Exchange Rate Forecasting with Advanced Machine Learning Methods

Historically, exchange rate forecasting models have exhibited poor out-of-sample performances and were inferior to the random walk model. Monthly panel data from 1973 to 2014 for ten currency pairs of OECD countries are used to make out-of sample forecasts with artificial neural networks and XGBoost models. Most approaches show significant and substantial predictive power in directional forecasts. Moreover, the evidence suggests that information regarding prediction timing is a key component in the forecasting performance.

Exchange Rate Forecasting Based on Combined Fuzzification Strategy and Advanced Optimization Algorithm

Exchange rate forecasting is a crucial but challenging task due to the uncertainty and fuzziness of the associated data caused by complex influence factors. However, most traditional forecasting methods ignore the ambiguity of the data itself. Thus, in this paper, a novel fuzzy time series forecasting system based on a combined fuzzification strategy and an advanced optimization algorithm was proposed for use in exchange rate forecasting, and was proven to have an excellent ability to deal with the uncertainties and ambiguities in data. Concretely, the data “decomposition and ensemble” strategy was applied to carry out the data preprocessing process. The combined fuzzification strategy was used in the fuzzification of the observed data, and the advanced optimization algorithm was developed to determine the optimal parameters in the models. The analysis of this experiment verified the effectiveness of the proposed forecasting system, which will benefit future research and decision-making related to investments.