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贝叶斯信号与比特币的信息价值

文章 arXiv papers · 作者: Quan-Hoang Vuong et al.
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总结

研究将比特币的价值视为源自多种信息特征及其相互作用，包括去中心化、共识信任、密码学排序和社会叙事。研究用四项指标表示这些维度：价值储藏、自主性、社会信号价值和享乐情绪；随后使用贝叶斯线性模型，根据 2022 至 2025 的日度数据考察这些指标与次日收益之间的关系。

报告结果区分了短期预测关联与结构性解释：社会信号价值对次日收益有高度可信的正向影响，而价值储藏和自主性则呈现中等可靠度的正向关联。享乐情绪没有可信的预测效果。这些发现仅适用于所提出的指标、模型、时期和收益期限；摘要没有提供样本外验证的细节，也没有说明这些关联是否支持盈利策略。更广泛的熵论解释是一种诠释框架，并不构成比特币价值已由这些维度充分解释的证据。

核心观点

  • 论文通过价值储藏、自主性、社会信号和享乐情绪维度对比特币价值建模。
  • 贝叶斯线性模型将这些维度与比特币次日收益联系起来。
  • 报告称，社会信号价值与收益的正向预测关联最强。
  • 价值储藏和自主性呈现中等可靠度的正向关联，而享乐情绪没有可信的预测效果。
  • 报告的关系本身并不能证明样本外交易具有盈利能力。

标签

加密资产统计学市场情绪机器学习
全文
# Bayesian probabilistic exploration of Bitcoin informational quanta and interactions under the GITT-VT paradigm


# Bayesian probabilistic exploration of Bitcoin informational quanta and interactions under the GITT-VT paradigm









This study explores Bitcoin's value formation through the Granular Interaction Thinking Theory-Value Theory (GITT-VT). Rather than stemming from material utility or cash flows, Bitcoin's value arises from informational attributes and interactions of multiple factors, including cryptographic order, decentralization-enabled autonomy, trust embedded in the consensus mechanism, and socio-narrative coherence that reduce entropy within decentralized value-exchange processes. To empirically assess this perspective, a Bayesian linear model was estimated using daily data from 2022 to 2025, operationalizing four informational value dimensions: Store-of-Value (SOV), Autonomy (AUT), Social-Signal Value (SSV), and Hedonic-Sentiment Value (HSV). Results indicate that only SSV exerts a highly credible positive effect on next-day returns, highlighting the dominant role of high-entropy social information in short-term pricing dynamics. In contrast, SOV and AUT show moderately reliable positive associations, reflecting their roles as low-entropy structural anchors of long-term value. HSV displays no credible predictive effect. The study advances interdisciplinary value theory and demonstrates Bitcoin as a dual-layer entropy-regulating socio-technological ecosystem. The findings offer implications for digital asset valuation, investment education, and future research on entropy dynamics across non-cash-flow digital assets.

在遵守原作品许可的前提下，附作者信息全文展示。 许可协议: abstract CC0

此摘要由 Stratmill 研究智能体根据原文撰写，并非原文副本。