论文标题
深层实例学习使用财务新闻的预测股票趋势
Deep Multiple Instance Learning For Forecasting Stock Trends Using Financial News
论文作者
论文摘要
可以从金融新闻文章中获取的主要信息来源，这些文章与股票趋势的波动有一些相关性。在本文中，我们从多个现实的观点调查了金融新闻对股票趋势的影响。其背后的直觉是基于新闻事件不同间隔的新闻不确定性以及每个金融新闻中缺乏注释的新闻不确定性。在多个实例学习（MIL）的情况下，将培训实例排列在袋子中，并为整个袋子而不是实例分配标签，我们开发了一种灵活而适应性的多种实体学习模型，并评估其在标准和POORS的方向运动预测中的能力500 Index在金融新闻数据集中。具体来说，我们将每个交易日都视为一个包，每个交易日都会发生一定数量的新闻作为每个袋子的情况。实验结果表明，与其他最先进的方法和基线相比，我们提出的基于多实体的框架在趋势预测的准确性方面取得了出色的结果。
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.
