q-fin updates on arXiv.org
Tue, 25 Feb 2020 06:01:58 GMT language
Faced with the growing research towards crude oil price fluctuations
influential factors following the accelerated development of Internet
technology, accessible data such as Google search volume index are increasingly
quantified and incorporated into forecasting approaches. In this paper, we
apply multi-scale data that including both GSVI data and traditional economic
data related to crude oil price as independent variables and propose a new
hybrid approach for monthly crude oil price forecasting. This hybrid approach,
based on divide and conquer strategy, consists of K-means method, kernel
principal component analysis and kernel extreme learning machine , where
K-means method is adopted to divide input data into certain clusters, KPCA is
applied to reduce dimension, and KELM is employed for final crude oil price
forecasting. The empirical result can be analyzed from data and method levels.
At the data level, GSVI data perform better than economic data in level
forecasting accuracy but with opposite performance in directional forecasting
accuracy because of Herd Behavior, while hybrid data combined their advantages
and obtain best forecasting performance in both level and directional accuracy.
At the method level, the approaches with K-means perform better than those
without K-means, which demonstrates that divide and conquer strategy can
effectively improve the forecasting performance.