Neural Networks and the Financial Markets

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.

Part 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

Part 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

Part 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

Part 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

Part V Optimising and Beyond

• 25 Portfolio Optimisation
• 26 Multi-Agent Modelling
• 27 Financial Prediction Modelling:Summary and Future
• Avenues
• Further Reading
• References