论文标题
与经济新闻有关意大利主权债券市场的神经预测
Neural Forecasting of the Italian Sovereign Bond Market with Economic News
论文作者
论文摘要
在本文中，我们在神经网络框架内采用经济新闻来预测意大利10年的利率传播。我们使用一个大型开源数据库，称为“全局”事件，语言和音调数据库，以提取与债券市场动态相关的局部和情感新闻内容。我们将这些信息部署在具有自回旋复发网络（DEEPAR）的概率预测框架中。我们的发现表明，基于长期术语记忆单元的深度学习网络优于经典的机器学习技术，并提供了预测性能，而这种预测性能超出了仅使用利率的常规决定因素而获得的。
In this paper we employ economic news within a neural network framework to forecast the Italian 10-year interest rate spread. We use a big, open-source, database known as Global Database of Events, Language and Tone to extract topical and emotional news content linked to bond markets dynamics. We deploy such information within a probabilistic forecasting framework with autoregressive recurrent networks (DeepAR). Our findings suggest that a deep learning network based on Long-Short Term Memory cells outperforms classical machine learning techniques and provides a forecasting performance that is over and above that obtained by using conventional determinants of interest rates alone.
