Main Article Content
This research constructed an optimal model to forecast the exchange rate of the Thai Baht against the Chinese Yuan by Support Vector Machine model and firefly algorithm. The software used for the construction was R program. The data used for modelling was secondary data collected from Bank of Thailand and the Ministry of Commerce, which consisted of the exchange rate of the Thai Baht against the Chinese Yuan, policy interest rate (per year), the Thai Baht index, import value, export value and international reserve fund. The collection of data was monthly records starting from January 2009 to June 2019, 126 data sets. The first 120 data sets were used for constructing the model and the last 6 data sets were used to verify the model. It was found that there were 3 factors which affected the exchange rate of the Thai baht against the Chinese yuan, with 5% statistical significance. The same direction factors were policy interest rate and import value and the opposite direction factor was international money fund. The optimal support vector machine model obtained was eps-regression type with radial basis function kernel, which had gamma parameter , epsilon parameter and cost value parameter . The model verification showed that the obtained model provided the root mean square error 0.1518 only whereas the classical multiple linear regression model provided the root mean square error 0.2614.
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Avakiat, S., & Prawatrungruang, S. (2016). The Influencing of Important Factors on Exchange Rate of Thai Baht Against the US Dollar, Thai Baht Against the Euro, and Thai Baht Against the Yen. Journal of Business Administration (The Association of Private Higher Education Institutions of Thailand). Year 5(4): 16-27.
Bank of Thailand. (2019). RMB Transaction. [On-line]. Available: https://www.bot.or.th/English/FinancialMarkets/ForeignExchangeMarket/LocalCurrencyMarkets/RMBTransaction/Pages/default.aspx.
Chao, C. F., & Horng M. H. (2015). The Construction of Support Vector Machine Classifier Using the Firefly Algorithm. Computational Intelligence and Neuroscience. (2015.) Article ID 212719. DOI: https://doi.org/10.1155/2015/212719
Deng, N., Tian, Y., & Zhang, C. (2013). Support Vector Machines Optimization Based Theory Algorithms, and Extensions. CRC Press, Taylor & Francis Group, Boca Raton.
Hamel, L. (2009). Knowledge Discovery with Support Vector Machines. John Wiley & Sons, Inc.
Mungmaipol, D., & Boonyanam, N. (2018). Exchange Rate Forecasting Between Thai Baht and
Japanese Yen. Veridian E-Journal. 11(3): 3301-3315.
Prajaksitai, D. (2016). When “Yuan” Was Accepted to Be a Part of SDRs. [On-line].
Sanguansut, P. (2019). Artificial Intelligence with Machine Learning, 1st. ed., IDC. Premiere.
Vapink, V. (1995). The nature of statistical learning theory. Springer-Verlag. New York.
Yang, X. S. (2010). Nature-Inspired Metaheuristic Algorithms 2nd ed. Luniver Press. UK.