一个案例研究,揭开深度学习在资产负债管理中的潜力
Thomas Krabichler1, Josef Teichmann2
1Centre for Banking and Finance, Eastern Switzerland University of Applied Sciences, St. Gallen, Switzerland.
Frontiers in artificial intelligence
|June 7, 2023
概括
深度资产负债管理 (Deep ALM) 使用深度学习来实现资产和负债管理的技术转型. 这种方法影响了财务决策,商品采购和电厂优化.
科学领域:
- 量化金融 量化金融
- 机器学习 机器学习
- 风险管理 风险管理
背景情况:
- 在定量风险管理中,正在出现深度学习应用.
- 传统的资产负债管理 (ALM) 面临着复杂的金融工具和长期负债的挑战.
- 需要技术进步来改变ALM实践.
研究的目的:
- 引入深度资产负债管理 (Deep ALM) 的概念.
- 探索深度学习在ALM技术转型中的潜力.
- 证明深度ALM在各种金融和运营领域的广泛适用性.
主要方法:
- 深度ALM的概念框架开发.
- 将深度学习技术应用于资产和负债管理.
- 用风格化的财务场景进行说明性案例研究.
主要成果:
- 深度ALM提供了一种新的方法来管理整个期限结构的资产和负债.
- 该方法对财政部职能中最佳决策具有重要意义.
- 潜在的应用包括商品采购和水电厂的优化.
结论:
- 深度ALM代表了金融风险管理的重大技术进步.
- 该方法有可能通过基于目标的投资和抽象的ALM来解决复杂的社会挑战.
- 深度ALM的进一步研究和应用是有必要的,以充分实现它的好处.
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