  Traditional stock market prediction approaches commonly utilize the
historical price-related data of the stocks to forecast their future trends. As
the Web information grows, recently some works try to explore financial news to
improve the prediction. Effective indicators, e.g., the events related to the
stocks and the people's sentiments towards the market and stocks, have been
proved to play important roles in the stocks' volatility, and are extracted to
feed into the prediction models for improving the prediction accuracy. However,
a major limitation of previous methods is that the indicators are obtained from
only a single source whose reliability might be low, or from several data
sources but their interactions and correlations among the multi-sourced data
are largely ignored.
  In this work, we extract the events from Web news and the users' sentiments
from social media, and investigate their joint impacts on the stock price
movements via a coupled matrix and tensor factorization framework.
Specifically, a tensor is firstly constructed to fuse heterogeneous data and
capture the intrinsic relations among the events and the investors' sentiments.
Due to the sparsity of the tensor, two auxiliary matrices, the stock
quantitative feature matrix and the stock correlation matrix, are constructed
and incorporated to assist the tensor decomposition. The intuition behind is
that stocks that are highly correlated with each other tend to be affected by
the same event. Thus, instead of conducting each stock prediction task
separately and independently, we predict multiple correlated stocks
simultaneously through their commonalities, which are enabled via sharing the
collaboratively factorized low rank matrices between matrices and the tensor.
Evaluations on the China A-share stock data and the HK stock data in the year
2015 demonstrate the effectiveness of the proposed model.
