Economics Job Candidate Seminar

Abstract: Valuations depend on how people categorize, perceive, or otherwise represent economic objects. This paper develops a measure of how the market represents firms, and uses this measure to study stock valuations. I train an algorithm to structure language from financial news into embeddings—vectors that quantify the economic features and themes in each firm's news coverage. I show that a firm's vector representation is informative of how the market perceives its business model. Representations explain cross-sectional variation in stock valuations, cash flow forecasts, and return correlations. Changes in representation help to explain changes in stock prices. Some changes in representations and prices are forecastable, and indicate that some of the explained variation in stock valuations stems from misperception. I find that misperception and misvaluation can intensify when a firm's news coverage includes attention-drawing features—like "internet" in the late 1990s or "AI" in the early 2020s.