Quantifying Outlierness of Funds from their Categories using Supervised Similarity

TL;DR

This paper introduces a machine learning approach using Random Forests to quantify fund miscategorization by detecting outliers, revealing its impact on future returns and aiding better classification accuracy.

Contribution

It presents a novel distance-based outlier detection method for mutual fund categorization using Random Forests, linking misclassification to future performance.

Findings

Outliers correlate with future fund returns.

The method effectively identifies miscategorized funds.

Misclassification impacts investment decisions.

Abstract

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.