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This paper provides a holistic study of how stock prices vary in theirresponse to financial disclosures across different topics. Thereby, wespecifically shed light into the extensive amount of filings for which no apriori categorization of their content exists. For this purpose, we utilize anapproach from data mining - namely, latent Dirichlet allocation - as a means oftopic modeling. This technique facilitates our task of automaticallycategorizing, ex ante, the content of more than 70,000 regulatory 8-K filingsfrom U.S. companies. We then evaluate the subsequent stock market reaction. Ourempirical evidence suggests a considerable discrepancy among various types ofnews stories in terms of their relevance and impact on financial markets. Forinstance, we find a statistically significant abnormal return in response toearnings results and credit rating, but also for disclosures regarding businessstrategy, the health sector, as well as mergers and acquisitions. Our resultsyield findings that benefit managers, investors and policy-makers by indicatinghow regulatory filings should be structured and the topics most likely toprecede changes in stock valuations.

Investor Reaction to Financial Disclosures Across Topics: An Application of Latent Dirichlet Allocation