Speaking the market into being: artificial intelligence, predictive language, and the communicative power of central banks in Southeast Asia

Authors

  • Mohammad Alief Hidayatullah Universitas Nurul Jadid Author

DOI:

https://doi.org/10.67490/ijle.v1i2.907

Keywords:

artificial intelligence, central-bank communication, communicative power, predictive language, Southeast Asia

Abstract

Background: Central-bank communication increasingly governs expectations through forecasts, conditional projections, and policy signals, while artificial intelligence expands the capacity to classify and compare such language across heterogeneous monetary regimes. Objective: This study examines how central banks in Indonesia, Singapore, Malaysia, and the Philippines construct economic futures, authorise policy judgement, and become analytically legible through AI-assisted textual modelling. Method: A comparative corpus-assisted discourse design analyses fourteen official English-language policy communications using predictive-language annotation, communicative-power coding, normalised institutional profiles, cosine similarity, concordance checking, and human validation. Results: Predictive discourse consistently combined epistemic modality, temporal projection, and inflation alignment, although quantification, conditionality, directional risk, and forecast revision varied across institutions. Communicative authority emerged through different configurations: Bank Indonesia foregrounded policy commitment and exchange-rate stability, MAS emphasised numerical forecasting and recalibration, BNM contextualised projections through broader macroeconomic conditions, and BSP combined formal decisions with forecast monitoring. Implication: Computational comparison identified substantial but incomplete institutional convergence, demonstrating that shared monetary vocabulary did not erase differences in mandate, genre, or policy orientation. Novelty: This study contributes an integrated account of predictive authority by linking linguistic futurity, institutional performativity, and explainable AI, while treating computational outputs as interpretive evidence requiring contextual and human scrutiny across linguistically and institutionally differentiated Southeast Asian monetary systems

References

[1] N. Fanta and R. Horváth, “Artificial intelligence and central bank communication: The case of the ECB,” Applied Economics Letters, vol. 32, no. 18, pp. 2600–2607, 2025, doi: 10.1080/13504851.2024.2337318.

[2] M. Pfeifer and V. P. Marohl, “CentralBankRoBERTa: A fine-tuned large language model for central bank communications,” The Journal of Finance and Data Science, vol. 9, Art. no. 100114, 2023, doi: 10.1016/j.jfds.2023.100114.

[3] Vyshnevskyi, W. Jombo, and W. Sohn, “The clarity of monetary policy communication and financial market volatility in developing economies,” Emerging Markets Review, vol. 59, Art. no. 101121, 2024, doi: 10.1016/j.ememar.2024.101121.

[4] M. Ehrmann and J. Talmi, “Starting from a blank page? Semantic similarity in central bank communication and market volatility,” Journal of Monetary Economics, vol. 111, pp. 48–62, 2020, doi: 10.1016/j.jmoneco.2019.01.028.

[5] A. Petropoulos and V. Siakoulis, “Can central bank speeches predict financial market turbulence? Evidence from an adaptive NLP sentiment index analysis using XGBoost machine learning technique,” Central Bank Review, vol. 22, no. 1, pp. 1–11, 2022, doi: 10.1016/j.cbrev.2021.12.002.

[6] L. Wansleben, “How expectations became governable: Institutional change and the performative power of central banks,” Theory and Society, vol. 47, no. 6, pp. 773–803, 2018, doi: 10.1007/s11186-018-09334-0.

[7] A. Abreu and D. S. Lopes, “Forward guidance and the semiotic turn of the European Central Bank,” Journal of Cultural Economy, vol. 15, no. 1, pp. 14–29, 2022, doi: 10.1080/17530350.2021.1921829.

[8] T. Walter, “One future to bind them all? Modern central banking and the limits of performative governability,” The British Journal of Politics and International Relations, vol. 26, no. 3, pp. 718–741, 2024, doi: 10.1177/13691481231210382.

[9] J. Černevičienė and A. Kabašinskas, “Explainable artificial intelligence (XAI) in finance: A systematic literature review,” Artificial Intelligence Review, vol. 57, Art. no. 216, 2024, doi: 10.1007/s10462-024-10854-8.

[10] R. Beaupain and A. Girard, “The value of understanding central bank communication,” Economic Modelling, vol. 85, pp. 154–165, 2020, doi: 10.1016/j.econmod.2019.05.013.

[11] O. Kryvtsov and L. Petersen, “Central bank communication that works: Lessons from lab experiments,” Journal of Monetary Economics, vol. 117, pp. 760–780, 2020, doi: 10.1016/j.jmoneco.2020.05.001.

[12] M. Niţoi, M. Pochea, and S. Radu, “Unveiling the sentiment behind central bank narratives: A novel deep learning index,” Journal of Behavioral and Experimental Finance, vol. 38, Art. no. 100809, 2023, doi: 10.1016/j.jbef.2023.100809.

[13] J. Park, H. Lee, and S. Cho, “Hot topic detection in central bankers’ speeches,” Expert Systems with Applications, vol. 230, Art. no. 120563, 2023, doi: 10.1016/j.eswa.2023.120563.

[14] M. Neuenkirch, “Managing financial market expectations: The role of central bank transparency and central bank communication,” European Journal of Political Economy, vol. 28, no. 1, pp. 1–13, 2012, doi: 10.1016/j.ejpoleco.2011.07.003.

[15] P. T. Hughes and S. Kesting, “A literature review on central bank communication,” On the Horizon, vol. 22, no. 4, pp. 328–340, 2014, doi: 10.1108/OTH-07-2014-0027.

[16] A. G. Karl, ““Bank talk,” performativity and financial markets,” Journal of Cultural Economy, vol. 6, no. 1, pp. 63–77, 2013, doi: 10.1080/17530350.2012.745441.

[17] L. C. La Berge, “How to make money with words: Finance, performativity, language,” Journal of Cultural Economy, vol. 9, no. 1, pp. 43–62, 2016, doi: 10.1080/17530350.2015.1040435.

[18] R. Baeriswyl, C. Cornand, and B. Ziliotto, “Observing and shaping the market: The dilemma of central banks,” Journal of Money, Credit and Banking, vol. 53, no. 6, pp. 1547–1576, 2021, doi: 10.1111/jmcb.12682.

[19] A. Zayim, “Inside the black box: Credibility and the situational power of central banks,” Socio-Economic Review, vol. 20, no. 2, pp. 559–586, 2022, doi: 10.1093/ser/mwaa011.

[20] A. Born, M. Ehrmann, and M. Fratzscher, “Central bank communication on financial stability,” The Economic Journal, vol. 124, no. 577, pp. F299–F333, 2014, doi: 10.1111/ecoj.12039.

[21] J. D. Morris, “The performativity, performance and lively practices in financial stability press conferences,” Journal of Cultural Economy, vol. 9, no. 3, pp. 245–260, 2016, doi: 10.1080/17530350.2015.1136831.

[22] A. E. Grigorescu, “Artificial intelligence in central banking,” Proceedings of the International Conference on Business Excellence, vol. 18, no. 1, pp. 1892–1901, 2024, doi: 10.2478/picbe-2024-0159.

[23] M. Vučinić and R. Luburić, “Artificial intelligence, fintech and challenges to central banks,” Journal of Central Banking Theory and Practice, vol. 13, no. 2, pp. 5–42, 2024, doi: 10.2478/jcbtp-2024-0021.

[24] J. Delgadillo, J. Kinyua, and C. Mutigwe, “FinSoSent: Advancing financial market sentiment analysis through pretrained large language models,” Big Data and Cognitive Computing, vol. 8, no. 8, Art. no. 87, 2024, doi: 10.3390/bdcc8080087.

[25] F. Khan, S. Mazhar, K. Mazhar, D. A. AlSaleh, and A. Mazhar, “Model-agnostic explainable artificial intelligence methods in finance: A systematic review, recent developments, limitations, challenges and future directions,” Artificial Intelligence Review, vol. 58, 2025, doi: 10.1007/s10462-025-11215-9.

[26] K. Mishev, A. Gjorgjevikj, I. Vodenska, L. T. Chitkushev, and D. Trajanov, “Evaluation of sentiment analysis in finance: From lexicons to transformers,” IEEE Access, vol. 8, pp. 131662–131682, 2020, doi: 10.1109/ACCESS.2020.3009626.

Downloads

Published

30-06-2026

How to Cite

Mohammad Alief Hidayatullah. (2026). Speaking the market into being: artificial intelligence, predictive language, and the communicative power of central banks in Southeast Asia. Indonesian Journal of Language and Economic Discourse, 1(2), 124-133. https://doi.org/10.67490/ijle.v1i2.907

Similar Articles

You may also start an advanced similarity search for this article.