NEURAL NETS FOR FINANCE
LOW-COMPLEXITY NEURAL NETWORKS FOR STOCK MARKET PREDICTION
Our most lucrative application is the prediction of financial data. The optimal (but not necessarily practically feasible) universal way of predicting finance data is discussed here.
An approach that is more feasible on current computers is based on a "minimum description length"-based argument. It shows that flat minima of typical neural network error functions correspond to low expected overfitting/high generalization. In stock market prediction benchmarks, Flat Minimum Search outperformed other widely used competitors.