The Integration of Artificial Neural Networks and Text Mining to Forecast Gold Futures Prices. Issue 4 (20th April 2016)

• Record Type: Journal Article
• Authors: Chen, Hsin-Hung; Chen, Mingchih; Chiu, Chun-Cheng
• Is Part Of: Communications in statistics. Volume 45:Issue 4(2016)
• Page Start: 1213
• Page End: 1225
• Publication Date: 2016-04-20
• Subjects: ARIMA -- Forecast -- Gold futures -- Neural network -- Text mining; 62M20 -- 91B84; Mathematical statistics -- Periodicals; Mathematical statistics -- Data processing -- Periodicals; Digital computer simulation -- Periodicals; 519.5
• DOI: 10.1080/03610918.2013.786780
• Languages: English
• ISSNs: 0361-0918

Abstract

Abstract : Although a previous study found that neural network forecasts were more accurate than time series models for predicting Latin American stock indexes, the forecasting accuracy of neural network for predicting gold futures prices has never been discussed. Therefore, the first objective of this study is to compare the forecasting accuracy of a neural network model with that of ARIMA models. Furthermore, the fluctuations in gold futures are not only influenced by the quantitative variables, but also by many nonquantifiable factors, such as wars, international relations, and terrorist attacks. The second objective of this study is therefore to propose the integration of text mining and an artificial neural network to forecast gold futures prices. The historical gold futures prices from 1999 to 2008 were used as training data and testing data, and the prices of 2009 were used to examine the effectiveness of the proposed model. The results of empirical analysis showed that an artificial neural network forecasted gold futures prices better than ARIMA models did. In addition, text mining provided a reasonable explanation of the trend in gold futures prices.