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

Deep Multiple Instance Learning For Forecasting Stock Trends Using Financial News