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Mutual fund categorization has become a standard tool for the investmentmanagement industry and is extensively used by allocators for portfolioconstruction and manager selection, as well as by fund managers for peeranalysis and competitive positioning. As a result, a (unintended)miscategorization or lack of precision can significantly impact allocationdecisions and investment fund managers. Here, we aim to quantify the effect ofmiscategorization of funds utilizing a machine learning based approach. Weformulate the problem of miscategorization of funds as a distance-based outlierdetection problem, where the outliers are the data-points that are far from therest of the data-points in the given feature space. We implement and employ aRandom Forest (RF) based method of distance metric learning, and compute theso-called class-wise outlier measures for each data-point to identify outliersin the data. We test our implementation on various publicly available datasets, and then apply it to mutual fund data. We show that there is a strongrelationship between the outlier measures of the funds and their future returnsand discuss the implications of our findings.

Quantifying Outlierness of Funds from their Categories using Supervised Similarity