Do Whitepaper Claims Predict Market Behavior? Evidence from Cryptocurrency Factor Analysis

Paper: arXiv 2601.20336

Authors: Murad Farzulla

Abstract

This study investigates whether cryptocurrency whitepaper narratives align with empirically observed market factor structure. We construct a pipeline combining zero-shot NLP classification of 38 whitepapers across 10 semantic categories with CP tensor decomposition of hourly market data (49 assets, 17,543 timestamps). Using Procrustes rotation and Tucker’s congruence coefficient (phi), we find weak alignment between claims and market statistics (phi = 0.246, p = 0.339) and between claims and latent factors (phi = 0.058, p = 0.751). A methodological validation comparison (statistics versus factors, both derived from market data) achieves significance (p < 0.001), confirming the pipeline detects real structure. The null result indicates whitepaper narratives do not meaningfully predict market factor structure, with implications for narrative economics and investor decision-making. Entity-level analysis reveals specialized tokens (XMR, CRV, YFI) show stronger narrative-market correspondence than broad infrastructure tokens.

Complexity vs Empirical Score

• Math Complexity: 6.5/10
• Empirical Rigor: 8.5/10
• Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: This paper presents a novel framework combining zero-shot NLP classification of whitepapers with tensor decomposition (CP/Tucker) of high-frequency market data to test narrative economics in crypto. It exhibits strong empirical rigor with rigorous statistical alignment tests (Procrustes rotation, Tucker’s congruence coefficient) and outstanding reproducibility with publicly available code and data.

Research Flowchart

  flowchart TD
    A[Research Goal: Do whitepaper claims predict crypto market behavior?] --> B[Data Inputs: 38 Whitepapers & Hourly Market Data for 49 Assets]
    
    subgraph Methodology [Parallel Analysis Pipeline]
        B --> C[Zero-Shot NLP Classification<br/>10 Semantic Categories]
        B --> D[CP Tensor Decomposition<br/>17,543 Timestamps to Latent Factors]
    end

    C --> E[Procrustes Rotation & Tucker's Congruence Test]
    D --> E

    E --> F[Validation Check: Statistics vs Factors<br/>Significant Correlation, p < 0.001]
    E --> G[Primary Outcome: Claims vs Market Structure<br/>Weak Alignment, phi = 0.058, p = 0.751]

    G --> H[Key Finding: Whitepaper narratives do NOT predict market factors<br/>Specialized tokens XMR, CRV show higher alignment]