股票回购:对遗传算法和数学选项性的理论探索
1Department of High-Tech Business and Entrepreneurship, University of Twente, Enschede, Netherlands.
Frontiers in artificial intelligence
|November 29, 2023
概括
本研究介绍了利用遗传算法 (GA) 和数学优化优化股票回购执行的三个新假设. 这些未经测试的想法探索模仿交易时间表,优化回购作为数学问题,并利用可选性来提高财务绩效.
科学领域:
- 金融经济学 金融经济学
- 计算金融是指计算金融.
- 数学优化的数学优化
背景情况:
- 股票回购是一个重要的企业融资工具.
- 现有的策略可能无法充分利用先进的计算和数学技术.
- 奥斯特里德,塞恩,马斯特斯和格昂的基础工作为新的方法提供了基础.
研究的目的:
- 为执行股票回购制定三个创新,未经测试的假设.
- 在这个领域探索遗传算法 (GA) 和数学优化的应用.
- 为优化企业财务战略提供一个新的视角.
主要方法:
- 理论假设的制定.理论假设的制定.
- 遗传算法 (GA) 的概念应用.
- 数学优化原则. 数学优化原则.
主要成果:
- 假设1:GA可以有效地模仿回购的最佳交易时间表.
- 假设2:股票回购执行可以作为数学问题进行框架和优化.
- 假设3:纳入可选性提高了股票回购策略的表现.
结论:
- 提出的假设为股票回购执行提供了新的理论见解.
- 这些假设为未来的经验测试和实际金融创新铺平了道路.
- 该研究强调了先进算法和数学框架在企业金融中的潜力.
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