Columbia Business School



The Informativeness of Historical Financial DisclosuresMichael S. DrakeMarriott School of ManagementBrigham Young Universitymikedrake@byu.eduDarren T. RoulstoneFisher College of BusinessThe Ohio State Universityroulstone.1@osu.eduJacob R. ThornockFoster School of BusinessUniversity of Washingtonthornocj@uw.eduNovember 2012We are especially grateful to Scott Bauguess and many others at the Securities and Exchange Commission and to Dick Dietrich for assistance in acquiring and implementing the proprietary data used in this study. We thank Scott Bauguess, Anne Beatty, Bob Bowen, Jonathan Brogaard, Nicole Cade, John Campbell, Ed deHaan, Thomas Gilbert, Frank Hodge, Dawn Matsumoto, Kathy Petroni, Terry Shevlin and workshop participants at the 2012 UBCOW Conference, 2012 Chicago Quantitative Alliance Annual Academic Competition, Brigham Young University, Massachusetts Institute of Technology, Michigan State University, the Division of Risk, Strategy, and Financial Innovation at the U.S. Securities and Exchange Commission, Rice University, University of Georgia, University of Hawaii, University of California-San Diego, and University of Pennsylvania for valuable comments. We thank Isaac Kelly and Joseph Tate for computational assistance and Bret Johnson and Nick Guest for excellent research assistance. Finally, we gratefully acknowledge the financial support of the Marriott School of Management, the Fisher College of Business, and the Foster School of Business. ABSTRACT:Investors have at their fingertips an almost unlimited supply of financial disclosures. Some of these disclosures are recently released but most have been in the public domain for months or even years. In this study, we evaluate whether such “stale” disclosures continue to be informative to investors. Using a novel dataset that tracks search requests on the SEC EDGAR database, we find evidence that the acquisition of historical disclosures from EDGAR is positively associated with absolute returns and trading volume within the next two hours. We investigate several potential explanations for why investors might find previously released disclosures informative and find evidence consistent with two explanations: first, investors appear to request historical information in order to provide context for new information releases (e.g., earnings announcements, 8-K filings); second, investors appear to request historical information to resolve cases of high valuation uncertainty. We find no evidence that the relation between requests for historical disclosures and market activity is driven by unsophisticated investors. Our results suggest that financial disclosures continue to remain useful beyond their initial release and highlight the value of historical disclosures and their archives to equity markets. Key Words: Information Acquisition, EDGAR, Trading Volume IntroductionThe set of public information available to an investor is vast, diverse and growing. Decades of capital market research provide evidence that the release of new financial disclosures is associated with increased market activity, suggesting that the disclosures are informative. However, new disclosures make up only a very small fraction of the information set available to investors—the overwhelming majority of public information can be considered historical. Given the dominance of old information in the total information set, and the considerable resources required to prepare and maintain financial disclosures, it is important to understand whether historical financial disclosures remain informative to investors. Accordingly, the objectives of this paper are to evaluate the informativeness of historical disclosures and to investigate settings when their informativeness is greatest. Two factors motivate our research questions. First, in theory, competitive forces quickly drive the net gains from trading on information to zero, thus rendering the information “stale.” Prior research finds that certain types of information can become stale. For example, the evidence in Tetlock [2011] suggests that the redundant articles in the business press are only traded on by unsophisticated investors; these articles provide no new information to rational investors. We examine whether historical (old) financial disclosures retain their informativeness to rational investors (and, if so, why). Second, there is prior, indirect, evidence that historical disclosures can be informative. For example, there is a large literature on the time-series properties of earnings; the fact that investors employ historical earnings to predict current earnings indirectly suggests that historical earnings are informative for benchmarking (Lev [1983]; Kothari [2001]). Other studies find that information signals based on historical accounting information can be predictive of future returns (e.g., Ou and Penman [1989]; Abarbanell and Bushee [1998]; and Piotroski [2000]). Although the evidence in prior research suggests that historical financial information is informative, that evidence is often a byproduct of a different research question rather than a direct test of the informativeness of historical financial disclosures. A challenge in addressing the informativeness of historical information is that researchers are generally unable to measure the timing of investors’ acquisition of historical information outside of news release windows. We overcome this measurement problem by using a novel dataset that tracks all investor requests for disclosures on the SEC’s EDGAR database. The dataset records every “click” made by an investor to request a regulated filing, such as a 10-K or 8-K, from EDGAR and thereby provides a direct proxy for investor information acquisition. These data allow us to empirically observe both the timing of investors’ acquisition of previously disclosed financial information and the age of the information acquired. Our proxy for historical information acquisition is the number of investor requests for disclosures that have been publicly available on EDGAR for at least 30 days at the time of the request. We make three assumptions regarding these data. First, we assume that if users request a particular company’s disclosure, it is highly likely that they are interested in the information the disclosure contains about the company; in other words, they consider the disclosure to be potentially informative. Second, given that investors are acquiring the actual SEC filing, we assume that they are interested in information unique to that filing that cannot be more easily obtained via other financial sources. Finally, although investors can gather financial information from a number of other sources, we assume that investors demand for disclosures is constant across all acquisition channels. We define informativeness as a temporal association between the acquisition of information and market activity; in doing so, we follow decades of research on the information content of earnings (Kothari [2001]). Note that we are not attempting to establish a causal relation between investor requests and market activity as we do not believe information requests cause investors to trade. Rather, we believe that when investors decide to trade they gather relevant information including financial disclosures (both recent and, possibly, historical). We test the informativeness of historical disclosures by examining the association between investor requests for these disclosures and subsequent short-term market activity, measured as absolute returns and trading volume. We conduct our analysis at the intraday level using hourly requests for disclosures on EDGAR and hourly aggregations of equity market data from the Trade and Quote (TAQ) database. Given the intense computational requirements to analyze intra-day data, we carry out our analyses on a random sample of 200 firms. We account for general news that can influence historical information acquisition by including as controls contemporaneous, absolute stock returns, lagged, absolute stock returns and trading volume, and prior-day returns and turnover. (Inclusion of past market activity also controls for the effect of past market activity—which is highly correlated with future market activity—on current requests for disclosures.)We find that requests for historical disclosures are positively associated with absolute returns and total trading volume in the subsequent two hours. When we focus on requests for periodic accounting filings (annual reports (10-Ks) and quarterly reports (10-Qs)), our results are uniformly stronger than results for all disclosures, underscoring the importance of historical, periodic accounting reports to equity markets. In sensitivity tests, we find that the association between historical disclosure acquisition and market activity holds even during periods of minimal firm-specific news (i.e., after removing periods when firm-specific news, such as earnings announcements, analyst forecasts, and news articles, is released), which suggests that these news events are not driving the association. Overall, our evidence is consistent with the notion that historical information is informative to equity markets. We test three potential reasons for the informativeness of historical financial disclosures. The first explanation is that investors acquire and use old information to provide important context for new information releases and current events. For example, investors may turn to previously filed 10-Ks to assess how managers’ previous discussions of strategic initiatives played out in current periods. We test this notion by examining whether the observed positive association between requests for historical disclosures and subsequent market activity is particularly strong (i.e., more positive) in the presence of two important events, earnings announcements and 8-K filings, while controlling for the normal level of market activity that arises from these events. For earnings announcements, we find that the positive association between requests for historical disclosures and market activities is stronger during a short window around the earnings release. For 8-K filings, we find that the positive association between requests for historical disclosures and market activity is stronger on the date the 8-K is filed. In sum, the evidence is consistent with historical financial information being informative to equity markets when acquired and used in conjunction with current information releases.A second potential reason for the informativeness of historical financial disclosures relates to valuation uncertainty. Our intuition is that valuation is a difficult and subjective process to begin with and is made more difficult and subjective when there is greater uncertainty about the valuation inputs. For certain firms, investors need more time to process and contextualize information, thus investors may continue to find previously filed financial disclosures helpful for valuation assessments. We use three different proxies for valuation uncertainty and find that the positive association between historical information acquisition and market activity is even greater when valuation uncertainty is higher. The third explanation for the association between historical disclosure acquisition and market activity is based on findings in prior research that unsophisticated investors acquire and trade on historical information because they are “late to the game”—that is, they access information in an untimely manner and fail to realize that the information is no longer value-relevant. In our empirical model, we follow prior research in assuming that unsophisticated traders initiate smaller trades on average (e.g., Frankel et al. [1999]). However, we find that requests for historical disclosures are positively associated with the mean dollar value of individual trades in the subsequent two hours. Thus, the evidence is inconsistent with the notion that demand for historical disclosures is driven by smaller, less sophisticated investors. Given the difficulty in reading and processing complex financial disclosures such as 10-Ks and our finding that historical disclosures are used in fundamental analysis, we conjecture that sophisticated investors are more likely to use the information in historical disclosures than unsophisticated investors. Our results are consistent with this conjecture. Our findings contribute to the emerging literature that investigates whether historical or redundant news is perceived as informative to at least some market participants. For example, Tetlock [2011] and Gilbert et al. [2012] find evidence consistent with individual investors trading on stale (specifically, redundant) news, in the context of media articles and macro-economic indicators. Our paper extends the literature by examining whether a firm’s financial disclosures, which are arguably more prominent, and certainly more regulated and complex than media articles and macro-economic indicators, become stale with age. Given the importance of these disclosures to market participants, examining their informativeness is important in its own right. Another point of contrast between our paper and the Gilbert et al. [2012] and Tetlock [2011] papers is that they examine reactions to the release of stale information (i.e., supply), we examine consequences of actual investor requests for stale information (i.e., demand). Finally, we find evidence that old information can be informative to rational investors, which stands in contrast the evidence in prior studies. In summary, this study revisits an old question of whether disclosures are informative, with an emphasis on historical disclosures. Providing direct evidence on this question is important because the vast majority of publicly available information is old. For example, at the start of our sample period (January, 2008), EDGAR hosted over 8.2 million financial disclosures, of which less than 0.2 million (representing 2.5% of all filings) had been in the public domain for less than 30 days and could be reasonably considered “new.” Prior research on the informativeness of financial disclosures has mostly focused on the release of new information; in contrast this paper is, to our knowledge, the first to directly study the informativeness of historical financial disclosures. Moreover, our evidence provides insights to regulators, who are concerned about the usefulness of financial information when investors are potentially overloaded by information (Paredes [2008]). The current U.S. regulatory regime requires a broad spectrum of disclosures, from complex, comprehensive period reports (10-Ks, 10-Qs) to small and frequent current disclosures (8-Ks, Form 4s). These disclosures must strike a balance between being so large that investors cannot process their information content quickly and so small that frequent updates are needed. Our findings suggest that historical disclosures (especially 10-Ks and 10-Qs) continue to be informative, especially at times of new information releases. We view this as providing some justification for the substantial costs to firms and regulators of preparing and archiving financial disclosures. 2. The Informativeness of Historical InformationA fundamental step in assessing the informativeness of a disclosure is to test whether investors acquire and use the information contained in that disclosure. Researchers in accounting and finance have long been interested in understanding the role that information acquisition plays in the price discovery process, but have been hampered by the lack of an available proxy for information acquisition. One line of research uses variation in broad firm characteristics such as firm size, analyst following, or institutional ownership to proxy for variation in incentives to acquire information about a particular firm (e.g., Atiase [1985], El-Gazzar [1998], Kirk [2011]). More recent research has begun to examine whether information acquisition can be inferred using different channels of information dissemination, including conference calls (Frankel et al. [1999]), investor conferences (Bushee et al. [2011]), and the media (Soltes [2009], Bushee et al. [2010], Engelberg and Parsons [2011]). In general, prior research infers information acquisition (and thereby informativeness) by examining how disclosure events are associated with differential trading activities by investors before, during and after the event. For example, Frankel et al. [1999] find that capital market activity, such as absolute returns and trading volume, is higher for all measures during conference calls than during the control period. Bushee et al. [2011] find significant short window increases in stock returns and trading volume during managerial presentation at conferences. Busse and Green [2002] and Engelberg et al. [2012] show that information is acquired through media television programs by documenting increases in market activity during and after a stock is mentioned on the shows. Another stream of research proposes internet search as a more direct measure of information acquisition. For example, Da et al. [2011] find that weekly Google requests for corporate stock ticker symbols are positively associated with contemporaneous weekly abnormal returns and share turnover, while Gao et al. [2011] find that daily Google requests are positively associated with greater trading volume and trade sizes. Drake et al. [2012b] find that Google search requests increase around earnings announcements and provide information that aids in the pricing of earnings news. Thus, the general evidence from studies using Google search as a proxy for information acquisition is that when contemporaneous information is acquired by investors, it leads to increases in trading activities, which suggests that these acquisition events are informative to investors. Using EDGAR search requests as a measure of information acquisition, we aim to assess the informativeness of historical information. There are several reasons that one would not expect historical financial disclosures to be informative to markets. First, some researchers argue that accounting information lacks timeliness, which in turn reduces its informativeness (Collins et al. [1994]); Ball and Shivakumar [2008]). Older financial disclosures, therefore, may have very little information content remaining months or years after they are made public. Second, there are volumes of macro-, industry-, and firm-specific information available on a more timely basis through other sources (e.g., analysts, the business press, the internet), which reduces the value of historical financial information. Finally, the semi-strong form of the efficient markets hypothesis predicts that the net gains to information in disclosures are traded away quickly. This conjecture holds especially true for disclosures that are costless to obtain and widely disseminated, as is the case with many of the disclosures available on EDGAR. On the other hand, prior research provides indirect evidence that investors use historical information to provide context for current information (and vice versa), which is consistent with historical information being informative (Freeman and Tse [1989]). In addition, prior research suggests that past information signals can be informative for future returns, which is again consistent with that information being informative. However, we argue that the question of whether and how historical financial disclosures are informative has not been directly addressed in prior research. That is, although prior research finds that the acquisition of new information is associated with market activity, such an association has not been tested for historical financial information. This discussion provides the basis for our first hypothesis, stated in the alternative form:H1: The acquisition of historical financial information is associated with subsequent market activity. If we find evidence for H1, an important follow-up question is why the acquisition of historical financial information is associated with market activity. One explanation for the informativeness of historical disclosures follows from the standard approach to fundamental analysis, which calls for the use of historical information in estimating firms’ intrinsic values. Newly disclosed information generally requires comparison with previously disclosed information; i.e., the older information contextualizes the new information and provides expectations against which the new information can be measured. Hence, new disclosures can contain information that cannot be fully exploited unless it is analyzed in connection with information provided in previously filed disclosures. Similarly, information can exist in old disclosures that cannot be fully exploited until subsequent disclosures are made. Moreover, market efficiency does not preclude investors from basing their expectations on information in prior disclosures. In fact, in an efficient market, investors form price expectations based all available conditioning information, including prior financial disclosures. It then follows that current disclosures can trigger demand for historical disclosures as investors seek context. This discussion leads to our second hypothesis, stated in the alternative form:H2: The association between historical financial information acquisition and market activity is positively influenced by contemporaneous information releases and contemporary information acquisition.Another explanation for the informativeness of historical disclosures is that there is high uncertainty about the value of the firm given the information available to investors. When there is greater valuation uncertainty, the market can rationally under-react to new information as investors need more time to process and contextualize the information. Consistent with this argument, prior research finds evidence of lower market reactions to earnings announcements when earnings quality is lower (Francis et al. [2007]) or when earnings credibility is lower (Teoh and Wong [1993]). Valuation uncertainty also leads to more frequent revision of investor beliefs. For example, Baker and Wurgler [2006] finds that the returns of hard-to-value firms are more affected by subjective measures (in their case, investor sentiment) which they attribute to the difficulty of objectively valuing these firms. Our intuition is that, all else equal, valuation uncertainty will result in investors need to re-evaluate historic information as time passes. Taken together, these findings and our intuition suggest that when information about investment payoffs is noisy or uncertain, or when a firm is difficult to value because of a lack of objective valuation inputs, investors will continue to examine and process financial information after it has been made public. This leads to our third hypothesis, stated in the alternative:H3: The association between historical financial information acquisition and market activity is positively influenced by valuation uncertainty.Our final explanation for the informativeness of historical financial disclosures is evidence from prior research that unsophisticated investors trade on “stale” information. Researchers have posited that a subset of unsophisticated investors are likely to use historical information because they (i) are slow to acquire and process disclosures, (ii) exhibit cognitive biases, and/or (iii) do not recognize when disclosures have become stale (Gilbert et al. [2012]). For example, Hand [1990] and Huberman and Regev [2001] provide examples of presumably unsophisticated investors underreacting to an important news announcement, and subsequently overreacting when the news announcement was re-released. Similarly, Gilbert et al. [2012] and Tetlock [2011] provide evidence that investor inattention is linked to trading on stale information, which leads to subsequent mispricing. On the other hand, it takes a degree of sophistication to know how to access, read and properly interpret financial disclosures. For example, the 2011 Form 10-K recently filed by General Electric is over 270 pages long and contains detailed information on the firm’s operations, risks and performance, all of which would require a substantial level of financial literacy and understanding. Moreover, the ability to perform fundamental analysis using current and past disclosures requires investment knowledge and skill that an unsophisticated investors would not likely possess. This discussion leads to our final hypothesis:H4: Historical information acquisition is associated with investor sophistication.We now turn to discussion of our unique dataset and how we use this dataset to test the hypotheses above.Data and Research Design3.1 Data and SampleThe primary data used in this study capture investor information acquisition of all disclosures archived on the SEC EDGAR servers. As these data are described in detail in Drake et al. [2012a], here we provide only a brief description. The SEC maintains server logs that record every request for financial disclosures hosted on the EDGAR servers. With the assistance of the SEC’s division of Risk, Strategy & Financial Innovation, we obtained the server log for the first six months of 2008. Each entry in the server log allows us to observe the partial IP address of the user, the date and time of the request, the Central Index Key (CIK) of the company that filed the requested form, and a link to the particular filing. Following Drake et al. [2012a], we focus our analyses on requests made by investors, rather than those made by automated webcrawlers. Drake et al. [2012a] provide evidence that webcrawlers are generally deployed for database building, pulling large volumes of historical filings. Our focus is on whether investors acquire and use historical filings in their short-term trading activities. We use these data as a direct measure of financial information acquisition. The data are subject to some caveats. First, we emphasize that our data on investor requests for filings in EDGAR represent a lower bound of total information acquisition of SEC filings. Many SEC filings are freely available to investors on company investor relations websites, financial websites (e.g., Yahoo! Finance), and brokerage websites. In addition, institutional investors can directly access SEC filings from commercial data aggregators such as Bloomberg, Capital IQ, or Morningstar Document Research. Institutions can also directly subscribe to the filings through the Public Dissemination Service. Second, we are unsure of the identity of the user. Although we cannot validate it, we reason that the typical EDGAR user is unlikely to be a novice or expert investor, but likely somewhere in between. Third, our sample period is limited to six months of data; thus, to the extent that information acquisition via EDGAR during this period is systematically different, our results may not generalize to other periods. On the other hand, the EDGAR dataset provides several key advantages, as described in Drake et al. [2012a]. First, it reflects actual information acquisition of regulatory financial filings by interested parties. That is, it reflects actions undertaken by individuals to acquire mandatory disclosures. Second, it reflects investor choice—given the myriad of financial disclosures at their fingertips, the EDGAR request data allow us to identify the specific piece of information (i.e., the actual filing) that the user chooses to download. Finally, the data come directly from the primary source of regulatory disclosure—the SEC. In summary, although these data have their limitations, they represent a significant improvement over static proxies used in prior research for information acquisition such as firm size, analyst following and institutional ownership. The EDGAR server log provides the precise time when a disclosure is acquired by an investor. This level of detail enables us to conduct our analyses at the intra-day level; we use this granularity to isolate the association between the acquisition of historical information in a given hour and market activity in the subsequent hours. This alleviates concerns that general firm characteristics drive the association between search and trading (e.g., Drake et al. [2012a] finds that investors request more filings from large firms while firm size is positively associated with trading volume). We obtain intra-day data on trading activity from the TAQ database available on WRDS. Due to the extreme computational demands involved in analyzing intra-day trading and intra-day EDGAR server log data, we use a random sample of 200 firms with data available from TAQ and our EDGAR dataset over the January 1, 2008 through June 30, 2008 time period. In Table 1, Panel A we present descriptive statistics (means and medians) of selected firm characteristics for our random sample of 200 firms and for all firms with available data in the intersection of the COMPUSTAT and CRSP database. We assess the representativeness of our random sample by testing for statistical differences between the means and medians of each firm characteristic, which we define in Appendix A. As Table 1, Panel A reports, we find that the Market Value of Equity, Total Assets, Book-to-Market, Return-on-Assets, Leverage, Analyst Following? and Institutional Ownership for the random sample are statistically indistinguishable from the universe of firms with available data. In Table 1, Panel B we compare the distribution of the randomly selected firms across industries (Fama-French Classification 17) to that of the universe of firms with available data and again find that the industry distributions are similar. Overall, we conclude from the results in Table 1 that our random sample of 200 firms is representative of the population of available firms in Compustat and CRSP. 3.2 Variables and Empirical ModelsTo measure the timing of investor requests, we divide each day into 24 one-hour periods. Our primary measure of information acquisition of a disclosure is the count of investor requests made for a firm’s disclosures in a particular hour. To separate information acquisition for historical disclosures from that for contemporaneous disclosures, we sum all investor requests for disclosures on EDGAR separately for disclosures that are publicly available for greater than or equal to 30 days (HistoricalDisc) and for disclosures that are publicly available less than 30 days (RecentDisc). These two variables are the explanatory variables of interest in the study. The choice of a 30-day threshold is arbitrary; we choose that threshold under the assumption that a month in the public domain is sufficient time for a disclosure to be accessed and processed. As noted above, we define informativeness as a temporal association between market activity and historical information acquisition. Following tests of informativeness in Beaver [1968] and others, we employ two measures of market activity as dependent variables: the absolute value of the raw stock return during the hour (AbsRet) and the total dollar value of trading volume during the hour (Volume). If no trades are recorded during the hour, these values are set to zero. For all of these variables, the unit of observation is measured at the firm-hour level.One potential concern is that requests for historical disclosures are endogenously determined by firm and market events that also lead to increased market activity. Hence, we include as control variables two hourly lags of the dependent variable, two hourly lags of absolute returns, and the prior day’s absolute return and share turnover. To the extent that hourly and daily measures of market activity are associated with events that trigger information acquisition, these controls will reduce the effects of endogenous, omitted variables. We also control for requests for recent disclosures (those filed within the most recent 30 days) in EDGAR because investor requests for historical and contemporaneous information are likely to be correlated; excluding the requests for recent disclosures would induce a correlated omitted variable problem. We also control for the decile rank of the market value of equity (RankMVE). We include this control because firm size is correlated with both market activity (returns and volume) and with information acquisition (Drake et al. [2012a]). Investors make disclosure requests on EDGAR throughout the day, but TAQ records trades only from 4am through 8pm (EST) each day the market is open; thus we construct indicator variables for times when the market is closed. Morning is set to one from midnight to 4am and to zero otherwise; Evening is set to one from 8pm to midnight and to zero otherwise; Weekend is set to one during the weekend and on holidays and to zero otherwise. In estimating the empirical models, we interact these indicator variables with both the requests for historical disclosures (HistoricalDisc) and the requests for recent disclosures (RecentDisc). Finally, we include hour-of-day fixed effects and we cluster the standard errors by day to control for cross-sectional residual correlation. We estimate the following general model for all firms i (subscripts omitted) and hours t:Market Activityt = f (HistoricalDisct-1, HistoricalDisct-2, RecentDisct-1, RecentDisct-2, Market Activityt-1, Market Activityt-2, AbsRett-1, AbsRett-2, RankMVE, PriorDay_AbsRet, PriorDay_Turnover, Controls for Market Closed, Hour Fixed Effects),(1)whereMarket Activity= one of two dependent variables: AbsRet or Volume as defined above; Controls for Market Closed = set of control variables which includes the Morning, Evening, and Weekend indicator variables defined above, as well as the interaction of each of these indicator variables with HistoricalDisct-1, HistoricalDisct-2, RecentDisct-1, and RecentDisct-2;all other variables are defined above. H1 predicts that the acquisition of historical information should be associated with trading activity. The coefficients on HistoricalDisct-1 and HistoricalDisct-2 are the test variables for H1 in estimations of model (1). A significantly positive coefficient on those variables is consistent with investor acquisition of historical disclosures leading to significant capital market activity, which is consistent with H1. All subsequent analyses are tests for potential reasons for H1. H2 posits that the relation between historical information acquisition and market activity is positively influenced by contemporaneous information releases and acquisition. We test H2 using two information release events: earnings announcements and 8-K filings. We investigate earnings announcements because they are one of the most important corporate disclosure events and they allow us to observe the precise time that the report was made public. From IBES, we obtain the timestamp of any quarterly earnings announcement issued by our sample firms during the sample period. We employ a very short window (+/- 5 hours) around the earnings announcement to increase the likelihood that the investor requests from EDGAR are motivated by the earnings announcement. The indicator variable (EarnAnnHour(-5,5)) is set equal to one for the 11-hour period centered on the earnings announcement hour and to zero otherwise. When a firm undergoes a significant current event, such as an acquisition, executive hiring or firing, changes in auditor, or bankruptcy, it is required to file a Form 8-K with the SEC. We employ the filing date of an 8-K as a proxy for the many important events that trigger demand for firm-specific information. We obtain 8-K filing dates from the Master Index File available in EDGAR and construct an indicator variable (Form8-K) set equal to one on the filing date and to zero otherwise. We then enter these variables separately into model (1) and interact them with the EDGAR request variables as a test of H2. We also interact RankMVE with both EDGAR request variables in the model to ensure that the earnings announcement or 8-K indicator variable is not contaminated with a size effect. The model is as follows:Market Activityt = f (HistoricalDisct-1, HistoricalDisct-2, RecentDisct-1, RecentDisct-2, CurrentEvent, HistoricalDisct-1 x CurrentEvent, HistoricalDisct-2 x CurrentEvent, RecentDisct-1 x CurrentEvent, RecentDisct-2 x CurrentEvent, RankMVE, HistoricalDisct-1 x RankMVE, HistoricalDisct-2 x RankMVE, RecentDisct-1 x RankMVE, RecentDisct-2 x RankMVE, Market Activityt-1, Market Activityt-2, AbsRett-1, AbsRett-2, PriorDay_AbsRet, PriorDay_Turnover, Controls for Market Closed, Hour Fixed Effects),(2)whereCurrentEvent= one of two variables: EarnAnnHour(-5,5) or Form8-K as defined above; all other variables are defined above. In Model (2), the main effect CurrentEvent captures the general increase in market activity associated with earnings announcements or 8-K filings. Our focus is on the interaction of the current event and historical information acquisition. A positive coefficient on HistoricalDisc x EarnAnnHour(-5,5) or on HistoricalDisc x Form8-K is consistent with H2 and indicates that the association between requests for historical disclosures and market activity is even stronger when there is a current information release. In a final test of H2, we examine whether the association between investor acquisition of historical disclosures and subsequent market activity is influenced by investor EDGAR requests for current financial disclosures. We test this conjecture by interacting the two EDGAR request variables for each one hour time period (HistoricalDisc x RecentDisc) and by entering these interactions into model (1). Given that investors’ disclosure requests are highly positively correlated with firm size (Drake et al. [2012a]), we also interact RankMVE with both EDGAR request variables in the model as well. Our third model is as follows:Market Activityt = f (HistoricalDisct-1, HistoricalDisct-2, RecentDisct-1, RecentDisct-2, HistoricalDisct-1 x RecentDisct-1, HistoricalDisct-2 x RecentDisct-2, RankMVE, HistoricalDisct-1 x RankMVE, HistoricalDisct-2 x RankMVE, RecentDisct-1 x RankMVE, RecentDisct-2 x RankMVE, Market Activityt-1, Market Activityt-2, AbsRett-1, AbsRett-2, PriorDay_AbsRet, PriorDay_Turnover, Controls for Market Closed, Hour Fixed Effects)(3)where all variables are defined above. A positive coefficient on the HistoricalDisc x RecentDisc provides further support for H2 and indicates that the association between requests for historical disclosures and market activity is stronger when there are more requests for contemporaneous financial information. Our third hypothesis, H3, examines whether valuation uncertainty plays a role in explaining the association between EDGAR requests for historical financial disclosures and subsequent market activity. We employ three proxies for valuation uncertainty. The first proxy is based on the level of intangible assets, Intang, under the assumption that firms with more intangibles assets are harder to value. We calculate Intang as the proportion of assets not related to capital assets following Kumar [2009]. The second proxy for valuation uncertainty relates to earnings quality and is based on the extent to which current accruals map into cash flows (e.g., Dechow and Dichev [2002], Francis et al. [2005], Francis et al. [2007]). We select an earnings-based measure of information uncertainty because earnings relates directly to equity-investment payoffs. We estimate information uncertainty, EarnQuality, using the residuals from the following model for all firms i in years T:CurrAccrT = f (CFOT-1, CFOT, CFOT+1, ΔREVT, PPET),(4)whereCurrAccr= total current accruals;CFO= cash flow from operations;ΔREV= change in revenues; andPPE= gross value of property, plant, and equipment.We provide more detailed variable definitions in Appendix A. We follow Francis et al. [2007] in estimating model (4) for each industry (Fama-French 48 classifications) with at least 20 observations and for each year T. We capture the residual for each sample firm and estimate the standard deviation of the residuals (EarnQuality) over the past 5 years (years T-4 through T). Larger variation in the residuals suggests that cash flows map poorly into accruals and thus indicates greater information uncertainty. The final proxy is based on dispersion in analyst forecasts of earnings, which are an important component of value. We assume that greater disagreement among analysts about future performance is indicative of greater valuation uncertainty. We define Dispersion as the standard deviation of analyst annual earnings forecasts at the beginning of the year, scaled by stock price.We test H3 by interacting the EDGAR request variables for each one hour time period with our measures of valuation uncertainty (HistoricalDisc x ValUncertain and RecentDisc x ValUncertain) and entering these interactions into model (1). Given that valuation uncertainty is likely negatively correlated with firm size we also interact RankMVE with the EDGAR requests variables (HistoricalDisc x RankMVE and RecentDisc x RankMVE). The model is as follows:Market Activityt = f (HistoricalDisct-1, HistoricalDisct-2, RecentDisct-1, RecentDisct-2, ValUncertain, HistoricalDisct-1 x ValUncertain, HistoricalDisct-2 x ValUncertain, RecentDisct-1 x ValUncertain, RecentDisct-2 x ValUncertain, RankMVE, HistoricalDisct-1 x RankMVE, HistoricalDisct-2 x RankMVE, RecentDisct-1 x RankMVE, RecentDisct-2 x RankMVE, Market Activityt-1, Market Activityt-2, AbsRett-1, AbsRett-2, PriorDay_AbsRet, PriorDay_Turnover, Controls for Market Closed, Hour Fixed Effects),(5)where ValUncertain= one of three variables: Intang, EarnQuality, or Dispersion as defined above; all other variables are defined above. A significantly positive coefficient on the HistoricalDisc x ValUncertain is consistent with H3 and indicates that the association between requests for historical disclosures and market activity is stronger when there is greater information uncertainty in the firm. Our final hypothesis, H4, posits that the relation between historical disclosure acquisition and market activity is related to sophisticated trading. Following prior research (e.g., Frankel et al. [1999]), we use the average trade size during the hour (TradeSize) as a proxy for sophisticated trading under the assumption that larger trades are unlikely to be initiated by unsophisticated investors. We enter TradeSize as the dependent variable in model (1) as follows: TradeSizet = f (HistoricalDisct-1, HistoricalDisct-2, RecentDisct-1, RecentDisct-2, TradeSizet-1, TradeSizet-2, AbsRett-1, AbsRett-2, RankMVE, PriorDay_AbsRet, PriorDay_Turnover, Controls for Market Closed, Hour Fixed Effects),(6)where all variables are defined above. A negative (positive) coefficient on HistoricalDisct-1 and/or HistoricalDisct-2 is consistent with historical information acquisition being associated with less (more) sophisticated trading.4. Findings4.1 Descriptive Statistics and CorrelationsIn Table 2, we provide descriptive statistics for disclosure requests by hour of the day. In Panel A of Table 2 we present the mean, standard deviation, and maximum number of historical and recent disclosure requests for any form in EDGAR during the hour, aggregated across all 200 firms. Broadly, we find that both historical and recent disclosures are in highest demand during normal trading hours, and in particular, in the hours just before the markets close, which is consistent with our data picking up the information acquisition activity of human investors (rather than robots). We also find that across all time periods the mean number of requests for historical disclosures is greater than the mean number of requests for recent disclosures. This finding should certainly be viewed in light of the fact that there is a much greater supply of historical financial disclosures available on EDGAR; however, it does underscore the value of historic financial information and the benefits of storing these disclosures in an archival database such as EDGAR. Untabulated findings show that the median number of days between the filing date and the request date is 370 days for the historical disclosure sample and 1 day for the recent disclosure sample. In Panel B of Table 2 we present descriptive statistics for the three dependent variables by hour of the day. Consistent with Kelly and Tetlock [2012], we find that AbsRet and Volume are highest during the middle hours of the day (from 10:00 to 15:00 EST). In contrast, TradeSize is generally greatest during the opening and closing hours of the market (around 9:00 EST and 16:00 EST). As described above, in our empirical models we include hour of day fixed effects to control for these observed differences in market activity throughout the hours of the day.In Table 3 we present pairwise Spearman correlations for all of the variables used in the regression models. We find that HistoricalDisct-1 is positively associated with RecentDisct-1 with a correlation coefficient of 0.32, providing univariate evidence that disclosures filed at different times are requested together. We also find that HistoricalDisc is positively associated with the market activity variables AbsRet and Volume, and with prior day absolute returns and turnover. We do not discuss the correlations between all of variables, but note that the pairwise correlations are all significantly greater than zero at the 10 percent level. We now turn to the test results of the formal hypotheses. 4.2 Tests of H1Figure 1 shows evidence that investors continue to download 10-Ks for a number of weeks after they are filed with the SEC. A visual inspection of the figure shows that investor requests for a 10-K are highest right after it is filed, but continue at levels above a baseline amount for about 18 months after it is filed. After that point, investor downloads of disclosures continue at roughly the same (lower) rate for years after the filing. To the extent that investor downloads of historical disclosures are evidence of their informativeness, we interpret the findings in this figure as prima facie evidence of the informativeness of historical disclosures. We next turn to tests of whether the acquisition of historical disclosures is associated with subsequent market activity. In Table 4 we present the estimation results for model (1). In Panel A of Table 4, we estimate the regressions using historical and recent disclosure requests for all disclosures in EDGAR; in Panel B of Table 4, we focus on requests for periodic accounting disclosures (i.e., requests for 10-Ks and 10-Qs only). The purpose of Panel B is to examine whether historical accounting disclosures are informative to equity markets. For parsimony, we do not tabulate the coefficients on variables used to control for time periods when the market is closed or the hour-of-day fixed effects.In column (1) of Table 4, Panel A we find that HistoricalDisct-1 is positively associated with AbsRett. Since the coefficient on HistoricalDisct-1 captures the market reaction to the acquisition of the historical disclosure, we interpret this result as evidence that historical financial information is informative to markets. The result remains significant after we control for requests for recent disclosures in EDGAR (RecentDisc), the magnitude of news that hit the market (lagged AbsRet), and the magnitude of news and trading volume realized in the prior day. In economic terms, the magnitude of the coefficient on HistoricalDisct-1 is significant: going from 1 to 10 requests during a particular hour is associated with a 1.04 basis point increase in absolute returns over a subsequent one-hour trading period. We also find that RecentDisct-1 is positively associated with AbsRett and that the coefficient magnitude is 2.5 times greater than that on HistoricalDisct-1. The larger magnitude is intuitive, given that more recently filed financial statements are more likely to disclose information not yet incorporated into prices. An untabulated F-test confirms that this difference in the coefficient is statistically significant (F = 11.13, p < 0.01). The results using Volumet as the dependent variable are presented in column (2) of Table 4 and provide similar evidence of a positive association between requests for historical information and market activity. Here, we find that HistoricalDisct-1 and HistoricalDisct-2 are both positively associated with Volumet. The magnitude of the coefficient on HistoricalDisct-1 suggests that going from 1 to 10 requests during a particular hour is associated with a $1.3 million increase in trading volume over a subsequent one-hour trading period. Although the coefficient on HistoricalDisct-1 is slightly higher than the coefficient on RecentDisct-1, an untabulated F-test reveals that the difference is not statistically significant (F= 1.77, p > 0.10). Panel B of Table 4 shows that our analysis of EDGAR requests for periodic accounting disclosures provides similar evidence to that in Panel A, in which we examine all disclosure requests. That is, in column (3) HistoricalDisct-1 is positively associated with AbsRett, and in column (4) HistoricalDisct-1 and HistoricalDisct-2 are both positively associated with Volumet. We note that while the coefficient magnitudes on HistoricalDisct-1 and HistoricalDisct-2 in the two panels are relatively similar when AbsRett is the dependent variable, the magnitudes are considerably larger in Panel B when Volumet is the dependent variable. Overall, the evidence presented in Table 4 is consistent with the notion that historical disclosures are informative in that increases in requests for historical disclosures are associated with increases in returns and trading volume within two hours of the request. The remaining tests examine three potential reasons for this result. 4.3 Tests of H2 – Historical disclosures as context for current newsH2 posits that contemporaneous events and information acquisition have a positive influence on the association between historical disclosure acquisition and market activity. In Table 5 we present the results of estimating model (2) using indicators for the announcement of earnings (EarnAnnHour(-5,5)) or the filing of an 8-K (Form8-K) to capture the contemporaneous event. In Panels A and B we present the results for all disclosure requests and periodic accounting report requests, respectively. Consistent with H2, in Panels A and B we find positive and significant coefficients on the HistoricalDisct-1 x EarnAnnHour(-5,5) interaction across all four model specifications. This evidence indicates that historical disclosures are more informative to equity markets when the disclosures are requested in short windows around the release of the quarterly earnings report. We note that the coefficients on the main effect, HistoricalDisct-1, remain positive and statistically significant for all models; however, the magnitude of the coefficient on the main effect relative to that on the interaction is considerably smaller. For example, in Table 5, Panel A, column (2), the coefficient on HistoricalDisct-1 is 362.77, which indicates that going from 1 to 10 requests during a particular hour is associated with a $0.62 million increase in subsequent hourly trading volume. The sum of the coefficients on HistoricalDisct-1 and HistoricalDisct-1 x EarnAnnHour(-5,5) is 1,827.87, which indicates that going from 1 to 10 requests during the 11-hour earnings announcement period is associated with a $3.1 million increase in subsequent hourly trading volume. Also consistent with H2, in Panels A and B we find positive and significant coefficients on the HistoricalDisct-1 x Form8-K interaction across all four model specifications. This evidence indicates that historical disclosures are particularly informative to equity markets when the disclosures are requested on days when material corporate events are disclosed to the market. We note that the coefficients on the main effect, HistoricalDisct-1, remain positive and statistically significant for all models. Taken together, the evidence presented in Table 5 highlights the importance of investor access to historical financial reports during periods when firms are announcing current news. As a final test of H2, we investigate whether the positive association between historical information acquisition and market activity is increasing in requests for contemporaneous information. In Table 6, Panels A and B we present the results of estimating model (3) using all disclosure requests and periodic accounting report requests, respectively. Consistent with H2, in Panel A we find positive and significant coefficients on the HistoricalDisc x RecentDisc interaction coefficients using requests during the prior hour when AbsRett is the dependent variable and during the prior two hours with Volumet as the dependent variable. We find similar evidence in Panel B using requests for periodic accounting reports, but HistoricalDisc x RecentDisc is significant only when Volumet is the dependent variable. This evidence is consistent with H2 in that historical disclosures appear to be informative to equity markets when they are used in conjunction with more recent disclosures, and vice versa. 4.4 Tests of H3 – Historical disclosures and valuation uncertaintyH3 posits that valuation uncertainty has a positive influence on the association between historical disclosure acquisition and market activity. In Table 7, Panels A and B we present the results of estimating model (5) using all disclosure requests and periodic accounting report requests, respectively. Consistent with H3, Panel B reveals positive and significant coefficients on the HistoricalDisc x ValUncertain interactions in ten of the twelve cases; two of the coefficients are negative and significant. This evidence suggests that the link between historical disclosure acquisition and trading is particularly strong when there is more prior uncertainty about the firm’s information. Furthermore, comparing the coefficient magnitudes across Panel A and Panel B, we find that the results are again stronger when the requests are for periodic accounting reports. Thus, our results are consistent with the prediction in H3. 4.5 Tests of H4 – Historical disclosures and investor sophisticationFinally, we examine whether the positive association between historical disclosure acquisition and market activity can be explained by investor sophistication. We employ a commonly used proxy for investor sophistication based on the average trade size that occurs during the hour (TradeSize). If unsophisticated (sophisticated) investors are trading, we expect that historical disclosure acquisition will be associated with smaller (larger) trade sizes. Table 8, columns (1) and (2) present the estimation of model (6) in which the dependent variable is TradeSizet. We find significantly positive coefficients on HistoricalDisct-1 and HistoricalDisct-2 when the sample includes requests for all disclosures (column (1)) or when the sample is restricted to requests for periodic accounting reports (column (2)). In particular, four out of a possible four coefficients on HistoricalDisc are positive and significant at the one percent level. Consistent with the results in Table 4, the coefficient magnitudes are generally greater when requests for periodic accounting reports are used. This finding suggests that the positive association between requests for historical information and total trading volume observed in Table 4 is being driven, in part, by larger values of individual trades. We urge caution in interpreting these results given evidence that institutions engage in stealth-trading by trading in smaller sizes than they would otherwise (Chakravarty [2001]). However, to the extent that larger dollar value trades are more likely to be initiated by more sophisticated traders, such as hedge funds and institutional investors, these results are inconsistent with unsophisticated investors driving the association between historical information acquisition and market activity. In summary, the evidence provided in the tests described above is consistent with investors appearing to use historical disclosures in trading when historical disclosures provide context for current disclosures or when there is greater information uncertainty about the firm. However, the evidence is inconsistent with an investor unsophistication explanation for the acquisition and use of historical disclosures. The next section discusses robustness tests of these findings. 5. Additional tests5.1 Endogenous Historical Information AcquisitionOur main tests provide evidence of a significant association between the acquisition of historical financial information and market activity in very short windows. As discussed earlier, we do not believe this relation is necessarily causal; rather, we believe that as part of their trading activity investors search for information regarding the firms they may trade. This trading activity is unlikely to be random and thus, there is a concern that whatever sparks interest in trading a particular firm also sparks interest in acquiring historical disclosures even if those disclosures are not related to the trading decisions. Factors associated with trading and information acquisition may be related to broad firm characteristics (i.e., investors acquire more filings of larger firms) or to events (i.e., investors acquire more filings around news events). We provide three additional tests to alleviate concerns that our results are driven by factors that affect both information search and trading without the search and trading being related.First, we estimate a determinants model that explains the level of information acquisition through EDGAR using firm characteristics. Specifically, for each hour of the day, we regress the number of requests for current and historical financial filings on a broad set of firm characteristic including the market value of equity, book-to-market ratio, return on assets, leverage, analyst following, and institutional ownership (see Table 1, Panel A for the distributions of these variables for our sample firms). We find that these firm characteristics explain 6.9 percent (13.7 percent) of the variation in current (historical) information acquisition. We then take the residuals from the regression as a measure of “abnormal” current and historical information acquisition and re-estimate our main tests using the abnormal values (results untabulated). We find that our inferences remain unchanged for those presented in the main tables using the raw acquisition values. Second, as an alternative to the determinants model just described, we estimate our models including firm fixed-effects. This approach removes the influence of unobserved, static firm characteristics associated with the level of historical information acquisition and exploits time-series variation within each firm. The analysis yields results that are consistent with those reported in the tables (untabulated). 5.2 Different Thresholds for Historical Information AcquisitionIn the tests above, we use a 30-day threshold to identify historical financial disclosures. Although this threshold provides a reasonable amount of time for information to become historical, we acknowledge that it is arbitrary. Here we test the robustness of the results to two different thresholds. First, we use a threshold of one week. That is, under this alternate research design, HistoricalDisc (RecentDisc) captures the number of investor requests for a financial disclosure that has been on EDGAR for more than or equal to (less than) seven days. We find that the results (untabulated) using this threshold are consistent with those presented in the previous section. Second, we use a mix of the 30- and 7-day thresholds. Under this alternative, HistoricalDisc measures the number of investor requests for a financial disclosure that has been on EDGAR for more than 30 days, and RecentDisc measures the number of investor requests for disclosures that have been on EDGAR for less than seven days. Requests for disclosures that have been on EDGAR for more than one week but less than one month are excluded. Again the results (untabulated) using these alternative thresholds are consistent with those discussed in the previous section. Overall, we conclude that, regardless of the cutoff threshold for the age of a financial disclosure, the tenor of results is consistent with investors finding historical disclosures informative.5.3 Earnings Announcements and 8-KsDue to the fact that earnings announcements are a subset of total 8-K filings, there is overlap between 8-K filings and earnings announcements. To ensure our 8-K results (in Table 6) are not driven by earnings announcements (in Table 5), we analyze the relation between 8-K filings and HistoricalDisc after excluding 8-K filings that disclose earnings news. With this exclusion, we estimate model (2) and find that the coefficient on the interaction term HistoricalDisct-1 x Form8-K remains positive and significant in explaining subsequent market activity. This finding reinforces the inference that the informativeness of historical disclosures for current material events is not driven solely by the need to provide context for current earnings numbers. 6. Summary and ConclusionWe provide novel evidence that investors appear to find historical disclosures informative. We use disclosure requests on EDGAR as a proxy for information acquisition and test whether the acquisition of historical disclosures is associated with subsequent short-term absolute returns and trading volume. We find evidence suggesting that investors trade on historical disclosures within two hours of acquiring the information and that this result is particularly strong when the acquired information is a periodic accounting report (10-K or 10-Q). We propose and test reasons that historical financial information continues to be informative even after it has been publicly available for an extended period of time. Our evidence suggests that historical disclosures are informative to markets when they provide context for current events and disclosures, and when there is greater prior information uncertainty about the firm. We find no evidence that the association between historical disclosure acquisition and market activity is related to unsophisticated trading. Although our results provide evidence consistent with historical disclosure informativeness, several caveats apply to our study. First, we do not observe actual usage of the information; we simply observe that investors request the information from EDGAR. We assume that investors request the information only if they plan to use it. Second, the computational difficulties of our analyses limit our sample to 200 randomly selected firms. Our analyses suggest that these firms are representative, but to the extent that they are not, our results will not apply to a broader cross-section of firms. The same caveat applies to our limited sample period, which may not be typical of other time periods. Finally, investors can gather financial disclosures from various channels. Because EDGAR is designed specifically to maintain historical disclosures, we may overestimate the informativeness of disclosures if investor information acquisition activities are not similar in other channels of information dissemination. With these caveats in mind, we interpret our results as an important first step in understanding the informativeness of historical financial disclosures. Our results contribute to a burgeoning literature that seeks to learn more about investor acquisition of information. As Charles Lee states, this line of research “…adopts a ’user,’ rather than a ’preparer,’ orientation toward accounting information. User-oriented research, such as valuation, is definitely a step in the right direction” (Lee, [1999]). While our study answers this call by providing new evidence on investors’ usage of historical disclosures, many questions remain. For example: what types of investors request historical information? Exactly how do the investors use the information? What substitutes for historical information exist? What components of financial disclosures (such as particular footnotes in an annual 10-K), are associated with trading activities? We look forward to future, user-oriented research that helps answer such questions.APPENDIX AVariable Definitions and Data SourcesVariableDescriptionSourceAbsRetThe absolute raw stock return during the hour.TAQAnalyst FollowingThe number of analysts in the consensus analyst forecast measured in December 2007.IBESBook-to-MarketCommon equity (CEQ) divided by the market value of equity (PRCC_F x CSHO) as measured in the fiscal year ending in pustatControls for Market ClosedAn indicator variable set equal to one during times when the market is closed (evenings, weekends, and holidays). The indicator variable is also interacted with Edgar Requests. TAQCurrentEventsOne of two proxies for current events: EarnAnnHour(-5,5) or Form8-K.DispersionAnalyst earnings forecast dispersion measured as the standard deviation of annual EPS forecasts as measured at the end of 2007, scaled by stock price at the end of 2007IBESEarnAnnHour(-5,5)An indicator variable set equal to one during an eleven-hour window centered on the earnings announcement hour.IBESForm8-KAn indicator variable set equal to one on 8-K filings dates and to zero otherwiseSECEarnQualityThe standard deviation of the residual value of a regression of current accruals on past, current and future cash flows as described in Dechow and Dichev [2002].CompustatInst. OwnershipThe percentage of outstanding shares owned by institutions as measured in the last reporting date of 2007.ThomsonIntangIntangible assets defined as one minus the ratio of property, plant, and equipment (PPENT) to total assets (AT) as measured at the end of pustatLeverageTotal liabilities (LT) divided by total assets (AT) as measured in the fiscal year ending in pustatMVEThe market value of equity (PRCC_F x CSHO) as measured in the fiscal year ending in pustatPriorDay_AbsRetThe absolute stock return for the previous day.CRSPPriorDay_TurnoverTotal trading volume (in shares) dividend by shares outstanding for the previous day.CRSPRankMVEThe quantile rank of the firm’s market value of equity as measured in the fiscal year ending in 2007.CRSPRecentDiscThe natural log of the count of the number of investor requests during the hour for disclosures that have been publicly available on EDGAR for less than 30 days.SECReturn on AssetsIncome before extraordinary items (IB) divided by total assets (AT) as measured in the fiscal year ending in 2007. CompustatHistoricalDiscThe natural log of the count of the number of investor requests during the hour for disclosures that have been publicly available on EDGAR for at least 30 days.SECTradeSizeThe average dollar value of individual trades executed during the hour. TAQValUncertainOne of three proxies for valuation uncertainty: Intang, EarnQuality, or DispersionVolumeThe dollar value of aggregate trading volume for a given firm during the hour. TAQREFERENCES:Abarbanell, B., and B. Bushee. “Abnormal returns to a fundamental analysis strategy.” The Accounting Review 73 (1998): 19-45.Aharony, J., and I. Swary. “Quarterly Dividend and Earnings Announcements and Stockholders’ Returns: An Empirical Analysis.” Journal of Finance 35 (1980): 1-12.Atiase, R. “Predisclosure information, firm capitalization and security price behavior around earnings announcements.” Journal of AccountingResearch 23 (1985): 21–36.Baker, M. and J. Wurgler. “Investor Sentiment and the Cross-Section of Stock Returns.” Journal of Finance (2006): 1,645-1680Ball, R. “The Global Financial Crisis and the Efficient Market Hypothesis: What Have We Learned?” Journal of Applied Corporate Finance (2009): 8-16.Ball, R., and P. Brown. “An Empirical Evaluation of Accounting Income Numbers.” Journal of Accounting and Economics 6 (1968): 159-178.Ball, R., and L. Shivakumar. “How much new information is there in earnings?” Journal of Accounting Research 46 (2008), 975-1016.Barberis, N., A. Shleifer, and R. Vishny. “A model of investor sentiment.” Journal of Financial Economics 49 (1998): 307-343.Beaver, W. “The information content of annual earnings announcements.” Journal of Accounting Research 6 (1968): 67–92.Bernard, V and J. Thomas. “Post-Earnings-Announcement Drift: Delayed Price Response or Risk Premium?” Journal of Accounting Research 27 (1989): 1-36Bloomfield, R. “The "Incomplete Revelation Hypothesis" and Financial Reporting” Accounting Horizons 16 (2002): 233-243.Brav, A., and J. Heaton. “Competing Theories of Financial Anomalies.” Review of Financial Studies 15 (2002): 575-606.Brown, S., and J. Warner. “Using Daily Stock Returns: The Case of Event Studies.” Journal of Financial Economics 14 (1985): 3-31. Bushee, B., J. Core, W. Guay, and S. Hamm. “The Role of the Business Press as an Information Intermediary.” Journal of Accounting Research 48 (2010): 1-19.Bushee, B., M. Jung, and G. Miller. “Conference Presentations and the Disclosure Milieu.” Journal of Accounting Research 49 (2011): 1163-92.Busse, J., and T. C. Green. “Market Efficiency in Real Time.” Journal of Financial Economics 65 (2002): 415-37.Chakravarty, S. “Stealth-trading: Which Traders’ Trades Move Stock Prices?” Journal of Financial Economics 61 (2001): 289-307.Colllins, D., S. Kothari, J. Shanken, and R. Sloan. “Lack of timeliness and noise as explanations for the low contemporaneous return-earnings association” Journal of Accounting and Economics 18 (1994): 289-324.D’Souza, J., K. Ramesh, and M. Shen. “The Interdependence between Institutional Ownership and Information Dissemination by Data Aggregators.” The Accounting Review 85(1) (2010): 159-193.Da, Z., J. Engelberg, and P. Gao. “In Search of Attention.” Journal of Finance 66 (2011): 1461-1499Dechow, P., and I. Dichev. “The quality of accruals and earnings: the role of accrual estimation errors.” The Accounting Review 77 (2002): 35–59. deHaan, E, T. Shevlin, and J. Thornock. “Investor attention and earnings announcement timing.” Working paper (2012)Dong, B., E. Li, K. Ramesh, and M. Shen. “The effects of regulation FD on informal and institutionalized leakages of information in earnings press releases”. Working paper (2011). Drake, M., D. Roulstone, and J. Thornock. “What Investors Want: Evidence from Investors’ Use of the EDGAR Database.” Working Paper (2012a).Drake, M., D. Roulstone, and J. Thornock. “Investor Information Demand: Evidence from Google Searches Around Earnings Announcements.” Journal of Accounting Research (2012) 50:1001-1040.El-Gazzar, S. “Predisclosure information and institutional ownership: A cross-sectional examination of market revaluations during earnings announcement periods.” The Accounting Review 73 (1998): 119-129. Engelberg, J., and C. Parsons. “The Causal Impact of Media in Financial Markets.” Journal of Finance 66 (2011): 67-97. Engelberg, J., C. Sasseville, and J. Williams. “Market Madness? The Case of Mad Money” Management Science 58 (2012): 351-364.Fama, E. “Efficient Capital Markets: II” Journal of Finance 46 (1991): 1575-1617.Fama, E., and K. French. “Dissecting Anomalies.” Journal of Finance 63 (2008): 1653-1678. Foster, G. “Stock market reactions to estimates of earnings per share by company officials.” Journal of Accounting Research (Spring 1973): 25-37.Francis, J., R. LaFond, P. Olsson, and K. Schipper. “The market pricing of accruals quality.” Journal of Accounting and Economics 39 (2005): 295-327. Francis, J., R. LaFond, P. Olsson, and K. Schipper. “Information Uncertainty and Post-Earnings-Announcement-Drift.” Journal of Business Finance & Accounting 34 (2007): 403-433. Frankel, R., M. Johnson, and D. Skinner. “An Empirical Examination of Conference Calls as a Voluntary Disclosure Medium.” Journal of Accounting Research 37 (1999): 133-150.Freeman, R., and S. Tse. “The Multiperiod Information Content of Accounting Earnings: Confirmations and Contradictions of Previous Earnings Reports. Journal of Accounting Research 27 (1989): 49-79.Gao, L., O. Li, and E. Yeung. “Information Acquisition and Investors Trading: Daily Analysis.” Working Paper, University of Georgia (2011).Gervais, S., R. Kaniel, and D. Mingelgrin. “The high-volume return premium” Journal of Finance 56 (2001): 877-919.Gilbert, T., S. Kogan, L. Lochstoer, and A. Ozyildirim. “Investors Inattention and the Market Impact of Summary Statistics.” Management Science 58 (2012): 336-350. Griffin, P. “A League of Their Own? Financial Analysts’ Responses to Restatements and Corrective Disclosures.” Journal of Accounting Auditing, and Finance (2003): 479-518.Hand, J. “A Test of the Extended Functional Fixation Hypothesis.” The Accounting Review 65 (1990): 740-763. Hirshleifer, D. and S. Teoh. “Limited Attention, Information Disclosure, and Financial Reporting” Journal of Accounting and Economics 36 (2003): 337-386.Huberman, G., and T. Regev. “Contagious Speculation and a Cure for Cancer: A Nonevent that Made Stock Prices Soar.” Journal of Finance 56 (2001): 387-396.Kelly, E. and P. Tetlock. “How (Un)Informed Is Trading?” Working Paper, Columbia University (2012).Kirk, M. “Research for Sale: Determinants and Consequences of Paid-For Analyst Research.” Journal of Financial Economics 100 (2011): 182-200.Kumar, A. “Hard-to-Value Stocks, Behavioral Biases, and Informed Trading.” Journal of Financial and Quantitative Analysis 44 (2009): 1375-1401.Kothari, S. and J. Warner. “Econometrics of Event Studies.” Handbook of Corporate Finance, 1 (2007): 3-36.Lee, C. “Accounting-based valuation: Impact on business practices and research.” Accounting Horizons 13 (1999): 413-425.Lev, B. “On the Usefulness of Earnings and Earnings Research: Lessons and Directions from Two Decades of Empirical Research.” Journal of Accounting Research 27 (1989): 153-192. Li, E. and K. Ramesh. “Market Reaction Surrounding the Filing of Periodic SEC Reports.” The Accounting Review 84 (2009): 1171-1208.Li, E., K. Ramesh, and M. Shen. “The Role of Newswires in Screening and Disseminating Value-Relevant Information in Periodic SEC Reports.” The Accounting Review 86(2) (2011): 669-701.Lerman, A., and J. Livnat. “The new form 8-K disclosures.” Review of Accounting Studies 15 (2010): 752-778.Malkiel, B. “The Efficient Market Hypothesis and its Critics.” Journal of Economic Perspectives 17 (2003): 59-82.Ou, J., and S. Penman. “Financial Statement Analysis and the Prediction of Stock Returns.” Journal of Accounting and Economics 11 (1989): 295-329.Palmrose, Z. V., V. Richardson, and S. Scholz. “Determinants of Market Reactions to Restatement Announcements.” Journal of Accounting and Economics 37 (2004): 59-89.Patell, S. “Corporate forecasts of earnings per share and stock price behavior: empirical tests.” Journal of Accounting Research 14 (1976): 246-76.Piotroski, J. “Value Investing: The Use of Historical Financial Statement Information to Separate Winners from Losers.” Journal of Accounting Research 38 (2000): 1-41.Soltes, E. “News dissemination and the impact of the business press.” Working paper, Harvard Business School. (2009).Teoh, S. H., and T. J. Wong. “Perceived Auditor Quality and the Earnings Response Coefficient.” The Accounting Review 68 (1993): 346-366.Tetlock, P. “All the News That’s Fit to Reprint: Do Investors React to Stale Information?” Review of Financial Studies 24 (2011): 1481-1512. FIGURE 1Average Investor Requests for 10-Ks by Age of the 10-K Filing This figure plots the average number of weekly requests for 10-Ks over the ten year period (520 weeks) following the filing date of the 10-K. The y-axis represents the cross-sectional average number of requests per firm-week. The x-axis represents the time that has passed since the filing of the 10-K.TABLE 1Comparison of the Random Sample to the Universe of FirmsPanel A: Firm CharacteristicsRandom Sample (N = 200)Universe (N = 5,307)Mean DiffMedian DiffVariablesMeanMedianMeanMedianT-StatZ-StatMVE ($M)4,9984013,5084220.83-0.39Total Assets ($M)10,83149513,626594-0.60-0.49Book-to-Market47.2%43.2%48.4%44.1%-0.18-1.16Return on Assets-3.3%1.6%-3.1%2.3%-0.10-1.37Leverage59.6%55.7%54.5%53.1%1.601.60Analyst Following5.13.04.83.00.690.16Inst. Ownership57.3%58.1%56.0%58.3%0.49-0.68Panel B: Industry DistributionRandom SampleUniverse (N = 200)(N = 5,307)Fama-French ClassificationN% of TotalN% of TotalFood74%1242%Mining and Minerals21%892%Oil and Petro Products84%2124%Textiles, Apparel, and Footwear42%701%Consumer Durables42%912%Chemicals32%892%Drugs, Soap, Perfumes, Tobacco137%3146%Construction84%1172%Steel21%541%Fabricated Products11%281%Machinery and Equipment1910%63512%Automobiles32%751%Transportation105%1894%Utilities95%1343%Retail Stores32%2334%Financial Institutions3819%1,12221%Other6633%1,68532%This table compares the random sample used in our analyses to the universe of CRSP/Compustat firms with available data. Panel A examines whether there is a significant difference in six firm characteristics between the random sample and the universe of firms. The measurement and source of all variables in the panel are described in Appendix A. Panel B compares the industry distribution of the random sample and the universe of firms. Industries are defined using the Fama-French 17 classification. TABLE 2Descriptive Statistics by Hour of the DayPanel A: Aggregate Requests for Disclosures in SEC EDGARRequests for Historical Disclosures?Requests for Recent DisclosuresHour of the DayMeanStdMax?MeanStdMax0:00 to 0:591345828665361861:00 to 1:591205632559341922:00 to 2:591065024856331493:00 to 3:59995027150311374:00 to 4:59975124447281335:00 to 5:591035130646301276:00 to 6:59964927253351657:00 to 7:591136431876522388:00 to 8:59190105421138954209:00 to 9:5933519170821715059010:00 to 10:59462266100626317968211:00 to 11:5951128697327619191812:00 to 12:5948024796225116769713:00 to 13:59489250102626818978714:00 to 14:59553289116827718773415:00 to 15:595422811037288200101616:00 to 16:59526269102929219490417:00 to 17:5944522686823915561618:00 to 18:5933516570518613155619:00 to 19:592741265371429051820:00 to 20:59212965131066431121:00 to 21:59198804201006237322:00 to 22:5918280373834723723:00 to 23:59156622977439198Panel B: Descriptive Statistics for Market VariablesAbsRetVolumeTradeSizeHour of the DayMeanStdMeanStdMeanStd0:00 to 0:59------------1:00 to 1:59------------2:00 to 2:59------------3:00 to 3:59------------4:00 to 4:590.000%0.05%283,90432215:00 to 5:590.000%0.01%313,70263536:00 to 6:590.001%0.07%34330,284247647:00 to 7:590.006%0.15%9,584886,1061381,8128:00 to 8:590.037%0.38%133,3083,509,98219,939661,4439:00 to 9:590.610%1.57%3,839,66427,637,5673,0166,41510:00 to 10:590.479%1.18%5,370,79932,838,4422,46214,66811:00 to 11:590.386%0.98%3,966,45722,335,8802,48717,84812:00 to 12:590.320%0.87%3,252,12718,275,3192,3509,33213:00 to 13:590.306%0.80%3,258,49518,226,5082,2927,31614:00 to 14:590.335%0.88%4,154,82923,716,5512,2966,56315:00 to 15:590.455%1.23%7,097,84539,514,8192,4095,74016:00 to 16:590.350%0.98%2,032,98516,795,43283,574358,22517:00 to 17:590.028%0.47%112,6212,314,36930,334481,83118:00 to 18:590.009%0.15%31,9491,294,72410,436335,25219:00 to 19:590.007%0.12%7,959347,2793,17881,67020:00 to 20:59------------21:00 to 21:59------------22:00 to 22:59------------23:00 to 23:59------------This table presents summary statistics by hour for our primary measures of historical information acquisition (HistoricalDisc) and recent information acquisition (RecentDisc) (Panel A). It also presents summary statistics by hour for our measures of capital market activity (AbsRet and Volume) and measure of investor sophistication (TradeSize) (Panel B). Volume is presented in thousands of dollars and TradeSize is presented in raw dollars. TAQ does not report trading activity between 8pm and 4pm, so those values are omitted in Panel B. The measurement and source of all variables in Panel are described in Appendix A. TABLE 3 Pairwise Rank CorrelationsVariables123456781HistoricalDisct-12HistoricalDisct-20.403RecentDisct-10.320.234RecentDisct-20.240.320.375AbsRet0.230.190.180.156Volume0.290.240.230.190.857TradeSize0.280.240.220.190.840.998PriorDay_AbsRet0.130.130.120.120.230.260.269PriorDay_Turnover0.250.260.210.210.310.370.360.66This table presents the univariate Spearman correlations between variables of interest. The measurement and source of all variables are described in Appendix A. All pairwise correlations are significant at the 10 percent level. TABLE 4The Association between Historical Disclosure Acquisition and Market ActivityPanel A: All DisclosuresPanel B: 10-Ks and 10-Qs?(1)?(2)(3)?(4)VariablesAbsRett?VolumetAbsRett?VolumetHistoricalDisct-10.000061***770.72***0.000057**1,198.34***(0.000019)(34.24)(0.000023)(56.50)HistoricalDisct-20.000013547.76***0.000009815.80***(0.000015)(30.39)(0.000017)(50.99)RecentDisct-10.000155***659.65***0.000112***657.34***(0.000023)(73.09)(0.000036)(156.30)RecentDisct-20.000005324.29***-0.000012429.53***(0.000020)(56.23)(0.000036)(111.31)AbsRett-10.185277***7,595.64**0.185593***9,196.42***(0.007113)(2,919.29)(0.007114)(2,900.57)AbsRett-20.086315***-15.440.086671***1,942.38(0.005027)(2,410.02)(0.005037)(2,402.00)Volumet-10.79***0.79***(0.02)(0.02)Volumet-2-0.07***-0.07***(0.02)(0.02)RankMVE-0.000573***830.03***-0.000540***837.04***(0.000036)(54.03)(0.000036)(57.70)PriorDay_ AbsRet0.012689***-412.08*0.012717***-201.18(0.000715)(236.58)(0.000714)(243.95)PriorDay_ Turnover0.002403***1,815.30**0.002500***2,606.97***(0.000764)(744.55)(0.000787)(929.06)Controls for Market ClosedYesYesYesYesHour Fixed EffectsYesYesYesYesDay Clustered SEYesYesYesYesN873,200?873,200873,200?873,200R-Squared0.157?0.5810.157?0.581This table presents the results of estimating equation (1), in which the dependent variables measure capital market activity (AbsRett and Volumet) in a given hour t and the independent variables of interest are the number of investor requests for historical information in the previous two hours (HistoricalDisct-1 and HistoricalDisct-2). The measurement and source of all variables are described in Appendix A. TABLE 5The Impact of Current Events on the Association between Historical Disclosure Acquisition and Market ActivityPanel A: All DisclosuresCurrentEvent =EarnAnnHour(-5,5)Form8-K?(1)?(2)(3)?(4)VariablesAbsRett?VolumetAbsRett?VolumetHistoricalDisct-10.000140***362.77***0.000141***356.62***(0.000023)(20.46)(0.000023)(21.09)HistoricalDisct-20.000065***272.19***0.000065***273.18***(0.000020)(19.39)(0.000020)(20.43)RecentDisct-10.000229***323.53***0.000216***335.78***(0.000030)(45.46)(0.000029)(49.58)RecentDisct-20.000047*150.06***0.000052**148.89***(0.000025)(35.56)(0.000025)(33.71)CurrentEvent0.001186***649.160.000268***-153.05(0.000247)(654.30)(0.000087)(277.98)HistoricalDisct-1 x CurrentEvent0.000571**1,465.10*0.000111**394.31*(0.000298)(891.14)(0.000066)(259.18)HistoricalDisct-2 x CurrentEvent0.00001686.740.00000712.47(0.000356)(511.50)(0.000072)(140.78)RecentDisct-1 x CurrentEvent-0.000066-607.200.000110-184.81(0.000282)(723.69)(0.000087)(305.38)RecentDisct-2 x CurrentEvent-0.000250-447.54-0.000143**-44.34(0.000269)(301.05)(0.000071)(189.38)Controls YesYesYesYesHour Fixed EffectsYesYesYesYesDay Clustered SEYesYesYesYesN873,200?873,200873,200?873,200R-Squared0.158?0.5830.158?0.583TABLE 5, continuedPanel B: 10-Ks and 10-QsCurrentEvent =EarnAnnHour(-5,5)Form8-K?(1)?(2)(3)?(4)VariablesAbsRett?VolumetAbsRett?VolumetHistoricalDisct-10.000157***465.21***0.000156***456.13***(0.000031)(32.12)(0.000031)(32.95)HistoricalDisct-20.000071***348.95***0.000068***351.61***(0.000025)(28.31)(0.000026)(29.29)RecentDisct-10.000250***351.02***0.000244***264.74***(0.000052)(87.61)(0.000052)(80.09)RecentDisct-20.000064175.74***0.000058129.01**(0.000051)(59.91)(0.000050)(57.41)CurrentEvent0.001387***652.140.000303***-215.11(0.000212)(533.13)(0.000074)(267.56)HistoricalDisct-1 x CurrentEvent0.000542**2,183.45**0.000150***667.62**(0.000273)(1,138.57)(0.000064)(395.40)HistoricalDisct-2 x CurrentEvent-0.000220-38.320.000009-61.63(0.000342)(586.18)(0.000076)(178.60)RecentDisct-1 x CurrentEvent-0.001050*-4,774.15-0.000183-167.41(0.000559)(2,985.87)(0.000156)(1,080.76)RecentDisct-2 x CurrentEvent0.0004332,342.160.000154998.45*(0.000616)(1,703.55)(0.000175)(585.45)ControlsYesYesYesYesHour Fixed EffectsYesYesYesYesDay Clustered SEYesYesYesYesN873,200?873,200873,200?873,200R-Squared0.158?0.5830.158?0.583This table presents the results of estimating equation (2), in which the dependent variables measure capital market activity (AbsRett and Volumet) in a given hour t and the independent variables of interest are the number of investor requests for historical information in the previous two hours (HistoricalDisct-1 and HistoricalDisct-2). Controls is a set of control variables that are included in the model (2), but not tabulated. The measurement and source of all variables are described in Appendix A. TABLE 6The Impact of Recent Disclosure Acquisition on the Association between Historical Disclosure Acquisition and Market ActivityPanel A: All DisclosuresPanel B: 10-Ks and 10-Qs?(1)?(2)(3)?(4)VariablesAbsRett?VolumetAbsRett?VolumetHistoricalDisct-10.000130***18.480.000169***371.29***(0.000024)(46.02)(0.000031)(49.72)HistoricalDisct-20.000063***159.10***0.000070***291.71***(0.000020)(31.66)(0.000025)(30.32)RecentDisct-10.000204***-364.02***0.000239***-161.20(0.000032)(59.06)(0.000054)(99.58)RecentDisct-20.000042-92.17**0.000065-54.11(0.000027)(41.19)(0.000054)(73.86)HistoricalDisct-1 x RecentDisct-10.000035**744.12***-0.000005746.99**(0.000014)(96.09)(0.000020)(290.62)HistoricalDisct-2 x RecentDisct-20.000006251.17***0.000008386.89**(0.000010)(64.63)(0.000019)(184.34)ControlsYesYesYesYesHour Fixed EffectsYesYesYesYesDay Clustered SEYesYesYesYesN873200?873200873200?873200R-Squared0.158?0.5840.157?0.583This table presents the results of estimating equation (3), in which the dependent variables measure capital market activity (AbsRett and Volumet) in a given hour t and the independent variables of interest are the interactions between the number of investor requests for historical information in the previous two hours (HistoricalDisct-1 and HistoricalDisct-2) and the number of investor requests for recent information in the previous two hours (RecentDisct-1 and RecentDisct-2). Controls is a set of control variables that are included in the model (3), but not tabulated. The measurement and source of all variables are described in Appendix A. TABLE 7The Impact of Valuation Uncertainty on the Association between Historical Disclosure Acquisition and Market ActivityPanel A: All DisclosuresValUncertain =IntangEarnQualityDispersion?(1)(2)(3)(4)(5)(6)VariablesAbsRettVolumetAbsRettVolumetAbsRettVolumetHistoricalDisct-10.000091**-761.81***0.000104***283.99***0.000149***366.65***(0.000042)(71.53)(0.000038)(21.17)(0.000025)(33.14)HistoricalDisct-2-0.000029-425.65***-0.000016310.54***0.000050*506.24***(0.000035)(53.79)(0.000035)(22.28)(0.000027)(37.47)RecentDisct-10.000176***-967.69***0.000142***306.51***0.000165***381.27***(0.000055)(141.08)(0.000054)(40.99)(0.000037)(74.30)RecentDisct-2-0.000059-392.39***0.000066226.97***0.000050362.37***(0.000047)(114.62)(0.000048)(36.05)(0.000036)(60.79)ValUncertain-0.000229***-595.02***-0.0005391,352.35***0.000210***909.05***(0.000034)(61.18)(0.000509)(195.02)(0.000022)(66.15)HistoricalDisct-1 x ValUncertain0.000068**1,432.61***0.0006464,591.04***0.000058***-529.57***(0.000041)(103.69)(0.000819)(449.35)(0.000029)(52.03)HistoricalDisct-2 x ValUncertain0.000118***868.59***0.001965**1,674.17***0.000040-564.59***(0.000036)(83.56)(0.000817)(378.25)(0.000034)(48.04)RecentDisct-1 x ValUncertain0.0000741,626.38***0.003132**2,431.14***0.000127***-621.27***(0.000056)(223.33)(0.001230)(775.71)(0.000042)(116.62)RecentDisct-2 x ValUncertain0.000134**672.48***-0.000171-646.45-0.000074**-488.13***(0.000052)(178.23)(0.001004)(664.58)(0.000034)(86.97)ControlsYesYesYesYesYesYesHour Fixed EffectsYesYesYesYesYesYesDay Clustered SEYesYesYesYesYesYesN873,200873,200602,508602,508515,188515,188R-Squared0.1580.5840.1650.5870.2290.588TABLE 7, continuedPanel B: 10-Ks and 10-QsValUncertain =IntangEarnQualityDispersion?(1)(2)(3)(4)(5)(6)VariablesAbsRettVolumetAbsRettVolumetAbsRettVolumetHistoricalDisct-10.000064-1,471.04***0.000040342.10***0.000204***421.94***(0.000050)(111.76)(0.000046)(32.97)(0.000032)(50.12)HistoricalDisct-2-0.000046-968.33***-0.000027347.59***0.000021586.32***(0.000042)(92.87)(0.000043)(31.72)(0.000030)(49.23)RecentDisct-10.000266***-1,025.73***0.000200**264.09***0.000123**280.47**(0.000082)(344.42)(0.000094)(76.08)(0.000049)(132.31)RecentDisct-2-0.000113-453.47**0.000057287.68***0.000109*430.33***(0.000081)(219.25)(0.000095)(57.60)(0.000064)(93.59)ValUncertain-0.000201***-421.35***-0.0000441,274.87***0.000226***787.41***(0.000032)(68.08)(0.000474)(179.48)(0.000022)(57.18)HistoricalDisct-1 x ValUncertain0.000130***2,502.45***0.002322**6,429.75***0.000093***-826.29***(0.000047)(161.19)(0.000983)(663.87)(0.000035)(77.26)HistoricalDisct-2 x ValUncertain0.000147***1,674.80***0.002353**2,728.88***0.000046*-872.11***(0.000045)(143.38)(0.000929)(573.91)(0.000035)(76.09)RecentDisct-1 x ValUncertain-0.0000381,631.97***0.0028082,343.97*0.000119**-682.22***(0.000080)(531.25)(0.002117)(1,381.07)(0.000056)(219.24)RecentDisct-2 x ValUncertain0.000229***815.89**0.000844-669.00-0.000064-621.45***(0.000077)(341.33)(0.001850)(911.14)(0.000056)(146.60)Controls YesYesYesYesYesYesHour Fixed EffectsYesYesYesYesYesYesDay Clustered SEYesYesYesYesYesYesN873,200873,200602,508602,508515,188515,188R-Squared0.1570.5840.1650.5860.2290.588This table presents the results of estimating equation (5), in which the dependent variables measure capital market activity (AbsRett and Volumet) in a given hour t and the independent variables of interest are the interactions between the number of investor requests for historical information in the previous two hours (HistoricalDisct-1 and HistoricalDisct-2) and one of three proxies for the level of valuation uncertainty. Controls is a set of control variables that are included in the model (3), but not tabulated. The measurement and source of all variables are described in Appendix A. TABLE 8The Association between Historical Disclosure Acquisition and Average Trade SizesPanel A: All Disclosures?Panel B: 10-Ks and 10-Qs?(1)?(2)VariablesTradeSizet?TradeSizetHistoricalDisct-16,431.19***7,769.01***(802.16)(829.32)HistoricalDisct-26,876.71***10,543.32***(766.46)(1,056.02)RecentDisct-13,974.74***5,872.97***(928.88)(1,294.83)RecentDisct-24,211.39***3,926.17***(1,102.52)(1,150.86)AbsRett-1-145,205.16***-129,882.56***(25,310.91)(25,218.73)AbsRett-2-91,520.52***-73,295.43***(21,843.94)(21,637.53)Volumet-10.02***0.02***(0.01)(0.01)Volumet-20.010.01(0.00)(0.00)RankMVE16,164.40***16,497.06***(1,349.29)(1,372.09)PriorDay_ AbsRet-13,341.64***-11,812.52**(4,772.53)(4,752.68)PriorDay_ Turnover8,972.63*16,005.36**(4,986.46)(6,178.22)Controls for Market ClosedYesYesHour Fixed EffectsYesYesDay Clustered SEYesYesN873,200?873,200R-Squared0.013?0.012This table presents the results of estimating equation (6), in which the dependent variable is a proxy for investor sophistication (TradeSizet) in a given hour t and the independent variables of interest are the number of investor requests for historical information in the previous two hours (HistoricalDisct-1 and HistoricalDisct-2). The measurement and source of all variables are described in Appendix A. ................
................

In order to avoid copyright disputes, this page is only a partial summary.

Google Online Preview   Download