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On data-driven robust portfolio optimization with semi mean absolute deviation via support vector clustering | ||
| Journal of Mathematics and Modeling in Finance | ||
| مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 12 خرداد 1404 اصل مقاله (188.87 K) | ||
| نوع مقاله: Research Article | ||
| شناسه دیجیتال (DOI): 10.22054/jmmf.2025.84881.1170 | ||
| نویسندگان | ||
| Eftekhar Kosarinia؛ Maziar Salahi* ؛ Tahereh Khodamoradi | ||
| Department of Applied Mathematics, Faculty of Mathematical Sciences, University of Guilan, Rasht, Iran | ||
| چکیده | ||
| In [14] the authors have studied robust semi-mean absolute deviation portfolio optimization model when assets expected returns involve uncertainty. They applied a data driven approach via support vector clustering to construct the uncertainty set using support vector clustering. In this paper, we show that their robust formulation is not the worst case counterpart of the original model. Then we give the true robust model of the underlying problems in the best an worst cases. Experiments are conducted to show the optimal objective value of the robust model in [14] belongs to the interval generated by our best and worst case models. | ||
| کلیدواژه‌ها | ||
| Portfolio optimization؛ Semi-mean absolute deviation؛ Uncertainty؛ Support vector clustering | ||
| مراجع | ||
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آمار تعداد مشاهده مقاله: 37 تعداد دریافت فایل اصل مقاله: 27 |
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