Zhang M. (ed.) Artificial Higher Order Neural Networks for Economics and Business

IGI Global, 2009. — 542 p.Artificial Neural Networks (ANNs) are known to excellence in pattern recognition, pattern matching and mathematical function approximation. However, they suffer from several limitations. ANNs are often stuck in local, rather than global minima, as well as taking unacceptable long times to converge in the real word data. Especially from the perspective of economics and financial time series predictions, ANNs are unable to handle non-smooth, discontinuous training data, and complex mappings. Another limitation of ANN is a ‘black box’ nature. It means that explanations for their decisions are not hard to use expressions to describe. This then is the first motivation for developing Higher Order Neural Networks (HONNs), since HONNs are ‘open-box’ models and each neuron and weight are mapped to function variable and coefficient.
SAS Nonlinear (NLIN) procedure produces least squares or weighted least squares estimates of the parameters of a nonlinear model. SAS Nonlinear models are more difficult to specify and estimate than linear models. Instead of simply generating the parameter estimates, users must write the regression expression, declare parameter names, and supply initial parameter values. Some models are difficult to fit, and there is no guarantee that the procedure can fit the model successfully. For each nonlinear model to be analyzed, users must specify the model (using a single dependent variable) and the names and starting values of the parameters to be estimated. However, the objective of the users is to find the model and its coefficients. This is the second motivation for using HONNs in economics and business, since HONNs can automatically select the initial coefficients for nonlinear data analysis.
Let millions of people working in economics and business areas know that HONNs are much easier to use and can have better simulation results than SAS NLIN, and understand how to successfully use HONNs software packages for nonlinear data simulation and prediction. HONNs will challenge SAS NLIN procedures and change the research methodology that people are currently using in economics and business areas for the nonlinear date simulation and prediction.
Millions of people who are using SAS and who are doing nonlinear model research, in particular, professors, graduate students, and senior undergraduate students in economics, accounting, finance and other business departments, as well as the professionals and researchers in these areas.Section I Artificial Higher Order Neural Networks for Economics
Artificial Higher Order Neural Network Nonlinear Models: SAS NLIN or HONNs?
Higher Order Neural Networks with Bayesian Confidence Measure for the Prediction of the EUR/USD Exchange Rate
Automatically Identifying Predictor Variables for Stock Return Prediction
Higher Order Neural Network Architectures for Agent-Based Computational Economics and Finance
Foreign Exchange Rate Forecasting Using Higher Order Flexible Neural Tree
Higher Order Neural Networks for Stock Index Modeling
Section II Artificial Higher Order Neural Networks for Time Series Data
Ultra High Frequency Trigonometric Higher Order Neural Networks for Time Series Data Analysis
Artificial Higher Order Pipeline Recurrent Neural Networks for Financial Time Series Prediction
A Novel Recurrent Polynomial Neural Network for Financial Time Series Prediction
Generalized Correlation Higher Order Neural Networks for Financial Time Series Prediction
Artificial Higher Order Neural Networks in Time Series Prediction
Application of Pi-Sigma Neural Networks and Ridge Polynomial Neural Networks to Financial Time Series Prediction
Section III Artificial Higher Order Neural Networks for Business
Electric Load Demand and Electricity Prices Forecasting Using Higher Order Neural Networks Trained by Kalman Filtering
Adaptive Higher Order Neural Network Models and Their Applications in Business.
CEO Tenure and Debt: An Artificial Higher Order Neural Network Approach
Modeling and Trading the Soybean-Oil Crush Spread with Recurrent and Higher Order Networks: A Comparative Analysis
Section IV Artificial Higher Order Neural Networks Fundamentals
Fundamental Theory of Artificial Higher Order Neural Networks
Dynamics in Artificial Higher Order Neural Networks with Delays
A New Topology for Artificial Higher Order Neural Networks: Polynomial Kernel Networks
High Speed Optical Higher Order Neural Networks for Discovering Data Trends and Patterns in Very Large Databases
On Complex Artificial Higher Order Neural Networks: Dealing with Stochasticity, Jumps and Delays
Trigonometric Polynomial Higher Order Neural Network Group Models and Weighted Kernel Models for Financial Data Simulation and Prediction