Prior research finds that the textual content of Item 1A risk factor disclosures in 10‐K filings provides valuable information about firm risk. However, less is known about whether the ordering of these disclosures conveys useful information. We examine whether the relative prominence of individual risk factors within Item 1A reflects firms' exposure to the underlying risks and predicts future adverse outcomes. Focusing on credit and goodwill risk disclosures, we find that risk factor prominence is associated with proxies for the underlying risks and predicts credit rating downgrades, bankruptcy filings, and goodwill impairments. We further find that prominence is more informative during periods of high information uncertainty, when the benefits of risk disclosure are predicted to be greater. Overall, our findings suggest that risk factor prominence offers a valuable signal of firm risk that complements the textual disclosures in Item 1A. Accordingly, investors, analysts, lenders, auditors, boards, and regulators should consider both the level of, and changes in, risk factor prominence when evaluating firm risk.

In this paper, we investigate the dynamic relationship between financial market volatility, macroeconomic fundamentals and investor sentiment, employing a two-factor model to decompose volatility into a persistent long run component and a transitory short run component. Using a structural VAR model with Bayesian sign restrictions, we show that adverse shocks to aggregate demand and supply cause an increase in the persistent component of both stock and bond market volatility, and that adverse shocks to the persistent component of either stock or bond market volatility cause a deterioration in macroeconomic fundamentals. We find no evidence of a relationship between the transitory component of volatility and macroeconomic fundamentals. Instead, we find that the transitory component is more closely associated with changes in investor sentiment. Our results are robust to a wide range of alternative specifications. Out-of-sample forecasting shows that the components of volatility can improve forecasts of macroeconomic fundamentals, and vice versa.