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