“Disclosure Softness of Corporate Language”

Job market paper

Abstract: I study economic incentives that determine the disclosure softness of corporate language. Using the MD&A section of 10-K filings, I measure disclosure softness by holistically aggregating linguistic attributes consisting of vague, tonal, forward-looking, numerical, and specific information, as well as novel metrics of historical and objective content based on natural language processing and machine learning algorithms. I find that firms provide softer disclosures during poorly performing years, and that this effect is stronger in less ambiguous settings, suggesting that the value of soft/hard information is conditional on the underlying information environment. These results are distinct from the effect of disclosure complexity as measured using the Fog index. In addition, I find that proprietary costs stemming from competition from incumbents versus potential entrants differentially influence disclosure softness. I corroborate my findings using the exogenous variation in entry threat resulting from import tariff rate reductions. In addition, my results suggest that performance and competitive incentives have a pervasive effect such that managers use an arsenal of linguistic attributes in shaping their disclosure strategy. Overall, I take a first attempt at measuring and studying the economic determinants of disclosure softness, a concept germane to the historical debate on relevance versus reliability.

:point_right: Click here to see how I used Machine Learning in my Job Market Paper! :robot:

“Protecting Forward Looking Statements”

with Maclean Gaulin and K. Ramesh

Abstract: We examine the increasingly prevalent managerial disclosure practice of listing specific keywords in SEC filings to identify forward-looking statements for obtaining “safe harbor” protection under the Private Securities Litigation Reform Act. We show that proxies for ex ante litigation risk, network/herding effects, disclosure supply, and economic uncertainty are strongly associated with the decision to include the keyword list and the number of keywords. Responding to transient economic circumstances, firms periodically change the number of keywords to customize their forward-looking disclosures. Using factor analysis we unravel the specific linguistic attributes implied by the keywords and show how they enable firms to tailor their disclosure of quantitative and qualitative forward-looking information. Finally, managers facing higher litigation risk find it imperative to protect disclosures that capital markets view as value relevant. Overall, the decision to include the list and the choice of the keywords it contains are neither boilerplate nor ad hoc. Together, our evidence provides an important first look at the determinants of firms’ decisions regarding a central feature of forward-looking disclosures’ “safe harbor” protection.

Works In Progress

  • “Disclosing Strategy” with Maclean Gaulin and K. Ramesh
  • “Regulating Forward-Looking Disclosures” with Maclean Gaulin, K. Ramesh, and Brian Rountree