Complex network analysis of cryptocurrency market during crashes

Paper: arXiv 2405.05642

Abstract

This paper identifies the cryptocurrency market crashes and analyses its dynamics using the complex network. We identify three distinct crashes during 2017-20, and the analysis is carried out by dividing the time series into pre-crash, crash, and post-crash periods. Partial correlation based complex network analysis is carried out to study the crashes. Degree density ($ρ_D$), average path length ($\bar{“l”}$), and average clustering coefficient ($\overline{“cc”}$) are estimated from these networks. We find that both $ρ_D$ and $\overline{“cc”}$ are smallest during the pre-crash period, and spike during the crash suggesting the network is dense during a crash. Although $ρ_D$ and $\overline{“cc”}$ decrease in the post-crash period, they remain higher than pre-crash levels for the 2017-18 and 2018-19 crashes suggesting a market attempt to return to normalcy. We get $\bar{“l”}$ is minimal during the crash period, suggesting a rapid flow of information. A dense network and rapid information flow suggest that during a crash uninformed synchronized panic sell-off happens. However, during the 2019-20 crash, the values of $ρ_D$, $\overline{“cc”}$, and $\bar{“l”}$ did not vary significantly, indicating minimal change in dynamics compared to other crashes. The findings of this study may guide investors in making decisions during market crashes.

Complexity vs Empirical Score

• Math Complexity: 6.5/10
• Empirical Rigor: 4.0/10
• Quadrant: Lab Rats — theoretically deep, empirically untested

Why this score: The paper employs advanced mathematical techniques like Hilbert transforms, Empirical Mode Decomposition, and partial correlation-based network construction, indicating high complexity. However, the empirical analysis focuses on descriptive network metrics (degree density, path length, clustering) on historical crypto data without clear backtesting protocols or implementation details for trading, leading to moderate rigor.

Research Flowchart

  flowchart TD
  A["Research Goal:<br>Analyze crypto market crash dynamics"] --> B["Data: BTC/ETH price series<br>2017-2020"]
  B --> C["Methodology:<br>Identify 3 market crashes"]
  C --> D["Divide timeline:<br>Pre-crash, Crash, Post-crash"]
  D --> E["Compute Partial Correlation<br>Complex Networks"]
  E --> F["Calculate Metrics:<br>ρ_D, bar{"l"}, bar{"cc"}"]
  F --> G{"Key Findings"}
  G --> H["Pre-crash: Lowest ρ_D & bar{"cc"}"]
  G --> I["Crash: Peak ρ_D & bar{"cc"},<br>Lowest bar{"l"}"]
  G --> J["Post-crash: Values remain<br>higher than pre-crash"]

Cited References (108)

1. Cryptocurrencies: applications and investment opportunities (2019)
2. The use of cryptocurrencies in the money laundering process (2019)
3. Bitcoin: Ringing the bell for a new asset class (2017)
4. Inferring the interplay between network structure and market effects in bitcoin (2014)
5. Do the rich get richer? An empirical analysis of the Bitcoin transaction network (2014)
6. Quantifying the COVID-19 shock in cryptocurrencies (2024)
7. The resilience of cryptocurrency market efficiency to COVID-19 shock (2022)
8. Network analysis of multivariate transfer entropy of cryptocurrencies in times of turbulence (2020)

0 of 108 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.