We characterize an information cliff in the stock market: the supply of information on aggregate cash flows drops precipitously beyond a one-year horizon, and so does analyst forecast accuracy. We use a generalized state-space model to explore the implications for expected cash-flow growth and expected returns. Identifying the state-space dimensionality is the only necessary step for sharpening the model structure. Once done, the information cliff has a direct mathematical representation: the expected cash-flow component of the state space must be non-persistent. Furthermore, the expected market returns only depend on the valuation wedge between the total market and one-year dividend strip.
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