Tech Report CS-93-36

Feedforward and Recurrent Neural Networks and Genetic Programs for Stock and Time Series Forecasting

Peter C. McCluskey

September 1993


Adding recurrence to neural networks improves their time series forecasts. Well chosen inputs such as a window of time-delayed inputs, or intelligently preprocessed inputs, are more important than recurrence. Neural networks do well on moderately noisy and chaotic time series, such as sunspot data. A single neural network or genetic program generalizes poorly on weekly stock market indices due to the low signal-to-noise ratio. When the responses of a number of networks are averaged, the resulting forecast shows substantial profits on historical data.

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