Quantifying Outlierness of Funds from their Categories using Supervised
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Dhruv Desai Ashmita Dhiman Tushar Sharma Deepika Sharma Dhagash Mehta Stefano Pasquali
Mutual fund categorization has become a standard tool for the investment
management industry and is extensively used by allocators for portfolio
construction and manager selection, as well as by fund managers for peer
analysis and competitive positioning. As a result, a (unintended)
miscategorization or lack of precision can significantly impact allocation
decisions and investment fund managers. Here, we aim to quantify the effect of
miscategorization of funds utilizing a machine learning based approach. We
formulate the problem of miscategorization of funds as a distance-based outlier
detection problem, where the outliers are the data-points that are far from the
rest of the data-points in the given feature space. We implement and employ a
Random Forest (RF) based method of distance metric learning, and compute the
so-called class-wise outlier measures for each data-point to identify outliers
in the data. We test our implementation on various publicly available data
sets, and then apply it to mutual fund data. We show that there is a strong
relationship between the outlier measures of the funds and their future returns
and discuss the implications of our findings.