Peter, Eckley (2015): Measuring economic uncertainty using news-media textual data.
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Abstract
We develop a news-media textual measure of aggregate economic uncertainty, defined as the fraction of Financial Times articles that contain uncertainty-related keyphrases, at frequencies from daily to annual, from January 1982 to April 2014. We improve on existing similar measures in several ways. First, we reveal extensive and irregular duplication of articles in the news database most widely used in the literature, and provide a simple but effective de-duplication algorithm. Second, we boost the uncertainty ‘signal strength’ by 14% through the simple addition of the word “uncertainties” to the conventional keyword list of “uncertain” and “uncertainty”, and show that adding further uncertainty-related keyphrases would likely constitute only a second-order adjustment. Third, we demonstrate the importance of normalising article counts by total news volume and provide the first textual uncertainty measure to do so for the UK. We empirically establish the plausibility of our measure as an uncertainty proxy through a detailed narrative analysis and a detailed comparative analysis with another popular uncertainty proxy, stock returns volatility. We show the relationship between these proxies is strong and significant on average, but breaks down periodically. We offer plausible explanations for this behaviour. We also establish the absence of Granger causation between the measures, even down to daily (publication) frequency.
Item Type: | MPRA Paper |
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Original Title: | Measuring economic uncertainty using news-media textual data |
Language: | English |
Keywords: | economic uncertainty; news-media; text-mining; stock returns volatility |
Subjects: | C - Mathematical and Quantitative Methods > C8 - Data Collection and Data Estimation Methodology ; Computer Programs > C80 - General D - Microeconomics > D8 - Information, Knowledge, and Uncertainty > D80 - General E - Macroeconomics and Monetary Economics > E6 - Macroeconomic Policy, Macroeconomic Aspects of Public Finance, and General Outlook > E66 - General Outlook and Conditions G - Financial Economics > G1 - General Financial Markets > G10 - General |
Item ID: | 64874 |
Depositing User: | Mr Peter Eckley |
Date Deposited: | 08 Jun 2015 14:04 |
Last Modified: | 29 Sep 2019 05:00 |
References: | Alexopoulos, M., & Cohen, J. (2009). Uncertain times, uncertain measures (No. 352). University of Toronto, Department of Economics Working Papers. Baker, S. R., Bloom, N., & Davis, S. J. (2013). Measuring Economic Policy Uncertainty. Retrieved from www.policyuncertainty.com Barndorff-Nielsen, O. E., Kinnebrock, S., & Shephard, N. (2008). Measuring downside risk — realised semivariance. Bekaert, G., Engstrom, E., & Xing, Y. (2009). Risk, uncertainty, and asset prices. Journal of Financial Economics, 91(1), 59–82. doi:10.1016/j.jfineco.2008.01.005 Bloom, N. (2009). The impact of uncertainty shocks. Econometrica, 77(3), 623–685. Bloom, N. (2014). Fluctuations in Uncertainty. Journal of Economic Perspectives, 28(2), 153–176. doi:10.1257/jep.28.2.153 Campbell, J. Y., Lettau, M., Malkiel, B. G., & Xu, Y. (2001). Have Individual Stocks Become More Volatile? An Empirical Exploration of Idiosyncratic Risk. The Journal of Finance, 56(1), 1–43. Retrieved from http://links.jstor.org/sici?sici=0022-1082(200102)56:1<1:HISBMV>2.0.CO;2-7 Cognitive Science Laboratory of Princeton University. (2010). About WordNet. Retrieved from http://wordnet.princeton.edu Dendy, C., Mumtaz, H., & Silver, L. (2013). An uncertainty index for the UK 1986-2012. Edwardes, M. D. (1995). A confidence interval for Pr(X < Y) − Pr(X > Y) estimated from simple cluster samples. Biometrics, 51(2), 571–8. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/7662846 Efron, B. (1987). Better Bootstrap Confidence Intervals. Journal of the American Statistical Association, 82(397), 171–185. Elliott, G., Rothenberg, T. J., & Stock, J. H. (1996). The Econometric Society. Econometrica, 64(4), 813–836. Feunou, B., Jahan-Par, M. R., & Tedongap, R. (2010). Modeling Market Downside Volatility. Granger, C. W. J. (1998). Extracting information from mega-panels and high-frequency data. Statistica Neerlandica, 52(3), 258–272. doi:10.1111/1467-9574.00084 Haddow, A., Hare, C., Hooley, J., & Shakir, T. (2013). Macroeconomic uncertainty: what is it, how can we measure it and why does it matter? Bank of England Quarterly Bulletin. Heber, G., Lunde, A., Shephard, N., & Sheppard, K. (2009). Oxford-Man Institute’s realized library, version 0.2. Oxford-Man Institute’s realized library. Retrieved January 20, 2015, from http://realized.oxford-man.ox.ac.uk/data Juselius, K. (2006). The Cointegrated VAR model: methodology and applications. Advanced Texts in Econometrics. Oxford: Oxford University Press. Knight, F. H. (1921). Risk, Uncertainty, and Profit. Boston, MA: Hart, Schaffner & Marx; Houghton Mifflin Co. Retrieved from http://www.econlib.org/library/Knight/knRUP.html Kothari, S. P., Li, X., & Short, J. E. (2009). The Effect of Disclosures by Management, Analysts, and Business Press on Cost of Capital, Return Volatility, and Analyst Forecasts: A Study Using Content Analysis. The Accounting Review, 84(5), 1639–1670. Kunsch, H. R. (1989). The jackknife and the bootstrap for general stationary observations. The Annals of Statistics, 17(3), 1217–1241. Leahy, J. V, & Whited, T. M. (1996). The Effect of Uncertainty on Investment: Some Stylized Facts. Journal of Money, Credit and Banking, 28(1), 64–83. Loughran, T., & McDonald, B. (2011). When Is a Liability Not a Liability? Textual Analysis, Dictionaries, and 10-Ks. The Journal of Finance, LXVI(1), 35–65. Mead, N., & Blight, G. (2014). Eurozone crisis timeline. The Guardian. Retrieved January 23, 2015, from http://www.theguardian.com/business/interactive/2012/oct/17/eurozone-crisis-interactive-timeline-three-years Merton, R. C. (1980). On estimating the expected return on the market. Journal of Financial Economics, 8(4), 323–361. Newson, R. (2002). Parameters behind “nonparametric” statistics: Kendall’s tau, Somers' D and median differences. The Stata Journal, 2(1), 45–64. Newson, R. (2005). Confidence intervals for rank statistics: Somers’ D and extensions. The Stata Journal, 6, 497–520. Ng, S., & Perron, P. (1995). Unit root tests in ARMA models with data-dependent models for the selection of the truncation lag. Journal of the American Statistical Association, 90(429), 268–281. Ng, S., & Perron, P. (2001). Lag Length Selection and the Construction of Unit Root Tests with Good Size and Power. Econometrica, 69(6), 1519–1554. Retrieved from http://www.jstor.org/stable/2692266 Rossi, B., & Sekhposyan, T. (2015). Macroeconomic Uncertainty Indices Based on Nowcast and Forecast Error Distributions. American Economic Review: Papers & Proceedings. Schwert, G. W. (1989a). Business cycles, financial crises, and stock volatility. Carnegie-Rochester Conference Series on Public Policy, 31, 83–126. Schwert, G. W. (1989b). Why Does Stock Market Volatility Change Over Time? The Journal of Finance, XLIV(5), 1115–1153. Segal, G., Shaliastovich, I., & Yaron, A. (2014). Good and Bad Uncertainty: Macroeconomic and Financial Market Implications. Working Paper, (January). Tetlock, P. C. (2007). Giving Content to Investor Sentiment: The Role of Media in the Stock Market. The Journal of Finance, LXII(3), 1139–1168. |
URI: | https://mpra.ub.uni-muenchen.de/id/eprint/64874 |
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