使用深度学习改进决策分析：财务披露案例
Improving Decision Analytics with Deep Learning: The Case of Financial Disclosures
摘要：
决策分析通常侧重于金融新闻源的文本挖掘，以提供管理决策支持和预测股市走势。现有的预测框架几乎完全应用传统的机器学习方法，而最近的研究表明，传统的机器学习方法不足以提取合适的特征并捕捉复杂任务的非线性性质。作为一种补救措施，新型深度学习模型旨在通过使用额外的隐藏层扩展传统的神经网络模型来克服这个问题。事实上，深度学习已经被证明在预测性能方面优于传统方法。在本文中，我们将新的深度学习技术应用于金融决策支持。在这种情况下，我们的目标是预测财务披露后股票走势的方向。因此，我们展示了深度学习如何将随机森林的准确性作为机器学习的基准提高5.66%。
英文摘要：
Decision analytics commonly focuses on the text mining of financial news sources in order to provide managerial decision support and to predict stock market movements. Existing predictive frameworks almost exclusively apply traditional machine learning methods, whereas recent research indicates that traditional machine learning methods are not sufficiently capable of extracting suitable features and capturing the non-linear nature of complex tasks. As a remedy, novel deep learning models aim to overcome this issue by extending traditional neural network models with additional hidden layers. Indeed, deep learning has been shown to outperform traditional methods in terms of predictive performance. In this paper, we adapt the novel deep learning technique to financial decision support. In this instance, we aim to predict the direction of stock movements following financial disclosures. As a result, we show how deep learning can outperform the accuracy of random forests as a benchmark for machine learning by 5.66%.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
