Data-efficient model building for financial applications: A semi-supervised learning approach
Purpose – Investors often rely on probabilistic models that were learned from small historical labeled datasets. The purpose of this article is to propose a new method for data-efficient model learning. Design/methodology/approach – The proposed method, which is an extension of the standard minimum relative entropy (MRE) approach and has a clear financial interpretation, belongs to the class of semi-supervised algorithms, which can learn from data that are only partially labeled with values of the variable of interest. Findings – This study tests the method on an artificial dataset and uses it to learn a model for recovery of defaulted debt. In both cases, the resulting models perform better than the standard MRE model, when the number of labeled data is small. Originality/value – The method can be applied to financial problems where labeled data are sparse but unlabeled data are readily available.
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