Abstract
This paper examines the predictive capacity of online news on the gold futures prices. The empirical results derived from the Chinese market demonstrate that the textual features extracted through natural language processing techniques contain complementary predictive content for gold futures prices, which enhance the 1-day ahead prediction accuracy across different machine learning methods and train-test sets.
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