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.
翻译：可以从金融新闻文章中获取主要信息来源,这些文章与股票趋势的波动有一定的关联性。在本文中,我们从多方面的角度调查金融新闻对股票趋势的影响。其背后的直觉是基于不同时期新闻事件的新闻不确定性和每个金融新闻缺乏注释。根据多例学习方案(多例学习方案),将培训活动安排在袋子里,为整个袋子贴上标签,而不是给整个袋子贴上标签。我们开发了一个灵活和适应性多层次学习模式,并评价其在金融新闻数据集标准普尔指数500指数的方向流动预测方面的能力。具体地说,我们把每一交易日都当作一个袋,每交易日都有某些新闻作为每袋中的实例。实验结果表明,我们提议的多层次学习框架在趋势预测的准确性方面,与其他最先进的方法和基线相比,取得了突出的成果。
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