Demonstrates a system that combines a neural network approach with an expert system to provide superior performance compared to either approach alone. Learning capability is provided in a software-based approach to commodity trading systems. The authors used the backpropagation network with some parameters selected experimentally. They used a human expert to implicitly define patterns, using hindsight, that an intelligent system might have been able to use for an accurate prediction. Desired outputs were found by a combination of observing the behavior of technical indices that normally precede a certain kind of market behavior, and by observing the actual market behavior in retrospect. Thus, the network learns to give signals based on data that look favorable to a human expert. The authors show the results of a rule-based daily trading system that has been augmented by a neural network market predictor.
- Accurate Prediction,
- Backpropagation Network,
- Commodity Trading,
- Commodity Trading Model,
- Expert Systems,
- Financial Data Processing,
- Learning Capability,
- Market Behavior,
- Neural Nets,
- Neural Network-Expert System Hybrid,
- Performance,
- Rule-Based Daily Trading System,
- Software-Based Approach,
- Technical Indices
Available at: http://works.bepress.com/donald-wunsch/77/