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E-raamat: Neural Networks and the Financial Markets: Predicting, Combining and Portfolio Optimisation

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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.

Muu info

Springer Book Archives
List of Contributors
xiii
Part I Introduction to Prediction in the Financial Markets
Introduction to the Financial Markets
3(8)
The Financial Markets
3(2)
Economics and the Markets
5(1)
Financial Markets and Economic Data
6(1)
What Are We Predicting?
7(1)
The Overall Process
8(3)
Univariate and Multivariate Time Series Predictions
11(12)
Philosophical Assumptions
11(5)
Data Requirements
16(6)
Summary
22(1)
Evidence of Predictability in Financial Markets
23(12)
Overview
23(1)
Review of Theoretical Arguments
24(2)
Review of Empirical Research
26(3)
Predictability Tests
29(3)
Beyond Tests for Market Efficiency
32(1)
Summary
32(3)
Bond Pricing and the Yield Curve
35(6)
The Time Value of Money and Discount Factors
35(1)
Pricing Bonds
36(1)
Bond Yield and the Yield Curve
37(1)
Duration and Convexity
38(1)
Summary
39(2)
Data Selection
41(8)
Introduction
41(1)
The General Economic Model
42(1)
Proxies
42(1)
Principal Components
43(2)
Summary
45(4)
Part II Theory of Prediction Modelling
General Form of Models of Financial Markets
49(6)
Introduction
49(1)
Cost Functions
49(2)
Parameterisation
51(1)
Econometric Models
52(1)
Summary
53(2)
Overfitting, Generalisation and Regularisation
55(6)
Overfitting and Generalisation
55(1)
Early Stopping
56(1)
Information Criteria
57(1)
Regularisation
57(1)
Weight Decay
58(1)
Forgetting
58(1)
Summary
59(2)
The Bootstrap, Bagging and Ensembles
61(8)
Introduction
61(1)
The Bias-Variance Trade-Off
61(1)
The Bootstrap
62(1)
Bagging
63(2)
Bootstrap with Noise
65(1)
Decorrelated Models
66(1)
Ensembles in Financial Market Prediction
67(2)
Linear Models
69(8)
Introduction
69(1)
Review of Linear Forecasting Methods
70(1)
Moving Average/Smoothing Methods
70(2)
ARMA, ARIMA and Time Series Regression Models
72(1)
Cointegration and Error Correction Models
73(1)
Ridge Regression
74(1)
State Space Models
75(1)
Summary
76(1)
Input Selection
77(10)
Introduction
77(1)
Input Selection
77(4)
Mutual Information
81(2)
Summary
83(4)
Part III Theory of Specific Prediction Models
Neural Networks
87(8)
What Are Neural Networks?
87(2)
The Living Neuron
89(1)
The Artificial or Formal Neuron
89(1)
Neural Network Architectures
90(2)
Neural Network Training Rules
92(1)
Further Comments on Neural Networks
93(2)
Learning Trading Strategies for Imperfect Markets
95(14)
Introduction
95(1)
Trading Predictability
96(2)
Modelling Trading Strategies
98(3)
Experimental Design and Simulation Experiments
101(7)
Summary
108(1)
Dynamical Systems Perspective and Embedding
109(8)
Introduction
109(3)
Practical Problems
112(1)
Characterising and Measuring Complexity
113(1)
SVD Smoothing
114(1)
Summary
115(2)
Vector Machines
117(6)
Introduction
117(1)
Support Vector Machines
117(1)
Relevance Vector Machines
118(2)
Optimising the Hyperparameters for Regression
120(1)
Optimising the Hyperparameters for Classification
120(1)
Summary
121(2)
Bayesian Methods and Evidence
123(10)
Bayesian Methods
123(1)
A Bayesian View of Probability
123(2)
Hypothesis Testing
125(2)
The Bayesian Evidence Ratio
127(3)
Conclusions
130(3)
Part IV Prediction Model Applications
Yield Curve Modelling
133(12)
Yield Curve Modelling
133(1)
Yield Curve Data
133(2)
Yield Curve Parameterisation
135(5)
Predicting the Yield Curve
140(2)
Conclusion
142(3)
Predicting Bonds Using the Linear Relevance Vector Machine
145(12)
Introduction
145(1)
The RVM as a Predictor
146(2)
Input Variable Selection
148(6)
Summary and Conclusions
154(3)
Artificial Neural Networks
157(10)
Introduction
157(1)
Artificial Neural Networks
157(6)
Models
163(2)
Summary
165(2)
Adaptive Lag Networks
167(8)
The Problem
167(1)
Adaptive Lag Networks
167(2)
Training the Adaptive Lag Network
169(1)
Test Results
170(1)
Modelling
171(3)
Summary and Conclusions
174(1)
Network Integration
175(6)
Making Predictions with Network Ensembles
175(2)
The Network Integrator
177(1)
The Random Vector Functional Link (RVFL)
178(1)
Summary
179(2)
Cointegration
181(12)
Introduction
181(2)
Construction of Statistical Mispricings
183(1)
Conditional Statistical Arbitrage Strategies
184(1)
Application of Cointegration-Based Methodology to FTSE 100 Stocks
185(1)
Empirical Results of Conditional Statistical Arbitrage Models
185(6)
Summary
191(2)
Joint Optimisation in Statistical Arbitrage Trading
193(10)
Introduction
193(1)
Statistical Mispricing
194(1)
Controlling the Properties of the Forecasting Model
195(1)
Modelling the Trading Strategy
196(1)
Joint Optimisation
197(1)
Empirical Experiments
197(4)
Summary
201(2)
Univariate Modelling
203(8)
Introduction
203(1)
Nearest Neighbours
203(2)
The Group Method of Data Handling (GMDH)
205(2)
The Support Vector Machine (SVM) Predictor Model
207(2)
The Relevance Vector Machine (RVM)
209(2)
Combining Models
211(10)
Introduction
211(1)
Linear Combiners
212(1)
A Temperature-Dependent SOFTMAX Combiner
212(1)
The Combiner Algorithm
213(3)
Results
216(1)
Conclusions
217(4)
Part V Optimising and Beyond
Portfolio Optimisation
221(26)
Portfolio Optimisation
221(1)
Notation and Terminology
222(2)
Scope of Portfolio Optimisation Methods
224(1)
Efficient Set Mathematics and the Efficient Frontier
225(4)
Construction of Optimised Portfolios Using Quadratic Programming
229(1)
Issues in Practical Portfolio Construction
230(3)
What Portfolio Selection Requires
233(1)
The Process of Building an Optimised Portfolio
234(2)
Example of an Asset Allocation Portfolio
236(5)
Alternative Measures of Risk and Methods of Optimisation
241(4)
Questions about Portfolio Optimisation and Discussion
245(2)
Multi-Agent Modelling
247(6)
Introduction
247(1)
The Minority Game
248(1)
A General Multi-agent Approach to the Financial Markets
249(2)
Conclusions
251(2)
Financial Prediction Modelling: Summary and Future Avenues
253(6)
Summary of the Results
253(2)
Underlying Aspects of the Approach
255(2)
Future Avenues
257(2)
Further Reading 259(2)
References 261(8)
Index 269