Abstract

In this paper we apply text mining methodologies on a set of 10,000 Central Bank speeches to construct a financial dictionary, based on which we use Google Trends indices to measure people’s interest in financial news. Particularly, we investigate the relationship between these indices and financial market turbulence leveraging on Deep Learning techniques, which are benchmarked against a variety of Machine Learning algorithms and traditional statistical techniques. Our main finding is that Google queries convey information able to predict future market turbulence in a short time period (one month), and that Deep Learning algorithms clearly outperform over benchmark techniques. Google Trends can provide useful input in the creation of crisis Early Warning Systems, as social data are more responsive compared to official financial indicators, which are usually available with a lag of several weeks or months. Thus, such an Early Warning System (EWS) that is continuously updated with current social data can be a valuable tool for policymakers, as it can immediately identify signs of whether a crisis is imminent or not.

摘要

在本文中，我们将文本挖掘方法应用于一组 10,000 份中央银行演讲来构建金融词典，在此基础上，我们使用谷歌趋势指数来衡量人们对金融新闻的兴趣。特别是，我们利用深度学习技术研究了这些指数与金融市场动荡之间的关系，这些技术以各种机器学习算法和传统统计技术为基准。我们的主要发现是，谷歌查询传达的信息能够在短时间内（一个月）预测未来的市场动荡，并且深度学习算法明显优于基准技术。谷歌趋势可以为创建危机预警系统提供有用的输入，因为与官方财务指标相比，社交数据更具响应性，通常会延迟数周或数月。因此，这种不断更新当前社会数据的早期预警系统 (EWS) 可以成为决策者的宝贵工具，因为它可以立即识别危机是否迫在眉睫的迹象。