UID:
almahu_9948026663602882
Format:
1 online resource (261 p.)
ISBN:
1-281-00826-5
,
9786611008260
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0-08-047965-0
Series Statement:
Academic Press advanced finance series
Content:
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.* Offe
Note:
Description based upon print version of record.
,
Front cover; Title page; Copyright page; Table of contents; 1 Introduction; 1.1 Forecasting, Classification, and Dimensionality Reduction; 1.2 Synergies; 1.3 The Interface Problems; 1.4 Plan of the Book; Part I Econometric Foundations; 2 What Are Neural Networks?; 2.1 Linear Regression Model; 2.2 GARCH Nonlinear Models; 2.2.1 Polynomial Approximation; 2.2.2 Orthogonal Polynomials; 2.3 Model Typology; 2.4 What Is A Neural Network?; 2.4.1 Feedforward Networks; 2.4.2 Squasher Functions; 2.4.3 Radial Basis Functions; 2.4.4 Ridgelet Networks; 2.4.5 Jump Connections
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2.4.6 Multilayered Feedforward Networks2.4.7 Recurrent Networks; 2.4.8 Networks with Multiple Outputs; 2.5 Neural Network Smooth-Transition Regime Switching Models; 2.5.1 Smooth-Transition Regime Switching Models; 2.5.2 Neural Network Extensions; 2.6 Nonlinear Principal Components: Intrinsic Dimensionality; 2.6.1 Linear Principal Components; 2.6.2 Nonlinear Principal Components; 2.6.3 Application to Asset Pricing; 2.7 Neural Networks and Discrete Choice; 2.7.1 Discriminant Analysis; 2.7.2 Logit Regression; 2.7.3 Probit Regression; 2.7.4 Weibull Regression
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2.7.5 Neural Network Models for Discrete Choice2.7.6 Models with Multinomial Ordered Choice; 2.8 The Black Box Criticism and Data Mining; 2.9 Conclusion; 2.9.1 MATLAB Program Notes; 2.9.2 Suggested Exercises; 3 Estimation of a Network with Evolutionary Computation; 3.1 Data Preprocessing; 3.1.1 Stationarity: Dickey-Fuller Test; 3.1.2 Seasonal Adjustment: Correction for Calendar Effects; 3.1.3 Data Scaling; 3.2 The Nonlinear Estimation Problem; 3.2.1 Local Gradient-Based Search: The Quasi-Newton Method and Backpropagation; 3.2.2 Stochastic Search: Simulated Annealing
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3.2.3 Evolutionary Stochastic Search: The Genetic Algorithm3.2.4 Evolutionary Genetic Algorithms; 3.2.5 Hybridization: Coupling Gradient-Descent, Stochastic, and Genetic Search Methods; 3.3 Repeated Estimation and Thick Models; 3.4 MATLAB Examples: Numerical Optimization and Network Performance; 3.4.1 Numerical Optimization; 3.4.2 Approximation with Polynomials and Neural Networks; 3.5 Conclusion; 3.5.1 MATLAB Program Notes; 3.5.2 Suggested Exercises; 4 Evaluation of Network Estimation; 4.1 In-Sample Criteria; 4.1.1 Goodness of Fit Measure; 4.1.2 Hannan-Quinn Information Criterion
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4.1.3 Serial Independence: Ljung-Box and McLeod-Li Tests4.1.5 Normality; 4.1.6 Neural Network Test for Neglected Nonlinearity: Lee-White-Granger Test; 4.1.7 Brock-Deckert-Scheinkman Test for Nonlinear Patterns; 4.1.8 Summary of In-Sample Criteria; 4.1.9 MATLAB Example; 4.2 Out-of-Sample Criteria; 4.2.1 Recursive Methodology; 4.2.2 Root Mean Squared Error Statistic; 4.2.3 Diebold-Mariano Test for Out-of-Sample Errors; 4.2.4 Harvey, Leybourne, and Newbold Size Correction of Diebold-Mariano Test; 4.2.5 Out-of-Sample Comparison with Nested Models
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4.2.6 Success Ratio for Sign Predictions: Directional Accuracy
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English
Additional Edition:
ISBN 0-12-485967-4
Language:
English
