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

TL;DR

This paper investigates Bitcoin's value formation through informational attributes and interactions, using a Bayesian model to analyze daily data, revealing social information's dominant short-term influence and structural factors' role in long-term value.

Contribution

It introduces a novel Bayesian approach to quantify informational factors influencing Bitcoin's value, emphasizing social entropy and structural anchors within the GITT-VT framework.

Findings

Social-signal value positively predicts next-day returns

Long-term value is moderately linked to autonomy and store-of-value

Hedonic-sentiment value shows no significant predictive effect

Abstract

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.