Expectations, Economic Uncertainty, and Sentiment
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Douglas de Medeiros Franco
Abstract
Objective: this article aims to help unravel if and how economic uncertainty interacts with the informational structure of sentiment. Methods: the empirical strategy is based on a non-linear and non-parametric causality test to investigate the interaction between variables as distributions. This article builds primarily on the literature on expectation formation. Results: it was found that uncertainty based on the media (ex-ante) precedes sentiment, at most, until the second moment of its distribution. In addition, sentiment helps predict the informational structure of fundamental uncertainty (ex-post) and higher order moments of ex-ante uncertainty. Conclusion: sentiment can be considered a channel for uncertainty through the tone of expectations and erroneous expectations. Ex-ante uncertainty measures can also help calibrate the rational cost-benefit calculation of attention by acting as a leading indicator of the increasing value of information.
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Franco, D. de M. (2022). Expectations, Economic Uncertainty, and Sentiment. Journal of Contemporary Administration, 26(5), e210029. https://doi.org/10.1590/1982-7849rac2022210029.en
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Theoretical-empirical Articles
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References
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Bekiros, S., Gupta, R., & Kyei, C. (2016). On economic uncertainty, stock market predictability and nonlinear spillover effects. The North American Journal of Economics and Finance, 36(C), 184-191. https://doi.org/10.1016/j.najef.2016.01.003
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Black, F. (1986). Noise. The Journal of Finance, 41(3), 528-543. https://doi.org/10.1111/j.1540-6261.1986.tb04513.x
Blanchard, O. J., L’Huillier, J-P., & Lorenzoni. G. (2013). News, noise, and fluctuations: An empirical exploration. American Economic Review, 103(7), 3045-3070. https://doi.org/10.1257/aer.103.7.3045
Blanchard, O. J., & Watson, M. W. (1982). Bubbles, rational expectations and financial markets [Working Paper n. 945]. National Bureau of Economic Research, Cambridge, MA, USA. https://doi.org/10.3386/w0945
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Bloom, N. (2014). Fluctuations in uncertainty. Journal of Economic Perspectives, 28(2), 153–176. https://doi.org/10.1257/jep.28.2.153
Bloomfield, R., Libby, R., & Nelson, M. (2000). Underreactions, overreactions and moderated confidence. Journal of Financial Markets, 3(2), 113–137. https://doi.org/10.1016/S1386-4181(00)00003-3
Carroll, C. D. (2003). Macroeconomic expectations of households and professional forecasters. The Quarterly Journal of Economics, 118(1), 269-298. https://doi.org/10.1162/00335530360535207
Caskey, J. A. (2009). Information in equity markets with ambiguity averse investors. Review of Financial Studies, 22(9), 3595-3627. https://doi.org/10.1093/rfs/hhn062
Cecchetti, S., Lam, P., & Mark, N. (2000). Asset pricing with distorted beliefs: Are equity returns too good to be true? American Economic Review, 90(4), 787-805. https://doi.org/10.1257/aer.90.4.787
Chahrour, R., & Jurado, K. (2018). News or noise? The missing link. American Economic Review, 108(7), 1702–1736. https://doi.org/10.1257/aer.20170792
Daniel, K. D., Hirshleifer, D., & Subrahmanyam, A. (2001). Overconfidence, arbitrage, and equilibrium asset pricing. The Journal of Finance, 56(3), 921-965. https://doi.org/10.1111/0022-1082.00350
Daniel, K. D., Hirshleifer, D., & Teoh, S. H. (2002). Investor psychology in capital markets: Evidence and policy implications. Journal of Monetary Economics, 49(1), 139–209. https://doi.org/10.1016/S0304-3932(01)00091-5
Dickey, D. A., & Fuller, W. A. (1981). Likelihood ratio statistics for autoregressive time series with a unit root. Econometrica, 49(4), 1057–1072. https://doi.org/10.2307/1912517
Dicks, D., & Fulghieri, P. (2021). Uncertainty, investor sentiment, and innovation. The Review of Financial Studies, 34(3), 1236–1279. https://doi.org/10.1093/rfs/hhaa065
Diks, C., & Panchenko, V. (2005). A note on the Hiemstra-Jones test for Granger noncausality. Studies in Nonlinear Dynamics & Econometrics, 9(2). https://doi.org/10.2202/1558-3708.1234
Diks, C., & Panchenko, V. (2006). A new statistic and practical guidelines for nonparametric Granger causality testing. Journal of Economic Dynamics and Control, 30(9-10), 1647-1669. https://doi.org/10.1016/j.jedc.2005.08.008
Doms, M., & Morin, N. (2004). Consumer sentiment, the economy, and the news media [Working Paper n. 2004-51]. Federal Reserve Bank of San Francisco, San Francisco, USA. Retrieved from https://www.federalreserve.gov/pubs/feds/2004/200451/200451pap.pdf
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