  A major source of information can be taken from financial news articles,
which have some correlations about the fluctuation of stock trends. In this
paper, we investigate the influences of financial news on the stock trends,
from a multi-instance view. The intuition behind this is based on the news
uncertainty of varying intervals of news occurrences and the lack of annotation
in every single financial news. Under the scenario of Multiple Instance
Learning (MIL) where training instances are arranged in bags, and a label is
assigned for the entire bag instead of instances, we develop a flexible and
adaptive multi-instance learning model and evaluate its ability in directional
movement forecast of Standard & Poors 500 index on financial news dataset.
Specifically, we treat each trading day as one bag, with certain amounts of
news happening on each trading day as instances in each bag. Experiment results
demonstrate that our proposed multi-instance-based framework gains outstanding
results in terms of the accuracy of trend prediction, compared with other
state-of-art approaches and baselines.
