  A recursive free cash flow model (FCFF) is proposed to determine the
corporate value of a company in an efficient market in which new market and
company-specific information is modelled by additive white noise. The
stochastic equations of the FCFF model are solved explicitly to obtain the
average corporate value and valuation risk. It is pointed out that valuation
risk can be reduced significantly by implementing a conventional two-step
Kalman filter in the recursive FCFF model, thus improving its predictive power.
Systematic errors of the Kalman filter, caused by intermediate changes in risk
and hence in the weighted average cost of capital (WACC), are detected by
measuring the residuals. By including an additional adjustment step in the
conventional Kalman filtering algorithm, it is shown that systematic errors can
be eliminated by recursively adjusting the WACC. The performance of the
three-step adaptive Kalman filter is tested by Monte Carlo simulation which
demonstrates the reliability and robustness against systematic errors. It is
also proved that the conventional and adaptive Kalman filtering algorithms can
be implemented into other valuation models such as the economic value added
model (EVA) and free cash flow to equity model (FCFE).
