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Using Textual and Economic Features to Predict the RMB Exchange Rate

Yi-Chen Chung, Hsien-Ming Chou, Chih-Neng Hung and Chihli Hung

Advances in Management and Applied Economics, 2021, vol. 11, issue 6, 8

Abstract: This research proposes an integrated framework for the use of textual and economic features to predict the exchange rate of the TWD (Taiwan dollar) against the RMB (Chinese Renminbi). The exchange rate is affected by the current economic situation and expectations for the future economic climate. Exchange rate forecasting studies focus mainly on overall economic indices and the actual exchange rate, but overlook the influence of news. This research considers both textual and economic factors and builds three basic prediction models, i.e. multiple linear regression (MLR), support vector regression (SVR), and Gaussian process regression (GPR) for the prediction of the RMB exchange rate. In addition to the three basic prediction models, this research uses ensemble learning and feature selection techniques to improve prediction performance. Our experiments demonstrate that textual features also play an important role in predicting the RMB exchange rate. The SVR model is shown to outperform the other models and the MLR model is shown to perform worst. The ensemble of three basic models performs better than its individual counterparts. Finally, the models which use feature selection techniques demonstrate improved results in general, and different feature selection techniques are shown to be more suitable for different prediction models. JEL classification numbers: D80, F31, F47.

Keywords: Exchange rate prediction; Text mining; Ensemble learning; Time series forecasting. (search for similar items in EconPapers)
Date: 2021
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