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When do retail investors pay attention to their trading platforms?

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  • Aharon, David Y.
  • Qadan, Mahmoud

Abstract

Buying and selling securities through online trading platforms has become increasingly popular among U.S. households in recent years. This study tracks U.S. households' attention to their online trading platforms using daily data for 2004 to August 2017. The analysis covers the 10 most popular online trading platforms among U.S. investors. The findings indicate that market shocks, captured by several proxies, as well as macroeconomic announcements attract investors' attention to trading platforms. We also document that the ostrich effect weakens when considering greater changes in the VIX. Our findings do not support the avoidance of information theory, but do support the theoretical argument that risk-averse agents engage in more information gathering when uncertainty prevails in hopes of reducing their risks.

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  • Aharon, David Y. & Qadan, Mahmoud, 2020. "When do retail investors pay attention to their trading platforms?," The North American Journal of Economics and Finance, Elsevier, vol. 53(C).
    • Handle: RePEc:eee:ecofin:v:53:y:2020:i:c:s1062940820301066
    DOI: 10.1016/j.najef.2020.101209
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    as
    1. Emily Oster & Ira Shoulson & E. Ray Dorsey, 2013. "Optimal Expectations and Limited Medical Testing: Evidence from Huntington Disease," American Economic Review, American Economic Association, vol. 103(2), pages 804-830, April.
    2. Baur, Dirk G. & Dimpfl, Thomas, 2016. "Googling gold and mining bad news," Resources Policy, Elsevier, vol. 50(C), pages 306-311.
    3. Philippas, Dionisis & Rjiba, Hatem & Guesmi, Khaled & Goutte, Stéphane, 2019. "Media attention and Bitcoin prices," Finance Research Letters, Elsevier, vol. 30(C), pages 37-43.
    4. Heyman, Dries & Lescrauwaet, Michiel & Stieperaere, Hannes, 2019. "Investor attention and short-term return reversals," Finance Research Letters, Elsevier, vol. 29(C), pages 1-6.
    5. Dan Galai, 2006. "The "Ostrich Effect" and the Relationship between the Liquidity and the Yields of Financial Assets," The Journal of Business, University of Chicago Press, vol. 79(5), pages 2741-2759, September.
    6. Foster, F Douglas & Viswanathan, S, 1990. "A Theory of the Interday Variations in Volume, Variance, and Trading Costs in Securities Markets," The Review of Financial Studies, Society for Financial Studies, vol. 3(4), pages 593-624.
    7. Stigler, George J., 2011. "Economics of Information," Ekonomicheskaya Politika / Economic Policy, Russian Presidential Academy of National Economy and Public Administration, vol. 5, pages 35-49.
    8. Kristoufek, Ladislav, 2015. "Power-law correlations in finance-related Google searches, and their cross-correlations with volatility and traded volume: Evidence from the Dow Jones Industrial components," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 428(C), pages 194-205.
    9. Thierry Foucault & David Sraer & David J. Thesmar, 2011. "Individual Investors and Volatility," Journal of Finance, American Finance Association, vol. 66(4), pages 1369-1406, August.
    10. Larry G. Epstein & Martin Schneider, 2008. "Ambiguity, Information Quality, and Asset Pricing," Journal of Finance, American Finance Association, vol. 63(1), pages 197-228, February.
    11. Taufiq Choudhry, 2000. "Day of the week effect in emerging Asian stock markets: evidence from the GARCH model," Applied Financial Economics, Taylor & Francis Journals, vol. 10(3), pages 235-242.
    12. Jones, Charles M. & Lamont, Owen & Lumsdaine, Robin L., 1998. "Macroeconomic news and bond market volatility," Journal of Financial Economics, Elsevier, vol. 47(3), pages 315-337, March.
    13. Larry G. Epstein & Stanley E. Zin, 2013. "Substitution, risk aversion and the temporal behavior of consumption and asset returns: A theoretical framework," World Scientific Book Chapters, in: Leonard C MacLean & William T Ziemba (ed.), HANDBOOK OF THE FUNDAMENTALS OF FINANCIAL DECISION MAKING Part I, chapter 12, pages 207-239, World Scientific Publishing Co. Pte. Ltd..
    14. Zhi Da & Joseph Engelberg & Pengjie Gao, 2011. "In Search of Attention," Journal of Finance, American Finance Association, vol. 66(5), pages 1461-1499, October.
    15. Brad M. Barber & Terrance Odean, 2001. "The Internet and the Investor," Journal of Economic Perspectives, American Economic Association, vol. 15(1), pages 41-54, Winter.
    16. Laura L. Veldkamp, 2006. "Information Markets and the Comovement of Asset Prices," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 73(3), pages 823-845.
    17. Nofsinger, John R., 2001. "The impact of public information on investors," Journal of Banking & Finance, Elsevier, vol. 25(7), pages 1339-1366, July.
    18. Yung, Kenneth & Nafar, Nadia, 2017. "Investor attention and the expected returns of reits," International Review of Economics & Finance, Elsevier, vol. 48(C), pages 423-439.
    19. Andersen, Torben G & Bollerslev, Tim, 1998. "Answering the Skeptics: Yes, Standard Volatility Models Do Provide Accurate Forecasts," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 885-905, November.
    20. Lars Forsberg & Eric Ghysels, 2007. "Why Do Absolute Returns Predict Volatility So Well?," Journal of Financial Econometrics, Oxford University Press, vol. 5(1), pages 31-67.
    21. Michael W. Brandt & Francis X. Diebold, 2006. "A No-Arbitrage Approach to Range-Based Estimation of Return Covariances and Correlations," The Journal of Business, University of Chicago Press, vol. 79(1), pages 61-74, January.
    22. Daniel Kahneman & Amos Tversky, 2013. "Prospect Theory: An Analysis of Decision Under Risk," World Scientific Book Chapters, in: Leonard C MacLean & William T Ziemba (ed.), HANDBOOK OF THE FUNDAMENTALS OF FINANCIAL DECISION MAKING Part I, chapter 6, pages 99-127, World Scientific Publishing Co. Pte. Ltd..
    23. Chang, Shao-Chi & Chen, Sheng-Syan & Chou, Robin K. & Lin, Yueh-Hsiang, 2008. "Weather and intraday patterns in stock returns and trading activity," Journal of Banking & Finance, Elsevier, vol. 32(9), pages 1754-1766, September.
    24. Ghysels, Eric & Santa-Clara, Pedro & Valkanov, Rossen, 2006. "Predicting volatility: getting the most out of return data sampled at different frequencies," Journal of Econometrics, Elsevier, vol. 131(1-2), pages 59-95.
    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. Lakonishok, Josef & Maberly, Edwin, 1990. "The Weekend Effect: Trading Patterns of Individual and Institutional Investors," Journal of Finance, American Finance Association, vol. 45(1), pages 231-243, March.
    27. Chronopoulos, Dimitris K. & Papadimitriou, Fotios I. & Vlastakis, Nikolaos, 2018. "Information demand and stock return predictability," Journal of International Money and Finance, Elsevier, vol. 80(C), pages 59-74.
    28. Parkinson, Michael, 1980. "The Extreme Value Method for Estimating the Variance of the Rate of Return," The Journal of Business, University of Chicago Press, vol. 53(1), pages 61-65, January.
    29. Kim, O & Verrecchia, Re, 1991. "Trading Volume And Price Reactions To Public Announcements," Journal of Accounting Research, Wiley Blackwell, vol. 29(2), pages 302-321.
    30. Gul, Faruk, 1991. "A Theory of Disappointment Aversion," Econometrica, Econometric Society, vol. 59(3), pages 667-686, May.
    31. Shane A. Corwin & Paul Schultz, 2012. "A Simple Way to Estimate Bid‐Ask Spreads from Daily High and Low Prices," Journal of Finance, American Finance Association, vol. 67(2), pages 719-760, April.
    32. French, Kenneth R. & Roll, Richard, 1986. "Stock return variances : The arrival of information and the reaction of traders," Journal of Financial Economics, Elsevier, vol. 17(1), pages 5-26, September.
    33. J. Hirshleifer, 1975. "Speculation and Equilibrium: Information, Risk, and Markets," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 89(4), pages 519-542.
    34. Vlastakis, Nikolaos & Markellos, Raphael N., 2012. "Information demand and stock market volatility," Journal of Banking & Finance, Elsevier, vol. 36(6), pages 1808-1821.
    35. Barclay, Michael J & Litzenberger, Robert H & Warner, Jerold B, 1990. "Private Information, Trading Volume, and Stock-Return Variances," The Review of Financial Studies, Society for Financial Studies, vol. 3(2), pages 233-253.
    36. Shefrin, Hersh & Statman, Meir, 1985. "The Disposition to Sell Winners Too Early and Ride Losers Too Long: Theory and Evidence," Journal of Finance, American Finance Association, vol. 40(3), pages 777-790, July.
    37. Yin, Libo & Feng, Jiabao, 2019. "Can investors attention on oil markets predict stock returns?," The North American Journal of Economics and Finance, Elsevier, vol. 48(C), pages 786-800.
    38. Rebecca L. Thornton, 2008. "The Demand for, and Impact of, Learning HIV Status," American Economic Review, American Economic Association, vol. 98(5), pages 1829-1863, December.
    39. Bomfim, Antulio N., 2003. "Pre-announcement effects, news effects, and volatility: Monetary policy and the stock market," Journal of Banking & Finance, Elsevier, vol. 27(1), pages 133-151, January.
    40. Niklas Karlsson & George Loewenstein & Duane Seppi, 2009. "The ostrich effect: Selective attention to information," Journal of Risk and Uncertainty, Springer, vol. 38(2), pages 95-115, April.
    41. Mark J. Flannery & Aris A. Protopapadakis, 2002. "Macroeconomic Factors Do Influence Aggregate Stock Returns," The Review of Financial Studies, Society for Financial Studies, vol. 15(3), pages 751-782.
    42. Russell Golman & David Hagmann & George Loewenstein, 2017. "Information Avoidance," Journal of Economic Literature, American Economic Association, vol. 55(1), pages 96-135, March.
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    4. Paritosh Chandra Sinha, 2021. "Attention to the Election-Economics-Politics (EEP) Nexus in the Indian Stock Markets," The Review of Finance and Banking, Academia de Studii Economice din Bucuresti, Romania / Facultatea de Finante, Asigurari, Banci si Burse de Valori / Catedra de Finante, vol. 13(1), pages 7-32, June.

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    More about this item

    Keywords

    Investor attention; Online trading; Trading platforms;
    All these keywords.

    JEL classification:

    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • G02 - Financial Economics - - General - - - Behavioral Finance: Underlying Principles
    • D03 - Microeconomics - - General - - - Behavioral Microeconomics: Underlying Principles
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness

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