  Portfolio optimization has been a major topic of research in finance, as it
has a significant impact on investment profit. In this paper, we investigate
the problem of data uncertainty in convex multi-objective portfolio
optimization. We extend Bertsimas definition of the robustness to the
multi-objective case, which has two important advantages over the previous
solutions. First, by restricting the maximum number of coefficients that are
allowed to deviate in each row of the uncertainty matrix, we control the
conservatism of our results. This modification renders our problem more
flexible. Second, by using box uncertainty to model a noisy environment, we
obtain an optimization problem with linear uncertainty set. This second
alteration makes the solution more favorable computationally compared with the
earlier nonlinear methods. Finally, using MANOVA analysis, we derive the exact
amount of uncertainty for which our solution outperforms the previous results.
