We develop a measure of how information events impact investors' expectations of risk. The measure is broadly applicable and simple to implement. We derive it from an option‐pricing model, where investors anticipate an announcement that simultaneously conveys information on the announcer's expected future cash flows and risk profile. We empirically implement the measure using firms' earnings announcements, showing that it closely aligns with our model's predictions and offers strong forecasting power for firms' risk profiles, costs of capital, and future investments. We further highlight pitfalls of using simple changes in option‐implied volatilities to study information gleaned from earnings announcements. Finally, we apply our measure to study disclosure regulation, the efficacy of text‐based proxies, and market‐wide events, which we use to illustrate our measure's uses, and illuminate its potential limitations.

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.

In this paper, I develop a measure of the difference in the amount of information that investors expect a forthcoming disclosure to contain should it reveal good news versus bad news (the disclosure’s “asymmetry”). To do so, I first show that this asymmetry is linked to the skewness of returns that the disclosure creates. I then show that this skewness can be measured using a weighted change in option-implied return skewness leading up to the disclosure’s release. The measure’s ability to capture investors’ prior beliefs regarding asymmetry is advantageous when studying ex ante decisions including contracting and information acquisition choices. I implement it on a sample of large firms’ quarterly earnings announcements, finding evidence that investors anticipate cross-sectional but not time-series variation in earnings’ asymmetry.

When valuing a firm, investors must assess not only its expected future cash flows but also the systematic risk inherent in these cash flows. In this paper, we model the process by which investors may learn about firms' betas from earnings and how this learning process affects the relationship between earnings, announcement returns, and expected future returns. The model's main predictions are: (i) earnings response coefficients vary with macroeconomic conditions and are lower in upswings than downturns; (ii) earnings positively and negatively predict future returns in economic upswings and downturns, respectively, leading to return autocorrelation; and (iii) real earnings management rises in economic downturns and contributes to systematic risk in the economy. These predictions are directly attributable to investors' uncertainty regarding firms' exposures to systematic risk.

While researchers and practitioners alike estimate firms' exposures to systematic risk factors, the disclosure literature typically assumes that exposures are common knowledge. We develop a model where the firm's exposure to a factor is unknown, and analyze the effects of factor-exposure uncertainty on share price and the effects of disclosure about the exposure. We find that: (1) factor-exposure uncertainty introduces skewness and excess kurtosis in the cash flow distribution relative to the commonly used normal distribution; (2) risk-factor disclosure affects all moments of that distribution; and (3) the pricing of higher moments affects the price response of disclosure and the incentives to disclose. For example, factor-exposure uncertainty may actually increase price when the uncertainty implies positive skewness in the cash flow distribution. Hence, a reduction in uncertainty through disclosure may increase cost of capital. We also extend our model to multiple firms and show that factor-exposure uncertainty manifests as uncertainty about a firm's CAPM beta.