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# This CITATION.cff file was generated for Zenodo indexing
# See: https://citation-file-format.github.io/

cff-version: 1.2.0
title: "Do Whitepaper Claims Predict Market Behavior? Evidence from Cryptocurrency Factor Analysis"
message: "If you use this research, please cite it using the metadata from this file."
type: dataset
authors:
  - family-names: Farzulla
    given-names: Murad
    orcid: "https://orcid.org/0009-0002-7164-8704"
    affiliation: "Farzulla Research"
repository-code: "https://github.com/studiofarzulla/tensor-defi"
url: "https://farzulla.org"
abstract: >-
  This paper investigates whether the functional claims made in cryptocurrency
  whitepapers exhibit measurable alignment with subsequent market behavior patterns.
  Using zero-shot NLP classification and tensor decomposition, we analyze 13 major
  cryptocurrency whitepapers against market data for 49 assets over 17,543 daily
  observations. Our CP tensor decomposition achieves 92.45% variance explained with
  rank-2 factors, revealing a "BTC dominance" factor (loading: 28.5) and a
  "diversified altcoin" factor. The primary claims-statistics alignment yields
  Tucker's φ = 0.331 (moderate alignment, p < 0.001), with "smart contracts" and
  "interoperability" categories showing strongest correspondence to market patterns.
  We contribute novel methodology combining NLP-extracted semantic features with
  tensor factor analysis for financial prediction.
keywords:
  - cryptocurrency
  - tensor decomposition
  - natural language processing
  - factor analysis
  - whitepaper analysis
  - market behavior
  - PARAFAC
  - Tucker congruence
license: CC-BY-4.0
version: "3.0.0"
date-released: "2025-12-12"
doi: "10.5281/zenodo.17917922"
identifiers:
  - type: doi
    value: "10.5281/zenodo.17917922"
    description: "Zenodo archive of research materials"
preferred-citation:
  type: article
  authors:
    - family-names: Farzulla
      given-names: Murad
      orcid: "https://orcid.org/0009-0002-7164-8704"
  title: "Do Whitepaper Claims Predict Market Behavior? Evidence from Cryptocurrency Factor Analysis"
  year: 2025
  journal: "Zenodo"
  doi: "10.5281/zenodo.17917922"