Paper: arXiv 2407.08069

Authors: An Pham Ngoc Nguyen, Martin Crane, Thomas Conlon, Marija Bezbradica

Abstract

Herding behavior has become a familiar phenomenon to investors, with potential dangers of both undervaluing and overvaluing assets, while also threatening market stability. This study contributes to the literature on herding behavior by using a recent dataset, covering the most impactful events of recent years. To our knowledge, this is the first study examining herding behavior across three different types of investment vehicle and also the first study observing herding at a community (subset) level. Specifically, we first explore this phenomenon in each separate type of investment vehicle, namely stocks, US ETFs and cryptocurrencies, using the Cross-Sectional Absolute Deviation model. We find mostly similar herding patterns for stocks and US ETFs. Subsequently, the same experiment is implemented on a combination of all three investment vehicles. For a deeper investigation, we adopt graph-based techniques including the Minimum Spanning Tree and Louvain community detection to partition the combination into smaller subsets to detect herding behavior for each subset. We find that herding behavior exists at all times across all types of investment vehicle at a subset level, although perhaps not at the superset level, and that this herding behavior tends to stem from specific events that solely impact that subset of assets. Lastly, we explore herding by examining the financial contagion effects between these types of investment vehicle. Results show that US ETFs not only have a tendency to propagate similar trading behaviors in stocks and especially cryptocurrencies but also show self-reinforcing herding behavior, acting as drivers of their own trends.

Complexity vs Empirical Score

  • Math Complexity: 4.5/10
  • Empirical Rigor: 7.5/10
  • Quadrant: Street Traders — practical and empirical, lighter on theory

Why this score: This paper presents a robust empirical evaluation of herding behavior across multiple asset classes using high-frequency data and CSAD regressions. While relying on established econometric and network algorithms rather than novel mathematical derivations, its community-level graph analysis and cross-asset contagion framework provide insightful, highly practical market findings.

Research Flowchart

  flowchart TD
    A[Goal: Analyze Herding Behavior & Financial Contagion Across Asset Classes] --> B[Data: Stocks, US ETFs, and Cryptocurrencies]
    B --> C[Single & Multi-Asset Analysis via CSAD Model]
    B --> D[Network Analysis: Minimum Spanning Tree & Louvain Community Detection]
    C --> E[Findings: Similar herding in Stocks & ETFs; no superset herding]
    D --> F[Findings: Persistent subset-level herding driven by asset-specific events]
    C & D --> G[Contagion Outcome: US ETFs drive market trends and propagate herding to Stocks & Crypto]