We initiate a novel approach to explain the out of sample performance of random forest (RF) models by exploiting the fact that any RF can be formulated as an adaptive weighted K nearest-neighbors model. Specifically, we use the proximity between points in the feature space learned by the RF to re-write random forest predictions exactly as a weighted average of the target labels of training data points. This linearity facilitates a local notion of explainability of RF predictions that generates attributions for any model prediction across observations in the training set, and thereby complements established methods like SHAP, which instead generates attributions for a model prediction across dimensions of the feature space. We demonstrate this approach in the context of a bond pricing model trained on US corporate bond trades, and compare our approach to various existing approaches to model explainability.

These categorization systems go deeper than the broader asset class based classification (equity, fixed income, etc) and provide Categorization of mutual funds or Exchange-Traded-funds (ETFs) further granular categories based on the portfolio breakdown. They have long served the financial analysts to perform peer analysis have been used to identify the top performing as well as worst for various purposes starting from competitor analysis, to quantifying performing funds within their peer groups, called peer analysis portfolio diversification. The categorization methodology of funds; to identify a home-grown fund to recommend against a usually relies on fund composition data in the structured format competitor's fund; to explain similarities and advantages of homegrown extracted from the Form N-1A. Here, we initiate a study to learn products compared to competitors' products for marketing the categorization system directly from the unstructured data as purposes; to quantify portfolio diversification of a given fund of depicted in the forms using natural language processing (NLP).