英文标题：
《Accelerated Share Repurchase and other buyback programs: what neural
networks can bring》
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作者：
Olivier Gu\\\'eant, Iuliia Manziuk, Jiang Pu
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最新提交年份：
2019
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英文摘要：
When firms want to buy back their own shares, they have a choice between several alternatives. If they often carry out open market repurchase, they also increasingly rely on banks through complex buyback contracts involving option components, e.g. accelerated share repurchase contracts, VWAP-minus profit-sharing contracts, etc. The entanglement between the execution problem and the option hedging problem makes the management of these contracts a difficult task that should not boil down to simple Greek-based risk hedging, contrary to what happens with classical books of options. In this paper, we propose a machine learning method to optimally manage several types of buyback contract. In particular, we recover strategies similar to those obtained in the literature with partial differential equation and recombinant tree methods and show that our new method, which does not suffer from the curse of dimensionality, enables to address types of contract that could not be addressed with grid or tree methods.
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中文摘要：
当公司想要回购自己的股份时，他们可以在几种选择中进行选择。如果他们经常进行公开市场回购，他们也越来越依赖银行，通过涉及期权成分的复杂回购合同，例如加速股票回购合同、VWAP减利润分享合同、，等等。执行问题和期权对冲问题之间的纠葛使这些合同的管理成为一项艰巨的任务，不应归结为简单的希腊式风险对冲，这与经典期权书的情况相反。在本文中，我们提出了一种机器学习方法来优化管理几种类型的回购合同。特别是，我们恢复了与偏微分方程和重组树方法在文献中获得的策略类似的策略，并表明我们的新方法不会受到维数灾难的影响，能够解决网格或树方法无法解决的合同类型。
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分类信息：

一级分类：Quantitative Finance 数量金融学
二级分类：Computational Finance 计算金融学
分类描述：Computational methods, including Monte Carlo, PDE, lattice and other numerical methods with applications to financial modeling
计算方法，包括蒙特卡罗，偏微分方程，格子和其他数值方法，并应用于金融建模
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一级分类：Quantitative Finance 数量金融学
二级分类：Trading and Market Microstructure 交易与市场微观结构
分类描述：Market microstructure, liquidity, exchange and auction design, automated trading, agent-based modeling and market-making
市场微观结构，流动性，交易和拍卖设计，自动化交易，基于代理的建模和做市
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