1. science
2. ecology
3. pollution

Externalities of Disclosure Regulation: The Case of Regulation FD
Michael Crawley,a Bin Ke,b and Yong Yuc
ABSTRACT
We use Regulation Fair Disclosure (REG FD) to examine a relatively neglected but important
effect of disclosure regulation: externalities. REG FD applies to all publicly traded U.S. firms,
but foreign firms cross-listed on U.S. stock exchanges are explicitly exempt. Despite the
exemption, we find that many cross-listed firms voluntarily adopt REG FD as part of their
disclosure policies. We hypothesize that REG FD imposes two externalities on cross-listed firms.
First, following REG FD previously disadvantaged U.S. investors have a lower demand for
shares of cross-listed firms that continue to follow a selective disclosure policy. Second, REG
FD creates an information spillover effect on cross-listed firms whose values and cash flows are
correlated with those of U.S. firms. We find evidence of both effects in cross-listed firms’
voluntary REG FD adoption decisions. Further, relative to non-adopters, cross-listed firms who
voluntarily adopt REG FD exhibit a significant reduction in the information asymmetry
component of cost of capital. REG FD adopters are also more likely than non-adopters to switch
to open disclosure post REG FD. These results suggest that cross-listed firms’ voluntary REG
FD adoption represents a credible commitment to increased disclosure transparency.
Keywords: Regulation Fair Disclosure; disclosure externalities; cross-listed firms
JEL codes: G, K2, M4, N2
December 8, 2010
We thank Kris Allee, Rob Bloomfield, Zhihong Chen, Fred Choi, Dain Donelson, Yuyan Guan, Jeff Hales, Steve
Huddart, John Jiang, Bob Lipe, Steve Kachelmeier, Bill Kinney, Sidney Leung, Craig Nichols, Sheridan Titman,
and workshop participants at Boston University, City University of Hong Kong, Cornell University, FASRI
Roundtable, Michigan State University, Pennsylvania State University brownbag seminar, Singapore Management
University, University of Texas at Austin, and the 2009 AAA annual meeting for helpful comments and Laura
Abrahamson and Mallory Valverde for able research assistance. The paper was formerly titled “why do cross-listed
firms voluntarily adopt regulation fair disclosure?”.
a
Kelley School of Business,
Email: [email protected].
Indiana University,
Bloomington,
IN
47405.
Tel:
+1
812-855-0951.
b
Nanyang Business School, Nanyang Technological University, S3-01B-39, 50 Nanyang Avenue, Singapore
639798. Tel: +65 6790-4832. Fax: +65 67913697. Email: [email protected].
c
McCombs School of Business, University of Texas at Austin, 1 University Station, Austin, TX 78712. Tel: +1 512471-6714. Email: [email protected].
I. INTRODUCTION
Disclosure plays a central role in many countries’ securities regulatory frameworks. Over
the past decade, a flurry of significant securities laws and regulations (e.g., Regulation Fair
Disclosure and the Sarbanes Oxley Act of 2002) and the world-wide mandatory adoption of
IFRS have generated a strong interest in understanding the economic consequences of disclosure
regulation. While a large body of empirical research has examined firm-specific costs and
benefits of disclosure regulation (see, e.g., Greenstone et al. 2006; Bushee and Leuz 2005;
Gintschel and Markov 2004; Zhang 2007; Gao et al. 2007), 1 Leuz and Wysocki (2008) conclude
after surveying the extant literature that there is a paucity of evidence on the externalities of
disclosure regulation.
Understanding the externalities of disclosure regulation is important because such
externalities provide a possible justification for the necessity of disclosure regulation. Firmspecific effects of disclosure regulation are undoubtedly relevant for evaluating the economic
consequences of disclosure regulation. However, the mere existence of firm-specific net benefit
from disclosure is not sufficient to justify mandatory disclosure. This is because when the firmspecific net benefit is positive, firms ought to have an incentive to voluntarily provide the
disclosure and thus the necessity of mandatory disclosure regulation becomes less clear.
Unfortunately, Leuz and Wysocki (2008) indicate that debates about disclosure regulation often
incorrectly focus on firm-specific costs and benefits of disclosure choices rather than the
aggregate effects of disclosure regulation.
The objective of this study is to use Regulation Fair Disclosure (REG FD) as a specific
setting to understand the economic forces that determine the degree of externalities associated
1
See Leuz and Wysocki (2008) for a detailed review of this literature.
1
with disclosure regulation. REG FD prohibits management of all publicly traded U.S. firms from
sharing material nonpublic information with select investors, particularly financial analysts and
institutional investors. However, American Depository Receipts (ADRs) traded on the U.S. stock
exchanges (referred to as cross-listed firms), which represent an important and growing segment
of the U.S. financial market, are explicitly exempt from the regulation. 2 Despite the exemption, a
2004 survey of 143 large cross-listed firms conducted by Broadgate Capital Advisors, Value
Alliance and the Bank of New York reported that 54% of the respondents voluntarily adopted
REG FD as part of their disclosure policies.
Why did so many cross-listed firms voluntarily follow REG FD? We conjecture that
cross-listed firms’ decision to voluntarily adopt REG FD represents externalities resulting from
the mandatory implementation of REG FD by U.S. firms. It is reasonable to assume that
managers of both U.S. firms and cross-listed firms optimized their disclosure policies prior to the
passage of REG FD. Managers that expected a positive (negative) net benefit from an open
disclosure policy should have chosen an open (selective) disclosure policy and investors should
have priced the firms’ shares accordingly. REG FD forced all U.S. firms to follow an open
disclosure policy. The evidence from existing research indicates that REG FD has been effective
in reducing the private communication between company management and sophisticated
investors (such as financial analysts and institutional investors) and increasing the level playing
field among investors with respect to access to management’s private information (see, e.g.,
Gintschel and Markov 2004; Francis et al. 2006; Ke et al. 2008).
2
From 1990 to 2000, the number of foreign firms listed on NYSE and NASDAQ grew steadily from about 170 to
over 750 with cumulative trading volume in these firms reaching more than $750 billion. As of 2002, foreign firms
listed on NYSE represented nearly 17% of all NYSE listings (see Coffee 2002).
2
We hypothesize that REG FD imposes two non-mutually exclusive externalities on crosslisted firms. First, following the passage of REG FD, the information asymmetry among external
investors should be reduced in U.S. firms and thus previously disadvantaged U.S. investors
(particularly retail investors) are likely to find it more attractive trading in U.S. firms than in
cross-listed firms (denoted as investor demand effect). Consistent with this prediction, Bushee et
al. (2004) find that following REG FD the amount of individual investor trading increased during
conference calls for U.S. firms that previously held closed calls relative to U.S. firms that
previously held open calls. There is also evidence that following REG FD, U.S. firms
experienced declines in return volatility and increases in both trading volume and informational
efficiency, consistent with a shift toward a more transparent information disclosure environment
(see, e.g., Heflin et al. 2003; Bailey et al. 2006).
If cross-listed firms choose not to follow REG FD, they risk losing investors to U.S. firms,
which in turn will reduce their stock liquidity and increase their expected returns in markets that
are not perfectly competitive (see Merton 1987; Fishman and Hagerty 1989; Pastor and
Stambaugh 2003). The risk of investor loss is particularly high for cross-listed firms because
their major investor clientele is U.S. retail investors (Matthew et al. 2007). As a result, crosslisted firms should face the pressure to follow REG FD, especially those firms that have a larger
U.S. retail investor clientele. 3
Second, U.S. firms’ implementation of REG FD may create an information spillover on
cross-listed firms (denoted as information spillover effect). Assuming that firm values and cash
flows of U.S. firms are correlated with those of cross-listed firms, previously disadvantaged
3
Disadvantaged investors may also take other actions to pressure cross-listed firms to adopt REG FD, such as
increased shareholder activism and the refusal to purchase products from cross-listed firms. Such actions should be
easier to justify in the post-REG FD period due to U.S. firms’ implementation of REG FD.
3
investors can use U.S. firms’ open disclosures mandated under REG FD to update their beliefs
about cross-listed firms’ firm values and cash flows and would assume the worst in the event of
no disclosure by cross-listed firms. As shown in Dye and Sridhar (1995), this revision in
investors' perception should induce cross-listed firms to voluntarily follow REG FD in order to
distinguish themselves from other cross-listed firms with worse information, resulting in an
unraveling of managers’ private information (Grossman 1981; Milgrom 1981). Therefore, in the
absence of disclosure costs, cross-listed firms should find it optimal to pool with U.S. firms by
voluntarily switching from a selective disclosure policy to an open disclosure policy.
We use both email and telephone surveys to obtain information on cross-listed firms’
REG FD adoption decision. 43% (181/422) of the active cross-listed firms responded to our
survey and 40% (72/181) of the respondents stated that they had voluntarily adopted REG FD as
part of their formal disclosure policy, a finding consistent with the 2004 Broadgate survey. In
terms of economic significance measured using total assets, the 72 REG FD adopters represent
approximately 36% of the 422 cross-listed firms in our initial sample and 60% of the 181 crosslisted firms in our final sample.
We use the institutional ownership of ADR shares as an inverse proxy for the investor
demand effect. We use the percentage of foreign sales and the stock return synchronicity
between cross-listed firms and U.S. firms per Piotroski and Roulstone (2004) as two alternative
proxies for the information spillover effect. Consistent with our expectations, we find that the
probability of REG FD adoption is higher for cross-listed firms that have a smaller institutional
ownership, a higher percentage of foreign sales and a higher stock return synchronicity.
Consistent with the existing voluntary disclosure literature, we also find that the probability of
REG FD adoption is lower for cross-listed firms that are expected to gain less from a voluntary
4
open disclosure policy, i.e., those firms that have a lower investment opportunity set, a lower
demand for external financing, and greater managerial agency problems.
We find evidence that cross-listed firms’ voluntary adoption of REG FD represents a
credible commitment to increased disclosure transparency. First, relative to the non-adopters,
adopters exhibit a reduction in the bid-ask spread and an increase in share turnover. Second,
adopters are more likely than non-adopters to switch to open conference calls after the voluntary
REG FD adoption. Specifically, we find that the majority of both adopters and non-adopters held
closed conference calls prior to the passage of REG FD. Following the passage of REG FD, the
majority of the adopters but not the non-adopters switched to open conference calls.
This study makes several important contributions. First, we are one of the few studies that
examine the externalities of disclosure regulation. While the earlier disclosure literature has
examined intra-industry information transfers associated with earnings announcements (see, e.g.,
Foster 1981), very few studies have investigated the externalities of disclosure regulation. 4 One
exception is Bushee and Leuz (2005). They examine a 1999 SEC regulatory change that
mandates all domestic firms quoted on the over-the-counter bulletin board to file periodic
financial reports with the SEC. They find the regulatory change resulted in positive externalities
for domestic firms previously filing with the SEC. Anand et al. (2006) also find weak evidence
that the Sarbanes and Oxley Act imposes externalities on Canadian firms not listed in the U.S.
However, neither Anand et al. (2006) nor Bushee and Leuz (2005) examine the specific
mechanisms through which the externalities of disclosure regulation work.
4
Even though we use cross-listed firms, which are exempt from REG FD, to illustrate the externalities of disclosure
regulation, it is important to note that externalities of disclosure regulation also exist among firms subject to a
common regulation. For example, one firm’s disclosure of information mandated by a regulation could be also
useful to the valuation and investment decisions of another firm that is subject to the same regulation (see Leuz and
Wysocki 2008).
5
Second, we contribute to the understanding of cross-listed firms’ voluntary disclosure
incentives. There appears to be a presumption in the extant literature that cross-listed firms do
not have an incentive to follow U.S. laws from which they are exempt (see, e.g., Licht 2003;
Gomes et al. 2007; Francis et al. 2006; Chen et al. 2010). We show that this presumption may be
premature. Our study should be of interest to researchers who wish to use cross-listed firms as a
control group. In the context of REG FD, the evidence from our study suggests that cross-listed
firms are not homogenous with respect to their voluntary compliance with U.S. securities laws
and thus using all cross-listed firms as a control sample may reduce a researcher’s ability to
detect REG FD-related effects for publicly traded U.S. firms.
Third, our study is relevant to the legal bonding versus reputational bonding debate in the
cross listing literature. The legal bonding hypothesis (see Coffee 1999; Stulz 1999) attributes at
least a portion of the benefit of a U.S. cross listing (e.g., an increase in stock market valuation or
access to outside finance) to more stringent U.S. securities laws, stronger SEC enforcement
power, and a greater threat of litigation by minority shareholders. However, recent research by
Siegel (2005) raises questions on the legal bonding hypothesis. 5 Siegel (2005) finds that the SEC
and minority shareholders have not effectively enforced U.S. laws against cross-listed firms who
violated U.S. laws. However, Siegel (2005) finds that cross-listed Mexican firms that abide by
the U.S. laws without exploiting minority shareholders during an economic downturn (i.e., firms
with a clean reputation) are able to receive more privileged long-term access to outside finance
than those that have a bad reputation. Siegel (2005) argues that reputational bonding better
explains the success of U.S. cross listings than legal bonding. Our results lend further support to
Siegel’s reputational bonding hypothesis. More importantly, we identify the economic forces that
5
See Karolyi (2006) and Benos and Weisbach (2004) for a comprehensive survey of the cross listing literature.
6
cause cross-listed firms to voluntarily bond with tougher U.S. securities laws from which they
are exempt.
Fourth, our study provides timely and relevant information to the SEC who indicated in
the final rule of REG FD that it would undertake a comprehensive review of cross-listed firms’
reporting requirements. Although we cannot directly assess the consequences of mandating all
cross-listed firms to adopt REG FD, our study provides the first ex-ante archival evidence on the
major costs and benefits involved in cross-listed firms’ existing disclosure choices and helps the
SEC better predict the economic consequences of imposing REG FD on all cross-listed firms.
The rest of the paper is organized as follows. The next section describes the sample
selection procedures and survey results. Section III discusses the regression results on the
determinants of cross-listed firms’ REG FD adoption decision. Section IV shows the results on
the effect of voluntary REG FD adoption on the information asymmetry component of cost of
capital. Section V reports the analysis of cross-listed firms’ open conference calls in the pre- and
post-REG FD periods. Section VI provides a series of sensitivity checks to rule out alternative
explanations. Section VII concludes.
II. SAMPLE SELECTION PROCEDURES AND SURVEY RESULTS
The Sample of Cross-Listed Firms
We use several data sources to identify our cross-listed firm sample, including CRSP,
Compustat, Citibank, and the Bank of New York. The initial sample includes all cross-listed
firms that were listed on the NYSE, AMEX or NASDAQ in the form of American Depository
Receipts (ADRs) at some point in time from January 1, 2000 to December 31, 2005. 6 As REG
6
We do not consider level I ADRs that trade over the counter or SEC Rule 144A private placements to qualified
institutional buyers because information for those firms is scarce. We do not expect these firms to adopt REG FD
7
FD took effect in October 2000, we exclude cross-listed firms that were delisted prior to January
1, 2000. As we started our data collection in 2006, we exclude cross-listed firms that became
listed after December 31, 2005. We exclude the few Canadian ADRs because most Canadian
firms are directly listed in the U.S. In addition, Canada had a “tipping” rule in effect designed to
limit selective disclosure prior to REG FD and thus REG FD may have little effect on crosslisted Canadian firms’ voluntary disclosure behavior. 7 The resulting sample contains 552 unique
cross-listed firms, of which 422 were active and 130 were inactive as of the end of 2006 when
we started our survey.
An Analysis of Survey Responses
To increase the survey response rate, our survey questionnaire asks two factual questions
about REG FD (see Appendix A for a sample copy of the survey instrument). We conducted our
survey in two stages. First, we emailed our survey questionnaire to all 522 cross-listed firms’
investor relations representatives in four rounds of emails. We obtained the email addresses from
the cross-listed firms’ U.S. or home country web sites. The first round emails were sent on
November 20, 2006 and the last round emails were sent on March 9, 2007. Second, starting in
the middle of March 2007, we started to make phone calls to the firms that did not respond to our
four rounds of email inquiries. We first called a firm’s U.S. office and then the headquarters at its
home country during business hours. We followed the same survey questionnaire in Appendix A
for the phone survey.
because their investors should be generally sophisticated and thus they should not face the same REG FD related
investor pressure as the ADRs that trade on the major stock exchanges.
7
In 2002 the Canadian Securities Administrators (CSA) issued NP 51-201 (Disclosure Standards) to further
interpret and clarify some issues in the “tipping” rule.
8
Of the 552 cross-listed firms, 203 firms responded to our inquiry, of which 10 firms
refused to answer our questions and 4 firms provided unusable answers. This leaves us with 189
usable firms, of which only 8 (or 6% of all inactive cross-listed firms) are inactive cross-listed
firms. Because of the inactive cross-listed firms’ low response rate and the high likelihood that
required financial data on inactive cross-listed firms are unavailable, we dropped the 130 inactive
cross-listed firms from our analyses. As a result, the usable response rate for the active crosslisted firms is 43% (181/422), which is high compared with the typical 10-15% survey response
rate documented in recent surveys of financial executives (e.g., Graham et al. 2005). As we do
not have the survey responses for all of the 422 active cross-listed firms, we also examine the
effect of this survey response bias on our empirical results in Appendix B. We find no evidence
that the survey response bias causes a material effect on our inferences.
Of the 181 active respondents, 75 answered the email inquiry, and 106 answered the
phone inquiry (79 provided the answers on the phone immediately and 27 provided the answers
in an email following the phone call). Appendix C provides the distribution of the 422 active
cross-listed firms and the number of firms that responded to our survey by country. 72 of the 181
respondents (40%) claimed to have voluntarily adopted REG FD. This result suggests that a
significant percentage of cross-listed firms chose to voluntarily follow REG FD, consistent with
the 2004 Broadgate survey. Due to missing values for some of the regression variables, we lose
another 3 firms and therefore our final sample contains 178 active cross-listed firms, of which 70
firms claimed to have adopted REG FD.
Regarding our second survey question, the adoption date is always the REG FD effective
rate (i.e., October 23, 2000) for the adopters listed on a U.S. stock exchange before October 23,
9
2000. The adoption date is the U.S. listing date for the adopters listed on a U.S. stock exchange
after October 23, 2000.
Survey Response Accuracy
A common concern with survey research is the accuracy of survey responses. This
concern could be more acute in our case because we did not include additional questions to
cross-check the validity of the responses to our two factual survey questions. However, it is
important to note that conducting a standard survey with multiple questions is not feasible in our
setting because the typical response rate for a standard survey is around 10-15% (see Graham et
al. 2005). Given that the population of active cross-listed firms is only 422 during our sample
period, conducting a standard survey would result in a useable sample that is too small for our
study. 8
We address potential survey response errors in two ways. First, in Sections IV-V, we
directly use cross-listed firms’ stock market liquidity and ex post open disclosure behavior
before vs. after the REG FD adoption date to check the validity of our survey responses. To the
extent that our survey responses are reliable and credible, both stock market liquidity and the
likelihood of open disclosure should be increased in the post-REG FD period for adopters
relative to non-adopters. However, if some survey respondents provided incorrect or
intentionally biased responses, we should be less likely to find the predicted effects in Sections
IV-V. Second, for the 178 cross-listed firms included in our final sample, we directly cross check
the accuracy of the survey responses with publicly available data sources, including the
FACTIVA data base, which covers all the major news wires, newspapers and magazines,
8
We wish to thank Bill Kinney for the suggestion of using a short survey to increase the response rate.
10
company web sites, SEC filings, and available conference call transcripts. We discuss the details
of this analysis in Section VI.
III. DETERMINANTS OF CROSS-LISTED FIRMS’ REG FD ADOPTION
Variable Definitions and Predictions
Proxies for the Investor Demand and Information Spillover Effects
Investor demand: REG FD was designed to level the playing field among investors of
publicly traded U.S. firms with regard to information access to company management. Following
REG FD, previously disadvantaged U.S. investors should find it more attractive trading in U.S.
firms than in cross-listed firms. Hence after the passage of REG FD we expect all cross-listed
firms to face some pressure to follow REG FD. For example, Remond (2000) reports that after
the passage of REG FD, many cross-listed foreign firms felt obligated to follow REG FD in
order to keep pace with U.S. firms’ disclosure standards. Leading advisors to ADR firms (e.g.,
Bank of New York Co., depositary bank for 65% of the world's depositary receipts) also
recommended that their ADR clients at least operate in the spirit of REG FD, if not the letter
(see, e.g., Remond 2000; Platt 2000; Rosenbaum 2001).
However, the investor demand for adopting REG FD is likely higher for firms that have a
larger base of U.S. retail investors, who were often at a disadvantage relative to analysts and
institutional investors in information access to company management prior to the passage of
REG FD. 9 Because the size of a cross-listed firm’s U.S. retail investor base is not observable, we
9
In theory the investor demand effect should apply to all previously disadvantaged retail investors, including home
country retail investors. However, in practice the investor demand effect is likely much smaller for home country
retail investors because the latter face greater transaction costs (e.g., foreign currency exchange risk and a host of
issues associated with setting up and managing a foreign stock trading account) in shifting their stock investments
from cross-listed firms to U.S. firms.
11
use INSTITUTIONOWN (defined below) as an inverse proxy for small investors’ pressure for
cross-listed firms to follow REG FD. INSTITUTIONOWN is defined as the fraction of crosslisted firm’s ADR shares owned by U.S. institutional investors as disclosed in the Spectrum
database. 10 , 11 For cross-listed firms listed on a U.S. stock exchange prior to the REG FD
effective date, INSTITUTIONOWN is measured at the end of the calendar quarter prior to the
REG FD effective date. For cross-listed firms listed on a U.S. stock exchange after the REG FD
effective date, INSTITUTIONOWN is measured at the end of the calendar quarter following the
firm’s U.S. stock listing date. We predict the coefficient on INSTITUTIONOWN to be negative.
Information spillover: U.S. firms’ implementation of REG FD may also induce crosslisted firms to voluntarily follow REG FD in order to avoid being labeled by investors as firms
with worse information. This information spillover effect is expected to increase with the
correlation between U.S. firms’ and cross-listed firms’ values and cash flows (see Dye and
Sridhar 1995). We capture this correlation in two ways. First, we use a cross-listed firm’s
percentage of annual foreign sales as disclosed in Compustat (denoted as FOREIGNSALE) to
proxy for this correlation. The idea is that as a cross-listed firm has more foreign sales, its value
and cash flows are more likely to be correlated with those of U.S. firms because it is very likely
that a significant portion of cross-listed firms’ foreign sales are in the U.S. 12 For cross-listed
firms listed on a U.S. stock exchange prior to the REG FD effective date, FOREIGNSALE is
10
An alternative and potentially preferred definition of INSTITUTIONOWN is to include the ownership of home
country institutional investors. Unfortunately, ownership data for home country institutions are not readily available.
11
To make sure INSTITUTIONOWN is not a proxy for the size of the ADR float relative to a cross-listed firm’s
worldwide market cap, we also include the latter as a control in Table 2 and obtain similar inferences (untabulated).
The coefficient on this new control variable is insignificant.
12
An alternative and better proxy for the correlation between U.S. firms’ and cross-listed firms’ value and cash
flows is the percentage of a cross-listed firm’s U.S. sales. Unfortunately many cross-listed firms do not separately
disclose U.S. sales.
12
measured at the end of the fiscal year prior to the REG FD effective date. For cross-listed firms
listed on a U.S. stock exchange after the REG FD effective date, FOREIGNSALE is measured in
the fiscal year prior to or in the year of the firm’s U.S. stock listing date. We expect the
coefficient on FOREIGNSALE to be positive.
Second, we measure the correlation between U.S. firms’ and cross-listed firms’ values
and cash flows using the stock return synchronicity (denoted as SYNCHRONICITY) derived
from the following firm-specific model in Piotroski and Roulstone (2004, 1123):
Ri ,t = α + β1 MARETi ,t −1 + β 2 MARETi ,t + β 3 INDRETi ,t −1 + β 4 INDRETi ,t + ε i ,t
(1)
where
i=firm index
t=time index
R=the weekly return of a cross-listed firm.
MARET=the weekly value weighted average market return of all U.S. firms.
INDRET=the weekly value weighted average return of all U.S. firms in the same two-digit SIC
code as the cross-listed firm.
For cross-listed firms listed in the U.S. prior to the REG FD effective date, equation (1) is
estimated using weekly returns from CRSP over a one-year period that ends one day prior to the
REG FD effective date. For cross-listed firms listed in the U.S. after the REG FD effective date,
equation (1) is estimated using weekly returns over a one-year period that starts with the firm’s
U.S. listing date. We require a minimum of 45 weeks for the estimation. SYNCHRONICITY is
defined as the natural logarithm of
R2
, where R2 is the coefficient of determination from
(1 − R 2 )
the estimation of equation (1). Higher values of synchronicity imply a stronger correlation in
13
firm values and cash flows between a cross-listed firm and U.S. firms in the same industry. We
expect the coefficient on SYNCHRONICITY to be positive.
Proxies for Other Voluntary Disclosure Determinants
Conditional on the REG FD-driven investor demand and information spillover effects,
cross-listed firms’ REG FD adoption decision should also depend on the common costs and
benefits of voluntary disclosure identified in the existing literature: investment opportunities,
demand for external equity finance, managerial agency costs, information asymmetry between
management and outside investors, proprietary costs, and disclosure complexity (see, e.g., Healy
and Palepu 2001; Bushee et al. 2003). 13 Below we introduce the proxies for each of the above
mentioned voluntary disclosure determinants. Unless stated otherwise, all the financial data
required for the subsequent regression variables are drawn from Compustat, Worldscope, CRSP,
or hand-collected from companies' SEC filings. Unless stated otherwise, the following control
variables are measured at the end of the fiscal year prior to the REG FD effective date for crosslisted firms listed on a U.S. stock exchange prior to the REG FD effective date, and measured in
the fiscal year prior to or in the year of the firm’s U.S. stock listing for cross-listed firms listed
on a U.S. stock exchange after the REG FD effective date.
Investment opportunity set and demand for external equity financing: Prior research
finds that the benefit of increased disclosure is larger for firms that have more investment
opportunities that cannot be satisfied by internally generated cash flows and debt financing. The
definitions of both the investment opportunity set and demand for external equity financing
follow Durnev and Kim (2005) and Demirguc-Kunt and Maksimovic (1998). A firm’s
13
We do not consider litigation risk as a determinant because as noted in Bushee et al. (2003), under existing fraudon-the-market rules, individuals can sue regardless of whether they heard the information directly or not.
14
investment opportunity set (INVEST_OPP) is defined as the 2-year geometric average of the
annual percentage growth in net sales. A firm’s demand for external equity financing
(EXTERNAL_FIN) is defined as the difference between the firm’s actual growth rate and the
sustainable growth rate with retained earnings and short-term and long-term debt financing that
maintain a constant debt-to-assets ratio. The actual growth rate is the 2-year geometric average
of the annual growth rate in total assets and the sustainable growth rate is the 2-year average of
ROE/(1-ROE), where ROE is the return on equity. Both INVEST_OPP and EXTERNAL_FIN
are winsorized at the 1% and 99% percentiles. As Durnev and Kim (2005) note, these two
proxies are measured using ex ante information and thus do not suffer from the same degree of
endogeneity as other proxies for the investment opportunity set (e.g., the book-to-market ratio)
and the demand for external equity financing (e.g., the future ex post realized equity financing).
We expect the coefficients on both variables to be positive.
Managerial agency costs: Following Gong et al. (2010), we measure the managerial
agency costs using the extent of divergence between the voting rights and cash flows rights of
the company management group (including officers, directors, and their immediate family
members) who are controlling shareholders of the firm. 14 We define a dummy variable
CONTROL_WEDGE that equals one if a cross-listed firm’s management group represents the
largest block holder of the firm by voting rights and its voting rights exceed cash flow rights. We
define a dummy variable CONTROL_NOWEDGE that equals one if a cross-listed firm’s
14
We collect the voting rights and cash flows rights as follows. First, we match our cross-listed firm sample with the
datasets used by Faccio and Lang (2002) and Lins (2003), which contain the voting rights and cash flow rights of the
firm management group for many non-U.S. firms. Second, for the cross-listed firms that are not included in the
above two studies, we hand collect the voting rights and cash flow rights of the firm management group using the
approach described in Lins (2003). The data sources include SEC filings (proxy statements, 20-F, 40-F, or 10-K),
company web sites, stock exchange web sites, as well as the other data sources listed in Faccio and Lang (2002), and
Lins (2003). For the firms cross listed prior to REG FD, we use the year 2000 data if available and the most recent
year data if year 2000 is not available. For the firms cross listed after REG FD, we use the year prior to the listing
year if available and the most recent available data otherwise.
15
management group represents the largest block holder of the firm by voting rights and its voting
rights do not exceed its cash flow rights. We expect management of CONTROL_WEDGE=1
firms to have both the ability and incentive to expropriate minority shareholders. Because
increased disclosure transparency is expected to reduce management’s private control benefits
(see, e.g., Shleifer and Wolfenzon 2002; Greenstone et al. 2006), we expect the coefficient on
CONTROL_WEDGE to be negative. While management of CONTROL_NOWEDGE=1 firms is
controlling shareholders and thus have the ability to expropriate minority shareholders, it does
not possess voting rights in excess of its cash flow rights and thus should have no incentive to
expropriate minority shareholders. Hence, we expect the coefficient on CONTROL_NOWEDGE
to be insignificant.
Information asymmetry: As noted at the beginning of this Section, all cross-listed firms
face the threat of losing investors to publicly traded U.S. firms following the passage of REG FD.
Fishman and Hagerty (1989) argue that it is costly for an investor to study the disclosure of every
firm and thus an investor who does not follow a stock will be less likely to spend time studying
and trading on the firm’s public disclosure; hence, losing investors will reduce a firm’s stock
price efficiency in less than perfectly competitive markets (see also Merton 1987). In addition, a
significant loss of investors’ interest in a stock may also reduce analysts’ incentive to cover the
stock, which would further exacerbate the information asymmetry problem. Therefore, crosslisted firms that suffer from a higher information asymmetry between management and outside
investors should be more likely to adopt REG FD. 15 We use the natural logarithm of total assets
(LN(ASSETS)) and the natural logarithm of one plus the number of analysts following the firm
from IBES (LN(ANALYST)) as proxies for the degree of information asymmetry. We expect the
15
However, U.S. firms’ open disclosure under REG FD should mitigate this information asymmetry to the extent
that there is a positive correlation between U.S. firms’ and cross-listed firms’ values and cash flows.
16
coefficients on both variables to be negative. None of the regression coefficients in the REG FD
adoption regression are affected if we drop either LN(ASSETS) or LN(ANALYST).
Proprietary costs: Similar to Bushee et al. (2003), we assume that relative to a policy of
selective disclosure, an open disclosure policy increases a firm’s risk of leaking proprietary
information to product market competitors. A common proprietary cost proxy used in prior
accounting research (e.g., Harris 1998) is the industry Herfindahl index, which intends to capture
the degree of industry-level product market competition. However, relying on recent industrial
organization studies, Karuna (2007) argues that product market competition is better captured
using three dimensions, including product substitutability (SUBSTITUTION), market size
(MKTSIZE), and entry costs (ENTRYCOST). Accordingly, we use the three dimensions of the
product market competition rather than the industry Herfindahl index to measure proprietary
costs. 16 Following Karuna (2007), SUBSTITUTION is the sum of sales in an industry (4-digit
SIC) divided by the sum of operating costs in the same industry. MKTSIZE is the sum of sales in
an industry (in millions of U.S. dollars). ENTRYCOST is the average gross PPE (in millions of
U.S. dollars) in an industry weighted by each firm’s sales in the same industry. We express both
MKTSIZE and ENTRYCOST in the natural logarithm form in the later regression model to
reduce skewness. Higher values of SUBSTITUTION and ENTRYCOST and lower values of
MKTSIZE represent lower industry competition.
Because our sample firms are cross-listed foreign firms and industry segment data are not
always available, we make a few simplifying assumptions in computing the three proprietary
cost proxies. First, if a cross-listed firm derives 50% or more of its sales from its home country,
we use the firm’s home country industry data to measure the three proxies. In addition, because
16
Inference is similar if using the industry Herfindahl index as a proprietary cost proxy. The coefficient on the
industry Herfindahl index is insignificant in the REG FD adoption regression.
17
cross-listed firms rarely disclose industry segment data, we assume that all of the cross-listed
firm’s sales are derived from its primary 4-digit SIC code industry. Second, if a cross-listed firm
derives less than 50% of its sales from the home country, we assume that the cross-listed firm’s
primary product market competition resides in the U.S. and therefore we use the U.S. 4-digit SIC
code industry segment data. 17
A priori the effect of proprietary costs on REG FD adoption is not clear cut. However,
prior research (see, e.g., Harris 1998) finds that firms that operate in less competitive product
markets are less willing to provide public disclosures that may hurt their competitive advantages.
Thus, we predict the coefficients on SUBSTITUTION and ENTRYCOST to be negative and the
coefficient on MKTSIZE to be positive.
Financial disclosure complexity: Existing research suggests that small (or naïve)
investors are not as sophisticated as large (or institutional) investors in processing financial
information (see, e.g., Battalio and Mendenhall 2005; Bhattacharya 2001; Bhattacharya et al.
2007; Walther 1997; Malmendier and Shanthikumar 2007; Mikhail et al. 2007). In a study of the
determinants of open versus closed conference calls, Bushee et al. (2003) find that firms that
have more complex financial information are less likely to choose open conference calls. Their
result is consistent with the idea that company management has a fear that the public disclosure
of complex financial information to all investors will increase the likelihood of naïve investors’
misinterpretation of the complex disclosure and lead to unnecessary stock return volatility.
Following Bushee et al. (2003), we use three proxies to capture the complexity of a
firm’s financial disclosure: HIGH_TECH, STD_REV, and INTANGIBLE. HIGH_TECH is a
dummy that is equal to 1 for firms in a high-tech industry (SIC codes: 2833-36, 3612-13, 362117
Results are similar if we use only U.S. industry segment data to measure the three proprietary cost proxies for all
cross-listed firms.
18
29, 3651-52, 3661-69, 3671-2, 3674, 3695, 4812-22, 4832-99, 7370-79). STD_REV is the
standard deviation of net sales scaled by the mean sales over the prior 3 years. INTANGIBLE is
the percentage of total assets that are intangible. STD_REV is winsorized at the 1% and 99%
percentiles while INTANGIBLE is winsorized at the 99% percentile. We expect the coefficients
on HIGH_TECH, STD_REV, and INTANGIBLE to be negative.
Although we attempt to use different variables for different theoretical constructs, it is
likely that some of the variables could be proxies for multiple constructs. For example,
INSTITUTIONOWN, a proxy for investor demand, could also be a proxy for information
asymmetry. Likewise, the variables for financial disclosure complexity could also capture
proprietary costs. As a result, readers should interpret the regression coefficients with this caveat
in mind.
Descriptive Statistics and Primary Regression Results
Table 1 provides descriptive statistics on the determinants of REG FD adoption for the
adopters in column (1) and the non-adopters in column (2). Consistent with our expectations,
relative
to
the
adopters,
the
non-adopters
have
higher
institutional
ownership
(INSTITUTIONOWN), lower foreign sales (FOREIGNSALE), lower stock return synchronicity
(SYNCHRONICITY), lower investment opportunities (INVEST_OPP), lower demand for
external equity finance (EXTERNAL_FIN), and greater divergence of the voting rights and cash
flow rights by top management (CONTROL_WEDGE). The other economic determinants do not
significantly differ across the two groups.
Table 2 reports the logistic regression results on the determinants of the REG FD
adoption decision based on the 178 active cross-listed firms that responded to our survey. The
19
two regression models in Table 2 are identical except that the proxy for the information spillover
effect is FOREIGNSALE in column (1) and SYNCHRONICITY in column (2). We discuss the
regression result in column (1) first. Consistent with the investor demand effect, the coefficient
on INSTITUTIONOWN is significantly negative. The significantly positive coefficient on
FOREIGNSALE is consistent with the information spillover effect, suggesting that when there is
a positive correlation between U.S. firms’ and cross-listed firms’ values and cash flows, crosslisted firms are more likely to pool with U.S. firms by voluntarily providing open disclosure.
Consistent with the existing theories of voluntary disclosure (see Healy and Palepu 2001), crosslisted firms are more likely to adopt REG FD when the investment opportunity set is higher, the
demand for external equity financing is higher, and the divergence of management’s voting
rights and cash flow rights is lower. We find little evidence that proprietary costs
(SUBSTITUTION, LN(MKTSIZE), and LN(ENTRYCOST)) and information asymmetry
(LN(ASSETS) and LN(ANALYST)) affect cross-listed firms’ REG FD adoption. There is mixed
evidence that financial disclosure complexity affects REG FD adoption. Only one of the three
proxies for disclosure complexity (i.e., STD_REV) is significant and consistent with our
prediction. Overall these results suggest that U.S. firms’ adoption of REG FD creates pressure
for cross-listed firms to voluntarily follow REG FD, but cross-listed firms whose management is
expected to benefit less from open disclosure are less likely to adopt REG FD.
The sample size for the regression in column (2) is smaller due to missing values for
SYNCHRONOCITY. With the exception of the significant coefficients on LN(MKTSIZE) and
LN(ENTRYCOST) in column (2), the inferences from the regression in column (2) are
qualitatively similar to those from the regression in column (1). Consistent with the coefficient
on FOREIGNSALE, the coefficient on SYNCHRONICITY is significantly positive. This finding
20
is not surprising because the Pearson (Spearman) correlation between the two information
spillover effect proxies is a significant 0.31 (0.34).
Robustness Checks
The regression results in Table 2 are robust to a series of untabulated sensitivity checks.
First, we exclude the cross-listed firms from UK and Australia because we learned in the process
of conducting the survey that around the passage of REG FD both the UK and Australian
regulators issued rules similar to REG FD though it is unclear whether those rules are strictly
enforced. Second, we include country fixed effects to control for potential omitted country-level
variables such as country investor protection. With the exception of the insignificant coefficients
on STD_REV and INVEST_OPP, none of the other significant coefficients are affected. Third,
we include in the regression a series of dummy variables indicating (1) whether a cross-listed
firm is listed on one of the three major U.S. stock exchanges; (2) whether a cross-listed firm is
listed on its home country exchange; and (3) whether a cross-listed firm is listed in any foreign
stock exchange other than the U.S. Finally, we retain only the cross-listed firms that were listed
on a U.S. stock exchange prior to the effective date of REG FD. Eliminating the foreign firms
cross-listed in the U.S. after the REG FD effective date helps reduce the concern that our results
could be partially due to alternative economic forces such as the mandatory adoption of IFRS
across Europe in 2005, which tends to increase disclosure transparency.
IV. THE EFFECT OF VOLUNTARY REG FD ADOPTION ON THE INFORMATION
ASYMMETRY COMPONENT OF COST OF CAPITAL
Predictions
21
Existing disclosure theories suggest that a credible commitment to increased disclosure
transparency should result in a reduction in a firm’s information asymmetry component of cost
of capital (see Verrecchia 2001). Hence, a natural question is whether cross-listed firms’
voluntary commitment to REG FD helps reduce the information asymmetry component of cost
of capital. 18
Ex ante it is difficult to predict the effect of voluntary REG FD adoption on the adopters’
information asymmetry component of cost of capital. If cross-listed firms’ stated REG FD
adoption represents a credible commitment to increased disclosure transparency, we should
expect the cross-listed firms who voluntarily followed REG FD to enjoy a significant reduction
in the information asymmetry component of cost of capital. Table 2 shows that cross-listed
firms’ REG FD adoption can be explained by economic incentives, suggesting that the voluntary
adoption should be credible. However, there are also reasons why cross-listed firms’ voluntary
REG FD adoption may not be credible. First, the REG FD adoption is voluntary and an adopting
firm can revert to a selective disclosure policy at any time without the fear of being prosecuted
by the SEC. However, cross-listed firms that are suspected of breaking their REG FD
commitment would bear other significant costs, such as loss of management’s reputation and
credibility. A few SEC enforcement cases against U.S. firms’ REG FD violations (see Fenwick
and West 2005) suggest that detecting REG FD violations is not that difficult because the
selective disclosure of material non-public information would inevitably result in significant
changes in stock prices, which is observable to all investors.
18
Though untabulated, we also conducted an event study of cross-listed firms’ stock market reactions to the events
that led to the final passage of REG FD. We find no evidence of either a positive or negative stock market reaction
to the events, suggesting that REG FD on average did not cause any change in shareholder valuation. However, we
would like to caution that the lack of significant results could reflect the difficulty of identifying the events relevant
to REG FD.
22
Second, even if cross-listed firms’ stated REG FD adoption is credible, the adoption itself
may not represent a significant increase in the voluntary disclosure transparency. The cross-listed
firms who claimed to have voluntarily followed REG FD could merely reconfirm their existing
voluntary open disclosure policy. However, REG FD adopters’ ex post open disclosures shown
in Section V suggest that this is not a significant concern.
Finally, REG FD may create a chilling effect among managers of both U.S. firms and
cross-listed firms who are pressured to follow REG FD. The primary objective of REG FD is to
level the playing field among different investors with regard to information access to company
management (i.e., increase management’s voluntary disclosure transparency) and does not intend
to force management to increase or decrease the total amount of voluntary (open and closed)
disclosures to all investors. However, in reality REG FD may also affect the total amount of
voluntary disclosures available to all investors. In particular, some commentators in the debate
leading to the passage of REG FD argued that REG FD would create a chilling effect among
corporate managers and discourage them from disclosing the private information that they would
otherwise disclose to select investors. If this were true, REG FD could increase rather than
decrease the information asymmetry among investors, especially between insiders and outside
investors. Thus, to the extent that a significant chilling effect exists, the previously discussed
investor demand and information spillover effects on cross-listed firms would be smaller and
therefore we will be less likely to find a negative effect of the voluntary REG FD adoption on the
information asymmetry component of cost of capital.
The non-adopters’ change in the information asymmetry component of cost of capital
around the passage of REG FD is hard to predict due to two offsetting effects. On one hand, due
to the investor demand effect resulting from U.S. firms’ adoption of REG FD, the non-adopters
23
should experience a reduction in investors’ interest and therefore a decrease in stock liquidity in
less than perfectly competitive financial markets (Fishman and Hagerty 1989). On the other hand,
due to the information spillover resulting from U.S. firms’ adoption of REG FD, more public
information about non-adopters’ values and cash flows could become available, resulting in an
increase in the non-adopters’ stock liquidity and a decrease in their cost of capital following
REG FD (see Lambert et al. 2007).
Research Design
Following Leuz and Verrecchia (2000), we use two approaches to test the effect of
adopting REG FD on the information asymmetry component of cost of capital. The first
approach compares the information asymmetry component of cost of capital in the post-REG FD
adoption period for the adopters versus non-adopters (the level regression). The second approach
compares the change in the information asymmetry component of cost of capital before versus
after the passage of REG FD for the adopters versus non-adopters (the change regression). We
use the non-adopters (i.e., a difference-in-difference design) to control for confounding factors
that affect all cross-listed firms during our sample period. We adopt both the level and change
approaches because both have pros and cons. The level regression has a larger sample because
we can include the firms cross listed after REG FD’s effective date. The disadvantage of the
level regression is that it is more prone to correlated omitted variables with the REG FD adoption
decision. To reduce the correlated omitted variable concern for the level regression, we add
several control variables (discussed below) and also use the Heckman’s (1978) approach to
correct for the self selection bias of the REG FD adoption decision.
24
The advantage of the change regression is that we can use the firm itself as a control for
the many unobservable confounding firm fixed effects (e.g., firm characteristics). As REG FD is
an exogenous shock to cross-listed firms, the change in the information asymmetry component of
cost of capital in our short event window (defined below) before versus after the passage of REG
FD for the adopters versus non-adopters is less likely caused by changes in the unobservable
confounding factors. However, the change regression has a smaller sample than the level
regression because it requires each firm to have data in both pre and post REG FD periods. In
addition, the change specification cannot capture the REG FD induced cost of capital effect
already reflected in the pre REG FD period.
Consistent with Leuz and Verrecchia (2000), the information asymmetry component of a
firm’s cost of capital in either the pre-REG FD period or the post-REG FD period is measured
using the bid-ask spread, trading volume, and share price volatility. 19 To reduce the confounding
effects associated with a firm’s gradual shift in voluntary disclosure, we follow Leuz and
Verrecchia (2000) by using a 6-month window to compute the three proxies, but results are
similar if we use a 3-month window. Leuz and Verrecchia (2000) argue that the bid-ask spread is
the most explicit measure of a firm’s information asymmetry while the other two proxies can be
influenced by many other factors unrelated to information, especially the share price volatility.
Indeed, Leuz and Verrecchia (2000) find significant results for the bid-ask spread and trading
volume but not for the share price volatility.
The bid-ask spread (SPREAD) is defined as the average relative closing bid-ask spread
from the daily CRSP (i.e., the absolute spread divided by the average of bid and ask) in a 6month window. Trading volume (TURNOVER) is defined as the median daily turnover ratio (i.e.,
19
We do not examine the implied cost of capital due to data limitations and the noise of existing implied cost of
capital measures (see Easton et. al. 2002; Easton and Sommers 2007).
25
the number of shares traded divided by the total shares outstanding from the daily CRSP) in a 6month window. 20 Share price volatility (VOLATILITY) is defined as the standard deviation of
daily stock returns in a 6-month window. All three variables are winsorized at the 99% percentile.
For the pre-REG FD period, the three cost of capital proxies are measured over the 6 months
before the REG FD effective month. Note that the three proxies in the pre-REG FD period
cannot be computed for the firms cross-listed after the REG FD effective date because these
firms did not trade on a U.S. stock exchange. For the post-REG FD period, the three proxies are
measured over the 6 months after the REG FD effective month if a firm is listed on a U.S. stock
exchange prior to the end of the REG FD effective month and over the 6 months after the firm’s
initial month of cross listing if a firm is listed on a U.S. stock exchange after the REG FD
effective month. Recall that the REG FD adopters that were cross listed prior to the REG FD
effective date all adopted REG FD on the REG FD effective date.
Following Leuz and Verrecchia (2000), we include several control variables in the level
regression to reduce the influence of confounding factors. For the bid-ask spread level regression,
we include as controls TURNOVER, VOLATILITY, LN(MARKETCAP), and FREEFLOAT.
LN(MARKETCAP) is defined as the natural logarithm of a firm’s total market capitalization
(i.e., the sum of the market values of all issues for firms with multiple share issues). For the firms
cross listed prior to the REG FD effective date, LN(MARKETCAP) is measured at the year end
prior to the 6-month measurement window of the dependent variable. For the firms cross listed
after the REG FD effective date, LN(MARKETCAP) is measured at the year end before the
firm’s cross listing date. FREEFLOAT indicates the presence of insiders and is defined as the
20
To ensure that the trading volume change around REG FD is not caused by a difference in the frequency of
earnings announcement events in the 6 months around REG FD, we also excluded the daily trading volumes in the
five trading days around the quarterly earnings announcement dates in computing TURNOVER and obtained similar
inference.
26
percentage of firm shares that are not closely held. FREEFLOAT is measured at the year-end
prior to the 6-month measurement window of the dependent variable. For the trading volume
level regression, we include as controls LN(MARKETCAP), VOLATILITY, and FREEFLOAT.
For the share price volatility level regression, we include as controls LN(MARKETCAP),
FREEFLOAT, and BETA. BETA is the market beta directly from the Worldscope database and
is a firm-specific constant over the sample period. We do not include the three control variables
(i.e., LN(MARKETCAP), FREEFLOAT, and BETA) in the change regression because
FREEFLOAT and BETA are not likely to change significantly over the test period. In addition,
we find no evidence from untabulated regression tests that the reduction in the bid-ask spread in
Table 4 is caused by stock price appreciation (i.e., LN(MAREKETCAP)) in the post-REG FD
period.
The Level Regression Results
Primary Regression Results
Table 3 reports the level regression results on the effect of REG FD adoption on the
information asymmetry component of cost of capital measured by the bid-ask spread (SPREAD),
share turnover (TURNOVER), and share price volatility (VOLATILITY) in the post REG FD
period. ADOPT is a dummy that equlas one for the REG FD adopters. Following Heckman
(1978), we use the model in column (1) of Table 2 to correct for the self selection bias of
ADOPT. However, the untabulated OLS regression results are similar.
We find that the adopters have a lower bid-ask spread and higher share turnover than
non-adopters. However, similar to Leuz and Verrecchia (2000), we find no evidence that the
adopters have lower share price volatility than non-adopters, suggesting that VOLATILITY may
27
not be a good proxy for a firm’s information asymmetry component of cost of capital. Many of
the control variables are significant and consistent with expectations. For the SPREAD
regression, larger firms with higher share turnover have lower bid-ask spread. For the
TURNOVER regression, larger firms with higher return volatility have higher share turnover.
For the VOLATILITY regression, the only significant control variable is BETA. Overall the
coefficients on the control variables are similar to those reported in Leuz and Verrecchia (2000).
The only noticeable exception is that the coefficient on FREEFLOAT is never significant in our
sample.
Robustness Checks
The regression results in Table 3 are robust to a battery of untabulated sensitivity checks.
First, to recognize the fact that ADOPT has a joint effect on both SPREAD and TURNOVER
regression, we jointly estimate a three-equation system including the REG FD adoption
regression, the bid-ask spread regression and the share turnover regression. Because this system
is only identified via the functional form, we also eliminate TURNOVER in the SPREAD
regression and estimate a reduced form. Second, we control for the number of analysts following
in Table 3 in order to rule out the possibility that the effect of ADOPT is due to a difference in
analyst following across the adopters and non-adopters. Third, we rerun the regressions in Table
3 after excluding cross-listed firms from the UK and Australia that introduced similar regulations
around the passage of REG FD. Fourth, we include a series of dummy variables indicating
whether a cross-listed firm is listed on one of the three major U.S. stock exchanges, whether a
cross-listed firm is listed on its home country exchange, and whether a cross-listed firm is listed
in any foreign stock exchange other than the U.S.
28
Results on Changes of Liquidity
Primary Results
Table 4 reports the changes of SPREAD, TURNOVER, and VOLATILITY from the 6
months before to the 6 months after the REG FD adoption for the cross-listed firms that were
listed on the U.S. stock exchanges in the pre REG FD period. The sample size here is smaller
than that in Table 1 because we require each firm to have data in both the pre and post REG FD
periods. For the non-adopters, the changes in the three proxies for the information asymmetry
component of cost of capital are insignificant except for the significant reduction in share
turnover, suggesting that the non-adopters’ share liquidity decreases in the post-REG FD period.
This finding suggests that following the passage of REG FD, i.e., investors were less willing to
trade in the shares of non-adopters. For the adopters, however, all three measures of the
information asymmetry component of cost of capital change significantly in the predicted
directions. In addition, a test of the differences in differences in the last column of Table 4 for the
adopters versus non-adopters in the two time periods indicates that the changes in the three
proxies of the information asymmetry component of cost of capital are significantly different for
adopters and non-adopters. Overall, the results in Table 4 are consistent with those from Table 3
and suggest that the adoption of REG FD is a credible commitment to increased disclosure
transparency that helps significantly reduce adopting firms’ information asymmetry component
of cost of capital.
Robustness checks
29
Although we have used the non-adopters to control for common economic shocks that
affect all cross-listed firms, the differences in results shown in Table 4 between the non-adopters
and adopters could be caused by some unknown changes other than REG FD that affect the
adopters but not the non-adopters. For example, it is possible that even in the absence of REG
FD, we may still observe the results in Table 4 due to the adopters’ increased commitment to
disclosure transparency over time. To rule out this alternative explanation, we rerun the analyses
of ∆LN(SPREAD), ∆LN(TURNOVER), and ∆LN(VOLATILITY) in Table 4 for the nonadopters and adopters in two alternative 12-month time periods: a) the 12-month period prior to
the REG FD effective date; and b) the 12-month period subsequent to the REG FD effective date
(see Table 5). To the extent that the results in Table 4 are due to the adopters’ gradual increase in
commitment to disclosure transparency over time rather than REG FD, the results in Table 5
ought to be similar to those in Table 4. We fail to find support for this alternative explanation in
Table 5, suggesting that the results in Table 4 are likely driven by REG FD.
V. CROSS-LISTED FIRMS’ EX POST VOLUNTARY DISCLOSURE: THE CASE OF
OPEN CONFERENCE CALLS
In this Section we examine whether cross-listed firms who claimed to have adopted REG
FD actually followed through on their initial commitment by providing voluntary open
disclosure in the post-REG FD period. Following Bushee et al. (2003), we use open conference
calls as an ex post validation of a firm’s ex ante open disclosure commitment. 21 We believe it is
reasonable to use open conference calls as a proxy for a firm’s ex post voluntary open disclosure
21
6-K filings could be an alternative measure of cross-listed firms’ extent of voluntary open disclosure. We decided
not to adopt this measure for two major reasons. First, not all 6-K filings are voluntary. Second, 6-K is not the only
method of disclosure endorsed by REG FD.
30
because over the past decade conference calls have become a popular medium publicly traded
firms use to communicate non-public information to outside investors.
We hand collected the open versus closed conference call data from Thomson Financial’s
StreetEvents database over the years 1999-2006. 1999 is the first year that the StreetEvents
database has a reasonable coverage of conference calls. For both the pre- and post-REG FD
periods separately, OPEN is coded one if a firm always held open conference calls, and zero if
the firm held no conference call at all or at least one closed call. 22 A firm is deemed to hold a
closed conference call if the conference call either provided no call/web information for
investors to listen in or restricted the call to analysts only.
Panel A of Table 6 shows the descriptive statistics of OPEN in the pre- and post-REG FD
periods for only the cross-listed firms that were listed in the U.S. stock exchanges prior to REG
FD’s effective date. Panel B of Table 6 shows the descriptive statistics of OPEN in the post
REG FD period for the combined sample of firms that were cross listed pre REG FD and the
firms that were cross listed post REG FD. 23
Focusing on the REG FD adopters in Panel A, we find that for the pre-REG FD period
only 25% of the adopters followed an open call policy. In the post-REG FD period 75% of the
adopters followed an open call policy, which is significantly higher than the percentage in the
pre-REG FD period (p value<0.001). These results suggest that the REG FD adoption is not
merely a restatement of a firm’s existing open disclosure policy. In addition, the REG FD
adoption represents a credible commitment to increased open disclosure.
22
All of the inferences in Table 6 are unchanged if firms who never held a conference call are excluded.
23
It is also interesting to examine the change in the information content of REG FD adopters’ conference calls in the
pre- and post-REG FD periods. Unfortunately, we could not conduct this analysis due to lack of enough usable
observations.
31
In contrast, for the non-adopters, the percentage of open conference call firms is only
17% in the pre-REG FD period and 29% in the post-REG FD period and the difference is only
marginally significantly different (p value=0.061). The difference in the percentage of open
conference call firms in the pre-REG FD period for the adopters and non-adopters (25% versus
17%) is not significantly different, suggesting that pre REG FD the adopters and non-adopters
followed a similar voluntary disclosure policy.
The increase in the percentage of open conference call firms post REG FD is significantly
higher for the adopters than for the non-adopters (p value<0.001). Overall, the results in Panel A
of Table 6 show that cross-listed firms’ adoption of REG FD represents a credible commitment
to increased open disclosure. 24
Panel B of Table 6 repeats the descriptive statistics of OPEN using all the cross-listed
firms that existed in the post-REG FD period. The percentages of open call firms for both the
adopters and non-adopters are very similar to those in Panel A, suggesting that excluding the
firms that were cross listed in the post-REG FD period in Panel A does not create any material
bias.
VI. CONFIRMED VS. UNCONFIRMED REG FD ADOPTERS
As noted in Section II, we employ publicly available data sources to verify the accuracy
of our survey responses. We did not find any public information suggesting REG FD adoption by
the 108 firms who said they did not follow REG FD in the survey. For 37 (53%) out of the 70
cross-listed firms who claimed to have followed REG FD in the survey, we were able to find
direct confirmation of their survey responses from publicly available data sources. We refer to
24
The inferences in Tables 3 and 4 are qualitatively similar if we delete the REG FD adopters who did not always
conduct open calls in the post-REG FD period (untabulated).
32
these 37 REG FD adopters as confirmed adopters and the remaining 33 REG FD adopters as
unconfirmed adopters. The majority of the confirmed REG FD adopters made the adoption
announcement on their company web sites.
There are two possible reasons why we could not find any public announcement of the
adoption for the 33 unconfirmed REG FD adopters. One possible reason is that our search is
incomplete (referred to as the incomplete search explanation). This is possible because we
conducted our search long after the REG FD effective date. For example, it is possible that the
REG FD adopters may have deleted their REG FD adoption announcements from their company
web sites. Another possible reason is that the unconfirmed REG FD adopters did not have the
same degree of commitment to disclosure transparency as the confirmed REG FD adopters or
simply lied to us (referred to as the lack of commitment explanation). The first explanation
would imply that our regression results in Tables 2, 3, 4, and 6 should be similar for the
confirmed and unconfirmed REG FD adopters. By contrast, the second explanation would imply
that the above regression results should be weaker for the unconfirmed REG FD adopters.
To distinguish between these two competing explanations, we rerun all the tests in Tables
2, 3, 4, and 6 for confirmed and unconfirmed REG FD adopters separately. The results are shown
in Table 7. Panel A of Table 7 replicates the regression result in column (1) of Table 2 for the
confirmed REG FD adopters and unconfirmed REG FD adopters separately. The regression
coefficients that are significant in Table 2 continue to be significant and in the same directions in
Panel A of Table 7, suggesting that the similar economic forces drive both unconfirmed adopters
and confirmed adopters’ decisions to follow REG FD.
Panel B of Table 7 replicates the regression models in Table 3 while Panel C of Table 7
replicates the results in Table 4 for the confirmed REG FD adopters and unconfirmed REG FD
33
adopters separately. As shown in Panel B, the coefficient on ADOPT is always significantly
negative in the SPREAD regression for both confirmed and unconfirmed adopters. For the
TURNOVER regression, the coefficient on ADOPT is still significantly positive for the
confirmed adopters but insignificant for the unconfirmed adopters. For the VOLATILITY
regression, the coefficient on ADOPT is insignificant for both confirmed and unconfirmed
adopters, consistent with the result in Table 3 for the full sample. As shown in Panel C, the
differences in ∆LN(SPREAD) and∆LN(TURNOVER) between the non -adopters and the
confirmed adopters or between the non-adopters and the unconfirmed adopters are in the
expected directions and all significant at the 10% level or lower. The difference in
∆LN(VOLATILITY) between the non-adopters and the confirmed adopters or between the nonadopters and the unconfirmed adopters is in the expected direction but not significant at the 10%
level. Overall, the results in Panels B and C of Table 7 suggest that the stock market perceived
both the confirmed adopters and unconfirmed adopters’ REG FD adoption to be credible
commitment to increased disclosure transparency.
Finally, Panel D of Table 7 replicates the results in Table 6 for confirmed adopters and
unconfirmed adopters separately. Again, there is no evidence that the two types of adopters
behave differently in terms of ex post voluntary open disclosure.
Overall, the results in Table 7 are consistent with the incomplete search explanation,
suggesting that the REG FD adoption for both confirmed adopters and unconfirmed adopters
represents a genuine commitment to increased disclosure transparency.
VII. CONCLUSIONS
34
We examine how U.S. firms’ implementation of Regulation Fair Disclosure (REG FD)
creates externalities on cross-listed firms who are explicitly exempt from the regulation. We
hypothesize that REG FD imposes two types of externalities on cross-listed firms. First,
following REG FD, previously disadvantaged U.S. investors have a reduced demand for shares
of cross-listed firms that continue to follow a selective disclosure policy (denoted as investor
demand effect). Second, U.S. firms’ adoption of REG FD creates an information spillover effect
on cross-listed firms whose values and cash flows are correlated with those of U.S. firms
(denoted as information spillover effect). We find that both effects explain cross-listed firms’
voluntary REG FD adoption decision. Consistent with the hypothesis that cross-listed firms’
voluntary adoption of REG FD is a credible commitment to increased disclosure transparency,
we find that REG FD adopters enjoy a greater reduction in the bid-ask spread and a greater
increase in share turnover than non-adopters. In addition, REG FD adopters are more likely than
non-adopters to switch to open disclosure post REG FD.
Our results have important implications for both researchers and regulators. For
researchers, our results suggest that securities regulation may have significant externalities and
thus it is important to study the causes and consequences of such externalities in order to better
understand the economic consequences of securities regulation. Future research may extend our
study to many other securities laws that cross-listed firms are exempt from (see Licht 2003). For
regulators, our study illustrates the underlying economic forces that may affect the extent of the
externalities of U.S. securities regulation. In addition, to the extent that the SEC plans to modify
cross-listed firms’ existing reporting requirements to be consistent with those of U.S. firms, our
study should provide useful information on the potential economic consequences of mandating
all cross-listed firms to follow such U.S. laws.
35
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39
Appendix A. The Survey Questionnaire
Dear Sir or Madam,
Professor XXX at the XXX University and I are conducting academic research on the factors
that influenced foreign private issuers’ decision to voluntarily adopt Regulation Fair Disclosure.
As you know, foreign issuers are explicitly excluded from Regulation FD in the SEC final rule
(see http://www.sec.gov/rules/final/33-7881.htm) and therefore it is up to each foreign issuer
whether to voluntarily adopt Regulation FD. We would appreciate your help in answering the
following two factual questions:
1. Has your company formally adopted Regulation FD as part of its disclosure policies?
(Yes or No): ________
2. If
yes, when did your company
_____/_____/_______ (mm/dd/yy)
formally
adopt
Regulation
FD?
We will keep your individual answers to the survey questions strictly confidential and will
release only summary statistics of the survey.
If you would like to receive the finished research, please indicate (Y or N):___
We thank you again for your support in our research.
Sincerely,
40
Appendix B. Tests of a Potential Survey Response Bias
The omission of the 60% active cross-listed firms who did not respond to our survey clearly
raises the concern about survey response bias. We perform several empirical tests to examine the
severity of the response bias and find no evidence of a severe survey response bias. First, we
compare the REG FD adoption rate for the early versus late survey respondents (see Graham et
al. 2005). The late survey respondents can be thought of as a sample from the non-response
group. We find little difference in the REG FD adoption rate between the cross-listed firms who
responded to our email inquiry and the cross-listed firms who responded to our phone inquiry,
suggesting that there is no evidence of a significant response bias (results untabulated).
Second, following Moore and Reichert (1983), we compare the characteristics of survey
responding firms to the characteristics of the non-responding firms. To the extent that the
samples of respondents and non-respondents are random draws from the same population, we
should expect the two samples’ characteristics to be similar. Untabulated ranksum tests indicate
that with the exception of firm size (LN(ASSETS)) and the divergence of the voting rights and
cash flow rights by top management (CONTROL_WEDGE), all the economic determinants
included in the REG FD adoption regression do not differ for the responding firms and nonresponding firms. Relative to the responding firms, on average the non-responding firms’ firm
size (LN(ASSETS)) is smaller while the degree of the divergence of the voting rights and cash
flow rights by top management (CONTROL_WEDGE) is greater. This evidence suggests mild
evidence of a survey response bias.
Third, we use a formal statistical method to directly control for the response bias in the
estimation of the REG FD adoption regression in Tables 2 and 3. Specifically, we first model the
economic determinants of the binary survey response decision; then we include the inverse
41
Mills’ ratio from this first stage regression in the second stage regression of the REG FD
adoption regression in Table 2 or the liquidity regressions in Table 3. In addition to the economic
determinants of the REG FD adoption regression, the first-stage survey response regression
includes the following additional plausible determinants of the survey response:
ENGLISH: a dummy equal to 1 if a country’s official language is English.
USCONTACT: a dummy equal to 1 for firms that have a U.S. contact phone number.
TIMEZONE: the natural logarithm of one plus the time zone difference between a firm’s home
country capital and the U.S. capital.
DISTANCE: the natural logarithm of the distance in miles between a firm’s home country
capital and the U.S. capital.
CONSUMER: a dummy equal to 1 for firms in the consumer product industry (2-digit SIC in 20,
23, 25, 35, 36, 55-58, 70, 72, 78-86). The SIC classification follows Frieder et al. (2008).
LN(ADR_SIZE): the natural logarithm of the market value of a cross-listed firm’s ADR shares
traded in the U.S. measured at the calendar quarter end prior to the REG FD effective date for
firms cross listed prior to the REG FD effective date and at the calendar quarter end immediately
following the firm’s U.S. stock listing date for firms cross listed after the REG FD effective date.
As we conducted our survey from the U.S. in English, we expect English speaking firms
that have a U.S. contact phone number and are closer to the U.S. both in terms of time zone and
geographic distance to be more likely to respond to our survey. We also believe that cross-listed
firms that have experience interacting with U.S. consumers and have a larger presence in the
U.S. stock market should be more likely to have designated personnel to handle questions in the
U.S. product and financial markets. Thus, we expect those firms to be more likely to answer the
survey.
42
We find in the unreported first-stage regression that the likelihood that a cross-listed firm
responds to our survey is higher for firms that are domiciled in English speaking (ENGLISH)
countries farther away from the U.S. (DISTANCE), larger (LN(ASSETS)), operate in industries
with smaller market sizes (LN(MKTSIZE), and report more foreign sales (FOREIGNSALE),
intangible assets (INTANGIBLE) and volatile sales (STD_REV). The other variables’
coefficients are insignificant in the first-stage regression. Our inferences in the second-stage
regressions for Tables 2 and 3 are unchanged (results untabulated).
43
Appendix C. Country Distribution of the Active Cross-listed Firms
Home country
Argentina
Australia
Austria
Belgium
Brazil
Chile
China
Columbia
Denmark
Finland
France
Germany
Greece
Hong Kong
Hungary
India
Indonesia
Ireland
Israel
Italy
Japan
Korea
Luxembourg
Mexico
Netherlands
New Guinea
N. Zealand
Norway
Peru
Philippines
Portugal
Russia
Singapore
S. Africa
Spain
Sweden
Switzerland
Taiwan
Thailand
Number of cross-listed firms that responded to
our survey
REG FD Adopters
Non-adopters
Number of
cross-listed
firms
13
18
2
1
30
16
36
1
3
4
25
19
3
10
1
13
2
10
8
10
30
14
3
20
21
1
1
4
1
2
2
5
2
9
5
4
11
8
1
3
4
2
6
6
6
10
1
1
2
5
6
3
3
2
2
1
2
2
9
8
2
3
8
2
1
1
3
1
2
4
2
44
2
7
4
5
2
1
3
3
1
1
1
1
2
1
5
3
1
2
Turkey
U.K.
Venezuela
Total
1
51
1
422
45
6
17
72
109
Table 1. Descriptive statistics for the survey respondents who adopted REG FD and survey
respondents who did not adopt REG FDa
Variable name
INSTITUTIONOWN
FOREIGNSALE
SYNCHRONICITY
INVEST_OPP
EXTERNAL_FIN
ASSETS
ANALYST
CONTROL_WEDGE
CONTROL_NOWEDGE
SUBSTITUTION
MKTSIZE
ENTRYCOST
HIGH_TECH
Mean (median) [STD]
(1)
(2)
Respondents who
Responders who did not
adopted REG FD
adopt REG FD
(N=70)
(N=108)
0.383
0.478
(0.233)
(0.460)
[0.385]
[0.360]
0.469
0.333
(0.524)
(0.239)
[0.353]
[0.326]
-0.860
-1.555
(-0.890)
(-1.518)
[1.451]
[0.923]
0.618
0.132
(0.211)
(0.078)
[1.545]
[0.351]
0.723
0.001
(0.093)
(-0.047)
[2.650]
[0.387]
65,689.169
29,353.982
(5,166.572)
(4,829.500)
[163,123.432]
[75,576.855]
3.743
2.757
(1.000)
(2.000)
[5.129]
[3.064]
0.057
0.336
(0.000)
(0.000)
[0.234]
[0.475]
0.214
0.206
(0.000)
(0.000)
[0.413]
[0.406]
1.168
1.163
(1.132)
(1.141)
[0.150]
[0.159]
160,616.030
66,495.688
(12,999.088)
(8,856.617)
[317,428.560]
[184,338.275]
5,240.115
6,429.626
(760.696)
(1,225.071)
[10,730.310]
[12,596.520]
0.357
0.290
(0.000)
(0.000)
46
Ranksum
test’s twotailed p value
0.088
0.013
<0.001
<0.001
0.002
0.862
0.571
<0.001
0.890
0.722
0.123
0.710
0.347
STD_REV
INTANGIBLE
[0.483]
0.284
(0.190)
[0.281]
0.090
(0.016)
[0.166]
[0.456]
0.217
(0.156)
[0.197]
0.072
(0.013)
[0.125]
0.187
0.910
a
INSTITUTIONOWN is the fraction of cross-listed firm’s shares traded in the U.S. that is owned by institutional
investors. FOREIGNSALE is sales outside the firm's home country divided by total sales. SYNCHRONICITY
measures the correlation between a cross-listed firm’s returns and the returns of U.S. firms and is defined as the
natural logarithm of
R2
, where R2 is the coefficient of determination from the estimation of equation (1) in
(1 − R 2 )
Section III. INVEST_OPP is the 2-year geometric average of the annual percentage growth in net sales.
EXTERNAL_FIN is the difference between the firm’s actual growth rate and the sustainable growth rate with
retained earnings and short-term and long-term debt financing that maintain a constant debt-to-assets ratio. The
actual growth rate is the 2-year geometric average of the annual growth rate in total assets and the sustainable
growth rate is the 2-year average of ROE/(1-ROE), where ROE is the return on equity. ASSETS is total assets.
ANALYST is the number of analysts following the firm. CONTROL_WEDGE is a dummy that equals one if a
cross-listed firm’s management group represents the largest blockholder of the firm by voting rights and its voting
rights exceed cash flow rights. CONTROL_NOWEDGE is a dummy that equals one if a cross-listed firm’s
management group represents the largest blockholder of the firm by voting rights and its voting rights do not exceed
cash flow rights. SUBSTITUTION is the sum of sales in an industry (4-digit SIC) divided by the sum of operating
costs in the same industry. MKTSIZE is the sum of sales in an industry (in millions of U.S. dollars). ENTRYCOST
is the average gross PPE (in millions of U.S. dollars) in an industry weighted by each firm’s sales in the same
industry. SUBSTITUTION, MKTSIZE and ENTRYCOST are measured using a cross-listed firm’s home country
industry data if the firm’s home-country sales as a percentage of total sales are 50% or higher, and using the U.S.
industry data otherwise. HIGH_TECH is a dummy that is equal to 1 for firms in a high-tech industry (SIC codes:
2833-36, 3612-13, 3621-29, 3651-52, 3661-69, 3671-2, 3674, 3695, 4812-22, 4832-99, 7370-79). STD_REV is the
standard deviation of net sales scaled by the mean sales over the prior 3 years. INTANGIBLE is the percentage of
total assets that are intangible. For the cross-listed firms listed on a U.S. stock exchange prior to the REG FD
effective date, all the variables are measured in the year immediately prior to the REG FD effective date. For the
cross-listed firms listed on a U.S. stock exchange after the REG FD effective date, all the variables are measured in
the year immediately prior to or in the year following the firm’s U.S. stock listing date. Certain continuous variables
are winsorized at either the 99% percentile or both the 1% and 99% percentiles (see section III for the details).
47
Table 2. The logistic regression results of the REG FD adoption decisiona
(1)
(2)
Dependent variable = ADOPT
Predicted
sign
INSTITUTIONOWN
-
FOREIGNSALE
+
SYNCHRONICITY
+
INVEST_OPP
+
EXTERNAL_FIN
+
LN(ASSETS)
-
LN(ANALYST)
-
CONTROL_WEDGE
-
CONTROL_NOWEDGE
?
SUBSTITUTION
-
LN(MKTSIZE)
+
LN(ENTRYCOST)
-
HIGH_TECH
-
STD_REV
-
INTANGIBLE
-
Constant
?
Regression coefficient
(standard error)
-1.293**
(0.562)
2.544***
(0.916)
N/A
2.841**
(1.300)
1.454***
(0.607)
0.140
(0.122)
0.251
(0.273)
-2.293***
(0.679)
-0.770
(0.527)
0.153
(1.313)
0.014
(0.153)
-0.139
(0.123)
0.893
(0.519)
-4.157**
(2.472)
-1.569
(1.453)
-1.359
(1.753)
178
0.28
67.51
<0.001
Observations
Pseudo R2
Likelihood ratio χ2
Model p-value
48
-0.925**
(0.546)
N/A
0.371**
(0.172)
2.841**
(1.332)
1.085**
(0.557)
0.015
(0.113)
0.217
(0.279)
-1.984***
(0.674)
-0.583
(0.525)
-1.153
(1.467)
0.283**
(0.124)
-0.240**
(0.123)
0.389
(0.483)
-4.531**
(2.519)
0.144
(1.348)
0.896
(1.792)
170
0.26
58.10
<0.001
a
ADOPT is one for REG FD adopters and zero for non-adopters. LN(ASSETS) is the natural logarithm of ASSETS.
LN(ANALYST) is the natural logarithm of one plus ANALYST. LN(MKTSIZE) is the natural logarithm of
MKTSIZE. LN(ENTRYCOST) is the natural logarithm of ENTRYCOST. For cross-listed firms listed on a U.S.
stock exchange prior to the REG FD effective date, all the variables are measured in the year immediately prior to
the REG FD effective date. For cross-listed firms listed on a U.S. stock exchange after the REG FD effective date,
all the variables are measured in the year immediately prior to or in the year following the firm’s U.S. stock listing
date. See Table 1 for other variable definitions. All significance tests are one-tailed if there is a prediction and twotailed otherwise. * significant at 10 percent; ** significant at 5 percent; *** significant at 1 percent.
49
Table 3. The effect of REG FD adoption on bid-ask spread, share turnover, and share price
volatility in the post REG FD perioda
(1)
(2)
(3)
The dependent variable =
LN(TURNOVER)
LN(VOLATILITY)
Regression coefficient
(standard error)
LN(SPREAD)
ADOPT
LN(MARKETCAP)
LN(TURNOVER)
LN(VOLATILITY)
FREEFLOAT
-0.937***
(0.258)
-0.141***
(0.029)
-0.155***
(0.046)
-0.083
(0.085)
-0.045
(0.203)
1.375***
(0.421)
0.110**
(0.048)
-3.783***
(0.469)
0.151
(0.179)
167
-4.691***
(0.699)
-0.316
(0.300)
167
0.556***
(0.135)
-0.490
(0.338)
BETA
Constant
Inverse Mills ratio
Observations
a
0.173
(0.239)
-0.036
(0.027)
-0.006
(0.192)
0.221**
(0.091)
-3.812***
(0.319)
-0.204
(0.170)
167
ADOPT is one for REG FD adopters and zero otherwise. LN(SPREAD) is the natural logarithm of the average
relative bid-ask spread from the daily CRSP (i.e., the absolute spread divided by the average of bid and ask) in a 6month window. LN(TURNOVER) is the natural logarithm of the median daily turnover ratio (i.e., the number of
shares traded divided by the total shares outstanding from the daily CRSP) in a 6-month window.
LN(VOLATILITY) is the natural logarithm of the standard deviation of daily stock returns in a 6-month window.
The three dependent variables are winsorized at the 99% percentile and measured over the 6 months after the REG
FD effective month if a firm is listed on a U.S. stock exchange prior to the end of the REG FD effective month and
over the 6 months after the firm’s initial month of cross listing if a firm is listed on a U.S. stock exchange after the
REG FD effective month. LN(MARKETCAP) is defined as the natural logarithm of a firm’s total market
capitalization (i.e., the sum of the market values of all issues for firms with multiple share issues). For the firms
cross listed prior to the REG FD effective date, LN(MARKETCAP) is measured at the year end prior to the 6-month
measurement window of the dependent variable. For the firms cross listed after the REG FD effective date,
LN(MARKETCAP) is measured at the year end before the firm’s cross listing date. FREEFLOAT indicates the
presence of insiders and is defined as the percentage of firm shares that are not closely held. FREEFLOAT is
measured at the year-end prior to the 6-month measurement window of the dependent variable. BETA is the market
beta directly from the Worldscope database. All significance tests are two-tailed. * significant at 10 percent; **
significant at 5 percent; *** significant at 1 percent.
50
Table 4. Changes in the bid-ask spread, share turnover, and share price volatility around the REG
FD effective date for adopters and non-adoptersa
(Mean (median) [STD])
Non-adopters
(N=63)
Adopters
(N=36)
Two-tailed ranksum
test of the difference
in difference
ΔLN(SPREAD)
0.088
(-0.037)
[0.827]
Signed
rank test
P=0.627
-0.386
(-0.601)
[1.192]
Signed
rank test
P=0.079
P=0.009
ΔLN(TURNOVER)
-0.205
(-0.086)
[1.443]
Signed
rank test
P=0.004
0.439
(0.519)
[1.016]
Signed
rank test
P=0.002
P=0.007
ΔLN(VOLATILITY)
-0.097
(0.003)
[0.740)
Signed
rank test
P=0.912
-0.376
(-0.165)
[0.819]
Signed
rank test
P=0.018
P=0.071
∆LN(SPREAD) is the change in natural logarithm of SPREAD from 6 months before to 6 months after the REG
FD effective month (i.e., October 2000). ∆LN(TURNOVER) and ∆LN(VOLATILITY) are defined similarly. See
Tables 2 and 3 for other variable definitions.
a
51
Table 5. Changes in the bid-ask spread, share turnover, and share price volatility in the 12-month
period before and after the REG FD effective date for adopters and non-adoptersa
(Mean (median) [STD])
Panel A. The 12-month period before the REG FD effective date
Non-adopters
(N=59)
ΔLN(SPREAD)
ΔLN(TURNOVER)
ΔLN(VOLATILITY)
Adopters
(N=34)
Two-tailed ranksum
test of the difference
in difference
0.013
(-0.029)
[0.349]
Signed
rank test
P=0.809
-0.101
(-0.086)
[0.291]
Signed
rank test
P=0.050
P=0.142
-0.094
(-0.090)
[0.534]
Signed
rank test
P=0.120
-0.117
(-0.236)
[0.878]
Signed
rank test
P=0.301
P=0.649
-0.122
(-0.145)
[0.288)
Signed
rank test
P=0.007
-0.015
(-0.114)
[0.427]
Signed
rank test
P=0.278
P=0.343
Panel B. The 12-month period after the REG FD effective date
Non-adopters
(N=63)
ΔLN(SPREAD)
ΔLN(TURNOVER)
ΔLN(VOLATILITY)
Adopters
(N=35)
Two-tailed ranksum
test of the difference
in difference
0.014
(-0.010)
[0.293]
Signed
rank test
P=0.036
0.125
(0.383)
[1.003]
Signed
rank test
P=0.302
P=0.093
-0.078
(0.004)
[1.174]
Signed
rank test
P=0.874
0.041
(-0.068)
[0.744]
Signed
rank test
P=0.492
P=0.868
0.054
(-0.050)
[0.702]
Signed
rank test
P=0.579
0.096
(0.056)
[0.326]
Signed
rank test
P=0.109
P=0.120
∆LN(SPREAD), ∆LN(TURNOVER), and ∆LN(VOLATILITY) are defined in the same way as in Table 4 except
that the changes are defined over the 12-month time period that ends on the day before the REG FD effective date in
panel A and over the 12-month time period that starts from the day after the REG FD effective date in Panel B.
a
52
Table 6. Descriptive statistics on the percentage of survey responding firms that always hold
open conference calls (OPEN) in the pre- or post-REG FD perioda
Panel A. Firms that were cross listed in the pre-REG FD period
Adopters
Non-adopters
(1)
(2)
N=44
N=77
Pre-REG FD period
(3)
Post-REG FD period
(4)
Two-tailed p value
from a t-test of the
difference in (3) and (4)
Two-tailed p value
from a t-test of the
difference in (1) and
(2)
25%
17%
0.285
75%
29%
<0.001
<0.001
0.060
<0.001
The two-tailed p value from a t-test of the difference in the percentage of open conference call
firms over the pre- and post-REG FD periods for adopters and non-adopters = <0.001
Panel B. all cross-listed firms in the post-REG FD period
Adopters
Non-adopters
(1)
(2)
N=70
N=108
Post-REG FD period
73%
30%
a
Two-tailed p value
from a t-test of the
difference in (1) and
(2)
<0.001
OPEN is coded 1 if a firm always holds open conference calls in the pre- or post-REG FD period, and zero
otherwise.
53
Table 7. Replications of Tables 2, 3, 4, and 6 for confirmed and unconfirmed adopters separatelya
Panel A. Replication of the regression in column (1) of Table 2
(1)
(2)
Confirmed
Unconfirmed
adopters vs.
adopters vs.
non-adopters
non-adopters
Dependent variable = ADOPT
Regression coefficient
(standard error)
Predicted sign
INSTITUTIONOWN
-
FOREIGNSALE
+
INVEST_OPP
+
EXTERNAL_FIN
+
LN(ASSETS)
-
LN(ANALYST)
-
CONTROL_WEDGE
-
CONTROL_NOWEDGE
?
SUBSTITUTION
-
LN(MKTSIZE)
+
LN(ENTRYCOST)
-
HIGH_TECH
-
STD_REV
-
INTANGIBLE
-
Constant
?
-1.332**
(0.734)
2.797**
(1.288)
2.688**
(1.357)
1.477**
(0.707)
0.187
(0.168)
0.466
(0.357)
-1.729**
(0.809)
-0.067
(0.647)
-0.600
(1.902)
0.109
(0.220)
-0.274**
(0.165)
1.045
(0.687)
-3.501*
(2.733)
-2.449
(1.978)
-2.346
(2.432)
145
0.34
55.70
<0.001
Observations
Pseudo R2
Likelihood ratio χ2
Model p-value
54
-1.189**
(0.719)
2.238**
(1.142)
3.390**
(2.058)
1.400**
(0.742)
0.068
(0.155)
-0.031
(0.370)
-3.283***
(1.273)
-1.662**
(0.773)
0.538
(1.583)
-0.095
(0.185)
0.013
(0.152)
0.743
(0.666)
-5.503*
(3.674)
-1.118
(1.825)
-1.096
(2.085)
141
0.25
38.86
<0.001
Panel B. Replication of the regressions in Table 3: showing only the coefficients and standard
errors (in parentheses) on ADOPT
The dependent variable
=
LN(SPREAD)
LN(TURNOVER)
LN(VOLATILITY)
(1)
Confirmed adopters vs.
non-adopters
(N=137)
(2)
Unconfirmed adopters vs. nonadopters
(N=133)
-0.969***
(0.304)
1.531***
(0.451)
0.143
(0.283)
-0.916***
(0.346)
0.808
(0.613)
0.190
(0.331)
55
Panel C. Replication of Table 4
Confirmed adopters vs. non-adopters
Non-adopters
(N=63)
Confirmed adopters
(N=18)
Two-tailed
ranksum test of the
difference in
difference
ΔLN(SPREAD)
0.088
(-0.037)
[0.827]
Signed
rank test
P=0.627
-0.336
(-0.432)
[1.335]
Signed
rank test
P=0.198
P=0.078
ΔLN(TURNOVER)
-0.205
(-0.086)
[1.443]
Signed
rank test
P=0.004
0.296
(0.455)
[0.907]
Signed
rank test
P=0.199
P=0.093
ΔLN(VOLATILITY)
-0.097
(0.003)
[0.740)
Signed
rank test
P=0.912
-0.457
(-0.241)
[0.890]
Signed
rank test
P=0.064
P=0.104
Unconfirmed adopters vs. non-adopters
Non-adopters
(N=63)
Unconfirmed adopters
(N=18)
Two-tailed
ranksum test of the
difference in
difference
ΔLN(SPREAD)
0.088
(-0.037)
[0.827]
Signed
rank test
P=0.627
-0.436
(-0.745)
[1.066]
Signed
rank test
P=0.157
P=0.021
ΔLN(TURNOVER)
-0.205
(-0.086)
[1.443]
Signed
rank test
P=0.004
0.582
(0.583)
[1.121]
Signed
rank test
P=0.008
P=0.011
ΔLN(VOLATILITY)
-0.097
(0.003)
[0.740)
Signed
rank test
P=0.912
-0.296
(-0.099
[0.757
Signed
rank test
P=0.157
P=0.233
56
Panel D. Replication of Panel A of Table 6
Confirmed adopters vs. non-adopters
Adopters
(1)
N=25
Pre-REG FD period
(3)
Post-REG FD period
(4)
Two-tailed p value
from a t-test of the
difference in (3) and
(4)
Non-adopters
(2)
N=77
Two-tailed p value from
a t-test of the difference
in (1) and (2)
28%
17%
0.228
76%
29%
<0.001
<0.001
0.060
The two-tailed p value from a t-test of the difference in the percentage of open conference
call firms over the pre- and post-REG FD periods for adopters and non-adopters = 0.004
Unconfirmed adopters vs. non-adopters
Adopters
(1)
N=19
Pre-REG FD period
(3)
Post-REG FD period
(4)
Two-tailed p value
from a t-test of the
difference in (3) and
(4)
Non-adopters
(2)
N=77
Two-tailed p value from
a t-test of the difference
in (1) and (2)
21%
17%
0.674
74%
29%
<0.001
0.002
0.060
The two-tailed p value from a t-test of the difference in the percentage of open conference
call firms over the pre- and post-REG FD periods for adopters and non-adopters = 0.005
a
Confirmed adopters (unconfirmed adopters) refer to REG FD adopters whose survey responses are confirmed
(not confirmed) by publicly available information sources.
57