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Market fragmentation and post-earnings announcement drift

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Abstract

This paper examines the effects of dark and lit market fragmentation around both earnings announcements and earnings surprises. Results indicate that both dark and lit market fragmentation increase around earnings announcements. Further, I test whether dark and lit fragmentation hinder the level of price discovery around the earnings announcement, resulting in greater post-earnings announcement drift, PEAD. The analysis reveals that lit fragmentation has no significant impact on PEAD while dark fragmentation reduces the level of PEAD for stocks with positive earnings surprises. This result is consistent with the notion that dark venues capture more uninformed trading around positive news events, resulting in greater informed trading and higher informational efficiency in the lit venue. However, the results also indicate that dark fragmentation leads to stronger PEAD for stocks with negative earnings surprises. This last finding suggests that informed traders migrate to dark venues around negative earnings surprises, consistent with previous research that argues informed traders follow passive trading strategies around negative news events.

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Notes

  1. Across 12 listed exchanges provided in Fidessa, the exchange with the highest market share is NASDAQ, which accounts for roughly 25% of all executed trades.

  2. Comerton-Forde and Putniņš (2015) provide empirical results in support of Zhu’s claim that increases in dark fragmentation result in improved price discovery on the lit venue.

  3. Chakrabarty and Shaw (2008) analyze hidden liquidity around earnings announcements. However, hidden liquidity, which refers to hidden limit orders within the lit venue limit order book, differs significantly from the use of off-exchange or dark trading venues. Degryse et al. (2015) provide an extensive review of how hidden liquidity and dark liquidity differentiate.

  4. This study compares the weekly total of reported trades and trade volume reported by NYSE’s Daily Trades and Quotes (DTAQ) to the reported trades and trade volume in FINRA and find that the reported dark trading volume in FINRA accounts for only 30–40% of the reported dark trading volume in DTAQ. Thus, other studies are likely underestimating the amount of off-exchange around earnings announcements.

  5. The time-series forecasts of SUE used in this study is consistent with prior studies. I do not have available analyst forecast data, however, Lorek and Pagach (2014) demonstrate that there are cases in which the time-series forecast is better than analyst-based forecasts of SUE.

  6. In verifying my trading and trading volume numbers, I compare the overall numbers reported in TAQ with those reported by CRSP. The numbers are not exact but are very close. Similarly, I sum the number of trades and trading volume reported in MIDAS with those reported only for exchange code ‘D’ in TAQ to verify that the total resembles the aggregate trading volume on CRSP.

  7. I also classify midpoint dark fragmentation if the thousandths place of the transaction price falls in the (0.4, 0.6) Zit interval. Likewise, I classify retail dark fragmentation if the thousandths place of transaction price falls in the (0,0.4) or (0.6,1.0) Zit interval. Boehmer et al. (2017) show that a considerable amount of dark or off-exchange trading that occurs away from the midpoint is likely retail executions.

  8. Following previous PEAD research (Battalio and Mendenhall 2011; and Pantzalis and Ucar 2014), returns are winsorized at the 1 and 99% levels to help mitigate the influence of outliers.

  9. My earnings announcement window covers the 21-day event window covering the 10 days before and after the announcement date.

  10. I do not include the variable RetDark in Table 4 as the regression coefficients and t-statistics for RetDark will have opposite signs of MidDark.

  11. I also measure excess abnormal dark and lit fragmentation using a standardized measure using the entire quarter to calculate average for both dark and lit fragmentation. I use this same time window to calculate the standard deviation of both dark and lit fragmentation. Using the standardized measure in determining quintiles does not alter my results.

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  1. Department of Finance, Walker College of Business, Appalachian State University, Boone, NC, 28608, USA

    Justin Cox

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Cox, J. Market fragmentation and post-earnings announcement drift. J Econ Finan 44, 587–610 (2020). https://doi.org/10.1007/s12197-020-09506-8

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  • DOI: https://doi.org/10.1007/s12197-020-09506-8

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