Using the Fundamental Analysis Method to Create a Matrix of Investor Views in the Black Literman Optimization Model and Comparing its Performance with Existing Models

Modern investment management began with Markowitz's theory on how to select an optimal portfolio based on the mean-variance model, and then continued with various optimization methods such as conditional value at risk model, half-variance model, and the mean-absolute deviation model. One of the most important weaknesses of these models is not paying attention to investors' views and relying on past information. The Black Literman model has largely eliminated this weakness by combining a matrix of investor views and past returns. In this research, using fundamental analysis, the matrix of investor views is formed and then the performance of the optimal portfolio of the Black Literman model is compared with existing models. The research period is between 2016 to 2021 and MATLAB software has been used to obtain the optimal portfolio. The results show that using different evaluation criteria, the fundamental Black Literman model performs better than other existing optimization models.