Forecasting exchange rates using feedforward and recurrent neural networks

Chung‐Ming Kuan · Tung Liu

428 citations0/0 files storedJournal of Applied Econometrics

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Forecasting Techniques and Applications Neural Networks and Applications Stock Market Forecasting Methods

Abstract

Abstract In this paper we investigate the out‐of‐sample forecasting ability of feedforward and recurrent neural networks based on empirical foreign exchange rate data. A two‐step procedure is proposed to construct suitable networks, in which networks are selected based on the predictive stochastic complexity (PSC) criterion, and the selected networks are estimated using both recursive Newton algorithms and the method of nonlinear least squares. Our results show that PSC is a sensible criterion for selecting networks and for certain exchange rate series, some selected network models have significant market timing ability and/or significantly lower out‐of‐sample mean squared prediction error relative to the random walk model.

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References

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