# Clustering Market Regimes using the Wasserstein Distance

arXiv: https://arxiv.org/abs/2110.11848 · HTML: https://mlsanity.com/paper/2110.11848/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 57 references that are in the map.
- First posted on arXiv 22 Oct 2021.

## Builds on (most cited first, 9 of 9)

- [Generalized Sliced Wasserstein Distances](https://mlsanity.com/paper/1902.00434/) (2019)
- [Statistical Inference for Generative Models with Maximum Mean Discrepancy](https://mlsanity.com/paper/1906.05944/) (2019)
- [A Data-driven Market Simulator for Small Data Environments](https://mlsanity.com/paper/2006.14498/) (2020)
- [Fast Discrete Distribution Clustering Using Wasserstein Barycenter with Sparse Support](https://mlsanity.com/paper/1510.00012/) (2015)
- [Finite sample properties of parametric MMD estimation: robustness to misspecification and dependence](https://mlsanity.com/paper/1912.05737/) (2019)
- [Two-sample Test using Projected Wasserstein Distance](https://mlsanity.com/paper/2010.11970/) (2020)
- [Multiple Change-point Detection: a Selective Overview](https://mlsanity.com/paper/1512.04093/) (2015)
- [Variational Wasserstein Clustering](https://mlsanity.com/paper/1806.09045/) (2018)
- [Estimation of copulas via Maximum Mean Discrepancy](https://mlsanity.com/paper/2010.00408/) (2020)

## Built on by (most cited first, 4 of 4)

- [Non-parametric online market regime detection and regime clustering for multidimensional and path-dependent data structures](https://mlsanity.com/paper/2306.15835/) (2023)
- [The geometry of financial institutions -- Wasserstein clustering of financial data](https://mlsanity.com/paper/2305.03565/) (2023)
- [An Agglomerative Clustering of Simulation Output Distributions Using Regularized Wasserstein Distance](https://mlsanity.com/paper/2407.12100/) (2024)
- [Automated regime classification in multidimensional time series data using sliced Wasserstein k-means clustering](https://mlsanity.com/paper/2310.01285/) (2023)

## Abstract (arXiv)

The problem of rapid and automated detection of distinct market regimes is a topic of great interest to financial mathematicians and practitioners alike. In this paper, we outline an unsupervised learning algorithm for clustering financial time-series into a suitable number of temporal segments (market regimes). As a special case of the above, we develop a robust algorithm that automates the process of classifying market regimes. The method is robust in the sense that it does not depend on modelling assumptions of the underlying time series as our experiments with real datasets show. This method -- dubbed the Wasserstein k-means algorithm -- frames such a problem as one on the space of probability measures with finite pth moment, in terms of the p-Wasserstein distance between (empirical) distributions. We compare our WK-means approach with a more traditional clustering algorithms by studying the so-called maximum mean discrepancy scores between, and within clusters. In both cases it is shown that the WK-means algorithm vastly outperforms all considered competitor approaches. We demonstrate the performance of all approaches both in a controlled environment on synthetic data, and on real data.