The information content of operations-related disclosures

Journal Pre-proof The Information Content of Operations-Related Disclosures Guang Ma

PII:

S0737-4607(19)30107-7

DOI:

https://doi.org/10.1016/j.acclit.2019.11.004

Reference:

ACCLIT 63

To appear in:

Journal of Accounting Literature

Received Date:

14 October 2019

Revised Date:

9 November 2019

Accepted Date:

9 November 2019

Please cite this article as: Ma G, The Information Content of Operations-Related Disclosures, Journal of Accounting Literature (2019), doi: https://doi.org/10.1016/j.acclit.2019.11.004

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The Information Content of Operations-Related Disclosures

Guang Ma Desautels Faculty of Management McGill University 1001 Sherbrooke St. W. Montreal, QC H3A 1G5, Canada

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[email protected]

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ABSTRACT This study examines the information content of firms’ operations-related disclosures (ORDs) and the importance of these disclosures as an information source to stock markets relative to other commonly examined sources of information. I find that ORDs constitute a large portion of corporate press releases. These disclosures are associated with significant stock price reactions and trading volume. The stock price reactions to ORDs are greater than the reactions to 10-K/Q reports and are of similar magnitudes to the reactions to 8-K filings. On average, ORDs explain variation in firms’ quarterly returns to a similar degree as management earnings forecasts and 10-K/Q reports for the full sample and to a greater degree for small firms and firms with lower earnings quality.

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Keywords: Nonfinancial disclosure; operations disclosure; information content; relative importance.

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JEL Classification: G14, L22, M4

1. INTRODUCTION This study examines firms’ disclosures about their operations—product developments; strategic alliances; clients; and business expansion, reorganization, and discontinuation—through corporate press releases (referred to as “operations-related disclosures,” “ORDs” for short). I describe the prevalence of ORDs, examine the information content of such disclosures, and quantify the contribution of ORDs as a source of information to stock markets relative to other commonly examined sources of information.

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It is important to examine ORDs because they are prevalent. For example, during 2002 to 2010, International Business Machines Corporation (IBM) made 3,423 operations-related disclosures, but only

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43 press releases of MEFs. There is a large body of accounting literature examining management earnings forecasts (MEFs) (Beyer et al. 2010; Hirst et al. 2008). However, there is limited research on ORDs and no

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large sample evidence (Maines et al. 2002). Examining the more prevalent ORDs is important for investors,

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regulators, and researchers to have a more comprehensive understanding of corporate disclosure. Using the Capital IQ Key Developments database, I obtain a sample of 76,366 ORDs made by

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3,647 U.S. public firms covered by Compustat and CRSP between 2002 and 2010 that do not occur concurrently with earnings announcements, MEFs, or 10-K/Q reports and are not accompanied with

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subsequent 8-K filings. In contrast, the same population of firms made a total of 21,078 MEFs over the same period. 1 I measure market reactions to ORDs using absolute abnormal returns, abnormal return volatility, and abnormal trading volume. I find that, on average, the absolute size-adjusted abnormal returns, abnormal return volatility, and abnormal volume are 2.55 percent, 79.14 percent, and 47.35 percent on the

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event day, respectively. These results indicate that ORDs have significant information content. Using signed stock returns, I find that the size-adjusted abnormal returns to ORDs are 72.8 basis points on the event day, suggesting that ORDs on average convey good news to capital markets. I compare the magnitude of the information content of ORDs with that of other corporate

disclosures. On average, the market reactions to ORDs are greater than the reactions to 10-K/Q reports and

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I only count the number of unique press releases of MEFs. An MEF press release may contain multiple forecasts.

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are similar in magnitude to the reactions to 8-K filings. I next adopt the procedure of Beyer et al. (2010) to examine the relative contribution of each type of disclosure in providing information to stock markets. Specifically, for each individual firm, I estimate a time-series regression of quarterly abnormal stock returns on aggregate abnormal returns on the days when ORDs are released during the quarter and on equivalent measures for other types of disclosures. I find that ORDs explain about 2.91% of time-series variation in quarterly returns. This magnitude is larger than the amount explained by 10-K/Q reports (2.34%), similar

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to the amount explained by MEFs (3.43%) and 8-K filings (3.71%), but smaller than the amount explained by earnings announcements (10.14%). 2 I also find that ORDs explain a larger portion of time-series

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variation in quarterly returns for small firms and firms with lower earnings quality than for other firms,

environment is poorer and earnings are less informative.

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suggesting that ORDs are a more important information source to stock markets when a firm’s information

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My study contributes to the literature in three ways. First, I provide large-sample evidence that investors react significantly to nonfinancial corporate disclosures. Prior studies examine the association

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between stock price and specific nonfinancial information using smaller samples (for example, Ittner and Larcker 1998 and Bushee et al. 2011). ORDs are a broader type of nonfinancial corporate disclosure, and I

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document market reactions to such nonfinancial disclosures on precisely identified event dates. Second, my study contributes to the literature on the relative contribution of different types of corporate disclosures. I find that ORDs are more frequent than MEFs, a widely examined type of voluntary disclosure. Moreover, ORDs contribute significantly beyond earnings announcements, MEFs, 10-K/Q reports, and 8-K filings in providing information to capital markets. My findings have implications for

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future studies that examine disclosure decisions and firms’ information environment as well as studies that compare the importance of multiple corporate disclosure venues.

These numbers are different from Beyer et al. (2010) due to different sample compositions. Beyer et al.’s sample of 2,747 firms is based on the intersection of First Call and IBES, which consists of mostly large firms that make MEFs and have analyst coverage, while my sample of 4,624 firms is based on the Compustat universe. In other words, Beyer et al.’s results are biased in favor of MEFs. My results are comparable to those in Basu et al. (2010). 2

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Last, my study has regulatory and practical implications. The materiality threshold for mandatory disclosures is ambiguous for nonfinancial information. Although containing significant information to capital markets, ORDs are largely voluntary. My study might draw regulators’ attention to ORDs. Companies aiming to convey timelier and value-relevant information to investors may differentiate themselves by providing more ORDs.

2.

NONFINANCIAL CORPORATE DISCLOSURE

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2.1 Prior Research

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One stream of literature on nonfinancial information examines the association of nonfinancial disclosures with market value and future financial outcomes. Amir and Lev (1996) find that two

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nonfinancial measures in the telecommunication industry—total population in a service area and the ratio of subscribers to total population—are significantly associated with stock prices. The relation between stock

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prices and nonfinancial measures has also been documented in different settings (Hirschey et al. 2001; Hughes 2000; Ittner and Larcker 1998). In addition, several studies have documented relation between

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nonfinancial measures and future financial performance (Al-Tuwaijri et al. 2004; Banker et al. 2000; Behn and Riley 1999; Ittner and Larcker 1998; Luft and Shields 2002; Nagar and Rajan 2001; Orlitzky et al.

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2003) as well as cost of capital (Dhaliwal et al. 2011). However, because the dates on which the nonfinancial information first becomes available are not identified, a major limitation of prior studies is that the associations they find may be confounded by other factors during the long investigation window. Investors could be using other information that is correlated with nonfinancial measures and future performance.

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Additionally, companies often present financial information along with nonfinancial measures, making it difficult to isolate their separate effects (Maines et al. 2002). Some studies examine market reactions to disclosures that are indirectly related to corporate

operations, such as write-offs, R&D, and product-market conference presentations. Francis et al. (1996) examine 507 write-off announcements published by PR Newswire during the period 1989 to 1992 and find significant negative reactions to write-offs. While write-offs likely result from business reorganization or

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discontinuation, they are distinct types of events.3 Narayanan et al. (2000) examine 501 Wall Street Journal announcements about the R&D stage and find that managerial intentions and government approval are associated with significant market reactions. Francis et al. (2002) examine the earnings announcement press releases of 30 firms during 1980-1999 and find that the market reactions to earnings announcements are associated with concurrent information in these press releases. Because they do not find significant evidence regarding income from discontinued operations and operating data such as R&D spending, one cannot infer

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from their study whether ORDs per se trigger market reactions. Lerman and Livnat (2010) examine the market reactions to 8-Ks filed under the new SEC regime

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2004 and find that the disclosed items are associated with abnormal volume and return volatility around both the event and SEC filing dates. Among the items required by SEC to disclose in 8-K filings, item 1.01

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“entry into a material definitive agreement,” item 1.02 “termination of a material definitive agreement,”

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and item 8.01 “other events” could be associated with ORD events. However, Lerman and Livnat (2010)’s results are primarily for disclosures mandated by the SEC. The ORD events that I examine are not required

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to be disclosed by the SEC. Firms may file Form 8-K for an ORD event under Item 7.01 or 8.01, both of which are considered voluntary items. Stock exchanges may impose additional requirements that firms should immediately release material information, but firms have leeway not to do so because of the vague

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criterion of “materiality” for nonfinancial information (Cao et al. 2018). To further differentiate ORDs from mandatory disclosure events in 8-K filings, I exclude any ORD that is subsequently accompanied by an 8K filing.

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2.2 Operations-related Disclosures

ORDs disseminate information about “critical success factors” that are necessary for a firm to

achieve its performance objective and future success (Daniel 1961; Rockart 1981). Wulff (2001) further

First, write-offs are expressed in accounting numbers, whereas I examine the qualitative information in a firm’s announcement of business reorganization or discontinuation. Second, investor reactions to business reorganization and discontinuation events reflect more than the write-off in the current period. Third, write-offs are often presented with other accounting information, making it difficult for researchers to isolate the effect of write-offs. 3

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points out that “most companies believe … that if they can effectively describe their strategy, their critical success factors, and their performance on those critical success factors, the financial markets will reward them.” This belief is confirmed by a survey conducted by the Institute for Responsible Investment at Boston College. The institute surveys 750 retail investors and 228 professional investors and concludes that all the investors demonstrate a substantial interest in using nonfinancial information in conjunction with their financial analysis for making investment decisions. Both retail investors and financial professionals agree

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that the most important kinds of nonfinancial information are product market share, customer relations, innovative products, and product safety (IRI 2008).

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Even though investors desire operations-related disclosures, it is not clear whether such disclosures outside of earnings announcements, 10-K/Q reports, and 8-K filings receive significant market reactions.

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First, these disclosures are not considered material enough by the SEC to require an 8-K filing. The new

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Form 8-K aims to “benefit markets by increasing the number of unquestionably or presumptively material events that must be disclosed currently.”4 In identifying these events, SEC considers “various factors to

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gauge, among other things, the extent to which we believe investors would consider the event important in making an investment or voting decision, …, the likely market reaction to the event, and the potential

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impact of the event on a company’s operations and financial statements.”5 Second, companies may not be willing to release material information in ORDs because of proprietary costs of disclosure (Entwistle 1999). Third, ORDs may lack credibility and comparability because of a lack of guidelines for disclosures and

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presentations (Maines et al. 2002).6 Last, it may be difficult for a company to determine when an operations-

See “Final Rule: Additional Form 8-K Disclosure Requirements and Acceleration of Filing Date” (SEC Release Nos. 33-8400; 34-49424; File No. S7-22-02), also available online at http://www.sec.gov/rules/ final/ 33-8400.htm #P195_29183. 5 See footnote 38 in “Proposed Rule: Additional Form 8-K Disclosure Requirements and Acceleration of Filing Date” (SEC Release Nos. 33-8106; 34-46084; File No. S7-22-02), also available online at http://www.sec.gov/rules /proposed/33-8106.htm#P75_12595. 6 Gigler (1994) models voluntary disclosure in a cheap talk framework and shows that product market competition and capital market concerns may make voluntary disclosure credible even without verification. In the model, a firm has incentives to overstate product demand to the capital market to raise more capital. At the same time, the firm has incentives to understate demand to the product market to reduce the likelihood of rivals reacting in a way that may adversely affect the firm. Trading off the benefits of overstating against those of understating can make the firm’s equilibrium public disclosures credible and informative. 4

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related event occurs and when to disclose such information. For example, termination of customer relations may occur over a period of time. Thus, whether investors react to ORDs is an open empirical question. 3. SAMPLE AND VARIABLES 3.1 Collecting Operations-Related Disclosures I obtain ORDs from the Capital IQ Key Developments database. The database constantly monitors more than 20,000 news sources, including various newswires, and collects and categorizes news and

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corporate events into 129 Key Development Types.7 No research of which I am aware has examined ORD events, which constitute 25.7% of all Key Developments of U.S. public firms from 2002 to 2010. I consider

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the following types of Key Developments ORD events:

Business Expansions: Growing the company by increasing current operations through internal

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growth such as entering new markets with existing products, opening new branches, establishing new

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divisions, increasing production capacity, and adding capital to the current business. This category does not include growth by acquisitions.

cost savings.

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Business Reorganization: Reorganizing a division, management, or operations for efficiency or

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Discontinued Operations or Downsizings: Phasing out of a product line, closing an individual facility (e.g., a plant, branch, division, or subsidiary), or reducing the workforce. Client Announcements: Announcing the beginning, ending, or changes in a relationship with the company’s current clients or potential future clients.

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Product-Related Announcements: Announcing the introduction, changes, improvement, or discontinuation of the company's products or services. This category includes all announcements ranging

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The 129 types of Key Developments are classified into nine main categories: financial transactions, company forecasts and ratings, corporate structure related, customer/product related, dividends/splits, listing/trading related, potential red flags/distress indicators, potential transactions, and results announcements. See detailed classifications at https://www. capitaliq.com/help/capital-iq-help/company-profiles/news,-events-and-filings-overview/what-typesof-key-developments-does-capital-iq-collect.aspx.

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from research & development to the final launch of products and any enhancements to the products after the launch. Strategic Alliances: Announcing agreements between the company and other entities that the involved parties will collaborate to achieve a common goal. This category includes disclosure events that the entities are in discussions to form an alliance. I combine Business Reorganization and Discontinued Operations or Downsizing into one category

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because they are similar in nature and usually occur concurrently. The Appendix presents examples of each type of ORDs.

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Following Bushee et al. (2010), I consider only announcements made on newswires. In addition, following Tetlock et al. (2008), I require that the company’s name appear in the release headline and within

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the first 10 words of the release content. This requirement ensures that a release is initiated by the company

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and that my sample excludes announcements in which the company is referred to in another company’s newswire release. After these procedures, among the initially identified ORDs, 70.8% are initiated by the

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disclosing firm. To ensure that the market reactions to ORDs are not contaminated by information from other disclosures, I drop ORDs that occur concurrently, in a three-day window, with earnings

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announcements, MEFs, 10-K/Q reports, or 8-K filings. The SEC requires firms to file a Form 8-K within four business days after material events. To ensure the ORDs that I collect are outside of 8-K filings, I drop an ORD if a Form 8-K is filed within four days following the release of the ORD. To better isolate the market reaction to each individual ORD, I drop those events when there are multiple ORDs within a three-

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day window.

I then merge my ORD data with Compustat and CRSP by the listing exchange, stock ticker, and

firm name. To facilitate trading volume analysis, I further require at least 240 trading days in the calendar year in which an ORD is made. I have a final sample of 76,366 ORDs released by 3,647 U.S. public firms. Given that there are 175,879 firm-quarters covered by Compustat and CRSP during my sample period, the

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number of 76,366 ORDs indicates that on average a firm-quarter issues 0.43 ORD.8 Table 1 summarizes the sample selection process. Client announcements and product-related announcements make up for a large portion of ORD events at 35,897 (47%) and 29,016 (38%), respectively. 3.2 Sample Distributions Table 2 reports the frequency distribution of ORDs along with earnings announcements, MEFs, 10-K/Q reports, and 8-K filings. Panel A shows by year the number of disclosure events, the average

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number of disclosure events per firm-quarter, and the number of disclosing firm-quarters. The total number of ORD events has in general increased over time from 7,672 in 2002 to 9,897 in 2010. The total number

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of firm-quarters in which a disclosure event occurred has also increased over time from 3,746 to 4,832, accounting for 16.99% and 27.85% of the firm-quarters in Compustat/CRSP, respectively. I also observe

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an increase in the average number of ORD events per firm quarter. In contrast, there is a notable decrease

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in the frequency of MEFs from 2,953 in 2002 to 2,031 in 2010. During the sample period, the frequency of 8-K filings has changed from 5,579 in 2002 to 19,260 in 2010. 9 Most of the increase occurred during 2004-

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2005—the first two years after the new SEC rule, “Additional Form 8-K Disclosure Requirements and Acceleration of Filing Date,” was adopted.

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Panel B shows the distribution across firm size groups. Every quarter, I assign firms into microcap, small-cap, and large-cap groups based on their market cap at the end of the previous quarter using the 20th and 50th percentiles of the NYSE stock market cap as the cutoffs. On average, micro-cap firms have the smallest number of ORD events per firm-quarter (0.309), followed by small-cap firms (0.415), and

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large-cap firms (0.790). This trend is also observed for MEFs, 10-K/Q reports, and 8-K filings and is consistent with the perception that large firms tend to have a richer information environment. Panel C shows the distribution across industries. The industries with the largest number of ORD

events per firm-quarter are Business Equipment (1.162), followed by Telecom (0.558), Healthcare (0.502),

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The number of 0.43 is a conservative estimate of the frequency of ORDs because I exclude the ORDs that occur concurrently with earnings announcements, MEFs, 10-K/Q reports, or 8-K filings. 9 In this study, I exclude MEFs, 10-K/Q reports, 8-K filings that occur in the three-day window around an earnings announcement.

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Consumer Durables (0.405), and Manufacturing (0.404). These industries also have the largest percentage of disclosing firm-quarters. Business Equipment industry has 45.40%, followed by Healthcare (30.07%), Telecom (25.95%), Consumer Durables (23.41%), and Manufacturing (21.07%). The pattern is much different for MEFs and 8-K filings. The industries with the largest number of MEFs per firm-quarter are Wholesale/Retail (0.289), followed by Utilities (0.209), Chemicals (0.195), and Consumer Non-durables (0.168). These industries also have the largest percentage of disclosing firm-quarters, 20.28% for

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Wholesale/Retail, followed by Utilities (15.79%), Chemicals (15.54%), and Consumer Non-durables (13.83%). The industries with the largest number of 8-K filings per firm-quarter are Utilities (1.529),

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followed by Energy (1.191), and Chemicals (1.028). These industries also have the largest percentage of disclosing firm-quarters, 52.71% for Utilities, followed by Chemicals (45.61) and Energy (43.07).

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4. MARKET REACTIONS TO ORDS

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I use the standardized absolute value of size-adjusted abnormal returns, abnormal return volatility, and abnormal trading volume to measure the market reactions to ORDs (Cready and Mynatt, 1991; Griffin,

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2003; Lerman and Livnat, 2010). I use the absolute values of returns because ORDs may contain either favorable or unfavorable signals. Following Lerman and Livnat (2010), I define the standardized absolute

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abnormal return as the difference between the absolute value of the mean size-adjusted abnormal returns in the event window (one-day or alternatively three-day) and that in the non-event period (i.e., days -63 to -8 relative to the event date), divided by the standard deviation of abnormal returns in the non-event period. The formula is as follows:

𝐴𝑣𝑔𝑑∈[𝑒𝑣𝑒𝑛𝑡] [|𝑅𝐸𝑇𝑖𝑗,𝑑 − 𝐷𝐸𝐶𝑅𝐸𝑇𝑗,𝑑 |] − 𝐴𝑣𝑔𝑑∈[non-event] [|𝑅𝐸𝑇𝑖𝑗,𝑑 − 𝐷𝐸𝐶𝑅𝐸𝑇𝑗,𝑑 |]

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𝑆𝐴𝑅𝑖𝑗 =

𝑆𝑡𝑑𝑑∈[non-event] [|𝑅𝐸𝑇𝑖𝑗,𝑑 − 𝐷𝐸𝐶𝑅𝐸𝑇𝑗,𝑑 |]

(1)

where 𝑅𝐸𝑇𝑖𝑗,𝑑 is the raw return of firm i on day d of event j, and 𝐷𝐸𝐶𝑅𝐸𝑇𝑗,𝑑 is the value-weighted return of the size decile that firm i belongs to on day d of event j. If investors do not react to ORDs, SAR is symmetrically distributed around the mean of zero and thus can be examined with a t-test (Cready and Mynatt, 1991; Griffin, 2003).

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I measure abnormal return volatility as the average squared abnormal returns in the event window divided by the variance of abnormal returns in the non-event period: 2

𝐴𝐵𝑉𝑂𝐿𝐴𝑇𝑖𝑗 =

Avg𝑑∈[event] [(𝑅𝐸𝑇𝑖𝑗,𝑑 − 𝐷𝐸𝐶𝑅𝐸𝑇𝑗,𝑑 ) ] 𝑉𝑎𝑟𝑑∈[non-event] [𝑅𝐸𝑇𝑖𝑗,𝑑 − 𝐷𝐸𝐶𝑅𝐸𝑇𝑗,𝑑 ]

−1

(2)

where 𝑅𝐸𝑇𝑖𝑗,𝑑 is the daily stock return on day d, and 𝐷𝐸𝐶𝑅𝐸𝑇𝑗,𝑑 is the daily return of size decile portfolios on day d.

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Kim and Verrecchia (1991) suggest that trading volume may magnify stock price reactions to public disclosures and can therefore be a more powerful measure of market reactions. Following Cready

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and Mynatt (1991) and Utama and Cready (1997), I examine the abnormal trading volume around ORD events. I measure abnormal trading volume as average daily share trading volume in the event window

Avg𝑑∈[event] [𝑉𝑂𝐿𝑖𝑗,𝑑 ] Avg𝑑∈[non-event] [𝑉𝑂𝐿𝑖𝑗,𝑑 ]

−1

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𝐴𝐵𝑉𝑂𝐿𝑈𝑀𝐸𝑖𝑗 =

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divided by the normal daily share volume during the non-event period:

(3)

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where 𝑉𝑂𝐿𝑖𝑗,𝑑 is the daily trading volume on day d. The abnormal trading volume measures the percentage of change in daily trading volume during the event window relative to the non-event period. I also use signed abnormal returns to explore whether the news conveyed by ORDs is on average

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favorable. The size-adjusted abnormal returns are calculated as the sample firm’s buy-and-hold stock return in the event window minus the buy-and-hold return of the value-weighted portfolio of firms in the same decile of market cap in the same window: 𝐶𝐴𝑅𝑖𝑗 =

∑

[𝑅𝐸𝑇𝑖𝑗,𝑑 − 𝐷𝐸𝐶𝑅𝐸𝑇𝑗,𝑑 ]

(4)

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𝑑∈[𝑒𝑣𝑒𝑛𝑡]

Panel A of Table 3 reports that, on average, ORDs trigger 14.06 percent standardized absolute

abnormal returns on the release day and 13.03 percent in the three-day window centered on the release date. Across the five types of ORDs, the standardized absolute abnormal returns are consistently positive and significant on the release day. Announcements of strategic alliances have the largest standardized absolute abnormal returns at 16.56 percent, followed by announcements of business reorganization (16.10 percent), 10

product-related announcements (14.81 percent), client announcements (14.75 percent), and announcements of business expansion (5.40 percent). Panel B reports that the abnormal return volatility exhibits a similar pattern except for announcements of business reorganization. ORDs, on average, have 79.14% abnormal return volatility on the release day and 94.60% in the three-day window. Across the five types of operations-related disclosures, announcements of strategic alliances have the largest abnormal return volatility at 110.19%, followed by

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product-related announcements (88.02%), client announcements (79.46%), announcements of business

volatility is consistently positive and significant across these types.

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reorganization (56.43%), and announcements of business expansion (24.27%). The abnormal return

Panel C reports that, on average, ORDs have 47.35% abnormal trading volume on the release day

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and 82.80% in the three-day window. Across the five types of ORDs, product-related announcements have

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the largest abnormal trading volume at 55.40%, followed by announcements of strategic alliances (54.46%), client announcements (46.62%), announcements of business reorganization (25.83%), and announcements

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of business expansion (15.20%).

Moreover, the above results also suggest that the market reactions to ORDs concentrate on the

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release day. Although unknown factors may be influencing the significance in days away from the release day, the results indicate that the market reactions to ORDs are prompt. Table 4 reports that, on average, ORDs trigger 72.8 basis point of size-adjusted signed abnormal returns on the release day, 9.2 basis points on the day immediately after the release day, and 82.6 basis

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points in the three-day window centered on the release day. These reactions are statistically significant. I also observe significantly positive abnormal returns for each type of ORDs. Announcements of business expansion trigger 26.3 basis points on the release day and 14.9 basis points the day after. Disclosures of business reorganization trigger -34.3 basis points on the release day, and -24.3 basis points the day after. Client announcements trigger 91 basis points on the release day, and 9.3 basis points the day after. Productrelated announcements trigger 63 basis points on the release day, and 9.8 basis points the day. Disclosures of strategic alliance trigger 83.9 basis points on the release day, and the abnormal return the day after is not 11

statistically significant in this case. The abnormal returns on other days in the [-5, +5] window are generally insignificant. The distribution of daily abnormal returns around an ORD event is plotted in Figure 1. While the distribution peaks around zero, it shows a long right-hand tail with a positive median and mean, suggesting ORD on average conveys good news to investors. Then I plot the distributions separately for each type of ORD event in Figure 2. The stock returns to business expansion, client announcements, product-related

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announcements, and strategic alliances have long right-hand tails and positive medians, whereas the stock returns to business reorganization have a long left-hand tail and a negative median. These results suggest

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that business reorganization on average conveys bad news to investors, whereas business expansion, client announcements, product-related announcements, and strategic alliances on average convey good news to

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5. RELATIVE IMPORTANCE OF ORDS

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investors.

Prior studies have assessed the relative information contribution of different types of disclosures.

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Ball and Shivakumar (2008) regress annual returns on four quarterly earnings announcement window returns and use the 𝑅 2 as a measure of information contribution. The authors then compare the information

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contributions of earnings announcements, MEFs, and analyst forecasts for firm-quarters that have at least one MEF and one analyst forecast, where they select the last forecast issued in a quarter if multiple MEFs or analyst forecasts exist for that quarter. The authors conclude that earnings announcements contain a moderate amount of new information.

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Basu et al. (2010) argue that examining only forecasting quarters overstates the importance of MEFs and analyst forecasts and that examining the last forecast when multiple MEFs or analyst forecasts exist understates the importance of the latter. Instead, Basu et al. accumulate forecast returns over the corresponding windows when multiple forecasts exist in a quarter. If there is no forecast during a quarter, the authors use stock returns from a randomly selected window in that quarter. They conclude that earnings announcements are an important source of new information in the equity market.

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Beyer et al. (2010) use a similar research design. They regress quarterly abnormal returns on aggregate abnormal returns of earnings announcements, pre-announcements, MEFs, analyst forecasts, 10K/Q reports, and 8-K filings, and obtain the incremental 𝑅 2of each type of disclosure. Here, the aggregate abnormal returns of a given type of disclosure are calculated as the sum of abnormal returns on the dates during the quarter when this type of disclosure is provided. If this type of disclosure is absent, then the aggregate abnormal return is assigned the value of zero. The authors conclude that stock price reactions to

announcements, pre-announcements, 10-K/Q reports, and 8-K filings.

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MEFs and analyst forecasts explain the variation in quarterly stock returns to a larger degree than earnings

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To address the relative importance of ORDs, I first compare the market reactions to ORDs with the market reactions to EADs, MEFs, 10-K/Q reports, and 8-K filings. For my sample period, I identify 177,533

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earnings announcements from Compustat, 21,078 MEFs from First Call’s Company Issued Guidelines

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dataset, 63,021 10-K/Q reports, and 142,205 8-K filings. Figure 3 compares the mean values of standardized absolute abnormal returns, abnormal trading volume, and abnormal return volatility between

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these types of disclosure and ORDs. The magnitudes of absolute abnormal returns and abnormal return volatility for ORDs are higher than those for 10-K/Q reports and 8-K filings but lower than those for

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earnings announcements and MEFs. The abnormal trading volume for ORDs is higher than that for 10-K/Q reports but lower than that for other types of disclosures. These results suggest that ORDs convey a significant amount of new information to the market, especially when compared with 10-K/Q reports and 8-K filing. Figure 4 compares the mean value of size-adjusted signed abnormal returns. In the three-day

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event window, ORDs have larger abnormal returns, in magnitude, than earnings announcements, 10-K/Q reports, and 8-K filings, but smaller than MEFs. Next, I use the procedure similar to Beyer et al. (2010) to take into account the frequency of each

type of disclosure in assessing the relative contribution of different types of disclosure. Specifically, for each type of corporate disclosure, I sum the size-adjusted abnormal returns of all the announcement days within a calendar quarter. I then estimate, for each sample firm i, the following time-series regression of quarterly abnormal returns on the total event day abnormal returns for each type of disclosure: 13

10−𝐾/𝑄

𝐸𝐴𝐷 𝑂𝑅𝐷 𝑀𝐸𝐹 𝐶𝐴𝑅𝑖,𝑄 = 𝛼 + 𝛽1 𝐶𝐴𝑅𝑖,𝑄 + 𝛽2 𝐶𝐴𝑅𝑖,𝑄 + 𝛽3 𝐶𝐴𝑅𝑖,𝑄 + 𝛽4 𝐶𝐴𝑅𝑖,𝑄

8−𝐾 + 𝛽5 𝐶𝐴𝑅𝑖,𝑄 + 𝜀𝑖,𝑄

(5)

where 𝐶𝐴𝑅𝑖,𝑄 is the quarterly cumulative size-adjusted abnormal returns for firm i in calendar quarter Q; 𝐸𝐴𝐷 𝐶𝐴𝑅𝑖,𝑄 is the sum of one-day or three-day size-adjusted abnormal returns for all earnings announcements 𝑂𝑅𝐷 made by firm i in quarter Q; 𝐶𝐴𝑅𝑖,𝑄 is the sum of one-day or three-day size-adjusted abnormal returns for

all operations-related releases made by firm i in quarter Q and zero if there no operations-related releases; 𝑀𝐸𝐹 𝐶𝐴𝑅𝑖,𝑄 is the sum of one-day or three-day size-adjusted abnormal returns for all management earnings

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10−𝐾/𝑄

forecasts made by firm i in quarter Q and zero if there are no management earnings forecasts; 𝐶𝐴𝑅𝑖,𝑄

is

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the sum of one-day or three-day size-adjusted abnormal returns for all 10-K/Q filings made by firm i in 8−𝐾 quarter Q and zero if there are no 10-K/Q filings; 𝐶𝐴𝑅𝑖,𝑄 is the sum of one-day or three-day size-adjusted

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abnormal returns for all 8-K filings made by firm i in quarter Q and zero if there are no 8-K filings. I require

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that each firm have at least 240 trading days within a year and 20 quarterly observations during the period from 2002 to 2010. I obtain a total of 143,345 firm-quarter observations for 4,579 unique firms. For each

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individual firm, I obtain the incremental 𝑅 2s for each type of disclosure as the 𝑅 2 of the full model minus the 𝑅 2 of a model without the type of disclosure.10

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When multiple disclosure events occur on the same day, I attribute the daily abnormal return to, in the order of priority, earnings announcements, MEFs, 10-K/Q reports, 8-K filings, and ORDs. Specifically, 𝑀𝐸𝐹 MEFs that are coincident with earnings announcements are excluded from calculating 𝐶𝐴𝑅𝑖,𝑄 , 10-K/Q or

8-K filings that are coincident with earnings announcements and MEFs are excluded from calculating 10−𝐾/𝑄

8−𝐾 or 𝐶𝐴𝑅𝑖,𝑄 , and ORDs that are coincident with any other disclosures are excluded from

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𝐶𝐴𝑅𝑖,𝑄

𝑂𝑅𝐷 calculating 𝐶𝐴𝑅𝑖,𝑄 . This treatment results in the most conservative estimate of the incremental R2 for

ORDs.

The incremental R2 is also called squared semi-partial correlation (r). It measures “the proportion of (unique) variance accounted for by the predictor X1, relative to the total variance of Y. Thus, the semi-partial correlation … is a better indicator of the ‘practical relevance’ of a predictor” than partial R2, because the semi-partial correlation is “scaled to (i.e., relative to) the total variability in the dependent (response) variable.” (see Hill and Lewicki, 2007, and StatSoft, Inc., 2012) 10

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The estimation of model (5) for each individual firm is reported in Table 5. On average, 24.14% of the variation in quarterly stock returns is explained by returns on days when the firm makes disclosures and 35.17% of the variation in quarterly stock returns is explained by returns on the three-day window centered on the disclosure date. When the event day is used to measure disclosure-event returns, ORDs provide 2.91% of total quarterly information on average, which constitutes approximately 12.06% of all information provided by corporate disclosures. When the three-day window is used to measure disclosure-event returns,

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ORDs provide 3.25% of total quarterly information on average, which constitutes 9.23% of all information provided by corporate disclosures. Earnings announcements contribute more than 40% of all information

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provided by corporate disclosures, 8-K filings contribute around 14%, and MEFs contribute slightly greater than 10%, consistent with Basu et al. (2010). This evidence suggests that ORDs contribute more

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information than 10-K/Q reports (t-value 5.51 for the one-day window and 6.64 for the three-day window),

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to a similar extent as MEFs and 8-Ks, but less than earnings announcements.

I repeat the analysis by size groups and industries and report the results in panels B and C of Table

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5. Disclosures are generally more informative for smaller firms, because they have a sparser information environment. Table 5 supports this view. The incremental 𝑅 2 is almost monotonically increasing from

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micro-cap to large-cap, with large-cap firms having the highest incremental 𝑅 2. However, the information provided by each type of disclosure, as a percentage of total information explained by corporate disclosures, differs across types. ORDs provide 12.70% of total information in corporate disclosures for micro-cap firms, 11.12% for small-cap firms, and 11.83% for large-cap firms. This evidence indicates that ORDs are

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relatively more important for micro-cap firms (t-value=1.74). Earnings announcements and 8-K filings have similar patterns. MEFs and 10-K/Q reports are relatively more important for large-cap firms (t-value 10.4 for MEFs and 7.16 for 10-K/Q reports). Across industries, ORDs are more important for Business Equipment, Energy, Healthcare, Consumer Durable and Other, but less important for Chemicals, Finance, and Wholesale/ Retail. Even though the time-series regression in Table 5 controls for firm-specific factors that might affect the inference, the method may be subject to survivorship bias. For startup companies that do not have a 15

long history for the time-series analysis, ORDs may be more informative for investors. Therefore, I also estimate model (5) cross-sectionally for each calendar quarter and report the results in Table 6. On average, ORDs explain 1.49% of cross-sectional variation in quarterly returns on the release day and 2.45% on the three-day window. These numbers are larger than those for 10-K/Q reports and MEFs, but smaller than those for earnings announcements and 8-K filings. Across size groups, ORDs are more important in explaining the cross-sectional variation in quarterly returns for micro-cap firms but less important for small-

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cap firms, consistent with the time-series results in Table 5. Across industries, ORDs are more important for Utilities, Energy, Business Equipment, and Other, but less important for Finance, Wholesale/Retail, and

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Chemicals. These results are generally consistent with my expectations. Micro-cap firms and firms in Business Equipment are more likely to be startup firms with a short history, and ORDs are more useful for

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investors in valuing these stocks.

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Some firms, especially those in the “mature” stage, may have little new operations-related information to disclose. Therefore, the evidence in Table 5 and Table 6, obtained from all U.S. public firms,

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may understate the importance of ORDs. I address this concern by examining firms that make at least one ORD during the sample period 2002 to 2010. Table 7 reports the incremental 𝑅 2 for this sample. When the

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event day (the three-day window) is used to measure disclosure-event return, ORDs explain 4.49% (4.84%) of time-series variation in quarterly returns, which is 13.86% (10.64%) of the total information provided by corporate disclosures, and explain 1.74% (3.12%) of cross-sectional variation in quarterly returns, which is 10.27% (9.01%) of the total information provided by corporate disclosures. The incremental 𝑅 2 of ORDs

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is significantly higher than that of MEFs and 10-K/Q reports, but significantly lower than that of earnings announcements and 8-K filings. When firms have lower earnings quality, investors may seek other information sources for

valuation. ORDs may be more important to investors of those firms. I examine the relationship between the importance of ORDs and earnings quality. Specifically, I measure earnings quality by the absolute value of discretionary accruals, earnings persistence, earnings predictability, and earnings smoothness. Each year

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for each two-digit SIC industry, I estimate modified Jones’ model and obtain the residual as a measure of discretionary accruals 𝐴𝐶𝐶𝑖𝑡 = 𝛼 + 𝛽(Δ𝑅𝐸𝑉𝑖𝑡 − Δ𝐴𝑅𝑖𝑡 ) + 𝛾𝑃𝑃𝐸𝑖𝑡 + 𝜖𝑖𝑡

(6)

where 𝐴𝐶𝐶𝑖𝑡 is accruals (earnings minus cash flow from operations), Δ𝑅𝐸𝑉𝑖𝑡 is the change in net sales from year t-1 to year t, Δ𝐴𝑅𝑖𝑡 is the change in accounts receivable from year t-1 to year t, 𝑃𝑃𝐸𝑖𝑡 is gross property, plant, and equipment at year t, and all variables are scaled by total assets at the end of year t-1.

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Earnings persistence is measured as the coefficient 𝛽 from the auto-regression of earnings 𝐸𝑖𝑡 = 𝛼 + 𝛽𝐸𝑖𝑡−1 + 𝜀𝑡

(7)

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where 𝐸𝑖𝑡 is earnings for year t, scaled by total assets, and earnings predictability is measured as the root

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mean squared error from the regression, multiplied by -1. Earnings smoothness is measured as the ratio of the standard deviation of earnings to the standard deviation of cash flow from operations, times -1.

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I then sort a firm into the “high”/“low” earnings quality group by its absolute discretionary accruals, earnings persistence, earnings predictability, or earnings smoothness, one measure at a time. A firm is sorted

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into the “high” earnings quality group if the earnings quality measure is higher than the sample median, and into the “low” earnings quality group if otherwise. The absolute value of discretionary accruals for each

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firm is averaged during the sample period before sorting, so that each individual firm is only sorted once and does not change its group to facilitate the estimation of the time-series Equation (5). Prior studies show that earnings are more informative if accompanied with lower discretionary accruals, higher earnings persistence, higher earnings smoothness, and higher earnings predictability (Dechow et al., 1995; Easton

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and Zmijewski, 1989; Francis et al., 2004; Lipe, 1990; Tucker and Zarowin, 2006). I, therefore, predict that the return variation explained by ORDs is higher for firms with higher discretionary accruals, lower earnings persistence, lower earnings smoothness, and lower earnings predictability. Table 8 presents evidence consistent with the prediction. When the event day (the three-day window) is used to measure disclosure-event return, ORDs explain 3.73% (3.98%), 3.25% (3.51%), 3.39% (3.71%), and 3.85% (4.23%) of the variation in quarterly return for firms with higher discretionary accruals,

17

lower earnings persistence, lower earnings smoothness, and lower earnings predictability, compared with 2.13% (2. 4%), 2.73% (3.12%), 2.59% (2.91%), and 2.12% (2.4%) of the variation in quarterly return variation for firms with lower discretionary accruals, higher earnings persistence, higher earnings smoothness, and higher earnings predictability. The difference is statistically different across four measures of earnings quality. However, the informativeness of earnings announcements does not monotonically increase with earnings smoothness and earnings predictability. MEFs, 10-K/Q reports, and 8-K filings do

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not show a consistent monotonic increase in informativeness with earnings quality measures. Overall, the evidence in Table 8 suggests that ORDs are a more important information source for firms with lower

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earnings quality.

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6. CONCLUSIONS

This study examines the information content of ORDs. I find that ORDs are associated with

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significant market reactions measured by abnormal returns, standardized absolute abnormal returns, abnormal trading volume, and abnormal return volatility. I also find that ORDs contribute a significant

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amount of information to capital markets, beyond earnings announcements, MEFs, 10-K/Q reports, and 8K filings. On average, an ORD conveys more information to capital markets than a 10-K/Q report and about

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the same amount of information as an 8-K filing. These findings have implications for investors, regulators,

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and researchers.

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APPENDIX EXAMPLES OF VOLUNTARY OPEARTIONS-RELATED DISCLOSURES, IBM

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Client Announcements Date: 2009-11-13; Source: PR Newswire Press Release: International Business Machines Corp. announced a $14.5 million agreement with Power and Water Corporation to help design and implement an asset management system aimed at delivering electricity, water and sewerage services to its customers more efficiently. Power and Water will tap IBM's expertise and technology for its Asset Management Capability (AMC) Project, a four-year program to improve asset management practices across the corporation. A key component in the delivery of the AMC Project lies in the implementation of IBM Maximo® Spatial Asset Management, a geospatially enabled asset management solution integrated with the latest Geographic Information System (GIS) technology from IBM Business Partner ESRI. IBM Maximo Spatial Asset Management is designed to enable users to visualize all assets and work in a geospatial context to help optimize resources and decisions. The solution enables users to capture, analyze, and display assets, locations, and work orders. For example, asset managers will not only know that an asset exists, but can also see information about its condition, cost, maintenance history and exactly where it is on a map, in relationship to other assets of various types. Power and Water will also be using IBM Cognos® TM1 to consolidate and analyze maintenance and capital works planning. This will help Power and Water to meet the future needs of the growing Northern Territory population and meet the goals of its recently increased capital maintenance plan allocated across power generation, power networks, water, sewerage and business services.

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Business Expansions Date: 2009-11-10; Source: PR Newswire Press Release: IBM announced that the opening of the sixth in a network of analytics solution centers-this one dedicated to helping federal agencies and other public sector organizations extract actionable insights from their data. The new IBM Analytics Solution Center in Washington, D.C., will draw on the expertise of more than 400 IBM professionals. These will include IBM researchers, experts in advanced software platforms, and consultants with deep industry knowledge in areas such as transportation, social services, public safety, customs and border management, revenue management, defense, logistics, healthcare and education. The company also plans to add an additional 100 professionals, through retraining or new hiring, as demand grows. The center's staff will work with federal agencies and other clients to apply breakthrough streaming technologies, mathematical algorithms, and modeling. Using these tools, the company will help clients optimize individual business decisions, processes and even entire business models, as well as manage risk and fraud and, ultimately, improve the delivery of public services. The new center will be located in IBM's Institute for Electronic Government at 1301 K Street, N.W. It will serve as the hub for collaboration with federal agencies, academia, and other institutions in the Washington Metro Area on analytics projects ranging from transportation and social services to defense logistics and homeland security systems. Washington, D.C. was chosen as the location for the new center because of its access to thinkers on the future of government systems, innovations taking place within the U.S. federal government, and the dozens of universities in the area.

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Discontinued Operations/Downsizings Date: 2008-07-09; Source: The Associated Press State & Local Wire Press Release: International Business Machines Corp. announced it has laid off 30 employees at its facility in Research Triangle Park as part of a national restructuring. The company reported about 150 jobs were cut nationally, including those in North Carolina. The employees will be able to apply for other jobs within IBM or receive severance packages if they decide to leave.

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Product-Related Announcements Date: 2005-06-28; Source: PR Newswire Press Release: IBM unveiled the Infoprint 6500, its next-generation line matrix printer series. The Infoprint 6500 is part of IBM's long-standing relationship with Printronix, the leading integrated supply-chain printing solutions. With the dynamic combination of IBM and Printronix technology powering the Infoprint 6500, users will be able to keep up with the demands of back office and data-processing applications.

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Strategic Alliances Date: 2007-12-18; Source: Market Wire Press Release: International Business Machines Corp. and Toshiba Corp. announced that they have entered into a joint development agreement on 32nm bulk complementary metal oxide semiconductor (CMOS) process technology. Under the new agreement, Toshiba joins a six company IBM Alliance for 32nm bulk CMOS process technology development based in East Fishkill, New York. Through this collaboration IBM and Toshiba plan to accelerate development of next-generation technology to achieve high-performance, energy-efficient chips at the 32nm process level, and to enhance the companies' leadership in the global semiconductor industry. In addition to continuing the successful collaboration on fundamental advanced research, Toshiba will jointly develop the state-of-the-art 32nm bulk CMOS process integration technology, as a member of the world-class seven-company IBM Alliance.

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Basu, S., T. X. Duong, S. Markov, and E.-J. Tan. 2010. How important are earnings announcements as an information source? In SSRN eLibrary: Singapore Management University.

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Behn, B., and R. Riley. 1999. Using nonfinancial information to predict financial performance: The case of the U.S. airline industry. Journal of Accounting, Auditing and Finance 14 (1):29-56. Beyer, A., D. A. Cohen, T. Z. Lys, and B. R. Walther. 2010. The financial reporting environment: Review of the recent literature. Journal of Accounting and Economics 50 (2-3):296-343.

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Bushee, B. J., J. E. Core, W. Guay, and S. J. W. Hamm. 2010. The role of the business press as an information intermediary. Journal of Accounting Research 48 (1):1-19.

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Bushee, B. J., M. J. Jung, and G. S. Miller. 2011. Conference presentations and the disclosure milieu. Journal of Accounting Research 49 (5):1163-1192.

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Cao, S. S., G. Ma, J. W. Tucker, and C. Wan. 2018. Technological peer pressure and product disclosure. The Accounting Review 93 (6):95-126. Daniel, D. R. 1961. Management information crisis. Harvard Business Review 39 (5):111-121.

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Dhaliwal, D. S., O. Z. Li, A. Tsang, and Y. G. Yang. 2011. Voluntary nonfinancial disclosure and the cost of equity capital: The initiation of corporate social responsibility reporting. The Accounting Review 86 (1):59-100. Entwistle, G. M. 1999. Exploring the R&D disclosure environment. Accounting Horizons 13 (4):323-341. Francis, J., J. D. Hanna, and L. Vincent. 1996. Causes and effects of discretionary asset write-offs. Journal of Accounting Research 34 (Supplement):117-134. Francis, J., K. Schipper, and L. Vincent. 2002. Expanded disclosures and the increased usefulness of earnings announcements. The Accounting Review 77 (3):515-546.

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Gigler, F. 1994. Self-enforcing voluntary disclosures. Journal of Accounting Research 32 (2):224-240. Hirschey, M., V. J. Richardson, and S. Scholz. 2001. Value relevance of nonfinancial information: The case of patent data. Review of Quantitative Finance and Accounting 17 (3):223-235. Hirst, D. E., L. Koonce, and S. Venkataraman. 2008. Management Earnings Forecasts: A Review and Framework. Accounting Horizons 22 (3):315-338. Hughes, K. E. 2000. The value relevance of nonfinancial measures of air pollution in the electric utility industry. Accounting Review 75 (2):209-228. IRI. 2008. The use of non-financial information: what do investors want? In White Paper: Report on Project Findings. Boston, MA: Institute for Responsible Investment, Boston College.

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Ittner, C. D., and D. F. Larcker. 1998. Are nonfinancial measures leading indicators of financial performance? An analysis of customer satisfaction. Journal of Accounting Research 36 (SUPPL.):1-35. Lerman, A., and J. Livnat. 2010. The new Form 8-K disclosures. Review of Accounting Studies 15 (4):752-778. Luft, J., and M. Shields. 2002. Learning the drivers of financial performance: Judgment and decision effects of financial measures, nonfinancial measures, and statistical models: Michigan State University. Maines, L. A., E. Bartov, P. M. Fairfield, D. E. Hirst, T. E. Iannaconi, R. Mallett, C. M. Schrand, D. J. Skinner, and L. Vincent. 2002. Recommendations on disclosure of nonfinancial performance measures. Accounting Horizons 16 (4):353-362. Nagar, V., and M. V. Rajan. 2001. The revenue implications of financial and operational measures of product quality. Accounting Review 76 (4):495-513.

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Narayanan, V. K., G. E. Pinches, K. M. Kelm, and D. M. Lander. 2000. The influence of voluntarily disclosed qualitative information. Strategic Management Journal 21 (7):707-722.

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Orlitzky, M., F. L. Schmidt, and S. L. Rynes. 2003. Corporate social and financial performance: A metaanalysis. Organization Studies 24 (3):403-441.

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Rockart, J. F. 1981. A primer on critical success factors. In The Rise of Managerial Computing: The Best of the Center for Information Systems Research, edited by C. V. Bullen and J. F. Rockart. Homewood, IL: Dow Jones-Irwin. Tetlock, P. C., M. Saar-Tsechansky, and S. Macskassy. 2008. More than words: quantifying language to measure firms' fundamentals. Journal of Finance 63 (3):1437-1467.

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Wulff, J. 2001. Improving business reporting: insights into enhancing voluntary disclosures. In AAA Annual Meeting. Atlanta, GA: American Accounting Association.

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12.5 10 5

7.5

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% Observations

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0 10 1-day size-adjusted abnormal returns (%)

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Figure 1. Distribution of 1-day Size-adjusted Abnormal Returns to Operations-Related Disclosure Events

Bus. Reorg.

0

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5

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10 15 20

Bus. Expan.

0

5

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10 15 20

Product

-20

-10

0

10

20

5

10 15 20

Strategic

0

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% Observations

Client

-20

-10

0

10

20

1-day size-adjusted abnormal returns (%)

Figure 2. Distribution of 1-day Size-adjusted Abnormal Returns to Each Type of Operations-Related Disclosure Events

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Fig 3. Mean Market Reactions to Firms’ ORDs (ORD), Earnings Announcements (EAD), Management Forecasts (MEF), 10-K/Q Reports (10-K/Q), and 8-K Filings (8-K) This figure shows the absolute abnormal returns, abnormal return volatility, and abnormal trading volume, during the one-day and three-day windows, to 76,366 operations-related disclosures, 177,533 earnings announcements, 21,078 management forecasts, 63,021 10-K/Q reports and 142,205 8-K filings, during the period 2002 to 2010. The sample consists of all disclosures with data available and more than 240 trading days in the year. Abnormal returns are calculated as the firm’s raw returns minus the value-weighted portfolio returns of the corresponding size decile, 𝐶𝐴𝑅𝑖𝑗 = ∑𝑑∈[event][𝑅𝐸𝑇𝑖𝑗,𝑑 − 𝐷𝐸𝐶𝑅𝐸𝑇𝑗,𝑑 ]; absolute abnormal returns are calculated as the absolute value of size-adjusted abnormal returns in the event; abnormal return volatility is calculated as the ratio of daily average return squared 2

to average return squared in the [-63,-8] non-event window, minus one, i.e., 𝐴𝐵𝑉𝑂𝐿𝐴𝑇𝑖𝑗 =

Avg 𝑑∈[event] [(𝑅𝐸𝑇𝑖𝑗,𝑑 −𝐷𝐸𝐶𝑅𝐸𝑇𝑗,𝑑 ) ] Var𝑑∈[non-event] [𝑅𝐸𝑇𝑖𝑗,𝑑 −𝐷𝐸𝐶𝑅𝐸𝑇𝑗,𝑑 ]

− 1; abnormal trading volume is

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calculated as the ratio of daily average shares traded in the event window to average shares traded in the [-63,-8] non-event window, minus one, i.e., 𝐴𝐵𝑉𝑂𝐿𝑈𝑀𝐸𝑖𝑗 =

Avg 𝑑∈[event] [𝑉𝑂𝐿𝑖𝑗,𝑑 ]

Avg 𝑑∈[non-event] [𝑉𝑂𝐿𝑖𝑗,𝑑 ]

− 1.

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Fig 4. Average Size-Adjusted Abnormal Returns to Firms’ ORDs (ORD), Earnings Announcements (EAD), Management Forecasts (MEF), 10-K/Q Reports (10-K/Q), and 8-K Filings (8-K)

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This figure shows the size-adjusted abnormal returns, during the one-day and three-day windows, to 76,366 operationsrelated disclosures, 177,533 earnings announcements, 21,078 management forecasts, 63,021 10-K/Q reports, and 142,205 8-K filings, during the period 2002 to 2010. The sample consists of all disclosures with data available and more than 240 trading days in the year. Abnormal returns are calculated as the firm’s raw returns minus the value-weighted portfolio returns of the corresponding size decile, i.e., CAR ij = ∑d∈[event][RETij,d − DECRETj,d ].

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Table 1. Sample Selection of Operations-Related Disclosure Events

Business Reorganization

20,015

3,015

14,124

Product Related Announcements

Strategic Alliance

Total

# FirmYears

# Firms

87,571

13,113

230,825

36,987

8,749

2,497

78,357

66,443

9,355

170,776

21,188

4,359

2,366

75,037

63,642

8,917

163,484

20,937

4,329

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13,522 9,813

1,339

57,638

48,039

6,376

123,205

18,623

4,060

9,666

1,214

56,731

47,476

6,270

121,357

18,493

4,044

9,577

1,175

56,355

47,168

6,215

120,490

18,456

4,040

7,434

1,008

39,303

31,732

4,264

83,741

18,179

3,959

6,708

902

35,897

29,016

3,843

76,366

16,537

3,647

Jo

ur na

2. Merging with Compustat and CRSP 3. Dropping events coincident with 10-K/Q filings, during the 3-day [-1, +1] window 4. Dropping events coincident with 8-K filings, during the 6-day [-1, +4] window 5. Dropping events coincident with earnings announcements, during the 3-day [-1, +1] window 6. Dropping events coincident with releases of management forecasts, during the 3-day [1, +1] window 7. Dropping overlapped operations-related disclosures, during the 3-day [-1, +1] window 8. Requiring returns and accounting data availability and more than 240 trading days in the calendar year

107,111

re

Observations remaining after:

Client Announcements

-p

Procedure 1. Number of operations-related newswire releases without concurrent other disclosures, by US public firms, for the period 2002-2010

Business Expansion

ro

This table shows the sample selection procedure and the corresponding number of observations and firm-years for the period from 2002 to 2010. Data on ORDs obtained from Capital IQ, on returns from CRSP, on financial data from Compustat, and on SEC filing dates from EDGAR.

26

Table 2. Distribution of Operations-Related Disclosure Events

2004 2005 2006 2007 2008 2009 2010 Total

171 (0.008) 122 (0.006) 58 (0.003) 46 (0.002) 47 (0.002) 44 (0.002) 161 (0.008) 180 (0.010) 73 (0.004) 902 (0.005)

3,935 (0.178) 3,610 (0.174) 3,998 (0.199) 3,808 (0.192) 3,776 (0.192) 3,777 (0.198) 4,481 (0.236) 4,281 (0.236) 4,231 (0.244) 35,897 (0.204)

2,610 (0.118) 2,749 (0.133) 2,626 (0.131) 2,756 (0.139) 2,833 (0.144) 3,058 (0.160) 4,111 (0.216) 4,116 (0.227) 4,157 (0.240) 29,016 (0.165)

417 (0.019) 440 (0.021) 363 (0.018) 331 (0.017) 391 (0.020) 459 (0.024) 471 (0.025) 493 (0.027) 478 (0.028) 3,843 (0.022)

Total

EAD

7,672 (0.348) 7,426 (0.358) 7,639 (0.380) 7,570 (0.381) 7,794 (0.397) 8,177 (0.429) 10,251 (0.540) 9,940 (0.549) 9,897 (0.571) 76,366 (0.434)

22,278 (1.011) 20,939 (1.010) 20,289 (1.010) 20,055 (1.010) 19,803 (1.009) 19,249 (1.009) 19,157 (1.008) 18,259 (1.008) 17,504 (1.009) 177,533 (1.009)

lP

2003

539 (0.024) 505 (0.024) 594 (0.030) 629 (0.032) 747 (0.038) 839 (0.044) 1027 (0.054) 870 (0.048) 958 (0.055) 6708 (0.038)

ur na

2002

Product Business Business Client Related Strategic Expansion Reorganization Announcements Announcements Alliance

Jo

Year

# Other Disclosure Events (#disclosure/total firm-quarters in Compustat)

re

# Operations-related Disclosure Events (#disclosure/total firm-quarters in Compustat)

-p

Panel A. Distribution of operations-related disclosure over time

ro

of

This table shows the distribution of 76,366 operations-related disclosure events for the period of 2002-2010. Panel A shows the distribution of events and firms over time. Panel B shows the distribution across size groups. Firms are assigned into micro-cap, small-cap, and large-cap groups based on their market cap at the end of the previous quarter, and the cutoff points are the 20 th and 50th percentiles of all NYSE stocks. Panel C shows the distribution across Fama-French 12 industries. EAD refers to earnings announcements; ORD refers to operations-related disclosures; MEF refers to management forecasts; 10-K/Q refers to SEC periodic filings of Form 10-K/Q; 8-K refers to SEC current filings of Form 8-K. 10-K/Q and 8-K filing dates are from EDGAR. MEF, 10-K/Q, and 8-K disclosures within the three-day [-1, +1] earnings announcement window are dropped.

27

MEF 10-K/Q 2,953 (0.134) 2,485 (0.120) 2,692 (0.134) 2,246 (0.113) 2,088 (0.106) 1,930 (0.101) 2,536 (0.133) 2,117 (0.117) 2,031 (0.117) 21,078 (0.120)

6,867 (0.311) 7,230 (0.349) 7,216 (0.359) 7,242 (0.365) 7,220 (0.368) 7,175 (0.376) 7,109 (0.374) 6,457 (0.357) 6,505 (0.375) 63,021 (0.358)

# Disclosing Firm-Quarters (percentage of all firm-quarters in Compustat)

8-K

EAD

ORD

MEF

10-K/Q

8-K

5,597 (0.254) 8,203 (0.396) 12,298 (0.612) 19,251 (0.969) 19,839 (1.011) 20,565 (1.078) 19,854 (1.045) 17,338 (0.958) 19,260 (1.110) 142,205 (0.809)

22,046 (100) 20,732 (100) 20,098 (100) 19,862 (100) 19,619 (100) 19,069 (100) 19,000 (100) 18,106 (100) 17,347 (100) 175,879 (100)

3,746 (16.99) 3,627 (17.49) 3,663 (18.23) 3,824 (19.25) 3,996 (20.37) 4,185 (21.95) 4,948 (26.04) 4,845 (26.76) 4,832 (27.85) 37,666 (21.42)

2,503 (11.35) 2,087 (10.07) 2,211 (11.00) 1,873 (9.43) 1,762 (8.98) 1,571 (8.24) 1,850 (9.74) 1,573 (8.69) 1,543 (8.89) 16,973 (9.65)

6,529 (29.62) 7,019 (33.86) 7,022 (34.94) 7,047 (35.48) 7,034 (35.85) 6,944 (36.42) 6,959 (36.63) 6,330 (34.96) 6,391 (36.84) 61,275 (34.84)

3,369 (15.28) 4,569 (22.04) 6,038 (30.04) 7,831 (39.43) 8,190 (41.75) 8,591 (45.05) 8,590 (45.21) 7,731 (42.70) 8,643 (49.82) 63,552 (36.13)

Panel B. Distribution of operations-related disclosure across size groups

Large

15,781

11,983

(0.020)

(0.003)

(0.155)

(0.118)

1440

167

7,243

5,652

(0.039)

(0.005)

(0.198)

(0.155)

3217

444

12,873

11,381

(0.085)

(0.012)

(0.341)

(0.302)

6708

902

35,897

29,016

(0.038)

(0.005)

(0.204)

(0.165)

1,293

8-K

31,399

102,774 5,998 26,558 61,339

(0.013) (0.309)

(1.012) (0.059) (0.261) (0.604)

661

15,163

(0.018) (0.415) 1,889

29,804

36,841

(0.050) (0.790) 3,843

5,518 15,625 32,843

# Disclosing Firm-Quarters (percentage of all firm-quarters in Compustat)

EAD

ORD

MEF 10-K/Q

101,597 17,675 5,271 25,561

8-K 29,101

(100) (17.40) (5.19) (25.16) (28.64) 36,546

7,508

4,504 15,257

14,707

(1.008) (0.151) (0.428) (0.899)

(100) (20.54) (12.32) (41.75) (40.24)

37,918

37,736 12,483 7,198 20,457

9,562 20,838 48,023

(1.005) (0.253) (0.552) (1.273)

76,366

177,533 21,078 63,021 142,205

(0.022) (0.434)

(1.009) (0.120) (0.358) (0.809)

Jo

ur na

lP

Total

291

MEF 10-K/Q

-p

Small

2051

EAD

re

Micro

Total

ro

Product Business Business Client Related Strategic Expansion Reorganization Announcements Announcements Alliance

Size

of

# Other Disclosure Events (#disclosure/total firm-quarters in Compustat)

# Operations-related Disclosure Events (#disclosure/total firm-quarters in Compustat)

28

19,744

(100) (33.08) (19.07) (54.21) (52.32) 175,879 37,666 16,973 61,275

63,552

(100) (21.42) (9.65) (34.84) (36.13)

Panel C. Distribution of operations-related disclosure across Fama-French 12 industries

739

812

(0.050)

(0.010)

(0.093)

(0.102)

Consumer

165

55

769

396

Durables

(0.045)

(0.015)

(0.211)

(0.109)

Energy Chemicals Business Equipment Telecom

426

188

3,653

1,417

(0.029)

(0.013)

(0.246)

(0.096)

249

36

369

664

(0.036)

(0.005)

(0.053)

(0.095)

221

33

395

305

(0.065)

(0.010)

(0.116)

837

148

18,706

(0.028)

(0.005)

(0.615)

333

22

1,460

(0.004)

(0.281)

(0.064) Utilities

201 (0.043)

Wholesale, Retail Healthcare

1577 (0.099) 342

Other Total

14,299

(0.470) 958

(0.184)

13

202

53

(0.003)

(0.044)

(0.011)

106

1,927

959

(0.007)

(0.121)

(0.060)

62

2,155

5,871

(0.003)

(0.119)

(0.324)

894

64

2,156

1,439

(0.022)

(0.002)

(0.054)

(0.036)

1069

93

3,366

1,843

(0.043)

(0.004)

(0.136)

(0.074)

Jo

(0.019)

Finance

(0.089)

6708

902

35,897

29,016

(0.038)

(0.005)

(0.204)

(0.165)

10-K/Q

8-K

EAD

ORD

MEF

10-K/Q

8-K

2,150

8,131

1,335

3,226

6,259

7,931

1,376

1,097

3,106

3,088

(0.016) (0.271) 89

(1.025) (0.168) (0.407)

1,474

3,736

(0.024) (0.405) 297 150

5,981

15,042

1,468

(0.022) (0.211) 132

1,086

(0.039) (0.318) 1,355

35,345

(0.045) (1.162) 128

2,901 559 4,690 9,085

(0.036) (0.502) 264

4,817

(0.007) (0.120) 439

6,810

(0.018) (0.275) 3,843

6,493

6,982

396

2,008

(1.003) (0.057) (0.289) 3,476

667

1,511

(0.789)

(100)

3,224

3,644

(0.885)

(100)

12,000

14,822

(0.810)

(100)

853

454

1,502

1,535

(23.41) (12.46) (41.22) (42.12) 3,123

1,695

6,329

5,953

(21.07) (11.44) (42.70) (40.16)

8,290

6,959

1,062

354

(1.191)

(100)

(15.26)

(5.09)

3,515

3,418

702

531

1,991

2,997

(28.61) (43.07) 1,474

1,559

(1.028)

(100)

(20.54) (15.54) (43.12) (45.61)

30,689

12,985

21,738

30,407

13,804

(1.009) (0.137) (0.427)

(0.715)

(100)

(45.40) (11.89) (41.50) (35.92)

3,810

5,195

1,348

245

(0.733)

(100)

(25.95)

(4.72)

4,166 271

1,328

7,087

4,635

452

(1.001) (0.209) (0.338)

969

(1.529)

(100)

(9.75)

16,197

4,587

1,567

1,287

10,921 1,586

(24.77) (30.53) 1,549

2,443

(15.79) (33.42) (52.71)

15,884 (100)

18,246

18,100

5,443

1,501

(1.008) (0.104) (0.333) (0.928)

(100)

(30.07)

(8.29)

(32.67) (40.38)

40,194

40,124

2,896

1,661

10,975

(1.002) (0.049) (0.275) (0.615)

(100)

(7.22)

(4.14)

(27.35) (26.11)

24,961

20,433

24,760

3,952

1,867

(1.008) (0.089) (0.328) (0.825)

(100)

(15.96)

(7.54)

1,977 2,207

6,035 11,054 8,117

16,799 24,674

76,366

177,533 21,078 63,021 142,205 (1.009) (0.120) (0.358) (0.809)

2,655

732

12,620

14,376

1,884

7,127

3,614

(1.020) (0.289) (0.449) (0.905)

(0.022) (0.434)

29

(17.35) (13.83) (39.16) (38.94)

(1.017) (0.195) (0.442)

4,638

(0.008) (0.295) 655

2,080

(1.009) (0.052) (0.256)

(0.019) (0.121) 121

1,570

(1.015) (0.140) (0.438)

5,241

(0.025) (0.558) 90

539

(1.025) (0.148) (0.431)

(0.020) (0.404)

lP

Manufacturing

123

MEF

-p

82

ur na

Non-Durables

394

EAD

re

Consumer

# Disclosing Firm-Quarters (percentage of all firm-quarters in Compustat)

Total

ro

Product Business Business Client Related Strategic Expansion Reorganization Announcements Announcements Alliance

Industry

of

# Other Disclosure Events (#disclosure/total firm-quarters in Compustat)

# Operations-related Disclosure Events (#disclosure/total firm-quarters in Compustat)

3,222

6,613 5,914

7,915

(21.42)

(9.65)

7,309 10,475 9,046

(31.97) (36.53)

175,879 37,666 16,973 61,275 (100)

6,640

(16.71) (20.28) (41.63) (41.80)

63,552

(34.84) (36.13)

Table 3. Mean Absolute Abnormal Returns, Return Volatility, and Trading Volume around OperationsRelated Disclosure Events Standardized absolute abnormal returns are the absolute value of size-adjusted abnormal returns in the event period minus the absolute value of size-adjusted abnormal returns in a non-event period, divided by the standard deviation of the absolute value of size-adjusted abnormal returns in the non-event period, i.e., 𝑆𝐴𝑅𝑖𝑗 = Avg𝑑∈[event][|𝑅𝐸𝑇𝑖𝑗,𝑑 −𝐷𝐸𝐶𝑅𝐸𝑇𝑗,𝑑 |]−Avg𝑑∈[non-event] [|𝑅𝐸𝑇𝑖𝑗,𝑑 −𝐷𝐸𝐶𝑅𝐸𝑇𝑗,𝑑 |] Std𝑑∈[non-event] [|𝑅𝐸𝑇𝑖𝑗,𝑑 −𝐷𝐸𝐶𝑅𝐸𝑇𝑗,𝑑 |]

. Abnormal volatility is the daily abnormal returns squared in

of

the period of interest scaled by the variance of abnormal returns in the non-event period, minus 1. Abnormal trading volume is the daily shares traded in the period of interest scaled by the average shares traded in the preceding non-event period, minus one. The non-event period is trading days -63 through -8 relative to the event date. All units are in %. t-tests for zero mean are conducted and t-statistics in parentheses are calculated based on standard errors clustered by firm and quarter. Significance at * 10% level, ** 5% level, and *** 1% level.

Panel A. Standardized absolute abnormal returns around operations-related disclosure events (3)

(4)

Total

Business Expansion

Business Reorganization

Client Announcements

2.8588

3.7589

Day -3

(1.54)

0.9229

1.7812

Day +2

[-1, +1] # Obs.

-0.7545

-0.0117

-2.3794

-0.8065

-2.0691

-1.3132 -2.1446

(-0.54)

-1.5554 (-0.45)

(-0.36)

0.0160

(0.00)

-2.2389 (-0.76)

-0.8789

(-0.33)

16.0969

(1.65)

(2.45)

14.0583

***

5.4035

*

(5.44) 1.1066

(1.94) 0.8836

(2.20) 5.0725

(0.40)

(0.30)

(0.87)

1.4469

5.0314* 3.6200

(1.19)

Day +5

1.3295

-1.3051

(-0.00)

1.3195

(0.35)

3.3536

(0.97) 3.6445 (0.98)

8.0704

**

**

(2.08)

-1.6216

(-1.02)

0.4358

-2.2603 (-1.01)

***

16.5611***

(4.08) 1.7532

(5.32) 3.1628

(0.11)

(0.61)

(0.78)

0.4272 5.4300* (1.85)

(0.12)

-1.7881 14.8051

-0.9426 (-0.24)

(6.22) 0.3059

(0.17)

11.7841**

-0.9659

(-0.68) ***

-2.1991 (-0.79)

(-0.39)

-2.4346 14.7527

(-0.24)

(-0.85)

(-0.69)

Strategic Alliances (-0.13)

(0.51)

Jo

Day +4

0.5268

(1.12)

(0.24)

(0.56)

Day +3

4.3698

ur na

Day +1

(1.24)

(6)

-0.3581

(0.74)

(-0.87) Day 0

(0.90)

2.8925

(0.47)

(-0.52) Day -1

2.9254

(0.37) (-0.56) Day -2

-p

Day -4

6.1388

re

(1.20)

(5) Product Related Announcements

ro

(2)

lP

Day -5

(1)

4.0577 (1.32)

2.7732 (1.12) 4.6695 (1.40) 3.2127 (1.04)

-0.3724 (-0.10) 5.3842* (1.80) 3.3083 (1.03)

3.7085

5.1640

2.5456

3.7562

3.6864

1.1653

(1.15) 13.0264* (1.71) 76,366

(1.29) 4.0481 (0.63) 6,708

(0.45) 20.4107** (2.04) 902

(1.21) 12.6306** (2.55) 35,897

(1.08) 14.7747** (2.35) 29,016

(0.34) 17.4635*** (2.60) 3,843

30

Panel B. Abnormal return volatility around operations-related disclosures (1)

(2)

(3)

(4)

Total

Business Expansion

Business Reorganization

Day -5

25.9102***

25.8730***

29.9811*

(5.70)

(4.95)

(1.87)

(4.64)

(3.45)

Day -4

18.1231**

12.2228

36.2892

18.5359***

19.8675**

(2.56)

(1.32)

(1.14)

(2.87)

(2.48)

7.7136

4.0489

(1.20) Day -2

-0.7570

(0.89)

8.3335* (1.78)

(-0.09)

2.1380

24.9745***

7.9840

(0.90)

7.1314 (0.89) 4.4227

(0.98)

(0.59)

14.2654**

10.2742

(2.28)

(0.99)

Day -1

0.7238 (0.14)

(-0.67)

(0.13)

(-0.01)

Day 0

79.1398***

24.2707***

56.4283***

79.4613***

88.0198***

110.1937***

Day +1

(8.23) 14.7192**

(3.41) 9.0720

(3.44) 11.3063

(6.46) 13.8753**

(4.04) 16.7837**

(4.52) 17.6713

(2.45)

(1.23)

(1.56)

Day +2

24.9191***

22.0934 (1.42)

ro

(0.52)

(2.27)

(2.38)

15.4556**

43.2450***

(2.54)

(3.76)

(0.65) ***

65.6278*

(2.95)

(2.22)

(3.99)

(3.54)

(1.75)

21.2208**

19.1640

27.8874***

27.8290***

16.3132*

(3.81)

(2.20)

(0.99)

(3.23)

(4.86)

(1.79)

(2.42) 30.0461* (1.85) 6,708

11.2363

29.5256

(0.68) 68.6209** (2.52) 902

(2.99) 93.3210*** (5.14) 35,897

Jo

# Obs.

(3.07) 94.5966*** (6.21) 76,366

29.8407

ur na

[-1, +1]

34.1269

lP

(4.31) 26.5940***

***

31

29.3847

5.7786

Day +4

**

33.2222

***

(-0.80)

33.0761

Day +5

48.1083

**

-3.9265

Day +3

***

27.5840

***

3.1999

-p

(0.91)

7.6385 (1.06)

***

-0.0272

re

(4.59)

0.6765

(0.82)

10.1866

4.6226

(0.39)

-3.2966

28.4902***

(1.28)

3.0000

(0.32)

Strategic Alliances

6.9646

(6)

of

Day -3

Client Announcements

(5) Product Related Announcements

43.8363

**

(2.12) 108.0190*** (4.41) 29,016

16.7031* (1.96) 123.9384*** (4.96) 3,843

Panel C. Abnormal trading volume around operations-related disclosures (1)

(2)

(3)

(4)

Total

Business Expansion

Business Reorganization

Client Announcements

(5) Product Related Announcements

11.6600***

12.1768***

9.4119***

(3.31)

(3.35)

10.3850***

Day -4

10.8062***

(6.33)

(3.70)

(3.65) Day -3

7.7398

5.7071*** (3.61)

***

(4.45) Day -2

7.4876***

(2.43)

4.6217

**

(2.49)

6.8172*** (4.59)

20.9301

(2.02)

4.2837* 4.3747**

(1.99)

(2.32)

(2.64)

(3.03)

(1.96)

15.2010***

25.8256***

46.6236***

55.4030***

54.4616***

Day +1

(5.67) 28.3269***

(3.97) 11.1995***

(4.41) 20.2754***

(6.61) 32.5871***

(5.69) 28.6086***

(4.42) 18.1919***

(3.56)

(2.70)

Day +2

19.0296*** 22.7585

Day +4

17.6229***

(1.93)

(6.46) (4.34) 82.8042*** (5.31) 76,366

8.2607**

10.5529

(2.34)

***

(4.35) 29.4136*** (3.98) 6,708

10.8574

**

(2.16) 54.3198*** (6.73) 902

Jo

# Obs.

(3.70) ***

20.3007 (4.02)

9.9084***

ur na

[-1, +1]

20.0493

14.4118

***

lP

(6.38)

-p

(3.08) *

(3.18)

(6.33)

(4.03)

15.5084***

25.7426***

21.8213**

(5.21)

(4.84)

(2.43)

re

(3.15)

Day +3

Day +5

(3.91)

32

ro

(3.38) 47.3480***

16.8944***

10.9825

(1.77)

***

Day 0

7.5213***

7.1788

(2.42)

10.1061*** (3.09)

***

5.6881**

8.1293

***

8.2187

(2.94) **

(2.56)

5.3572*** (2.74)

**

8.0086

7.0817***

Day -1

(5.29)

3.0131

7.9934

5.7531*** (3.13)

14.6754* (1.84)

***

(3.62)

6.8333** **

(3.18)

9.0455*** (4.76)

**

(2.01)

1.8527 (1.01)

***

10.1995**

Strategic Alliances

of

Day -5

(6)

***

19.6702***

(4.95)

(3.59)

(3.46)

15.8224***

22.0927***

16.3651***

(5.50)

(4.44)

(4.19)

19.2415

23.0567

***

***

(3.36) 86.3982*** (4.11) 35,897

29.5248

19.7132

***

(4.62) 94.9942*** (4.59) 29,016

13.2135*** (3.43) 77.082*** (4.22) 3,843

Table 4. Mean Size-Adjusted Abnormal Returns around Operations-Related Disclosure Events Abnormal returns are the one-day size-adjusted abnormal return around the release of operations-related announcements. All units are in %. t-tests for zero mean are conducted and t-statistics in parentheses are calculated based on standard errors clustered by firm and quarter. Significance at * 10% level, ** 5% level, and *** 1% level. (2)

(3)

(4)

Total 0.0316

Business Expansion -0.0623*

Business Reorganization -0.2124

Client Announcements 0.0316

Day -3

(0.43)

0.0739

(-2.22) ***

(4.05) Day -1

0.7282

Day +1

0.0923***

Day +2

(6.26) 0.0634*** (2.82) 0.0069 (0.47) -0.0079 (-0.36)

Day +5 [-1, +1]

(5.47) 0.1487*** (4.39) -0.0019

-0.3434

(-0.05)

-0.0079 (-0.20)

0.0552 (1.31)

***

(-2.22) -0.2431** (-2.13) -0.1345 (-0.99)

0.1105

(1.25)

0.0247

0.0540

(0.85)

0.9097

0.1001

***

0.0276

***

(14.65)

0.0927***

(0.87) 0.6300

-0.0044 (-0.08) 0.0607 (1.02)

***

(8.80) 0.0979***

0.8389*** (7.74) 0.0269

(4.73) 0.0492*

(3.76) 0.1079***

(0.55)

(1.92)

(3.54)

(0.43)

-0.0357

-0.0201

0.0444*

(0.68)

(1.73)

-0.0129

-0.0097

(-0.13)

0.0646

(2.90)

0.0107 (0.48)

**

(-0.42)

(-1.39) -0.0158 (-0.44)

0.0207

(-0.44) -0.0399 (-0.84)

0.0056

0.0303

0.1519

0.0228

-0.0289

0.0290

(0.33) 0.826*** (11.06) 76,366

(0.74) 0.429*** (5.52) 6,708

(0.95) -0.639*** (-2.94) 902

(0.99) 0.991*** (11.42) 35,897

(-1.13) 0.745*** (8.35) 29,016

(0.48) 0.924*** (6.15) 3,843

Jo

# Obs.

0.2628

(-0.11) ***

0.0361

(1.09)

0.0713 (2.62)

-0.0120

ur na

Day +4

(-0.59)

(0.89) ***

(2.26)

Strategic Alliances -0.0070 (-0.13)

(1.36)

0.0632**

-0.0588

0.0255

(9.07)

Day +3

0.0372

0.0381 (1.80)

0.0456 (0.27)

(0.89)

0.0207 (0.77)

Day 0

(1.76)

-0.0792**

0.0354 (1.50)

Day -2

0.2117

(2.18) *

(6)

of

(1.68)

0.0168

(1.28) *

(5) Product Related Announcements 0.0660**

ro

0.0388

(-1.25)

-p

Day -4

(-1.87) *

re

(1.11)

lP

Day -5

(1)

33

Table 5. Incremental 𝑹𝟐 from Firm-Specific Time Series Regressions of Quarterly Abnormal Returns on Returns to Corporate Disclosures

of

This table reports the incremental 𝑅2 of each disclosure from a firm-specific time series regression: 10−𝐾/𝑄 𝐸𝐴𝐷 𝑂𝑅𝐷 𝑀𝐹 8−𝐾 𝐶𝐴𝑅𝑖,𝑄 = 𝛼 + 𝛽1 𝐶𝐴𝑅𝑖,𝑄 + 𝛽1 𝐶𝐴𝑅𝑖,𝑄 + 𝛽1 𝐶𝐴𝑅𝑖,𝑄 + 𝛽1 𝐶𝐴𝑅𝑖,𝑄 + 𝛽1 𝐶𝐴𝑅𝑖,𝑄 + 𝜀𝑖,𝑄 𝐸𝐴𝐷 where 𝐶𝐴𝑅𝑖,𝑄 is the quarterly cumulative size-adjusted abnormal returns for firm i in calendar quarter Q; 𝐶𝐴𝑅𝑖,𝑄 is the sum 𝑂𝑅𝐷 of one-day or three-day size-adjusted abnormal returns for all earnings announcements made by firm i in quarter Q; 𝐶𝐴𝑅𝑖,𝑄 is the sum of one-day or three-day size-adjusted abnormal returns for all operations-related releases made by firm i in quarter 10−𝐾/𝑄 𝑀𝐹 Q, zero if no operations-related releases; similarly for management forecasts 𝐶𝐴𝑅𝑖,𝑄 , 10-K/Q filings 𝐶𝐴𝑅𝑖,𝑄 , and 8-K 8−𝐾 filings 𝐶𝐴𝑅𝑖,𝑄 . Management forecasts overlapped with the 3-day earnings announcement window are deleted; 10-K/Q and 8-K filings overlapped with the 3-day window of earnings announcements and management forecasts are deleted; ORDs overlapped with the 3-day window of earnings announcements, management forecasts, 10-K/Q filings, and 8-K filings are deleted. The sample consists of all firms with available data in Compustat and CRSP, 143,345 firm-quarter observations, 4,624 individual firms, and 36 calendar quarters. At least 20 observations are required for each firm.

Panel A. Distribution of Incremental 𝑅 2 from time-series regression for each sample firm

Q1

(p-value, H0=1)

(% of total)

0.842 0.960 (0.59) 1.096

lP

MEF: Management earnings forecasts

0.827

(% of total)

(% of total) 14.39

(15.54)

(41.99)

(27.82)

(39.77)

0.00

2.91

0.20

2.93

0.00

(12.06) 3.43

(0.99) 0.11

(8.11) 3.45

(14.20)

(0.54)

(9.52)

2.34

0.08

2.46

0.00

(9.69)

(0.39)

(6.81)

0.00

3.71

0.24

4.11

8.76

(15.36) 24.14

(1.16) 20.36

(11.36) 36.19

0.952

4.88

16.79

13.83

24.90

(0.00)

(27.49)

(47.73)

(39.12)

(48.98)

0.792

0.00

3.25

0.31

3.45

0.00

(9.23) 3.67

(0.87) 0.21

(6.79) 4.11

(10.44)

(0.58)

(8.09)

2.51

0.16

2.72

(7.13)

(0.46)

(5.34)

4.89

0.61

6.41

(13.89)

(1.72)

(12.60)

35.17

35.34

50.84

0.827

ur na

Total 𝑅2

(% of total)

5.66

(0.00)

8-K filings

Q3

10.14

(0.32)

10-K/Q filings

Median

1.36

re

(0.00) ORD: Operations-related disclosures

Mean

-p

1-day event window EAD: Earnings announcements

Mean

ro

Incremental 𝑹𝟐

Coefficient

(0.00)

3-day event window

EAD: Earnings announcements

Jo

ORD: Operations-related disclosures

MEF: Management earnings forecasts 10-K/Q filings

(0.00) 0.955 (0.25) 0.752

0.00

(0.00) 8-K filings

0.790

0.00

(0.00) Total 𝑅

2

17.75

34

Panel B. Average incremental 𝑅 2 for individual firms in each market cap group

1-day event window EAD: Earnings announcements

Full

Micro

Small

Large

All-but-micro

(% of total)

(% of total)

(% of total)

(% of total)

(% of total)

10.05

9.78

10.77

10.23

(46.51)

(39.72)

(36.26)

(37.97)

ORD: Operations-related disclosures

2.91

2.75

2.74

3.51

3.09

MEF: Management earnings forecasts

(12.06) 3.43

(12.70) 2.10

(11.12) 4.50

(11.83) 5.37

(11.48) 4.90

(14.20)

(9.73)

(18.29)

(18.07)

(18.18)

2.34

1.96

2.39

3.19

2.76

(9.69)

(9.07)

(9.71)

Total 𝑅2 3-day event window EAD: Earnings announcements

3.71

3.51

3.57

(15.36) 24.14

(16.24) 21.61

(14.50) 24.61

16.79

15.43

18.21

(47.73)

(50.91)

(10.75)

(48.70)

(10.24)

4.36

3.93

(14.67) 29.71

(14.59) 26.96

17.97

18.10

(40.95)

(44.83)

ro

8-K filings

-p

10-K/Q filings

of

10.14 (41.99)

3.25

3.00

2.63

4.28

3.39

MEF: Management earnings forecasts

(9.23) 3.67

(9.90) 2.29

(7.02) 4.77

(9.76) 5.89

(8.39) 5.28

(7.57)

(13.41)

(13.08)

re

ORD: Operations-related disclosures

(12.76)

2.04

2.57

3.44

2.97

(7.13)

(6.72)

(6.88)

(7.83)

(7.36)

4.89

4.65

4.64

6.12

5.32

(13.89)

(15.35)

(12.40)

(13.94)

(13.17)

35.17

30.31

37.39

43.88

40.38

2434

1183

(10.44) 2.51

lP

10-K/Q filings 8-K filings

4624

Jo

Total 𝑅 # Firms

ur na

2

35

1007

2190

One-day event window

Energy Chemicals Business Equipment

Utilities Wholesale, Retail

Jo

Healthcare Finance Other

8-K

12.81

2.81

3.53

2.51

2.99

(47.86)

(10.51)

(13.17)

(9.39)

(11.17)

11.63

3.16

3.51

2.97

2.84

(45.21)

(12.28)

(13.66)

(11.53)

(11.04)

12.09

2.94

3.53

2.47

(45.23)

(11.00)

(13.22)

(9.24)

6.94

3.49

2.07

2.81

(31.55)

(15.86)

(9.43)

11.11

2.04

4.19

(37.50)

(6.90)

(14.16)

9.03

4.52

4.83

(35.20)

(17.59)

12.46

2.74

(54.04) 7.95

Total 𝑅

3.32

26.77 25.73 26.73

(12.41) 5.55

(12.78)

(25.24)

3.78

5.64

(12.76)

(19.03)

2.32

3.33

(18.82)

(9.03)

(12.96)

1.61

2.73

2.52

(11.90)

(6.99)

(11.82)

(10.93)

2.45

5.56

2.38

5.13

(32.57)

(10.02)

(22.78)

(9.74)

(20.99)

10.83

2.35

5.38

2.61

4.14

(39.98)

(8.68)

(19.87)

(9.62)

(15.30)

10.48

3.93

3.11

2.18

5.57

(38.37)

(14.38)

(11.40)

(8.00)

(20.39)

9.68

1.42

2.02

1.97

2.94

(50.26)

(7.35)

(10.49)

(10.21)

(15.25)

9.90

3.10

3.09

2.26

3.56

(42.53)

(13.31)

(13.29)

(9.73)

(15.27)

36

EAD

ORD

MEF

10-K/Q

19.28

2.83

3.81

2.77

4.10

(51.17)

(7.52)

(10.10)

(7.34)

(10.88)

18.87

3.41

3.50

2.70

3.67

(50.33)

(9.10)

(9.33)

(7.21)

(9.80)

19.12

3.11

3.69

2.37

4.51

(50.35)

(8.18)

(9.71)

(6.24)

(11.89)

11.16

3.01

1.64

2.63

7.85

(35.90)

(9.66)

(5.28)

(8.44)

(25.25)

19.10

2.20

4.36

3.14

5.77

(47.53)

(5.47)

(10.86)

(7.81)

(14.35)

18.18

5.17

5.22

2.27

4.16

(44.88)

(12.76)

(12.90)

(5.61)

(10.27)

18.31

4.04

1.18

2.49

4.58

(53.91)

(11.89)

(3.48)

(7.33)

(13.50)

13.98

1.63

5.59

2.32

7.42

(39.23)

(4.58)

(15.68)

(6.50)

(20.83)

18.02

2.28

6.24

3.34

5.46

(45.29)

(5.72)

(15.68)

(8.38)

(13.72)

16.01

4.16

3.43

2.18

7.73

(43.45)

(11.30)

(9.30)

(5.91)

(20.99)

14.21

1.51

2.28

2.06

3.90

(53.77)

ro

10-K/Q

ur na

Telecom

MEF

2

-p

Manufacturing

ORD

re

Consumer Durables

Three-day event window

EAD

lP

Consumer NonDurables

of

Panel C. Average incremental 𝑅 2 for individual firms in each industry

22.01 29.62 25.67 23.05 24.42 27.09 27.30 19.27 23.28

8-K

(5.71)

(8.61)

(7.79)

(14.75)

17.33

3.85

3.41

2.95

4.77

(48.37)

(10.75)

(9.52)

(8.22)

(13.31)

Total 𝑅2 37.68 37.50 37.97 31.10 40.19 40.51 33.97 35.64 39.80 36.84 26.43 35.82

Table 6. Incremental 𝑹𝟐 from Cross-Sectional Regressions of Quarterly Abnormal Returns on Returns to Corporate Disclosures This table reports the incremental 𝑅2 of each disclosure from a cross-sectional regression for each quarter: 10−𝐾/𝑄 𝐸𝐴𝐷 𝑂𝑅𝐷 𝑀𝐹 8−𝐾 𝐶𝐴𝑅𝑖,𝑄 = 𝛼 + 𝛽1 𝐶𝐴𝑅𝑖,𝑄 + 𝛽1 𝐶𝐴𝑅𝑖,𝑄 + 𝛽1 𝐶𝐴𝑅𝑖,𝑄 + 𝛽1 𝐶𝐴𝑅𝑖,𝑄 + 𝛽1 𝐶𝐴𝑅𝑖,𝑄 + 𝜀𝑖,𝑄 𝐸𝐴𝐷 where 𝐶𝐴𝑅𝑖,𝑄 is the quarterly cumulative size-adjusted abnormal returns for firm i in calendar quarter Q; 𝐶𝐴𝑅𝑖,𝑄 is the sum of one-day or three-day size-adjusted abnormal returns for all earnings announcements made by firm i in 𝑂𝑅𝐷 quarter Q; 𝐶𝐴𝑅𝑖,𝑄 is the sum of one-day or three-day size-adjusted abnormal returns for all operations-related releases made by firm i in quarter Q, zero if no operations-related releases; similarly for management forecasts 10−𝐾/𝑄

𝑀𝐹 𝐶𝐴𝑅𝑖,𝑄 , 10-K/Q filings 𝐶𝐴𝑅𝑖,𝑄

8−𝐾 , and 8-K filings 𝐶𝐴𝑅𝑖,𝑄 . Management forecasts overlapped with the 3-day

of

earnings announcement window are deleted; 10-K/Q and 8-K filings overlapped with the 3-day window of earnings announcements and management forecasts are deleted; ORDs overlapped with the 3-day window of earnings announcements, management forecasts, 10-K/Q filings, and 8-K filings are deleted. The sample consists of all firms with available data in Compustat and CRSP, 175,728 firm-quarter observations, 7,882 individual firms, and 36 calendar quarters.

ro

Panel A. Distribution of incremental 𝑅 2 from cross-sectional regression for each calendar quarter

Mean (% of total)

Median (% of total)

Q3 (% of total)

4.15

5.71

5.68

7.21

(45.70)

(51.51)

(48.60)

(54.40)

0.44

1.58

1.22

2.24

lP

Mean (p-value, H0=1)

1.154

(4.82) 0.77

(14.23) 1.11

(10.41) 1.06

(16.93) 1.41

(0.00)

(8.48)

(10.03)

(9.08)

(10.63)

0.837

0.19

0.34

0.32

0.47

(0.00)

(2.07)

(3.03)

(2.75)

(3.52)

0.887

1.07

2.23

2.00

2.97

(0.00)

(11.84)

(20.11)

(17.10)

(22.44)

9.08

11.08

11.69

13.26

0.942 (0.01)

ORD: Operations-related disclosures

0.818

(0.00)

10-K/Q filings 8-K filings

ur na

MEF: Management earnings forecasts

Total 𝑅2 3-day event window

Jo

EAD: Earnings announcements

ORD: Operations-related disclosures MEF: Management earnings forecasts 10-K/Q filings 8-K filings

Q1 (% of total)

re

1-day event window EAD: Earnings announcements

-p

Incremental 𝑹𝟐

Coefficient

0.949

10.82

13.51

14.28

16.87

(0.02)

(54.48)

(56.46)

(56.89)

(58.64)

0.815

1.24

2.40

1.95

2.86

(0.00) 0.970

(6.26) 1.05

(10.02) 1.78

(7.79) 1.62

(9.95) 2.33

(0.38)

(5.28)

(7.45)

(6.44)

(8.10)

0.696

0.41

0.70

0.65

0.88

(0.00)

(2.08)

(2.92)

(2.58)

(3.06)

0.791

1.75

4.14

4.68

5.57

(0.00)

(8.83)

(17.30)

(18.65)

(19.35)

19.87

23.93

25.09

28.76

Total 𝑅2

37

Panel B. Average incremental 𝑅 2 from cross-sectional regressions for each calendar quarter in each market cap group Micro

Small

Large

All-but-micro

(% of total)

(% of total)

(% of total)

(% of total)

(% of total)

5.71 (51.51)

5.35

7.28

7.48

7.28

(51.34)

(51.35)

(47.20)

(50.34)

ORD: Operations-related disclosures

1.58

1.61

1.31

1.81

1.45

MEF: Management earnings forecasts

(14.23) 1.11

(15.48) 0.83

(9.25) 2.48

(11.43) 2.68

(10.04) 2.48

(10.03)

(7.97)

(17.52)

(16.89)

(17.14)

0.34

0.32

0.55

(3.03)

(3.05)

(3.88)

2.23

2.18

2.45

(20.11)

(20.86) 10.43

8-K filings

2.54

18.70

17.83

18.28

(51.01)

(55.55)

2.02

3.37

2.48

(10.80) 1.35

(6.28) 3.79

(9.63) 4.06

(7.55) 3.81

(6.15)

(11.73)

(11.62)

(11.57)

0.70

0.61

1.12

1.48

1.17

(2.92)

(2.76)

(3.48)

(4.22)

(3.56)

13.51

12.44

ORD: Operations-related disclosures

2.40

2.38

MEF: Management earnings forecasts

(10.02) 1.78

lP

(7.45)

re

(56.46)

(56.55)

(17.55) 14.47

3.96

4.55

5.90

5.00

(17.30)

(18.01)

(14.09)

(16.88)

(15.19)

23.93

22.00

32.27

34.96

32.90

7,882

4,870

1,731

1,281

3,012

ur na

4.14

Jo

Total 𝑅 # Firms

2.86

(57.94)

EAD: Earnings announcements

2

(3.88)

(18.06) 15.84

11.08

8-K filings

(4.51)

(17.30) 14.18

Total 𝑅2 3-day event window

10-K/Q filings

0.56

ro

10-K/Q filings

0.71

-p

1-day event window EAD: Earnings announcements

of

Full

38

One-day event window

Energy Chemicals Business Equipment

Utilities Wholesale, Retail

Jo

Healthcare Finance Other

10-K/Q

8-K

Total 𝑅

9.58

1.79

1.22

0.91

2.03

(57.72)

(10.78)

(7.38)

(5.51)

(12.25)

6.66

1.95

1.68

1.97

2.45

(42.21)

(10.63)

(12.46)

(15.52)

ro

(12.34)

11.00

1.42

1.49

0.53

(61.71)

(7.96)

(8.38)

(2.98)

4.24

1.82

0.90

0.54

(37.67)

(16.15)

(8.04)

10.17

1.80

1.45

2.41 3.26

(4.81)

(28.96)

0.93

2.97

(9.92)

(7.99)

(5.15)

(16.41)

5.57

2.49

1.55

0.41

1.90

(46.28)

(20.68)

(12.85)

(3.45)

(15.77)

6.50

1.20

1.55

0.59

3.01

(49.39)

(9.09)

(11.78)

(4.47)

(22.87)

5.68

4.74

3.14

1.50

4.16

(29.83)

(24.85)

(16.48)

(7.87)

(21.85)

8.31

0.91

2.56

0.66

2.63

(53.37)

(5.81)

(16.42)

(4.25)

(16.89)

4.40

2.88

0.91

0.57

4.28

(33.60)

(22.01)

(6.91)

(4.39)

(32.65)

4.65

0.27

1.06

0.37

1.77

(56.67)

16.60 15.78 17.82

(13.54)

(56.19)

ur na

Telecom

MEF

-p

Manufacturing

ORD

re

Consumer Durables

Three-day event window

2

EAD

lP

Consumer NonDurables

of

Panel C. Average incremental 𝑅 2 from cross-sectional regressions for each calendar quarter in each industry

(3.29)

(12.96)

(4.56)

(21.54)

5.29

2.17

1.17

0.56

2.01

(46.34)

(19.06)

(10.26)

(4.92)

(17.60)

39

11.25 18.09 12.04 13.16 19.06 15.58 13.10 8.21 11.41

EAD 18.79

ORD 1.92

MEF 1.84

10-K/Q 1.38

8-K 3.51

(62.41)

(6.38)

(6.11)

(4.57)

(11.66)

16.37

2.58

2.54

2.01

3.80

(53.91)

(8.49)

(8.38)

(6.62)

(12.51)

20.30

2.29

1.83

1.17

3.13

(64.32)

(7.24)

(5.79)

(3.72)

(9.92)

8.68

1.84

1.00

0.48

5.93

(44.22)

(9.39)

(5.10)

(2.47)

(30.20)

16.48

2.30

1.96

1.03

5.18

(56.94)

(7.95)

(6.78)

(3.57)

(17.91)

17.00

3.67

2.63

0.71

4.06

(57.10)

(12.34)

(8.83)

(2.37)

(13.65)

15.26

1.81

1.47

0.80

4.30

(60.90)

(7.23)

(5.88)

(3.20)

(17.17)

12.75

3.99

3.48

1.84

8.79

(37.30)

(11.67)

(10.19)

(5.38)

(25.72)

16.10

1.77

3.60

1.39

4.45

(55.24)

(6.08)

(12.37)

(4.76)

(15.28)

9.63

3.52

1.31

0.77

6.92

(41.47)

(15.15)

(5.63)

(3.34)

(29.79)

12.14

0.46

1.52

0.87

2.79

(64.38)

(2.44)

(8.08)

(4.61)

(14.82)

12.54

3.05

1.73

0.79

3.30

(55.02)

(13.38)

(7.58)

(3.47)

(14.49)

Total 𝑅2 30.10 30.36 31.55 19.63 28.94 29.77 25.06 34.18 29.14 23.22 18.85 22.80

Table 7. Incremental 𝑹𝟐 from Regressions of Quarterly Abnormal Returns on Returns to Corporate Disclosures for Firms with At Least One Operations-related Disclosure during the Sample Period This table reports the incremental 𝑅2 of each disclosure from the following regression: 10−𝐾/𝑄 𝐸𝐴𝐷 𝑂𝑅𝐷 𝑀𝐹 8−𝐾 𝐶𝐴𝑅𝑖,𝑄 = 𝛼 + 𝛽1 𝐶𝐴𝑅𝑖,𝑄 + 𝛽1 𝐶𝐴𝑅𝑖,𝑄 + 𝛽1 𝐶𝐴𝑅𝑖,𝑄 + 𝛽1 𝐶𝐴𝑅𝑖,𝑄 + 𝛽1 𝐶𝐴𝑅𝑖,𝑄 + 𝜀𝑖,𝑄 𝐸𝐴𝐷 where 𝐶𝐴𝑅𝑖,𝑄 is the quarterly cumulative size-adjusted abnormal returns for firm i in calendar quarter Q; 𝐶𝐴𝑅𝑖,𝑄 is the sum 𝑂𝑅𝐷 of one-day or three-day size-adjusted abnormal returns for all earnings announcements made by firm i in quarter Q; 𝐶𝐴𝑅𝑖,𝑄 is the sum of one-day or three-day size-adjusted abnormal returns for all operations-related releases made by firm i in quarter 10−𝐾/𝑄

𝑀𝐹 Q, zero if no operations-related releases; similarly for management forecasts 𝐶𝐴𝑅𝑖,𝑄 , 10-K/Q filings 𝐶𝐴𝑅𝑖,𝑄

, and 8-K

8−𝐾 𝐶𝐴𝑅𝑖,𝑄 .

ro

of

filings The model is estimated in a time series for each individual firm and cross-sectionally for each calendar quarter. Management forecasts overlapped with the 3-day earnings announcement window are deleted; 10-K/Q and 8-K filings overlapped with the 3-day window of earnings announcements and management forecasts are deleted; ORDs overlapped with the 3-day window of earnings announcements, management forecasts, 10-K/Q filings, and 8-K filings are deleted. The sample consists of firms that make at least one operations-related disclosure in the period 2002 to 2010, 71,968 firm-quarter observations, 2,224 individual firms, and 36 calendar quarters. At least 20 observations are required for each firm in the time-series regression.

-p

Mean incremental 𝑅2 (% of total 𝑅2 )

Time-series

8-K filings

ur na

10-K/Q filings

lP

ORD: Operations-related disclosures MEF: Management earnings forecasts

Total 𝑅2 3-day event window

EAD: Earnings announcements

Jo

ORD: Operations-related disclosures

MEF: Management earnings forecasts 10-K/Q filings 8-K filings Total 𝑅

10.12

7.77

(31.26)

(45.90)

4.49

1.74

(13.86) 3.40

(10.27) 0.57

(10.50)

(3.38)

4.18

0.87

(12.91)

(5.14)

re

1-day event window EAD: Earnings announcements

2

40

Cross-sectional

6.43

5.13

(19.86) 32.38

(30.29) 16.93

16.89

16.23

(37.12)

(46.81)

4.84

3.12

(10.64) 2.92

(9.01) 0.78

(6.43)

(2.24)

4.23

1.73

(9.29)

(5.00)

8.11

8.50

(17.82)

(24.53)

45.50

34.67

of

Table 8. Incremental 𝑹𝟐 of Disclosures for Firms with Different Earnings Quality: Firm-Specific Time Series Regressions of Quarterly Abnormal Returns on Returns to Corporate Disclosures

re

-p

ro

This table reports the incremental 𝑅2 of each disclosure for firms with different earnings quality. Earnings quality is measured by the absolute value of discretionary accruals, earnings persistence, earnings smoothness, and earnings predictability. Each year, I estimate modified Jones’ model 𝐴𝐶𝐶𝑖𝑡 = 𝛼 + 𝛽(Δ𝑅𝐸𝑉𝑖𝑡 − Δ𝐴𝑅𝑖𝑡 ) + 𝛾𝑃𝑃𝐸𝑖𝑡 + 𝜖𝑖𝑡 for each two-digit SIC industry, and obtain the residual as a measure of discretionary accruals. The absolute value of discretionary accruals for each firm is then averaged during the sample period. 𝐴𝐶𝐶𝑖𝑡 is accruals (earnings minus cash flow from operations); Δ𝑅𝐸𝑉𝑖𝑡 is the change in net sales from year t-1 to year t; Δ𝐴𝑅𝑖𝑡 is the change in accounts receivable from year t-1 to year t; 𝑃𝑃𝐸𝑖𝑡 is gross property, plant, and equipment at year t; all variables are scaled by total assets at year t-1. Earnings persistence is measured as the coefficient 𝛽 from the regression 𝐸𝑖𝑡 = 𝛼 + 𝛽𝐸𝑖𝑡−1 + 𝜀𝑡 , where 𝐸𝑖𝑡 is earnings for year t, scaled by total assets, and earnings predictability is measured as the root mean squared error from the regression, times -1. Earnings smoothness is measured as the ratio of the standard deviation of earnings to standard deviation of cash flow from operations, times -1. A firm is classified into the high group if its average absolute discretionary accruals, earnings persistence, and earnings smoothness are higher than the sample median. The sample consists of all firms with available data in Compustat and CRSP, 143,345 firm-quarter observations, 4,624 individual firms, and 36 calendar quarters. At least 20 observations are required for each firm.

Panel A. 1-day event window

lP

Mean Incremental 𝑹𝟐 (% of Total R2)

announcements

ur na

EAD: Earnings

ORD: Operations-related disclosures

MEF: Management earnings forecasts

Jo

10-K/Q filings 8-K filings Total 𝑅2

Discretionary Accruals High – Low High Low (t-value) 10.11 8.85 -1.256** (44.01) (39.79) (-2.21) 2.13

3.73

(9.27) (16.77) 3.69

2.56

(16.06) (11.51) 2.39

2.44

(10.40) (10.97) 2.95

3.45

(12.84) (15.51) 22.97 22.24

1.598***

Earnings Persistence High – Low High Low (t-value) 9.48 10.45 0.966*** (41.56) (45.63) (2.70) 3.25

2.73

(5.08) -1.133***

(14.25) (11.92)

(-3.73)

(12.85) (12.62)

0.047

(0.20) 0.491 (1.51) -0.735 (-0.85)

2.93 2.36

2.89 2.37

(10.35) (10.35) 3.09

2.8

(13.55) (12.23) 22.81 22.9

41

-0.519**

Earnings Smoothness High – Low High Low (t-value) 9.78 10.16 0.378 (42.71) (44.84) (1.06) 3.39

2.59

(-2.57) -0.045

(14.80) (11.43)

(-0.24)

(12.10) (13.37)

0.006 (0.04) -0.289 (-1.47) 0.087 (0.16)

2.77 2.38

3.03 2.29

(10.39) (10.11) 2.99

2.84

(13.06) (12.53) 22.9 22.66

Earnings Predictability High – Low High Low (t-value) 9.8 10.12 0.323 (41.74) (45.52) (0.90)

-0.805***

3.85

2.12

-1.736***

(-3.97) 0.254

(16.40)

(9.54)

2.51

3.31

(-8.67) 0.800***

(1.37)

(10.69)

(14.89)

(4.33)

-0.094

2.35

2.38

0.030

(-0.63)

(10.01)

(10.71)

(0.20)

-0.152

3.36

2.52

-0.841***

(-0.78) -0.234 (-0.44)

(14.31) 23.48

(11.34) 22.23

(-4.28) -1.250** (-2.35)

of

Panel B. 3-day event window Mean Incremental 𝑹𝟐 (% of Total R2)

ORD: Operations-related

2.4

3.98

1.582

(7.05) (12.13)

disclosures MEF: Management earnings

3.59

2.47

(10.54) (7.53)

10-K/Q filings 8-K filings

(-3.78)

2.66

2.45

-0.208

(7.47)

(-0.79)

3.79

4.38

0.589

34.06

32.8

(49.15) (51.08) 3.51

(1.54)

-1.259

3.12

(1.87)

(8.78)

(49.45) (50.86)

(0.59) ***

2.91

(11.01)

(8.75)

2.78

(-1.81) -0.161

2.81

2.92

(-3.68) 0.106

(8.21)

(-0.84)

(8.34)

(8.78)

(0.55)

-0.392

(10.48) (9.22) 2.94

*

3.71

-0.799

Earnings Predictability High – Low High Low (t-value) 16.72 17.03 0.309 (48.60)

(51.72)

(0.70)

4.23

2.4

-1.824***

(12.30)

(7.29)

2.46

3.26

(-8.47) 0.806***

(7.15)

(9.90)

(4.21)

2.42

2.55

0.130

2.48

2.44

-0.038

2.44

2.54

0.096

(7.23)

(7.53)

(0.82)

(7.36)

(7.34)

(-0.25)

(7.09)

(7.71)

(0.61)

3.99

3.73

-0.261

3.86

3.81

-0.048

4.16

3.56

-0.607**

(-0.20)

(12.09)

(10.81)

(-2.54)

-0.442

34.4

32.93

-1.470**

(11.91) (11.02) 33.49

33.85

(-1.09) 0.361 (0.58)

ur na

(-1.26)

Jo

Total 𝑅

(4.96) -1.127***

(7.81)

(11.13) (13.35) 2

***

lP

forecasts

(-1.72)

Earnings Smoothness High – Low High Low (t-value) 16.66 16.91 0.259

ro

(50.38) (48.63)

announcements

-p

EAD: Earnings

Earnings Persistence High – Low High Low (t-value) 16.46 17.29 0.829*

re

Discretionary Accruals High – Low High Low (t-value) 17.16 15.95 -1.216*

42

(11.46) (11.46) 33.69

33.25

(-0.71)

(-2.36)