Prediction of Malaysian Exchange Rate Using Microstructure Fundamental and Commodities Prices: A Machine Learning Method
Shamaila Butt1, Suresh Ramakrishnan2, Muhammad Ali Chohan3, Suresh Kumar Punshi4
1Shamaila Butt, Azman Hashim International Business School, Universiti Teknologi Malaysia, Johor Bahru, Johor, Malaysia Department of Management Sciences, Universiti of Wah, Wah Cantt, Pakistan.
2Suresh Ramakrishnan, Azman Hashim International Business School, Universiti Teknologi Malaysia, Johor Bahru, Johor, Malaysia.
3Muhammad Ali Chohan, Azman Hashim International Business School, Universiti Teknologi Malaysia, Johor Bahru, Johor, Malaysia.
4Suresh Kumar Punshi, Azman Hashim International Business School, Universiti Teknologi Malaysia, Johor Bahru, Johor, Malaysia.
Manuscript received on 19 October 2019 | Revised Manuscript received on 25 October 2019 | Manuscript Published on 02 November 2019 | PP: 987-993 | Volume-8 Issue-2S9 September 2019 | Retrieval Number: B11890982S919/2019©BEIESP | DOI: 10.35940/ijrte.B1189.0982S919
Open Access | Editorial and Publishing Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The key objective of this research is to investigate the short run dynamics of the exchange rate using commodity prices and microstructure market variables for developing economies, Malaysia. The analysis of the literature revealed different school of thought where one claims the strong correlation among the variables while other significantly reject the relationship. There is mixed results that support and reject the accurate forecasting of the exchange rate through different determinants. Therefore, in this study the machine learning approach is applied to perform the extensive experiments and investigate the relationship between the commodity prices, bid-ask spread and exchange rate. Three techniques were selected from the machine learning i.e. artificial neural network, RandomForest and support vector machine. The experimental results revealed that randomforest perform better than SVM and ANN, both in the performance and accuracy. Thus, the exchange rate can be predicted will reasonable accuracy using the commodity prices in the combination of the bid-ask spread. Thus, the policy maker can be utilized these results for strategies development, corporate planning and building investment plan.
Keywords: Exchange Rate; Commodity Price; Bid-Ask Spread; Random Forest; Support Vector Machine; Artificial Neural Network.
Scope of the Article: Machine Learning