基于经济新闻的意大利主权债券市场神经网络预测
Neural Forecasting of the Italian Sovereign Bond Market with Economic News
摘要：
在本文中，我们采用神经网络框架内的经济新闻预测意大利10年期利率利差。我们使用一个大型的开源数据库，称为全球事件、语言和语气数据库，以提取与债券市场动态相关的主题和情感新闻内容。我们将这些信息部署在具有自回归递归网络（DeepAR）的概率预测框架内。我们的研究结果表明，基于长短期记忆细胞的深度学习网络优于经典的机器学习技术，并提供了一个预测性能，超过了单独使用传统的利率决定因素所获得的预测性能。
英文摘要：
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.
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。
