Central Bank Communication during Economic Recessions: Evidence from Nigeria

Omotosho, Babatunde S. (2020): Central Bank Communication during Economic Recessions: Evidence from Nigeria.

Abstract

This paper analyses the communication strategy of the Central Bank of Nigeria (CBN) during the 2016 economic recession. Applying text mining techniques, useful insights are derived regarding the linguistic intensity, readability, tone, and topics of published monetary policy communiques. Our results provide evidence of increased central bank communication during the recession. However, the ease of reading the published policy communiques declined, especially at the outset of the recession. In terms of tone, we find that negative policy sentiments were expressed during the 2015-2017 period; reflecting the economic uncertainties that trailed the oil price slump of 2014 and its implications for the domestic economy. The negativity of the policy sentiment score reached its trough in July 2016 and recorded an inflexion; signalling the economy’s turning point towards recovery. Based on the results of the estimated topic model, issues relating to “oil price shocks”, “external reserves”, and “inflation” were of concern to the Monetary Policy Committee (MPC) a few quarters preceding the recession while the topics relating to “exchange rate management” as well as “output growth and market stability” were dominant during the recession. Expectedly, the topic proportion for “prices and macroeconomic policies” remain relatively sizeable across the sample period, reflecting the MPC’s commitment to the CBN’s primary mandate of maintaining price stability.

• Item Type: MPRA Paper
• Language: English
• Keywords: Monetary policy, central bank communication, economic recession, text mining
• Subjects: E - Macroeconomics and Monetary Economics > E3 - Prices, Business Fluctuations, and Cycles > E32 - Business Fluctuations ; Cycles; E - Macroeconomics and Monetary Economics > E5 - Monetary Policy, Central Banking, and the Supply of Money and Credit > E52 - Monetary Policy; E - Macroeconomics and Monetary Economics > E5 - Monetary Policy, Central Banking, and the Supply of Money and Credit > E58 - Central Banks and Their Policies; E - Macroeconomics and Monetary Economics > E6 - Macroeconomic Policy, Macroeconomic Aspects of Public Finance, and General Outlook > E61 - Policy Objectives ; Policy Designs and Consistency ; Policy Coordination; E - Macroeconomics and Monetary Economics > E6 - Macroeconomic Policy, Macroeconomic Aspects of Public Finance, and General Outlook > E65 - Studies of Particular Policy Episodes

References

Amadxarif, Z., Brookes, J., Garbarino, N., Patel, R. & Walczak, E. (2019). ‘The Language of Rules: Textual Complexity in Banking Reforms. Bank of England Working Paper.

Amato, J. D., Morris, S. & Shin, H. S. (2002). Communication and Monetary Policy. Oxford Review of Economic Policy, 18(4), 495–503.

Amusa, L., Yahya, W. & Balogun, A. (2016). Data Mining of Nigerian’s Sentiments on the Administration of Federal Government of Nigeria. Annals. Computer Science Series, 14(2).

Apel, M. & Grimaldi, M. (2012). The Information Content of Central Bank Minutes. Riksbank Research Paper Series, (92).

Apel, M. & Grimaldi, M. B. (2014). How Informative are Central Bank Minutes? Review of Economics, 65(1), 53–76.

Barbagallo, D., Bruni, L., Francalanci, C. & Giacomazzi, P. (2012). An Empirical Study on the Relationship between Twitter Sentiment and Influence in the Tourism Domain, in ‘Information and Communication Technologies in Tourism 2012’, Springer, pp. 506– 516.

Berger, H., Ehrmann, M. & Fratzscher, M. (2011). Monetary Policy in the Media. Journal of Money, Credit and Banking, 43(4), 689–709.

Bergholt, D. (2014). Monetary Policy in Oil Exporting Economies. Working paper 0023, Centre for Applied Macro-and Petroleum economics (CAMP), BI Norwegian Business School.

Bernanke, B. S., Gertler, M., Watson, M., Sims, C. A. & Friedman, B. M. (1997). Systematic Monetary Policy and the Effects of Oil Price Shocks. Brookings papers on economic activity, 1997(1), 91–157.

Bernanke, B. S. & Reinhart, V. R. (2004). Conducting Monetary Policy at very Low Short Term Interest Rates. American Economic Review 94(2), 85–90.

Bholat, D., S. Hansen, P. Santos, & C. Schonhardt-Bailey (2015). Text Mining for Central Banks. Centre for Central Banking Studies Handbook (33), 1-19.

Binette, A. & Tchebotarev, D. (2019). Canada’s Monetary Policy Report: If Text could Speak, what would it Say? Bank of Canada Staff Analytical Note.

Blei, D. M. & Lafferty, J. D. (2009). Topic models, in Text Mining. Chapman and Hall/CRC, pp. 101–124.

Blei, D. M., Ng, A. Y. & Jordan, M. I. (2003). Latent Dirichlet Allocation. Journal of Machine Learning research. 3(Jan), 993–1022.

Blinder, A. S. (1999). Central Banking in Theory and Practice. MIT press.

Blinder, A. S., Ehrmann, M., Fratzscher, M., De Haan, J. & Jansen, D.J. (2008). Central Bank Communication and Monetary Policy: A Survey of Theory and Evidence. Journal of Economic Literature. 46(4), 910–45.

Born, B., Ehrmann, M. & Fratzscher, M. (2014). Central Bank Communication on Financial Stability. The Economic Journal 124(577), 701–734.

Bruno, G. (2016). Text Mining and Sentiment Extraction in Central Bank Documents, in 2016 IEEE International Conference on Big Data (Big Data). IEEE, pp. 1700–1708.

Bruno, G. (2017). Central Bank Communications: Information Extraction and Semantic Analysis. IFC Bulletins chapters 44.

Calvo-González, O., Eizmendi, A. & Reyes, G. J. (2018). Winners never Quit, Quitters never Grow: Using Text Mining to Measure Policy Volatility and its Link with Long-Term Growth in Latin America. World Bank Policy Research Working Paper.

Clarida, R., Gali, J. & Gertler, M. (1999). The Science of Monetary Policy: A New Keynesian Perspective’, Journal of Economic Literature. 37(4), 1661–1707.

Clarida, R., Gali, J. & Gertler, M. (2000). Monetary Policy Rules and Macroeconomic Stability: Evidence and some Theory. The Quarterly journal of economics. 115(1), 147– 180.

Coenen, G., Ehrmann, M., Gaballo, G., Hoffmann, P., Nakov, A., Nardelli, S., Persson, E. & Strasser, G. (2017), ‘Communication of Monetary Policy in Unconventional Times. ECB Working Paper. No. 2080.

Coleman, M. & Liau, T. L. (1975), ‘A Computer Readability Formula Designed for Machine Scoring. Journal of Applied Psychology. 60(2), 283.

Correa, R., Garud, K., Londono, J. M. & Mislang, N. (2017). Sentiment in Central Banks’ Financial Stability Reports. International Finance Discussion Papers 1203.

Crone, S. F. & Koeppel, C. (2014). Predicting Exchange Rates with Sentiment Indicators: An Empirical Evaluation using Text Mining and Multilayer Perceptrons, in 2014 IEEE Conference on Computational Intelligence for Financial Engineering & Economics (CIFEr), IEEE. pp. 114–121.

Daas, P. J. & Puts, M. J. (2014). Social Media Sentiment and Consumer Confidence. ECB Statistics Paper.

Debortoli, S., Müller, O., Junglas, I. & Vom Brocke, J. (2016). Text Mining for Information Systems Researchers: An Annotated Topic Modelling Tutorial. Communications of the Association for Information Systems. 39(1), 7.

Demiralp, S., Kara, H. & Ozlu, P. (2012). Monetary Policy Communication in Turkey’, European Journal of Political Economy. 28(4), 540–556.

Ehrmann, M. & Fratzscher, M. (2005). Communication and Decision-Making by Central Bank Committees. ECB Working Paper.

Ekor, M., Adeniyi, O. & Saka, J. (2013). Central Bank Communication and Monetary Policy Effectiveness: Empirical Evidence from Nigeria. University Library of Munich, Germany. 118-152.

Eusepi, S. (2010). Central Bank Communication and the Liquidity Trap. Journal of Money, Credit and Banking. 42(2-3), 373–397.

Eusepi, S. & Preston, B. (2010). Central Bank Communication and Expectations Stabilization. American Economic Journal: Macroeconomics. 2(3), 235–71.

Farina, V., Galloppo, G. & Previati, D. A. (2018). Central Bank’s Communication Strategies: Just Words? in Contemporary Issues in Banking, Springer, pp. 91–121.

Gefen, D., Endicott, J. E., Fresneda, J. E., Miller, J. L. & Larsen, K. R. (2017). A Guide to Text Analysis with Latent Semantic Analysis in R with Annotated Code: Studying Online Reviews and the Stack Exchange Community. CAIS. 41, 21.

Gentzkow, M., Kelly, B. & Taddy, M. (2019). Text as Data. Journal of Economic Literature. 57(3), 535–74.

Geraats, P. M. (2002), ‘Central Bank Transparency. The Economic Journal. 112(483), F532– F565.

Hayo, B. & Neuenkirch, M. (2011). Canadian Interest Rate Setting: The Information Content of Canadian and US Central Bank Communication. Southern Economic Journal. 78(1), 131–148.

Hoeberichts, M., Tesfaselassie, M. F. & Eijffinger, S. (2008). Central Bank Communication and Output Stabilization. Oxford Economic Papers. 61(2), 395–411.

Hopper, G. P. (1997), ‘What Determines the Exchange Rate: Economic Factors or Market Sentiment. Business Review. 5, 17–29.

Huang, K. X. (2007). Effective Central Bank Communication under Uncertainty. Journal of Economic Issues. 41(3), 661–680.

Hubert, P. & Fabien, L. (2017). Central Bank Sentiment and Policy Expectations. Bank of England Staff Working Paper 648.

Iwantoro, T. & Koesrindartoto, D. P. (2017). Exchange Rate Directional Forecasting using Sentiment Analysis on Social Media in Indonesia 2015. Australian Academy of Accounting and Finance Review. 1(2), 132–147.

Janetzko, D. (2014). Predictive Modelling in Turbulent Times–What Twitter Reveals about the EUR/USD Exchange Rate’, NETNOMICS: Economic Research and Electronic Networking. 15(2), 69–106.

Kahveci, E. & Odabas, A. (2016). Central Bank’s Communication Strategy and Content Analysis of Monetary Policy Statements: The Case of Fed, ECB and CBRT. Procedia-Social and Behavioural Sciences. 235, 618–629.

Khatri, S. K., Singhal, H. & Johri, P. (2014). Sentiment Analysis to Predict Bombay Stock Exchange Using Artificial Neural Network, in Proceedings of 3rd International Conference on Reliability, Infocom Technologies and Optimization. IEEE. pp. 1–5.

Kumar, S., Maskara, S., Chandak, N. & Goswami, S. (2015). Empirical Study of Relationship between Twitter Mood and Stock Market from an Indian Context. International Journal of Applied Information Systems. 8(7), 33–37.

Luangaram, P. & Sethapramote, Y. (2016). Central Bank Communication and Monetary Policy Effectiveness: Evidence from Thailand. Puey Ungphakorn Institute for Economic Research.

Luangaram, P. &Wongwachara, W. (2017). More than Words: A Textual Analysis of Monetary Policy Communication. PIER Discussion Papers. 54.

Miner, G., Elder IV, J., Fast, A., Hill, T., Nisbet, R. & Delen, D. (2012). Practical Text Mining and Statistical Analysis for Non-Structured Text Data Applications. Academic Press.

Mittal, A. & Goel, A. (2012). Stock Prediction using Twitter Sentiment Analysis. Standford University Working Paper 15.

Mordi, C. N., Adebiyi, M. A. & Omotosho, B. S. (2019), Modelling Interest Rates Passthrough in Nigeria: An Error Correction Approach with Asymmetric Adjustments and Structural Breaks. In Contemporary Issues in the Nigerian Economy: A Book of Readings. Central Bank of Nigeria. 3 – 20.

Omotosho, B. S. (2020). Central Bank Communication in Ghana: Insights From A Text Mining Analysis. Noble International Journal of Economics and Financial Research, 5(1), 01-13.

Oshima, Y. & Matsubayashi, Y. (2018). Monetary Policy Communication of the Bank of Japan: Computational Text Analysis. Graduate School of Economics Working paper, Kobe University.

Ozturk, S. S. & Ciftci, K. (2014). A Sentiment Analysis of Twitter Content as a Predictor of Exchange Rate Movements. Review of Economic Analysis. 6(2), 132–140.

Park, K. Y., Lee, Y. & Kim, S. (2019). Deciphering Monetary Policy Board Minutes through Text Mining Approach: The Case of Korea. Bank of Korea Working Paper.

Plakandaras, V., Papadimitriou, T., Gogas, P. & Diamantaras, K. (2015). Market Sentiment and Exchange Rate Directional Forecasting. Algorithmic Finance. 4(1-2), 69–79.

Ranco, G., Aleksovski, D., Caldarelli, G., Grčar, M. & Mozetič, I. (2015). The Effects of Twitter Sentiment on Stock Price Returns. PloS One. 10(9).

Sanusi, A. R. (2011). Signalling Effects of Monetary Policy in Nigeria: Evidence from the New Central Bank’s Communication Regime: 2005-2011. In 52nd Annual Conference of the Nigerian Economics Society.

Shapiro, A. H. & Wilson, D. (2019). Taking the Fed at its Word: Direct Estimation of Central Bank Objectives using Text Analytics. Federal Reserve Bank of San Francisco Working Paper 2019-02.

Shirota, Y., Hashimoto, T. & Sakura, T. (2015). Topic Extraction Analysis for Monetary Policy Minutes of Japan in 2014, in Industrial Conference on Data Mining. Springer, pp. 141–152.

Siklos, P. L. (2013). The Global Financial Crisis and The Language of Central Banking: Central Bank Guidance in Good Times and in Bad. CAMA Working Paper Series Paper 58/2013.

Sturm, J. E. & De Haan, J. (2011). Does Central Bank Communication Really Lead to Better Forecasts of Policy Decisions? New Evidence Based on a Taylor Rule Model for the ECB. Review of World Economics. 147(1), 41–58.

Sumbureru, P. T. (2013). Analysis of Tweets for Prediction of Indian Stock Markets. International Journal of Science and Research (IJSR). Volume 4.

Tumala, M. M., & Omotosho, B. S. (2019). A Text Mining Analysis of Central Bank Monetary Policy Communication in Nigeria. CBN Journal of Applied Statistics, 10(2).