  This paper provides a holistic study of how stock prices vary in their
response to financial disclosures across different topics. Thereby, we
specifically shed light into the extensive amount of filings for which no a
priori categorization of their content exists. For this purpose, we utilize an
approach from data mining - namely, latent Dirichlet allocation - as a means of
topic modeling. This technique facilitates our task of automatically
categorizing, ex ante, the content of more than 70,000 regulatory 8-K filings
from U.S. companies. We then evaluate the subsequent stock market reaction. Our
empirical evidence suggests a considerable discrepancy among various types of
news stories in terms of their relevance and impact on financial markets. For
instance, we find a statistically significant abnormal return in response to
earnings results and credit rating, but also for disclosures regarding business
strategy, the health sector, as well as mergers and acquisitions. Our results
yield findings that benefit managers, investors and policy-makers by indicating
how regulatory filings should be structured and the topics most likely to
precede changes in stock valuations.
