arXiv subjects

Murad Farzulla

Publications and source records attributed to Murad Farzulla.

7 recordsLinked to original sources

arXiv2026

Are Whitepaper Claims Reflected in Market Structure? A Contamination-Aware Pipeline and a Power-Limited Null

Do the functional narratives in cryptocurrency whitepapers correspond to their tokens' market behaviour? We compare ten-category topical-emphasis profiles for 43 screened documents with seven market statistics calculated from 2023--2024 exchange data. The primary specification reconstructs USD notional turnover from hourly bars. Dimension-matched Procrustes congruence is $ϕ=0.280$ (permutation $p=0.507$), slightly below its permutation-null mean of $0.284$; the zero-padded statistic gives the same non-detection. A four-leg comparison separates document replacement from changes in the assets included. Replacing documents on the 34 common assets changes padded congruence by $-0.014$ under USD turnover and $-0.009$ under base-token volume. Entity rankings and threshold crossings depend on both composition and specification, so an earlier contamination-only attribution is withdrawn. The documents are not a verified historical corpus: at least two postdate the market window. Excluding these documents, or excluding all seven assets with shorter histories, does not produce a significant alignment. Fresh numerical simulations distinguish injected signal from fitted congruence and compare noise restricted to the market subspace with noise throughout the text space. At the lowest classifier-agreement scenario, detection remains below $43\%$ even at the largest injected signal. These are conditional checks of the alignment stage, not validation of the text instrument or exclusion bounds on economic effects. The contribution is an auditable non-detection and a specification-sensitive corpus diagnosis, with the inferential limits made explicit.

q-fin.CP↗

arXiv2026

Does Crypto Sentiment Extremity Widen Estimated Spreads? Evidence Depends on the Specification

We examine whether extreme values of the Crypto Fear & Greed Index are associated with a daily high-low spread estimate for Bitcoin. The sample contains 2,896 BTC/USDT observations from February 2018 to January 2026. We find an unconditional extreme-minus-neutral gap of 61.99 basis points. After close-to-close realised-volatility-quintile demeaning it is 24.79 basis points, although none of the five separate quintile contrasts survives Holm correction. With quadratic realised-volatility and strictly lagged momentum controls, the HAC estimate is 11.81 basis points (95% CI [-2.31,25.93], p=.101). A fixed non-parametric stratification gives 20.44 basis points (p=.0195 under circular shifts), while separate models for a zero-floored estimate's incidence and positive magnitude are imprecise. The results therefore show only a descriptive, specification-dependent association. We conclude that they do not establish a stable or causal liquidity premium.

q-fin.ST↗

arXiv2026

Do Cryptocurrency Markets Differentiate Infrastructure from Regulatory Shocks? A Multi-Moment Event Study with Dependence-Robust Inference

Do cryptocurrency markets respond differently to infrastructure and regulatory shocks? We study returns and conditional variance on a shared sample of 50 events and six assets (January 2019--August 2025), using GJR-GARCH-X models and dependence-aware inference. Treating event inclusion as a design parameter, we trace the variance differential across inclusion screens. Curated high-salience events yield a $3.49\times$ point-estimate multiplier, whereas a mechanical impact filter on a broad reconstructed candidate pool yields approximately $0.5$--$1.6\times$. This pattern is descriptive and selection-conditional, not an inferential comparison between screens. The curated variance differential is not significant against its fitted sharp per-asset-equality null under the conditional fixed-path Student-$t$-copula bootstrap ($p\approx0.39$). Floored recursive sensitivity gives one-sided $p=0.025$ at baseline and $0.041$ with asset-specific high-variance-regime controls, so the verdict depends on inference scheme and implementation. Across reported dependence inputs, the six-contrast effective sample size is $1.33$--$2.35$; one-sided effective-df sensitivity gives $p=0.044$--$0.116$. Earlier significance from treating correlated per-asset coefficients as independent samples is not robust to dependence and heavy-tail corrections. The cumulative-abnormal-return difference is $+8.69$ percentage points (event-level block-bootstrap $p=0.202$). The asymmetry remains directional, selection-conditional and unresolved. The contribution is an inference ladder and an internal Monte-Carlo calibration study, demonstrated through correction of the author's earlier significance claim.

q-fin.ST↗

arXiv2026

The Replicator-Optimization Mechanism: A Scale-Relative Formalism for Persistence-Conditioned Dynamics with a Conditional Consent-Friction Instantiation

Persistence models often conflate propagation, survival, and cross-scale loss. The Replicator-Optimization Mechanism (ROM) is a replicator-mutator template separating baseline weight, bounded survival, and a transfer kernel at a declared scale. Its equation conserves mass but guarantees neither invariance, convergence, a potential, nor a preferred scale. For finite static density-independent continuous time, an irreducible weighted kernel yields a unique positive Perron-Frobenius composition; discrete-time power convergence needs primitivity. The componentwise ranking proved here is guaranteed under exact uniform-residual transfer. Strong lumpability gives universal first-order transfer closure, and blockwise effective fitness gives an exact quotient. An institutional instantiation uses normalized stakes, signed preference-decision alignment, information loss, and descriptive effective voice. It specifies conditional survival, not legitimacy or normative authority. A companion mixed-motive MARL battery reports exploratory evidence against the implemented proxy ratio in its environment: a positive signed target-coordinate-correlation effect survives held-out evaluation under shared-state contention, while a reduced feasible-centred frozen-policy crossing reverses the predicted correlation-noise interaction. The treatment varies ideal-point correlation inside a fixed reward family, not objective- or reward-function alignment. Lean checks mapped algebraic identities and scalar monotonicities, not the stationary theorem, empirical mapping, or normative bridge. ROM is an assumptions ledger and model-construction discipline, not a cross-substrate law.

econ.TH↗

arXiv2026

Context-Dependent Affordance Reports in Vision-Language Models

Vision-language models produce different object and use descriptions under different persona prompts, but low overlap alone does not identify an affordance effect. We audit an earlier seven-prompt study and add matched-question controls. In the historical Qwen pilot, 363 of 3,213 parsed responses contain empty object lists. These affect 2,037 of 9,244 comparisons, with the implementation assigning zero lexical overlap to every affected pair. Conditioning on nonempty reports raises pooled word Jaccard from 0.095 to 0.121 and sentence cosine from 0.415 to 0.511. A previously named chef-specific Tucker factor loses its concentrated loading under missing-cell and complete-nonempty analyses. We withdraw the functional-manifold interpretation and the conversion of similarity scores into percentages of meaning. A new experiment uses 48 images absent from the original pilot, four personas, a shared three-object task, two wordings, and two requested seeds in each of two model configurations. Qwen3.5-9B's persona-minus-wording cosine-distance contrast is 0.0146 (95% CI [-0.0003, 0.0295]; 37 complete images). Ollama llava:13b's persona-minus-wording cosine-distance contrast is -0.0266 (95% CI [-0.0412, -0.0121]; 32 complete images). Persona-associated variation does not uniformly exceed wording or sampling variation. The study provides a reproducible analysis of context-conditioned reports while separating response availability, content similarity, and the limits of inference from linguistic outputs.

cs.CL↗

arXiv2026

The Axiom of Consent: Authorization, Friction, and Multi-Agent Coordination

Coordination research collapses four objects: operative control, authorization, a model-derived friction score, and observed outcomes. The Axiom of Consent is a stake-weighted unanimity principle; majority and supermajority thresholds are explicit relaxations, not versions of the axiom. Decision loci are structural facts, whereas authorization and legitimacy require normative and measurement premises. Alignment, calibrated stakes, and information deficit are candidate coordinates, and F = sigma(1 + epsilon)/(1 + alpha) is a phenomenological ansatz. The Replicator-Optimization Mechanism supplies a conditional persistence interface: irreducibility suffices for its finite, static, positive-fitness continuous-time Perron result; primitivity is required only for the corresponding discrete-time power convergence, and the componentwise ranking is narrower. Neither persistence result derives authorization. A resource-allocation instantiation specifies an identification contract but observes no authorization acts or effective voice. Its exploratory MARL companion uses target-vector correlation and observation noise as narrow proxy treatments, not measures of general alignment or information deficit. Under that proxy and reward-gap design, the composite loses to an independent-effects model and a feasible-centred frozen crossing yields the opposite interaction direction. Cooperative target correlation lowers the gap under shared-state contention; separable IQL is structurally invariant and separable VDN is a non-detection. Paired partial sharing modulates the gradient without establishing an exact dose law or endpoint equivalence. Target support changes opposition and residual-policy conclusions. The surviving contribution is an authorization architecture and measurement discipline, not a universal friction law.

cs.MA↗

arXiv2026

ASRI: An Aggregated Systemic Risk Index for Cryptocurrency Markets

Cryptocurrency markets exceed USD 3 trillion in capitalisation, yet practitioners lack an interpretable, channel-decomposed composite for characterising crypto-native systemic stress. We introduce the Aggregated Systemic Risk Index (ASRI), built from four weighted sub-indices -- Stablecoin Concentration Risk (30%), DeFi Liquidity Risk (25%), Contagion Risk (25%, implemented as a TradFi-stress proxy), and Regulatory Opacity Risk (20%) -- with a Diebold--Yilmaz connectedness series computed on the sub-indices as network benchmark. We evaluate ASRI retrospectively against four crises (Terra/Luna, Celsius/3AC, FTX, SVB) and give a methodological account of how autocorrelation- and block-structure-robust inference reshapes apparent crisis-detection strength. The event-study signal is inconclusive: heavily serially correlated (AR(1) $\approx 0.8$--$0.9$), with placebo dates clearing the nominal threshold as often as crises. Fixed-threshold detection flags three of four events with $\approx$19-day average lead ($\approx$5 days under a responsive specification); walk-forward thresholds flag 4/4 but at high false-positive cost -- evidence against look-ahead bias, not a clean prediction record. ASRI's day-level discrimination (AUROC 0.866) beats only the circular D--Y comparator (0.670); it is statistically indistinguishable from its strongest sub-index (0.851), PC1 (0.858), and a standalone VIX series (0.875, $p=0.58$). We read aggregation's value as interpretive -- channel attribution, lead time, and regime structure in one auditable composite -- not as discriminative gain. With four crisis events the binding power limit, ASRI is a transparent, reproducible, retrospective monitoring framework targeting crypto-native vulnerabilities that SRISK and CoVaR are not built to capture, not a validated early-warning system. Out of sample it classifies the 2025 Bybit hack as non-systemic.

q-fin.RM↗