Bitcoin Returns and AI-Measured Monetary Policy Expectations

Summary

This study examines whether central-bank policy narratives help explain Bitcoin returns, separating market expectations from realized policy actions. It constructs a weekly Monetary Policy Expectations index by using a large language model to classify market messages as hawkish or dovish. The index is tested for predictive information at short to medium horizons, and the analysis reports significant Granger-causality results at multiple lags. The excerpt does not describe the message sources, classification validation, or the exact return horizons.

For nonlinear and regime-dependent relationships, the researchers use an LSTM model and SHAP explanations. They report that hawkish policy narratives are associated with negative Bitcoin price responses beyond those accounted for by contemporaneous Federal Funds Rate adjustments. This frames Bitcoin as responsive to central-bank communication, rather than solely to implemented rate changes. The findings establish reported predictive associations, not proof that the index causes returns or that a trading strategy based on it would be profitable. The description gives no trading costs, benchmark strategy, or out-of-sample trading results.

Key ideas

• The study builds a weekly monetary policy expectations index from language-model classification of market messages.
• Changes in the index are reported to contain predictive information for Bitcoin returns at several horizons.
• Granger-causality tests and an LSTM with SHAP are used to examine linear and nonlinear relationships.
• Hawkish narratives are associated with negative Bitcoin price responses beyond contemporaneous rate changes.
• Predictive association does not establish causation or demonstrate profitable trading.

Tags

# Is Bitcoin A Hedge Against Central Banking? Evidence from AI-Driven Monetary Policy Expectations


# Is Bitcoin A Hedge Against Central Banking? Evidence from AI-Driven Monetary Policy Expectations









This study investigates the transmission of monetary policy narratives to Bitcoin prices, distinguishing policy expectations from realized policy implementation. We introduce a weekly Monetary Policy Expectations (MPE) index derived from the Large Language Model (LLM)-based classification of 118,000+ market messages, providing a granular measure of hawkish and dovish monetary policy discourse. We demonstrate that changes in the MPE index provide evidence of significant linear predictive information for Bitcoin returns at short-to-medium horizons, with significant Granger causality at multiple lags. A Long Short-Term Memory (LSTM) framework combined with SHapley Additive exPlanations (SHAP) further identifies nonlinear and regime-dependent relationships between monetary-policy expectations and Bitcoin returns, indicating that Bitcoin functions as a sensitive barometer of central bank signaling. In particular, hawkish monetary-policy narratives are associated with negative price responses that are not accounted for by contemporaneous Federal Funds Rate adjustments. These findings highlight Bitcoin's structural sensitivity to global monetary discourse, establishing LLM-derived monetary-policy sentiment as a high-frequency measure of central-bank communication and as an informative leading macroeconomic indicator for the digital asset landscape.

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