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Local investor attention and post-earnings announcement drift

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Abstract

We show that local investor attention, as a proxy for the arrival rate of informed trading, has an impact on post-earnings announcement drift. Measured by monthly abnormal Google search volume before the earnings announcement, high (low) local investor attention is associated with weak (strong) delayed market reaction to the earnings announcement and strong (weak) abnormal trading volume in the pre-earnings announcement period. The evidence documented in this paper supports both “rational structural uncertainty” and attention allocation theories that argue that information distribution among investors plays an important role in explaining market anomalies.

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Notes

  1. Previous papers that apply daily or weekly SVI data, such as the papers of Da et al. (2011) and Drake et al. (2012) using weekly and daily SVI data, respectively, are focused on aggregate level of attention. For queries at local or state level, Google provides a breakdown of its data by month, not by week or day. Under this condition, we can only construct valid measure for local attention in the calendar month before earnings announcement.

  2. We also construct an alternative sample that includes earnings announcements that are made no less than 10 calendar days before the end of the announcement month. Our results still hold for this alternative sample. Results are available upon request.

  3. Ivkovic and Weisbenner (2005) find that the average household can generate an additional annualized return of 3.2% from its local investment relative to nonlocal holdings, and posit that local bias stems from the information advantage of local firms over nonlocal firms instead of from simple familiarity. However, Seasholes and Zhu (2010), using two types of calendar-time portfolios based on holdings and transactions, find that portfolios of local investment do not generate abnormal returns relative to their nonlocal holdings and conclude that individual investors do not appear to have value-relevant information advantages on their local investments.

  4. While previous studies on individual investors typically use a small set of individual investors from a number of brokerage firms, Wang and Zhang (2015) use a comprehensive dataset covering individual investors on NYSE and find that individual trading improves stock market informativeness.

  5. In a recent study, Wu and Gau (2017) argue that domestic investors who are partially informed are also likely to be overconfident and therefore, they could overreact to new information of domestic stocks, which would result in a home bias.

  6. In an empirical study, Hur and Singh (2016) find that investors’ poor attention to a stock because of its low visibility could cause delayed market reaction to firm specific information.

  7. Since attention variable should lose much of its efficacy when there is a large interval between the reporting data of SVI index and the date of earnings announcement, we expect to observe a much weaker effect of local attention on PEAD when we use announcements made in the latter half of the month. See our further discussion on this issue in Sect. 5.1.

  8. http://www.google.com/insights/search/.

  9. We focus on S&P 500 firms because Google Insights for Search applies some minimum thresholds for inclusion in the tool and provides results mainly for searches that exhibit a significant amount of volume.

  10. Barber and Lyon (1997) and Daniel and Titman (1997) suggest that matching sample firms to firms of similar sizes and book-to-market (BM) ratios, rather than using factor betas to measure cumulative abnormal returns, yields better-specified test statistics. However, to make sure the results are robust, abnormal returns calculated by market model are used.

  11. The models shown below are those for decile ranks. The corresponding quintile ranks models are not shown for brevity.

  12. In a study testing the relation between attentions through blog contents in the internet and trading behavior of investors, Hun et al. (2013) find that blog coverage of a stock is negatively associated with stock returns, whereas such negative relation is predominant for the stocks with low institutional ownership.

  13. http://www.census.gov/popest/data/intercensal/state/state2010.html.

  14. Appendix B provides a detail account of the list of firms located in different states and population figures of the corresponding states.

  15. A more detailed discussion on this issue can be found in Sect. 3.

  16. Nonetheless, institutional investors are better at collecting and processing firm-specific information and can make stronger impact in the market (e.g., Kao 2007; Frijns et al. 2014).

  17. Regarding these studies, Glosten and Milgrom (1985) demonstrate how trade imbalances might force prices to their full information value, whereas Pan and Poteshman (2006) examine the relation between trading volume, price and private information in the derivative markets. On the other hand, Da et al. (2011) find that direct measure of investor attention by SVI is highly correlated with abnormal trading volume. Another study of Gao et al. (2012) shows that daily abnormal dollar trading volume is positively associated with contemporaneous abnormal ticker search.

  18. Theoretical work by Holthausen and Verrecchia (1990) and Kim and Verrecchia (1994) implies that investors’ opinions may diverge more as the information content of a news announcement increases. Garfinkel (2009) confirms this prediction empirically.

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Acknowledgements

We are highly grateful to the Editor, Cheng-Few Lee, and to the anonymous referee, who provide comments that help us make a substantial improvement of the paper. We are also grateful to Incheol Kim, Joseph J. Cabral, the participants at the EFA 2013 Annual Meeting and FMA 2013 Annual Meeting for their comments and suggestions. We are responsible for all errors.

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Authors and Affiliations

  1. College of Business Administration, Marquette University, 312 David A. Straz, Jr. Hall, Milwaukee, WI, 53233, USA

    Bin Wang

  2. College of Business and Entrepreneurship, University of Texas Rio Grande Valley, Edinburg, TX, 78539, USA

    Wonseok Choi

  3. College of Management, Long Island University Post, Brookville, NY, 11548, USA

    Ibrahim Siraj

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  1. Bin Wang
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  2. Wonseok Choi
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  3. Ibrahim Siraj
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Correspondence to Bin Wang.

Appendix

Appendix

See Tables 9 and 10.

Table 9 Variable definitions
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Table 10 Location of headquarters of sample firms
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Wang, B., Choi, W. & Siraj, I. Local investor attention and post-earnings announcement drift. Rev Quant Finan Acc 51, 219–252 (2018). https://doi.org/10.1007/s11156-017-0669-2

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