The Essay Explores Central Banking, Sentiment Analysis, And The European Central Bank's Data Governance Framework, Utilizing Big Data.

Central Banking

A central bank can be referred to as a public institution that is involved in managing a country’s or a group of countries’ currency and controlling the supply of money that the amount of money circulated. Ensuring price stability is the main objective of central banking. In some countries, law requires the central banks to act to support full employment. Setting the interest rate is one of the primary tools used by any central bank, and it can be regarded as the cost of money that is a key part of the monetary policy. A central bank cannot be considered as a commercial bank. It is not possible for an individual to open an account at a central bank. A central bank is such a public body that is not motivated by profit. A central bank acts as a bank to the commercial banks and this is the manner in which it influence the money flow and credit in the economy to attain price stability (ecb.europa.eu 2022).

Sentiment analysis can be regarded as a process used for ascertaining whether a piece of writing is positive, negative or neutral. Such analysis helps in gauging different types of opinions. Analysis of the central bank discussion papers by using the sentiment analysis shows that certain key words have been used by the central bank for expressing positive and negative financial and economic situations. The positive words include achieve, benefit, outperform, efficiency, progress, stabilize, versatility, strength, favourable, enhance, upgraded, improve, strengthened and smooth. The negative words are corrupt, bottleneck, bankruptcy, fallout, downgrade, stagnant, imbalance, monopolize, challenge, adverse, deteriorate, escalate, stagnation, worsen and vulnerability (bbvaresearch.com 2022). A discussion paper of the European Central Bank is “Product market structure and monetary policy: evidence from the Euro Area” (ecb.europa.eu 2022). In this paper, the words ‘stressed countries’ are used for several times. Since ‘stress’ is a negative word, the tone of this discussion paper can be considered as negative. Another discussion paper of the Central European Bank is “Monetary policy in a low interest rate environment: reversal rate and risk-taking” where the word ‘benefit’ is used for many times indicating that the tone one of this paper is positive since ‘benefit’ is a positive word (ecb.europa.eu 2022).

The European Central Bank has adopted a specific approach for the enterprise data governance framework which is supported by the Office of the Chief Data Officer (OCDO). This framework include the Board Data Council which consists of seven voting members and ten liaisons, Data Governance Committee which consists of the Chief Data Steward, Enterprise Data Domain Data Steward Team which consists of the Chief DS, Business DS, Source DS and Technical DS, and Data Management team (ecb.europa.eu 2022). Each committee and team is interconnected to each other. The Data Management team is responsible for data architecture and modelling, fulfilling requirements, metadata, data quality, data and content management, data security and access, and data operations (ecb.europa.eu 2022). The Board Data Council and Data Governance Committee come under the strategic aspects of the framework whereas the Enterprise Data Domain Data Steward Team comes under tactical aspect. The Data Management team comes under the functional aspect of the enterprise data governance framework (ecb.europa.eu 2022).

Sentiment Analysis

Big data can potentially assist with policy rate-setting that helps in stabilising inflation and reducing unemployment to its natural rate in the following manners:

1. Big data can assists the local government in collecting vast amount of microlevel administrative data which helps in better tracking and forecasting economic activity.
2. The statistical power of big data can be seen in extending the possibilities of panel studies to shed light on different economic aspects like distribution of income (bsi-economics.org 2022).
3. Big data represents massive amount of data to track the economic activities in private sector on real time basis and it constitutes a powerful tool for tracking economic activity with a higher level of frequency and granularity as compared to the traditional survey data.
4. Indirect measure of big data like social media posts or search queries can largely be utilised as accurate proxies like consumer confidence or employment (bsi-economics.org 2022).
5. A large size of data are represented by the big data which would largely contribute to a major enhancement of different economic measurements.
6. The use of big data provides a better perception of the influence and effects created by the economic policies and events (bsi-economics.org 2022).

Big data have the capability to be used for supporting financial stability. Central banks around the world are in the process to improve their big data usage to support financial stability. There are certain operating systems that generate big data like the settlement and payment systems, and these systems are used for gathering these data for the purpose of different analyses. These systems are collecting daily data on the money market transactions, and these data are used for monitoring the impact of different economic and financial policies employed for assessing financial stability (bis.org 2022). Now, the central banks seen to select their new data sources selectively. Financial market data are routinely streamed from Bloomberg or Reuters in order to monitor different trends. The use of scanner data or online prices is another area of interest of the central banks as these data help to measure inflation and its tenacity. All these aspects indicate that big data have much potential to ensure financial stability. The use of big data in different purposes makes a strong case to improve financial stability analysis with the utilisation of big data sources, like bank balance sheets, trade repositories, credit registries and others (bis.org 2022).

Big data are being actively used by the central banks for different purposes and in variety of areas, which include research, monetary policy and financial stability. Other areas where big data are used include supervision and regulation. The economic analysis undertaken by the central banks is supported by big data with the help of nowcasting models drawing on big data. High frequency forecast is used by these models that the central banks can update on real time basis. While nowcasting is used primarily for forecasting gross domestic product or private consumptions, it is now used by the central banks in other advanced areas (bis.org 2022). For instance, inflation dynamics are predicted by some models from online retail sales and others unemployment from different job portals. The usefulness of nowcasting and real-time economic indicators is seen during the time of heightened economic uncertainty, such as COVID-19 crisis. Big data are largely used with the smart document management systems in the central banks and this mechanism is used for proactively pushing contents to staff on the basis of past reading preferences (Bholat 2015). Other aspects of economic activities are measured by the central banks through the use of big data. For instance, natural language processing is used by them for producing economic or policy uncertainty indices from textual data or for gauging sentiment regarding the monetary policies announced, including the unconventional monetary policy measures. It has already been discussed in the above parts that how big data can be used for supporting financial stability (bis.org 2022). Therefore, the use of big data in these manners could change the central bank’s internal operations while transforming the external economic and financial systems.

Central banks have started to use big data to an increased and advanced extent in almost all of their operations. Big data is transforming the culture of the central banks towards data-driven and data-centric. As a result, the central banks have become able in developing best practices by focusing on data management and data governance (ecb.europa.eu 2022). Therefore, the recommendation to the central banks is to optimize their analytical environment to gain enhanced insights on different domains, data, trends, and others. Due to the use of big data, the central banks will be able to embrace transparency and open data engagement with efficiency.

Conclusion

The use of sentiment analysis shows that the European Central Bank uses certain key terms which can be used to assess the tone of its discussion papers. Terms like benefit, achieve, strength and others are used as positive terms whereas terms as corrupt, stress and others are used as negative tone. The enterprise data governance framework used by the European Central Bank use different committees and teams to ensure proper management of data. Big data provides the scope to analyse large amount of data which involves different categories of data, and analysis of these data helps in policy-rate setting by providing different insights. In addition, big data are largely used by the central banks to maintain financial stability, changing internal operations and transforming economic and financial systems. Due to these benefits, it is recommended to the central banks to use big data for advanced purposes.

References

Bank, E. 2021. What is a central bank?, European Central Bank. Available at:

https://www.ecb.europa.eu/ecb/educational/explainers/tell-me/html/what-is-a-central-bank.en.html (Accessed: 25 January 2022).

Bbvaresearch.com. 2022. A Big Data approach to understand Central Banks. Available at: https://www.bbvaresearch.com/wp-content/uploads/2018/11/Understanding_CentralBanks_BigDataSpain2018_.pdf (Accessed: 25 January 2022).

Bholat, D., 2015. Big data and central banks. Big Data & Society, 2(1), p.2053951715579469.

Bis.org. 2022. BIS Working Papers No 930 Big data and machine learning in central banking. Available at:

https://www.bis.org/publ/work930.pdf (Accessed: 25 January 2022).

Bis.org. 2022. Financial instability: can Big Data help connect the dots? Available at:

https://www.bis.org/speeches/sp181203.pdf (Accessed: 25 January 2022).

Bsi-economics.org. 2022. Big Data or the renewal of the economic analysis. Available at:

https://www.bsi-economics.org/images/ENbig-data-eco-analysis.pdf (Accessed: 25 January 2022).

Ecb.europa.eu. 2022. Emerging Opportunities and Challenges with Central Bank Data. Available at:

https://www.ecb.europa.eu/events/pdf/conferences/141015/presentations/Emerging_opportunities_and_chalenges_with_Central_Bank_data-presentation.pdf?6074ecbc2e58152dd41a9543b1442849 (Accessed: 25 January 2022).

Ecb.europa.eu. 2022. Monetary policy in a low interest rate environment: reversal rate and risk-taking. Available at:

https://www.ecb.europa.eu/pub/pdf/scpwps/ecb.wp2593~c97d059554.en.pdf?e913a317e7f48d5afcda925925e4f27d (Accessed: 25 January 2022).

Ecb.europa.eu. 2022. Product market structure and monetary policy: evidence from the Euro Area. Available at:

https://www.ecb.europa.eu/pub/pdf/scpwps/ecb.wp2632~d05437c47c.en.pdf?2a64a2ecb790b9dc4a159a7e086c89fc (Accessed: 25 January 2022).