q-fin updates on arXiv.org
Fri, 07 Feb 2020 06:01:24 GMT language
Crude oil price forecasting has attracted substantial attention in the field
of forecasting. Recently, the research on text-based crude oil price
forecasting has advanced. To improve accuracy, some studies have added as many
covariates as possible, such as textual and nontextual factors, to their
models, leading to unnecessary human intervention and computational costs.
Moreover, some methods are only designed for crude oil forecasting and cannot
be well transferred to the forecasting of other similar futures commodities. In
contrast, this article proposes a text-based forecasting framework for futures
commodities that uses only future news headlines obtained from Investing.com to
forecast crude oil prices. Two marketing indexes, the sentiment index and the
topic intensity index, are extracted from these news headlines. Considering
that the public's sentiment changes over time, the time factor is innovatively
applied to the construction of the sentiment index. Taking the nature of the
short news headlines into consideration, a short text topic model called SeaNMF
is used to calculate the topic intensity of the futures market more accurately.
Two methods, VAR and RFE, are used for lag order judgment and feature
selection, respectively, at the model construction stage. The experimental
results show that the Ada-text model outperforms the Adaboost.RT baseline model
and the other benchmarks.