Forecasting Malaysian exchange rate using machine learning techniques based on commodities prices

Abstract

This article investigates the dynamic interactions between four commodities prices and the exchange rate for an emerging economy, Malaysia. The literature has identified a series of contradictory claims in the support and against the accurate prediction of the exchange rate. This article provides a new methodology to perform a comparative analysis of the three machine learning techniques, namely: Support Vector Machine, Neural Networks, and RandomForest. The experimental results demonstrate that the RandomForest is comparatively better than Support Vector Machine and Neural Networks, for accuracy and performance. This shows that the fluctuation in the Malaysian exchange rate can be evaluated accurately using RandomForest as compare to other techniques. Furthermore, this paper reveals that Malaysian specific commodities prices-crude oil, palm oil, rubber, and gold, are the strong dynamic parameters that influence Malaysian exchange rate. Hence, these results are beneficial for policy making, investment modeling, and corporate planning.