Osterrieder, Jörg Robert (2023). Share buybacks: a theoretical exploration of genetic algorithms and mathematical optionality Frontiers in Artificial Intelligence, 6 Frontiers Research Foundation 10.3389/frai.2023.1276804
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This article exclusively formulates and presents three innovative hypotheses related to the execution of share buybacks, employing Genetic Algorithms (GAs) and mathematical optimization techniques. Drawing on the foundational contributions of scholars such as Osterrieder, Seigne, Masters, and Guéant, we articulate hypotheses that aim to bring a fresh perspective to share buyback strategies. The first hypothesis examines the potential of GAs to mimic trading schedules, the second posits the optimization of buyback execution as a mathematical problem, and the third underlines the role of optionality in improving performance. These hypotheses do not only offer theoretical insights but also set the stage for empirical examination and practical application, contributing to broader financial innovation. The article does not contain new data or extensive reviews but focuses purely on presenting these original, untested hypotheses, sparking intrigue for future research and exploration. JEL Classification: G00.
Item Type: |
Journal Article (Original Article) |
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Division/Institute: |
Business School > Institute for Applied Data Science & Finance Business School |
Name: |
Osterrieder, Jörg Robert 0000-0003-0189-8636 |
Subjects: |
H Social Sciences > HG Finance |
ISSN: |
2624-8212 |
Publisher: |
Frontiers Research Foundation |
Funders: |
[7] Swiss National Science Foundation ; [UNSPECIFIED] European Cooperation in Science and Technology |
Language: |
English |
Submitter: |
Yiting Liu |
Date Deposited: |
06 Dec 2023 10:53 |
Last Modified: |
06 Dec 2023 10:53 |
Publisher DOI: |
10.3389/frai.2023.1276804 |
ARBOR DOI: |
10.24451/arbor.20560 |
URI: |
https://arbor.bfh.ch/id/eprint/20560 |
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