论文标题
随着时间的推移，多任务学习和单词极性的库存指数预测
Stock Index Prediction with Multi-task Learning and Word Polarity Over Time
论文作者
论文摘要
基于情感的股票预测系统旨在探索在线语料库中的情感或事件信号，并试图将信号与股票价格变化联系起来。基于功能和基于神经网络的方法都取得了令人鼓舞的结果。但是，股票价格经常发生的微小波动限制了从价格模式中学习文本的情感，如果文本与基础市场无关，则可能会偏向文本中的市场情感。另外，当使用离散的单词功能时，特定术语的极性会根据不同的事件随时间而变化。为了解决这些问题，我们提出了一个两阶段的系统，该系统由一个情感提取器组成，以提取对市场趋势的意见和一个摘要器，以预测下一周的索引运动的方向，因为本周新闻的意见。我们采用多任务学习的BERT，还可以预测新闻的价值，并提出一个称为“ Pallerity-time”的指标，以在不同的事件期间提取极性一词。还提出了一个每周一个月的预测框架和一个新的数据集，即10年的路透社金融新闻数据集。
Sentiment-based stock prediction systems aim to explore sentiment or event signals from online corpora and attempt to relate the signals to stock price variations. Both the feature-based and neural-networks-based approaches have delivered promising results. However, the frequently minor fluctuations of the stock prices restrict learning the sentiment of text from price patterns, and learning market sentiment from text can be biased if the text is irrelevant to the underlying market. In addition, when using discrete word features, the polarity of a certain term can change over time according to different events. To address these issues, we propose a two-stage system that consists of a sentiment extractor to extract the opinion on the market trend and a summarizer that predicts the direction of the index movement of following week given the opinions of the news over the current week. We adopt BERT with multitask learning which additionally predicts the worthiness of the news and propose a metric called Polarity-Over-Time to extract the word polarity among different event periods. A Weekly-Monday prediction framework and a new dataset, the 10-year Reuters financial news dataset, are also proposed.
