Prediction of exchange rates with machine learning

In this study a macroeconomic model is considered to predict the next month's monthly average exchange rates via machine learning based regression methods including the Ridge, decision tree regression, support vector regression and linear regression. The model incorporates the domestic money supply, real interest rates, Federal Funds rate of the USA, and the last month's monthly average exchange rate to predict the next month's exchange rate. Monthly data with 148 observations from the US Dollar and Turkish Lira exchange rates are considered for the empirical testing of the model. Empirical results show that the Ridge regression offers accurate estimation for investors or policy makers with relative errors less than 60 basis points. Policy makers can obtain point estimates and confidence intervals for analyzing the effects of interest rate cuts on the exchange rates.

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