...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Decision support systems >Decision support from financial disclosures with deep neural networks and transfer learning
【24h】

Decision support from financial disclosures with deep neural networks and transfer learning

机译：借助深层神经网络和转移学习从财务披露中提供决策支持

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Company disclosures greatly aid in the process of financial decision-making; therefore, they are consulted by financial investors and automated traders before exercising ownership in stocks. While humans are usually able to correctly interpret the content, the same is rarely true of computerized decision support systems, which struggle with the complexity and ambiguity of natural language. A possible remedy is represented by deep learning, which overcomes several shortcomings of traditional methods of text mining. For instance, recurrent neural networks, such as long short-term memories, employ hierarchical structures, together with a large number of hidden layers, to automatically extract features from ordered sequences of words and capture highly non-linear relationships such as context-dependent meanings. However, deep learning has only recently started to receive traction, possibly because its performance is largely untested. Hence, this paper studies the use of deep neural networks for financial decision support. We additionally experiment with transfer learning, in which we pre-train the network on a different corpus with a length of 139.1 million words. Our results reveal a higher directional accuracy as compared to traditional machine learning when predicting stock price movements in response to financial disclosures. Our work thereby helps to highlight the business value of deep learning and provides recommendations to practitioners and executives. (C) 2017 Elsevier B.V. All rights reserved.
机译：公司披露在财务决策过程中有很大帮助；因此，在行使股票所有权之前，金融投资者和自动交易员会咨询他们。尽管人们通常能够正确解释内容，但是计算机决策支持系统却很少遇到这种情况，因为该系统难以应对自然语言的复杂性和歧义性。深度学习代表了一种可能的解决方法，它克服了传统文本挖掘方法的一些缺点。例如，递归神经网络（例如长时短时记忆）采用分层结构以及大量隐藏层，以自动从单词的有序序列中提取特征并捕获高度非线性的关系（如上下文相关的含义） 。但是，深度学习直到最近才开始受到关注，这可能是因为其性能在很大程度上未经测试。因此，本文研究了深度神经网络在财务决策支持中的使用。我们还进行了转移学习的实验，在该学习中，我们在不同语料库上对网络进行了预训练，长度为1.391亿字。与传统的机器学习相比，我们的结果表明，在预测因应财务披露而发生的股价波动时，其方向准确性更高。因此，我们的工作有助于强调深度学习的商业价值，并为从业者和高管提供建议。 （C）2017 Elsevier B.V.保留所有权利。

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