Abstract
This study investigates the price discovery process in equity markets with informed institutional investors. Consistent with extant theories, we show empirically that institutional investors, in contrast to retail investors, trade based on the leaked sign of unanticipated news and then (partially) reverse their trades when the news become public. We also find that the longer the leakage period for institutional investors to exploit, the less informative the news is when it becomes public. These results are robust to controls for firm press releases and news articles and endogeneity concerns.
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Notes
We use the terms “reporting lag” and “filing lag” interchangeably.
A concurrent study by Ben-Rephael et al. (2017) examines institutional and retail investor attention around 8 K event and filing dates. They measure institutional (retail) attention by the number of searches on Bloomberg terminals (google). They find heightened attention by institutional investors around the event and filing dates, and no change in attention by retail investors. They then provide evidence that price discovery increases with attention. Our paper differs from Ben-Rephael et al. (2017) on the following dimensions: (1) They do not test the same hypotheses as this study and, in particular, the implications of the “buy on the rumor, sell on the news” strategy of Hirshleifer et al. (1994) and Brunnermeier (2005) on price formation. (2) Because of data limitations, they do not provide evidence related to the extent, magnitude, and economic benefits associated with strategic trading. (3) Our research methodology allows us to provide direct evidence on price discovery on each day during the event window. (4) Our sample is much larger, covering all 8 K items whereas their analysis is restricted to five news items only. Excluding all filings with a filing gap of less than two days, their sample comprises around 17,000 8 K filings, whereas our sample comprises over 95,000 filings.
Lerman and Livnat (2010) show that 8Ks often trigger market reactions in the form of abnormal equity returns and trading volumes. Segal and Segal (2016) show that managers disclose negative non-earnings information from the 8 K report strategically when investor attention is low. Rubin et al. (2017) examine analysts’ reaction to 8 K information. Bird and Karolyi (2016) examine the effect of institutional ownership on the quality of the 8 K disclosure.
With the exception of auditor changes (resignation, firing, hiring) and director resignations that had to be reported within five business days
While clearly some of the events are predictable, the overall evidence in the literature suggests that 8 K forms are by and large unpredictable, as their filing results in significant abnormal returns and analysts’ revisions.
Indeed, Lerman and Livnat (2010) provide evidence of heightened trading around both the 8 K event and the filing date.
Abel Noser stopped reporting unique institutional identifiers after 2011 to further protect their clients’ privacy. Hence, it is impossible to track an institution’s trades across stocks and time after 2011. Also, note that the 8 K sample period is longer than the institutional data. Therefore, we elect to use the expanded 8 K sample whenever institutional data are not required, in order to increase the power of the tests.
Hu et al. (2018) provide a comprehensive review of the data and note that the data have been widely used in the accounting and finance literatures. Their analysis indicates that that the database contains 12 years of data covering 233 million transactions with $37 trillion traded. They find that institutional trade sizes decline dramatically over time, rendering institutional trade size–based inferences problematic, and that the data cover 12% of CRSP volume.
The data include separate codes for the fund family (e.g., Fidelity) and the specific fund within the fund family (e.g., Fidelity’s Magellan Fund). We examine the trading around 8 K filings at the specific fund level.
Although companies indicate the “event date” on the 8 K form, it is possible that the event occurs after trading hours. Hence, if the company files the 8 K report on the day after the event date, we cannot determine whether the informed trader was aware of the news as early as on the event date.
The event date is typically the date on which the information first becomes known. However, in many cases, information about the impending event can leak out. For example, consider director resignations, where the event date is the date on which the director resigns. Information about the director’s intention to resign is likely to leak out prior to the actual resignation date as the director contemplates whether and when to resign.
Total volume transacted during the day is an alternative potential deflator. However, results are quite sensitive to scaling by total volume. The reason is that total volume, which includes institutional investor volume not accounted for in Ancerno, increases considerably during the event and filing windows—on average, we observe an increase of roughly 22% in total volume. Consequently, when scaling (the increasing) daily trading volume of institutional traders by (the increasing) total volume, we do not observe any change during the aforementioned periods.
We define non-news days as the days on which the company did not issue preliminary earnings or 10-Q or 10-K reports. In other words, we exclude, from the sample, the days on which the company reported preliminary earnings and the 10-Q and 10-K reports.
The differences between the coefficients on the days within the event window are highly significant (p value<0.01). Also, untabulated tests show a spike in the coefficient on the first filing day, relative to the last day of the event window.
We use a three day window to allow for the possibility that 8 K forms are reported on the last trading day of the week, and to give institutional investors sufficient time to reverse their trading position
The ostensibly small number of institutions that engage in strategic trading is explained by the observation that for 75% of the 8 K forms, there is no strategic trading. There are two reasons for this: First, not all 8Ks contain significant economic news. Second, institutional investors do not always reverse their position (i.e., they trade according to the sign of the news but do not reverse their position post filing); hence, they are not captured by our sample of “strategic traders.” To further ascertain the magnitude of strategic trading, we exclude 8 K reports that did not yield strategic trading. We find that the mean number of institutional investors that engage in strategic trading is 7.4.
With the exception of the Average Filing Volume for five- to seven-day filing lags, which is significant at the 5% level, all figures in the table are significant at the 1% level. Further analysis indicates that the difference between the long (five- to seven-day) and short (two- to four-day) filing lags is also significant.
We elected to focus on the larger sample and not control for news reports in most of our analyses for two reasons. First, given the large volume of data, we cannot determine whether the media content is indeed related to the event itself. Second, Raven Pack data cover less than 50% of our sample firms (6193 of 12,601). Nevertheless, our results hold for the smaller sample as well.
Note that the average filing lag in the pre period of 2001–2003 (3.3 days) is significantly greater (p value<0.01) than the average filing lag in the post period of 2005–2007 (2.85 days).
Note that since both the numerator and denominator are scaled by the same constant (number of shares outstanding in the case of the volume ratio, and average daily volatility in the case of the volatility ratio), the volume ratio is equivalent to the ratio of total volume during the filing window scaled by total volume from the event date through one day after the filing date. Similarly, the volatility ratio is equivalent to the ratio of total equity volatility during the filing window scaled by equity volatility from the event date through one day after the filing date.
Untabulated results indicate that the rate of the incorporation of the information related to the event is independent of the reporting lag. This suggests that while institutions may learn about the filing lag, they do not seem to have superior information regarding the firm’s strategy as to when to file; hence, they trade immediately as they learn about the event.
The Ancerno data are based on the client-manager level, where the manager is typically an investment house. A client can have investments with different managers. Because most investment houses have funds with different investment characteristics (e.g., indexers, hedge funds, mutual funds) and because the data fail to include a description of fund type, we treat each client-manager as an independent observation, although it is likely that the same fund is represented more than once in the data (with different clients). To alleviate this concern, we examine the results at the investment house level, but the downside here is that we aggregate funds with different objectives.
We obtain similar results when we examine retail trades from 2004 to 2013, where 2013 is the end year of the 8 K sample.
Collin-Dufresne and Fos (2015) explain their findings by showing that informed traders trade during times of high liquidity and use limit orders.
Interestingly, the coefficient on the negative news indicator is positive but not significant post 2004, indicating that negative news is not reported later than positive news. However, the negative aspect of the news is captured by CMAR, and its coefficient is negative and significant at the 1% level. Furthermore, excluding CMAR and its interaction with the negative news indicator from the regression yields a positive and highly significant coefficient (p value<0.01) for the negative news indicator.
In a sensitivity analysis we measure ROA as operating income scaled by total assets. Results are very similar.
References
Ben-Rephael, A., Zhi D., Easton, P.D. & Israelsen, R.D. 2017. Who pays attention to sec form 8K? Kelley School of Business Research Paper no. 17-24. Available at SSRN: https://ssrn.com/abstract=2942503.
Bird, A., and S.A. Karolyi. 2016. Do institutional investors demand public disclosure? Review of Financial Studies 29 (12): 3245–3277.
Boehmer, E. 2005. Dimensions of execution quality: Recent evidence for U.S. equity markets. Journal of Financial Economics 78 (3): 553–582.
Boehmer, E., Jones, C.M.X., and Zhang, X. 2021. Tracking retail investor activity. Journal of Finance, Forthcoming.
Brunnermeier, M.K. 2005. Information leakage and market efficiency. Review of Financial Studies 18 (2): 417–457.
Bushee, B.J., J. Gerakos, and L.F. Lee. 2018. Corporate jets and private meetings with investors. Journal of Accounting and Economics 65 (2–3): 358–379.
Bushee, B.J., M.J. Jung, and G.S. Miller. 2017. Do investors benefit from selective access to management? Journal of Financial Reporting 2 (1): 31–61.
Christophe, S.E., M.G. Ferri, and J. Hsieh. 2010. Informed trading before analyst downgrades: Evidence from short sellers. Journal of Financial Economics 95 (1): 85–106.
Collin-Dufresne, P., and F. Vyacheslav. 2015. Do prices reveal the presence of informed trading? Journal of Finance. 70 (4): 1555–1582.
Fong, C., C. Hazlett, and K. Imai. 2018. Covariate balancing propensity score for a continuous treatment: Application to the efficacy of political advertisements. Annals of Applied Statistics 12 (1): 156–177.
Hirshleifer, D., Subrahmanyam, A., & and Titman, S., (1994). Security analysis and trading patterns when some investors receive information before others. Journal of Finance, 49(5), 1665–1698.
Ho, D., K. Imai, G. King, and E. Stuart. 2007. Matching as nonparametric preprocessing for reducing model dependence in parametric causal inference. Political Analysis 15: 199–236.
Hu, G., K. M. Jo, Y. A. Wang, and J. Xie. 2018. Institutional trading and abel noser data. Journal of Corporate Finance 52: 143–167.
Imai, K., and Marc M. Ratkovic. 2014. Covariance balancing propensity score. Journal of the Royal Statistical Society Series B 76: 243–263.
Irvine, P., M. Lipson, and M. Puckett. 2007. Tipping. Review of Financial Studies 20: 741–768.
Glosten, L.R., and P.P.R. Milgrom. 1985. Bid, ask and transaction prices in a specialist market with heterogeneously informed traders. Journal of Financial Economics 14: 71–100.
Kadan, O., R. Michaely, and P.C. Moulton. 2018. Trading in the presence of short-lived private information: Evidence from analyst recommendation changes. Journal of Financial and Quantitative Analysis 53: 1509–1546.
Kothari, S.P., S. Shu, and P.D. Wysocki. 2009. Do managers withhold bad news? Journal of Accounting Research 47: 241–276.
Lerman, A., and J. Livnat. 2010. The new form 8K disclosures. Review of Accounting Studies 15: 752–778.
Rubin, A., B. Segal, and D. Segal. 2017. The interpretation of unanticipated news arrival and analysts' skill. Journal of Financial and Quantitative Analysis 52: 1491–1518.
Segal, B., and D. Segal. 2016. Are managers strategic in reporting non-earnings related items in 8K filings? Evidence on timing and news bundling. Review of Accounting Studies 21: 1203–1244.
Solomon, D., and E. Soltes. 2015. What are we meeting for? The consequences of private meetings with investors. Journal of Law and Economics 58: 325–355.
Stuart, E.A. 2010. Matching methods for causal inference: A review and look forward. Statistical Science 25 (1): 1–21.
Subasi, M. 2014. Investor conferences and institutional trading in takeover targets. In Working paper. University of.
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Appendices
Appendix I Determinants of the Filing Lag
Because the impact of the filing lag on informed trading may be influenced by the determinants of the filing lag, we provide empirical evidence regarding the determinants of the filing lag in this appendix.
The empirical evidence that has accumulated to date indicates that managers systematically delay disclosing bad news but time or accelerate the disclosure of good news to create circumstances that are beneficial to themselves or to the firm (Kothari et al. 2009). While it is to be expected that these incentives play a lesser role as far as 8 K reporting is concerned—especially after 2004 when the reporting lag was reduced to no more than four business days—the 8 K filing lag appears to be longer for negative news, as evidenced by the higher likelihood of 8 K reporting on Fridays for negative news (Segal and Segal 2016). In addition, the filing lag is likely positively related to the economic magnitude of the event, especially if the news is negative. This probably occurs because managers require more time to ensure the veracity of larger reported magnitudes, both because such events are bound to be more complex and also to minimize potential litigation risk.
Table A1 shows the results from regressing the filing lag on 8 K characteristics and control variables. The main variables of interest are the sign of the news, the economic magnitude of the news, and an interaction term involving both. The economic magnitude of the news is measured as the cumulative market adjusted return (CMAR) during the filing window. To test the relation between negative news and the filing lag, we include a negative news indicator, which takes the value of one if CMAR is negative and zero otherwise, and an interaction variable of CMAR and the negative news dummy. Given the decrease in the filing lag over the sample period, the regressions also control for the time trend. The firm-level control variables include profitability, leverage, return volatility, log of the market value of equity, book-to-market, and the number of analysts following the firm.
Columns (1) and (2) of Table A1 give the main results. Column (1) reports the results of an OLS regression, whereas Column (2) reports the results of a Poisson regression to account for the fact that the dependent variable is a count variable. The results across both regressions indicate that the economic magnitude of the event and the sign of the news are statistically significant determinants of the filing lag. Companies tend to report negative news later than positive news, and the filing lag increases (decreases) with the economic magnitude of negative (positive) news. Hence, these results are consistent with the incentives of managers to delay the reporting of negative news (potentially because of litigation costs). In contrast, managers rush to issue positive 8 K reports, and the filing speed increases with the economic magnitude of the positive news. Taken together, the relation between the filing lag and the economic magnitude of the news is conditional on the sign of the news, implying that the sign of the news has a first order effect on the filing lag.
Consistent with the evidence in Table 1, we find that the filing lag decreases with time. The coefficients on the control variables indicate that firms with greater equity volatility tend to delay the reporting of 8 K news, whereas large firms tend to report earlier. Interestingly, we observe that the coefficient on the number of analysts following is by and large positive and significant, whereas one might expect a negative coefficient given that the number of analysts could proxy for the demand for more timely information. Notwithstanding the statistical significance, the economic magnitude appears to be low. The interquartile difference in the number of analysts increases the reporting lag by only 0.144 (=0.018*8) days.
The next two columns report the results when we restrict the sample to the old and new 8 K forms, respectively. The results are very similar to those in Column 1, except that the coefficient on the negative news indicator in the “New 8 K” column is not significant. While the results related to the “Old” regime indicate a larger filing lag on average (as evidenced by the intercept) and a larger sensitivity to the economic magnitude of the news, the results post 2004 also indicate that the filing lag is substantial (3.3 days), especially considering that the speed of trade execution is measured in seconds (Boehmer 2005). Also, the impact of the economic magnitude of the news is statistically significant. The difference between positive and negative news is captured by the coefficient on the interaction variable (6.054), so a 10% negative event, for example, would be reported 0.6 days later (or equivalently one more trading day) than a 10% positive event.Footnote 25
We conduct further sensitivity analyses (untabulated). First, to eliminate the possibility that the results are due primarily to changes to the 8 K report in 2004, we evaluate only the news items that are reported both on the old and the new 8 K forms. Specifically, we include “Entry into a Material Definitive Agreement,” “Termination of a Material Definitive Agreement,” “Bankruptcy or Receivership,” and “Changes in Certifying Accountant”—Items 1.01, 1.02, 1.03, and 4.01 from the new 8 K form and Items 2, 3, and 4 from the old 8 K form. While there are differences in the definitions of the items across the two 8 K regimes, they are close in substance. In another analysis, we restrict the sample to a particular news item for the most frequent news items. The overall results are similar for all items—the filing lag is significantly related to the sign of the news; and conditional on the sign of the news, the filing lag increases (decreases) with the economic magnitude of the news for negative (positive) news.
Table A1 Determinants of Filing Lag
(1) All-OLS | (2) All-Poisson | (3) Old 8 K | (4) New 8 K | |
Constant | 4.786*** | 6.671*** | 3.339*** | |
(0.000) | (0.000) | (0.000) | ||
Market Adjusted Return | −3.607*** | −1.231*** | −4.790*** | −3.136*** |
(0.000) | (0.000) | (0.000) | (0.000) | |
Negative News Indicator | 0.040*** | 0.013*** | 0.080*** | 0.014 |
(0.000) | (0.000) | (0.002) | (0.189) | |
(Market Adjusted Return) X (Negative News Indicator) | 7.573*** | 2.551*** | 10.937*** | 6.054*** |
(0.000) | (0.000) | (0.000) | (0.000) | |
Time | −0.108*** | −0.036*** | −0.391*** | −0.043*** |
(0.000) | (0.000) | (0.000) | (0.000) | |
Return on Assets | −0.070 | −0.038*** | −0.086 | −0.153** |
(0.329) | (0.000) | (0.581) | (0.014) | |
Equity Return Volatility | 2.688*** | 0.798*** | 4.597*** | 1.078** |
(0.000) | (0.000) | (0.002) | (0.017) | |
Book-to-Market | −0.003 | 0.002 | 0.029 | 0.008 |
(0.820) | (0.278) | (0.328) | (0.332) | |
Leverage | 0.043 | 0.024*** | 0.045 | 0.055 |
(0.575) | (0.009) | (0.786) | (0.403) | |
Log Market Value of Equity | −0.103*** | −0.029*** | −0.035 | −0.009 |
(0.000) | (0.000) | (0.362) | (0.533) | |
Analysts Following | 0.018*** | 0.004*** | −0.017** | 0.008** |
(0.000) | (0.000) | (0.020) | (0.012) | |
Observations | 384,373 | 383,212 | 133,650 | 250,723 |
R-squared | 0.160 | 0.164 | 0.167 |
Appendix II
1.1 New Form 8 K Items Number and Description
Item Description | |
Entry into a Material Definitive Agreement | 1.01 |
Termination of a Material Definitive Agreement | 1.02 |
Bankruptcy or Receivership | 1.03 |
Mine Safety - Reporting of Shutdowns and Patterns of Violations | 1.04 |
Completion of Acquisition or Disposition of Assets | 2.01 |
Results of Operations and Financial Condition | 2.02 |
Creation of a Direct Financial Obligation or an Obligation under an Off-Balance Sheet Arrangement of a Registrant | 2.03 |
Triggering Events That Accelerate or Increase a Direct Financial Obligation or an Obligation under an Off-Balance Sheet Arrangement | 2.04 |
Costs Associated with Exit or Disposal Activities | 2.05 |
Material Impairments | 2.06 |
Notice of Delisting or Failure to Satisfy a Continued Listing Rule or Standard; Transfer of Listing | 3.01 |
Unregistered Sales of Equity Securities | 3.02 |
Material Modification to Rights of Security Holders | 3.03 |
Changes in Registrant’s Certifying Accountant | 4.01 |
Non-Reliance on Previously Issued Financial Statements or a Related Audit Report or Completed Interim Review | 4.02 |
Changes in Control of Registrant | 5.01 |
Departure of Directors or Certain Officers; Election of Directors; Appointment of Certain Officers; Compensatory Arrangements of Certain Officers | 5.02 |
Amendments to Articles of Incorporation or Bylaws; Change in Fiscal Year | 5.03 |
Temporary Suspension of Trading Under Registrant’s Employee Benefit Plans | 5.04 |
Amendment to Registrant’s Code of Ethics, or Waiver of a Provision of the Code of Ethics | 5.05 |
Change in Shell Company Status | 5.06 |
Submission of Matters to a Vote of Security Holders | 5.07 |
Shareholder Director Nominations | 5.08 |
Asset-Backed Securities | 6.01–6.05 |
Regulation FD Disclosure | 7.01 |
Other Events | 8.01 |
Financial Statements and Exhibits | 9.01 |
1.2 Old Form 8 K Items Number and Description
Item Description | |
Changes in Control | 1 |
Acquisition or Disposition of Assets | 2 |
Bankruptcy or Receivership | 3 |
Changes in Certifying Accountant | 4 |
Other Important Events Resignation of Directors | 5 6 |
Other Exhibits | 7 |
Change in Fiscal Year | 8 |
Appendix III Variable Definitions
1.1 Market Value of Equity – Computed at fiscal year end
Leverage – Short-term debt (DLC) plus long-term debt (DLTT) scaled by average total assets (AT) Return on Assets – Income before extraordinary items (IB) scaled by average total assets (AT)Footnote 26 Equity Return Volatility – Standard deviation of daily equity returns during the fiscal year Book-to-Market – Common stockholders’ equity (CEQ) scaled by market value of equity at the fiscal year-end Number of Analysts – Number of unique analysts providing at least one earnings forecast during the year Absolute Market Adjusted Return – Computed as the absolute cumulative market adjusted equity returns in the three days around the filing date Filing Lag – Number of business days between the filing and event dates of the 8 K report Log of Total Strategic Trading – Log of the total number of institutional investors with the sign of net trading during the event window equal to the sign of the news (sign of market adjusted returns in the filing window) and sign of net trading during the filing window opposite to the sign of the news Proportion of Total Strategic Trading – Total Strategic Trading scaled by total number of institutions that traded shares during the event window Log of Total Consistent Trading – Log of the total number of institutional investors with the sign of net trading during the event window equal to the sign of the news (sign of market-adjusted returns in the filing window) Proportion of Total Consistent Trading – Computed as Total Consistent Trading scaled by total number of institutions that traded shares during the event window Institutional Abnormal Filing Volume – Average volume of institutional investors during the filing window scaled by average daily volume of institutional investors during the year over non-news days Overall Trading Volume – Volume (CRSP) scaled by number of shares outstanding (in 00) Daily Equity Return Volatility – Market adjusted equity returns squared Abnormal Filing Trading Volume – Average trading volume during the filing window divided by average daily trading volume during the year over non-news days Abnormal Filing Equity Return Volatility – Average market adjusted equity returns squared during the filing window divided by average market adjusted equity returns squared during the year over non-news days Total Cumulative Market Adjusted Return – Computed as the cumulative market adjusted return from the event date through the day after the filing date (inclusive) Volatility Ratio – Ratio of total abnormal daily market adjusted equity returns squared (abnormal daily volatility) during the filing window to total abnormal daily volatility from the event date through the day after the filing date. The abnormal daily volatility is computed as the daily market adjusted equity returns squared scaled by average daily market adjusted equity returns squared over non-news days. Volume Ratio – Ratio of total abnormal daily volume scaled by number of shares outstanding (abnormal daily volume) during the filing window to total abnormal daily volume from the event date through the day after the filing date. The abnormal daily volume is computed as the ratio of daily volume scaled by average daily volume over non-news days. Trading Volume Event Window – Ratio of the number shares traded during the event (filing) window scaled by the number of shares held by informed investors on the day prior to the 8 K event date Trading Volume Filing Window – Ratio of the number shares traded during the filing window scaled by the number of shares held by informed investors on the day prior to the 8 K event date Profit – Change in the share price, measured as the average share price during the filing window scaled by the average share price during the event window minus 1. If informed traders take a short position in the event window, then Profit is measured as the average share price during the event window scaled by the average share price during the filing window minus 1. |
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Callen, J.L., Kaniel, R. & Segal, D. Filing speed, information leakage, and price formation. Rev Account Stud 28, 1618–1656 (2023). https://doi.org/10.1007/s11142-022-09673-5
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DOI: https://doi.org/10.1007/s11142-022-09673-5