# Clustering Digital Assets Using Path Signatures: Application to Portfolio Construction

arXiv: https://arxiv.org/abs/2410.23297 · HTML: https://mlsanity.com/paper/2410.23297/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 30 references that are in the map.
- First posted on arXiv 15 Oct 2024.

## Builds on (most cited first, 6 of 6)

- [A Primer on the Signature Method in Machine Learning](https://mlsanity.com/paper/1603.03788/) (2016)
- [Embedding and learning with signatures](https://mlsanity.com/paper/1911.13211/) (2019)
- [Developing the Path Signature Methodology and its Application to Landmark-based Human Action Recognition](https://mlsanity.com/paper/1707.03993/) (2017)
- [A signature-based machine learning model for bipolar disorder and borderline personality disorder](https://mlsanity.com/paper/1707.07124/) (2017)
- [An Attention Free Long Short-Term Memory for Time Series Forecasting](https://mlsanity.com/paper/2209.09548/) (2022)
- [Deep Fusion of Lead-lag Graphs: Application to Cryptocurrencies](https://mlsanity.com/paper/2201.02040/) (2022)

## Built on by (most cited first, 1 of 1)

- [SigGate: Enhancing Recurrent Neural Networks with Signature-Based Gating Mechanisms](https://mlsanity.com/paper/2502.09318/) (2025)

## Abstract (arXiv)

We propose a new way of building portfolios of cryptocurrencies that provide good diversification properties to investors. First, we seek to filter these digital assets by creating some clusters based on their path signature. The goal is to identify similar patterns in the behavior of these highly volatile assets. Once such clusters have been built, we propose "optimal" portfolios by comparing the performances of such portfolios to a universe of unfiltered digital assets. Our intuition is that clustering based on path signatures will make it easier to capture the main trends and features of a group of cryptocurrencies, and allow parsimonious portfolios that reduce excessive transaction fees. Empirically, our assumptions seem to be satisfied.