Neural network protocols and model performance

Abstract The benchmark performance of neural network models in exchange rate forecasting has been established in a number of recent papers. In this paper, we drop a number of customary features that are used in neural modeling to improve performance yet are able to predict daily returns for the four main exchange rates with accuracy above levels previously reported for comparable data sets. Significant outperformance relative to a random walk without drift and against the mean value predictor is reported, indicating that the transparent neural network used in the study is finding new and potentially profitable information.

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