This book explores the intuitive appeal of neural networks and the genetic algorithm in finance. It demonstrates how neural networks used in combination with evolutionary computation outperform classical econometric methods for accuracy in forecasting, classification and dimensionality reduction. McNelis utilizes a variety of examples, from forecasting automobile production and corporate bond spread, to inflation and deflation processes in Hong Kong and Japan, to credit card default in Germany to bank failures in Texas, to cap-floor volatilities in New York and Hong Kong. * Offers a balanced, critical review of the neural network methods and genetic algorithms used in finance * Includes numerous examples and applications * Numerical illustrations use MATLAB code and the book is accompanied by a website
Author(s): Marc Israel, J. Steven Jones, Steve Jones
Series: Advanced Finance
Publisher: Academic Press
Year: 2005
Language: English
Pages: 261
1Introduction......Page 18
1.2 Synergies......Page 21
1.3 The Interface Problems......Page 23
1.4 Plan of the Book......Page 25
2What Are Neural Networks?......Page 30
2.2 GARCH Nonlinear Models......Page 32
2.3 Model Typology......Page 37
2.5 Neural Network Smooth-Transition RegimeSwitching Models......Page 55
2.7 Neural Networks and Discrete Choice......Page 66
2.8 The Black Box Criticism and Data Mining......Page 72
2.9 Conclusion......Page 74
2.9.1 MATLAB Program Notes......Page 75
3.1 Data Preprocessing......Page 76
3.2 The Nonlinear Estimation Problem......Page 82
3.2.1 Local Gradient-Based Search: The Quasi-NewtonMethod and Backpropagation......Page 84
3.2.2 Stochastic Search: Simulated Annealing......Page 87
3.2.3 Evolutionary Stochastic Search: The GeneticAlgorithm......Page 89
3.2.5 Hybridization: Coupling Gradient-Descent,Stochastic, and Genetic Search Methods......Page 92
3.3 Repeated Estimation and Thick Models......Page 94
3.4 MATLAB Examples: NumericalOptimization and Network Performance......Page 95
3.4.2 Approximation with Polynomials andNeural Networks......Page 97
3.5 Conclusion......Page 100
4Evaluation of Network Estimation......Page 102
5Estimating and Forecasting withArtificial Data......Page 132
5.2 Stochastic Chaos Model......Page 134
5.2.2 Out-of-Sample Performance......Page 137
5.3 Stochastic Volatility/Jump Diffusion Model......Page 139
5.4 The Markov Regime Switching Model......Page 143
5.5 Volatility Regime Switching Model......Page 147
5.6 Distorted Long-Memory Model......Page 152
5.7 Black-Sholes Option Pricing Model: ImpliedVolatility Forecasting......Page 154
5.8 Conclusion......Page 159
6.1 Forecasting Production in the AutomotiveIndustry......Page 162
6.2 Corporate Bonds: Which Factors Determinethe Spreads?......Page 173
