The Stochastic Parameter Approach to Residual Income Valuation
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- Discussion Papers 
In residual income valuation the information dynamics (ID) provides a connection between current observable information and unobservable forecasts of future residual income (RI). Traditionally the ID is formulated as multivariate auto-regressive processes, where the auto-regressive parameters (persistence parameters) are assumed fixed and known. For this reason uncertainty (risk) is captured in a zero-mean noise term, and risk does not affect the process of expected RI. Put differently, future projects accepted by a firm are by assumption zero-NPV undertakings. Even without information of a specific firm’s future activities, one should acknowledge the fact that some firms will outperform the industry average, while some will underperform. Ignoring the existence of risk that systematically affects RI by applying a linear ID, a downward valuation bias will occur. In this paper we treat the persistence parameters as stochastic variables in order to capture the overall effect of “systematic risk” on firm level. The realization of the stochastic term will be positive for firms succeeding in business and negative for failing firms. Based on OWAM a valuation model is developed, where analysts’ forecasts embed available information (accounting information and “other information”) at the time of valuation. OWAM: Ohlson’s weighted average model (Ohlson 95). Consistent with several recent empirical studies on ID, the revised model predicts a positive relationship between “systematic risk” and expected return.