The errors-in-variable model in the optimal portfolio construction

In the paper we consider a modification of Sharpe's method used in classical portfolio analysis for optimal portfolio building. The conventional theory assumes there is a linear relationship between asset's return and market portfolio return, while the influence of all the other factors is not included. We propose not to neglect them any more, but include them into a model. Since the factors in question are often hard to measure or even characterize, we treat them as a disturbances on random variables used by classical Sharpe's method. The key idea of the paper is the modification of the classical approach by application of the errors-in-variable model. We assume that both independent (market portfolio return) as well as dependent (given asset's return) variables are randomly distributed values related with each other by linear relationship and we build the model used for parameters' estimation. To verify the model, we performed an analysis based on archival data from Warsaw Stock Exchange. The results are also included.