Refining expected market return estimation: fusing multiple valuation models as an approach to reducing uncertainty

Abstract

This paper presents a methodology aimed at reducing the uncertainty associated with estimating the expected market return within a bounded rationality framework. The proposed approach involves calculating the implicit rate of return using various valuation models and subsequently merging them using the Kalman Filter technique to minimize estimation errors. The contribution of this study lies in the application of the Kalman Filter, which enables the expected market return to be refined and provides a more accurate estimate by mitigating uncertainty. The ability to determine an accurately expected market return assumes critical significance in investment decision-making. Therefore, investors can utilize this methodology as a tool to enhance the precision of their investment choices. By reducing uncertainty in estimating the expected market return, this approach empowers investors to make more informed and confident decisions.