Parameterized Neural Networks for Finance
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 16 of its 28 references that are in the map. First posted on arXiv 18 Apr 2023.
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References · 16
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4 more
- 2020Emerging Relation Network and Task Embedding for Multi-Task Regression Problemscited by 8
- 2020Differential Machine Learningcited by 8
- 2022Task Embedding Temporal Convolution Networks for Transfer Learning Problems in Renewable Power Time-Series Forecastcited by 4
- 2019Learning Internal Representations (PhD Thesis)cited by 1
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