Clustering Digital Assets Using Path Signatures: Application to Portfolio Construction

TL;DR

This paper introduces a novel clustering method for cryptocurrencies using path signatures to identify similar behavioral patterns, enabling the construction of diversified and cost-effective portfolios.

Contribution

It presents a new clustering approach based on path signatures for digital assets, improving portfolio diversification and reducing transaction costs.

Findings

Clustering based on path signatures effectively groups similar cryptocurrencies.

Portfolios constructed from these clusters outperform unfiltered assets.

The method captures main trends and features of volatile digital assets.

Abstract

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.