Measuring economic uncertainty using news-media textual data

Peter, Eckley (2015): Measuring economic uncertainty using news-media textual data.

Abstract

We develop a news-media textual measure of aggregate economic uncertainty – the fraction of Financial Times articles containing uncertainty-related keyphrases – for 1982–2014 at daily to annual frequencies. We contribute to the literature in three areas. First, we provide a measurement framework that links observed expressions of uncertainty in newspaper articles to a latent propensity to express uncertainty, which we argue is an ordinal proxy for the uncertainty that matters for economic decision-making, namely the intensity of the cognitive state of uncertainty. We use this framework to estimate how the noise-to-signal ratios varies with sample size (or frequency) and show that noise variance is modest at monthly and lower frequencies, and approaching signal variance at daily frequency. Second, we study key choices in the empirical implementation of such measures more deeply than has been done previously, focusing on uncertainty keyphrase selection, isolating economic uncertainty, de-duplication of articles, and appropriate scaling of the uncertainty measure, with a critique of scaling methods commonly used in the literature. Our findings provide empirical foundations for the extant literature, and evidence-based recommendations for methodological improvements. Third, we conduct the first detailed comparative analysis of a news-media uncertainty measure with another uncertainty proxy, stock returns volatility. Our narrative analysis establishes the plausibility of our news-media measure. Our quantitative analysis reveals a strong relationship to stock volatility on average. But this relationship breaks down periodically, with timing that suggests that the semantics of the word “uncertainty” may be biased towards downside uncertainty or risk. Finally, we establish the absence of Granger causation between the measures down to daily frequency, except for a one-day lead of stock volatility over news-media uncertainty, which is to be expected given that the FT is published before the market opens.

• Item Type: MPRA Paper
• 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; 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

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.