Deep Multiple Instance Learning For Forecasting Stock Trends Using Financial News
This addresses stock prediction for investors by applying MIL to handle news uncertainty, though it is incremental as it adapts existing MIL methods to financial data.
The paper tackles stock trend forecasting using financial news by developing a multiple instance learning model that treats each trading day as a bag of news instances, achieving outstanding accuracy compared to state-of-the-art approaches.
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.