Network-based diversification of stock and cryptocurrency portfolios

Paper: arXiv 2408.11739

Abstract

Maintaining a balance between returns and volatility is a common strategy for portfolio diversification, whether investing in traditional equities or digital assets like cryptocurrencies. One approach for diversification is the application of community detection or clustering, using a network representing the relationships between assets. We examine two network representations, one based on a standard distance matrix based on correlation, and another based on mutual information. The Louvain and Affinity propagation algorithms were employed for finding the network communities (clusters) based on annual data. Furthermore, we examine building assets’ co-occurrence networks, where communities are detected for each month throughout a whole year and then the links represent how often assets belong to the same community. Portfolios are then constructed by selecting several assets from each community based on local properties (degree centrality), global properties (closeness centrality), or explained variance (Principal component analysis), with three value ranges (max, med, min), calculated on a maximal spanning tree or a fully connected community sub-graph. We explored these various strategies on data from the S&P 500 and the Top 203 cryptocurrencies with a market cap above 2M USD in the period from Jan 2019 to Sep 2022. Moreover, we study into more details the periods of the beginning of the COVID-19 outbreak and the start of the war in Ukraine. The results confirm some of the previous findings already known for traditional stock markets and provide some further insights, while they reveal an opposing trend in the crypto-assets market.

Complexity vs Empirical Score

• Math Complexity: 6.5/10
• Empirical Rigor: 6.0/10 (capped from 8.0: transaction costs not modelled; see penalty flags)
• Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper employs advanced mathematical concepts from network science (community detection algorithms, centrality measures, spanning trees) and statistical methods (mutual information, PCA), while also conducting extensive backtests across multiple asset classes (S&P 500 and cryptocurrencies) and specific crisis periods with detailed performance analysis.

Research Flowchart

  flowchart TD
  A["Research Goal: Network-Based Diversification for Stocks vs. Cryptocurrencies"] --> B["Data & Inputs<br>S&P 500 & Top 203 Cryptos<br>Jan 2019 - Sep 2022"]
  B --> C["Network Representation<br>1. Correlation Distance Matrix<br>2. Mutual Information Matrix"]
  C --> D{"Community Detection<br>Algorithms"}
  D --> D1["Louvain"]
  D --> D2["Affinity Propagation"]
  D1 & D2 --> E["Portfolio Construction<br>Selection Strategies:<br>Centrality (Degree/Closeness) & PCA"]
  E --> F["Key Findings/Outcomes"]
  F --> F1["Stocks: Diversification benefits align with traditional finance"]
  F --> F2["Cryptos: Opposing trend observed<br>(High volatility impact)"]
  F --> F3["Network metrics remain viable predictors across asset classes"]

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0 of 91 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.