The impact of earnings announcements (EAs) on investor uncertainty depends not only on how much new information they contain but also on how long it would take comparable information to arrive in the future through alternative sources, which I term “earnings horizon.” Using a structural model of periodic EAs, I show that earnings horizon is not captured by standard empirical measures of earnings informativeness or timeliness based on the event-study approach. However, earnings horizon can be estimated using patterns in return volatility over firms’ reporting cycles, which indicate that EAs have a short horizon and thus reduce investor uncertainty by one-third the amount suggested by event studies. Moreover, these patterns indicate that it takes investors considerably longer than the three- to five-day windows commonly applied in event studies to fully process EAs and that more frequent financial reporting may significantly enhance EAs’ informativeness. Data Availability: Data are available from the public sources cited in the text.

We study debt and equity valuation when investors have private information and may exhibit differences of opinion. Our model generates several predictions that are consistent with empirical evidence but difficult to reconcile with traditional models. Belief dispersion relates to expected equity and debt returns in opposite directions. Similarly, expected debt (equity) returns typically increase (decrease) with default risk, though these relationships reverse for firms close to bankruptcy. Firms’ capital structures affect their valuations even without classical capital structure frictions (e.g., tax shields, distress costs) – when liquidity is higher in the equity than in the debt market, leverage can raise firm value.

We model the “feedback effect” of a firm's stock price on investment in projects exposed to a systematic risk factor, like climate risk. The stock price reflects information about both the project's cash flows and its discount rate. A cash‐flow‐maximizing manager treats discount rate fluctuations as “noise,” but a price‐maximizing manager interprets such variation as information about the project's net present value. This difference qualitatively changes how investment behavior varies with the project's risk exposure. Moreover, traditional objectives (e.g., cash flow or price maximization) need not maximize welfare because they do not correctly account for hedging and risk‐sharing benefits of investment.