Abstract

The stability of exchange rates plays a decisive role in a country’s economic development and internal and external equilibrium. Therefore, the prediction of short-term exchange rate is of vital importance to maintain a country’s economic stability and financial security. Traditional time series forecasting models are only based on historical data, which cannot reflect other important factors, like investors’ currency exchange expectations and their emotions. This study aims to build a web-searching index to predict the short-term exchange rate, which is based on web search data, the natural language processing and information retrieval sharing platform (NLPIR) which is a Chinese segmentation technique and the TextRank keywords extraction system. In this way, the study establishes a Conditional Autoregressive Model (CAR) model integrated with our web-searching index to include the investor’s expectations and emotions in the prediction, and therefore enhance prediction accuracy. The outcome shows that the accuracy of the CAR model based on search data can be significantly higher than other models. Besides, compared with traditional exchange rate prediction models, the integrated CAR model has a better fitting effect and a lower prediction error.

Key words:  Exchange rate prediction, web-searching index, big data, TextRank, NLPIR.