pith. sign in

arxiv: 2604.08825 · v1 · submitted 2026-04-09 · 💰 econ.GN · q-fin.EC

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

Maxime L. D. Nicolas , Fran\c{c}ois Sicard , Marion Laboure , Zixin Sun , Anah\'i Rodr\'iguez-Mart\'inez This is my paper

Pith reviewed 2026-05-10 16:37 UTC · model grok-4.3

classification 💰 econ.GN q-fin.EC
keywords Bitcoinmonetary policy expectationscentral bankingLLM classificationGranger causalitycryptocurrency pricessentiment analysisLSTM SHAP
0
0 comments X

The pith

Bitcoin prices fall in response to hawkish central bank narratives even when actual interest rates stay unchanged.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper tests whether Bitcoin serves as a hedge against central banking by separating the effects of expected policy from implemented rate changes. Using an AI model to classify over 118,000 market messages, the authors construct an index of monetary policy expectations that distinguishes hawkish from dovish sentiment. They find that hawkish signals produce clear negative Bitcoin price moves that do not require any actual Federal Funds Rate shift. The same index also shows the ability to forecast Bitcoin returns over short and medium time spans through linear causality tests, while revealing more complex nonlinear patterns tied to broader economic conditions. If these relationships hold, Bitcoin appears structurally linked to monetary discourse rather than insulated from it.

Core claim

The study introduces a high-frequency Monetary Policy Expectations index built from Large Language Model classification of over 118,000 market messages into hawkish and dovish categories. Bitcoin exhibits consistent negative price responses to hawkish narratives that operate independently of actual Federal Funds Rate adjustments. The MPE index demonstrates Granger causality with Bitcoin returns at short-to-medium horizons, indicating linear predictive power, while LSTM networks combined with SHAP explanations uncover nonlinear and regime-dependent interactions between these factors.

What carries the argument

The Monetary Policy Expectations (MPE) index, created by LLM-based hawkish/dovish classification of market messages, which isolates ex-ante policy sentiment and tests its direct impact on Bitcoin prices.

If this is right

  • Hawkish policy expectations trigger Bitcoin price declines without any accompanying change in the Federal Funds Rate.
  • The MPE index carries leading information for Bitcoin returns at short-to-medium horizons via Granger causality.
  • Bitcoin price reactions to policy signals are nonlinear and depend on the prevailing macroeconomic regime.
  • Market messages processed by language models can function as a real-time leading indicator for digital asset prices.
  • Bitcoin's response pattern highlights structural sensitivity to global monetary narratives rather than insulation from them.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Traders could treat AI-processed central bank commentary as an input for short-term Bitcoin positioning.
  • The same narrative sensitivity might appear in other major cryptocurrencies if the channel is general rather than Bitcoin-specific.
  • Central bank communication teams may need to account for crypto market spillovers when shaping public messaging.

Load-bearing premise

The LLM classification of market messages accurately measures true ex-ante monetary policy expectations without introducing bias or capturing unrelated factors.

What would settle it

Reclassifying the same market messages by human experts and finding that the resulting sentiment scores no longer produce negative Bitcoin price responses or Granger causality would falsify the central results.

Figures

Figures reproduced from arXiv: 2604.08825 by Anah\'i Rodr\'iguez-Mart\'inez, Fran\c{c}ois Sicard, Marion Laboure, Maxime L. D. Nicolas, Zixin Sun.

Figure 1
Figure 1. Figure 1: Bitcoin Prices, Returns, and Monetary Policy Indicators [PITH_FULL_IMAGE:figures/full_fig_p006_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Weekly Event Study: MPE Reaction by Pre-Announcement Regime. [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗
Figure 5
Figure 5. Figure 5: Mean Absolute SHAP Values: Feature Importance. [PITH_FULL_IMAGE:figures/full_fig_p022_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Mean Absolute SHAP values: Feature Importance and Lag ( [PITH_FULL_IMAGE:figures/full_fig_p024_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Comparative Temporal Attribution Signatures of Top 10 Features. [PITH_FULL_IMAGE:figures/full_fig_p025_7.png] view at source ↗
Figure 11
Figure 11. Figure 11: Narrative Validation: Inflation Discourse and the MPE Index [PITH_FULL_IMAGE:figures/full_fig_p036_11.png] view at source ↗
read the original abstract

This study investigates the transmission of monetary policy narratives to Bitcoin prices, distinguishing the impact of ex-ante expectations from ex-post interest rate implementation. We introduce a high-frequency Monetary Policy Expectations (MPE) index, using a Large Language Model (LLM)-based classification of 118,000+ market messages to achieve a precise hawkish/dovish decomposition. Results from a framework combining Long Short-Term Memory (LSTM) networks with SHapley Additive exPlanations (SHAP) indicate that Bitcoin functions as a sensitive barometer of central bank signaling; specifically, hawkish narratives consistently trigger negative price responses independently of actual Federal Funds Rate adjustments. We demonstrate that the MPE index Granger-causes Bitcoin returns at short-to-medium horizons, establishing linear predictive causality, while the LSTM-SHAP framework reveals pronounced non-linear, macroeconomic regime-dependent interactions. These findings highlight Bitcoin's structural sensitivity to global monetary discourse, establishing LLM-derived sentiment as a potent leading macroeconomic indicator for the digital asset landscape.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

4 major / 2 minor

Summary. The paper introduces a high-frequency Monetary Policy Expectations (MPE) index constructed via LLM classification of over 118,000 market messages into hawkish and dovish categories. Using an LSTM-SHAP framework, it claims that hawkish narratives trigger negative Bitcoin price responses independently of actual Federal Funds Rate adjustments, that the MPE index Granger-causes Bitcoin returns at short-to-medium horizons, and that non-linear regime-dependent interactions exist between monetary narratives and BTC prices.

Significance. If validated, the work would be significant for demonstrating Bitcoin's role as a barometer of central bank signaling through AI-derived sentiment. The combination of LLM-based high-frequency indexing with LSTM-SHAP for non-linear effects offers a novel methodological template for linking macroeconomic discourse to asset prices, potentially establishing reproducible AI indicators as leading tools in financial econometrics.

major comments (4)
  1. [MPE index construction] The abstract and methods description provide no details on LLM prompt design, human validation of labels against known FOMC surprises, or inter-annotator agreement metrics for the 118k+ message classification. This directly undermines the central claim that MPE measures unbiased ex-ante expectations independent of price movements.
  2. [Empirical framework and results] The independence of narrative effects from actual rate changes is asserted but not demonstrated via explicit controls (e.g., residualizing MPE against contemporaneous FFR surprises or including them in the LSTM model). Without these, the reported negative price responses and Granger results risk confounding with policy implementation.
  3. [Granger causality analysis] Granger causality from MPE to BTC returns is claimed at short-to-medium horizons, but no lag structure, stationarity tests, or controls for equity/volatility shocks are specified. This leaves open the possibility of reverse causality or omitted factors, especially given potential contemporaneous posting of messages with price ticks.
  4. [LSTM-SHAP framework] The LSTM-SHAP evidence for pronounced non-linear, regime-dependent interactions requires details on regime identification (e.g., macroeconomic state variables) and robustness to alternative architectures or feature sets; absent these, the non-linear claim is not load-bearing.
minor comments (2)
  1. [Abstract] The abstract would be strengthened by reporting specific quantitative magnitudes (e.g., average price response size or Granger F-statistics) rather than qualitative statements.
  2. [Data and methods] Clarify the exact sample period, message sources, and any filtering for temporal precedence to ensure ex-ante measurement.

Simulated Author's Rebuttal

4 responses · 0 unresolved

We thank the referee for the insightful and constructive comments on our manuscript. These suggestions will help improve the clarity and robustness of our findings regarding the relationship between monetary policy expectations and Bitcoin prices. Below, we address each major comment point by point, indicating the revisions we plan to make.

read point-by-point responses

  1. Referee: The abstract and methods description provide no details on LLM prompt design, human validation of labels against known FOMC surprises, or inter-annotator agreement metrics for the 118k+ message classification. This directly undermines the central claim that MPE measures unbiased ex-ante expectations independent of price movements.

    Authors: We agree that additional details on the MPE index construction are warranted for transparency. In the revised version, we will include in the main text a description of the LLM prompt used for classifying messages as hawkish or dovish, along with human validation results comparing labels to known FOMC surprises and inter-annotator agreement metrics (such as Fleiss' kappa) from a double-annotated subsample. These elements are currently detailed in the supplementary materials and will be moved to the primary methods section to better substantiate the unbiased nature of the ex-ante expectations measure. revision: yes

  2. Referee: The independence of narrative effects from actual rate changes is asserted but not demonstrated via explicit controls (e.g., residualizing MPE against contemporaneous FFR surprises or including them in the LSTM model). Without these, the reported negative price responses and Granger results risk confounding with policy implementation.

    Authors: We acknowledge this point and will strengthen the empirical framework in the revision. Specifically, we will residualize the MPE index against contemporaneous FFR surprises and include the residuals as well as the original FFR changes in the LSTM model. We will report these results to show that the negative price responses and Granger causality findings hold independently of actual rate changes, addressing potential confounding with policy implementation. revision: yes

  3. Referee: Granger causality from MPE to BTC returns is claimed at short-to-medium horizons, but no lag structure, stationarity tests, or controls for equity/volatility shocks are specified. This leaves open the possibility of reverse causality or omitted factors, especially given potential contemporaneous posting of messages with price ticks.

    Authors: We will clarify and expand the Granger causality section in the revision by explicitly stating the lag structure (selected via information criteria up to 20 lags for short-to-medium horizons), reporting the results of Augmented Dickey-Fuller stationarity tests, and incorporating controls for equity returns and volatility shocks in the VAR specification. Additionally, we will address potential reverse causality by examining the timing of message postings relative to price movements and conducting robustness checks with lagged variables. These additions will mitigate concerns about omitted factors and strengthen the causal interpretation. revision: yes

  4. Referee: The LSTM-SHAP evidence for pronounced non-linear, regime-dependent interactions requires details on regime identification (e.g., macroeconomic state variables) and robustness to alternative architectures or feature sets; absent these, the non-linear claim is not load-bearing.

    Authors: In the revised manuscript, we will provide detailed information on how regimes are identified using key macroeconomic state variables, such as GDP growth, inflation rates, and unemployment figures, to define high and low uncertainty periods. We will also report robustness checks employing alternative model architectures (e.g., Transformer-based models) and varying feature sets to confirm the stability of the non-linear, regime-dependent interactions identified via SHAP values. This will make the non-linear findings more robust and load-bearing. revision: yes

Circularity Check

0 steps flagged

No significant circularity in derivation chain

full rationale

The paper constructs the MPE index via LLM classification of external market messages (118k+ items) and then applies out-of-sample statistical tests (Granger causality, LSTM-SHAP) to relate the index to Bitcoin returns. No equations or steps reduce the claimed predictive relationships to the index construction by definition or by fitting a parameter that is then renamed as a prediction. The central results are empirical associations tested on separate data rather than tautological. Self-citations, if present, are not load-bearing for the uniqueness or validity of the MPE-to-returns link. The derivation remains self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 0 invented entities

The central claim rests on the assumption that market messages contain clean signals of policy expectations and that the statistical models isolate causal effects. No new physical entities are postulated.

free parameters (1)
  • LLM prompt design and classification threshold
    Choices in how the language model is instructed and how hawkish/dovish labels are assigned directly shape the MPE index.
axioms (1)
  • domain assumption Market messages from the sampled sources accurately and unbiasedly reflect prevailing monetary policy expectations
    Invoked when the 118,000+ messages are treated as the ground truth for ex-ante expectations.

pith-pipeline@v0.9.0 · 5496 in / 1485 out tokens · 60735 ms · 2026-05-10T16:37:30.182591+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

51 extracted references · 51 canonical work pages

  1. [1]

    Akiba, T., Sano, S., Yanase, T., Ohta, T., and Koyama, M. (2019). Optuna: A next-generation hyperparameter optimization framework. In Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining , pages 2623--2631

    work page 2019

  2. [2]

    and Tercero-Lucas, D

    Auer, R. and Tercero-Lucas, D. (2022). Distrust or speculation? the socioeconomic drivers of us cryptocurrency investments. Journal of Financial Stability , 62:101066

    work page 2022

  3. [3]

    G., Hong, K., and Lee, A

    Baur, D. G., Hong, K., and Lee, A. D. (2018). Bitcoin: Medium of exchange or speculative assets? Journal of International Financial Markets, Institutions and Money , 54:177--189

    work page 2018

  4. [4]

    S., Ehrmann, M., Fratzscher, M., De Haan, J., and Jansen, D.-J

    Blinder, A. S., Ehrmann, M., Fratzscher, M., De Haan, J., and Jansen, D.-J. (2008). Central bank communication and monetary policy: A survey of theory and evidence. Journal of economic literature , 46(4):910--945

    work page 2008

  5. [5]

    Carb \'o , J. M. and Gorj \'o n, S. (2024). Determinants of the price of bitcoin: An analysis with machine learning and interpretability techniques. International Review of Economics & Finance , 92:123--140

    work page 2024

  6. [6]

    R., Moraes, M

    Carvalho, V. R., Moraes, M. F., Braga, A. P., and Mendes, E. M. (2020). Evaluating five different adaptive decomposition methods for eeg signal seizure detection and classification. Biomedical Signal Processing and Control , 62:102073

    work page 2020

  7. [7]

    Choi, D., Gao, Z., and Jiang, W. (2020). Attention to global warming. The Review of Financial Studies , 33(3):1112--1145

    work page 2020

  8. [8]

    W., Ghosh, P., Li, J., and Ruan, Q

    Cong, L. W., Ghosh, P., Li, J., and Ruan, Q. (2024). Inflation expectation and cryptocurrency investment. Technical report, National Bureau of Economic Research

    work page 2024

  9. [9]

    Conlon, T., Corbet, S., and McGee, R. (2024). Enduring relief or fleeting respite? bitcoin as a hedge and safe haven for the us dollar. Annals of Operations Research , 337(1):45--73

    work page 2024

  10. [10]

    and McGee, R

    Conlon, T. and McGee, R. (2020). Safe haven or risky hazard? bitcoin during the covid-19 bear market. Finance Research Letters , 35:101607

    work page 2020

  11. [11]

    Corbet, S., Larkin, C., Lucey, B., Meegan, A., and Yarovaya, L. (2020a). Cryptocurrency reaction to fomc announcements: Evidence of heterogeneity based on blockchain stack position. Journal of Financial Stability , 46:100706

    work page

  12. [12]

    M., Meegan, A., and Yarovaya, L

    Corbet, S., Larkin, C., Lucey, B. M., Meegan, A., and Yarovaya, L. (2020b). The impact of macroeconomic news on bitcoin returns. The European Journal of Finance , 26(14):1396--1416

    work page

  13. [13]

    P., de la Fuente, G., and Perote, J

    de la Horra, L. P., de la Fuente, G., and Perote, J. (2019). The drivers of bitcoin demand: A short and long-run analysis. International Review of Financial Analysis , 62:21--34

    work page 2019

  14. [14]

    Dickey, D. A. and Fuller, W. A. (1979). Distribution of the estimators for autoregressive time series with a unit root. Journal of the American statistical association , 74(366a):427--431

    work page 1979

  15. [15]

    Dmonte, A., Ko, E., and Zampieri, M. (2024). An evaluation of large language models in financial sentiment analysis. In 2024 IEEE International Conference on Big Data (BigData) , pages 4869--4874. IEEE

    work page 2024

  16. [16]

    and Zosso, D

    Dragomiretskiy, K. and Zosso, D. (2013). Variational mode decomposition. IEEE transactions on signal processing , 62(3):531--544

    work page 2013

  17. [17]

    Elsayed, A. H. and Sousa, R. M. (2024). International monetary policy and cryptocurrency markets: dynamic and spillover effects. The European Journal of Finance , 30(16):1855--1875

    work page 2024

  18. [18]

    Democratizing access to alternative investments for America's workers

    Executive Office of the President (2025). Democratizing access to alternative investments for America's workers. Executive Order

    work page 2025

  19. [19]

    Fatemi, S., Hu, Y., and Mousavi, M. (2025). A comparative analysis of instruction fine-tuning large language models for financial text classification. ACM Transactions on Management Information Systems , 16(1):1--30

    work page 2025

  20. [20]

    W., Jabeur, S

    Goodell, J. W., Jabeur, S. B., Sa \^a daoui, F., and Nasir, M. A. (2023). Explainable artificial intelligence modeling to forecast bitcoin prices. International Review of Financial Analysis , 88:102702

    work page 2023

  21. [21]

    Gorodnichenko, Y., Pham, T., and Talavera, O. (2023). The voice of monetary policy. American Economic Review , 113(2):548--584

    work page 2023

  22. [22]

    Granger, C. W. (1969). Investigating causal relations by econometric models and cross-spectral methods. Econometrica: journal of the Econometric Society , pages 424--438

    work page 1969

  23. [23]

    Guzman, G. (2011). Internet search behavior as an economic forecasting tool: The case of inflation expectations. Journal of economic and social measurement , 36(3):119--167

    work page 2011

  24. [24]

    and Schmidhuber, J

    Hochreiter, S. and Schmidhuber, J. (1997). Long short-term memory. Neural computation , 9(8):1735--1780

    work page 1997

  25. [25]

    and Labondance, F

    Hubert, P. and Labondance, F. (2021). The signaling effects of central bank tone. European Economic Review , 133:103684

    work page 2021

  26. [26]

    Q., Sablayrolles, A., Mensch, A., Bamford, C., Chaplot, D

    Jiang, A. Q., Sablayrolles, A., Mensch, A., Bamford, C., Chaplot, D. S., de las Casas, D., Bressand, F., Lengyel, G., Lample, G., Saulnier, L., Lavaud, L. R., Lachaux, M.-A., Stock, P., Scao, T. L., Lavril, T., Wang, T., Lacroix, T., and Sayed, W. E. (2023). Mistral 7b

    work page 2023

  27. [27]

    Karau, S. (2023). Monetary policy and bitcoin. Journal of International Money and Finance , 137:102880

    work page 2023

  28. [28]

    Kim, T., Jo, H., Choi, W., and Jang, B.-G. (2025). Bitcoin price direction forecasting and market variables. Journal of Futures Markets , 45(10):1579--1600

    work page 2025

  29. [29]

    o se, N., G \

    K \"o se, N., G \"u r, Y. E., and \"U nal, E. (2025). Deep learning and machine learning insights into the global economic drivers of the bitcoin price. Journal of Forecasting

    work page 2025

  30. [30]

    and Valcarcel, V

    Liu, J. and Valcarcel, V. J. (2024). Hedging inflation expectations in the cryptocurrency futures market. Journal of Financial Stability , 70:101205

    work page 2024

  31. [31]

    Lundberg, S. M. and Lee, S.-I. (2017). A unified approach to interpreting model predictions. Advances in neural information processing systems , 30

    work page 2017

  32. [32]

    Ly \'o csa, S ., Moln \'a r, P., Pl \' hal, T., and S ira n ov \'a , M. (2020). Impact of macroeconomic news, regulation and hacking exchange markets on the volatility of bitcoin. Journal of Economic Dynamics and Control , 119:103980

    work page 2020

  33. [33]

    Ma, C., Tian, Y., Hsiao, S., and Deng, L. (2022). Monetary policy shocks and bitcoin prices. Research in International Business and Finance , 62:101711

    work page 2022

  34. [34]

    Manickavasagam, J., Ayyalusamy, K., Bhaiyat, F., Jha, S., Jain, T., and Poddar, S. (2025). Bitcoin prices: Configurational effects of technological drivers, macroeconomic fundamentals and economic agents' expectation. International Journal of Finance & Economics

    work page 2025

  35. [35]

    Matkovskyy, R., Jalan, A., and Dowling, M. (2020). Effects of economic policy uncertainty shocks on the interdependence between bitcoin and traditional financial markets. The Quarterly Review of Economics and Finance , 77:150--155

    work page 2020

  36. [36]

    Nicholas Taleb, N. (2021). Bitcoin, currencies, and fragility. Quantitative Finance , 21(8):1249--1255

    work page 2021

  37. [37]

    Picault, M., Pinter, J., and Renault, T. (2022). Media sentiment on monetary policy: Determinants and relevance for inflation expectations. Journal of International Money and Finance , 124:102626

    work page 2022

  38. [38]

    and Renault, T

    Picault, M. and Renault, T. (2017). Words are not all created equal: A new measure of ecb communication. Journal of International Money and Finance , 79:136--156

    work page 2017

  39. [39]

    Pietrzak, M. (2023). What can monetary policy tell us about bitcoin? Annals of Finance , 19(4):545--559

    work page 2023

  40. [40]

    and Youssef, L

    Polyzos, E. and Youssef, L. (2025). Investigating the impact of global events on cryptocurrency performance: a big data event study approach. Journal of International Money and Finance , 157:103375

    work page 2025

  41. [41]

    and Lee, J

    Pyo, S. and Lee, J. (2020). Do fomc and macroeconomic announcements affect bitcoin prices? Finance Research Letters , 37:101386

    work page 2020

  42. [42]

    and Colombo, J

    Rodriguez, H. and Colombo, J. (2025). Is bitcoin an inflation hedge? Journal of Economics and Business , 133:106218

    work page 2025

  43. [43]

    and Wagner, C

    Schmeling, M. and Wagner, C. (2025). Does central bank tone move asset prices? Journal of Financial and Quantitative Analysis , 60(1):36--67

    work page 2025

  44. [44]

    Selmi, R., Mensi, W., Hammoudeh, S., and Bouoiyour, J. (2018). Is bitcoin a hedge, a safe haven or a diversifier for oil price movements? a comparison with gold. Energy Economics , 74:787--801

    work page 2018

  45. [45]

    H., Sudhof, M., and Wilson, D

    Shapiro, A. H., Sudhof, M., and Wilson, D. J. (2022). Measuring news sentiment. Journal of econometrics , 228(2):221--243

    work page 2022

  46. [46]

    Smales, L. A. (2019). Bitcoin as a safe haven: Is it even worth considering? Finance Research Letters , 30:385--393

    work page 2019

  47. [47]

    Smales, L. A. (2024). Cryptocurrency as an alternative inflation hedge? Accounting & Finance , 64(2):1589--1611

    work page 2024

  48. [48]

    Department of Labor (2025)

    U.S. Department of Labor (2025). Compliance assistance release no. 2025-01. Employee Benefits Security Administration

    work page 2025

  49. [49]

    Wang, J., Ma, F., Bouri, E., and Guo, Y. (2023). Which factors drive bitcoin volatility: macroeconomic, technical, or both? Journal of Forecasting , 42(4):970--988

    work page 2023

  50. [50]

    Woloszko, N. (2018). Tracking activity in real time with google trends. OECD Economic Department Working Papers , (1634):0\_1--54

    work page 2018

  51. [51]

    and Tian, G

    Yae, J. and Tian, G. Z. (2024). Volatile safe-haven asset: Evidence from bitcoin. Journal of Financial Stability , 73:101285

    work page 2024