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

Abstract This study investigates whether cryptocurrency whitepaper narratives align with empirically observed market factor structure. Using zero-shot NLP classification of 38 whitepapers across 10 semantic categories combined with CP tensor decomposition of hourly market data (49 assets, 17,543 timestamps), we find weak alignment between claims and market statistics (φ = 0.246, p = 0.339) and between claims and latent factors (φ = 0.058, p = 0.751). Key Findings Null result: Whitepaper narratives do not meaningfully predict market factor structure Pipeline validated: Statistics-vs-factors comparison achieves significance (p < 0.001), confirming the pipeline detects real structure Entity-level variation: Specialized tokens (XMR, CRV, YFI) show stronger narrative–market correspondence than broad infrastructure tokens Implications: Challenges narrative economics assumptions about crypto markets; investor decisions may not be driven by whitepaper content Status With Editor at Digital Finance (Springer). Also on arXiv. Working Paper DAI-2508. Links arXiv: arXiv:2601.20336 SSRN: SSRN:5918302 Code & Data: github.com/studiofarzulla/tensor-defi ASCRI: systems.ac/3/DAI-2508 Research Lab: Dissensus AI