• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>IEEE International Conference on Data Mining Workshops >Stock Market Prediction Analysis by Incorporating Social and News Opinion and Sentiment
【24h】

Stock Market Prediction Analysis by Incorporating Social and News Opinion and Sentiment

机译：结合社会新闻舆情对股市的预测分析

获取原文
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

The price of the stocks is an important indicator for a company and many factors can affect their values. Different events may affect public sentiments and emotions differently, which may have an effect on the trend of stock market prices. Because of dependency on various factors, the stock prices are not static, but are instead dynamic, highly noisy and nonlinear time series data. Due to its great learning capability for solving the nonlinear time series prediction problems, machine learning has been applied to this research area. Learning-based methods for stock price prediction are very popular and a lot of enhanced strategies have been used to improve the performance of the learning based predictors. However, performing successful stock market prediction is still a challenge. News articles and social media data are also very useful and important in financial prediction, but currently no good method exists that can take these social media into consideration to provide better analysis of the financial market. This paper aims to successfully predict stock price through analyzing the relationship between the stock price and the news sentiments. A novel enhanced learning-based method for stock price prediction is proposed that considers the effect of news sentiments. Compared with existing learning-based methods, the effectiveness of this new enhanced learning-based method is demonstrated by using the real stock price data set with an improvement of performance in terms of reducing the Mean Square Error (MSE). The research work and findings of this paper not only demonstrate the merits of the proposed method, but also points out the correct direction for future work in this area.
机译：股票价格是公司的重要指标，许多因素都会影响它们的价值。不同的事件可能会不同地影响公众的情绪和情感，这可能会影响股票市场价格的趋势。由于依赖各种因素，股票价格不是静态的，而是动态的，高度嘈杂的和非线性的时间序列数据。由于其解决非线性时间序列预测问题的强大学习能力，因此机器学习已应用于该研究领域。基于学习的股票价格预测方法非常流行，许多增强策略已用于改善基于学习的预测变量的性能。但是，执行成功的股票市场预测仍然是一个挑战。新闻文章和社交媒体数据在财务预测中也非常有用和重要，但是目前尚不存在可以将这些社交媒体考虑在内以提供对金融市场的更好分析的好的方法。本文旨在通过分析股价与新闻情绪之间的关系来成功预测股价。提出了一种新的基于学习的增强型股票价格预测方法，该方法考虑了新闻情绪的影响。与现有的基于学习的方法相比，这种新的基于学习的增强方法的有效性通过使用实际股票价格数据集得到了证明，并且在减少均方误差（MSE）方面具有改进的性能。本文的研究工作和发现不仅证明了该方法的优点，而且为该领域的未来工作指明了正确的方向。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号