Embed Size (px)
Citation preview
Re-examining the Effects of Regulation Fair Disclosure Using Foreign Listed Firms to Control for Concurrent Shocks
Jennifer Francis* (Duke University)
Dhananjay Nanda (Duke University)
Xin Wang
(Duke University)
We re-examine prior studies’ findings concerning the effects of Regulation Fair Disclosure (Reg FD) on properties of US firms’ information environments. Our study’s innovation is the identification of a sample of industry- and size-matched foreign listed firms (ADRs) explicitly exempt from the provisions of Reg FD. We use the ADR sample to control for events occurring at the same time as Reg FD and which affected all traded firms’ information environments. Specifically, by examining the relative difference in changes in pre- versus post- information proxies for US versus ADR firms, we provide a more powerful test of effects unique to Reg FD. Across all tests, we find that US firms’ information environments changed no more nor no less than those of ADRs, suggesting that Reg FD itself had little effect on the aspects of US firms’ environments we consider.
Draft: June 2004
* Corresponding author: Fuqua School of Business, Duke University, Durham, NC, 27708. Email address, [email protected]. We gratefully acknowledge financial support from Fuqua School of Business, Duke University. We have benefited from helpful comments from Linda Bamber, Larry Brown, Stan Markov, Michael Mikhail, Per Olsson, Mohan Venkatachalam, Richard Willis, and Amy Zang, and workshop participants at Duke University and the 2004 SESARC conference at Georgia State University. Analyst forecast data are from Zacks Investment Research database.
.
Re-examining the Effects of Regulation Fair Disclosure Using Foreign Listed Firms to Control for Concurrent Shocks
We re-examine prior studies’ findings concerning the effects of Regulation Fair Disclosure (Reg FD) on properties of US firms’ information environments. Our study’s innovation is the identification of a sample of industry- and size-matched foreign listed firms (ADRs) explicitly exempt from the provisions of Reg FD. We use the ADR sample to control for events occurring at the same time as Reg FD and which affected all traded firms’ information environments. Specifically, by examining the relative difference in changes in pre- versus post- information proxies for US versus ADR firms, we provide a more powerful test of effects unique to Reg FD. Across all tests, we find that US firms’ information environments changed no more nor no less than those of ADRs, suggesting that Reg FD itself had little effect on the aspects of US firms’ environments we consider.
.
Re-examining the Effects of Regulation Fair Disclosure Using Foreign-Listed Firms to Control for Concurrent Shocks
1. Introduction
We re-examine prior studies’ conclusions regarding the effect that Regulation Fair Disclosure
(“Reg FD”) had on firms’ information environments. For the most part, this body of work finds that US
firms experienced post-Reg FD declines in returns volatility (both in general and around earnings
announcements), increases in trading volume, increases in informational efficiency, increases in the
dispersion of analysts’ forecasts, and deteriorations in both the accuracy and the informativeness of
analysts’ reports. In terms of capital market effects (as proxied by returns volatility, trading volume and
information efficiency), this research suggests that Reg FD achieved many of the goals advanced by its
proponents. In terms of its effects on analysts’ activities, the evidence suggests that the quality of
analysts’ reports suffered as a result of Reg FD, consistent with some of the unintended consequences
predicted by opponents.
The research designs used in these studies compare proxy variables measured in one or more pre-
Reg FD periods with values of these proxies measured in one or more post-Reg FD periods, with the pre-
and post- periods chosen to avoid the fourth calendar quarter of 2000 (the effective quarter of Reg FD).
In general, this type of within-firm, pre- versus post- research design works well if there are no
confounding events occurring at the same time as the event under study. In the case of Reg FD, it is
highly questionable whether this assumption is met. Over the time period studied by most Reg FD
research (1998-2002, or some subset thereof), several events occurred which likely affected properties of
firms’ information environments. These include: the US economic recession (began March 2001); the
crash of the Internet bubble (began October 2000); the decimalization of stock trading of the US stock
exchanges (late January 2001 for NYSE and AMEX stocks, April 2001 for NASDAQ stocks); the demise
of both Enron (began September 2001, and ended on December 1, 2001 when it filed for Chapter 11
protection) and Arthur Andersen (indicted in March 2002, found guilty in June 2002, and ceased audits of
. 1
SEC registrants as of August 31, 2002); and general trends in technology, movements through business
cycles and changes in macroeconomic indicators, such as interest rates and gross domestic product. The
potential confluence of these events makes it difficult to rule out any of these explanations as being
equally likely (as Reg FD) to have caused changes in firms’ information environments.
The key to resolving this inference problem is to control for the effects of industry-specific and
economy-wide changes that occurred at the same time as Reg FD. Some studies attempt to do this by
including, as independent variables, measures of industry performance (such as industry-specific
measures of growth or profitability) or by including measures of macroeconomic performance (such as
interest rates or GDP). An example is Heflin, Subramanyam and Zhang [2003a] who include earnings-
price ratios to capture changes in growth expectations and the yield on 30-year bond indexes to capture
changes in interest rates. We note that this approach is limited to only those alternative explanations that
can be proxied by some readily measurable variable, such as interest rate movements.
Other studies focus on one alternative explanation and attempt to exploit variation in it to parse
out the effects of Reg FD. An example is Bailey, Li, Mao and Zhong’s [2003] analysis of the effects of
decimalization on returns volatility and trading volume. Specifically, they examine whether changes in
volatility and trading volume between pre- and post-Reg FD periods are driven by the concurrent-in-time
reduction of tick size. For this analysis, they compare measures of returns volatility and trading volume
around fiscal quarter IV 2000 earnings announcements made in the first few months of 2001 (the only
post-Reg FD quarter with data for both decimalized and undecimalized firms), with similar measures
calculated around fiscal quarter III 2000 and fiscal quarter IV 1999 earnings announcements (pre-Reg FD
quarters with data on undecimalized firms). They find that decimalization appears to explain the finding
of post-Reg FD decreases in returns volatility, but does not explain post-Reg FD increases in trading
volume. The limitations of this approach are twofold. First, while this approach sheds light on the
alternative explanation studied (i.e., decimalization in Bailey et al.), it does not address whether other
explanations drive the results. Second, in the specific case of decimalization, the power of the tests is
. 2
limited by the fact that observations for both decimalized and undecimalized stocks are available for only
one quarter.
A third approach seeks to identify cross-sectional variation in the degree to which firms are
expected to be affected by Reg FD. An example is Bushee, Matsumoto and Miller’s [2004] analysis of
the relative effects of Reg FD on firms which had previously held open versus closed conference calls.
They argue that Reg FD should have more significant effects on conference call related behaviors of
formerly-closed conference call firms because it forced these firms to either stop holding closed
conference calls or to open them. A limitation of this approach is that it potentially restricts the
researcher to examining constructs related to the source of cross-sectional variation (i.e., conference call
behaviors in Bushee et al.), rather than broader outcome-based measures (such as returns volatility over
longer periods which do not necessarily encompass conference calls). In particular, by focusing on a
subset of information activities, this empirical design is unable to test whether firms substitute one
method of information dissemination for another with no discernible change in outcomes. Note that the
power of this approach hinges on the difference in the degree to which the two groups of firms (i.e.,
closed versus open firms in Bushee et al.) are expected to respond to Reg FD.
We adopt the third approach by identifying a sample of firms where we expect the greatest
likelihood of finding extreme variation in responses to Reg FD and where the set of responses can be
broadly measured. Specifically, our research design exploits the fact that Rule 243.101(b) of Reg FD
explicitly excludes foreign issuers trading on US exchanges.1 Practically, this means that Canadian firm
listings and firms that trade as American Depository Receipts (ADRs) are excluded from the provisions of
Reg FD; hereafter we refer to all such firms as ADRs for simplicity. To control for potential differences
between ADRs and US listed firms, we screen our samples by identifying ADRs that are in the same
1 Reg FD also explicitly exempts communications made in connection with security offerings (initial and secondary) and communications to news reporters, credit analysts, any person who owes the firm a duty of trust or confidence (e.g., an attorney, investment banker or accountant), and any person who agrees to maintain the information in confidence. Further, the rule applies to communications made by senior company officials; it does not apply to employees below the executive ranks.
. 3
industry and are of similar size as a matched US firm.2 We compare pre- and post-Reg FD changes in the
sample US firms’ information proxies with pre- and post-Reg FD changes in ADRs’ information proxies.
To the extent that the pre- versus post-changes in ADRs’ proxies capture all changes that are not due to
Reg FD, this approach allows us to interpret any relative difference found for US firms as reflecting the
unique effects of Reg FD.3
There are at least two reasons for using ADR firms’ information environments as the benchmark
of comparison for US firms’ information environments. First, ADRs lobbied for exclusion from the
provisions of RegFD and, in response, Rule 243.101(b) explicitly excludes them from complying with the
regulation (Remond [2000]). It seems unlikely that foreign firms that sought exemption from Reg FD
would then turn around and comply fully with its provisions. Second, foreign-listed firms generally had
less incentive to avoid selective disclosure because their home country disclosure regulations and/or
enforcement were not as stringent as the US post-Reg FD disclosure environment. Regardless of whether
the rules existed, in few (if any) cases does it appear that actual disclosure practices in these countries
resulted in the absence of selective disclosure (Eisinger, Hagerty and Kueppers [2001]; Pottinger [2001]).
To provide additional evidence on whether ADRs responded to Reg FD, we surveyed the investor
relations directors of our sample ADRs, asking them whether their overall disclosure had increased,
decreased or remained unchanged after the passage of Reg FD. Of the 46 responses, 39 ADRs reported
their disclosures were unchanged, six said their disclosures increased, and one reported a decline in
disclosures in response to Reg FD. We believe the survey evidence supports the view that while some
2 Monthly stock returns for the matched US and ADR firms exhibit a correlation of 0.91 over the sample period, indicating the two samples face similar economic shocks. Further, as we discuss later, we explicitly control for any remaining differences between the sample US and ADR firms. 3 In concurrent work, Gomes, Gorton and Madureira [2004] and Mathew, Hughen and Ragan [2002] also consider the effects of Reg FD on ADRs. Gomes et al. summarize the results of a robustness test showing that large (small) ADRs had similar responses to Reg FD as large (small) US firms. They do not report sample sizes or detail the test performed. Mathew et al. re-examine prior studies finding of reduced returns volatility post Reg FD using a non-matched sample of 30-46 ADRs and 117-430 US firms. They report a significant immediate increase in returns volatility for US firms in the first post Reg FD quarter; no similar increase is found for ADR firms. Relative to these studies, we believe our study is more comprehensive: we use a longer series of pre- and post Reg FD quarters, we have a larger sample of ADRs, we employ matching procedures to ensure that firm-specific factors known to affect information environments do not drive our results, and we examine a broader set of information environment proxies.
. 4
ADRs may have responded to Reg FD, taken as a whole, ADRs responded less to Reg FD than did their
US counterparts.4
Our results show no evidence that Reg FD affected any of the market-based metrics (i.e., returns
volatility, trading volume, or information efficiency) or analyst-based metrics (forecast dispersion,
forecast accuracy, report newsworthiness). That is, we find that the pre- versus post-Reg FD changes in
the information environment of US firms are indistinguishable from changes in the information
environment of foreign-listed firms who are not required to comply with this regulation. These findings
suggest that Reg FD had no net effect on the properties of US firms’ information environments that we
consider.
The remainder of the paper is organized as follows. Section 2 provides a background of Reg FD,
summarizes the main results from prior research, describes the use of ADRs as a control sample for tests
for Reg FD, and details the specific hypotheses that we test. Section 3 describes the sample selection
procedures. Section 4 presents the main results, section 5 reports sensitivity tests, and section 6 concludes.
2. Institutional Background and Prior Research
2.1. Reg FD
On October 23, 2000, the Securities and Exchange Commission (SEC) implemented Regulation
Fair Disclosure (hereafter, Reg FD). Reg FD prohibits firms from privately communicating material
information to selected market participants (such as securities analysts or institutional investors) without
simultaneous public disclosure of the same information.5 The public disclosure must be made as soon as
4 Janvrin and Kurtenbach [2002] report that 45% of the financial executives of US firms that were surveyed report an increase in the amount of information provided to analysts and investors. 5 Reg FD does not define materiality, but relies on case law which characterizes a material disclosure as one which a reasonable investor would consider important or as significantly altering the total mix of information available (TSC Industries, Inc. v. Northway, Inc., 426 US 438, 1976; Basic v. Levinson, 485 US 224, 231, 1988). Reg FD also provides a list of items which the SEC suggests should be reviewed for materiality: earnings announcements, merger and acquisition activity, new products or customer/supplier developments, changes in management or auditor, events related to the firm’s securities (i.e., stock split), and bankruptcy.
. 5
practical, but no later than 24 hours after the initial disclosure. The intent of Reg FD, at least according to
regulators, was to eliminate favored access to information for a subset of investment professionals.
Proponents of Reg FD claimed that the new regulation would improve the flow of information to
financial markets by reducing, if not eliminating, analysts’ reliance on management-provided information
and by increasing the amount and quality of independent research performed by securities analysts. By
reducing reliance on management-provided information, proponents argued that analysts would not be as
pressured to provide favorable reports in order to maintain or increase their access to management. By
reducing incentives to issue favorable reports, proponents further argued that the accuracy of analyst
reports would increase after Reg FD because analysts would likely invest in research that is less prone to
be influenced by managements.
Opponents countered by arguing that rather than increasing the amount of management-provided
information, Reg FD would reduce such disclosures. Reg FD would potentially eliminate the benefit that
managers derive from selective disclosure, consequently mitigating managerial incentives to disclose
information. Reduced disclosure is expected both because management will be less willing to
communicate complex information in public disclosures (because analysts will not be available to provide
guidance and context for interpreting the information) and because management will be less willing to
disclose information to investment professionals due to concerns with respect to the precise standards and
enforcement of the regulation. In addition to reducing the amount of information, opponents argued that
Reg FD would change the timing of corporate disclosures. In particular, the concern was that continuous
information flows between firms and analysts would be replaced with discrete information flows related
to management-provided public disclosure. For these reasons, opponents argued that Reg FD would
likely result in deteriorations of information efficiency and accuracy rather than the intended effects
claimed by regulators (of improved information flow and increased accuracy of information). In
particular, they argued that returns volatility would increase, informational efficiency would decline,
trading volume would decrease, and analyst guidance would be less accurate.
. 6
The security analyst industry largely opposed Reg FD. Survey evidence reported by both the
Securities Industry Association [2001] and the Association for Investment Management and Research
[2001] indicates that the majority of analysts believed that Reg FD reduced both the overall quality of
information (disseminated in general by companies and communicated specifically to securities analysts)
and the accessibility and responsiveness of firm management. Analysts’ opposition to Reg FD largely
centered on two issues. First, analysts argued that management-provided information, including
information guidance, is a primary means by which firms communicate information to capital markets. In
particular, many firms are more willing to convey complex and potentially proprietary information to
securities analysts, rather than issue public disclosures of such information, because analysts have the
skills and knowledge necessary to interpret and screen this information. Curtailing such communications
with management would result in non-disclosure or boilerplate disclosures, and thus, impair the amount
and quality of information available to all investors, not just to analysts. Second, by reducing the amount
of detailed information available to analysts, Reg FD would impair the quality of analysts’ reports by
reducing the ability of analysts to forecast earnings and make stock recommendations. Since investors
largely rely on analysts’ reports to guide their investment decisions, the quality of information provided to
investors will be further impaired.
2.2. Prior studies’ evidence on the effects of Reg FD
Numerous studies investigate the impact of Reg FD on several properties of firms’ information
environments, including returns volatility (both in general and in response to specific events, such as
earnings announcements), information efficiency, trading volume, dispersion in analysts’ forecasts, and
proxies capturing the quality of analysts’ forecasts (e.g., accuracy and information content).
One of the most well-studied properties is the volatility of returns in short windows around
earnings announcements; we label this “event returns volatility” to distinguish it from returns volatility
measured over longer intervals which includes, primarily, non-event days (we term the latter “general
returns volatility”). Event returns volatility is typically measured as the sum of squared (or absolute value)
daily abnormal returns over a short window surrounding the news event, such as the three-day window
. 7
surrounding the announcement of a firm’s quarterly earnings. In terms of prior research, at least five
studies have examined event returns volatility around quarterly earnings announcements made before
versus after the implementation of Reg FD: Heflin, Subramanyam and Zhang [2003b] (hereafter HSZ
[2003b]), Bailey, Li, Mao and Zhong [2003] (hereafter BLMZ), Eleswarapu, Thompson and
Venkataraman [2003], Gadarowski and Sinha [2002], and Shane, Soderstrom and Yoon [2001]. In all
cases, these studies show significantly lower event returns volatility in post-Reg FD periods. (Although
BLMZ attribute the lower event returns volatility to decimalization of the stock exchanges which
occurred at roughly the same time as the implementation of Reg FD.)
HSZ [2003b] also examine whether general returns volatility changed following implementation
of Reg FD. Their measure of general returns volatility is similar to that described above, except that the
window over which volatility is calculated is extended to begin about 65 trading days prior to the earnings
announcement. They compare both the total daily, and the average per day, return volatility of their
sample firms for all trading days in the three quarters preceding Reg FD and the three quarters following
Reg FD. Their results for both measures (total and per day) show that general returns volatility declined
significantly in the post-Reg FD period.
Related to returns volatility is the concept of informational efficiency. Informational efficiency
refers to the speed and extent to which stock prices anticipate information in upcoming information
events. When stock prices reflect information early and fully, the market is highly informationally-
efficient with respect to the information reported in the subsequent disclosure. In contrast, when the stock
market has little anticipation of the news, the market is viewed as being highly informationally-inefficient.
Measures of informational efficiency provide a calibration of the aggregate effects of private and public
disclosures on security prices, and hence, provide a mechanism for evaluating the net effect of Reg FD on
total information flows. Understanding this net effect is important given critics claimed that Reg FD
would, on net, reduce information flows between companies and the market. To the extent that critics’
claims were realized, informational efficiency would have declined.
. 8
Informational efficiency is measured as the gap between the full-information stock price and a
pre-event price, where the latter is measured over various windows prior to the announced event. For
example, in their analysis of information efficiency prior to quarterly earnings announcements, Heflin,
Subramanyam and Zhang [2003a] (hereafter HSZ [2003a]) examine pre-event periods extending as much
as 65 days prior to the earnings announcement date. The specific metric that HSZ [2003a] examine is the
absolute cumulative abnormal return over h days prior to the earnings announcement:
2
,( , 2) [1 ( )] 1j q j tt h
ACAR h AR+
=−
− + = + −∏ , . Smaller (larger) values of this metric indicate a smaller (larger)
gap between the full-information stock price and the pre-event stock price and, therefore, suggest greater
information efficiency (inefficiency). HSZ [2003a] contrast ,( , 2) j qACAR h− + for quarterly earnings
announcements preceding and following implementation of Reg FD. Their results show smaller mean
and median ACAR’s for earnings announcements made after Reg FD went into effect. They note that this
pattern of results is inconsistent with critics’ claims that Reg FD would reduce total information flows to
the market; rather, it is consistent with the view that Reg FD improved informational efficiency.
BLMZ also examine the effect of Reg FD on abnormal trading volume, a proxy for the extent of
disagreement among investors. They find substantial increases in volume in post-Reg FD periods,
controlling for other factors known or expected to influence this variable. Together with their results
concerning increases in analyst forecast dispersion (described below), they conclude that Reg FD
substantially increased the amount of disagreement among investors.
Research has also examined the effect of Reg FD on the quality of analysts’ reports, as proxied by
the dispersion and accuracy of their earnings forecasts and the newsworthiness of their reports.
Investigations of these aspects of quality are motivated by the argument that the elimination of private
communications would render analysts’ reports less insightful and, therefore, less useful to investors. The
claim of reduced usefulness stems from three concerns. First, management will be less willing to
communicate complex and potentially proprietary information in public disclosures, because analysts will
not be available to provide guidance and context for interpreting the information. Absent analyst
. 9
guidance and context, there is an increased threat of litigation over mis-interpretations and mis-
understandings of management-provided public disclosures. Second, management will be less willing to
disclose information to analysts (and institutional investors) due to concerns over the precise standards
and enforcement of Reg FD. Third, analysts may not be able to, or may choose not to, compensate for the
lost private communications.6 For these reasons, opponents of Reg FD claimed that curtailing private
communications between companies and analysts would impair the ability of analysts to form opinions
and reach consensus on interpreting earnings information, which in turn would increase forecast
dispersion and forecast errors,7 and reduce the overall information content of analysts’ reports.
Of the seven studies examining forecast dispersion, five report a significant increase following
implementation of Reg FD (Agrawal and Chadha [2002], BLMZ, Irani and Karamanou [2003],
Mohanram and Sunder [2001] and Topaloglu [2002]), while two show no change in dispersion (HSZ
[2003a], and Shane, Sonderstrom and Yoon [2001]. Of the five studies examining changes in the
accuracy of analysts’ earnings per share forecasts between pre- and post-Reg FD periods, two find no
change in forecast accuracy (HSZ [2003a]; Shane, Soderstrom and Yoon [2001]), two report a decrease
(Agrawal and Chandha [2003]; Mohanram and Sunder [2001]); and one reports an increase for one set of
tests (BLMZ, time-series tests) and no change for another set of tests (BLMZ, consensus-based tests).8
Finally, in terms of the newsworthiness of analysts’ reports (as measured by the absolute price impact of
the analyst’s report on the day it was issued), prior research finds that the average price impact of
analysts’ reports is significantly lower in the post-Reg FD periods relative to pre-Reg FD periods
(Gintschel and Markov [2004])
6 According to an AIMR [2001] survey, the majority of analysts believed that the accuracy of their earnings forecasts and their stock recommendations would not suffer as a result of Reg FD. This evidence suggests that securities analysts believed that they would be able to replace the management-provided information lost by Reg FD with information gained by additional private information search. 7 Opdyke [2000] quotes an analyst as indicating that Reg FD would lower the accuracy of forecasts by about 25%. 8 Mohanram and Sunder further find that the decline in forecast accuracy is more pronounced for less-skilled analysts; in particular, they find that All-Star analysts (as defined by Institutional Investor annual rankings of analysts) experienced a smaller average increase in absolute forecast errors than did non-All-Star analysts.
. 10
What can one conclude from this body of work? In terms of market-based measures of firms’
information environments, the results suggest that Reg FD improved information flows, resulting in lower
returns volatility in general and around earnings announcements, improved information efficiency, and
increased trading volume. In terms of analyst-based measures, the results suggest a deterioration in the
quality of analysts’ reports, as evidenced by less consensus in analysts’ forecasts, less (or no more)
accurate forecasts, and lower information content of analyst reports. These inferences rely, however, on
the crucial assumption that the documented effects are caused by Reg FD and not some other event that
occurred at or near the same time as the implementation of Reg FD.9 The main innovation of our
analyses is the identification of a sample of foreign-listed firms (ADRs) which we expect to react less, or
not at all, to Reg FD.
2.3. American Depositary Receipts (ADRs) and their exclusion from Reg FD
As noted in the Introduction, foreign firms listed on US stock exchanges are explicitly exempt
from the provisions of Reg FD (Rule 243.101(b)). In the US, foreign listed firms generally trade as
American Depositary Receipts (ADRs).10 ADRs are created when a foreign company’s shares are
purchased abroad and delivered to a depository’s local custodian bank (usually a major-center
commercial bank) and placed in a special trust. The depository bank then issues the ADRs, which may
represent a multiple or a fraction of the deposited shares. Like US companies’ common stocks, owners of
ADRs have a legal claim on the cash flows of the deposited shares. The depository bank receives any
dividends, and distributes them to the holders of ADRs, charging a small handling fee. ADRs trade like
any other security listed on the NYSE, AMEX or NASDAQ.
When the SEC first began considering the issues related to Reg FD in late 1999, they [the SEC]
planned to include foreign companies within the provisions of the new regulation. Ultimately, however,
9 Most studies do not state this assumption explicitly; an exception is HSZ [2003b] who note that “FD was implemented at a time of considerable change in the U.S. economy. While we have attempted to control for these changes, we can never completely rule out the possibility a correlated event drives our results.” 10 The ADR system was created to facilitate the purchase of foreign firms’ shares by US investors. While foreign companies’ shares are typically written in the language of the issuer, ADRs are usually issued in English. ADR prices are quoted in US dollars and dividends are paid in US dollars.
. 11
foreign listed corporations were excluded based on concerns that stricter disclosure requirements would
scare away foreign listers (Remond [2000]). Whether and to what extent ADRs would respond to Reg FD
is debatable. Some ADRs clearly indicated their resolve to not follow Reg FD: for example, Alcatel
management was quoted as saying that “the company will continue to hold one-to-one meetings with US
analysts…We want to avoid that extremely conservative approach [Reg FD] but also recognize that
investor confidence is built through openness” (Rosenbaum [2001]). Other ADRs believed that investors
would lose confidence if they did not respond to Reg FD; for example, Nokia management claimed they
planned to review and adhere to Reg FD (Rosenbaum [2001]); DaimlerChrylser AG and TV Azteca SA
also indicated their intent to adapt to their US counterparts (Remond [2000]). In addition, Bank of New
York, the largest depositary bank for ADRs, claimed that they were advising ADR clients to “at least
operate in the spirit of FD, if not the letter” (Remond [2000]).
Whether ADRs adopted Reg FD-compliant disclosure practices likely depends both on the firm’s
incentives to do so and on the disclosure requirements of their home countries. In terms of the latter,
some jurisdictions (such as Canada, France, and the UK) had selective disclosure requirements similar to
Reg FD, while in other countries, selective disclosure requirements were either less strict (Mexico) or
nonexistent (Japan). Regardless of whether the rules existed, in few (if any) cases does it appear that
actual disclosure practices in these countries resulted in the absence of selective disclosure. As examples,
Eisinger, Hagerty and Kueppers [2001] characterize much European practice as resembling the US before
Regulation FD,11 and Pottinger [2001] reports that Hong Kong and Chinese companies routinely provide
material information to select market participants (institutional investors and analysts) without informing
the public at large. Even for the arguably-most comparable country to the US (Canada, which prohibits
selective disclosures), these disclosure laws are not strictly enforced. In particular, a 1999 survey of
corporate disclosure practices in Canada (prepared under the supervision of the Ontario Securities
Commission, and reported on by Canada NewsWire [December 22, 1999]) found that 71% of the 170
11 Eisinger et al. also quote investment bankers as saying that the gap in disclosure practices between the US and Europe is narrowing. Such claims are consistent with the European Union’s proposed directive on market abuse (released on May 30, 2001) which contains a similar fair disclosure clause as Reg FD (OxResearch [2001]).
. 12
respondents had no written disclosure policies, 81% of the 170 respondents held one-on-one meetings
with analysts (only 2% indicated they did not comment on analysts’ reports), and only 19% held open
quarterly conference calls. As late as July 2002, the Ontario Securities Commission released guidance for
selective disclosures saying that “Canadian law has specifically prohibited selective disclosure for
decades, but Canadian companies needed guidance on what terms such as ‘necessary course of business’
actually meant.” [Investor Relations Business, May 20, 2002]
To further examine ADR firms’ response to Reg FD we conducted an email survey of the
investor relations directors of our sample of foreign listed firms. Our questionnaire asked whether the
firm’s overall disclosure level had increased, decreased or remained unchanged after the passage of Reg
FD. We also asked whether certain types of disclosure practices such as phone calls to analysts, emails to
analysts, meetings with analysts, reviewing analyst forecasts, conference calls, press releases and
management forecasts, had changed in response to the regulation. The questionnaire was emailed to 218
ADR firms for which we could obtain email addresses of investor relations offices; 43 investor relations
directors (20%) responded.12 Of these, 36 said their overall disclosure practices were unchanged after the
passage of Reg FD, and six (one) stated they increased (decreased) the overall level of disclosure in
response to Reg FD. These results support the view that, by and large, foreign listed firms did not change
their disclosure practices in response to the passage of Reg FD.
Based on our reading of survey evidence and articles in the popular press, we believe that while
some ADRs were hesitant to comply with Reg FD, market pressures may have encouraged some to
respond somewhat to the new rule. Importantly, however, it seems implausible to believe that ADRs had
incentives to respond more to Reg FD than did US firms. As a consequence, we expect that, as a whole,
ADRs likely responded less to Reg FD than did their US counterparts. The less that ADRs responded to
Reg FD, the more powerful is our research design.
12 The 20% response rate compares favorably with other studies that have surveyed financial personnel. For example, in a study surveying chief financial officers of US firms, Graham and Harvey [2001] report a response rate of 8.8%.
. 13
2.4 Hypotheses
We revisit prior studies’ results which show decreases in returns volatility, increases in trading
volume, improvements in informational efficiency, increases in forecast dispersion, and decreases in
forecast accuracy and newsworthiness of analysts’ reports for US firms following implementation of Reg
FD. Our tests focus on whether these findings maintain on a relative basis, where relative differences are
based on contemporaneous changes in these proxies experienced by ADRs. We state the six hypotheses
in alternative form, using prior studies’ results to specify the predicted direction (assuming Reg FD did
affect US firms disproportionately more than it affected ADR firms):
Hypothesis 1 (H1): Reg FD reduced returns volatility of US stocks (more than ADR stocks) both in
general and around quarterly earnings announcements.
Hypothesis 2 (H2): Reg FD increased the trading volume of US stocks (more than ADR stocks) around
quarterly earnings announcements.
Hypothesis 3 (H3): Reg FD improved the information efficiency of US stocks (more than ADR stocks).
Hypothesis 4 (H4): Reg FD increased the dispersion of analysts’ forecasts of US firms (more than ADR
firms).
Hypothesis 5 (H5): Reg FD reduced the accuracy of analysts’ forecasts of US firms (more than ADR
firms).
Hypothesis 6 (H6): Reg FD reduced the newsworthiness of analysts’ forecasts of US firms (more than
ADR firms).
3. Sample Selection and Descriptive Statistics
3.1. Sample selection
Our research design requires that we identify a sample of ADRs and a sample of matched US
firms, with market data and analyst forecast data for periods before and after implementation of Reg FD.
Our sample identification and matching procedures are extensive and strict, for two reasons. First, we
need to control for firm- and industry-specific factors that may give rise to differences between ADR and
. 14
US firms’ information environments. Second, it is important that we control for calendar-time specific
economy-wide shocks that occurred at or near the same time as Reg FD. Rather than attempt to control
for these factors by including proxies for firm-, industry-, and calendar-time specific factors in a
multivariate regression, we choose instead to identify samples of firms that are matched on all these
dimensions. As discussed by Kothari, Leone and Wasley [2005], matching is superior to the control
variable approach because it does not impose a specific functional form on the relation linking the
variable of interest (effects of Reg FD in our study) on the control variables. Barber and Lyon [1996,
p.396] also conclude that matching by industry and size yields well-specified and powerful test statistics.
We begin by describing the sample of ADRs. We identify ADRs from data on CRSP,13 and
impose the additional requirements that ADRs have stock price data available for at least 18 months prior
to the effective quarter of Reg FD (i.e., April 1999 through October 2000) and that each firm have annual
Compustat data on assets and sales for 1999, 2000 and 2001. These restrictions result in an initial sample
of 417 ADR firms.
Next we identify a sample of matched US common stocks, with similar CRSP and Compustat
requirements as the ADR firms. We match US firms with ADR firms by industry, firm size and sales
performance, as measured in fiscal year 1999. Industry is defined using the 48 industry codes identified
by Fama and French [1997], firm size is measured as total assets (Compustat #6), and sales performance
as measured as total sales (Compustat #12). Matches are identified by an algorithm that calculates the
distance between each ADR firm k and its matched US counterpart j; our algorithm is similar to that used
by Lo [2003]. Specifically, for each US firm j in the same Fama-French industry as ADR firm k, we
calculate the percentage difference in assets, ,j k
j kk
Assets AssetsAssetDistance
Assets−
= , and the percentage
difference in sales, ,j k
j kk
Sales SalesSalesDistance
Sales−
= . The sum of the two distance measures yields a
13 ADRs are indicated by CRSP share codes whose first digit is three. Canadian firms are identified by CRSP incorporation codes (STATE=99 and FINC=9).
. 15
matching score for each US firm j that is in the same industry as ADR firm k. From the set of matching
scores that are less than two, we choose the US firm with the smallest matching score for each ADR
firm;14 we then remove the matched pair (the ADR and its US counterpart) from the lists of ADR and US
firms. In some cases, a single US firm is the best match for several ADRs. In this case, we control for
the order in which we match a US firm to an ADR firm by first calculating all possible matching scores,
and the assigning the US firm j to the ADR firm k whose matching score is the smallest among the
candidate ADRs. For the remaining candidate ADRs, we repeat the above steps using the remaining US
firms. In total, application of these procedures produces a final sample of 399 matched ADR and US
firms. This sample is reduced further by data requirements specific to each test, as summarized below.
Market-based metrics: Many of our tests of changes in market-based metrics (returns volatility,
information efficiency, and trading volume) are based on proxy variables calculated around earnings
announcement dates. We assign each fiscal quarter earnings announcement to a calendar quarter based on
the announcement date. We use calendar quarters, rather than fiscal quarters, because we do not wish to
restrict our sample further by requiring that our sample firms have December fiscal year ends. Note that
absent this assumption, it is inappropriate to compare market measures linked to fiscal quarters because
the events that occurred concurrent with Reg FD are calendar-time specific, not fiscal-time specific.
From the sample period, we exclude the third and fourth calendar quarters of 2000 (III.2000 and IV.2000)
because these quarters cover Reg FD discussion and implementation periods. Each of our pre- and post-
periods consists of six calendar quarters: I.1999 to II.2000 for the pre-Reg FD period and I.2001 to
II.2002 for the post-Reg FD period.
Our market tests require that each pair of matched firms have earnings announcements in the
same calendar quarters of both the pre-period and the post-period. The quarters are matched as follows:
I.1999 with I.2001; II.1999 with II.2001; III.1999 with III.2001; IV.1999 with IV.2001; I.2000 with
14 Our results and sample sizes are qualitatively unchanged if we set the maximum matching score to either one or three.
. 16
I.2002; and II.2000 with II.2002. The requirement of matched earnings announcement quarters reduces
the market-based sample from 399 pairs to 384 pairs of ADR and US firms, representing 3,188 pairs of
pre- versus post-FD quarters. The 3,188 quarter-matches are roughly evenly distributed across the six
pre- vs. post-quarter pairings: I.1999 vs. I.2001 (n=460); II.1999 vs. II.2001 (n=560); III.1999 vs. III.2001
(n=606); IV.1999 vs. IV.2001 (n=556); I.2000 vs. I.2002 (n=462); and II.2000 vs. II.2002 (n=544).
For the most part, the size of the market-based sample is not affected by other data requirements,
including those for returns and trading volume data and for control variables used in the multivariate
regressions. An exception is the multivariate tests of trading volume; here, BLMZ include the dispersion
in analysts’ forecasts as a control variable. We calculate the dispersion of analysts forecasts for firm j in
quarter q (DISPq) as the standard deviation of all earnings-per-share forecasts (related to next fiscal
quarter earnings) that are made in the prior calendar quarter, q-1, as reported on the Zacks Investment
research database; following HSZ [2003a], we scale the forecast dispersion measure by stock price. The
sample of 384 matched US-ADR pairs is reduced by requirements that we have data on DISP for both the
pre- and post-FD matched quarters and for both the US firms and its ADR counterpart. There are 248 US
firms (1,842 matched quarters) with data on DISP, compared to 52 ADR firms (378 matched quarters).15
Combining the two samples (to obtain the match of the US and ADR firms) results in 35 pairs of firms
and 456 matched calendar-quarters. Because this sample is relatively small, we report multivariate results
for trading volume which include and exclude DISP as a control variable.
Analyst-based metrics: Our tests of the effects of Reg FD on properties of analysts’ forecasts focus on the
dispersion, accuracy, and newsworthiness of their reports in the pre- versus post- Reg FD periods. Our
tests of dispersion use the same sample as described above for trading volume, where we require the DISP
measure.
For tests of accuracy, we use the same matching procedures (industry, assets, and sales) as used
in the market-based tests to obtain pairs of US-ADR firms, except we apply these procedures within the 15 Of the 384 ADRs, 203 are not followed by any analyst during 1998-2002.
. 17
set of firms each analyst follows. For example, if analyst A follows nine firms in a given industry, of
which two are ADRs and seven are US firms, we use the seven US firms as the population to identify the
best match for each of the two ADRs; we refer to the resulting match as a same-analyst pairing. We
further require that the same-analyst pairing have quarterly earnings forecasts for quarter q+1 in the
matched pre- and post-Reg FD quarters (e.g., the same pairing is available for quarter q=I.1999 and
q=I.2001); the quarter q+1 forecasts are used to calculate our measure of forecast accuracy. This
requirement ensures that our pre- versus post-Reg FD comparison of forecast accuracy holds constant, for
each firm, the fiscal quarter earnings being forecast. In total, we have 102 same-analyst pairs (reflecting
472 calendar quarter pairings, 110 unique firms [46 ADRs and 64 US firms], and 80 unique analysts) with
measures of forecast accuracy for both the pre- and post- Reg FD matched quarters.
We limit the sample to firms with same-analyst forecasts because by doing so we avoid any
potential bias that would arise if the composition of analysts following the firm changed between the pre-
and post- Reg FD periods. In fact, Gomes, Gorton and Madureira [2004] find that, post Reg FD, the
number of analyst forecasts increased for large US firms and decreased for small US firms.16 Our
approach also controls for any analyst-specific differences in forecasting ability, forecasting resources, or
consistency of forecast definitions which have been shown to affect forecast accuracy (see, for example,
Mikhail, Walther and Willis [1997], Clement [1999], Jacob, Lys and Neale [1999], and Abarbanell and
Lehavy [2002]). That is, by examining differences in the forecast accuracy of same-analyst forecasts
between the pre- versus post-Reg FD periods, we have more confidence that we have controlled for the
experience of the analyst, the resources of her employer, and the definition of earnings that this analyst is
forecasting for this firm (e.g., operating earnings versus net income before extraordinary income).17 A
potential cost of the same-analyst design is that statistical power is reduced if ADR and US firms’
16 For our sample, the mean (median) analyst following in 1999 for US firms was 13.61 (12) and for ADRs was 10.04 (8). In 2001, the mean (median) analyst following for US firms was 15.08 (12) and for ADRs was 12.11 (9). 17 It is especially important to control for the definitional consistency of the earnings being forecast for ADR firms since the different accounting rules these firms follow make it difficult to identify exactly what earnings number analysts are forecasting. In addition, analysts following a given firm (US or ADR) may forecast different earnings numbers. Our use of same-analyst forecasts assumes that the analyst exhibits over-time consistency in her definition of earnings.
. 18
information are perfect complements as would be the case, for example, if an analyst reduced coverage of
an ADR if its US counterpart restricted its information.18
For tests of newsworthiness, we use all earnings forecasts made by the same-analyst in a given
calendar quarter, regardless of the horizons of those forecasts. This sample consists of 112 same-analyst
pairs (656 calendar quarter pairings, 118 unique firms [47 ADRs and 71 US firms], and 88 unique
analysts). Because analysts often issue several forecasts (of different horizons) on the same date, we
consider newsworthiness measures which treat these multiple forecasts as unique events and, separately,
which consolidate them as reflecting a single report. Our results are not sensitive to how we treat multiple
events, so we report results using the consolidated (single-report) approach.
3.2. Descriptive statistics
Table 1 reports descriptive information about the 384 matched pairs of US and ADR firms in the
market-based sample; we obtain similar results if we examine the analyst-based sample (not reported).
Panel A shows the distribution of the 384 ADRs across countries. The largest concentration of ADRs is
associated with Canada, and the second largest with England. In total, there are 38 countries associated
with the 384 ADRs. Panel B reports selected financial information about the ADRs. For purposes of this
table, we measure the financial variables as of the end of fiscal year 1999, and we winsorize the financial
variables at the 1% and 99% values. As is evident from these data, both the US and ADR firms in our
sample are large: the mean value of total assets is $12.2 billion and $15.6 billion for the US and ADR
firms, respectively; for sales, the comparison figures are $4.5 billion and $5.7 billion. Tests of the
difference between the US and ADR firms show few meaningful differences between the two samples. In
particular, with the exception of higher leverage for the US firms (and a weak indication of smaller mean
sales for US firms, p-value is 0.08), both two-sample t-tests (for means) and two-sample Wilcoxon rank
sums tests (for medians) show no difference along any of the financial dimensions examined: firm size,
performance (as proxied by return on assets and return on equity), and growth (as proxied by earnings-
price ratios). 18 We thank Stan Markov for bringing this possibility to our attention.
. 19
ADRs can be further identified by their level. Level II and Level III ADRs must reconcile their
financial reports to U.S. GAAP and meet the listing requirements of the US stock exchange on which they
are listed; Level I ADRs need not. For comparability with their matched US firms, we examine the
distribution of levels of our sample ADR firms. We note first that only Level II and Level III ADRs are
listed on US exchanges, and, therefore, have price data included on CRSP. Excluding Canadian firms
(n=106), our sample of 278 ADRs consists of 130 Level III ADRs, 122 Level II ADRs and 26 ADRs for
which the listing level could not be identified (but which must either be Level II or Level III given our use
of CRSP data). On the whole, the fact that our sample ADRs are exclusively Level II and Level III
suggests that the ADR and US firms are matched on disclosure and reporting dimensions.
We also examined the correlation between the stock returns of the matched ADR and US firms.
The correlation between the matched pairs’ average monthly stock returns over 1999-2001 is 0.91
(Pearson) and 0.90 (Spearman), both significant at the 0.0001 level. These data suggest that our
assumption that ADR firms faced similar economic shocks over the pre- and post-Reg FD periods as their
US counterparts is descriptively valid for our sample.
On the whole, we conclude that our matching procedures were successful in identifying pairs of
US and ADR firms that are similar along dimensions expected to affect dimensions of the firm’s
information environment. The high correlations between the US and ADR firms’ returns provides further
evidence that the pairs of firms are equally sensitive to economic events that occurred concurrent in time
with Reg FD.
4. Empirical Tests and Results
In this section, we report three sets of empirical results. First, for each metric examined, we
summarize univariate comparisons and the results of multivariate tests in prior studies, to the extent that
both are reported. Second, we show that our sample of US firms exhibits similar findings concerning pre-
versus post- Reg FD changes in information environments as documented in prior research. To be
consistent with the designs used in prior research, we report results that compare US firms’ pre-Reg FD
. 20
values of a given metric with their post-Reg FD values (univariate comparison), and then control for other
factors potentially affecting this difference (multivariate test). These tests focus on whether we find the
same signs and significance of 1α as reported in prior research, where 1α is the coefficient estimate
obtained from the following regressions:
, 0 1i q q i qMetric PostRegFD ,α α= + + ε
,
(1)
, 0 1 ,( )i q q c i q i qc
Metric PostRegFD Control cα α β= + + + ε∑ (2)
where = 1 if quarter q is after IV.2000; 0 otherwise. qPostRegFD
Documenting similar effects for our US sample firms is important because our US sample likely differs
from prior studies, which do not restrict their samples in the same ways that our matching procedures
restrict our US firms. In particular, both the smaller number of firms in our sample and the bias toward
larger firms (where some prior research shows that Reg FD had a less pronounced effect than on smaller
firms) may reduce the power of our tests. By documenting results similar to those found in prior research,
we provide some confidence that any relative difference between US firms and their ADR counterparts
(found in our third set of tests) cannot be attributed to sample bias or to low power tests.19
Our third and main set of tests examines the relative difference that Reg FD had on US firms,
using ADRs as the benchmark. Our tests of relative differences include the matched US and ADR
quarters and estimate the following regressions:
, 0 1 2 3 *i q q i q i i qMetric PostRegFD US PostRegFD US ,β β β β= + + + + ε
,)
(3)
, 0 1 2 3 ,* (i q q i q i c i q i qc
Metric PostRegFD US PostRegFD US Control cβ β β β β= + + + + + ε∑ (4)
where = 1 firm i is a US firm, 0 if firm i is an ADR. iUS
19 Our multivariate tests use control variables that are similar, but not always identical, to those used in prior research. In some cases, we include additional control variables; in other cases, we use a measure that is highly correlated with a measure used in prior research; in still other cases, the inclusion of a particular control variable changes the sample significantly due to the data requirements needed to calculate the variable. We describe in the text cases where our results differ from prior research due to one or more control variables.
. 21
In equations (3) and (4), the coefficient on , qPostRegFD 1β , now captures the difference in the
metric between the pre-Reg FD period and the post-Reg FD period. Importantly, because both US and
ADR firms are included in estimating these regressions, 1β captures the change in each metric that is not
uniquely associated with Reg FD; that is, it captures the average change in the metric that occurred for all
firms. To the extent prior studies’ results are replicable on our sample, we expect that 1β will be reliably
non-zero, with a sign and magnitude analogous to the findings of prior research. The coefficient on the
interaction term, , (*q iPostRegFD US 3β ) captures the change in the metric that is uniquely associated
with Reg FD. Here, we expect that if Reg FD affected US firms (more than it affected ADR firms), then
we will observe a significant value of 3β , with the expected sign of 3β indicated by our hypotheses
(which are based on the findings from prior research). We include as a separate variable to control
for any levels-differences between US and ADR firms that may persist despite our matching procedures.
For example, if US firms have lower returns volatility than ADR firms in both the pre- and post-Reg FD
periods, then
iUS
2 0β < . Including as a main effect avoids concerns that any effects we document are
merely the result of US firms differing systematically from ADR firms along dimensions not explicitly
controlled for in our tests.
iUS
4.1. Evidence concerning pre- versus post-Reg FD changes in market-based metrics
Prior research examines several metrics capturing whether and how Reg FD affected capital
markets: returns volatility (around earnings announcements and in general); informational efficiency, and
trading volume. We summarize our results for each of these metrics below.
Event returns volatility (around earnings announcements): Following HSZ [2003b], we use the sum of
daily squared abnormal returns as our measure of event returns volatility around firm j’s quarter q
earnings announcement,1
2, ,
1
( 1, 1) j qt
SqrCAR AR+
=−
− + = ∑ j t , where 2,j tAR = firm j’s squared abnormal return on
day t, and abnormal returns are calculated using the Fama and French [1993] three-factor asset pricing
. 22
regression. HSZ [2003b] estimated the three-factor model over the one year period ending the day before
the start of the pre-Reg FD quarter; our estimation period of days (-266,-66) is roughly similar. HSZ
[2003b] document that US firms’ ( 1, 1)SqrCAR − + declined in the post-Reg FD period: in particular, Table
2, Panel A shows that they report a mean univariate decline of -0.0011 (p-value = 0.00), and a mean
decline of -0.0017 (p-value = 0.00) in tests which control for other factors affecting .( 1, 1)SqrCAR − + 20
The control factors considered by HSZ [2003b] include the standard deviation of the firm’s returns over
the same interval used to estimate the Fama-French three-factor model (RETVAR), the absolute value of
the firm’s cumulative abnormal return during the quarter q (ABSCAR), a dummy variable that equals one
if the firm’s cumulative abnormal return in quarter q is negative, and zero otherwise (NEGRET), the
absolute value of the equally-weighted return on the market index in quarter q (ABSINDX), and the firm’s
earnings-price ratio measured as the firm’s quarter q earnings divided by end of quarter q price
(EPRATIO).
Replication of these tests on our sample of US firms (Panel B) show a similar decline in event
returns volatility for the multivariate test,21 where we document a decline of -0.0012 (p-value = 0.004).
While we do not find a decline in SqrCAR(-1,+1) for US firms using the univariate (means) test, we do
find a significant decline in the median value of SqrCAR(-1,+1) of -0.0005 (p-value = 0.016, results not
tabulated). To examine the sensitivity of these results to other measures of event returns volatility, we
repeat our tests using BLMZ’s measure, equal to the absolute value of the daily one-factor abnormal
returns summed over days (-1,+1), where abnormal returns are based on the CAPM, estimated over days
20 More precisely, HSZ [2003b] reports results after multiplying by 1000. We divide their reported number by 1000 to obtain figures that are comparable to our own. 21 We note three differences in our calculations versus those in HSZ [2003b]. First, we estimate the Fama-French model over days (-266,-66) relative to the earnings announcement date. This period roughly corresponds to HSZ’s [2003b] estimation period. For consistency, we measure RETVAR over the same (-266,-66) window. Second, in measuring the return on the market (ABSINDX), we use value-weighted returns rather than equally-weighted returns because our sample consists (primarily) of large firms. Third, we include as additional controls, a variable capturing firm size (SIZE, as proxied by the firm’s total assets at the end of quarter q) and a variable capturing whether the firm announced a loss in quarter q (LOSS=1 if quarter q earnings are less than zero, 0 otherwise). The inclusion of these two variables and the differences in calculations of RETVAR and ABSINDX do not change inferences about the main test variables (not reported).
. 23
-200 to -11). Results using this variable show significant (at the 0.00 level) declines in both the univariate
test and the multivariate test. Based on the weight of the evidence, we conclude that our sample US firms
exhibit similar declines in event returns volatility between pre- and post-Reg FD periods as documented
by prior research.
Our main tests focus on the results of estimating regressions (3) and (4) using the combined
sample of US and ADR firms. Panel C shows that both univariate and multivariate tests provide no
evidence that US firms’ returns volatility around earnings announcement decreased (or increased) any
more than ADR firms’ returns volatility changed. In particular, estimations of both (3) and (4) show
insignificant values of 3β . We also repeat these tests using the BLMZ measure of event returns volatility
and find the same insignificant values for 3β . Based on this evidence, we conclude that Reg FD had no
effect on the volatility of returns around US firms’ earnings announcements that is incremental to any
effects associated with contemporaneous events, as proxied by the concurrent change in event returns
volatility experienced by ADR firms.
General returns volatility: Our measure of general returns volatility also follows that used by HSZ
[2003b], who calculate the squared value of daily three-factor abnormal returns, for the interval beginning
three days after the announcement of quarter q-1 earnings and ending two days after the announcement of
quarter q earnings, 2
2, ,
3
( 3, 2) j qt
SqrCAR ARτ
τ+
= +
+ + = ∑ j t , where τ is the q-1 earnings announcement date.
(This interval corresponds, roughly, to days -65 to +2, measured relative to the quarter q earnings
announcement on day 0.) They compare values of ( 3, 2)SqrCAR τ + + for US firms between pre- and
post-Reg FD quarters. Their results, summarized in Panel A, Table 3, show an unconditional decline of
-0.0062 (p-value of 0.02) and a decline of -0.0109 (p-value of 0.00) controlling for other factors known to
affect volatility. The control variables in their tests are the same as used in the event returns volatility
tests.
. 24
While we do not find evidence of a univariate (mean) decline for this sample, tests based on
medians (not reported) show a decline of -0.011 (p-value = 0.00). Consistent with HSZ’s [2003b] finding,
the multivariate results in Panel B show a decline in general returns volatility for our sample US firms.
We note that the conditional decline we document for our sample US firms (of -0.0225) is about double
the decline found by HSZ [2003b] of -0.0109.
Our tests of the relative change in general returns volatility (Panel C) indicate a general decline in
general returns volatility between the pre- and post-Reg FD periods ( 1 0.0151β = − , p-value = 0.000), but
this decline is not unique to US firms as demonstrated by the fact that the estimate of 3β is not different
from zero in either equation (3) or equation (4). We interpret the insignificance of 3β as indicating that
Reg FD had no meaningful effect on the general volatility of US firms’ returns, as measured relative to
the general returns volatility effects experienced by ADR firms. These results (like those for event
returns volatility) suggest that any decrease in returns volatility experienced by US firms between the pre-
and post-Reg FD periods is unlikely to be the result of Reg FD, and is more likely to be attributable to
other concurrent shocks affecting all traded firms.
Informational efficiency: Recall that informational efficiency measures the gap between the full-
information stock price and a pre-event price. We follow the calculation of this construct developed by
HSZ [2003a] who examine absolute cumulative abnormal returns over h days prior to the earnings
announcement:2
,( , 2) [1 ( )] 1j q j tt h
ACAR h AR+
=−
− + = + −∏ , , where abnormal returns are calculated using the
CAPM estimated over days (-266,-66). Similar to these authors, we examine several values for h,
. As shown in Panel A, Table 4, HSZ [2003a] report smaller mean and median
values of these ACAR measures for post-Reg FD quarters, conditional on other factors expected to affect
[ 1, 2, 5, 10, 30,]h∈ − − − − −
. 25
informational efficiency.22 The control variables considered by HSZ [2003a] are similar to those used in
their tests of returns volatility (HSZ [2003b]) except the authors also include a measure of loss incidence
(LOSS, measured as previously described), a measure of negative special items (NEGSPEC, equal to the
absolute value of negative special items in quarter q, and zero otherwise), and they use the yield on the
30-year bond index at the end of the fiscal quarter to control for overall market changes (BOND30). The
authors interpret their finding of smaller post-Reg FD values of ACAR as indicating that US firms’
information environments became more efficient following Reg FD.
Our sample of US firms also shows smaller values of ACAR in post-Reg FD quarters relative to
pre-Reg FD quarters for most values of h. Specifically, conditional tests (shown in Panel B, Table 4)
generally indicate a significant negative coefficient on the PostRegFD variable; exceptions are
insignificant values of 1α for the shortest windows, h = -1 and -2.23
Is the improvement in informational efficiency documented for our US firms driven by Reg FD?
The results in Panel C suggest the answer is no. Specifically, when we include the ADR matched quarters,
we find an overall increase in informational efficiency for both US and ADR firms ( 1 0β < for all values
of h, at the 0.04 level or better); however, there is no evidence of an incremental decline in ACAR values
for US firms relative to their ADR counterparts (that is, 3β is indistinguishable from zero). This result
suggests that the increase in informational efficiency noted between the pre- and post- Reg FD periods is
likely to be attributable to some factor(s) other than Reg FD itself.
Trading volume: We follow BLMZ’s definition to calculate abnormal trading volume around earnings
announcements. Specifically, we measure abnormal trading volume (ATV) as the difference the average
daily trading volume over days (-1,+1) relative to the announcement date and the average daily volume
22 For brevity, we do not report the results of unconditional (univariate) tests; in all cases, the unconditional results show the same patterns and significance as the multivariate tests. Results based on medians are similar and are also not reported. 23 Our control variables are the same as those in Tables 2 and 3 Panel B, with the exception that we report results using CAPM abnormal returns for consistency with HSZ [2003a]. Our findings are not sensitive to the model of expected returns or to the use of ABSINDX rather than BOND30 to control for absolute market movements (not reported).
. 26
for that stock over days (-200, -11), divided by the latter:
,,
,
[ ( 1, 1) ] [ ( 200, 11) ]( 1, 1)
[ ( 200, 11) ]i q i q
i qi q
avg TV avg TVATV
avg TV− + − − −
− + =− −
, , where trading volume on day t is measured
as the percentage of outstanding shares traded that day. As summarized in Table 5, Panel A, BLMZ’s
univariate tests are inconclusive: they find that ATV is significantly negative for one quarter pair,
significantly positive for three quarter pairs, and insignificantly different from zero for two quarter pairs.
Their multivariate tests show a significant increase in ATV conditional on other factors affecting trading
volume: abnormal return variability (ARV, calculated as the sum of the daily absolute abnormal returns
over days (-1,+1), where abnormal returns are based on the CAPM estimated over days -200 to -11), firm
size (SIZE)), and analyst forecast dispersion (DISP, defined in section 3.1).
In repeating BLMZ’s tests on our US firms (Panel B), we find that the inclusion of analyst
forecast dispersion as a control variable has a significant influence on the sample size and the results. In
terms of sample size, if we consider any US firm that has data on all variables except for the dispersion
measure (DISP), we obtain a sample of 3,188 matched quarters; if we require data on DISP, this sample
reduces to 1,842 quarters; if we further require that the US firms be matched to ADR firms, the sample
drops to 248 US firm-quarters (recall from footnote 15 that many ADR firms do not have analyst forecast
data). For these samples, we find an increase in abnormal trading volume only for the regression which
includes DISP and all other control variables.24 That is, for the sample and test that are closest to the
regressions estimated by BLMZ, we find a similar result: abnormal trading volume is higher in post-Reg
FD periods. For all other samples and regression specifications, however, we do not find an increase (or a
decrease) in ATV. (In section 5, we summarize the results of pooled tests which use all US and ADR
firms with available data. For this sample, we do observe a significant increase in trading volume for US
firms in multivariate tests; however, similar to the results in Table 5, we find no relative difference in
trading volume changes of US and ADR firms between pre- and post-Reg FD periods.)
24 We use log of firm size, ln(SIZE), rather than SIZE in our tests of abnormal trading volume because most of the prior literature on trading volume uses the logged form of this variable (Bamber, Barron and Stober [1997] and Barron [1995]). Our results are not, however, sensitive to how we specify the size control variable.
. 27
Turning to our main tests, Panel C shows that when the change in ATV for US firms is measured
relative to the change in ATV for ADR firms, there is no evidence of a bigger change (either upward or
downward) for US firms. That is, in all specifications, the coefficient estimate on PostRegFD*US is
insignificantly different from zero.
4.2. Evidence concerning pre- versus post- Reg FD changes in analyst-based metrics
Prior studies have examined several proxies for the quality of analysts’ reports, including the
dispersion among analysts’ earnings forecasts, the accuracy of their earnings forecasts, and the price
impact (or newsworthiness) of their reports. We summarize our results for each of these metrics below.
Forecast dispersion: At least two prior studies (HSZ [2003a] and BLMZ [2003]) have examined the
change in analyst forecast dispersion between pre- and post-Reg FD periods; their results are summarized
in Panel A, Table 6. In a univariate comparison of mean dispersion, BLMZ report an increase in
dispersion of 0.19, significant at the 0.00 level; they do not report a multivariate test. HSZ [2003a] also
report a univariate comparison of changes in mean dispersion, showing an increase of 0.243, significant at
the 0.00 level. However, in tests which control for other factors affecting dispersion (shown in the far
right columns of Panel A), HSZ find that dispersion is unchanged.
We repeat HSZ tests on the sample of US firms where we have data on forecast dispersion and
the control variables. (Our measure of forecast dispersion is defined in section 3.1.) If we do not require
the US firm to have a matched ADR counterpart, we identify 1,842 US firm-quarters; if we require the
ADR match (for use in our third set of tests), we identify a sample of 248 US firm-quarters. For the
larger sample, our univariate and multivariate results are very similar to those reported by HSZ [2003a]:
in particular, we find an increase in dispersion (of 0.254, significant at the 0.067 level) when control
variables are absent, and no change in dispersion when control variables are present; unconditional results
. 28
are similar using median values (not reported).25 For the smaller sample of US firms with matched ADRs,
we find no change in dispersion using either univariate tests or multivariate tests.
Our main tests, summarized in Panel C, show no evidence that US firms experienced any larger
(or smaller) change in forecast dispersion than did their ADR counterparts.
Forecast accuracy: Our tests of forecast accuracy use the 102 analyst-pairings (944 quarters) where we
have data on analyst k’s forecast accuracy and the control variables. We define forecast accuracy (AFEi,j,q)
as the absolute value of the difference between firm j’s actual earnings for quarter q and the value of
analyst k’s forecasts (made in the prior calendar quarter), scaled by the stock price ten trading days before
the forecast release date. We average AFE across all forecasts made by a given analyst in quarter q to
obtain an analyst-quarter-specific measure of forecast accuracy. Results using the last forecast made by
analyst k during the calendar quarter (rather than all forecasts she issued) produce similar results and are
not reported.
Prior researchers’ findings concerning forecast accuracy are shown in Table 7, Panel A. Both
BLMZ and HSZ [2003a] find a significant (at the 0.00 level) increase in absolute forecast errors using
univariate comparisons. When control variables are included, HSZ [2003a] show that the increase is no
longer significant at conventional levels; the control variables they include are the same as used in their
tests of forecast dispersion. Panel B shows that we obtain very similar results using our sample of US
firms; results based on median values are similar and are not reported. In particular, we find a significant
(at the 0.00 level) increase in AFE, which disappears in the conditional tests. Finally, tests in Panel C
probe the relative pre- versus post-Reg FD change in forecast accuracy between US and ADR firms. 25 The control variables included by HSZ [2003a] in their tests of forecast dispersion are: the absolute value of firm j’s unexpected earnings in quarter q, calculated as the difference between the firm’s announced earnings for quarter q and the same quarter earnings one year ago, scaled by share price (ABSUE); a dummy variable equal to 1 if the firm’s unexpected earnings for quarter q are less than zero, 0 otherwise (NEGUE); the absolute value of the quarterly change in the seasonal growth rate in the gross domestic product (GDPSHOCK); the log of the average number of days that the forecast precedes the earnings announcements (DAYS); the lagged value of the dispersion in analysts’ forecasts (Lag_DISP); as well as LOSS and NEGSPEC (defined previously). Our control variables are roughly similar except we do not include NEGSPEC, GDPSHOCK or Lag_DISP. The exclusion of the first two variables (NEGSPEC and GDPSHOCK) has no material effect on our inferences. Because our sample size is reduced considerably if we require data on Lag_DISP, we cannot evaluate the sensitivity of our results to the effects of this variable.
. 29
Here univariate tests show weak evidence of an increase in AFE (significance level is 0.102), while
conditional tests show no evidence of any differential change in forecast accuracy between pre- and post-
Reg FD periods for US versus ADR firms.
Newsworthiness: Gintschel and Markov [2004] (GM) examine the information content, or
newsworthiness, of analysts’ reports, and investigate whether it changed after the implementation of Reg
FD. They measure the change in newsworthiness as the change in the absolute value of the standardized
daily return around analyst reports issued in the pre- versus post- Reg FD quarters. As shown in Panel A
of Table 8, GM find a substantial decline in the absolute market reaction to analyst reports in the post-Reg
FD period; for their sample, mean newsworthiness declines by 0.0185, significant at the 0.001 level.
We are unable to replicate this decline for the US firms in our sample. As shown in Panel B, our
US firms show no change in our measures of newsworthiness between the pre-and post-Reg FD periods.
We measure newsworthiness as the absolute value of the 1-day [3-day] market-adjusted return on the day
[days(-1,0,+1)] when the forecast is made. Further tests which control for the effect of Reg FD on ADR
firms, show no evidence that Reg FD had any more, or any less, of an effect on the newsworthiness of
analyst reports about US firms.
4.3. Inferences and additional evidence
In summary, our results comparing the effects of Reg FD on US firms’ information environments
with similar comparisons conducted for ADRs shows no differential effect for US firms. Since Reg FD
explicitly excludes ADR firms from its provisions, we interpret the absence of relative differences as
indicating that prior studies’ findings concerning the effects of Reg FD are more likely attributable to
events that occurred contemporaneous with Reg FD than to Reg FD itself. This interpretation is
consistent with BLMZ’s conclusion that the apparent decrease in event returns volatility between pre- and
post- Reg FD periods is attributable to decimalization of the exchanges rather than Reg FD.
Our conclusion that Reg FD had no effect on US firms’ information environments is predicated
on the assumption that Reg FD had less of an effect on ADRs than it did on US firms. As discussed in
. 30
section 2.3, we believe that the weight of the anecdotal evidence and survey evidence suggests this
assumption is descriptively valid. More specifically, while it is certainly plausible that some ADRs
responded to Reg FD to the same degree as their US counterparts, it seems unlikely that all ADRs
responded fully (or that some ADRs responded even more than their US counterparts such that they offset
the less-than-full responses of other ADRs).
Our findings are also very consistent with the results of surveys of US firm executives (one by
PriceWaterhouseCoopers (PwC) and another by the National Investor Relations Institute (NIRI), reported
by Barbash [2001] and in the CPA Journal [2001]); both studies showed that most US executives believe
that Reg FD had little impact on their disclosure practices. For example, of the 164 respondents to the
PwC survey, 51% reported that Reg FD had no impact on the frequency of their disclosures; those who
saw an impact were roughly equally split between Reg FD increasing disclosures (48%) and decreasing
disclosures (52%); about 75% of respondents indicated that Reg FD had no effect on their share price or
their share price volatility. The NIRI study showed very similar results concerning overall disclosure
practices (i.e., roughly half reporting no change, with the other half equally split between increases and
decreases); it also revealed that 74% of respondents indicated having the same number of one-on-one
meetings with analysts as they did pre-Reg FD.
5. Sensitivity tests
We consider the sensitivity of our results to several omitted factors and to alternative estimation
procedures. We note first that our matched sample design significantly restricts our sample and creates a
concern that our tests are low powered and consequently biased towards accepting the null hypothesis.
To alleviate this concern we re-ran all tests using the pooled sample of all ADRs and US (matched and
unmatched) firms with the necessary data for each test; the pooled sample used for the market-based tests
consists of 4,788 US firms (49,724 firm-quarters) and 408 ADR firms (3,446 firm-quarters), while for the
analyst-based tests, the pooled sample contains 2,276 US firms and 70 ADR firms. We note the
following two observations about the results of the pooled tests. First, using the pooled sample, we are
. 31
able to replicate the findings from previous research regarding the effects of Reg FD on US firms (the
exception is newsworthiness, where we continue to find no change for US firms.). Second, when we
estimate equations (3) and (4) using both US and ADR firms, our results are similar in all respects to
those reported in Tables 2 through 8; that is, the pooled results show no evidence of larger effects for US
firms relative to ADR firms.
A second concern is that because our matching procedure does not require that ADR firms be
listed on the same stock exchange as their US counterpart, our tests may be confounded by an exchange
effect. To address this concern, we repeat our tests including an exchange indicator variable (NYSE=1 if
the firm (ADR or US) is listed on the New York Stock Exchange, 0 otherwise) as a main effect and
interacted with the PostRegFD variable. The latter captures exchange-specific changes in firms’
information environments which occurred in the post-Reg FD period. Results of these tests (not tabulated)
are similar in all respects to those documented.
A third concern is that one or more unspecified features of the information environments of ADR
issuers’ home countries affect ADR firms’ market metrics and analyst metrics in systematic ways that
offset (true) Reg FD effects in US firms. To address this issue, we repeat our tests controlling for
potential country effects. Specifically, we add indicator variables for the five issuer countries with the
largest concentration of ADRs in our sample (Table 1 shows that these five countries are Canada,
England, Japan, France and Mexico); we include a sixth indicator for all ADRs issued by countries other
than these five. While the country indicators are significantly different from zero in some tests, in no case
do they alter inferences about the effects of Reg FD on US firms.
We also examine whether our results are stronger (or possibly driven by) the pairings of US firms
with non-Canadian firms. This analysis is motivated by the fact that Canadian firms are explicitly exempt
from US reporting requirements (by the Multijurisdictional Disclosure System) precisely because of the
comparability of disclosure requirements between the US and Canada (see Leuz [2003] for a discussion).
To address this issue, we estimate all regressions separately for the sample of US-non-Canadian ADR
pairings and the sample of US-Canadian firm pairings. We find that for all measures of information
. 32
environment, the results of our main tests are similar to those reported. We conclude that our results are
not sensitive to whether we use Canadian or non-Canadian ADR firms as the benchmark.
Our fifth sensitivity check focuses on some prior studies’ evidence that Reg FD had a differential
effect on small firms. The argument here is that smaller firms may be more likely to be affected by Reg
FD because these firms generally have fewer public disclosures and smaller analyst following than larger
firms (see, for example, Gomes, Gorton and Madureira [2004]). While our tests attempt to explicitly
control for size effects (both by the matching procedures and by the inclusion of size as a control variable
in the multivariate regressions), we further probe whether size effects exist for our sample, by repeating
our tests on size quintiles. For this purpose, we first calculate the quintile break points for all Compustat
firms, ranked on the basis of total assets in 1999. Using these cutoffs, we classify our sample firms into
size quintiles (1 being the smallest firms, 5 being the largest firms). Because our matching requirements
bias our sample towards larger firms (see discussion in section 3), it is not surprising that most of our
sample firms are included in the two largest quintiles: of the 384 US-ADR matched pairs, 198 are
included in quintile 5 and 81 are in quintile 4, with the remaining 105 pairs split 57 (quintile 3), 26
(quintile 2) and 22 (quintile 1). When we repeat our tests on each quintile, we find similar results to those
reported for the full sample. In particular, there is no evidence that our findings are driven by the smallest
firms (or, for that matter, by the largest firms).
As a final sensitivity check, we examine whether our results are robust to firm-level regressions
which use a single observation for each firm for each of the pre- and post-Reg FD periods. Specifically,
for each firm, we calculate the average value of each variable over the six pre-Reg quarters (I.1999 to
II.2000) and the six post-Reg FD quarters (I.2001 to II.2002); we repeat our tests substituting these means
values for the firm-quarter values. Results are similar to those reported.
6. Conclusion
Our re-examination of the effects of Reg FD on firms’ information environments focuses on
whether US firms subject to this regulation were affected more by it than foreign-listed firms that were
. 33
explicitly exempt from its provisions. As such, our tests contrast the relative differences in pre-versus
post-changes in proxies for the information environment between US firms and their industry- and size-
matched ADR counterparts. By benchmarking the over-time changes experienced by US firms against
the over-time changes experienced by ADR firms, this design explicitly controls for several events that
impacted the economy at the same time as the implementation of Reg FD (e.g., the crash of the Internet
bubble, the start of the US economic recession, and decimalization of the stock exchanges). Our results
indicate that these contemporaneous effects are important insofar as we find no evidence that Reg FD had
any meaningful effect on US firms’ information environments.
There are at least two interpretations of this general result. The first is that Reg FD was a non-
event: it had no net effect on the information environment. The second is that Reg FD was an event, and
one so big that even foreign firms that did not have to comply with it chose to comply and to the same
extent as US firms. While our empirical-archival results do not allow us to distinguish between these two
conclusions, our reading of the popular press and our survey results of ADR disclosure practices indicates
that ADRs did not, on average, adopt Reg FD-like practices. Moreover, survey evidence conducted on
US firms indicates that most US corporate executives reported no change in their disclosure practices as a
result of Reg FD. On the whole, this evidence suggests that the first interpretation of our results is more
descriptive than the second.
Our findings do not imply that Reg FD did not influence the types of information provided by
management and sought by analysts, or affect the mechanisms used to communicate such information. In
particular, there is considerable anecdotal evidence that Reg FD affected these inputs: e.g., by increasing
the amount and timing of company-initiated earnings pre-announcements (Opdyke [2001]; although
Maremont [2001] claims that some firms are less willing to offer earnings guidance post-Reg FD); by
increasing the use of webcasts of earnings disclosures (Vinzant [2001]); and by altering the way that
analysts and other investment professionals acquire and process information (Bodow [2001]; Opdyke
. 34
[2000]; Opdyke and Nelson [2000]; Clifford [2000]).26 Our tests shed light on the net effect of these
changes on outcome measures derived from the securities markets. Our finding that the net outcome
effect was essentially zero is consistent with this regulation having neither the unintended consequences
nor the unintended benefits documented in some prior research.
26 It is also possible that US firms did not alter their disclosure practices in response to Reg FD. Barnett [2001] makes the observation that Reg FD does not materially change the “rules of conduct” between companies, analysts and shareholders, but did make these rules easier to enforce. The key question then becomes the enforcement of Reg FD. On this point, the popular press is replete with examples of managers’ confusion with respect to how the SEC will interpret the “materiality” of a disclosure (e.g., Appin [2001]; Cowan [2001]; The Economist [February 10, 2001]).
. 35
Table 1
Descriptive Statistics For Samples of US and ADR Sample Firms
Panel A: Distribution of sample ADRs, by country
Country # firms % firms # qtr.obs. % obs.Canada 106 27.6% 1,028 32.2%England 55 14.3% 420 13.2%Japan 23 6.0% 134 4.2%France 20 5.2% 164 5.1%Mexico 18 4.7% 132 4.1%Chile 16 4.2% 120 3.8%Australia 13 3.4% 88 2.8%Netherlands 12 3.1% 106 3.3%Ireland 11 2.9% 86 2.7%Other 110 28.6% 910 28.5%Total 384 100% 3,188 100%
Panel B: Comparison of selected financial data for US and ADR firmsa
US Sample ADR Sample p-value for diff.Variable # obs Mean Median Std Dev Mean Median Std Dev t-stat. Sign stat.Total assets ($ mil) 384 12,162 1,376 37,751 15,584 1,737 42,940 0.24 0.21Sales ($mil) 384 4,474 845 8,694 5,722 1,103 11,157 0.08 0.32Earnings-price ratio 384 0.002 0.035 0.151 0.016 0.036 0.139 0.20 0.87Leverage 384 0.251 0.224 0.214 0.188 0.155 0.161 0.00 0.00Return on assets 384 0.002 0.025 0.125 0.007 0.032 0.128 0.58 0.28Return on equity 384 0.039 0.099 0.397 0.013 0.085 0.377 0.33 0.47
Sample description and variable definitions: We report descriptive evidence for the market-based sample, which includes 384 US-ADR matched pairs (768 firms total), and 3,184 matched pre- and post-Reg FD quarter-pairs (6,368 quarters total). The financial variables (Panel B) are measured at the end of fiscal year 1999. Leverage is defined as long-term debt to total assets; the remaining variables are self-explanatory. We winsorize the financial variables to the 1% and 99% values. a We report the mean, median and standard deviation of each variable for each of the US firms and ADR samples. The far right columns of Panel B report the p-values of tests of differences in mean and median values between the US and ADR samples. P-values for means are from two sample t-tests; p-values for medians are from Wilcoxon two-sample tests.
. 36
Table 2
Comparison of the Pre- versus Post-Reg FD Changes in Event Returns Volatilitya
Panel A: Changes in pre- versus post Reg FD event returns volatility found for US firms in prior research
HSZ [2003b]Variable coef. est. p-value. coef. est. p-value.Intercept -- -- 0.003 0.00PostRegFD -0.0011 0.00 -0.0017 0.00Control variables:RETVAR -- -- 2.045 0.00ABSCAR -- -- 0.004 0.00NEGCAR -- -- 0.001 0.02 EPRATIO -- -- -0.021 0.00ABSINDX -- -- 0.004 0.00Adjusted R2 29.11
Panel B: Changes in pre- versus post Reg FD event returns volatility found for our US firms
Variable coef. est. p-value. coef. est. p-value.Intercept 0.0074 0.000 -0.0027 0.000PostRegFD -0.0003 0.570 -0.0012 0.004Control variables:RETVAR -- -- 0.1965 0.000ABSCAR -- -- 0.0126 0.000NEGCAR -- -- 0.0005 0.203LOSS -- -- -0.0004 0.402EPRATIO -- -- -0.0137 0.000SIZE -- -- -0.0000 0.455ABSINDX -- -- 0.0027 0.506Adjusted R2 -- 23.06
Panel C: Changes in pre- versus post Reg FD relative event returns volatility for US and ADR firms
Variable coef. est. p-value. coef. est. p-value.Intercept 0.0068 0.000 -0.0040 0.000PostRegFD -0.0005 0.255 -0.0005 0.271US 0.0006 0.198 0.0007 0.073US*PostRegFD 0.0003 0.668 -0.0005 0.399Control variables:RETVAR -- -- 0.1924 0.000ABSCAR -- -- 0.0136 0.000NEGCAR -- -- 0.0011 0.000LOSS -- -- -0.0004 0.319EPRATIO -- -- -0.0086 0.000SIZE -- -- -0.0000 0.191ABSINDX -- -- 0.0055 0.063Adjusted R2 0.07 22.64
. 37
See Table 1 for sample description. Variable definitions: Event returns volatility is measured as
, where1
2, ,
1( 1, 1) j q j t
tSqrCAR AR
+
=−
− + = ∑ 2,j tAR = firm j’s squared abnormal return on day t, and abnormal returns are
calculated using the Fama and French [1993] three-factor asset pricing regression estimated over days (-266,-65) relative to the earnings announcement date; RETVAR = the standard deviation of the firm’s returns over the same interval used to estimate the Fama-French three-factor model; ABSCAR = the absolute value of the firm’s cumulative abnormal return during the quarter q; NEGRET = a dummy variable that equals one if the firm’s cumulative abnormal return in quarter q is negative, and zero otherwise; ABSINDX = the absolute value of the value-weighted return on the market index in quarter q; EPRATIO = the firm’s earnings-price ratio measured as the firm’s quarter q earnings divided by end of quarter q price; LOSS = 1 if the firm reported negative earnings during quarter q, 0 otherwise; SIZE = the firm’s total assets at the end of quarter q; PostRegFD = 1 if quarter q follows the implementation date of Reg FD, 0 otherwise; US = 1 if firm j is a US firm, 0 if it is an ADR. We winsorize all continuous variables to the 99th percentile of the distributions of their absolute values. a Panel A summarizes the results concerning event returns volatility reported by HSZ [2003b; Table 2 and Table 3, column “Earnings Announcements—Self”]. We divide their values by 1000 for comparability with our figures. Panel B reports the results of univariate and multivariate tests of the effect of Reg FD on US firms for our sample of US firms. Panel C reports the results of tests which compare the relative effects of Reg FD on US and ADR firms.
. 38
Table 3
Comparison of the Pre- versus Post-Reg FD Changes in General Returns Volatilitya
Panel A: Changes in pre- versus post Reg FD general returns volatility found for US firms in prior research
HSZ [2003b]Variable coef. est. p-value. coef. est. p-value.Intercept -- -- 0.018 0.00PostRegFD -0.0062 0.02 -0.0109 0.00Control variables:RETVAR -- -- 34.017 0.00ABSCAR -- -- 0.055 0.00NEGCAR -- -- 0.024 0.00EPRATIO -- -- -0.402 0.00ABSINDX -- -- 0.077 0.00Adjusted R2 52.38
Panel B: Changes in pre- versus post Reg FD general returns volatility found for our US firms
Variable coef. est. p-value. coef. est. p-value.Intercept 0.1151 0.000 -0.0876 0.000PostRegFD 0.0024 0.677 -0.0225 0.000Control variables:RETVAR -- -- 4.5808 0.000ABSCAR -- -- 0.1355 0.000NEGCAR -- -- 0.0073 0.054LOSS -- -- -0.0076 0.120EPRATIO -- -- -0.3168 0.000SIZE -- -- -0.0000 0.904ABSINDX -- -- 0.0681 0.065Adjusted R2 -- 58.47
Panel C: Changes in pre- versus post Reg FD relative general returns volatility for US and ADR firms
Variable coef. est. p-value. coef. est. p-value.Intercept 0.1193 0.000 -0.0922 0.000PostRegFD -0.0101 0.093 -0.0151 0.000US -0.0042 0.480 -0.0017 0.674US*PostRegFD 0.0125 0.130 -0.0047 0.402Control variables:RETVAR -- -- 4.5157 0.000ABSCAR -- -- 0.1607 0.000NEGCAR -- -- 0.0109 0.000LOSS -- -- -0.0011 0.756EPRATIO -- -- -0.2379 0.000SIZE -- -- -0.0000 0.515ABSINDX -- -- 0.0686 0.018Adjusted R2 0.00 55.22
. 39
See Table 1 for sample description. Variable definitions: General returns volatility is measured as the sum of the squared value of daily three-factor abnormal returns, for the interval beginning three days after the announcement of quarter q-1 earnings and ending two days after the announcement of quarter q earnings,
, where 2
2, ,
3( 3, 2) j q j t
tSqrCAR AR
τ
τ+
= +
+ + = ∑ τ is the q-1 earnings announcement date. For our tests (Panels B and C),
we measure the sum of the squared daily abnormal returns over days (-65,+2) relative to the earnings announcement on day 0. All other variables are described in Table 2. We winsorize all continuous variables to the 99th percentile of the distributions of their absolute values. a Panel A summarizes the results concerning total returns volatility reported by HSZ [2003b, Table 1]. We divide their values by 1000 for comparability with our figures. Panel B reports the results of univariate and multivariate tests of the effect of Reg FD on US firms for our sample of US firms. Panel C reports the results of tests which compare the relative effects of Reg FD on US and ADR firms.
. 40
Table 4
Comparison of the Pre- versus Post-Reg FD Changes in Informational Efficiencya
Panel A: Changes in pre- versus post Reg FD informational efficiency found for US firms in prior research, with controls
HSZ [2003a]ACAR(-30,+2) ACAR(-10,+2) ACAR(-5,+2) ACAR(-2,+2) ACAR(-1,+2)
Variable coef. est. p-value. coef. est. p-value. coef. est. p-value. coef. est. p-value. coef. est. p-value.Intercept 0.335 0.00 0.081 0.00 0.061 0.00 0.064 0.00 0.068 0.00PostRegFD -0.053 0.00 -0.016 0.00 -0.011 0.00 -0.012 0.00 -0.012 0.00Control variables:RETVAR 1.475 0.00 1.449 0.00 1.304 0.00 1.236 0.00 1.207 0.00NEGCAR 0.000 0.99 0.002 0.31 0.004 0.02 0.004 0.02 0.003 0.04ABSCAR 0.336 0.00 0.120 0.00 0.078 0.00 0.051 0.00 0.040 0.00LOSS 0.025 0.00 0.012 0.00 0.008 0.00 0.001 0.65 0.001 0.70NEGSPEC 0.476 0.00 0.127 0.05 0.186 0.00 0.112 0.02 0.146 0.00BOND30 -0.049 0.00 -0.009 0.03 -0.007 0.06 -0.007 0.02 -0.008 0.01EPRATIO 0.010 0.93 -0.048 0.58 -0.069 0.34 -0.107 0.10 -0.138 0.03Adjusted R2 40.590 22.33 18.90 15.02 13.84
Panel B: Changes in pre- versus post Reg FD informational efficiency found for US in our sample, with controls
ACAR(-30,+2) ACAR(-10,+2) ACAR(-5,+2) ACAR(-2,+2) ACAR(-1,+2)Variable coef. est. p-value. coef. est. p-value. coef. est. p-value. coef. est. p-value. coef. est. p-value.Intercept 0.0332 0.000 0.0247 0.000 0.0151 0.000 0.0188 0.000 0.0180 0.000PostRegFD -0.0201 0.000 -0.0071 0.027 -0.0054 0.045 -0.0026 0.259 -0.0024 0.271Control variables:RETVAR 0.9184 0.000 0.8130 0.000 0.8962 0.000 0.7550 0.000 0.7529 0.000ABSCAR 0.3746 0.000 0.1515 0.000 0.1164 0.000 0.0801 0.000 0.0677 0.000NEGCAR 0.0133 0.001 0.0074 0.019 0.0082 0.002 0.0054 0.019 0.0039 0.073LOSS 0.0072 0.148 0.0056 0.135 -0.0028 0.385 -0.0032 0.246 -0.0016 0.547EPRATIO -0.0648 0.002 -0.0640 0.000 -0.0489 0.000 -0.0319 0.006 -0.0309 0.005SIZE -0.0000 0.898 -0.0000 0.268 -0.0000 0.223 -0.0000 0.191 -0.0000 0.292ABSINDX -0.0373 0.352 0.0880 0.004 0.0674 0.008 0.0398 0.070 0.0313 0.132Adjusted R2 38.78 21.34 20.92 16.33 15.85
Panel C: Changes in pre- versus post Reg FD relative informational efficiency found for US and ADR firms, with controls
ACAR(-30,+2) ACAR(-10,+2) ACAR(-5,+2) ACAR(-2,+2) ACAR(-1,+2)Variable coef. est. p-value. coef. est. p-value. coef. est. p-value. coef. est. p-value. coef. est. p-value.Intercept 0.0335 0.000 0.0223 0.000 0.0152 0.000 0.0169 0.000 0.0170 0.000PostRegFD -0.0196 0.000 -0.0088 0.007 -0.0057 0.038 -0.0053 0.024 -0.0046 0.036US -0.0033 0.428 -0.0029 0.361 -0.0028 0.286 -0.0024 0.293 -0.0022 0.308US*PostRegFD -0.0009 0.874 0.0022 0.626 0.0016 0.675 0.0037 0.242 0.0034 0.261Control variables:RETVAR 0.9849 0.000 0.9410 0.000 0.8962 0.000 0.7814 0.000 0.7472 0.000ABSCAR 0.3776 0.000 0.1617 0.000 0.1210 0.000 0.0858 0.000 0.0717 0.000NEGCAR 0.0075 0.013 0.0064 0.005 0.0067 0.000 0.0057 0.001 0.0046 0.002LOSS 0.0133 0.000 0.0050 0.069 0.0002 0.922 -0.0011 0.572 0.0002 0.901EPRATIO -0.0480 0.002 -0.0306 0.009 -0.0131 0.183 -0.0057 0.502 -0.0117 0.139SIZE -0.0000 0.428 -0.0001 0.027 -0.0001 0.040 -0.0000 0.045 -0.0000 0.031ABSINDX -0.0146 0.634 0.0822 0.000 0.0909 0.000 0.0585 0.000 0.0546 0.000Adjusted R2 40.67 23.73 21.44 17.78 16.70
. 41
See Table 1 for sample description. Variable definitions: Informational efficiency is measured as the absolute cumulative abnormal returns over h days prior to the earnings announcement:
2
,( , 2) [1 ( )] 1j q j tt h
ACAR h AR+
=−
− + = + −∏ , , where abnormal returns are calculated using the CAPM and
. NEGSPEC = absolute value of negative special items in quarter q, zero if there are no negative special items; BOND30 = the yield on the 30-year bond index at the end of the fiscal quarter. All other variables are described in Table 2. We winsorize all continuous variables to the 99
[ 1, 2, 5, 10, 30, ]h∈ − − − − −
th percentile of the distributions of their absolute values. a Panel A summarizes the results concerning information efficiency reported by HSZ [2003a] in their Table 2 panel C. Panel B reports the results of multivariate tests of the effect of Reg FD on US firms for our sample of US firms. Panel C reports the results of tests which compare the relative effects of Reg FD on US and ADR firms.
. 42
Table 5Comparison of the Pre- versus Post-Reg FD Changes in Abnormal Trading Volumea
Panel A: Changes in pre- versus post Reg FD abnormal trading volume found for US firms by prior research
Variable coef. est. # qtrs coef. est. # qtrsIntercept -- -- NA NAPostRegFD 0.0599 1/3/2b 0.3104 6/6Control variables:SIZE*1000 0.0013 2/6ARV -- -- 8.2257 6/6ARV*PostRegFD -- -- -1.1279 6/6DISP -- -- -18.3850 6/6
Panel B: Changes in pre- versus post Reg FD abnormal trading volume found for our US firms
Results for all US firms in our sample (not matched with ADRs necessarily) Results for Reduced Sample of US firmsIncluding DISP measure (n=1,842 qtrs) Excluding DISP measure (n=3,188 qtrs) (n=248 qtrs)
Variable coef. est. p-value. coef. est. p-value. coef. est. p-value. coef. est. p-value. coef. est. p-value. coef. est. p-value.Intercept 0.4783 0.000 -0.1988 0.000 0.4409 0.000 -0.2274 0.000 0.5456 0.000 0.0701 0.640PostRegFD -0.0056 0.900 0.2107 0.002 -0.0190 0.649 -0.0019 0.975 -0.0741 0.520 -0.1866 0.302Control variables:ln(SIZE) -- -- 0.0154 0.157 -- -- 0.0507 0.000 -- -- -0.0420 0.170ARV -- -- 7.4157 0.000 -- -- 6.6119 0.000 -- -- 5.1232 0.000ARV*PostRegFD -- -- -1.8970 0.001 -- -- 0.1124 0.822 -- -- 2.1869 0.148DISP -- -- -24.2869 0.000 -- -- -- -- -- -- -20.8739 0.410Adjusted R2 20.10 17.71 23.17
Panel C: Relative changes in pre- versus post Reg FD abnormal trading volume for US versus ADR firms
Including DISP measure (n=496 qtrs) Excluding DISP measure (n=6,376 qtrs)Variable coef. est. p-value. coef. est. p-value. coef. est. p-value. coef. est. p-value.Intercept 0.6055 0.000 0.2050 0.099 0.4344 0.000 -0.2628 0.000PostRegFD -0.0965 0.446 -0.3386 0.023 -0.0972 0.060 0.0243 0.697US -0.0599 0.634 -0.1022 0.346 0.0065 0.897 -0.0179 0.702US*PostRegFD 0.0224 0.899 -0.0145 0.924 0.0782 0.262 0.0723 0.264Control variables:ln(SIZE) -- -- -0.0330 0.148 -- -- 0.0457 0.000ARV -- -- 4.7057 0.000 -- -- 7.1593 0.000ARV*PostRegFD -- -- 3.8544 0.000 -- -- -0.8947 0.038DISP -- -- -19.3314 0.339 -- -- -- --Adjusted R2 25.78 13.66
BLMZ [2003]
Sample description and variable definitions: Tests reported in this table are based on two different samples. Tests which exclude forecast dispersion (DISP) as a control variable use the market-based sample described in Table 1. Tests which include DISP use a sub-sample of 248 US firms (1,842 matched quarters used in Panel B) and reduced sample of 35 US firms (248 quarters used in Panel B) whose matched ADR firms’ DISP is available; the matched sample (used in Panel C) includes 35 US-ADR pairs (496 matched quarters). ATV = abnormal trading volume is the difference the average daily trading volume over days (-1,+1) relative to the announcement date and the average daily volume for that stock over days (-200, -11), normalized by average volume; ln(SIZE) = log of total assets for quarter q; ARV = absolute value of daily abnormal returns summed over days (-1,+1); DISPq = the dispersion of analysts forecasts for firm j in quarter q (measured as the standard deviation of all earnings-per-share forecasts, related to next fiscal quarter earnings, that are made in calendar quarter, q-1), scaled by stock price at the end of calendar quarter q-1. We winsorize all continuous variables to the 99th percentile of the distributions of their absolute values. b Panel A summarizes the results concerning abnormal trading volume reported by BLMZ [2003, Tables II and III]. We report the average value of their results for the six quarter-pairs. Instead of p-values, we report the number of quarter pairs where their p-value is less than 0.10; they find that mean ATV is significantly negative for 1 quarter pair, significantly positive for 3 quarter pairs, and insignificant for 2 quarter pairs. Panel B reports the results of multivariate tests of the effect of Reg FD on US firms for our sample of US firms. Panel C reports the results of tests which compare the relative effects of Reg FD on US and ADR firms.
. 43
Table 6
Comparison of the Pre- versus Post-Reg FD Changes in Analyst Forecast Dispersiona
Panel A: Changes in pre- versus post Reg FD forecast dispersion found for US firms by prior research
HSZ [2003a] BLMZ [2003] HSZ [2003a]Variable coef. est. p-value. coef. est. p-value. coef. est. p-value.Intercept -- -- -- -- 0.000 0.00PostRegFD (x1000) 0.243 0.00 0.190 0.00 -0.068 0.24Control variables:ABSUE -- -- -- -- 0.072 0.00NEGUE -- -- -- -- 0.000 0.00LOSS -- -- -- -- 0.001 0.00NEGSPEC -- -- -- -- 0.004 0.03GDPSHOCK -- -- -- -- 0.000 0.00DAYS -- -- -- -- -0.000 0.39Lag_DISP -- -- -- -- 0.156 0.00Adjusted R2 -- -- 36.95
Panel B: Changes in pre- versus post Reg FD forecast dispersion found for our US firms
US firms not necessarily matched US firms matched with ADRs
Variable coef. est. p-value. coef. est. p-value. coef. est. p-value. coef. est. p-value.Intercept 0.0018 0.000 0.0021 0.000 0.0014 0.000 0.0010 0.143PostRegFD (x1000) 0.2540 0.067 -0.0424 0.748 -0.0251 0.918 -0.1753 0.476Control variables:ABSUE -- -- 0.0198 0.000 -- -- 0.0141 0.000NEGUE -- -- 0.0002 0.068 -- -- 0.0001 0.642LOSS -- -- 0.0012 0.000 -- -- 0.0003 0.306SIZE -- -- -0.0000 0.000 -- -- -0.0000 0.481DAYS -- -- -0.0001 0.014 -- -- -0.0000 0.977EPRATIO -- -- 0.0031 0.047 -- -- 0.0159 0.004Adjusted R2 -- 14.69 -- 7.65
Panel C: Relative changes in pre- versus post Reg FD forecast dispersion for US versus ADR firms (n= 496 qtrs)
Variable coef. est. p-value. coef. est. p-value.Intercept 0.0014 0.000 0.0007 0.126PostRegFD (x1000) 0.1850 0.443 0.1468 0.538US (x1000) 0.0456 0.854 0.0839 0.731US*PostRegFD (x1000) -0.2101 0.531 -0.3314 0.312Control variables:ABSUE -- -- 0.0103 0.000NEGUE -- -- 0.0004 0.024LOSS -- -- 0.0002 0.356SIZE -- -- -0.0000 0.000DAYS -- -- 0.0000 0.193EPRATIO -- -- 0.0115 0.002Adjusted R2 -- 5.14
with ADRs (n=1,842 qtrs) (n=248 qtrs)
. 44
Sample description and variable definitions: Tests reported in this table are based on two different samples. Tests based on US firms without requiring matched ADR firms use 248 US firms with DISP (1,842 quarters, Panel B). Requiring DISP for the matched ADR firms result in 35 US-ADR firm pairs left (248 US quarters in Panel B, 496 total quarters in Panel C). DISP= the dispersion of analysts forecasts for firm j in quarter q (measured as the standard deviation of all earnings-per-share forecasts, related to next fiscal quarter earnings, that are made in calendar quarter, q-1), scaled by stock price at the end of calendar quarter q-1; ABSUEi,q = the absolute value of firm i’s unexpected earnings in quarter q; NEGUEi,q = 1 if the firm’s unexpected earnings for quarter q are less than zero, 0 otherwise; LOSSi,q =1 if firm i reports loss in quarter q, 0 otherwise; NEGSPEC = absolute value of negative special items in quarter q, zero if there are no negative special items; GDPSHOCK = the absolute value of the quarterly change in the seasonal growth rate in the gross domestic product; SIZEi,q = total assets for quarter q; DAYSi,q = the average number of days that the forecasts precede the earnings announcements; EPRATIOi,q = the firm’s earnings-price ratio measured as the firm’s quarter q earnings divided by end of quarter q price. Lag_ DISP = the lagged value of DISP. We winsorize all continuous variables to the 99th percentile of the distributions of their absolute values. a Panel A summarizes the results concerning analyst forecast dispersion reported by HSZ[2003a, Table 4] and by BLMZ [2003, Table VII]. Panel B reports the results of multivariate tests of the effect of Reg FD on US firms for our sample of US firms and US firms in reduced sample, respectively. Panel C reports the results of tests which compare the relative effects of Reg FD on US and ADR firms.
. 45
Table 7Comparison of the Pre- versus Post-Reg FD Changes in Analyst Forecast Accuracya
Panel A: Changes in pre- versus post Reg FD forecast accuracy found for US firms in prior research
BLMZ [2003]Time series FE Consensus FE HSZ [2003a]
Variable coef. est. p-value. coef. est. p-value. coef. est. p-value. coef. est. p-value.Intercept -- -- -- -- -- -- -0.001 0.00PostRegFD 0.0013 0.00 -0.0001 0.42 0.0009 0.00 0.0002 0.24Control variables:ABSUE -- -- -- -- -- -- 0.216 0.00NEGUE -- -- -- -- -- -- 0.000 0.05LOSS -- -- -- -- -- -- 0.003 0.00NEGSPEC -- -- -- -- -- -- 0.024 0.03GDPSHOCK -- -- -- -- -- -- -0.000 0.50DAYS -- -- -- -- -- -- 0.001 0.00Lag_AFE -- -- -- -- -- -- 0.031 0.00Adjusted R2 -- -- -- 40.65
Panel B: Changes in pre- versus post Reg FD forecast accuracy found for our US firms
Variable coef. est. p-value. coef. est. p-value.Intercept 0.0040 0.003 0.0027 0.297PostRegFD 0.0109 0.000 0.0013 0.317Control variables:ABSUE -- -- 0.1242 0.000NEGUE -- -- 0.0003 0.803LOSS -- -- 0.0038 0.043SIZE -- -- -0.0000 0.852DAYS -- -- 0.0000 0.274EPRATIO -- -- -0.3244 0.000Adjusted R2 -- -- 63.06
Panel C: Changes in pre- versus post Reg FD forecast accuracy for US and ADR firms (944 qtrs)
Variable coef. est. p-value. coef. est. p-value.Intercept 0.0077 0.000 0.0002 0.928PostRegFD 0.0059 0.009 0.0005 0.707US -0.0037 0.094 -0.0001 0.939US*PostRegFD 0.0051 0.102 -0.0001 0.969Control variables:ABSUE -- -- 0.1442 0.000NEGUE -- -- -0.0009 0.394LOSS -- -- 0.0039 0.009SIZE -- -- 0.0000 0.868DAYS -- -- 0.0001 0.000EPRATIO -- -- -0.3496 0.000Adjusted R2 3.11 62.53
. 46
Sample description and variable definitions: We report analyst accuracy for the analyst-based sample, which includes 102 analyst-pairings (944 quarters) where we have data on analyst k’s forecast accuracy and the control variables. AFEi,j,q = forecast accuracy, measured as the absolute value of the difference between firm j’s actual earnings for quarter q and the mean value of analyst k’s forecasts (made in the prior calendar quarter), scaled by the stock price ten trading days before forecast releases; ABSUEi,q = the absolute value of firm i’s unexpected earnings in quarter q (based on a seasonal-random walk model), scaled by share price; NEGUEi,q = 1 if the firm’s unexpected earnings for quarter q are less than zero, 0 otherwise; LOSSi,q =1 if firm i reports loss in quarter q, 0 otherwise; SIZEi,q = total assets for quarter q; DAYSi,q = the average number of days that the forecasts precede the earnings announcements; EPRATIOi,q = the firm’s earnings-price ratio measured as the firm’s quarter q earnings divided by end of quarter q price. Lag_ DISP = the lagged value of AFE. We winsorize all continuous variables to the 99th percentile of the distributions of their absolute values. a Panel A summarizes the results concerning analyst forecast accuracy reported by HSZ[2003a, Table 4] and by BLMZ [2003, Table VII]. Panel B reports the results of multivariate tests of the effect of Reg FD on US firms for our sample of US firms. Panel C reports the results of tests which compare the relative effects of Reg FD on US and ADR firms.
. 47
Table 8
Comparison of the Pre- versus Post-Reg FD Changes in the Newsworthiness of Analysts Reportsa
Panel A: Changes in pre- versus post Reg FD newsworthiness found for US firms in prior research GM[2004] Variable coef. est. standard error Intercept 0.0497 0.006 PostRegFD -0.0185 0.008 Panel B: Changes in pre- versus post Reg FD newsworthiness found for our US firms 1-day return 3-day return Variable coef. est. p-value. coef. est. p-value. Intercept 0.0377 0.000 0.0602 0.000 PostRegFD -0.0014 0.592 -0.0027 0.491 Panel C: Changes in pre- versus post Reg FD newsworthiness for US and ADR firms 1-day return 3-day return Variable coef. est. p-value. coef. est. p-value. Intercept 0.0326 0.000 0.0598 0.000 PostRegFD 0.0027 0.304 0.0030 0.471 US 0.0052 0.052 0.0004 0.916 US*PostRegFD -0.0042 0.268 -0.0057 0.329
Sample description and variable definitions: We report newsworthiness for the analyst-based sample, which includes 112 analyst-pairings (1,312 quarters). Newsworthiness is measured as the absolute value of 1-day [3-day] market-adjusted return on the day [days(-1,0,+1)] when the forecast is made. a Panel A summarizes the results concerning newsworthiness reported by GM[2003, Table 5]. Panel B reports the results of multivariate tests of the effect of Reg FD on US firms for our sample of US firms. Panel C reports the results of tests which compare the relative effects of Reg FD on US and ADR firms.
. 48
References Abarbanell, Jeffrey and Reuven Lehavy. 2002. “Differences in commercial database reported earnings:
implications for empirical research.” Working paper, University of North Carolina and University of Michigan
Agrawal, A. and S. Chadha. 2003. “Who is afraid of Reg FD? The behavior and performance of sell-side
analysts following the SEC’s fair disclosure rules.” Working paper, University of Alabama. Appin, R. October 29, 2001. “SEC wants to extend Regulation FD.” Bank Loan Report. Bailey, W., H. Li, C. Mao, and R. Zhong. 2003. “Regulation fair disclosure and earnings information:
market, analyst, and corporate Responses.” Journal of Finance, LVIII, December 2003: 2487-2514. Bamber, L.S., O.E.Barron and T.L.Stober. 1997. “Trading volume and different aspects of disagreement
coincident with earnings announcements.” The Accounting Review 72 (4):575-597. Barbash, F. April 22, 2001. “The earthquake that didn’t happen.” Wall Street Journal. Barber, B. and J. Lyon. 1996. “Detecting abnormal operating performance: the empirical power and
specification of test statistics.” Journal of Financial Economics (41): 359-400. Barnett, T. September 2001. “To speak or not to speak.” Oil & Gas Investor, Vol. 21, No. 9: 73-75. Barron, O.E. 1995. “Trading volume and belief revisions that differ among individual analysts.” The
Accounting Review 70 (4): 581-597. Bodow, S. December 23, 2001. “It’s not what they know, but whom.” New York Times. Bushee, B., D. Matsumoto, and G. Miller. 2004. “Managerial and investor responses to disclosure
regulation: the case of Reg FD and conference calls.” Forthcoming, The Accounting Review. Canada Newswire. December 22, 1999. “OSC releases preliminary results of corporate disclosure survey.” Clement, M. 1999. “Analyst forecast accuracy: do ability, resource and portfolio complexity matter? Journal of Accounting and Economics 27, 3: 285-303. Clifford, L. November 13, 2000. “The SEC wants to open the info vault.” Fortune, 142, 11: 434. Cowan, L. October 30, 2001. “Disclosure rule is receiving mixed reviews. Wall Street Journal. The CPA Journal. December, 2001. “Research studies show differing views on regulation FD.”
8,10,12,14. The Economist. February 10, 2001. “Fairwell, fair disclosure?” Eisinger, J., J. Hagerty and A. Kueppers. 2001. “Europe feels heat on profit warnings – pressure grows for
firms to take US attitude when shortfalls arise.” Wall Street Journal, April 9, 2001.
. 49
Eleswarapu, V., R. Thompson, and K. Venkataraman. 2003. “Measuring the fairness of regulation fair disclosure through its impact on trading costs and information asymmetry.” Forthcoming, Journal of Financial Quantitative Analysis.
Fama, E. and K. French. 1993. “Common risk factors in the returns on stocks and bonds.” Journal of Financial Economics 33: 3-56. Fama, E. and K. French. 1997. Industry costs of equity. Journal of Financial Economics 43: 153-193. Gadarowski, C. and P. Sinha. 2002. “On the efficacy of regulation fair disclosure: theory and evidence.”
Working paper, Cornell University. Gintschel, A. and S. Markov. 2004. “The effectiveness of regulation FD.” Forthcoming, Journal of
Accounting and Economics. Gomes, A., G. Gorton and L. Madureira. 2004. “SEC regulation fair disclosure, information and the cost
of capital.” Working paper, The Wharton School, University of Pennsylvania. Graham, J.R., and C.R. Harvey, 2001. “The theory and practice of corporate finance: Evidence from the
field.” Journal of Financial Economics, 60: 187-243. Heflin, F., K.R. Subramanyam, and Y. Zhang. 2003a. “Regulation FD and the financial information
environment: early evidence.” The Accounting Review 78 (1): 1-37. Heflin, F., K.R. Subramanyam, and Y. Zhang. 2003b. “Stock return volatility before and after regulation
FD.” Working paper, Purdue University and University of Southern California. Investor Relations Business. 2002. “Canadian regulator clarifies disclosure rules.” Investor Relations
Business, May 20, 2002. Irani, A. J, and I. Karamanou. 2003. “Regulation fair disclosure, analyst following, and analyst forecast
dispersion.” Accounting Horizon 17 (1): 15-29. Jacob, J., T. Lys and M. Neale. 1999. “Expertise in forecasting performance of security analysts.” Journal of Accounting and Economics 28, 1: 51-82. Janvrin, D., and J. Kurtenbach. 2002. “The impact of Regulation Fair Disclosure on the information environment: evidence from providers and users.” Working paper, Iowa State University. Kothari, S.P., A. Leone and C. Wasley. 2005. “Performance matched discretionary accrual measures.” Journal of Accounting and Economics, forthcoming. Leuz, C. 2003. “Discussion of ADRs, analysts, and accuracy: does cross-listing in the United Stated
improve a firm’s information environment and increase market value?” Journal of Accounting Research 41:347-362.
Lo, K. 2003. “Economic consequences of regulated changes in disclosure: the case of executive
compensation” Journal of Accounting and Economics, 35: 285-314 Maremont, M. January 29, 2001. “Gillete reports loss for fourth period, backs away from short-term
guidance.” Wall Street Journal.
. 50
Mathew, P., J. C. Hughen, and K. Ragan. 2002. “A Reexamination of information flow in financial markets: the impact of regulation FD and decimilization.” Working paper, University of Saskatchewan, Bowling Green State University, and Southwest Missouri State University. Mikhail, M., B. Walther and R.Willis. 1997. “Do security analysts improve their performance with experience?” Journal of Accounting Research 35: 131-157. Mohanram, P. and S. Sunder. 2001. “Has regulation fair disclosure affected financial analysts’ ability to
forecast earnings?” Working paper, New York University. Opdyke, J. October 23, 2000. “Deal & deal makers – the big chill: street feels effect of ‘fair disclosure’
rule – regulation is altering the way analysts approach their jobs.” Wall Street Journal. Opdyke, J. March 2, 2001. “How much are stocks hurting from recent rash of profit preannouncements
tied to new rules?” Wall Street Journal. Opdyke, J. and E. Nelson. October 31, 2000. “New SEC rule gives analysts headaches as conference calls
become hectic affairs.” Wall Street Journal. OxResearch. 2001. “European Union: market abuse under attack.” Oxford Analytica Ltd. Pottinger, M. December 26, 2001. “Cathay Pacific meetings show cozy relationships with analysts raise
questions beyond the U.S.” Wall Street Journal. Remond, C. October 23, 2000. “Many foreign companies entering US vow to adopt stricter disclosure
policies.” Wall Street Journal. Rosenbaum, E. January 1, 2001. “Reg FD’s global ripples – II magazine platinum.” Institutional Investor. Shane, P., N. Soderstrom, and S. Yoon. 2001. “Earnings and price discovery in the post-Reg FD
information environment: a preliminary analysis.” Working paper, University of Colorado at Boulder. Topaloglu, S. 2002. “An examination of institutional trading activity before and after Regulation FD.” Working paper, Purdue University.
Vinzant, C. June 14, 2001. “Keeping an ear on Wall Street; corporate webcasts earn a growing audience
of investors.” The Washington Post.
. 51
