Measuring Qualitative Information in Capital Markets Research: Comparison of Alternative Methodologies to Measure Disclosure Tone
Measuring Qualitative Information in Capital Markets Research: Comparison of Alternative Methodologies to Measure Disclosure Tone
复制标题
DOI：
10.2308/accr-51161
复制
发表时间：
2016-01-01
影响因子：
4.1
通讯作者：
Leone, Andrew J.
中科院分区：
文献类型：
--
作者：
Henry, Elaine;Leone, Andrew J.
这项研究评估了财务叙事语气的其他衡量标准。我们提出的证据表明，与一般词表相比，基于特定领域词表的词频音调测量更好地预测了市场对收益公告的反应，在短窗口事件研究中具有更大的统计能力，并显示出更经济上一致的公告后漂移。此外，Loughran和McDonald(2011)倡导的反向文件频率权重对等权重的替代方法几乎没有改进。我们还提供了证据表明，在对MD&A声调未来收益的回归中，词频声调测量与Li(2010)的朴素贝叶斯机器学习声调测量一样有效。总体而言，尽管更复杂的技术在某些背景下具有潜在优势，但在财务披露和资本市场背景下，等权、特定领域、词频的语调衡量通常也同样强大。这样的措施也更直观、更容易实施，更重要的是，更容易被复制。
This study evaluates alternative measures of the tone of financial narrative. We present evidence that word-frequency tone measures based on domain-specific wordlists-compared to general wordlists-better predict the market reaction to earnings announcements, have greater statistical power in short-window event studies, and exhibit more economically consistent post-announcement drift. Further, inverse document frequency weighting, advocated in Loughran and McDonald (2011), provides little improvement to the alternative approach of equal weighting. We also provide evidence that word-frequency tone measures are as powerful as the Naive Bayesian machine-learning tone measure from Li (2010) in a regression of future earnings on MD&A tone. Overall, although more complex techniques are potentially advantageous in certain contexts, equal-weighted, domain-specific, word-frequency tone measures are generally just as powerful in the context of financial disclosure and capital markets. Such measures are also more intuitive, easier to implement, and, importantly, far more amenable to replication.