  Stock prices move as piece-wise trending fluctuation rather than a purely
random walk. Traditionally, the prediction of future stock movements is based
on the historical trading record. Nowadays, with the development of social
media, many active participants in the market choose to publicize their
strategies, which provides a window to glimpse over the whole market's attitude
towards future movements by extracting the semantics behind social media.
However, social media contains conflicting information and cannot replace
historical records completely. In this work, we propose a multi-modality
attention network to reduce conflicts and integrate semantic and numeric
features to predict future stock movements comprehensively. Specifically, we
first extract semantic information from social media and estimate their
credibility based on posters' identity and public reputation. Then we
incorporate the semantic from online posts and numeric features from historical
records to make the trading strategy. Experimental results show that our
approach outperforms previous methods by a significant margin in both
prediction accuracy (61.20\%) and trading profits (9.13\%). It demonstrates
that our method improves the performance of stock movements prediction and
informs future research on multi-modality fusion towards stock prediction.
