We discuss and analyze a neural network architecture, that enables learning a model class for a set of different data samples rather than just learning a single model for a specific data sample. In this sense, it may help to reduce the overfitting problem, since, after learning the model class over a larger data sample consisting of such different data sets, just a few parameters need to be adjusted for modeling a new, specific problem. After analyzing the method theoretically and by regression examples for different one-dimensional problems, we finally apply the approach to one of the standard problems asset managers and banks are facing: the calibration of spread curves. The presented results clearly show the potential that lies within this method. Furthermore, this application is of particular interest to financial practitioners, since nearly all asset managers and banks which are having solutions in place may need to adapt or even change their current methodologies when ESG ratings additionally affect the bond spreads.

翻译：我们讨论并分析了一种神经网络结构，它可以学习一组不同数据样本的模型类，而不仅仅是针对某个特定数据样本学习一个单一模型。在这个意义上，它可以帮助减少过拟合问题，因为在学习基于这些不同数据集的模型类之后，只需要调整一些参数就可以为具体问题建模。 在理论上和通过不同一维问题的回归示例分析该方法之后，我们最终将该方法应用于资产管理人员和银行面临的标准问题之一：调整价差曲线。所展示的结果清楚地显示了这种方法的潜力。此外，这种应用对金融从业者来说尤其重要，因为几乎所有具有解决方案的资产管理人员和银行在ESG评级对债券价差产生影响时可能需要调整甚至更改他们当前的方法。