bayesian Methods and Evidence....Pages 123-130
Front Matter....Pages 131-132
Yield Curve Modelling....Pages 133-143
Predicting Bonds Using the Linear Relevance Vector Machine....Pages 145-155
Artificial Neural Network....Pages 157-166
Adaptive Lag Networks....Pages 167-174
Network Integration....Pages 175-179
Front Matter....Pages 131-132
Cointegration....Pages 181-191
Joint Optimisation in Statistical Arbitrage Trading....Pages 193-201
Univariate Modelling....Pages 203-210
Combining Models....Pages 211-217
Front Matter....Pages 219-219
Portfolio Optimisation....Pages 221-246
Multi-Agent Modelling....Pages 247-252
Financial Prediction Modelling: Summary and Future Avenues....Pages 253-258
Back Matter....Pages 259-273
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