Impact of Internet financial reporting on emerging markets.
Hunter, Shirley A. ; Smith, L. Murphy
INTRODUCTION
The use of information technology for competitive advantage is well
known and often applied by business firms. Using stock data for firms
listed on the emerging market stock exchanges in Brazil, India,
Indonesia, Russia, and South Africa, this study provides empirical
evidence as to the positive dispersions in price and volume regarding
the economic event of investments in the Internet. We show that in spite
of operating in highly volatile capital markets, some emerging market
firms attempt to distinguish themselves in the 1990s by investing in
Internet technology. Our study contributes to prior disclosure
literature by providing evidence regarding the integrity and speed of
adjustment (efficiency) of emerging markets to the new (value relevant)
qualitative information that is released electronically by public firms.
Internet financial reporting refers to the use of a company's
website to distribute information about the financial performance of the
corporations. Use of Internet financial reporting is effectually a
method of marketing a company to shareholders and investors (Poon et al.
2003). According to Wagenhofer (2003), Internet financial reporting has
at least two major economic effects. First, the Internet alters
information processing costs and with it the demand and supply of
financial information in capital markets. Second, Internet financial
reporting creates a demand for standardization; this led to development
of XBRL (Wagenhofer 2003).
Brown and Warner (1980) state that event studies provide a direct
test of market efficiency; their assumptions are that the event will be
either reflected in traded asset prices or in trading volume, if the
corporate news announcement is deemed value-relevant to their investors.
The authors that a major concern in event studies is that they tend to
assess the extent to which security prices perform around the time of
the economic event as abnormal. They also state that nonzero abnormal
security returns that persist after a particular type of event are
inconsistent with the efficient market hypothesis that security prices
adjust quickly in order to fully reflect new information (Brown and
Warner 1980).
In this study, we analyze two economic events to assess the impact
of information technology in Brazil, India, Indonesia, Russia, and South
Africa. The first event regards the effect of the Internet at the
macro-economic level. The purpose of the first event analysis is to
measure the total market response to the introduction of a new
communications medium that resulted from reforms made to the
telecommunications sector. The second event regards use of the Internet
at the micro-economic level. The purpose of the second event analysis is
to measure the market performance of those firms that have invested in
the new technology. The firms with websites send a strong signal to the
government of their endorsement of the privatization initiative.
This study uses valuation methodologies from prior literature to
measure security price performances relative to the two event dates,
i.e. the Mean Returns, the Market, and the Market Adjusted Returns
models (Megginson 1997; Brown and Warner 1980). Each methodology was
applied to publicly listed firms in Brazil, India, Indonesia, Russia,
and South Africa that commercialized the Internet and announced the
existence of their websites during the sampling period of 1991 to 2001.
These five securities markets are, arguably, the most important and
volatile emerging markets in the world and are often clustered into the
same market indices by virtue of their systematic risks (Posner 1998;
International Finance Corporation Annuals 1992-1999; Standard &
Poor's Emerging Market Factbook (S&P EMF) 2000).
This study proceeds as follows. The next section briefly describes
the research environment. Following this section, the research questions
are formulated based on the conceptual model of the Internet as a
reporting medium. Next, the theoretical development of the hypotheses is
presented, followed by a section on the research methodology. Results
are then described. The final section presents the conclusions and the
limitations of the study, and suggests opportunities for future
research.
RESEARCH ENVIRONMENT
The key objective of this event study is to determine whether any
value enhancing benefits accrue for those emerging market firms that
invest in Internet technology. Bhattacharya et al. (2000) state that
event studies are used to measure the impact of an economic phenomenon
on firm value. The economic event that motivates this study is the
liberalization of the telecommunications sector in Brazil, India,
Indonesia, Russia, and South Africa. In the 1990s, Brazil, India,
Indonesia, Russia, and South Africa issued equity of $1 billion or more
and BARRA rated these markets as highly volatile (Posner 1998). The
research question posits whether investors attach any incremental value
to consistent and accurate electronic disclosure by website firms in
their attempt to alleviate some of the uncertainty attributed to
investments in highly speculative markets. If the website is perceived
as value enhancing by global investors, then the expectation is a
positive response in abnormal returns.
Financial reporting issues, such as information integrity,
associated with traditional paper reporting are equally relevant when
companies use their website for reporting. Companies around the globe
are making increased use of Internet financial reporting (Khlifi 2007,
Pervan 2006, Oyelere et al. 2003, CTM 2003). Research has examined
determinants of Internet financial reporting and how management might
implement controls to ensure Internet financial reporting integrity
(DiNapoli 2007, Khlifi 2007, Debreceny et al. 2002, PKF 2002).
Ismail et al. (2007) identify potential problems associated with
Internet financial reporting. These problems are particularly
troublesome if reporting objectives are not well designed, if the data
is improperly formatted, if the system is flawed, and if the users are
unable to use the data. They conclude that companies should carefully
plan before implementing Internet financial reporting (Ismail et al.
2007). Bonson and Escobar (2006) examine how the European Union (EU) has
developed a series of norms with the objective of increasing the
transparency of both companies and the financial markets via
distribution of company information on the Internet. Their study focuses
on how incorporation of countries of Eastern Europe into the EU affects
the transparency of the markets.
Once financial information is available on the Internet, it can be
used in myriad ways, from traditional analyses to state-of-the-art. For
example, Bovee et al. (2005) describe the development and applications
of Financial Reporting and Auditing Agent with Net Knowledge (FRAANK).
FRAANK is used to assimilate accounting numbers with other financial
information publicly available on the Internet and calculates key
financial ratios and other financial-analysis indicators. Coupland
(2006) offers an analysis of Internet-based financial and corporate
social responsibility (CSR) reports, and raises questions regarding
prominence given CSR issues due to physical positioning or language
used.
EMERGING MARKETS
The term "emerging market" implies a stock market that is
in transition, increasing in size, activity, or level of sophistication
(International Finance Corporation Annuals (IFC) 1992-1999; Standard
& Poor's Emerging Market Factbook (S&P EMF) 1999-2000). A
stock market is classified as emerging if it is located in a low or
middle-income economy, as defined by the World Bank, and its
"investable" market capitalization is low relative to its most
recent Gross National Product per capita (S&P EMF 2000). In the
Standard & Poor's Emerging Market Database, the first test of a
stock's "investability" is determining whether the market
is open to foreign institutions. Standard & Poor's determine
the extent to which foreigners can buy and sell shares on local
exchanges and repatriate capital, capital gains, and dividend income
without undue constraint. Standard & Poor's also examines
company statutes that could for impose limits on foreign ownership that
may be more restrictive than national law.
Table 1 shows that the sample countries have created an enabling
environment for investments in the Internet. Column 3 in Table 1 shows
that South Africa was ranked 17th in terms of total market
capitalization in 1997, demonstrating a drastic decline in total market
capitalization from its 1st place ranking in 1990. Brazil experienced a
similar decline in market rank in total market capitalization to 18th in
1997 from its prior 3rd place rank in 1990. The volatility in market
capitalization was attributed to some extent to such macroeconomic
factors as inflation and currency devaluation that were impeding the
operating performance of some of the listed firms. For India, Indonesia,
and Russia, Column 4 reports Gross National Product per capita as
systematically below that of Brazil and South Africa during most of the
1990s.
The conclusions reached in this study concerning Brazil, India,
Indonesia, Russia, and South Africa could be generalized to other
emerging markets in Africa, Asia, Eastern Europe, and Latin America with
similar characteristics such as low liquidity and restrictions on
foreign investors.
THE INTERNET IN EMERGING MARKETS
This event study looks for a positive market response to the
introduction of the new electronic stimulus. The assumption is that any
firm disseminating its information electronically to its investors has
already altered its assets structure by making substantial investments
in information technology (IT). The Internet represents a tangible
benefit from those IT investments, with electronic reporting made
available as the main communication process.
The Internet is not the sole catalyst that will propel these five
countries in this study into the global market. In this study, it is the
communications medium that is leading to a reduction in information
asymmetry in securities markets, which are characterized by low
liquidity. Each of the markets in this study is already in some stage of
Internet development, but none have achieved the level of Internet
penetration experienced in the United States.
If the emerging stock markets are truly efficient as defined by
Fama (1970), then firms that voluntarily develop websites send a costly
signal to investors that future reporting will be timelier than in the
past and, if that signal is deemed credible, the market should respond.
The prediction is that both local and global stock markets will reward
those emerging market stock companies that engage in electronic
reporting over their non-website competitors, because website firms are
attempting to reduce information asymmetry between investors and
themselves with the expectation of monetary rewards.
THEORETICAL DEVELOPMENT OF THE HYPOTHESES
Prior disclosure literature shows that the quality of supplemental
voluntary printed disclosures is associated with a lower cost of capital
for manufacturing firms in the United States (Botosan 1997). Table 2
reports the results from prior studies, on the impact of the Internet as
a reporting medium various countries which suggest that investors prefer
timely and accurate financial information that is electronically
disseminated, over printed material for decision making (Ashbaugh et al.
1999; Deller et al. 1999; Financial Accounting Standards Board [FASB]
2000; Marston and Leow 1998; and Westarp et al. 1998).
A study by Lymer et al. (1999) of 660 companies from 22 countries
shows that 62% of the firms had websites that disclosed financial
information. Driven by the market demand for more business information,
Lymer et al.'s (1999) descriptive statistics show that more than
70% of the listed firms in Canada, Germany, Sweden, the U.K. and the
U.S. use the Web and 52% of the firms in Chile have websites. Lymer et
al.'s (1999) study provides evidence that publicly traded firms are
using the Internet to disclose relevant information about their
operations to foreign investors and creditors. However, prior literature
has not examined the impact of electronic reporting on firm value. We
predict that emerging market firms with websites will continue to incur
the incremental costs of voluntary electronic disclosure as long as they
are signaling value-relevant information to investors and creditors.
The current study posits that the market response to a company
announcing that it is establishing a website could be empirically
measured ex-post by examining daily abnormal returns using a short-event
window. This study suggests that if the magnitude of the residuals from
abnormal returns surrounding the event window exceeds that which occurs
during the non-event estimation period, then the market has assigned
some incremental value to that information. This incremental value
serves as the incentive that motivates managers to establish a website.
Our prediction is that the incremental value of this signal should be
more apparent in emerging market firms with ownership structures that
exhibit greater information asymmetry than comparable firms in developed
markets.
The efficient market hypothesis (EMH) is one of the theoretical
axioms supporting prior disclosure literature that forms the basis for
this study (Fama et al.1969; Fama 1970). The EMH posits that stock
markets react to new (value relevant) information that is disclosed by
public companies through variations in either the stock price, or in
trading volume. Prior literature has analyzed abnormal performance
indices for publicly traded firms in developed countries, providing
evidence of a spike in abnormal returns during the event window of
announced earnings (Ball and Brown 1968). The EMH leads to the premise
for Hypothesis One, which examines the overall market response to the
release of new information by the government that it has allowed the
telecommunications sector to permit commercialization of the Internet:
H1: The market performance of securities listed on emerging market
stock exchanges is higher in the post-event period following
commercialization of the Internet.
This study also questions if any value-enhancing benefits accrue
for the emerging market stock companies that voluntary engage in
electronic disclosure. For example, does the Internet matter for those
firms with concentrated ownership structures that are also listed on
emerging market exchanges? In addition, do market incentives exist which
motivate manages of these firms to take action to reduce information
asymmetry between their firms and external stakeholders? If so, is the
Internet the reporting medium being used to communicate the
manager's response (acceptance) of the market incentive?
Alternatively, are these managers merely mimicking the behavior of
listed firms on stock exchanges in developed nations? Prior literature
shows that the benefits of voluntary management forecast disclosure
increase when private information exists (Baginski et al. 2002).
Our prediction is a positive reaction to corporate news of a
website, a non-financial market event, because global investors and
creditors value manager's efforts to reduce information asymmetry
in the market. This suggests that we are specifically testing for a
non-zero or positive abnormal returns during the event period, implying
that the market participants value the information. Brown and Warner
(1980) state that positive abnormal returns signal a good news response
to the economic event as opposed to negative abnormal performance or bad
news. If the economic event results in an unconditional abnormal
performance equal to zero, then the null hypothesis of no abnormal
sample security returns is realized (Brown and Warner 1980). For the
study, the prediction of a good news response to the micro-economic
event leads to Hypothesis Two:
H2: The market responds positively to emerging market firms that
announce the launching of a website.
RESEARCH METHODOLOGY
Research methodology includes two sections. The first reports on
the analysis of the country level event, using monthly mean differences.
The second reports on the analysis of the firm level event, using daily
abnormal returns.
MONTHLY MEAN DIFFERENCES TEST--COUNTRY LEVEL EVENT
To test the first hypothesis, the Wilcoxon Signed Rank test, a
non-parametric test rank order statistical test measuring the volatility
of means returns, was calculated and is presented in a format following
a methodology proposed by Bhattacharya et al. (2000) and Corrado (1989).
A two-tailed test at the 5% percent level was used to rank the order of
the absolute value of equal and value weighted returns for each firm (i)
(Bhattacharya et al. 2000 and Corrado 1989). Table 3 presents the
monthly event periods (-36, + 36 months, -24, + 24 months, and -12, +12
months) for phase 1 of the study. The mean differences in returns are
examined relative to the null hypothesis for each firm (i) in specific
country (c).
To reject the null hypothesis, the differences in mean returns
during the pre-and post-event windows should not equal zero. The
one-tailed test at the 2.5% percent level examines Hypothesis One,
indicating that the magnitude of the parameter should be significantly
higher in the post-event periods. For this study, the Wilcoxon
Signed-Rank test is used because it is more analogous to the parametric
Correlated or Related sample t-test; it takes into account the magnitude
as well as the direction of the difference for each pair.
The z test statistics are as follows, for each firm (i) in country
(c) for event period (t):
z = T - [[mu].sub.T]/[[sigma].sub.T] Formula (1)
where [[mu].sub.T] = [n.sub.1]([n.sub.1] + [n.sub.2] + 1)/2
and the standard deviation ([[sigma].sub.T]) is calculated below
for the entire event period
[[sigma].sub.T.sup.2] = [n.sub.1][n.sub.2]([n.sub.1] + [n.sub.2] +
1)/12
The rejection regions for specified levels of [alpha] are:
Reject [H.sub.0] if z [not equal to] [z.sub.[??]/2], for [alpha] =
0.050
Reject [H.sub.1] if z < [z.sub.[??]], for [alpha]/2= 0.025
DAILY ABNORMAL RETURNS--FIRM LEVEL EVENT
In the second phase of the study, two event windows (-10, +10 days
and -15, + 15 days) are used to examine the impact of the Internet on
daily returns for those firms listed on the emerging market stock
returns in Brazil, India, Indonesia, Russia, and South Africa. A 260-day
(-60 to +200) estimation period leading up to these event windows is
defined around the corporate news announcements of the Internet launch
dates for each firm. The use of a benchmark pre-Internet window allows
us to establish a baseline of normal market behavior for these firms
following a methodology proposed in other event studies (Brown and
Warner 1985). It also allows for the use of these sample firms as their
own control group, since it has proven difficult for us to obtain data
on a matched group of emerging market non-web firms. The post-Internet
period is defined as the event window after the corporate news
announcement of the launch date for the website.
We propose Abnormal Returns ([[member of].sub.ict]) as the
performance measure of the market valuation models, as opposed to
Botosan's (1997) cost of capital, as the variable of interest
because stock returns ([R.sub.ict]) for firm (i) in country (c) at time
(t) serve as an independent measure of the market's response to the
event. Prior literature shows that when an event occurs whose impact may
be highly significant to the welfare of the firms, there should be an
economic reaction by the market (Fama 1970). The performance measures
show that any deviations from the expected return are interpreted as
Abnormal Returns ([[member of].sub.ict]). The expectation is that
Abnormal Returns should equal zero (E([[member of].sub.ict]) = 0) and
provide evidence of an efficient market (Brown and Warner 1980).
The Market Model ([R.sub.ict] = [a.sub.ict] +
[B.sub.ict][R.sub.mct] + [[member of].sub.ict]) and the Market Adjusted
Returns Model ([R.sub.ict] = [R.sub.fct] + [[R.sub.mct] -
[R.sub.fct]][B.sub.ict] + [[member of].sub.ict]) are used to calculate
the Cumulative Abnormal Average Residuals effect ([CAR.sub.ict] =
[CAR.sub.ict-1] + [AAR.sub.ict]) on the stock returns for those emerging
market firms in the pre-Internet and post-Internet periods following the
methodology proposed by Brown and Warner (1980).
Brown and Warner (1980) suggest that one could also use the
Cumulative Abnormal Average Residual method to investigate abnormal
performance when there is incomplete information about the event date.
This method is appropriate for this study since the event date is
defined as a random variable. The method calculates the Cumulative
Abnormal Average Residuals in the pre-event window ([CAR.sub.ict-1])
plus the current value of the Average Residuals ([AAR.sub.ict]) in order
to determine if they are systematically different from zero.
The decision by firm management to establish a website signal that
the firm is trying to distinguish itself from non-website firms is
examined in Hypothesis Two. Under the signaling hypothesis, this event
should have informational value and affect security prices (Beaver
1998). To the extent that the firm's investment in information
technology results in the electronic disclosure of financial statements,
the economic consequences of the investment action should positively
alter stock prices, as reflected in the residual error terms of the
post-Internet period. Formulae for this analysis are shown below:
[R.sub.ict] = [a.sub.ict] + [B.sub.ict][R.sub.mct] + [[member
of].sub.ict] Formula (2)
[R.sub.ict] = [R.sub.fct] + [[R.sub.mct] - [R.sub.fct]][B.sub.ict]
+ [[member of].sub.ict] Formula (3)
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] Formula (4)
E ([[member of].sub.ict]) = 0 Formula (5)
[CAR.sub.ict] = [CAR.sub.ict-1] + [AAR.sub.ict] Formula (6)
E ([CAR.sub.ict]) = 0 Formula (7)
The rejection regions for specified levels of [alpha] are:
Reject [H.sub.0] if z [not equal to] [z.sub.[??]/2], for
BOL97\f"Symbol"\s12 = 0.050
Reject [H.sub.2] if z < [z.sub.[??]], for [alpha]/2= 0.025
SAMPLE DATA
The country level data on the telecommunications industry,
Internet, and stock market statistics for Brazil, India, Indonesia,
Russia, and South Africa were obtained from the International
Telecommunications Union Annuals (ITU 1994-2002) and the Emerging Stock
Markets Factbook (IFC 1992-1999). The first phase of the study examines
the effects of commercialization of the Internet in emerging markets.
The sampling period for this analysis was from 1991 to 1997. This phase
of the study provides empirical evidence that an enabling information
technology infrastructure exists in Brazil, India, Indonesia, Russia,
and South Africa, which is necessary to support investments in web
technology at the firm level. The event date (MMYY) is the Internet
access day for each country (ITU Annuals 1994-2002). Monthly returns are
used to measure the market performance in the pre-Internet and
post-Internet periods. The event estimation windows for this analysis
are (-36, +36 months), (-24, + 24 months), and (-12, +12 months).
Table 4 reports some descriptive statistics on the stock exchanges
in Brazil, India, Indonesia, Russia, and South Africa using mean market
data for the pre-Internet and post-Internet periods. The data represents
the total number of firms listed in each country during the pre- and
post- event periods. The results are reported in U.S. dollars reflecting
ESMF foreign currency, devaluation, and inflationary adjustments.
Over the entire pre-and post-event periods, the mean level of
market liquidity appears to have been more stable in Brazil, at an
average turnover ratio of 4.5x, relative to India, Indonesia, Russia,
and South Africa. Although a late comer to the investment indices, the
mean market capitalization has been consistently higher in South Africa
in comparison to the other countries, from a low of $103.5 million in
the pre-Internet period (t-24) for the 683 listings, to more than
doubling in the post-Internet period (t+24) to $249.2 million for a mean
number of 642 listed stocks. The stock exchanges in India report the
largest increase in new, small firm listings based on a mean market
capitalization of $138.2 million for 5,898 firms in the post-Internet
period (t+36), from an initial level of $66.2 million for 2,590 listings
in the pre-Internet period (t-36). Although Russia's stock exchange
is rather embryonic, the turnover ratio at 1.1x in the post-Internet
period (t+36) outperformed South Africa's market.
Firm level data were obtained from the Emerging Stock Markets
Factbook (IFC 1992-1999), the Global Researcher Worldscope (Global
2006), and Standard and Poor's Emerging Markets (Standard and
Pooor's 2000) databases. The second phase of the event study
measures the impact of the web on the market performance of listed
firms. Eventus Software was used for the second phase of this study. The
sampling period was from 1998 to 2001. The event date (DDMMYY) was
obtained using an electronic survey and the Global database. This date
represents the date that knowledge of the firm 's website was
either announced by the firm or disclosed by a third party intermediary
(Global). The event windows for testing Hypothesis Two are (-10, +10
days) and (-15, +15 days).
RESULTS
Results include two parts. The first concerns commercialization of
the Internet in emerging markets. The second involves impact of
corporate websites on emerging markets.
COMMERCIALIZATION OF THE INTERNET IN EMERGING MARKETS
Panel A of Table 5 reports the results from using equally weighted
stock returns to test mean differences in the event periods. There are
11,992 firm-month observations in the portfolio. The z-statistics are
significantly positive for India, Indonesia, and South Africa in the
event window (t+12) - (t-12), leading to a rejection of the null
hypothesis of equal means. Thus, Hypothesis 1 is accepted that the
market performance of securities listed on emerging market stock
exchanges is higher in the post-event period following commercialization
of the Internet, with regard to India, Indonesia, and South Africa. For
example, regarding India, the z-statistics are significantly positive in
the pre-Internet period (t-12) - (t-24) which persists for 2 years
through to the post-Internet period (t+24) - (t+12), most likely due to
the significant increase in the number of firms trading in India during
these periods. An analysis of value weighted mean returns yielded the
same results as the analysis of equal weighted mean returns.
The mean differences in trading volume are reported in Panel B of
Table 5. The volume differences are significantly positive at the 0.05
level on the event date (t+12) - (t-12) for Brazil, India, and South
Africa, leading to a rejection of the null hypothesis. Thus, the
analysis of mean trading volume corroborates the analysis of mean
returns.
THE IMPACT OF CORPORATE WEBSITES ON EMERGING MARKETS
Table 6 reports the results from using the Market Model to
calculate Daily Abnormal Returns in the event period. The same results
were obtained, but not shown in the table, from the Market Adjusted
Returns Model. The t-statistics are significantly positive at the 0.05
level, rejecting the null hypothesis that the residual error term is
equal to zero. Thus, Hypothesis 2 is accepted that the market responds
positively to emerging market firms that announce the launching of a
website. As expected, positive abnormal returns are realized at the
0.025 significance level (for a = 0.05/2), signaling a good news market
response to the corporate news announcement of the launch of a website.
In Table 7, the Market Model method is used to report the
t-statistics on the sample data that has been disaggregated by country.
The t-statistics are significantly positive for Indonesia and South
Africa in each of the event windows (-10, +10 days) and (-15, +15 days),
a strong indication of a good news market response to the economic
event. This suggests that for listed firms in Indonesia and South
Africa, a significant market response resulted from the Web
announcement. The t-value for India in the event window (-15, +15 days)
is positive and significant supporting a lag in the market response to
the news event.
Hypothesis 2 that predicts positive returns in the post-event
window is rejected for Brazil and Russia in both event periods due to
their significantly negative results. The magnitude of the t-values show
a general trend of declining levels of significant negative abnormal
returns being realized in each subsequent event window for Brazil and
Russia. However, since the cut-off date for this study is set at 31
days, further analysis beyond that period could conflict with other
confounding market events.
In Brazil and Russia, the residual error terms were significantly
negative throughout the event periods, thus causing a rejection of the
second null hypothesis. One of the factors contributing to this negative
reaction may be that many of the websites in Brazil and Russia only
display information in Portuguese and Russian, respectively. The
language obstacles create some difficulties when trying to manipulate
these websites.
In a low liquidity market characterized by high information
asymmetry, prior literature predicts a lag in the market response to new
information (Bhattacharya et al. 2000). This was true for India, which
registered significantly positive abnormal returns in the event window
(-15, +15 days). Bhattacharya et al. (2000) also state that an economy
may be information inefficient, and that prices may be left with no
announcement stimuli against which to respond. The corporate news
announcement of an emerging market firm launching a website, a
technology induced stimulus, could possibly fit this analogy. In
countries where individuals are economically marginalized, use of the
Internet as a communications medium may be value-relevant to a subgroup
of investors who are attempting to diversify their portfolios.
SUMMARY AND CONCLUSIONS
This study contributes to prior literature on the Internet by
providing empirical evidence of the longitudinal effects of the Internet
technology on emerging markets. It demonstrates that in markets that
suffer from low liquidity, firms that invest in Internet technology are
able to use the electronic medium to attract foreign investors,
analysts, and creditors who might not have otherwise consider the
emerging market securities within their portfolios. As predicted for the
first hypothesis, the market performance of securities listed on
emerging market stock exchanges is higher in the post-event period
following commercialization of the Internet, with regard to India,
Indonesia, and South Africa.
Some disparities were observed in the hypothesized effects accruing
from a firm launching a website in second phase of the study. As
predicted by the second hypothesis, managers of website firms will incur
the costs of electronically disclosing private information to potential
investors as long as their wealth is increased. The incremental price
effect is the pecuniary payoff sought by these managers. The value of
the website firms in India, Indonesia, and South Africa appears to be
incrementally enhanced due to their investments in Web technology. The
magnitude of the price effects was more significant for website firms in
Indonesia and South Africa than for those in India. These price
dispersions could be driven by such country specific factors as the
existence of an extractive iron ore-mining sector in South Africa that
attracts more foreign investors' interest than the agricultural
sectors in India.
In many ways, this study could serve as a benchmark for future
studies on Internet financial reporting that might replicate these
phenomena in other emerging markets. The findings from this study could
assist analysts seeking new markets for investments in order to balance
some of their portfolio's risk. It is also of interest to
policymakers because the Internet and website firms show support at the
micro-level for a national policy on privatization.
Some caveats to these markets should be considered when reaching
these conclusions. They are characterized by low liquidity and business
practices suggest that the protection of foreign investors may be
minimal. This study shows that there is a lag in the speed of adjustment
of these emerging markets to new information. This lag in response
cannot be interpreted that these markets are less efficient than
developed markets because these markets are bombarded by different
macroeconomic factors than those that exist in for example, either the
United States or United Kingdom. If these markets are truly inefficient
in the theoretical context of the Efficient Market Hypothesis, then
well-informed investors could institute a profitable trading strategy.
However, this may not be practicable due to limited foreign investments
in emerging markets' firms.
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TABLE 1
Investment Restrictions and Investors Information by County
Chronological Time Series 1990-2000
MARKET BARRIERS TO ENTRY AND EXIT
TIME MARKET GNP PER
PERIOD COUNTRY RANKING CAPITA $
1990-1991 Brazil 3 2,810
India 10 310
Indonesia 11 680
Russia n.a. 2,820
South Africa 1 2,830
1992-1994 Brazil 15 3,020
India 22 290
Indonesia 27 730
Russia 35 2,350
South Africa 11 2,900
1995-1996 Brazil 17 3,640
India 20 340
Indonesia 25 980
Russia 33 2,240
South Africa 16 3,160
1997-1998 Brazil 18 4,790
India 21 370
Indonesia 38 1,110
Russia 36 2,680
South Africa 17 3,210
1999-3000 Brazil 19 4,630
India 23 440
Indonesia 33 640
Russia 29 2,260
South Africa 18 3,310
MARKET BARRIERS TO ENTRY AND EXIT
RESTRICTIONS
TIME ON FOREIGHT REPATRIATION REPATRIATION
PERIOD INVESTORS OF INCOME OF CAPITAL
1990-1991 None None None
Authorized Investors Same Some
Some Some Some
Closed Closed Closed
None None None
1992-1994 None None None
Authorized Investors None None
Some Same Some
Closed Closed Closed
None None None
1995-1996 None None None
Authorized Investors None None
Same Same Some
None None None
None None None
1997-1998 None None None
Authorized Investors None None
Some Some Some
None None None
None None None
1999-3000 None None None
Authorized Investors None None
Same Same Same
None None None
None None None
WITHHOLDING TAX
TIME LONG TERM
PERIOD INTEREST DIVIDENTS CAPITAL GAINS
1990-1991 15% 15% 15%
10% 10% 10%
20% 20% 20%
32% 32% n.a.
0% 15% 0%
1992-1994 15% 15% 0%
20% 20% 20%
15% 15% 15%
32% 32% 32%
0% 15% 0%
1995-1996 15% 0% 0%
20% 20% 10%
20% 20% 1%
15% 15% 20%
0% 0% 0%
1997-1998 15% 0% 0%
20% 20% 10%
20% 20% 0%
0% 10% n.a.
0% 0% 0%
1999-3000 15% 0% 0%
20% 20% 10%
20% 20% 0%
0% 10% n.a.
0% 0% 0%
QUALITY OF INFORMATION
TIME INVESTOR(h) ACCOUNTING
PERIOD PROTECTION STANDARDS
1990-1991 Good Adequate
Good Good
Adequate Poor
Poor Poor
Good Good
1992-1994 Good/69.9 Adequate
Adequate/28.0 Adequate
Adequate/63.4 Poor
Poor/n.a. Poor
Adequate/n.a. Good
1995-1996 68.5 Adequate
16.8 Adequate
56.3 Poor
n.a. Poor
22.4 Good
1997-1998 69.7 Adequate
47.3 Adequate
63.5 Poor
n.a. Poor
47.4 Good
1999-3000 64.0 Adequate
43.6 Adequate
58.6 Poor
n.a. Poor
255 Good
Notes:
(a.) None = Foreign investors have free entry and exit privileges to
purchase stocks.
(b.) Some = Foreigners are required to register with the Central Bank
to ensure Expatriation rights.
(c.) Foreigners approved by the Central Bank may buys tocks.
(d.) Closed = Closed to foreign investors, e. Good = International
acceptable quality. F. Adequate = Local market quality.
(g.) Poor = Require reform h. Operational Risk Benchmark = IFC
quantified the data -100 points scale.
Table 2: Internet Financial Reporting Studies
STUDY PUBLICATION RESEARCH
DATE FOCUS
Financial Accounting 2000 Financial
Standards disclosure on
Board (FASB) the Internet
International Accounting 1999 Annual financial
Standards Board on the Internet
(IASB)
(Lymer, Debrecency,
Gray, and Rahman)
Ashbaugh, Johnstone, 1999 Financial
and Warfield information
contained on
website
Deller, Stubenrath, 1999 World wide web,
and Weber shareholder
structure, and
network effects
Marston and Leow 1998 Firm
characteristics
and electronic
disclosure
Westarp, Ordelheid, 1998 Internet
Stubenrath, Bukman disclosure and
and Konig GAAP
STUDY GEOGRAPHICAL
REGION
Financial Accounting United States
Standards
Board (FASB)
International Accounting Australia, Brazil,
Standards Board Canada,
(IASB) France, Japan,
(Lymer, Debrecency, Norway,
Gray, and Rahman) South Africa,
United Kingdom,
and United States,
Ashbaugh, Johnstone, United States
and Warfield
Deller, Stubenrath, United States,
and Weber United Kingdom,
and Germany
Marston and Leow United Kingdom
Westarp, Ordelheid, United States,
Stubenrath, Bukman United Kingdom,
and Konig and Germany
STUDY RESEARCH
FINDINGS
Financial Accounting Financial reporting
Standards on web by U.S.
Board (FASB) firms around 1997. Legal
hazards of excerpted
financial data, hyperlinks,
and transcriptions.
International Accounting 22 countries, U.S.
Standards Board and U.K. dominate
(IASB) 660 sample firms, 86%
(Lymer, Debrecency, of firms have websites,
Gray, and Rahman) and information content
of website.
Ashbaugh, Johnstone, Investors valued timely
and Warfield disseminated accurate
information by
website firms
Deller, Stubenrath, Financial disclosure
and Weber is more prevalent
in the United States
where the Internet
is pervasive and
private investors
dominate the
stock market
Marston and Leow Firm size and
financial services
sector had significant
impact on quality of
electronic disclosure
Westarp, Ordelheid, Selective electronic
Stubenrath, Bukman disclosure
and Konig
Table 3: Longitudinal Event Time Line (1991 - 2001)
ANALYSIS DATA LEVEL ECONOMIC EVENT
& PREDICTION
Phase 1 Country Commercialization
specific of the Internet
Hypothesis One
Positive differences
Phase 2 Firm Launch of Website
specific and Investor Relations
Hypothesis Two
Positive differences
ANALYSIS RETURNS ESTIMATION EVENT WINDOWS
INTERVAL
Phase 1 Monthly (-36, +36 months)
Returns (-24, +24 months)
(-12, +12 months)
Phase 2 Daily (-10, +10 days)
Abnormal (-15, +15 days)
Returns
ANALYSIS EVENT
PERIOD
Phase 1 1991-1997
Phase 2 1998-2001
Table 4: Market Performance Statistics of Listed Firms
Mean Monthly Market Data (U.S. $)
Sampling Period 1991 to 1997
PANEL A - Pre-Internet Period
t -36
COUNTRY Total Market Value Days
Firms Capital. Traded Traded
Brazil 573 31,753.2 768.5 20.0
India 2,590 66,169.3 2,129.0 15.8
Indonesia 139 7,955.1 260.4 20.7
Russia - - - -
South
Africa - - - -
PANEL B: Post-Internet Period
t +12
Brazil 542 151,194.0 8,396.7 20.5
India 4,671 135,616.0 1,760.9 19.2
Indonesia 209 44,058.1 943.1 20.6
Russia - - - -
South
Africa 637 189,828.3 1,261.7 20.8
PANEL A - Pre-Internet Period
t -36
COUNTRY S.D. Turn- S.D. Total Market
Firms Capitalization
Brazil 1.00 2.8 1.14 569 55,072.7
India 3.44 3.7 2.06 2,828 66,533.7
Indonesia 1.97 3.3 0.79 153 12,423.4
Russia - - - - -
South
Africa - - - 683 103,536.6
PANEL B: Post-Internet Period
Brazil 1.25 5.8 1.30 543 152,329.2
India 1.95 1.3 0.47 5,549 144,955.9
Indonesia 1.24 2.2 0.51 234 65,331.0
Russia - - - 100 15,632.5
South
Africa 1.96 0.7 0.16 642 249,180.9
PANEL A - Pre-Internet Period
t -24
COUNTRY Value Days S.D.. Turn S.D.
Traded
Brazil 1,891.5 20.6 0.92 3.7 0.56
India 1,313.3 18.3 2.22 2.0 0.58
Indonesia 394.9 20.4 1.16 3.2 1.34
Russia - - - - -
South
Africa 341.1 21.0 0.0
PANEL B: Post-Internet Period
t +24
Brazil 6,487.8 20.4 1.35 4.3 0.51
India 6,477.1 19.9 1.93 4.4 2.70
Indonesia 1,767.6 20.6 1.93 2.7 0.46
Russia 143.9 20.0 1.87 0.8 0.64
South
Africa 1,557.4 20.8 1.54 0.6 0.11
PANEL A - Pre-Internet Period
t -24
COUNTRY Total Market Value Days S.D.
Firms Capitalli Traded
Brazil 556 74,959.8 3,317.4 20.5 1.25
India 3,475 104,331.7 2,424.4 18.4 3.20
Indonesia 170 25,802.4 957.4 20.4 1.38
Russia - - - - -
South
Africa 664 134,885.6 822.6 21.2 1.34
PANEL B: Post-Internet Period
t +36
Brazil 548 191,720.8 9,757.8 20.8 1.29
India 5,898 138,162.8 11,163.8 19.8 1.75
Indonesia 252 87,847.7 3,314.5 20.6 1.78
Russia 97 53,867.7 560.7 20.8 1.30
South
Africa 633 256,168.5 2,250.0 20.9 1.38
PANEL A - Pre-Internet Period
t -12
COUNTRY Turn S.D.
Brazil 5.0 1.12
India 2.6 1.04
Indonesia 3.8 0.81
Russia - -
South
Africa 0.6 0.12
PANEL B: Post-Internet Period
Brazil 5.2 0.80
India 8.0 2.13
Indonesia 3.8 0.64
Russia 1.1 0.35
South
Africa 0.9 0.11
Notes:
(a.) Data source - Emerging Stock Markets Factbook and Standard & Poor
Emerging Markets Database.
(b.) Country Internet Access Dates - Brazil Jan 94, India Aug 94,
Indonesia May 94, Russia Jun 94, and South Africa Jan 94.
(c.) Russia - Information available Jun 96, t+24 represents 5 months
data, t+36 represents 12 months data.
(d.) South Africa - Information available Dec 92, t+24 represents 1
month data, thereafter 12 months data.
* + Market Capital. = Market Capitalization; Turn = turnover
ratio; **SD = standard Deviation; ***Days = days traded;
Table 5: Test of Mean Differences in Price and Volume
Mean Monthly Returns and Trading Volume (U.s. $)
Panel a - Equal Weighted Mean Returns
COUNTRY Internet Total PRE - INTERNET PERIOD
Access Firms
Date t -36 t -24 t -12
Brazil Jan-94 55 2.571 1.057 2.437
India Aug-94 45 1.044 0.990 1.051
Indonesia May-94 23 0.970 0.999 1.025
Russia Jun-94 25
South Africa Jan-94 59 1.061
Panel B - Mean Trading Volume
Brazil Jan-94 55 733.925 795.238 1,505.471
India Aug-94 45 1.280 1.009 1.634
Indonesia May-94 23 2.392 3.958 6.528
Russia Jun-94 25 - - -
South Africa Jan-94 59 - - 1.451
Panel a - Equal Weighted Mean Returns
COUNTRY POST - INTERNET PERIOD MEAN DIFFERENCES IN
EVENT PERIODS
z- statistics
t +12 t +24 t +36 (t-24) -
(t-36)
Brazil 2.732 2.319 1.365 2.307 **
India 0.964 0.983 0.993 3.753 **
Indonesia 0.975 1.001 0.998 2.565 **
Russia 1.072
South Africa 1.013 1.034 0.971
Panel B - Mean Trading Volume
Brazil 1,367.904 1,699.092 1,891.581 5.453 **
India 0.753 2.745 8.819 5.727 **
Indonesia 7.014 11.453 18.289 3.665 **
Russia - - 38.250 -
South Africa 2.432 2.236 3.395 -
Panel a - Equal Weighted Mean Returns
COUNTRY MEAN DIFFERENCES IN EVENT PERIODS
z- statistics
(t-12) - (t+12) - (t+24) - (t+36) -
(t-24) (t-12) (t+12) (t+24)
Brazil 0.644 0.366 6.357 ** 4.688 **
India 7.930 ** 11.367 ** 2.426 ** 0.116
Indonesia 1.208 3.688 ** 2.689 ** 0.258
Russia
South Africa 4.924 ** 1.808 12.140 **
Panel B - Mean Trading Volume
Brazil 8.552 ** 4.403 ** 0.623 2.463 **
India 7.330 ** 14.763 ** 6.882 ** 11.736 **
Indonesia 6.666 ** 0.777 6.118 ** 4.017 **
Russia - - - -
South Africa - 8.010 ** 3.922 ** 13.533 **
Note: z- statistics based on the Wilcoxon Signed Rank Test at
two-tailed significance levels 0.05 ** or less for the mean differences
in event periods.
Table 6: Market Model Daily Abnormal Returns Composite Firm Level Data
Panel A: n = 151
DATE AVERAGE MEDIAN
ABNORMAL ABNORMAL
RETURN RETURN t-statistics
Window -15
-14 -0.05 0.00 -0.09
-13 0.04 0.01 0.07
-12 -0.37 -0.10 -0.71
-11 0.48 0.03 0.91
Window -10 0.21 -0.20 0.40
-9 0.06 -0.15 0.11
-8 0.16 -0.01 0.31
-7 -0.01 0.01 -0.03
-6 -0.49 -0.50 -0.93
-5 -0.09 -0.26 -0.17
-4 0.46 0.14 0.88
-3 0.05 -0.04 0.10
-2 0.61 0.15 1.16
-1 0.68 0.15 1.28 *
Event Day 0 1.02 -0.17 1.94 **
+1 -0.03 -0.26 -0.06
+2 -0.64 -0.29 -1.22
+3 0.35 0.30 0.66
+4 -0.32 -0.18 -0.61
+5 0.21 -0.13 0.40
+6 0.69 0.27 1.32 *
+7 -0.29 -0.16 -0.55
+8 0.12 0.02 0.23
+9 0.27 -0.06 0.52
Window +10 -0.05 -0.07 -0.10
+11 0.66 0.15 1.25
+12 1.42 -0.04 2.70 ***
+13 0.91 0.06 1.73 **
+14 1.22 -0.10 2.31 **
Window +15 0.86 0.11 1.63 *
Panel A: n = 151
DATE
POSITIVE SIGN TEST
NEGATIVE z-statistics
Window -15
-14 76:75 0.65
-13 77:74 0.82
-12 71:80 -0.16
-11 77:74 0.82
Window -10 68:83 -0.65
-9 67:84 -0.81
-8 75:76 0.49
-7 79:72 1.14
-6 57:94 -2.44 ***
-5 62:89 -1.63 *
-4 78:73 0.98
-3 72:79 0.00
-2 81:70 1.47 *
-1 82:69 1.63 *
Event Day 0 69:82 -0.49
+1 65:86 -1.14
+2 68:83 -0.65
+3 86:65 2.28 **
+4 73:78 0.17
+5 74:77 0.33
+6 82:69 1.63 *
+7 70:81 -0.32
+8 80:71 1.31 *
+9 70:81 -0.32
Window +10 70:81 -0.32
+11 82:69 1.63 *
+12 73:78 0.17
+13 79:72 1.14
+14 72:79 0.00
Window +15 84:67 1.96 **
Panel B: n = 151
CUMULATIVE MEDIAN
EVENT WINDOWS AVERAGE CUMULATIVE t-statistics
ABNORMAL ABNORMAL
RETURNS RETURNS
(-10+10) 2.98 2.88 1.23
(-15+15) 8.58 4.00 2.92 ***
EVENT WINDOWS POSITIVE: SIGN TEST
NEGATIVE z-statistics
(-10+10) 89:62 2.77 ***
(-15+15) 87:64 2.45 ***
Notes:
(a.) Significance levels 0.01 ***, 0.05 **, and 0.10 *;
(b.) Abnormal Returns used value-weighted index;
(c.) Sign test based on one-tailed test.
Table 7: Market Model Daily Abnormal Returns
COUNTRY n EVENT WINDOW t-value
Brazil 44 (-10,10) -7.776
44 (-15,15) -1.790
India 42 (-10,10) -3.579
42 (-15,15) 13.967
Indonesia 22 (-10,10) 16.467
22 (-15,15) 69.060
Russia 8 (-10,10) -15.926
8 (-15,15) -2.404
South Africa 35 (-10,10) 17.951
35 (-15,15) 12.123
COUNTRY t statistics MEAN
DIFFERENCES
Brazil 0.000 *** -0.0013
0.074 * -0.0003
India 0.000 *** -0.0003
0.000 *** 0.0015
Indonesia 0.000 *** 0.0033
0.000 *** 0.2496
Russia 0.000 *** -0.0076
0.016 ** -0.0006
South Africa 0.000 *** 0.0015
0.000 *** 0.0013
Notes:
(a.) One - sample t-test of null hypothesis, test value = 0.
(b.) Significance levels 0.01 ***, 0.05 **, and 0.10 * based on
two-tailed test.
