  Given the surge in popularity of mutual funds (including exchange-traded
funds (ETFs)) as a diversified financial investment, a vast variety of mutual
funds from various investment management firms and diversification strategies
have become available in the market. Identifying similar mutual funds among
such a wide landscape of mutual funds has become more important than ever
because of many applications ranging from sales and marketing to portfolio
replication, portfolio diversification and tax loss harvesting. The current
best method is data-vendor provided categorization which usually relies on
curation by human experts with the help of available data. In this work, we
establish that an industry wide well-regarded categorization system is
learnable using machine learning and largely reproducible, and in turn
constructing a truly data-driven categorization. We discuss the intellectual
challenges in learning this man-made system, our results and their
implications.
