Data Science Opportunities at Central Banks: Overview
a National Bank of Ukraine, Kyiv, Ukraine
Abstract

This paper reviews the main streams of Data Science algorithm usage at central banks and shows their rising popularity over time. It contains an overview of use cases for macroeconomic and financial forecasting, text analysis (newspapers, social networks, and various types of reports), and other techniques based on or connected to large amounts of data. The author also pays attention to the recent achievements of the National Bank of Ukraine in this area. This study contributes to the building of the vector for research the role of Data Science for central banking.

420
views
63
downloads
Full Text
Citation
Cite as: Krukovets, D. (2020). Data Science Opportunities at Central Banks: Overview. Visnyk of the National Bank of Ukraine, 249, 13-24. https://doi.org/10.26531/vnbu2020.249.02
Citation Format

Metrics
References

Abe, N., Shinozaki, K. (2018). Compilation of experimental price indexes using Big Data and Machine Learning:
A comparative analysis and validity verification. Bank of Japan Working Paper Series, No. 18-E-13. Bank of Japan.
Retrieved from https://www.boj.or.jp/en/research/wps_rev/wps_2018/data/wp18e13.pdf

Angelico, C., Marcucci, J., Miccoli, M., Quarta, F. (2018). Can we measure inflation expectations using Twitter?
Harnessing Big Data & Machine Learning Technologies for Central Banks (Rome, March 26). Retrieved from https://www.bancaditalia.it/pubblicazioni/altri-atti-convegni/2018-bigdata/Miccoli_Presentazione_Twitter_Workshop.pdf

Apergis, N., Pragidis, I. (2019). Stock price reactions to wire news from the European Central Bank: evidence from
changes in the sentiment tone and international market indexes. International Advances in Economic Research, 25,
91–112. https://doi.org/10.1007/s11294-019-09721-y

Arora, R., Fan, C., Leblanc, G. (2019). Liquidity management of Canadian corporate bond mutual funds: A
machine learning approach. Staff Analytical Note, 2019-7. Bank of Canada. Retrieved from https://www.bankofcanada.ca/2019/02/staff-analytical-note-2019-7/

Bazarbash, M. (2019). FinTech in financial inclusion machine learning applications in assessing credit risk. IMF Working Papers, WP/19/109. International Monetary Fund. Retrieved from https://www.imf.org/en/Publications/WP/Issues/2019/05/17/FinTech-in-Financial-Inclusion-Machine-Learning-Applications-in-Assessing-Credit-Risk-46883

Bholat, D., Hansen, S., Santos, P., Schonhardt-Bailey, C. (2015). Text mining for central banks. Centre for Central
Banking Studies Publication. Bank of England. Retrieved from https://www.bankofengland.co.uk/-/media/boe/files/ccbs/resources/text-mining-for-central-banks.pdf

Bolhuis, M., Rayner, B. (2020). Deus ex machina? A framework for macro forecasting with machine learning.
IMF Working Papers, WP/20/45. International Monetary Fund. Retrieved from https://www.imf.org/en/Publications/
WP/Issues/2020/02/28/Deus-ex-Machina-A-Framework-for-Macro-Forecasting-with-Machine-Learning-49094

Cavallo, A. (2013). Online and official price indexes: Measuring Argentina's inflation. Journal of Monetary
Economics, 60(2), 153-165. http://dx.doi.org/10.1016/j.jmoneco.2012.10.002

Cedervall, A., Jansson, D. (2018). Topic classification of Monetary Policy Minutes from the Swedish Central
Bank. Examensarbete Inom Technology, Grundnivå, 15 Hp. Stockholm, Sverige. Retrieved from http://www.diva-portal.org/smash/get/diva2:1272108/FULLTEXT01.pdf

Chakraborty, C., Joseph, A. (2017). Machine learning at central banks. Staff Working Paper, 674. Bank of England.
Retrieved from https://www.bankofengland.co.uk/-/media/boe/files/working-paper/2017/machine-learning-at-centralbanks.pdf

Choudhary, A., Haider, A. (2012). Neural network models for inflation forecasting: an appraisal. Applied Economics,
44(20), 2631-2635. https://doi.org/10.1080/00036846.2011.566190

Corea, F. (2016). Can Twitter proxy the investors’ sentiment? The case for the technology sector. Big Data Research, 4(C), 70–74. https://dl.acm.org/doi/10.5555/2991306.2991336

Faryna, O., Talavera, O., Yukhymenko, T. (2018). What drives the difference between online and official price
indexes? Visnyk of the National Bank of Ukraine, 243, 21-32. https://doi.org/10.26531/vnbu2018.243.021

Fayad, G., Huang, C., Shibuya, Y., Zhao, P. (2020). How do member countries receive IMF policy advice: Results
from a state-of-the-art sentiment index. IMF Working Papers, WP/20/7. International Monetary Fund. Retrieved from https://www.imf.org/en/Publications/WP/Issues/2020/01/17/How-Do-Member-Countries-Receive-IMF-Policy-Advice-Results-from-a-State-of-the-art-Sentiment-48937

Fulop, A., Kocsis, Z. (2018). News-based indices on country fundamentals: do they help explain sovereign credit spread fluctuations. MNB Working Papers, 1. Magyar Nemzeti Bank. Retrieved from https://www.mnb.hu/letoltes/
mnb-wp-2018-1-final-1.pdf

Gogas, P., Papadimitriou, T., Matthaiou, M., Chrysanthidou, E. (2014). Yield curve and recession forecasting in a
machine learning framework. Computational Economics, 45, 635–645. https://doi.org/10.1007/s10614-014-9432-0

Gogas, P., Papadimitriou, T., Sofianos, E. (2019). Money neutrality, monetary aggregates, and machine learning.
Algorithms, 12(7), 137. https://doi.org/10.3390/a12070137

Hansen, S. (2018). Measuring market and consumer sentiment and confidence. Bank Indonesia International Workshop and Seminar on “Big Data for Central Bank Policies / Building Pathways for Policy Making with Big
Data” (Bali, Indonesia, 23-26 July 2018). Retrieved from https://www.bis.org/ifc/publ/ifcb50_21.pdf

Hansen, S., McMahon, M., Prat, A. (2018). Transparency and deliberation within the FOMC: a computational linguistics approach. The Quarterly Journal of Economics, 133(2), 801–870. https://doi.org/10.1093/qje/qjx045

Hardoon, D. (2018). Exploring big data to sharpen financial sector risk assessment. Bank Indonesia International Workshop and Seminar on “Big Data for Central Bank Policies / Building Pathways for Policy Making with Big
Data” (Bali, Indonesia, 23-26 July 2018). Retrieved from https://www.bis.org/ifc/publ/ifcb50_28.pdf

Hatko, S. (2017). The Bank of Canada 2015 retailer survey on the cost of payment methods: nonresponse.
Technical Report, No. 107. Bank of Canada. Retrieved from https://www.bankofcanada.ca/wp-content/uploads/2017/03/tr107.pdf

Jung, J., Patnam, M., Ter-Martirosyan, A. (2018). An algorithmic crystal ball: forecasts-based on machine learning.
IMF Working Papers, WP/20/7. International Monetary Fund. Retrieved from https://www.imf.org/en/Publications/WP/Issues/2018/11/01/An-Algorithmic-Crystal-Ball-Forecastsbased-
on-Machine-Learning-46288

Kuhn, M., Johnson, K. (2013). Applied Predictive Modeling. Springer. https://doi.org/10.1007/978-1-4614-6849-322

Lacroix, R. (2018). The Bank of France datalake. Bank Indonesia International Workshop and Seminar on “Big
Data for Central Bank Policies / Building Pathways for Policy Making with Big Data” (Bali, Indonesia, 23-26 July 2018). Retrieved from https://www.bis.org/ifc/publ/ifcb50_26.pdf

Liang, F., Das, V., Kostyuk, N., Hussain, M. (2018). Constructing a data-driven society: China’s Social Credit
System as a state surveillance infrastructure. Policy & Internet, 10(4), 415-453. https://doi.org/10.1002/poi3.183

Liberti, J., Petersen, M. (2019). Information: hard and soft. The Review of Corporate Finance Studies, 8(1), 1–41.
https://doi.org/10.1093/rcfs/cfy009

Medeiros, M., Vasconcelos, G., Veiga, A., Zilberman, E. (2019). Forecasting inflation in a data-rich environment: the
benefits of machine learning methods. Journal of Business & Economic Statistics. https://doi.org/10.1080/07350015.2019.1637745

Mueller, H., Rauh, C. (2017). Reading between the lines: prediction of political violence using newspaper
text. American Political Science Review, 112(2), 358-375. https://doi.org/10.1017/S0003055417000570

Munkhdalai, L., Munkhdalai, T., Namsrai, O., Yun Lee, J., Ho Ryu, K. (2019). An empirical comparison of machine learning methods on bank client credit assessments. Sustainability, 11(3), 699. https://doi.org/10.3390/su11030699

Nakamura, E. (2005). Inflation forecasting using a neural network. Economics Letters, 86(3), 373-378. https://doi.org/10.1016/j.econlet.2004.09.003

Nymand-Andersen, P. (2017). Big data in central banks: Central Banking focus report. Central Banking Publications. Retrieved from https://www.centralbanking.com/media/download/24906/download

Nymand-Andersen, P. (2018). Google econometrics: nowcasting euro area car sales and big data quality
requirements. Statistics Paper Series, No. 30. European Central Bank. Retrieved from https://www.ecb.europa.eu/pub/pdf/scpsps/ecb.sps30.en.pdf

Petropoulos, A., Siakoulis, V., Stavroulakis, E., Klamargias, A. (2018). A robust machine learning approach for credit risk analysis of large loan-level datasets using deep learning and extreme gradient boosting. Ninth IFC Conference on “Are post-crisis statistical initiatives completed?” (Basel, 30-31 August 2018). Retrieved from https://www.bis.org/ifc/publ/ifcb49_49.pdf

Pokidin, D. (2015). National Bank of Ukraine econometric model for the assessment of banks’ credit risk and
support vector machine alternative. Visnyk of the National Bank of Ukraine, 234, 52-72. https://doi.org/10.26531/vnbu2015.234.052

Rashkovan, V., Pokidin, D. (2016). Ukrainian banks’ business models clustering: application of Kohonen neural
networks. Visnyk of the National Bank of Ukraine, 238, 13-38. https://doi.org/10.26531/vnbu2016.238.013

Richardson, A., Mulder, T., Vehbi, T. (2019). Nowcasting GDP using machine learning algorithms: A real-time
assessment. Discussion Paper, 2019-03. Reserve Bank of New Zeland. Retrieved from https://www.rbnz.govt.nz/research-and-publications/discussion-papers/2019/dp2019-03

Rijanto, E. (2018). Opening remarks. International Seminar on Big Data “Building Pathways for Policy-Making with Big Data” (Bali, 26 July 2018). Retrieved from https://www.bis.org/ifc/publ/ifcb50_02.pdf

Robinson, P. (2018). Big data: new insights for economic policy – The Bank of England Experience. IFC – Bank
Indonesia International Workshop and Seminar on “Big Data for Central Bank Policies / Building Pathways for Policy Making with Big Data” (Bali, Indonesia, 23-26 July 2018). Retrieved from https://www.bis.org/ifc/publ/ifcb50_12.pdf

Romer, C., Romer, D. (2004). A new measure of monetary shocks: Derivation and implications. NBER Working Paper Series, 9866. National Bureau Of Economic Research. Retrieved from https://www.nber.org/papers/w9866.pdf

Rybinski, K. (2019). A machine learning framework for automated analysis of central bank communication and
media discourse. The case of Narodowy Bank Polski. Bank i Kredyt, 50(1), 1-20. Retrieved from http://bankikredyt.nbp.pl/content/2019/01/BIK_01_2019_01.pdf

Soramaki, K. (2018). Introduction to network science & visualization. IFC – Bank Indonesia International Workshop and Seminar on “Big Data for Central Bank Policies / Building Pathways for Policy Making with Big Data” (Bali, Indonesia, 23-26 July 2018). Retrieved from https://www.bis.org/ifc/publ/ifcb50_10.pdf

Stiefel, M., Vives, R. (2019). ’Whatever it Takes’ to change belief: evidence from Twitter. AMSE Working Papers,
Nr. 07. Aix-Marseille School of Economics. Retrieved from https://www.amse-aixmarseille.fr/sites/default/files/working_papers/wp_2019_-_nr_07_0.pdf

Thorsrud, L. (2016). Words are the new numbers: A newsy coincident index of business cycles. Journal of Business & Economic Statistics, 38(2), 393-409. https://doi.org/10.1080/07350015.2018.1506344

Zulen, A., Wibisono, O. (2018). Measuring stakeholders’ expectations for the central bank’s policy rate. Ninth IFC Conference on “Are post-crisis statistical initiatives completed?” (Basel, 30-31 August 2018). Retrieved from https://www.bis.org/ifc/publ/ifcb49_50.pdf

Rights and Permissions
This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material.
Submit Your Paper