McNelis Paul D. Neural networks in finance: gaining predictive edge in the market

Preface xi.Forecasting, Classification, and Dimensionality.
Reduction.
Synergies.
The Interface Problems.
Plan of the Book.
Econometric Foundations.
What Are Neural Networks?
Linear Regression Model.
GARCH NonlinearModels.
Model Typology.
What Is A Neural Network?
Feedforward Networks.
Neural Network Smooth-Transition Regime Switching.
Models.
Nonlinear Principal Components: Intrinsic.
Dimensionality.
Neural Networks and Discrete Choice.
The Black Box Criticism and Data Mining.Estimation of a Network with Evolutionary Computation.
Data Preprocessing.
The Nonlinear Estimation Problem.
Repeated Estimation and ThickModels.
MatLAB Examples: Numerical Optimization and.
Network Performance.
Numerical Optimization.Evaluation of Network Estimation.
n-Sample Criteria.
Out-of-Sample Criteria.
nterpretive Criteria and Significance of Results.
mplementation Strategy.Applications and Examples.
Estimating and Forecasting with Artificial Data.
ntroduction.
Stochastic ChaosModel.
Stochastic Volatility/Jump Diffusion Model.
TheMarkov Regime SwitchingModel.
olatality Regime SwitchingModel.
Distorted Long-MemoryModel.
Black-Sholes Option Pricing Model: Implied Volatility.
Forecasting.Times Series: Examples from Industry and Finance.
Forecasting Production in the Automotive Industry.
Corporate Bonds: Which Factors Determine the.
Spreads.
Contents ix.Inflation and Deflation: Hong Kong and Japan.
Hong Kong.
Japan.Classification: Credit Card Default and Bank Failures.
Credit Card Risk.
Banking Intervention.Dimensionality Reduction and Implied Volatility.
Forecasting.
Hong Kong.
United States.
The Data.