# Quantifying Outlierness of Funds from their Categories using Supervised Similarity

arXiv: https://arxiv.org/abs/2308.06882 · HTML: https://mlsanity.com/paper/2308.06882/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 45 references that are in the map.
- First posted on arXiv 14 Aug 2023.

## Builds on (most cited first, 5 of 5)

- [Geometry- and Accuracy-Preserving Random Forest Proximities](https://mlsanity.com/paper/2201.12682/) (2022)
- [Fund2Vec: Mutual Funds Similarity using Graph Learning](https://mlsanity.com/paper/2106.12987/) (2021)
- [A Tutorial on Distance Metric Learning: Mathematical Foundations, Algorithms, Experimental Analysis, Prospects and Challenges (with Appendices on Mathematical Background and Detailed Algorithms Explanation)](https://mlsanity.com/paper/1812.05944/) (2018)
- [Machine Learning Fund Categorizations](https://mlsanity.com/paper/2006.00123/) (2020)
- [Learning Mutual Fund Categorization using Natural Language Processing](https://mlsanity.com/paper/2207.04959/) (2022)

## Built on by (most cited first, 6 of 6)

- [Open Set Recognition for Random Forest](https://mlsanity.com/paper/2408.02684/) (2024)
- [Enhanced Local Explainability and Trust Scores with Random Forest Proximities](https://mlsanity.com/paper/2310.12428/) (2023)
- [Quantile Regression using Random Forest Proximities](https://mlsanity.com/paper/2408.02355/) (2024)
- [Can an unsupervised clustering algorithm reproduce a categorization system?](https://mlsanity.com/paper/2408.10340/) (2024)
- [Case-based Explainability for Random Forest: Prototypes, Critics, Counter-factuals and Semi-factuals](https://mlsanity.com/paper/2408.06679/) (2024)
- [STRAPSim: A Portfolio Similarity Metric for ETF Alignment and Portfolio Trades](https://mlsanity.com/paper/2509.24151/) (2025)

## Abstract (arXiv)

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.