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Measuring investment opportunities under uncertainty

Author
Castro, J.; Gabarro, J.; Serna, M.
Type of activity
Presentation of work at congresses
Name of edition
15th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Date of publication
2020
Presentation's date
2019-09-18
Book of congress proceedings
Symbolic and Quantitative Approaches to Reasoning with Uncertainty, 15th European Conference, ECSQARU 2019: Belgrade, Serbia, September 18-20, 2019: proceedings
First page
481
Last page
491
Publisher
Springer
DOI
10.1007/978-3-030-29765-7_40
Project funding
2017 SGR 786 - Algorísmia, Bioinformàtica, Complexitat i Mètodes Formals ALBCOM
Graph-based Models and Methods for Computing in the Large
Management and Analysis of Complex DATA
Repository
http://hdl.handle.net/2117/178256 Open in new window
URL
https://link.springer.com/chapter/10.1007/978-3-030-29765-7_40 Open in new window
Abstract
In order to make sound economic decisions it is important to measure the possibilities offered by a market in relation to investments. Provided an investment scheme S = (r; R1, . . . , Rn), where r is a lower bound on the desired investment return and the Ri’s are the asset yields, the power to invest measures the capability of the scheme to fulfill requirement r. The power to invest is inspired in the Coleman’s power of a collectivity to act. We exemplify this approach considering subsets o...
Citation
Castro, J.; Gabarró, J.; Serna, M. Measuring investment opportunities under uncertainty. A: European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty. "Symbolic and Quantitative Approaches to Reasoning with Uncertainty, 15th European Conference, ECSQARU 2019: Belgrade, Serbia, September 18-20, 2019: proceedings". Berlín: Springer, 2019, p. 481-491.
Keywords
Investment opportunities, Power to act, Power to invest, Uncertainty
Group of research
ALBCOM - Algorithms, Computational Biology, Complexity and Formal Methods
LARCA - Laboratory of Relational Algorithmics, Complexity and Learnability

Participants

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