[This paper presents a model that relates properties of the analysts' information environment to the properties of their forecasts. First, we express forecast dispersion and error in the mean forecast in terms of analyst uncertainty and consensus (that is, the degree to which analysts share a common belief). Second, we reverse the relations to show how uncertainty and consensus can be measured by combining forecast dispersion, error in the mean forecast, and the number of forecasts. Third, we show that the quality of common and private information available to analysts can be measured using these same observable variables. The relations we present are intuitive and easily applied in empirical studies.]

We show analytically that mean analyst forecasts inefficiently aggregate information by assigning too much weight to analysts’ common information relative to their private information when used as a summary forecast measure of forthcoming earnings. A more precise summary forecast of earnings than the current mean forecast is the current mean forecast plus a positive multiple of the change in the mean forecast.

We document that borrowing costs and credit ratings are less sensitive to off-balance sheet lease financing than to on-balance sheet debt financing, particularly for firms that are financially constrained and firms that have limited ability to use tax shields. This evidence is consistent with theoretical predictions based on tax benefits as well as bankruptcy costs. Our evidence on borrowing costs and credit ratings suggests that credit markets treat operating leases differently from balance sheet debt. Consistent with this interpretation, we document that firms closer to ratings borderlines lease more, particularly around the investment grade borderline.

A substantial and increasing proportion of corporate assets consists of intangible assets. Despite their growing importance, internally-generated intangible assets are largely absent from balance sheets and other corporate reports. Consequently, the empirical capital structure research has struggled to evaluate the effects of intangible assets on financial leverage. High valuation risk and poor collateralizability of some intangible assets — e.g. goodwill, may discourage debt financing. In contrast, identifiable intangible assets may support debt because they are separately identifiable, valuable, and potentially collateralizable, and are instrumental in generating cash flows. Utilizing a recent accounting rule change that allows us to observe granular market-based valuations of intangible assets, we find a strong positive relation between identifiable intangible assets and leverage. Overall, identifiable intangible assets support debt financing as much as tangible assets do, in particular in firms that lack abundant tangible assets.

Abstract This paper presents a model that relates properties of the analysts' information environment of the properties of their forecasts. First, we express forecast dispersion and error in the mean forecast in terms of analyst uncertainty and consensus (that is, the degree to which analysts share a common belief). Second, were reserve the relations to show how uncertainability and consensus cab be measured by combining forecast dispersion, error in the mean forecast, and the number of forecasts. Third, we show that the quality of common and private information available to analysts can be measured using these same observable variables. The relations we present are intuitive and easily applied in empirical studies.