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Investors’ attention and information losses under market stress

Author

Listed:
  • Dionisis Th Philippas
  • Catalin Dragomirescu-Gaina
  • Stéphane Goutte

    (Cemotev - Centre d'études sur la mondialisation, les conflits, les territoires et les vulnérabilités - UVSQ - Université de Versailles Saint-Quentin-en-Yvelines, PSB - Paris School of Business - HESAM - HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université)

  • Duc Khuong Nguyen

    (IPAG Business School, VNU - Vietnam National University [Hanoï])

Abstract
The paper proposes a novel point-wise entropy approach to measure the time-varying losses in the value of information that investors associate with market signals, financial and economic indicators, and news. We cast our approach in a Bayesian framework and assume that market agents update their beliefs to incoming signals based on a prior information set. By exploiting the distribution rather than the time-series properties of information signals, our method is able to construct univariate signal-specific, but also composite proxies of information loss, with the latter being more efficient in reducing misleading effects and interpretation errors. As an empirical illustration, we construct information loss proxies for the US equity market from several mainstream information signals and find that the majority of information loss indicators can influence investors' attention, which then intermediates the impact of information signals on market outcomes. Finally, we show that, by relying on composites rather than univariate proxies, market agents can diversify and thus reduce their information losses when interpreting signals associated with the same underlying event.

Suggested Citation

  • Dionisis Th Philippas & Catalin Dragomirescu-Gaina & Stéphane Goutte & Duc Khuong Nguyen, 2021. "Investors’ attention and information losses under market stress," Post-Print hal-03434918, HAL.
    • Handle: RePEc:hal:journl:hal-03434918
    DOI: 10.1016/j.jebo.2021.09.040
    Note: View the original document on HAL open archive server: https://hal.science/hal-03434918
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    1. Drew Fudenberg & Philipp Strack & Tomasz Strzalecki, 2018. "Speed, Accuracy, and the Optimal Timing of Choices," American Economic Review, American Economic Association, vol. 108(12), pages 3651-3684, December.
    2. Chang, Eric C. & Cheng, Joseph W. & Khorana, Ajay, 2000. "An examination of herd behavior in equity markets: An international perspective," Journal of Banking & Finance, Elsevier, vol. 24(10), pages 1651-1679, October.
    3. Jakub Steiner & Colin Stewart & Filip Matějka, 2017. "Rational Inattention Dynamics: Inertia and Delay in Decision‐Making," Econometrica, Econometric Society, vol. 85, pages 521-553, March.
    4. X. Frank Zhang, 2006. "Information Uncertainty and Stock Returns," Journal of Finance, American Finance Association, vol. 61(1), pages 105-137, February.
    5. Hamid, Alain & Heiden, Moritz, 2015. "Forecasting volatility with empirical similarity and Google Trends," Journal of Economic Behavior & Organization, Elsevier, vol. 117(C), pages 62-81.
    6. Victor DeMiguel & Lorenzo Garlappi & Raman Uppal, 2009. "Optimal Versus Naive Diversification: How Inefficient is the 1-N Portfolio Strategy?," The Review of Financial Studies, Society for Financial Studies, vol. 22(5), pages 1915-1953, May.
    7. Bartosz Mackowiak & Mirko Wiederholt, 2009. "Optimal Sticky Prices under Rational Inattention," American Economic Review, American Economic Association, vol. 99(3), pages 769-803, June.
    8. Hsieh, Shu-Fan & Chan, Chia-Ying & Wang, Ming-Chun, 2020. "Retail investor attention and herding behavior," Journal of Empirical Finance, Elsevier, vol. 59(C), pages 109-132.
    9. Marcelo S. Perlin & João F. Caldeira & André A. P. Santos & Martin Pontuschka, 2017. "Can We Predict the Financial Markets Based on Google's Search Queries?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 36(4), pages 454-467, July.
    10. Zhi Da & Joseph Engelberg & Pengjie Gao, 2011. "In Search of Attention," Journal of Finance, American Finance Association, vol. 66(5), pages 1461-1499, October.
    11. Malcolm Baker & Jeffrey Wurgler, 2007. "Investor Sentiment in the Stock Market," Journal of Economic Perspectives, American Economic Association, vol. 21(2), pages 129-152, Spring.
    12. Paul C. Tetlock, 2011. "All the News That's Fit to Reprint: Do Investors React to Stale Information?," The Review of Financial Studies, Society for Financial Studies, vol. 24(5), pages 1481-1512.
    13. Daniel Andrei & Michael Hasler, 2015. "Investor Attention and Stock Market Volatility," The Review of Financial Studies, Society for Financial Studies, vol. 28(1), pages 33-72.
    14. Scott R. Baker & Nicholas Bloom & Steven J. Davis, 2016. "Measuring Economic Policy Uncertainty," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 131(4), pages 1593-1636.
    15. Schepanski, A. & Uecker, W. C., 1984. "The value of information in decision making," Journal of Economic Psychology, Elsevier, vol. 5(2), pages 177-194, June.
    16. Laura L. Veldkamp, 2006. "Media Frenzies in Markets for Financial Information," American Economic Review, American Economic Association, vol. 96(3), pages 577-601, June.
    17. Michael S. Drake & Darren T. Roulstone & Jacob R. Thornock, 2012. "Investor Information Demand: Evidence from Google Searches Around Earnings Announcements," Journal of Accounting Research, Wiley Blackwell, vol. 50(4), pages 1001-1040, September.
    18. Markus K. Brunnermeier & Stefan Nagel, 2008. "Do Wealth Fluctuations Generate Time-Varying Risk Aversion? Micro-evidence on Individuals," American Economic Review, American Economic Association, vol. 98(3), pages 713-736, June.
    19. H. J. Turtle & Kainan Wang, 2017. "The Value In Fundamental Accounting Information," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 40(1), pages 113-140, March.
    20. Challet, Damien & Marsili, Matteo & Zhang, Yi-Cheng, 2000. "Modeling market mechanism with minority game," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 276(1), pages 284-315.
    21. Dragomirescu-Gaina, Catalin & Philippas, Dionisis & Tsionas, Mike G., 2021. "Trading off accuracy for speed: Hedge funds' decision-making under uncertainty," International Review of Financial Analysis, Elsevier, vol. 75(C).
    22. Bertsch, Christoph & Hull, Isaiah & Zhang, Xin, 2021. "Narrative fragmentation and the business cycle," Economics Letters, Elsevier, vol. 201(C).
    23. Joseph E. Engelberg & Christopher A. Parsons, 2011. "The Causal Impact of Media in Financial Markets," Journal of Finance, American Finance Association, vol. 66(1), pages 67-97, February.
    24. Giuseppe Moscarini & Lones Smith, 2002. "The Law of Large Demand for Information," Econometrica, Econometric Society, vol. 70(6), pages 2351-2366, November.
    25. Thomas Dimpfl & Stephan Jank, 2016. "Can Internet Search Queries Help to Predict Stock Market Volatility?," European Financial Management, European Financial Management Association, vol. 22(2), pages 171-192, March.
    26. Fabio Verona, 2014. "Investment Dynamics with Information Costs," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 46(8), pages 1627-1656, December.
    27. Roberto C. Gutierrez & Eric K. Kelley, 2008. "The Long‐Lasting Momentum in Weekly Returns," Journal of Finance, American Finance Association, vol. 63(1), pages 415-447, February.
    28. Philippas, Dionisis & Philippas, Nikolaos & Tziogkidis, Panagiotis & Rjiba, Hatem, 2020. "Signal-herding in cryptocurrencies," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 65(C).
    29. Matthew Gentzkow & Jesse M. Shapiro, 2010. "What Drives Media Slant? Evidence From U.S. Daily Newspapers," Econometrica, Econometric Society, vol. 78(1), pages 35-71, January.
    30. P. G. Bissiri & C. C. Holmes & S. G. Walker, 2016. "A general framework for updating belief distributions," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 78(5), pages 1103-1130, November.
    31. Sims, Christopher A., 2003. "Implications of rational inattention," Journal of Monetary Economics, Elsevier, vol. 50(3), pages 665-690, April.
    32. Dierick, Nicolas & Heyman, Dries & Inghelbrecht, Koen & Stieperaere, Hannes, 2019. "Financial attention and the disposition effect," Journal of Economic Behavior & Organization, Elsevier, vol. 163(C), pages 190-217.
    33. Marcin Kacperczyk & Stijn Van Nieuwerburgh & Laura Veldkamp, 2016. "A Rational Theory of Mutual Funds' Attention Allocation," Econometrica, Econometric Society, vol. 84, pages 571-626, March.
    34. David H. Solomon, 2012. "Selective Publicity and Stock Prices," Journal of Finance, American Finance Association, vol. 67(2), pages 599-638, April.
    35. Michael Woodford, 2014. "Stochastic Choice: An Optimizing Neuroeconomic Model," American Economic Review, American Economic Association, vol. 104(5), pages 495-500, May.
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    Cited by:

    1. Tsionas, Mike G. & Philippas, Dionisis & Philippas, Nikolaos, 2022. "Multivariate stochastic volatility for herding detection: Evidence from the energy sector," Energy Economics, Elsevier, vol. 109(C).
    2. Zeng, Hongjun & Abedin, Mohammad Zoynul & Zhou, Xiangjing & Lu, Ran, 2024. "Measuring the extreme linkages and time-frequency co-movements among artificial intelligence and clean energy indices," International Review of Financial Analysis, Elsevier, vol. 92(C).
    3. Dragomirescu-Gaina, Catalin & Philippas, Dionisis & Goutte, Stéphane, 2023. "How to ‘Trump’ the energy market: Evidence from the WTI-Brent spread," Energy Policy, Elsevier, vol. 179(C).
    4. Lu, Shuai & Li, Shouwei, 2023. "Is institutional herding efficient? Evidence from an investment efficiency and informational network perspective," Journal of Behavioral and Experimental Finance, Elsevier, vol. 39(C).
    5. Chortane, Sana Gaied & Pandey, Dharen Kumar, 2022. "Does the Russia-Ukraine war lead to currency asymmetries? A US dollar tale," The Journal of Economic Asymmetries, Elsevier, vol. 26(C).
    6. Fang Xu & Xiaoru Zhang & Di Zhou, 2024. "Does digital financial inclusion reduce the risk of returning to poverty? Evidence from China," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 29(3), pages 2927-2949, July.

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    More about this item

    Keywords

    Attention; Google search volume; Information loss; Point-wise entropy;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

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