Jackknife Learning Algorithms for the Neural-Network Model of Exchange Rate

In this paper, we propose two grouped jackknife algorithms and apply them to a separate multi-layer feed-forward neural-network model of noisy financial time series, such as the spot Canadian/US foreign exchange rate. The integrated method delivers a reasonably reliable forecast of the spot rate along with a large amount of statistical information associated with the historical data.