Explaining Returns Through Valuation

Abstract

This article develops an analytically coherent yet parsimonious framework which explains market returns in terms of contemporaneous information. It anchors on the idea that valuation (static perspective) can be connected to the dynamics that explain returns, and vice versa. The framework requires two components. First, an explicit function that maps information to an estimate of value—a valuation heuristic. Second, the framework assumes that the difference between a firm’s actual value and value-per-heuristic follows an autoregressive stochastic process with a contraction parameter and no intercept. The contraction parameter can be estimated efficiently and nonparametrically. This modeling suffices to derive implied returns. Using scaled Earnings Per Share (``EPS'') forecasts as valuation heuristics, we empirically evaluate the framework’s validity and robustness. Its explanatory power compares favorably to that of traditional ordinary least squares (``OLS'') regressions, despite only requiring a single parameter. In a setting with pooled annual data, the implied and realized returns correlations range between 64% and 73%.