  The investment on the stock market is prone to be affected by the Internet.
For the purpose of improving the prediction accuracy, we propose a multi-task
stock prediction model that not only considers the stock correlations but also
supports multi-source data fusion. Our proposed model first utilizes tensor to
integrate the multi-sourced data, including financial Web news, investors'
sentiments extracted from the social network and some quantitative data on
stocks. In this way, the intrinsic relationships among different information
sources can be captured, and meanwhile, multi-sourced information can be
complemented to solve the data sparsity problem. Secondly, we propose an
improved sub-mode coordinate algorithm (SMC). SMC is based on the stock
similarity, aiming to reduce the variance of their subspace in each dimension
produced by the tensor decomposition. The algorithm is able to improve the
quality of the input features, and thus improves the prediction accuracy. And
the paper utilizes the Long Short-Term Memory (LSTM) neural network model to
predict the stock fluctuation trends. Finally, the experiments on 78 A-share
stocks in CSI 100 and thirteen popular HK stocks in the year 2015 and 2016 are
conducted. The results demonstrate the improvement on the prediction accuracy
and the effectiveness of the proposed model.
