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Authors test the suggested model on high-frequency time series data of USD/CAD and examine the ability to forecast exchange rate values for the horizon of one day.

To determine the forecasting efficiency, they perform a comparative statistical out-of-sample analysis of the tested model with autoregressive models and the standard neural network.

Forex markets are among the most active markets in the world in terms of dollar volume.

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Finally, the authors find out that their suggested hybrid neural network is able to produce more accurate forecasts than the standard models and can be helpful in eliminating the risk of making the bad decision in decision-making process. Profits or losses accrue as the exchange rate of that currency fluctuates on the open market.The authors suggest a new hybrid neural network which is a combination of the standard RBF neural network, a genetic algorithm, and a moving average.The moving average is supposed to enhance the outputs of the network using the error part of the original neural network.The real exchange rate (RER) is the purchasing power of a currency relative to another at current exchange rates and prices.It is the ratio of the number of units of a given country's currency necessary to buy a market basket of goods in the other country, after acquiring the other country's currency in the foreign exchange market, to the number of units of the given country's currency that would be necessary to buy that market basket directly in the given country.

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