Evolving artificial neural networks to combine financial forecasts

Abstract

We conduct evolutionary programming experiments to evolve artificial neural networks for forecast combination. Using stock price volatility forecast data we find evolved networks compare favorably with a naive average combination, a least squares method, and a kernel method on out-of-sample forecasting ability-the best evolved network showed strong superiority in statistical tests of encompassing. Further, we find that the result is not sensitive to the nature of the randomness inherent in the evolutionary optimization process.

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