Global Demand and Supply Sentiment: Evidence From Earnings Calls*
利用财报电话会议文本计算需求与供给情绪，结合贝叶斯向量自回归模型识别冲击，对比2009年大衰退与新冠疫情对全球供需的不同影响。
Abstract This paper quantifies global demand and supply conditions and compares two major global recessions: the 2009 Great Recession and the COVID‐19 pandemic. First, we compute demand and supply sentiment by applying Natural Language Processing techniques on earnings call transcripts. Second, we corroborate our sentiment measure by identifying demand and supply shocks using a structural Bayesian vector autoregression model. The results highlight sharp contrast in the size of supply and demand conditions over time and across sectors. While the Great Recession was characterized by weak demand, COVID‐19 caused sizable disruptions to both demand and supply, with varying relative importance across major sectors. Furthermore, certain sub‐sectors, such as professional and business services, internet retail, and grocery/department stores, fared better than others during the pandemic.