Explaining Returns through Valuation

Abstract

This paper 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 explains 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 but no intercept. The contraction parameter can be estimated efficiently and non-parametrically. This modelling suffices to correlate implied returns with realized returns. Using scaled EPS forecasts as valuation heuristics, we empirically evaluate the framework’s validity and robustness. Its explanatory power compares favorably to that of traditional OLS regressions, despite only requiring a single parameter. In a setting with pooled annual data, the implied and realized returns correlations are 73% and 64%, respectively.