据CAR官网显示，来自新加坡管理大学的Richard M. Crowley、香港理工大学的黄文利、多伦多大学的卢海 ，合作的论文“Discretionary dissemination on Twitter”在国际会计学顶刊《Contemporary Accounting Research》线上正式发表。
Title: Discretionary dissemination on Twitter
在Twitter上的自主传播
Richard M. Crowley
新加坡管理大学
黄文利
香港理工大学
卢海
多伦多大学
The study provides large-scale descriptive evidence on the timing and nature of corporate financial tweeting. Using an unsupervised machine learning approach to analyze 24 million tweets posted by S&P 1500 firms from 2012 to 2020, we find that firms are more likely to tweet financial information around significantly negative or positive news events, such as earnings announcements and the filing of financial statements. This convex U-shaped relation between the likelihood of posting financial tweets and the materiality of accounting events becomes stronger over time. Whereas research based on early samples concludes that firms are less likely to disseminate financial information on Twitter when the news is bad and material, the symmetric dissemination behavior we find suggests that these conclusions should be revised. We also show that a machine learning algorithm (Twitter-Latent Dirichlet Allocation) is superior to a dictionary approach in classifying short messages like tweets.
这项研究提供了关于企业财务推特发布时机和性质的广泛描述性证据。使用无监督机器学习方法来分析2012年至2020年间标准普尔1500指数公司发布的2400万条推文，我们发现公司在显著负面或正面新闻事件（如盈利公告和财务报表备案）周围更有可能发布财务信息。发布财务推文的可能性与会计事件的重要性之间的凸形U型关系随着时间的推移而增强。基于早期样本的研究得出结论，当新闻不好且重要时，公司不太可能通过推特发布财务信息，但我们发现的这种对称传播行为表明，这些结论应该被修正。我们还展示了机器学习算法（Twitter-Latent Dirichlet Allocation）在分类像推文这样的短消息方面优于词典方法。
Tips：“机器学习在经济金融领域的应用”研讨会即将举办，欢迎对机器学习方法及其应用感兴趣的学者和学生报名！
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