In this study, we propose an alternative technique for estimating the cost of equity capital. Specifically, we use a discounted residual income model to generate a market implied cost‐of‐capital. We then examine firm characteristics that are systematically related to this estimate of cost‐of‐capital. We show that a firm's implied cost‐of‐capital is a function of its industry membership, B/M ratio, forecasted long‐term growth rate, and the dispersion in analyst earnings forecasts. Together, these variables explain around 60% of the cross‐sectional variation in future (two‐year‐ahead) implied costs‐of‐capital. The stability of these long‐term relations suggests they can be exploited to estimate future costs‐of‐capital. We discuss the implications of these findings for capital budgeting, investment decisions, and valuation research.

This study presents a general approach for selecting comparable firms in market‐based research and equity valuation. Guided by valuation theory, we develop a “warranted multiple” for each firm, and identify peer firms as those having the closest warranted multiple. We test this approach by examining the efficacy of the selected comparable firms in predicting future (one‐ to three‐year‐ahead) enterprise‐value‐to‐sales and price‐to‐book ratios. Our tests encompass the general universe of stocks as well as a sub‐population of so‐called “new economy” stocks. We conclude that comparable firms selected in this manner offer sharp improvements over comparable firms selected on the basis of other techniques.

This study compares four broadly available industry classification schemes in a variety of applications common to capital market research. Standard Industrial Classification (SIC) codes have been available since 1939 but are being replaced by North American Industry Classification System (NAICS) codes. The Global Industry Classifications Standard (GICS) SM system, jointly developed by Standard & Poor's and Morgan Stanley Capital International (MSCI), is popular among financial practitioners, whereas the Fama and French [1997] algorithm is used primarily by academics. Our results show that GICS classifications are significantly better at explaining stock return comovements, as well as cross‐sectional variations in valuation multiples, forecasted and realized growth rates, research and development expenditures, and various key financial ratios. The GICS advantage is consistent from year to year and is most pronounced among large firms. The other three methods differ little from each other in most applications.