In this paper, we propose a deep learning approach to extract emotional information from the audio of earnings conference calls and empirically examine the influences of these emotional variables on securities analysts' follow-up behavior. Our findings suggest that, in the statement section, positive emotional information tended to positively influence the analysts' willingness to issue rating reports, while the inverse was true for negative emotional information; non-negative emotional information in the question section had a positive influence, while negative emotional information in the response section had a negative influence. Secondly, for the specific rating of the issued reports, negative emotional information in the response section tended to result in a lower rating, and neutral emotional information might also have caused a lower rating. Thirdly, in terms of rating adjustments, non-negative emotional information in the question section tended to cause an upgrade revision, while the inverse was true for the negative emotional information in this section. Positive emotional information in the response section also caused an upgrade revision. The approach we proposed provides new insight for understanding analysts' follow-up behavior and offers practical implications for analysts, management, investors, and regulators.

在本文中，我们提出了一种深度学习方法来从收益电话会议的音频中提取情绪信息，并通过实证检验这些情绪变量对证券分析师后续行为的影响。我们的研究结果表明，在声明部分，积极的情绪信息倾向于对分析师发布评级报告的意愿产生积极影响，而消极情绪信息则相反；问题部分的非负面情绪信息具有积极影响，而回答部分的负面情绪信息具有负面影响。其次，对于已发布报告的具体评级，响应部分的负面情绪信息往往导致评级较低，中性的情绪信息也可能导致较低的评级。第三，在评级调整方面，问题部分的非负面情绪信息倾向于引起升级修正，而该部分的负面情绪信息则相反。回应部分积极的情绪信息也引发了升级改版。我们提出的方法为理解分析师的后续行为提供了新的见解，并为分析师、管理层、投资者和监管机构提供了实际意义。回应部分积极的情绪信息也引发了升级改版。我们提出的方法为理解分析师的后续行为提供了新的见解，并为分析师、管理层、投资者和监管机构提供了实际意义。回应部分积极的情绪信息也引发了升级改版。我们提出的方法为理解分析师的后续行为提供了新的见解，并为分析师、管理层、投资者和监管机构提供了实际意义。