Into the Unknown: Uncertainty, Foreboding and Financial Markets
Springer Science and Business Media LLC - Trang 1-23 - 2023
Tóm tắt
While the link between financial market movement and economic policy uncertainty indices is well-established in literature, uncertainty in the form of ‘foreboding’ emanating from catastrophic events has not been explored in literature. This paper explores “foreboding”, which reflects uncertainty at its extreme, following the Covid-19 pandemic. Using Natural Language Processing on minute-by-minute news data, I construct two Foreboding Indices, representing ‘foreboding’ or ‘fearful apprehension’, for 28,622 Covid-related news for the period July 2020–August 2021. The impact of foreboding on financial market volatility is explored using a logistic regression model. Both the indices show a marked increase in June–July, 2020, in January 2021, April, 2021, and July–August, 2021 and have a positive impact on volatility for hourly S&P 500 Index. Understanding of foreboding sentiment is crucial for central banks looking to monitor financial market volatility. Appropriate signaling in accordance to sentiment can help central banks handle detrimental impacts of market volatility. Moreover, FI can be used for market practitioners to gauge the sentiment and take effective trading decisions.
Tài liệu tham khảo
Ahir, H., Bloom, N., & Furcer., D. (2020). 60 Years of Uncertainty. March, Finance and Development. https://www.imf.org/external/pubs/ft/fandd/2020/03/pdf/imf-launches-world-uncertainty-indexwui-furceri.pdf.
Algaba, A., Borms, S., Boudt, K., & Van Pelt, J. (2020). The economic policy uncertainty index for flanders, Wallonia and Belgium. Available at SSRN: https://ssrn.com/abstract=3580000 or https://doi.org/10.2139/ssrn.3580000.
Arbatli, E. C. (2017). Policy uncertainty in Japan. In Working paper 23411. National Bureau of Economic Research, Cambridge, MA, USA.
Azqueta-Gavaldon, A. (2017). Financial investment and economic policy uncertainty in the UK. In Proceedings of the 1st international conference on internet of things and machine learning, IML ’17. New York: Association for Computing Machinery. https://doi.org/10.1145/3109761.3158380.
Baker, S. R., Bloom, N., Davis, S. J., & Terry, S. J. (2020). COVID-Induced Economic Uncertainty. NBER Working Paper 26983. https://doi.org/10.3386/w26983.
Baillon, A., Bleichrodt, H., Keskin, U., L'Haridon, O., & Li, C. (2013). Learning under ambiguity: An experiment using initial public offerings on a stock market, WP 2013–31. http://scholar.google.co.in/scholar_url?url=https://crem-doc.univ-rennes1.fr/wp/2013/201331.pdf.
Baillon, A., Cabantous, L., & Wakker, P. (2012). Aggregating imprecise or conflicting beliefs: An experimental investigation using modern ambiguity theories. Journal of Risk and Uncertainty, 44, 115–147. https://doi.org/10.1007/s11166-012-9140-x.
Baker, S. R., Bloom, N., & Davis, S. J. (2016). Measuring economic policy uncertainty. The Quarterly Journal of Economics, 131(4), 1593–1636. https://doi.org/10.1093/qje/qjw024.
Bloom, N. (2009). The impact of uncertainty shocks. Econometrica, 77, 623–685. https://doi.org/10.3982/ECTA6248.
Bonatti, E., Kuchukhidze, G., Zamarian, L., Trinka, E., Bodner, T., Benke, T., & Delazer, M. (2009). Decision making in ambiguous and risky situations after unilateral temporal lobe epilepsy surgery. Epilepsy Behaviour, 14(4), 665–673. https://doi.org/10.1016/j.yebeh.2009.02.015. Epub 2009 Feb 20. PMID: 19233314.
Boudoukh, J., Feldman, R., Kogan, S., & Richardson, M. (2018). Information, trading, and volatility: Evidence from firm-specific news. Review of Financial Studies, 32, 992–1033.
Brogaard, J., & Detzel, A. (2015). The asset-pricing implications of government economic policy uncertainty. Management Science, 61(1), 3–18.
Chari, A. (2007). Heterogeneous market-making in foreign exchange markets: Evidence from individual bank responses to central bank interventions. Journal of Money, Credit and Banking, 39(5), 1131–1162.
Da, Z., Engelberg, J., & Gao, P. (2015). The sum of All FEARS investor sentiment and asset prices. The Review of Financial Studies, 28(1), 1–32.
Davis, S. J. (2016). An index of global economic policy uncertainty. Macroeconomic Review, October.
De Groot, K., & Thurik, R. (2018). Disentangling risk and uncertainty: When risk-taking measures are not about risk. Frontiers in Psychology, 9, 2194. https://doi.org/10.3389/fpsyg.2018.02194.
Ellsberg, D. (1961). Risk, ambiguity, and the savage axioms. The Quarterly Journal of Economics, 75(4), 643–669.
García, D. (2013). Sentiment during recessions. Journal of Finance, 68, 1267–1300.
Heston, S. L., & Sinha, N. R. (2017). News vs. sentiment: Predicting stock returns from news stories. Financial Analysts Journal, 73(3), 67–83. https://doi.org/10.2469/faj.v73.n3.3.
Kahneman, D., & Tversky, A. (1973). On the psychology of prediction. Psychological Review, 80, 237–251.
Kniesner, T. J., & Sullivan, R. (2020). The forgotten numbers: A closer look at COVID-19 non-fatal valuations. Journal of Risk Uncertainty, 61, 155–176. https://doi.org/10.1007/s11166-020-09339-0.
Levy, I., Snell, J., Nelson, A. J., Rustichini, A., & Glimcher, P. W. (2010). Neural representation of subjective value under risk and ambiguity. Journal of Neurophysiology, 103(2), 1036–1047. https://doi.org/10.1152/jn.00853.
Li, X., Shen, D., Zhang, W., (2018). Do Chinese internet stock message boards convey firm-specific information? Pacific-Basin Finance Journal, 49, 1–14.
Loughran, T., & Mcdonald, B. (2011). When is a liability not a liability? Textual analysis, dictionaries, and 10-Ks. The Journal of Finance, 66, 35–65. https://doi.org/10.1111/j.1540-6261.2010.01625.x.
Martens, M., & van Dijk, D. (2007). Measuring volatility with the realized range. Journal of Econometrics, 138(1), 181–207.
Murray, K., Jassi, A., Mataix-Cols, D., Barrow, F., & Krebs, G. (2015). Outcomes of cognitive behaviour therapy for obsessive–compulsive disorder in young people with and without autism spectrum disorders: A case controlled study. Psychiatry Research, 228(1), 8–13. https://doi.org/10.1016/j.psychres.2015.03.012.
Pyo, D.-J., & Kim, J. (2021). News media sentiment and asset prices in Korea: Text-mining approach. Asia-Pacific Journal of Accounting & Economics, 28, 183–205.
Renault, T. (2017). Intraday online investor sentiment and return patterns in the U.S. stock market. Journal of Banking & Finance, 84, 25–40.
Roy Trivedi, S. (2020). The Moses effect: Can central banks really guide foreign exchange markets? Empirical Economics, 58, 2837–2865.
Sprenger, T. O., Tumasjan, A., Sandner, P. G., & Welpe, I. M. (2014). Tweets and trades: The information content of stock microblogs. European Financial Management, 20, 926–957. https://doi.org/10.1111/j.1468-036X.2013.12007.
Tetlock, P. C. (2007). Giving content to investor sentiment: The role of media in the stock market. The Journal of Finance, 62(3), 1139–1168.
Tetlock, P. C., Saar-Tsechansky, M., & Macskassy, S. (2008). More than words: Quantifying language to measure firms’ fundamentals. The Journal of Finance, 63, 1437–1467.
Tversky, A., & Kahneman, D. (1974). Judgment under uncertainty: Heuristics and biases. Science, 185, 1124–1131.
Yono, K., Sakaji, H., Matsushima, H., Shimada, T., & Izumi, K. (2020). Construction of macroeconomic uncertainty indices for financial market analysis using a supervised topic model. Journal of Risk and Financial Management, 13, 79. https://doi.org/10.3390/jrfm13040079.