  Stock trend forecasting, which forecasts stock prices' future trends, plays
an essential role in investment. The stocks in a market can share information
so that their stock prices are highly correlated. Several methods were recently
proposed to mine the shared information through stock concepts (e.g.,
technology, Internet Retail) extracted from the Web to improve the forecasting
results. However, previous work assumes the connections between stocks and
concepts are stationary, and neglects the dynamic relevance between stocks and
concepts, limiting the forecasting results. Moreover, existing methods overlook
the invaluable shared information carried by hidden concepts, which measure
stocks' commonness beyond the manually defined stock concepts. To overcome the
shortcomings of previous work, we proposed a novel stock trend forecasting
framework that can adequately mine the concept-oriented shared information from
predefined concepts and hidden concepts. The proposed framework simultaneously
utilize the stock's shared information and individual information to improve
the stock trend forecasting performance. Experimental results on the real-world
tasks demonstrate the efficiency of our framework on stock trend forecasting.
The investment simulation shows that our framework can achieve a higher
investment return than the baselines.
