Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
An Empirical Analysis of Data Requirements for Financial Forecasting with Neural Networks
Journal of Management Information Systems
Lag-Dependent Regularization for MLPs Applied to Financial Time Series Forecasting Tasks
ICCS 2009 Proceedings of the 9th International Conference on Computational Science
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This paper presents a generative approach to direction-of-change time series forecasting. Kernel methods are used to estimate densities for the distribution of positive and negative returns, and these distributions are then combined to produce probability estimates for return forecasts. An advantage of the technique is that it involves very few parameters compared to regression-based approaches, the only free parameters being those that control the shape of the windowing kernel. A special form is proposed for the kernel covariance matrix. This allows recent data more influence than less recent data in determining the densities, and is important in preventing overfitting. The technique is applied to predicting the direction of change on the Australian All Ordinaries Index over a 15 year out-of-sample period.