This study investigates uncommon information in firm disclosures. Using a method from computational linguistics called Latent Semantic Analysis (LSA), I measure the difference in topics discussed in the Management's Discussion and Analysis (MD&A) section of a firm's 10-K filing relative to its peers. I hypothesize that the presence of uncommon information signals to investors that the firm's valuation function differs from its peers'. Consistent with this hypothesis, I find that uncommon information is associated with higher firm-specific stock returns. I also hypothesize that investors will process uncommon information slowly because uncommon information is textually and conceptually complex and investors have little data with which to estimate its value. Consistent with this hypothesis, I find that the market response to uncommon information is delayed. Drawing on the information theory and linguistics literature, I further hypothesize and find that text containing uncommon information is longer and less readable, suggesting that readability is at least partially a function of the information presented in the text. This study makes several contributions to the literature. First, I identify a mechanism through which investors identify firm-specific components of value. Second, I link the information content of a disclosure to higher information processing costs. In doing so, I identify a cost-benefit tradeoff faced by managers when crafting their disclosures and by investors when they analyze those disclosures. Third, I introduce Latent Semantic Analysis to the accounting literature as a method for comparing firm disclosures.
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