Combining Fundamental Measures for Stock Selection

Securities selection attempts to distinguish prospective winners from losers conditional on beliefs and available information. This article surveys relevant academic research on this subject, including work about the combining of forecasts (Bates and Granger 1969), the Black-Litterman model (1991, 1992), the combining of Bayesian priors and regression estimates (Pastor 2000), model uncertainty and Bayesian model averaging (Hoeting, Madigan, Raftery, and Volinsky 1999; Cremers 2002), the theory of competitive storage (Deaton and Laroque 1992), and the combination of valuation estimates (Yee 2007). Despite its wide-ranging applicability, the Bayesian approach is not a license for data snooping. The second half of this article describes common pitfalls in fundamental analysis and comments on the role of theory in mitigating these pitfalls