Möller Rouven, Reichmann Doron
Ruhr-University Bochum, Universitätsstraße 150, Bochum 44801, North Rhine Westphalia, Germany.
Q Rev Econ Finance. 2023 Feb;87:95-109. doi: 10.1016/j.qref.2022.11.007. Epub 2022 Dec 5.
We investigate a novel dataset of more than half a million 15 seconds transcribed audio snippets containing COVID-19 mentions from major US TV stations throughout 2020. Using the Latent Dirichlet Allocation (LDA), an unsupervised machine learning algorithm, we identify seven COVID-19 related topics discussed in US TV news. We find that several topics identified by the LDA predict significant and economically meaningful market reactions in the next day, even after controlling for the general TV tone derived from a field-specific COVID-19 tone dictionary. Our results suggest that COVID-19 related TV content had nonnegligible effects on financial markets during the pandemic.
我们研究了一个包含超过50万个15秒转录音频片段的新数据集，这些音频片段来自2020年美国各大电视台对新冠疫情的报道。我们使用潜在狄利克雷分配（LDA）这一无监督机器学习算法，识别出美国电视新闻中讨论的七个与新冠疫情相关的主题。我们发现，即使在控制了从特定领域的新冠疫情语气词典得出的一般电视语气之后，LDA识别出的几个主题仍能预测次日显著且具有经济意义的市场反应。我们的结果表明，在疫情期间，与新冠疫情相关的电视内容对金融市场产生了不可忽视的影响。
