This paper elucidates the impact of uncertainty on the Markowitz asset allocation and how it performs. The findings imply that when evaluated out-of-sample, estimation errors in parameters might significantly affect how well an allocation performs. Numerous publications that address this ambiguity have been highlighted to emphasize our findings further. In our work, we compare these approaches to alternative allocation strategies and explain their performance in both expected and real out-of-sample events. We find that the Markowitz framework can be improved by using tactics that take uncertainty into account. Longer sample numbers, however, may not always translate into better outcomes. Applying a short-sale constraint can also enhance the initial portfolio. Finally, we find that even more basic approaches to asset allocation, such as equally weighted allocation, perform reasonably well.
Computer Science and Mathematics, Data Structures, Algorithms and Complexity
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