论文标题
FOMC新闻发布会周围的风险和回报：计算机视觉的新颖观点
Risk & returns around FOMC press conferences: a novel perspective from computer vision
论文作者
论文摘要
我提出了一种新工具，以表征FOMC新闻发布会周围不确定性的分辨率。它依赖于构建一项措施，以捕捉Q＆A会议期间美联储主席与记者之间的讨论水平。我表明，复杂的讨论与较高的权益回报和实现波动率下降有关。该方法通过量化椅子需要多少依靠阅读内部文档来回答问题来创建注意力评分。这是通过构建新闻发布会的视频图像并利用计算机视觉的最新深度学习算法来完成的。此替代数据提供了有关非语言通信的新信息，这些信息无法从广泛分析的FOMC成绩单中提取。本文可以看作是概念证明，某些视频包含用于研究金融市场的宝贵信息。
I propose a new tool to characterize the resolution of uncertainty around FOMC press conferences. It relies on the construction of a measure capturing the level of discussion complexity between the Fed Chair and reporters during the Q&A sessions. I show that complex discussions are associated with higher equity returns and a drop in realized volatility. The method creates an attention score by quantifying how much the Chair needs to rely on reading internal documents to be able to answer a question. This is accomplished by building a novel dataset of video images of the press conferences and leveraging recent deep learning algorithms from computer vision. This alternative data provides new information on nonverbal communication that cannot be extracted from the widely analyzed FOMC transcripts. This paper can be seen as a proof of concept that certain videos contain valuable information for the study of financial markets.
