NEURAL NETS FOR FINANCE LOW-COMPLEXITY NEURAL NETWORKS FOR STOCK MARKET PREDICTION Many of our machine learning algorithms, in one way or another, discover and exploit initially unknown environmental regularities. Regularity implies algorithmic compressibility - inductive learning and generalization are closely related to data compression. 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. Sepp Hochreiter and J. Schmidhuber. Flat Minima. Neural Computation, 9(1):1-42, 1997, (201 K). HTML. S.  Hochreiter and J.  Schmidhuber. Simplifying neural nets by discovering flat minima. In G. Tesauro, D. S. Touretzky and T. K. Leen, eds., Advances in Neural Information Processing Systems 7, NIPS'7, pages 529-536. MIT Press, Cambridge MA, 1995. PDF . HTML.