Abstract: If stock prices do not follow random walks, what
processes do they follow? This question is important not only for
forecasting purpose, but also for theoretical analyses and derivative
pricing where a tractable model of the movement of underlying stock
prices is needed. Although several models have been proposed to
capture the predictability of stock returns, their empirical
performances have not been evaluated. This paper evaluates some
popular models using a Kalman Filter technique and finds that they
have serious flaws. The paper then proposes an alternative
parsimonious state-space model in which state variables characterize
the stochastic movements of stock returns. Using equal-weighted CRSP
monthly index, the paper shows that (1) this model fits the
autocorrelations of returns well over both short and longer horizons
and (2) although the forecasts obtained with the state-space model are
based solely on past returns, they subsume the information in other
potential predictor variables such as dividend yields.
Keywords: State-space model, stock returns
Full paper (215 KB PDF)
| Full paper (218 KB Postscript)
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