  Categorization of mutual funds or Exchange-Traded-funds (ETFs) have long
served the financial analysts to perform peer analysis for various purposes
starting from competitor analysis, to quantifying portfolio diversification.
The categorization methodology usually relies on fund composition data in the
structured format extracted from the Form N-1A. Here, we initiate a study to
learn the categorization system directly from the unstructured data as depicted
in the forms using natural language processing (NLP). Positing as a multi-class
classification problem with the input data being only the investment strategy
description as reported in the form and the target variable being the Lipper
Global categories, and using various NLP models, we show that the
categorization system can indeed be learned with high accuracy. We discuss
implications and applications of our findings as well as limitations of
existing pre-trained architectures in applying them to learn fund
categorization.
