Abstract
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.
Keywords:
computational intelligence; financial innovation; genetic algorithms; mathematical optimization; optionality; share buybacks; trading schedules.
Copyright © 2023 Osterrieder.
Grants and funding
The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This work has been supported by several institutions, each of which has provided vital resources and expertise to the research project. We also appreciate the funding for the project on Anomaly and Fraud Detection in Blockchain Networks (IZSEZ0-211195), and for the project on Narrative Digital Finance: a tale of structural breaks, bubbles and market narratives (IZCOZ0-213370). In addition, our research has benefited from funding from the European Union's Horizon 2020 research and innovation program under the grant agreement no. 825215 (Topic: ICT-35-2018, Type of action: CSA). This grant was provided for the FIN-TECH project, a training programme aimed at promoting compliance with financial supervision and technology. We gratefully acknowledge the support of the Marie Skłodowska-Curie Actions under the European Union's Horizon Europe research and innovation program for the Industrial Doctoral Network on Digital Finance, acronym: DIGITAL, project no. 101119635. We would like to express our gratitude to the Swiss National Science Foundation for its financial support across multiple projects. This includes the project on Mathematics and Fintech (IZCNZ0-174853), which focuses on the digital transformation of the Finance industry.