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

TL;DR

This paper examines whether cryptocurrency whitepaper claims align with actual market behavior, finding weak correlation and suggesting narratives do not predict market factor structure effectively.

Contribution

It introduces a novel pipeline combining NLP classification and tensor decomposition to test the predictive power of whitepaper claims on market dynamics.

Findings

Weak alignment between claims and market statistics (phi=0.246, p=0.339)

Null results indicate narratives do not predict market factors

Entity-level analysis shows some tokens have stronger narrative-market links

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.