Textual analysis, in some form, resides across many disciplines under various aliases. The notion of parsing text for patterns has a long history. In accounting and finance, the online availability of news articles, earnings conference calls, and text from social media provide ample fodder for applying the technology. Can we read news articles and trade before humans can read and assimilate the information? If Twitter’s tweets provide the pulse of information, can we monitor these messages in real time to gain an informational edge? Do textual artifacts provide an additional attribute that predicts bankruptcies? Are there subtle cues in managements’ earnings conferences that computers can discern better than analysts?
Textual analysis is an emerging area in accounting and finance. The corresponding taxonomies are still somewhat imprecise. Textual analysis can be considered as a subset of what is sometimes labeled qualitative analysis. The words selected by managers to describe their operations and the language used by media to report on firms and markets have been shown to be correlated with future stock returns, earnings, and even future fraudulent activities of management. The burgeoning literature in textual analysis is already summarized well in other papers, although the increasing popularity of the method quickly dates any attempt to distill research on the topic.
The literature needs to be less centered on finding ways to apply off-the-shelf textual methods and instead be more motivated by hypotheses “closely tied to economic theories” (Li [2010a, p. 158). Kearney and Liu [2014] provide a more recent survey of methods and literature with a focus on textual sentiment. We add value beyond simply offering an updated literature review by also underscoring the methodological tripwires for those approaching this relatively new technique. We emphasize the importance of exposition and transparency in this transformation process.
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