Abstract
We describe a concrete on-going industry project on advanced portfolio optimization based on machine-learning techniques, and we report on attempts and results of successful and advantageous application of QMC methods in this project. We are also carrying out an approach to determine a measure for dispersion in an opportunity set, which cannot trivially be found, because of the uncertainty of the shape of an opportunity set. Finally, we state some still open problems and questions in this context.
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Acknowledgements
The authors are supported by the Austrian Science Fund (FWF), Project F5507-N26, which is part of the Special Research Program Quasi-Monte Carlo Methods: Theory and Applications, and by the Land Upper Austria research funding. The authors thank an anonymous referee for very carefully reading our manuscript and for many valuable remarks and suggestions for improving this paper!
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Brunhuemer, A., Larcher, G. (2022). Quasi-Monte Carlo Methods in Portfolio Selection with Many Constraints. In: Botev, Z., Keller, A., Lemieux, C., Tuffin, B. (eds) Advances in Modeling and Simulation. Springer, Cham. https://doi.org/10.1007/978-3-031-10193-9_5
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