ME2F: Memecoin Fragility Framework

• Memecoin Ecosystem Fragility Framework (ME2F) is a quantitative methodology that defines memecoin vulnerabilities via volatility, whale dominance, and sentiment amplification.
• The framework employs financial econometrics, ownership concentration analysis, and social-diffusion theory to create multidimensional fragility profiles for risk assessment.
• Empirical applications reveal significant fragility differences, with politically themed memecoins showing higher risk than established tokens due to extreme volatility and insider concentration.

Memecoin Ecosystem Fragility Framework (ME2F) is a quantitative methodology for assessing the intrinsic vulnerabilities of memecoin markets. Designed specifically for crypto assets whose valuations are governed by viral attention, narrative-driven sentiment, and speculative capital flows—as opposed to protocol fundamentals—ME2F formalizes fragility along three orthogonal axes: volatility dynamics, ownership concentration, and sentiment shock amplification. The framework yields multidimensional fragility profiles for both individual tokens and the ecosystem, supporting comparative risk assessment, early warning, and governance recommendations. Empirical applications demonstrate highly uneven fragility: politically themed memecoins cluster at the system’s most fragile frontier, while established tokens and institutional-grade benchmarks display markedly greater resilience (Xiang et al., 29 Nov 2025).

1. Framework Objective and Structural Rationale

ME2F is engineered to systematically quantify structural fragility in memecoin markets where price discovery, liquidity depth, and network participation deviate fundamentally from technology-driven cryptocurrencies. The framework is underpinned by three principal risk factors:

• Volatility Dynamics: Persistent and episodic price fluctuations, adjusted for scale effects and base-chain spillovers.
• Whale Dominance: Concentration of supply among a small cohort of holders, measuring both macroscopic dominance and microscopic internal inequality.
• Sentiment Amplification: The translation of rapid sentiment swings (as quantified by the Fear & Greed Index) into nonlinear price perturbations.

This approach responds to the unique fragility drivers of the memecoin domain: viral social media propagation, network-induced capital flight, and the prevalence of large, often unvested insider allocations. By establishing transparent, formula-driven metrics, ME2F provides a platform for ecosystem-wide surveillance, regulatory oversight, and tokenomic best practices (Xiang et al., 29 Nov 2025).

2. Formalized Fragility Dimensions

ME2F operationalizes fragility through three orthogonal scores. Each dimension is grounded in a distinct theoretical tradition—financial econometrics, ownership structure analysis, and social-diffusion theory—and implemented via explicit, cross-normalized quantitative metrics.

2.1 Volatility Dynamics Score (VDS)

Theoretical Basis: Volatility clustering in memecoin prices arises from FOMO-driven capital surges and panic-driven outflows; thin order books further intensify daily ranges. Base-chain relationships (e.g., SHIB on Ethereum) generate volatility spillovers.

Mathematical Formulation:

• Daily range-based volatility:

$V_t = \frac{P_t^{\max} - P_t^{\min}}{P^c_{t-1}}$

• Aggregate measures:

$$\bar v_i = \frac{1}{T} \sum_{t=1}^T V_t, \qquad v^{\max}_i = \max_{t} V_t$$

Normalized:

$$v_i^{(a)} = \frac{\bar v_i}{\max_j \bar v_j},\qquad v_i^{(m)} = \frac{v^{\max}_i}{\max_j v^{\max}_j}$$

• Baseline volatility and scaling:

$$\mathcal{V}_i = 0.5\,v_i^{(a)} + 0.5\,v_i^{(m)}, \qquad S_i = \frac{2}{1/z_i + 1/c_i}, \qquad R_i = \frac{1}{1 + 0.5\,S_i}$$

• Final scale-adjusted scores:

$\Phi_i = \mathcal{V}_i \cdot R_i$

If on base-chain:

$$\Phi'_i = \Phi_i \left(1 + 0.5\, \frac{v^{(a)}_\text{base} + v^{(m)}_\text{base}}{2} \ln(1 + S_\text{base})\right)$$

$$\mathrm{VDS}_i = \begin{cases} \sqrt{\Phi_i} & \text{(standalone chain)} \ \sqrt{\Phi'_i} & \text{(base-chain token)} \end{cases}$$

(Xiang et al., 29 Nov 2025).

2.2 Whale Dominance Score (WDS)

Theoretical Basis: Highly concentrated memecoin allocations facilitate market manipulation (“rug pulls”) and undermine retail participation.

Mathematical Formulation:

• Top-100 cumulative share:

$C_i = \sum_{k=1}^{100} o_{k,i}$

• Herfindahl-Hirschman internal concentration of top-100:

$H_i = \sum_{k=1}^{100} o_{k,i}^2$

• Normalization:

$$\mathcal{N}_i = \frac{H_i / C_i^2 - 1/100}{1 - 1/100}$$

• Final:

$\mathrm{WDS}_i = C_i \mathcal{N}_i$

(Xiang et al., 29 Nov 2025).

2.3 Sentiment Amplification Score (SAS)

Theoretical Basis: Attention-driven events (e.g., celebrity tweets) produce abrupt sentiment shifts, which, when coincident with price jumps, generate market discontinuities.

Mathematical Formulation:

• Input: token-specific Fear & Greed Index $F_{i,t}$
• Baseline sentiment instability:

$$R_{F,i} = F_{\max,i} - F_{\min,i},\quad Q_{g,i} = \text{Pr}(FGI \ge 80),\quad Q_{f,i} = \text{Pr}(FGI \le 19)$$

• Normalized mean:

$$U_i = \frac{1}{3} \Bigg( \frac{R_{F,i}}{\max_j R_{F,j}} + \frac{Q_{g,i} + Q_{f,i}}{\max_j (Q_{g,j} + Q_{f,j})} + \frac{|\bar F_i - 50|}{\max_j |\bar F_j - 50|} \Bigg)$$

• Shock index:

$$\Delta F^{\max}_i = \max_t |F_{i,t} - F_{i,t-1}|,\qquad \Delta P^{\max}_i = \max_t \frac{|P^c_{i,t} - P^c_{i,t-1}|}{P^c_{i,t-1}}$$

$$K_i = \frac{\Delta F^{\max}_i}{\max_j \Delta F^{\max}_j} \cdot \frac{\Delta P^{\max}_i}{\max_j \Delta P^{\max}_j}$$

• Final:

$\mathrm{SAS}_i = U_i \cdot (K_i)^{1.5}$

(Xiang et al., 29 Nov 2025).

3. Empirical Application and Comparative Results

ME2F was empirically applied to the leading memecoins (accounting for over 65% of sector capitalization) and major blockchain benchmarks. Data cover daily quotes and on-chain snapshots from January 2023 through September 2025.

Token	VDS	WDS	SAS
ETH	0.0150	0.149	0.209
SOL	0.0320	0.002	0.145
DOGE	0.0330	0.064	0.129
SHIB	0.0760	0.225	0.235
PEPE	0.1530	0.030	0.266
FLOKI	0.2260	0.234	0.142
TRUMP	0.1210	0.654	0.608
MELANIA	0.3100	0.196	0.279
LIBRA	0.7350	0.334	–

Key findings include:

• Politically themed tokens (TRUMP, MELANIA, LIBRA) cluster in the top fragility tier across all three metrics (e.g., TRUMP WDS = 0.654, SAS = 0.608).
• Established community memecoins (SHIB, PEPE, FLOKI) exhibit intermediate fragility, with significant volatility and sensitivity but more moderate whale risk.
• Benchmarks (ETH, SOL, DOGE) remain consistently resilient (all three scores <0.21) due to deep liquidity and low concentration (Xiang et al., 29 Nov 2025).

4. Integration with Multivariate Fragility Index Theory

ME2F can be extended with a block-partitioned fragility index grounded in multivariate extreme value theory (Ferreira et al., 2011). Blocks $\mathcal{D}$ may correspond to categories (on-chain, wrapped, governance tokens, etc.):

• For token return vectors $X=(X_1,\dots,X_d)$, block-exceedance count $$N_x = \sum_{j=1}^s \mathbf{1}\{\max_{i\in I_j} X_i > x\}$$.
• The generalized index:

$$\mathrm{FI}(X, \mathcal{D}) = \lim_{x\to\infty} E[N_x \mid N_x > 0]$$

A high $\mathrm{FI}$ (>1) implies that shocks in any block cascade disproportionately through others, reflecting systemic risk. Real-time ME2F deployments may track $\widehat{\mathrm{FI}}$ in rolling windows, raising alerts if critical thresholds (e.g., 1.5) are breached (Ferreira et al., 2011).

5. Advanced Liquidity and Entity-Linked Risk Analytics

To counteract manipulation and artificial decentralization, ME2F incorporates multi-dimensional address linkage and on-chain liquidity analyses (Liu et al., 22 May 2025):

• Entity-Linked Identification: Clustering via source/destination of funds, behavioral similarity, and anomalous transaction cycles. DBSCAN and isolation forest algorithms refine clusters, adjusting all supply and liquidity computations for actual (not apparent) decentralization.
• Liquidity Risk Indicators:
  • Top-10 concentration $C_{10}$, Herfindahl-Hirschman Index (HHI)
  • Trading ratios (VMTV: 24h Vol/Market Cap)
  • Liquidity pool valuation $L_\text{pool}$, influenced by on-chain AMM reserves
  • Holder count $H$ (post-linkage)
• Composite Fragility Score $F$, integrating all normalized metrics:

$$F = \alpha C_{10} + \beta\,\mathrm{HHI} + \gamma(1-\mathrm{VMTV}) + \delta(1-\frac{V_{24h}}{L_\text{pool}}) + \varepsilon(1-\frac{L_\text{pool}}{L_\text{max}}) + \zeta(1-\frac{H}{H_\text{max}})$$

Empirical studies reveal that entity linkage increases detected concentration by up to 58%. Appending this to ME2F exposes camouflage strategies used to simulate liquidity or distribution (Liu et al., 22 May 2025).

6. Manipulation Detection and Tokenomics-Based Early-Warning

ME2F integrates manipulation taxonomies and quantitative detectors (Mongardini et al., 16 Apr 2025):

• Wash Trading: Detects volume spikes ($V_t/V_{t-1} > 6$) unaccompanied by price movement, zero-risk maker round-tripping ($|\text{BuyVol} - \text{SellVol}| \leq 2\%$), and persistent multi-day manipulator presence.
• Liquidity-Pool-Based Price Inflation (LPI): Identifies single-actor induced price surges out of proportion to volume.
• Tokenomics Indicators: Ownership concentration ($C>0.3$), fresh-address involvement, airdrop clustering, bundle-buy ratios, and low liquidity ratios ($\Lambda < 10^{-3}$) are synthesized into a Token Fragility Score:

$$\mathrm{TFS} = w_C C + w_F F + w_A A + w_B B + w_\Lambda (1 - \Lambda)$$

Empirical correlations show that tokens with early-stage wash trading or LPI (within 90 days of launch) have a $\sim$63% probability of subsequent pump/dump or exploit, compared to 1% for uncontaminated launches—a strong longitudinal link between early manipulation and ultimate fragility (Mongardini et al., 16 Apr 2025).

7. Governance Implications, Limitations, and Future Directions

ME2F’s dimensionality enables actionable governance:

• Risk Protocols: Rolling quantile triggers on VDS and SAS serve as early-warning signals. WDS governs whale monitoring and vesting policy.
• Token Design: Lock-up structures, circuit breakers, and volume throttles are recommended to reduce fragility.
• Regulatory Policy: Mandated public disclosure of whale addresses, on-chain risk scores as listing requirements, real-time fragility badges, and automated surveillance for manipulations.

Limitations include reliance on aggregate daily data (excluding microstructure), a single-cycle empirical window, and broad-brush sentiment indices. Proposed extensions include high-frequency, on-chain network analysis, cross-chain transaction mapping, and NLP-based sentiment decoding for token-level SAS calibration. Integration with system-wide block fragility indices could enable real-time threat assessment and systemic stress-testing (Xiang et al., 29 Nov 2025, Ferreira et al., 2011, Liu et al., 22 May 2025, Mongardini et al., 16 Apr 2025).