Corporate Organizational Structure, Tax Havens, Analyst Forecast Properties
and Information Environment
Audrey Wen-Hsin Hsu
Professor
Department of Accounting
College of Management
National Taiwan University
Taipei City 106, Taiwan (R.O.C.)
Tel: +886-2-33661131
E-mail: [email protected]
Sophia Hsin-Tsai Liu
Assistant Professor
Department of Accounting
College of Management
National Taiwan University
Taipei City 106, Taiwan (R.O.C.)
Tel: +886-2-33661123
E-mail: [email protected]
Siva Nathan*
Associate Professor
School of Accountancy
Robinson College of Business
Georgia State University
Atlanta, GA 30345, USA
Tel: +1-404-413-7225
E-mail; [email protected]
September 2, 2016
*Corresponding Author
1
Corporate Organizational Structure, Tax Havens, Analyst Forecast Properties and Information
Environment
Abstract
This study investigates how corporate organizational complexity and shell investees established
in tax havens affect analyst forecast behavior and the information environment of the firm. Using a
unique sample of publicly traded Taiwan companies which are required to disclose information on all
of their affiliates, we capture the feature of organizational complexity by measuring the span of
investment layers as the number of layers connecting the parent firm and the lowest-tiered subsidiary.
We argue that firms with a long span of investment layers, associated with higher information
asymmetry and agency costs, are likely to reduce quality of financial reporting, complicate analyst
forecast tasks and lead to low level of revelation of firm specific information. Our results show that
more investment layers adversely affect analyst forecast accuracy, increase forecast dispersion, and
increase stock price synchronicity (high stock price synchronicity is an indicator low level of firm
specific information). Furthermore, we find that the negative (positive) association between the
number of layers and analyst forecast accuracy (dispersion) is stronger for firms with more investees
in tax havens. We find similar results for stock price synchronicity. These results are consistent with
the notion that firms maintain opacity to hide the extent of their tax avoidance activities because
transparency of aggressive tax avoidance activities may anger citizen groups and customers (e.g.,
Houlder, 2010), provoke scrutiny from foreign tax authorities (e.g., Bergin, 2012), and potentially
imposes reputational damage on the firm (Graham et al., 2014). We also find that the negative (positive)
association between the number of layers and analyst forecast accuracy (dispersion) is stronger for
firms with higher deviation between control rights and cash flow rights. We find similar results for
stock price synchronicity. These results are consistent with the argument that deviation between control
rights and cash flow rights increases information asymmetry and agency costs. Overall, we document
that organizational complexity and shell investees in tax havens matters in terms of analyst forecast
tasks and revelation of firm specific information thus indirectly affecting capital markets.
Keywords: layers, tax havens, agency costs; information asymmetry; analyst forecast, stock price
synchronicity
2
1. Introduction
As business enterprises have grown in size and complexity, it is common to find them owning
and/or controlling one or more subsidiary corporations. 1 The consolidated firms not only acquire
subsidiaries across borders and across industries, but also develop a “vertical structure” by pyramiding
more investment layers, in which controlling shareholders of parent firm controls a firm, which in turn
controls another firm, and so on. Though pyramidal structure is not common in the U.S. due to the
double-taxation system (Morck 2005), it is prevalent in emerging markets as well as in many developed
countries (e.g., La Porta et al. 1999; Claessens et al. 2000; Faccio and Lang 2002; Morck et al. 2005).
Bena and Ortiz-Molina (2013) supplement the theoretical work of Almeida and Wolfenzon (2006) by
empirically documenting that pyramids arise because they provide a financing advantage in setting up
new firms when external financing is limited and thus, the financing advantage of pyramidal structure
is an economically important underpinning of entrepreneurial activity. Prior studies such as Morck et
al. (2005), Gopalan et al. (2007), Riyanto and Toolsema (2008), etc suggest that a pyramidal structure
facilitates intra-group financial support as a way to overcome external capital constrains. However,
based on the prior studies which suggest that organizational complexity is associated with greater
information asymmetry (e.g., Duru and Reeb 2002; Bens and Monahan 2004; Bushman et al. 2004;
Demirkan et al. 2011; Gilson et al. 2011),2 we anticipate that a larger number of investment layers in
a corporate pyramid implies higher information asymmetry. Pyramidal structure is also criticized for
their entrenchment against minority shareholders and creditors, which leads to severe agency problems,
poor financial performance, and lower earnings credibility (Fan and Wong 2002).
Since financial analysts play a crucial role in uncovering and disseminating information into the
capital market, leading to market being more efficient (e.g., Andrade et al. 2013; Zhang 2008), we aim
to understand capital market more by investigating whether such organizational complexity is
1
For example, as of April 2015, Google has acquired more than 180 subsidiaries; as of October 2015, Apple has
acquired more than 70 subsidiaries while the undisclosed actual acquisitions could be larger.
2
Here the organizational complexity is measured as industry diversification and/or geographical diversification. 3
associated with analyst forecast behaviors. We also examine the effect of organizational complexity on
the revelation of firm specific information as measured by stock price synchronicity. We capture inner
organizational complexity of parent-subsidiary firms by measuring the number of investment layers
connecting the parent firm and the lowest-tiered subsidiary. We argue that a firm with many investment
layers makes it difficult for external market participants to access to firm information, monitor firm
operation, and evaluate performance. Consistent with this argument, prior studies find that more
number of investment layers within a consolidated entity is associated with larger information
asymmetry (Hsu and Liu, 2016), higher cost of debts (Chan and Hsu 2013), and lower investment
efficiency (Hsu et al. 2015). Consequently, we argue that information asymmetry between insiders and
outsiders increase with the number of investment layers, complicating analyst forecast tasks and
thereby adversely affecting analyst forecast accuracy. In addition, as public financial information is
not enough or of lower quality for multi-layered firms, analysts have to rely more on the private
information to make earnings forecasts. Unequal ability and constraints due to cost of seeking and
processing private information will lead to larger forecast dispersion among analysts (Hermann and
Thomas 2005). Therefore, we form the testable hypotheses that the number of investment layers is
inversely related to analyst forecast accuracy and increases forecast dispersion. Finally, we directly
test whether the number of investment layers leads to lower revelation of firm specific information
(measured by stock price synchronicity)
Furthermore, we investigate whether setting up investees in tax havens affect corporate
information environment, and thereby adversely affect analyst forecast property and the information
environment. Although investees in tax havens provide expected tax savings, it often also
simultaneously reduces financial transparency (e.g., Chen et al. 2011; Wang 2011). Bushman et al.
(2004) for example suggests that firms may employ complex transfer pricing mechanism to shift profits
to low tax jurisdictions such as tax havens, accelerating information asymmetry. In addition,
obfuscatory feature of tax avoidance through investees in tax havens could be exploited by
4
opportunistic managers and controlling shareholders to extract private interests at shareholders’
expenses (e.g., see Desai and Dharmapala 2006). To shield tax avoidance activities or expropriation
from being detected, obfuscatory feature of tax haven investees may complicate earnings forecast tasks.
Therefore, we also investigate given the number of investment layers, whether a firm operates more
investees in tax havens further affect analyst forecast properties and the information environment.
Moreover, if the parent firm does not hold 100% equity shares of the lower-tiered subsidiaries,
building more investment layers will enable the controlling shareholders of the parent firm to leverage
up their control rights more disproportionately to their cash flow rights over subsidiaries sitting on the
lower tiers of the corporate structure (Claessens et al. 2002; La Porta et al. 1999). Such separation of
the cash flow rights from the control rights facilitates expropriation of minority shareholders and
creditors (Johnson et al. 2000; Bebchuk et al. 2000; Bae et al. 2002; Bertrand et al. 2002; Morck et al.
2005). Thus, building a long span of layers not only creates organizational complexity and information
asymmetry but also induces a significant conflict of interest between controlling and non-controlling
shareholders and creditors. Such conflict of interest increases the probability that insiders will begin
extracting private benefits (Bebchuk et al. 2000; Johnson et al. 2000) and also adversely affects
financial reporting quality. Specifically, the controlling shareholders have incentives to manipulate
earnings to mask outright expropriation or selectively disclose financial information in order to shield
themselves from outsiders’ scrutiny. Consistent with this argument, Fan and Wong (2002) document
that the controlling owners of consolidated firms in East Asian countries are perceived to report lowerquality financial information, leading to lower credibility of financial reporting; Using the US firms as
the sample, Francis et al. (2005) find that earnings are less informativeness when cash flow rights are
separated from control rights. Therefore, we further investigate whether given the number of
investment layers, the deviation between control rights and cash flow rights further adversely affects
analyst forecast properties and the information environment.
5
We conduct our analyses using a sample of listed non-financial firms in Taiwan which have
analyst earnings forecast data on I/B/E/S for 2000-2013. Although firms in many countries are not
required to disclose the structure of corporate pyramids3, all listed companies in Taiwan are required
to disclose information on all of their affiliated enterprises according to “Criteria Governing
Preparation of Affiliation Reports, Consolidated Business Reports and Consolidated Financial
Statements of Affiliation Enterprises” (hereafter CGPAR). Such mandatory disclosures in Taiwan
provide a natural setting to investigate our research questions.
Consistent with our predictions, we find that analyst forecast accuracy decreases and forecast
dispersion increases as the number of investment layers increases. The association between the number
of investment layers and analyst forecast properties is stronger as firms operating more subsidiaries in
tax havens and as the deviation between control rights and cash flow rights increases. We find similar
results for revelation of firm specific information, i.e., more investment layers, more subsidiaries in
tax havens and more deviation between control rights and cash flow rights leads to lower revelation of
firm specific information (measured as higher stock price synchronicity). Collectively, these findings
are consistent with the argument that corporate features such as tax haven subsidiaries and deviation
between control rights and cash flow rights further increase information asymmetry and agency costs.
Since the complex pyramidal structure is common in Taiwan and in other East Asia countries and
unique in terms of information asymmetry and agency problems and enormous attention tax havens
have received in policy making and media, a complete understanding of analyst forecast properties
should also enrich literature on organizational structure and analyst forecasting performance. To the
best of our knowledge, our study is the first to systematically investigate the impact of organizational
3
For example, in the U.S., publicly traded firms are required to disclose their significant subsidiaries in Exhibit 21 of
10K. i.e., publicly traded U.S. firms are not required to disclose all of their subsidiaries in the financial statements.
Furthermore, from Exhibit 21 sections of each firm’s annual 10-K report, the whole structure of pyramidal ownership is
not available as firms are not required to disclose all the subsidiaries and the number of layers in which each subsidiary is
located.
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structures, captured by the number of investment layers and the effect of subsidiaries in tax havens on
analyst forecast properties. In addition, analysts as sophisticated participants and information
intermediaries are perceived by managers as one of the most important parties who affect the share
price (Graham et al. 2005). Understanding the effect of organizational structure and tax haven
subsidiaries on analyst forecast properties provides insight to the capital market. We also supplement
our analyst analysis with capital market tests. We also contribute to the parent-subsidiary literature by
demonstrating that the lower-level organizational structure is related to analyst earnings forecast
properties. The unique disclosure requirement in Taiwan grants such opportunities to do so.
The rest of the paper is organized as follows. Section 2 provides related literature review and
Section 3 develops the hypotheses. Section 4 outlines the research design. Section 5 describes the
sample selection and reports the results. Section 6 outlines the additional analyses and we conclude in
Section 7.
2. Review of Related Literature
2.1 Organizational Complexity and Information Opacity
Pyramidal ownership structure is defined as a business entity whose ownership structure displays
a top-down chain of control (La Porta et al. 1999). That is, the parent company at the apex of an
investment structure indirectly controls subsidiaries sitting on the lowest tier of investment structure
through intermediate subsidiaries. Bushman et al. (2014) captures two aspects of organizational
complexity, industry and geographic diversification which impose significant operational and
informational complexity. Specifically, multi-industry firms demand high managerial ability, reduce
CEO focus, and face the capital allocation inefficiency (e.g., Stein 1997), and conflicting operational
styles and cultures among the parent and subsidiaries within the consolidated firm. In additional to the
challenges that multi-industry firms face, multinational firms further face cultural and institutional
diversity across countries. Information complexities also arise when combining diverse operations
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creates information aggregation problems that can result in information asymmetries between
corporate insiders and outside investors and creditors (Krishnaswami and Subramaniam 1999; Gilson
et al., 2001). For example, firms may employ complex transfer pricing schemes to conduct income
shifting, which affect outsiders to understand firms’ operations and reported earnings (e.g., Harris 1993;
Jacob 1996; Rego 2003). Even with required segment reporting, outside investors observe aggregate
earnings but cannot fully understand operations within subsidiaries. Thus reported earnings of a
consolidated firm may convey less value-relevant information (Bushman et al., 2004). Consistent with
the argument that the complex organizational structure tend to limit transparency of firms’ operations
to outside investors, prior studies document that organizational complexity is associated with higher
information asymmetry (e.g., Duru and Reeb 2002; Khuranna et al. 2003; Bens and Monahan 2004;
Gilson et al. 2011), poorer earnings quality (Demirkan et al. 2011), and poor earnings timeliness
(Bushman et al., 2004). Based on the same spirit underlying the argument for organizational
complexity, Chan and Hsu (2013) find a positive association between the number of investment layers
and cost of debts since information asymmetry between corporate insiders and outside creditors and
agency cost of debts increases with organizational complexity. Likewise, Hsu et al. (2015) argue that
a longer span of corporate pyramid implies greater information opaqueness of the company. Consistent
with the notion that information asymmetry and agency problems between corporate insiders and
outside capital providers increase with the number of layers, and therefore reduce the creditors'
willingness to provide capital, Hsu et al. (2015) find that investment layers decrease investment
efficiency.
2.2 Tax avoidance, Tax Havens, and Information Opacity
A tax haven is a country or territory where corporate tax rates are so low that firms have incentives
to establish shell subsidiaries to shift their income to save tax payments. While there is no official
definition of a tax haven, referred to the definition by Organization for Economic Cooperation and
8
Development (OECD) in 1998, tax havens are a jurisdiction which has no or only nominal taxes and
lack of transparency and effective exchange of relevant information with other governments on
taxpayers. Tax avoidance saves payments to governmental authorities while also reduces financial
transparency (e.g., Chen et al. 2011; Wang 2011; Bushman et al. 2004). Specifically, Wagener and
Watrin (2014) document that firms make use of complex structures to facilitate tax avoidance. A
prominent example of structuring more layers in order to shift income to tax havens is Google whose
tax avoidance strategy is known as the “Double Irish and Dutch Sandwich” (Drucker 2010). Tax
avoidance activities via offshore tax havens or creating complex structures involving income shifting,
often reduces financial transparency that are designed to obscure the underlying intent and to avoid
detection by tax authorities (Desai and Dharmapala 2006; Kim et al. 2011; Hope et al. 2013; Akamah
et al. 2014). In addition, obfuscatory feature of tax avoidance through subsidiaries in tax havens could
be exploited by opportunistic managers and controlling shareholders. Bennedsen and Zeume (2015)
suggest that tax havens are used by insiders for activities that go beyond the pure tax saving motives
and such motives decrease shareholder value. Enron for example set offshore subsidiaries in tax havens
for entrenchment. If entrenched managers and controlling shareholders use subsidiaries in tax havens
for their own private benefits, it is important to conceal intention and behaviors via a complex structure
that deters non-controlling shareholders, creditors, auditors, and tax authorities from unveiling the
intra-subsidiary transactions (Bennedsen and Zeume, 2015). Both obfuscatory feature inherent in tax
avoidance and managers and controlling shareholders attempting to conceal their tax avoidance
activities beyond the pure tax saving motives has been shown to decrease firm transparency (Kim et
al. 2011; Chen et al. 2011; Wang 2011).
2.3 Analyst Forecast Properties
Prior studies have investigated various determinants of analysts’ forecast properties. Among
which, analysts’ uncertainty in predicting earnings (proxied by the analyst forecast error and forecast
dispersion) is positively associated with a firm’s essential production, investment, and financial risk
9
(Parkash et al. 1995), earnings volatility (Kross et al. 1990), earnings surprises (Lang and Lundholm
1996), and industry and geographic diversification (Dunn and Nathan 2009; Duru and Reeb 2002)
since consistent with the argument by Brown et al. (1987) that analyst forecast accuracy depends on
the difficulty and complexity of the forecasting tasks, business risks which imply volatile
circumstances, earnings volatility, earnings surprise, and corporate diversification complicate
forecasting tasks. Analysts also have difficulty in forecasting earnings for firms with sticky cost pattern
(Weiss 2010). In addition, analysts’ uncertainty in forecasting earnings tends to be asymmetric; that is
analysts tend to provide optimistic earnings forecast for loss firms and the average forecast error for
loss firms is around ten times the forecast error for profitable firms (Hwang et al. 1996). Several studies
also document the inverse relation between the level of reported earnings and forecast error and this
association reflects earnings surprise due to unanticipated events and earnings management (e.g.,
Brown 2001b; Eames and Glover 2003).
Another set of studies find that analysts’ uncertainty in forecasting earnings is negatively
associated with the amount and the quality of information available regarding the firm (e.g., Atiase
1985; Parkash et al. 1995). Brown et al. (1987) model analyst forecast accuracy in the context of a
firm’s information environment and similarly, Barron et al. (1998) model that both analysts’ forecast
error and dispersion decrease with the quality of public information. Lang and Lundholm (1996)
provide empirical evidence that analysts’ forecasts are more accurate, less dispersed, and less volatile
in revisions for firms which provide more and higher-quality disclosures, as rated by analysts (i.e. FAF
Report, 1985-89), for large firms since firm size is a common proxy for the amount of information
available to the capital market, and for firms followed by more analysts. Likewise, Barron et al. (1999)
document that high-quality MD&A disclosures are associated with smaller forecast error and
dispersion. Using international data, Hope (2003a, 2003b) also demonstrates that enforcement of
accounting standards which directly increases financial disclosure quality and the level of disclosure
about accounting policy choices is positively related to analyst forecast accuracy and negatively to
10
forecast dispersion. In contrast, analysts have difficulty in forecasting earnings for firms with opaque
disclosure measured by annual report readability (Lehavy et al. 2011) and for firms that disclose
internal control weakness over financial reporting (Kim et al. 2009; Xu and Tang 2014). Among
information sources, management voluntary disclosure directly helps analyst to process information
and forecast earnings (e.g., Waymire 1986; Kimbrough 2005; Chen et al. 2011) although analysts
sometimes sacrifice forecast accuracy to curry favor with management (e.g., Feng and McVay 2010).
Not only financial information matters for analyst forecast performance, using the issuance of
stand-alone corporate social responsibility (CSR) reports to proxy for disclosure of nonfinancial
information, Dhaliwal et al. (2012) indicate that the nonfinancial information (CSR report) plays a role
complementary to financial disclosures, further increase analyst forecast accuracy. Research also
suggests that corporate governance disclosure is related to analyst forecast accuracy and dispersion.
For example, using cross-country data, Bhat et al. (2006) document that governance transparency as a
means to improve the information environment is positively associated with analyst forecast accuracy
when financial transparency is low, especially in the countries where the legal enforcement is weak.
Yu (2010) also finds that the quantity of corporate governance disclosures is associated with better
analyst forecast accuracy and lower forecast dispersion. Overall, the studies suggest that analysts
update their beliefs about the integrity or credibility of financial disclosure after learning information
on corporate governance and such information facilitates analysts to use the financial information and
improve their forecasting performance.
In addition to corporate governance disclosure, better corporate governance practice per se also
improves forecast accuracy (Beekes and Brown 2006). For example, using eight East Asian countries
which introduced voluntary national governance codes after the Asian crisis as a natural experimental
setting, Nowland (2008) find that improvement of corporate governance practice after introduction of
the corporate governance codes is associated with lower analyst forecast error. Analyst forecasting
performance is also positively associated with board independence (Byard et al. 2006) and board
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gender diversity (Gul et al. 2013) and negatively associated with board size (Byard et al. 2006; Cheng
2008) and the presence of CEO duality (Byard et al. 2006).
Auditors also play a role facilitating information environment since auditor monitoring improve
the quality of accounting reporting in terms of reducing both intentional earnings management (Becker
et al. 1998) and unintentional measurement errors (Watkins et al. 2004). Consistent with the argument
that analysts’ forecasting ability depends on the quality of financial information they use to predict
future earnings, Behn et al. (2008) find that a high-quality audit (proxyed by Big N auditors and
industry specialist auditors) is associated with higher analyst forecast accuracy and lower forecast
dispersion.
Although analyst earnings forecasts attract wide interests, prior research has not yet investigated
the association between decomposed parent-subsidiary corporate organizational structure, tax haven
subsidiaries, and properties of analysts’ earnings forecasts. Our study aims to fill this gap.
3. Hypotheses Development
3.1 Effect of Investment Layers
We argue that information environment is less transparent and more agency problems exist as the
number of investment layers increases for the two reasons. First, making earnings forecasts requires
detailed knowledge of how parent and subsidiaries operate within the pyramidal structure. Based on
the findings that organizational complexity can limit corporate transparency and increase information
asymmetry (Bushman et al. 2004) and that the number of layers within a corporate pyramid makes the
firm more opaque and more difficult for market participants to evaluate (Chan and Hsu 2013; Hsu et
al. 2015), we expect forecasting task be more difficult as the number of investment layers increases.
Since the accuracy of an earnings forecast is the function of difficulty or complexity of the forecasting
task, we expect that multi-layered corporate structure reflects an unique dimension of forecasting
difficulty which is not captured by previously identified proxies. Second, in additional to discretionary
accruals management and real-activity earnings management, parent firms can engage in earnings
12
management by using transactions among affiliated companies (Okuda and Shiiba 2010), which can
complicate forecasting tasks. Moreover, Hope and Thomas (2008) find that managers are more likely
to engage in non-value-maximizing investments when information asymmetries increase. To mask the
adverse effect of suboptimal investment decisions related to firm performance, managers have the
incentive to engage in aggressive financial reporting (Leuz et al. 2003), which adversely affect the
quality of information available to analysts. In sum, as the number of layers increase, information
asymmetry and agency problems between insiders and outsiders also increase, reducing the quality of
mandatory and voluntary financial disclosure, complicating analyst forecast tasks, and thus adversely
affect analyst forecast accuracy.
In addition, analysts’ forecast dispersion can be viewed as a measure of ex ante earnings
uncertainty (Imhoff and Lobo 1992). If financial information provided by firms are perceived to be not
credible or of poor quality for pyramidal firms (Fan and Wong 2002), analysts need to collect and
process more private information. Since not all the analysts are able to and/or willing to collect and
process private information, processing and relying more on private information will lead to a larger
dispersion among analysts regarding forecasted earnings. Stated more clearly, as public financial
information is not enough or is perceived to be of lower quality as the number of investment layers
increases, analysts need to rely more on private information and thus less likely to achieve consensus
in earnings forecasts. Therefore, the first hypothesis of the study is framed as follows.
H1a: Ceteris paribus, analyst forecast accuracy is lower as the number of investment layers
increases.
H1b: Ceteris paribus, analyst forecast dispersion is larger as the number of investment layers
increases.
On the other hand, we are not able to rule out the possibility that insiders may be willing to
disclose more information for their own purposes, such as to enhance reputation, to lower the cost of
capital, to lower the EPS forecast consensus in order to meet or beat it, or to time some events like
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shares issuances, etc. Another possibility is that limited public disclosure may induce analysts seeking
more private information as doing so brings more rewards. Both possibilities could relate the number
of investment layers to higher analyst forecast accuracy and lower forecast dispersion.
3.2 Subsidiaries in Tax Havens
A tax haven where certain taxes are levied at a low rate, or even zero, attracts corporate entities
to establish shell subsidiaries to avoid taxes both by permitting firms to shift domestic income away
from high-tax jurisdictions to the tax haven and by reducing the burden of home country taxation of
foreign income. (e.g., see Hines and Rice 1994 and Grubert and Slemrod 1998). To avoid the direct
costs of engaging in tax avoidance activities, such as tax audit, penalties, and reputation loss upon
detection, managers typically have to shield these actions from tax authorizes and public scrutiny. For
example, in the inquiry on the collapse of Enron, the Senate Finance Committee asked Enron to
disclose its corporate income tax returns as Enron created 881 offshore subsidiaries, 692 of them in
the Cayman Islands, a well known tax haven and engaged in unusually complicated transactions to
preclude tax auditing (The New York Times 2002). The case of Enron’s complicated tax avoidance
tactics and reporting scandal supports the argument that tax avoidance strategies entail complexity and
obfuscation in order to prevent detection (Chen et al. 2011; Dhaliwal et al. 2011; Balakrishnan et al.
2012). Bennedsen and Zeume (2015)’s findings that the signing of Tax Information Exchange
Agreements (TIEAs) increases average shareholder value by 2.5 percent support the notion that
insiders use tax haven subsidiaries beyond tax saving motives; such positive valuation effect is stronger
for firms with more complex organizational structure within the tax haven. Their findings suggest that
complex structure coupled with lack of transparency of tax havens adversely affect firm transparency,
provide managers and controlling shareholders with opportunities to pursue private gains and thus
reduces shareholder value. Akamah et al. (2014) also find that firms with greater use of tax havens are
more likely to aggregate their geographic disclosure (i.e., low-quality disclosure). This evidence is
14
consistent with firms’ use of discretion in disclosure requirements to hide the extent of their tax
avoidance activities.
In addition, consistent with the argument that the adverse impact on firm information environment
may induce managers to pursue self-serving objectives and extract rents that can be concealed by tax
avoidance activities (Desai and Dharmapala 2009), Frank et al. (2009) find that aggressive corporate
tax avoidance is associated with extent earnings management; Dyreng et al. (2012) also find that
profitable firms with extensive tax haven subsidiaries manage earnings more than other firms. Along
the similar line, Kim et al. (2011) provide evidence that tax avoidance allows managers to conceal
negative news and thus, facilitates bad news hoarding activities for extended periods, leading to more
future stock price crash risk.
Due to essential feature inherent in tax havens, which leading to obfuscation and even earnings
management, we argue that firms with more subsidiaries in tax havens have more incentives and/or
opportunities to manage earnings or conceal financial information, increasing analyst forecast
difficulty. In which the subsidiaries in a tax haven country can adversely affect corporate transparency,
we form the third hypothesis in an alternative form as follows.
H2a: Ceteris paribus, the negative association between analyst forecast accuracy and number of
investment layers is stronger with the increase in the number of subsidiaries in tax havens.
H2b: Ceteris paribus, the positive association between analyst forecast dispersion and number of
investment layers is stronger with the increase in the number of subsidiaries in tax havens.
3.3 Information Environment and Deviation between Control Rights and Cash Flow Rights
For brevity we do not discuss in detail and develop formal hypotheses pertaining to the information
environment (revelation of firm specific information measured as stock price synchronicity) and the
effect of the deviation between control rights and cash flow rights on analyst forecast properties and
the information environment. There is brief discussion of the theory when the results for these effects
15
are reported in a later section of the paper.
4. Research Design
4.1 The Number of Investment Layers
To measure the number of investment layers, we focus only on subsidiaries as defined by R.O.C
accounting standards No. 7, Consolidated Financial Statements, because the parent firm can control
only these firms’ operating and financing decisions. We identify all intermediate layers connecting the
parent company and the lowest-tiered subsidiaries. For firms with multiple chains in the pyramidal
structure, we focus on the longest chain, the one with the largest number of intermediate layers between
the parent company and the lowest-tiered subsidiary.
Figure 1 for example shows the investment structure of ASUS Co., with the parent company
ASUS (TW) indirectly controls the lowest-tiered firm Tubesonic Technology Ltd. (China) at layer 6
through Pegatron Corp. (layer 1), HuaWei Investing Corp (layer 2), Kinsus Interconnect Technology
Corp. (layer3), Kinsus Holding (Samoa) (layer 4) and Kinsus Holding (Cayman) (layer 5). ASUS set
more than twelve tax haven subsidiaries (Few are not included in Figure 1).
[Insert Figure 1 here]
As firms build more layers, such pyramidal structure enables the controlling shareholders of the
parent firm to establish control more disproportionately to the amount of ownership he has farther
down in the corporate pyramid (Claessens et al. 2000). Thus, the more the number of layers is, the
more divergent between the controlling parent’s cash flow rights and the control rights in the lowerlayered subsidiaries.
Separation of cash flow rights and control rights somewhat insulates controlling shareholders
from negative consequences as a result of the misconduct. With such disproportionate consequence,
the parent is encouraged to venture into more risky investment, using firms located at the lower tiers
of the pyramid (Morck et al. 2005). In addition, if with control rights disproportionate to ownership,
more number of layers allows controlling shareholders to easily expropriate wealth from minority
16
shareholders and creditors (e.g., Bebchuk et al. 2000; Morck et al. 2005) and facilitate non-arm’slength transactions (Bebchuk et al. 2000; Morck et al. 2004; Johnson et al. 2000).
To increase the chance of executing rent-seeking behaviors, controlling shareholders would
minimize and/or delay information disclosure so that other shareholders cannot intervene such
behaviours, which increases information asymmetry between insiders and outsiders (e.g., Attig et al.
2006).
4.2 Analyst Forecast Accuracy and Dispersion
Following Lang and Lundholm (1996), forecast accuracy (ACCY) is defined as the negative value
of the absolute value of forecast error, scaled by the year end stock price of t-14:
ACCYit  (1)  (
FORECASTit  EPSit
PRICEit 1
)
(1)
where subscripts i and t denote firm i and year t, respectively. FORCAST is the mean I/B/E/S
consensus forecast for year t’s earnings during year t ending three days before actual earnings
announcement. EPS is the corresponding actual earnings per share of year t; both forecast and actual
earnings per share are obtained from the I/B/E/S database to ensure consistency. We scale the forecast
errors by the year end stock price per share of t-1 (PRICE) to facilitate comparisons across firms.
The dispersion of analysts’ forecasts (DISP) for firm i is defined as the standard deviation of
earnings forecasts issued by individual analysts for firm i within year t, scaled by the year end stock
price per share of t-1.
DISPit 
STD ( FORECASTit )
PRICEit 1
(2)
Using mean EPS of year t instead of stock price per share in the year end t-1 as a scalar does not qualitatively alter the
results.
4
17
4.3 Empirical Model for H1a and H1b, Investment Layer
Following prior studies on analyst forecasts (e.g., Lang and Lundholm 1996; Behn et al. 2008),
we estimate the following OLS model to test H1a:
ACCYit =  0 +  1 LAYERit +  2 SIZEit +  3 NANAit +  4 MBit +  5 ADRit
+ 6 STD_EPS it +  7 STD _ ROEit +  8 SALES it +  9 LOSS it + 10 ZMIJ it
+11 HORIZON it + 12 BIGN it + 13 EPS it +  14 SURPRISEit +  15 DUALITYit
(3)
+ 16 INSIDEBit +  16 INSIDEM it +  18 DUALITYit +  19 FOREIGN _ INSTit
+ 20 INDEit +  21 BOARDSIZ Eit +YEARdummy + INDUSTRYdummy +  it
where the dependent variable is ACCY, analyst forecast accuracy. Our variable of interest, LAYER
is the number of layers of the longest investment span in the firm’s pyramidal structure. We take the
natural log of LAYER to avoid the non-linearity issue.5
We incorporate several control variables capturing information environments which are likely to
affect forecast accuracy. Specifically, we control for size (SIZE, natural logarithm of total assets) as a
proxy for a firm’s overall information environment since prior studies document that the amount of
information acquired by analysts is positively related to firm size (Atiase 1985; Lang and Lundholm,
1996). We also control the number of analysts following for firm i (NANA, natural logarithm of the
number of analysts following the firm i through the year) since prior studies such as Lys and Soo (1995)
and Lang and Lundholm (1996) suggest that greater analyst following indicates more intense
competition among analysts and, hence, greater incentives for analysts to enhance forecast accuracy.
We also control for firms’ growth opportunity (market-to-book ratio, MB), which provides analysts
with greater incentives to acquire corporate information. ADR is an indicator which equals one if firm
cross-listed in the U.S. stock exchange via American Depositary Receipt (ADR) program as firms
cross-listed in the U.S. are more likely to be subject to more investor interest, capital market pressure,
For the robustness check, we also include WIDTH, the natural logarithm of number of subsidiaries at the widest
horizontal span in the firm’s pyramidal structure to control for another dimension of organizational complexity and the
coefficient on WIDTH is not significant.
5
18
and stringent reporting regulation of the US, which in turn to improve the information environment of
the firms (Lang et al. 2003).
We capture volatility of earnings characteristics by including the standard deviation of earnings
per share over the previous five years (STD_EPS), the standard deviation of returns on equity over the
previous five years (STD_ROE) 6 , and change in current sales (∆SALES) since analysts’ earnings
forecasts are less accurate for firms with higher long-term earnings, profit, or sales volatility (e.g.,
Kross et al. 1999; Dichev and Tang 2009). We also include loss indicator (LOSS), which equals one if
pre-tax income is smaller than zero and zero otherwise as Hwang et al. (1996) suggests that it is more
difficult to forecast earnings for loss firms and analysts’ forecasts for loss firms are on average less
accurate than forecasts for portable firms. In addition, Zmijewski’s (1984) financial distress score
(ZMIJ) is incorporated as it is more difficult to make earnings forecasts for financially distressed firms
(Behn et al. 2008). We control for forecast horizon (HORIZON), the natural logarithm of the average
number of calendar days between the forecast announcement date and the actual earnings
announcement date. HORIZON is expected to be positively associated with forecast errors since
forecast horizon is likely to affect the amount of information available to analysts and a forecast made
closer to the earnings announcement date is expected to be more accurate than a forecast made in the
earlier period (Brown 2001a). Appendix 1 presents the detailed variable definitions.
We further incorporate a variable indicating whether a firm hires a big N audit firm (BIGN) or not
as such audit attribute has been documented to be inversely associated with analyst forecast errors
(Behn et al. 2008). Because Eames and Glover (2003) report that earnings level is related to forecast
accuracy, we include the earnings per share variable (EPS). Absolute value of the earnings surprise
(SURPRISE) is controlled as Lang and Lundholm (1996) find that larger changes in earnings are
associated with less accurate forecasts.
We also use STD_ROA, the standard deviation of return on assets over the five-year window to capture earnings
performance volatility as the robustness check and the results are not altered.
6
19
The strength of corporate governance is controlled via a set of corporate governance and board
characteristics, since prior studies suggest that corporate governance and board structure affects analyst
forecast properties (e.g., Gul et al. 2013; Hope 2003; Nowland 2008). They are DUALITY, INSIDEB,
INSIDEM, FOREIGN_INST, INDE, and BOARDSIZE of which the definitions are as in Appendix1.
Year and industry effects are controlled by including indicators in the model. We winsorize all the
continuous variables at the 1 percent and 99 percent levels to remove the outliers. The t-statistics are
adjusted for heteroskedasticity and firm-level clustering. H1a predict 1 be negative.
We then estimate the following OLS model to test H1b:
DISPit = 0 + 1 LAYERit +  2 SIZEit +  3 NANAit +  4 MBit +  5 ADRit
+ 6 STD_EPSit +  7 STD _ ROEit + 8 SALESit +  9 LOSSit + 10 ZMIJ it
+11 HORIZON it + 12 BIGN it  13 SURPRISEit + 14 DUALITYit
(4)
+15 INSIDEBit + 16 INSIDEM it + 18 DUALITYit + 19 FOREIGN _ INSTit
+ 20 INDEit +  21 BOARDSIZEit +YEARdummy + INDUSTRYdummy +  it
where DISP has commonly been discussed as a measure for uncertainty about future earnings since
it represents the consensus among analysts regarding future firm prospects (see, for instance, Imhoff
and Lobo 1992; Barron and Stuerke 1998). The control variables are the same as those incorporated in
Eq. (3).7 Year and industry effects are controlled by including indicators in the model. We winsorize
all the continuous variables at the 1 percent and 99 percent levels to remove the outliers. The t-statistics
are adjusted for heteroskedasticity and firm-level clustering. H1b predicts 1 be positive.
4.4 Empirical Model for H2a and H2b, Tax Havens
To investigate whether the intensity that consolidated entity has more subsidiaries in tax havens
Since prior literature has not documented the association between of earnings per share (EPS) and forecast dispersion,
we exclude the variable of earnings per share (EPS) in Eq. (4) for the robustness check and the results are qualitatively
the same.
7
20
can affect the negative (positive) association between the number of layers and analyst forecast
accuracy (dispersion), we employ the following OLS regression:
ACCYit =  0 +  1 LAYERit +  2TAXH it +  3 LAYERit  TAXH it +  4 SIZEit
+ 5 NANAit +  6 MBit +  7 ADRit +  8 STD_EPSit +  9 STD _ ROEit
+10 SALES it + 11 LOSS it + 12 ZMIJ it + 13 HORIZON it + 14 BIGN it
+15 EPS it +  16 SURPRISEit +  17 DUALITYit +  18 INSIDEBit
(5)
+ 19 INSIDEM it +  20 DUALITYit +  21 FOREIGN _ INSTit +  22 INDEit
+ 23 BOARDSIZEit +YEARdummy + INDUSTRYdummy +  it
DISPit =  0 + 1 LAYERit +  2TAXH it +  3 LAYERit  TAXH it +  4 SIZEit
+ 5 NANAit +  6 MBit +  7 ADRit + 8 STD_EPSit +  9 STD _ ROEit
+10 SALESit + 11 LOSSit + 12 ZMIJ it + 13 HORIZON it + 14 BIGN it
+15 EPSit + 16 SURPRISEit + 17 DUALITYit + 18 INSIDEBit
(6)
+ 19 INSIDEM it +  20 DUALITYit +  21 FOREIGN _ INSTit +  22 INDEit
+ 23 BOARDSIZEit +YEARdummy + INDUSTRYdummy +  it
where TAXH is defined as the number of subsidiaries in tax havens divided by the total number
of subsidiaries. We identify tax havens based on the list in Durnev et al. (2013) and construct a variable
denoted as TAXH.8 Their list of offshore financial centers (tax havens) comes from the International
Monetary Fund (IMF) and Financial Stability Forum (2000) and is presented in Appendix 2. We then
interact LAYER with TAXH as our variable of interest. We winsorize all continuous variables at the 1
percent and 99 percent level. H2 predicts that the coefficient on the interaction term of LAYER and
TAXH in Eq. (5) is significantly negative and the coefficient on the interaction term of LAYER and
TAXH in Eq. (6) is expected to be significantly positive.
5. Sample, Summary Statistics, and Results
5.1 Sample Selection and Data Sources
8
We also identify tax havens based on the list in Dyreng and Lindsey’s (2009) as the robustness check and the results
are qualitatively the same.
21
We focus on publicly traded firms in Taiwan which have analyst forecast data on I/B/E/S between
2000 and 2013. We pull firms’ financial information, stock returns, and corporate governance
characteristics from Taiwan Economic Journal (TEJ) database, construct all the variables in need and
calculate the number of investment layers of pyramidal firms as well.
The number of publicly traded firm-year observations in period of 2000-2013, excluding financial
and insurance industries which have unique industry characteristics and capital structure is total 45,210.
We exclude firm-year observations without data on analyst forecast properties, leading to 3,179
observations. We then remove firms that do not issue “Quanxi Business Operation Report” and
observations with the missing value on the control variables, ending up with the final sample of 2,514
firm-year observations.
5.2 Descriptive Statistics
Table 1 summarizes the sample descriptive statistics for 2,514 firm-year observations on the
variables included in our empirical models. The mean (median) of the number of the layers is 3.443
(3.000), which indicates that the use of pyramidal layers is common for parent-subsidiary investments.
Consistent with prior studies, the mean (median) value of forecast accuracy (ACCY) is -3.6% (-1.5%),
indicating that the difference between analyst forecasted earnings and the corresponding actual
earnings is about 3.6 % of the lagged stock price per share. The mean (median) value of forecast
dispersion (DISP) is 2.5% (1.5%), indicating that the standard deviation on forecasts is about 2.5 % of
the lagged stock prices. Firms have 3.75 subsidiaries in tax havens based on the list of tax havens as
in Durnev et al. (2013)9 and TAXH, the ratio of the number of subsidiaries in tax havens to the total
number of subsidiaries is on average 0.312. On average, NANA, the natural logarithm of the mean
9
4.21 investees based on Dyreng and Lindsey’s (2009) list of tax haven countries.
22
(median) of the number of analyst following is 1.557 (1.386), equivalent to 6.031 (3.000) analysts
following over the year. 4.8% of firms in our sample issue American Depositary Receipt (ADR) and
have to follow the US listed regulations. The mean standard deviation of EPS (STD_EPS) is 1.74 and
the mean standard deviation of return on equity (STD_ROE) is 0.075. The 10.5 % firms in our sample
experience loss. The Zmijewski’s financial distress score (ZMIJ) has a mean (median) of -2.119 (2.047), indicating that our sample firms on average are not in the financial distress. On average, 86.3%
of the sample firm-year observations hire a Big N audit firm (BIGN). The mean (median) earnings per
share (EPS) each year is $3.38 ($2.81) and earnings surprise is 8% of the lagged stock price a year
(SURPRISE). Directors of the board in general hold 19.4% outstanding shares; a CEO on average holds
1.5% outstanding shares. Foreign institutions, the most important institutional investors in Taiwan on
average hold 15.8% of outstanding shares. A board on average consists of 7.596 directors and 12.7%
of them are independent (INDE).
[Insert Table 1 here]
Table 2 presents Pearson and Spearman correlations for our sample of firm-year observations.
Our measure of layers is significantly positive correlated to forecast accuracy, number of subsidiaries
in tax havens, firm size, number of analyst following, ADR issuance, distress risk; and negatively
associated with analyst forecast dispersion, market-book ratio, volatility in terms of earnings and return
on assets. This is the correlation without incorporating any control variables.
.
[Insert Table 2 here]
We perform multivariate analyses using two analyst forecast properties, forecast accuracy (ACCY)
and forecast dispersion (DISP) as the dependent variable respectively in the regression analysis of Eq.
(3) and Eq. (4). Column (1) of Table 3 presents the results of estimating Eq. (3) using analyst forecast
accuracy, ACCY as the dependent variable. We find that the coefficient on LAYER is negative (-0.018)
and significant at the 1% level, consistent with the prediction of H1a that firms which have more layers
tend to have less accurate analyst forecasts. Regarding the control variables, consistent with prior
23
literature such as Lang and Lundholm (1996), analyst forecasts are more accurate when firms are
followed by more analysts over the year (coefficient on NANA is 0.006, significant at the 10% level).
Moreover, consistent with findings in the prior studies, analysts tend to issue less accurate earnings
forecasts for loss firms (e.g., Hwang et al. 1996), for firms which have more bankruptcy risk (e.g.,
Brown 2001; Behn et al. 2008), and for firms which have larger earnings surprise (e.g., Lang and
Lundholm 1996) (i.e., coefficient on LOSS is -0.057, significant at the 1% level; coefficient on ZMIJ
is -0.008, significant at the 1% level; coefficient on SURPRISE is -0.291, significant at the 1% level).
The coefficient on HORIZON is -0.008, negative and significant at 10% level, consistent with the
argument that forecasts made earlier are in general less accurate. Consistent with Behn et al. (2008),
firms audited by the big N audit firms are associated with more accurate forecasts (coefficient on BIGN
is 0.012, significant at the10% level).
Governance variables are also controlled in Column (2) of Table 3 and the results are qualitatively
the same (coefficient on LAYER is -0.018, significant at the 1% level). The coefficient on INDE is
0.036, and the coefficient on BOARDSIZE is 0.019, both positive and significant at the 1% level,
suggesting that board independence and board size facilitate information transparency, thus positively
associated with analyst forecast accuracy.
In column (3) of Table 3, we report the results of estimating Eq. (4) using analyst forecast
dispersion DISP as the dependent variable. We find that the coefficient on LAYER is 0.004, positive
and significant at the 5% level, consistent with the prediction of H1b that firms which have more
investment layers tend to have high analyst forecast dispersion. Consistent with prior studies, firms
with higher standard deviation of return on equity (e.g., Lim 2001; Kross et al. 1990), loss firms
(Hwang et al. 1996), firms have high bankruptcy risks (Brown 2001b; Behn et al. 2008), and firms
have larger earnings surprise (Lang and Lundholm 1996), and firms have longer forecast horizon
(Clement et al. 2004) are associated with larger analyst forecast dispersion (coefficient on STD_ROE
is 0.059, significant at the 5% level; coefficient on LOSS is 0.013, significant at the 1% level;
24
coefficient on ZMIJ is 0.004, significant at the 1% level; coefficient on HORIZON is 0.009, significant
at the 1% level; coefficient on SURPRISE is 0.076, significant at the 1% level); In contrast, consistent
with Behn et al. (2008), firms audited by big N audit firms are associated with lower dispersion
(coefficient on BIGN is -0.012, significant at the 1% level). Colum (4) reports the results for the full
model, which are qualitatively the same as Column (3). The coefficient on LAYER is 0.004, also
positive and significant at the 5% level. In addition, firms whose CEOs have more ownership, firms
whose foreign institutional investors own more ownership, firms which have more independent board
and larger board size are associated with smaller analyst forecast dispersion.
Overall, these results support H1, suggesting that the complex structures, captured by the number
of investment layers, induce more information asymmetries, which complicates analyst forecast task
and thus reduces analyst forecast accuracy and increase forecast dispersion.
[Insert Table 3 here]
Table 4 presents the regression analysis of Eq. (5) and Eq. (6). Without incorporating the number
of layers (LAYER) as the variable of interest, Column (1) of Table 4 first demonstrates that the
coefficient on TAXH is -0.040, negative and significant at 5% level, indicating that a firm operates
more subsidiaries in tax havens adversely affect analyst forecast accuracy. Column (3) of Table 4
reports that the coefficient on TAXH is 0.36, positively significant at the 5% level, suggesting that a
firm operateing more tax haven subsidiaries increase analyst forecast dispersion. In sum, the results
are consistent with the argument that essential feature inherent in tax havens, which leading to
information opacity and even earnings management, increasing analyst forecast difficulty and
adversely affecting analyst forecasting performance.
Column (2) of Table 4 demonstrates that the coefficient on TAXH is negative and significant at
the 10% level. The coefficient on the interaction term between LAYER and TAXH is -0.038, negative
and significant at the 5% level, suggesting that firms which have more subsidiaries in the tax havens
along with longer span of investment layers are associated with less accurate analyst forecasts. Column
25
(4) of Table 4 documents that the coefficient on TAXH is 0.023, positive and significant at the 5 %
level. The coefficient on the interaction term between LAYER and TAXH is 0.016, positive and
significant at the 10% level, suggesting that firms which have more subsidiaries in the tax havens
countries along with longer span of investment layers are associated with larger analyst forecast
dispersion. Overall, the results support H2, that due to essential feature inherently associated with tax
havens, which leading to obfuscation and/or even earnings management, it is more difficult for analysts
to forecast earnings and achieve consensus for firms which have more subsidiaries located in a tax
haven countries.
[Insert Table 4 here]
6. Additional Analyses
6.1 The Deviation between Control Rights and Cash Flow Rights
Furthermore, if the parent firm does not hold 100% equity shares of the lower-tiered subsidiaries,
building more layers will enable the controlling shareholders of parent firm to leverage up their control
rights more disproportionately to their cash flow rights over subsidiaries sitting on the lower layers of
pyramids (Claessens et al. 2002; La Porta et al. 1999). Such separation of control rights from cash flow
rights facilitates expropriation of minority shareholders through transferring resources out of the
bottom-layered firm for the benefit of the controlling shareholders (Johnson et al. 2000; Bebchuk, et
al. 2000; Bae et al. 2002; Bertrand et al. 2002; Morck et al. 2005). Moreover, if operating decision of
the subsidiaries which is controlled by the parent fails as a result of the parent’s morally hazardous
behavior, only a small portion of the parent’s actual wealth is jeopardized. Thus, creating a long span
of investment layers not only creates organizational complexity but also induces a significant conflict
of interest between controlling and non-controlling shareholders. Such agency problems also affect
financial reporting. That is, the controlling owners who obtain effective control to intervene financial
reporting have incentives to manipulate earnings or selectively disclose financial information in order
to mask outright expropriation and shield themselves from the board’s and outsiders’ scrutiny.
26
Consistent with this argument, Fan and Wong (2002) document that the controlling owners of firms in
East Asian countries are perceived to report financial information for self-serving purpose, leading to
low credibility of financial reporting. Using a sample of 9 East Asian and 13 Western European
countries, Haw et al. (2004) document that firms with controlling shareholders tend to manage earnings
more aggressively and report less conservative earnings. Moreover, the deviation between control
rights and cash flow rights is negatively associated with earnings informativeness and earnings
conservatism and positively associated with earnings management (Fan and Wong 2002; Haw et al.
2004). Similarly, ownership concentrated firms in Taiwan provide less accurate and more optimistic
management mandatory earnings forecasts than do wildly diversified firms (Chin et al. 2006),
supporting the argument that controlling shareholders mask their private control benefits by making
less accurate and more optimistic earnings forecasts to shield from outside intervention. Therefore, we
further test (a) whether the negative association between analyst forecast accuracy and number of
investment layers is stronger as the deviation between control rights and cash flow rights increase and
(b) whether the positive association between analyst forecast dispersion and number of investment
layers is stronger as the deviation between control rights and cash flow rights increase.
We use the following Eq. (7) and (8) to test our predictions:
ACCYit =  0 +  1 LAYERit +  2 DEVit +  3 LAYERit  DEVit +  4 SIZEit
+ 5 NANAit +  6 MBit +  7 ADRit +  8 STD_EPS it +  9 STD _ ROEit
+10 SALES it + 11 LOSS it + 12 ZMIJ it + 13 HORIZON it + 14 BIGN it
+15 EPS it +  16 SURPRISEit +  17 DUALITYit +  18 INSIDEBit
+ 19 INSIDEM it +  20 DUALITYit +  21 FOREIGN _ INSTit +  22 INDEit
+ 23 BOARDSIZEit +YEARdummy + INDUSTRYdummy +  it
27
(7)
DISPit = 0 + 1 LAYERit +  2 DEVit +  3 LAYERit  DEVit +  4 SIZEit
+ 5 NANAit +  6 MBit +  7 ADRit + 8 STD_EPSit +  9 STD _ ROEit
+10 SALESit + 11 LOSSit + 12 ZMIJ it + 13 HORIZON it + 14 BIGN it
+15 EPSit + 16 SURPRISEit + 17 DUALITYit + 18 INSIDEBit
(8)
+19 INSIDEM it +  20 DUALITYit +  21 FOREIGN _ INSTit +  22 INDEit
+ 23 BOARDSIZEit +YEARdummy + INDUSTRYdummy +  it
where all the variables in Eq. (7) and (8) are the same as in Eq. (3) and (4) except we include
DEV, an indicator which equals one for the high deviation (between control rights and cash flow rights)
group and zero for the low deviation group, where the high deviation group is defined as the parent
firms’ owning less than 50% of any subsidiary within the longest chain of the investment structure. We
incorporate the interaction between LAYER and DEV in the model as an experimental variable and we
predict that the association between analyst forecast property and the number of investment layers
varies systematically with the ownership of subsidiaries by the controlling shareholders (through the
parent company).
Table 5 presents the regression analysis of Eq. (7) and Eq. (8). Column (1) of Table 5 demonstrates
that the coefficient on the interaction term between LAYER and DEV is -0.048, negative and significant
at the 5% level, suggesting that firms with longer span of investment layers and larger deviation
between control rights and cash flow rights are associated with less accurate analyst forecasts. Column
(2) of Table 5 demonstrates that the coefficient on the interaction term between LAYER and DEV is
0.027, positive and significant at the 10% level, suggesting that firms with longer span of investment
layers and larger deviation between control rights and cash flow rights are associated with larger
analyst forecast dispersion. Overall, the results reported in Table 5 support our argument for agency
costs that adversely affecting analyst forecasting performance.
[Insert Table 5 here]
28
6.2 Stock Price Synchronicity and Layers
6.2.1 Stock price synchronicity for investment layers with respect to high/low deviation
We also test the impact of investment layers on a firm’s information environment in terms of
stock price synchronicity. Roll (1988) suggests that the extent to which stock prices co-move depends
on the relative amounts of firm-specific, industry-level, and market-level information impounded into
stock prices (Piotroski and Roulstone, 2004). Prior literature suggests that the findings of larger Rsquared obtained from a market index regression (sometimes along with the industry index) indicates
more price co-movement and less firm-specific information incorporated into stock prices, leading to
higher level of stock price synchronicity (Roll 1988; Morck et al. 2000). Based on the same premises
and measures, studies document the negative association between stock price synchronicity and
earnings informativeness (Durnev et al., 2003), corporate transparency (Jin and Myers, 2006),
voluntary disclosure (Haggard et al., 2008), and press freedom (Kim et al., 2014). Likewise as we
expect that less firm-specific information incorporated into stock prices as more deviation between
control right and cash flow rights arisen with more investment layers, we anticipate that such high
deviation is associated with high level of stock price synchronicity.
If the parent firm does not hold 100% of the subsidiaries down below, as the number of investment
layers increases, the controlling shareholders of the parent firm will possess more control over the
bottom-layered subsidiaries than their equity ownership indicates. The separation of control and cash
flow rights begets questions of controlling shareholders’ incentives to take actions in the best interests
of other shareholders. Theories predicts that when controlling shareholders hold less equity in the
bottom-layered subsidiaries than the control they actually possess, incentives arise for controlling
shareholders to pursue rent-seeking behaviors so the controlling shareholders become entrenched
(Johnson et al. 2000; Bebchuk et al. 2000; Bae et al. 2002; Bertrand et al. 2002; Morck et al. 2005).
Outside investors are likely to recognize such agency problem. Therefore, contracts are written or
monitoring mechanisms are built, often based on accounting information (Warfield et al. 1995) to
29
mitigate controlling shareholders’ opportunistic behaviors. However, when controlling shareholders
effectively controls a firm, they usually also control the production of the firm’s accounting
information and disclosure policies. The controlling shareholders have substantial discretionary power
to disclose information out of self-interest rather than as a reflection of the firm’s true underlying
economic transactions (Fan and Wong 2002). To hide any opportunistic behaviors without being
detected by minority shareholders and creditors or to keep their controlling status secure, opacity is a
good strategy. Controlling shareholders can either reduce information disclosed to outsiders, publish
irrelevant information, or just delay disclosure. Therefore we argue that less firm-specific information
is incorporated into the market stock as more deviation between control right and cash flow rights.
Several empirical studies support the above argument by documenting that controlling shareholders
under the deviation between control and cash flow rights keep obtaining higher private benefits of
control rights without being detected by minority shareholders through minimizing and delaying
information disclosure (Attig et al. 2006), reducing corporate voluntary disclosure (Lee 2007),
delaying loss recognition (Bona-Sanchez et al. 2011).
Our measure of stock price synchronicity following prior studies such as Morck et al. (2000) and
Boubaker et al. (2014) is the R2 derived from the market and industry model for each firm-year t:
ri,w =  0  1i mrw-1   2 i mrw   3i mrw 1   4 i irw-1   5 i irw   6 i irw+1   i,w
(9)
where riw is the current weekly return for firm i in week w; mrw is the value-weighted market
return in the current week w. irw is the value-weighted industry return in the current week w
1011
We
include the lead and lag terms for the market index return and industry return for nonsynchronous
trading (Dimson, 1979; Kim et al., 2011) and the impact that informed traders have timely information
capitalizing into stock prices (Piotroski and Roulstone, 2004). The stock’s synchronicity with the
10
We require at least 26 weeks in each year estimation.
We also use only the market model without incorporating current, lagged, and lead value-weighted weekly industry
returns into Eq. (9) for the robustness check and the inference does not change.
11
30
market is captured by R-square from Eq. (9), denoted R2it. Given that R2it is bounded within the unit
interval, following Morck et al. (2000), we apply a logistic transformation of (R2it./ 1 − R2it) to obtain
the dependent variable, denoted as SYNCH. Thus, the variable SYNCH measures stock price
synchronicity, the extent to which stock returns capitalize market- and industry-related information. A
high value of SYNCH indicates a low level of revelation of firm-specific information. To test whether
firms with more deviation between control rights and cash flow rights arisen from more investment
layers reveal less firm-specific information to the market and thus leading to higher level of stock price
synchronicity, we employ the following OLS regression:
SYNCH i,t =  0  1 LAYERit 1   2 DEVit 1   3 LAYERit 1  DEVit 1
+  ' MarketControlit +  ' FirmControlit 1 +  ' GovernanceControlit 1
(10)
+ IndustryDummy + YearDummy +  i,t
where variable SYNCH i,t measures the level of stock price synchronicity for firm i in year t, as
defined above; LAYER and DEV are defined as above. If more investment layers and higher deviation
between control rights and cash flow rights are associated with less firm-specific information available
in the market, we expect a positive sign for regression coefficient β1 and β3 in Eq. (10).
We include control variables that are documented to be related to stock price synchronicity: first
set is market-related variables, including stock liquidity (TURNOVER), the average monthly stock
turnover over the year t, where the monthly share turnover is calculated as the monthly trading volume
divided by the total number of shares outstanding during the month; return level (AVE_RET), the
average firm-specific weekly returns over year t; return volatility (STD_RET), the standard deviation
of firm-specific weekly returns over the firm-year period t; a set of firm characteristics including firm
size (SIZE), a natural log of the market value of the equity in year t; leverage (LEV_LT), the ratio of
the long-term liability over the total assets in year t; profitability (ROA), the net income in year t scaled
by the total assets at the beginning of year t; firm growth (MB) captured by the market to the book
31
value of equity at the beginning of year t; opacity measured by absolute value of abnormal accruals
(ABACC); analyst following (NANA) is the natural logarithm of one plus the number of analysts
following the firm through the year t; an indicator for firms which are cross-listed in the U.S. (ADR)
and a set of governance characteristics including an indicator that specifies a firm being audited by
BigN audit firms or not (BIGN); whether the CEO also serve as the chairman of the board (DUALITY);
director ownership (INSIDEB); managerial ownership (INSIDEM); foreign institutional ownership
(FOREIGN_INST) is the average percentage of shares held by institutional owners over year t; board
independence (INDE, the percentage of independent directors on the board) and board size
(BORADSIDE, the natural logarithm of number of directors sitting on the board).
More specifically, we include a range of variables capturing market-related characteristics;
including stock liquidity (TURNOVER) as Chordia et al. (2008) suggest that greater liquidity speeds
price adjustment and improves market informational efficiency by incorporating more firm-specific
information into stock prices12; average stock return (AVE_RET) is controlled as firms with higher
level of stock returns can incentivize investors to seek more firm-specific information, thus leading to
more information transparency; we control for return volatility (STD_RET) as firms with more volatile
returns produce more firm-specific information and are hence less impacted by market-wide
information (Chan and Hameed, 2006). Moreover, we also include a set of variables capturing firm
characteristics; in order to take into account the extent of the width of the corporate pyramid, another
dimension of the corporate pyramid; we control for firm size (SIZE) as Roll (1988) documents a strong
positive association between firm size and R2, suggesting that the stock prices of larger firms tend to
incorporate more market-wide information than those of small firms. Furthermore, Piotroski and
Roulstone (2004) indicate that large firms act as leading market indicators by revealing
macroeconomic or industrial events, resulting in higher stock price synchronicity. On the other hand,
12
Instead, we also use stock turnover and trading volume respectively to control for stock liquidity as the robustness
check and the results are qualitatively the same.
32
larger firm attract more investors’ and analysts’ interests, thus leading to more information about that
firm available to investors (e.g., Lang and Lundholm, 1996). Therefore price synchronicity could
decrease with firm size. We incorporate firm leverage (LEV_LT) as a control since Hutton et al. (2009)
argue that higher leveraged firms shifts the risk from equityholders to debtholders, who bear higher
idiosyncratic volatility, hence reducing stock price synchronicity. In contrast, Rajgopal and
Venkatachalam (2011) argue that levered firms are exposed to higher financial distress, leading to
stock returns being more volatile. We also control for fundamental firm characteristics, profitability
(ROA) and growth (MB). We capture accounting opacity via the absolute vale of abnormal accruals. In
addition, more number of analysts following (NANA) is associated with more industry-level
information revealed into the market (Piotroski and Roulstone 2004). Firms cross-listed in the U.S.
stock exchange are expected to have more transparent information environment (ADR indicator).
Moreover, prior literature shows that strong monitoring by the board and large shareholders have a
positive impact on information environment, and thus we include a set of governance variables.
We estimate Eq. (10) using a pooled ordinary least squares (OLS) regression with year fixed
effects. We winsorize the continuous variable at 1% and 99% level to eliminate outliers and also correct
standard errors for firm-level clustering. Column (1) of Table 6 reports the results of Eq. (10).
Consistent with our expectation, the coefficient on the interaction term LAYER*DEV is 0.858
significantly positive at 0.1% level, suggesting that the number of investment layers is positively
associated with stock price synchronicity in firms with high deviation between control rights and cash
flow right.
6.2.2 Stock price synchronicity for investment layers with respect to more subsidiaries in tax havens
Similarly, we also anticipate that the positive association between the number of investment layers
and level of stock price synchronicity is stronger with the increase in the number of investees in tax
havens. The controlling shareholders can conduct tax avoidance activities by creating complex
structures or transactions to obscure the underlying intent and to avoid detection by the tax authorities.
33
Desai and Dharmapala (2009) argue that tax avoidance activities comprising complex and obscure
transactions simultaneously provide a shield for controlling shareholders engaging in rent extraction
activities as well (e.g. Desai and Dharmapala 2008; Chen and Chu 2005; Crocker and Slemrod 2005;
Slemrod 2004). For example, while related party transactions can be promoted as saving taxes for the
company of interest, the controlling shareholder can also benefit themselves by extract rents through
affiliates.
While there is not an official definition of a tax haven, one of the characteristics the Organization
for Economic Cooperation and Development (OECD) defined a tax haven in 1998 is a jurisdiction
which has a lack of transparency and laws or administrative practices which prevent the effective
exchange of relevant information with other governments on taxpayers. Tax havens can facilitate
concealing financial information and earnings management. For example, Erickson et al. 2004) find
that that a firm can overstate the income of foreign subsidiary located in tax havens to management
earnings upward for the parent company but does not generate additional taxes. Dyreng et al. (2012)
also find that profitable firms with extensive tax haven subsidiaries manage earnings more than other
firms. The well-known illustration is as in the inquiry on the collapse of Enron. Robert McIntyre,
director of Citizens for Tax Justice, pointed out that Enron created 880 subsidiaries in tax havens, 692
of them in the Cayman Islands and engaged actions that obscure the underlying intent of the transaction
(Rense.com, 2002). The case of Enron’s complicated tax avoidance tactics and reporting scandal
supports the argument that tax avoidance strategies demands such obfuscation to retain the tax benefits.
(Chen et al. 2011; Dhaliwal et al. 2011). The coexistence of the organizational complexity (i.e. more
investment layers) and tax avoidance through tax havens can prevent firm-specific information
revealed into the market, leading to higher level of stock price synchronicity.
We employ the following OLS regression:
34
SYNCH i,t =  0  1 LAYERit 1   2TAXH it 1   3 LAYERit 1  TAXH it 1
+  ' MarketControlit +  ' FirmControlit 1 +  ' GovernanceControlit 1
(11)
+ IndustryDummy + YearDummy +  it
where variable SYNCH i,t measures level of stock price synchronicity for firm i in year t, as
defined above; LAYER and TAXH are defined ad above. Other variables are the same as in Eq. (10). If
more investment layers and more subsidiaries located in tax havens associated with less firm-specific
information available in the market (higher level of stock price synchronicity), we expect a positive
sign for regression coefficient β1 and β3 in Eq. (11).
Column (2) of Table 6 reports the results of Eq. (11). Consistent with our expectation, the
coefficient on the interaction term LAYER*TAXH is 0.497 significantly positive at 1% level, suggesting
that the number of investment layers is positively associated with stock price synchronicity in firms
with more subsidiaries located in tax havens.
6.3 Propensity-score Matching
As the numbers of investment layers firms develop is a firm’s choice. The possibility of
endogeneity in which omitted determinants that lead to longer span of pyramidal firms may also affect
analyst forecasts. We use propensity score matching to identify a control group of short-span firms and
account for the endogeneity of a firm’s decision to build long span. This nonparametric matching
technique facilitates causal inference in non-experimental settings by constructing control group of
short-span firms that is similar to a treatment group of long-span of firms (Rosenbaum and Rubin 1983;
Rosenbaum 2002).13 Then we can compare the long-span firms to the matched short-span firms which
are similar along all other observable firm characteristics.
First, we estimate the propensity of a firm developing a long-span of organizational structure
using the follow probit regression model:
13
See Shipman et al. (2016) for a detailed discussion of the usefulness and limitations of propensity score matching
relative to multiple regression analysis in accounting research.
35
P(DLARGE it ) =  0 +  1 SIZE it +  2 MBit +  3 LEVit +  4  SALES it +  5 INVESTEE it
+  6TAXHAVEN it +  7 DUALITYit +  8 INSIDEBit +  9 INSIDEM it
+  10 FOREIGN _ INSTit +  11 INDE it +  12 PLEDGE it +  13 BOARDSIZE it
(12)
+IndustryDunmmy + YearDummy +  it
where DLARGE is an indicator variable which equals one if the number of layers equals to or is
larger than three, and zero otherwise. We consider two sets of variables to capture a firm’s decisions
to build more number of investment layers. The first set of variables is related to firm characteristics,
including firm size (SIZE, measured by the nature logarithm of total assets of the firm), growth
opportunities (MB, measured by the market to book ratio), leverage (LEV, the sum of short-term debt
and long-term debt scaled by total assets), sales growth (∆SALES, change in sales from the prior year.),
the number of investees (INVESTEEE, the natural logarithm of the number of investees), and the
number of tax haven subsidiaries (TAXHAVEN, the natural logarithm of the number of subsidiaries in
tax havens). The second set of variables captures the firms’ governance dimension, including DUALITY,
an indicator variable which equals one if the CEO also serves as chairman of the board and zero
otherwise; INSIDEB, which denotes director ownership; INSIDEM, management ownership;
FOREIGN_INST, foreign institutional ownership; INDE, the percentage of independent directors
serving on the board; PLEDGE, the percentage of equity shares used by the blockholders as the pledge
for financing; BOARDSIZE, the natural log of the number of directors on the board. We also
incorporate indicator variables for two-digit TEJ industry codes and each fiscal year. The detailed
variable definitions are presented in Appendix 1. We get the propensity scores, which are the predicted
probabilities from Eq. (12). We then match each long-span firm to one short-span firm in the same year
by the nearest propensity score, in which we use a 1% caliper distance of propensity score. By doing
so, we match 670 short-span firms which are similar to 670 long-span firms in terms of firm and
governance characteristics except organizational complexity in terms of the number of investment
layers.
36
We then evaluate the covariate balance between long-span firms and short-span control firms
identified in Eq. (12). If the covariates are small enough, then the differences in analyst forecast
properties can be attributed to a firm’s organizational complexity rather than other firm characteristics.
In particular, we perform test of differences in means (t-test) and medians (Wilcoxon rank-sum tests)
of variables related to firm characteristics and governance characteristics (as discussed above)
respectively. The results (unreported) do not show these 13 variables used in Eq. (9) are significantly
different between long-span firms and control firms at a 90% confidence level. When all the covariates
are tested jointly, Hotelling’s T2 test suggests that long-span firms are not significantly different from
the short-span firms. The results therefore indicate that the propensity score matching process balances
the firm and governance characteristics.
[Insert Table 7 here]
We then compare 670 long-span firms to the propensity score matched control sample of 670
short-span firms with the following OLS regression:
ACCYit =  0 +  1 DLARGEit +  2TAXH it +  3 DLARGEit  TAXH it +  4 SIZEit
+ 5 NANAit +  6 MBit +  7 ADRit +  8 STD_EPS it +  9 STD _ ROEit
+10 SALES it + 11 LOSS it + 12 ZMIJ it + 13 HORIZON it + 14 BIGN it
(13)
+15 EPS it +  16 SURPRISEit +  17 DUALITYit +  18 INSIDEBit
+ 19 INSIDEM it +  20 DUALITYit +  21 FOREIGN _ INSTit +  22 INDEit
+ 23 BOARDSIZEit +YEARdummy + INDUSTRYdummy +  it
DISPit =  0 +  1 DLARGEit +  2TAXH it +  3 DLARGEit  TAXH it +  4 SIZEit
+ 5 NANAit +  6 MBit +  7 ADRit + 8 STD_EPSit +  9 STD _ ROEit
+10 SALESit + 11 LOSSit + 12 ZMIJ it + 13 HORIZON it + 14 BIGN it
+15 EPSit +  16 SURPRISEit + 17 DUALITYit +  18 INSIDEBit
(14)
+ 19 INSIDEM it +  20 DUALITYit +  21 FOREIGN _ INSTit +  22 INDEit
+ 23 BOARDSIZEit +YEARdummy + INDUSTRYdummy +  it
Where DLARGE is a variable indicating whether a firm has a long-span vertical organizational
structure or not discussed above and all other variables are the same as in Eq. (5) and (6). Panel B of
37
Table 7 presents the regression analysis. First, we investigate the effect of long-span structure
(DLARGE) on analyst forecast properties; consistent with H1a, Column (1) of Panel B demonstrates
that the coefficient on DLARGE is -0.019, negative and significant at the 1% level. The results indicate
that relative to short-span control firms, long-span firms are associated with less accurate analyst
forecasts, supporting the notion that organizational complexity captured by a firm’s investment layers
complicates analyst forecast task, leading to lower analyst forecast accuracy while the coefficient on
DLARGE in Column (3) is positive but not statistically significant.
Column (2) of Panel B demonstrates that the coefficient on DLARGE is negative and significant
at the 1% level. The coefficient on the interaction term between DLARGE and TAXH is -0.043, negative
and significant at the 10% level, consistent with H1a and H2a, suggesting that relative to short-span
firms, long-span firms which have more subsidiaries in the havens are associated with less accurate
analyst forecasts. Column (4) of Panel B documents that the coefficient on DLARGE is 0.008, positive
and significant at the 1% level. The coefficient on the interaction term between DLARGE and TAXH
is 0.026, positive and significant at the 10% level, suggesting that long-span firms which have more
tax haven subsidiaries associated with larger analyst forecast dispersion. Overall, after we control the
endogeneity of a firm’s decision to develop a long-span of organizational structure, the results still
support H1 and H2, suggesting that organizational complexity complicate analyst forecast tasks, and
thus adversely affect analyst forecast properties; the essential feature inherently associated with tax
havens, which leading to obfuscation and/or earnings management further reduce analyst forecast
accuracy and increase forecast dispersion.
[Insert Table 7 here]
38
7. Conclusion
We examine whether a parent-subsidiary consolidated firms’ analyst forecast properties and
information environment are related to its organizational structure captured by the number of
investment layers and tax haven subsidiaries. To address this research question, we employ a unique
sample of publicly traded Taiwan companies, since all publicly traded companies in Taiwan are
required to disclose information on all of their affiliates. We construct a measure, the number of
vertical layers in the parent-subsidiary relationship, from the ultimate parent company down to the
lowest-tiered subsidiary, which can serve as a measure of organizational complexity as well as indicate
an opaque information environment.
We argue that firms with more investment layers create more information asymmetry and agency
problems between insiders and outsiders, which increase the difficulties for analysts to make earnings
forecasts and facilitate information transparency. We also argue that firms with more investment layers
will reveal less firm specific information. Our empirical results show that firms with more number of
investment layers are associated with less accurate and more dispersed analyst earnings forecasts and
revelation of less firm specific information.
In addition, consistent with the notion that tax avoidance activities via offshore tax havens often
comprise very complex transactions that reduce corporate transparency and provide opportunities for
earnings manipulation, we find that negative (positive) association between the number of layers and
analyst forecast accuracy (dispersion) is stronger for firms which set more subsidiaries in tax havens.
We also find that the association between layers and analyst forecast properties is stronger for firms
which have higher deviation between control rights and cash flow rights, consistent with the agency
cost argument. We find similar results for revelation of firm specific information, measured by stock
price synchronicity. After addressing for the endogenous concern via propensity score matching, the
results still hold.
We look at the decomposed subsidiary structure within consolidated groups and show that the
39
subsidiary structure itself also affects a consolidated firm’s information transparency, captured by
analyst forecast properties. While a corporate pyramidal structure is common in other East Asian
countries, we limit our test to firms in Taiwan since many other countries do not require disclosure of
the structure of the corporate pyramid. Different institutional and regulatory environments limit the
extent to which the results can be generalized. Despite the limitation, this study provides valuable
insights for both practitioners and academia into the role of investment layers, subsidiaries in tax
havens, and deviation between control rights and cash flow rights in the analyst forecasting process
and the revelation of firm specific information.
40
References
Akamah, H., O-K Hope, and W.B. Thomas. 2014. Tax havens and disclosure aggregation. Working
paper .University of Oklahoma and University of Toronto.
Almeida, H. V., and D. Wolfenzon. 2006. A theory of pyramidal ownership and family business groups.
Journal of Finance 6: 2637–2680.
An, H. and T. Zhang. 2013. Stock price synchronicity, crash risk, and institutional investors. Journal
of Corporate Finance 21: 1-15.
Andrade, S., J. Bian, and T. Burch.2013. Analyst coverage, information, and bubbles. Journal of
Financial and Quantitative Analysis 48 (5):1573-1605.
Atiase, R. 1985. Predisclosure information, firm capitalization, and security price behavior around
earnings announcements. Journal of Accounting Research 23: 21–36.
Attig, N., W.-M. Fong, Y. Gadhoum, and L. Lang. 2006. Effects of large shareholding on information
asymmetry and stock liquidity. Journal of Banking and Finance 30 (10): 2875–2892.
Bae, K. H., J. Kang, and J. M. Kim. 2002. Tunneling or value added? Evidence from mergers by
Korean business groups. The Journal of Finance 11: 2695-2740.
Balakrishnan, K., J. Blouin, and W. Guay. 2012. Does tax aggressiveness reduce corporate
transparency? Working Paper.
Barron, O. E., and P. S. Stuerke. 1998. Dispersion in analysts’ earnings forecasts as a measure of
uncertainty. Journal of Accounting, Auditing and Finance 13 (Summer): 245–270.
Barron, O.E., C. Kile, and T.O’Keefe. 1999. MD&A quality as measured by the SEC and analysts’
earnings forecasts. Contemporary Accounting Research 16: 75-109.
Bebchuk, L., R. Kraakman, and G. Triantis. 2000. Stock pyramids, cross-ownership, dual-class equity:
the creation of agency costs of separating control from cash flow rights. In: Morck, R.K. (Ed.),
Concentrated Corporate Ownership. University of Chicago Press, Chicago, pp. 295-318.
Becker, C. L., M. L. DeFond, J. Jiambalvo, and K. R. Subramanyam. 1998. The effect of audit
quality on earnings management. Contemporary Accounting Research 15 (Spring): 1–24.
Beekes, W., and P. Brown. 2006. Do better-governed Australian firms make more informative
disclosures? Journal of Business Finance and Accounting 33(3-4): 422450.
Behn, B. K., J. Choi, and T. Kang. 2008. Audit quality and properties of analyst earnings forecasts.
The Accounting Review 83 (2): 327–349.
Bena, J., and H. Ortiz-Molina. 2013. Pyramidal ownership and the creation of new firms. Journal
of Financial Economics 108, 798-821.
Bens, D.A., and S.J. Monahan. 2004. Disclosure quality and the excess value of diversification.
Journal of Accounting Research 42, 691-730.
Bennedsen, M. and S. Zeume. 2015. Corporate tax havens and shareholder value. Working paper.
Bergin, T. 2012. How Starbucks Avoids U.K. Taxes’, Reuters, 15 October.
Bertrand, M., P. Mehta, and S. Mullanaithan. 2002. Ferreting out tunneling: an application to Indian
business group. Quarterly Journal of Economics 23: 121-148.
Bhat, G., O-K Hope, and T. Kang. 2006. Does corporate governance transparency affect
the accuracy of analyst forecasts? Accounting and Finance 46: 715-732.
Bona-Sanchez, C., Perez-Aleman, J., and D.J. Santana-Martin. 2011. Ultimate ownership
and earnings conservatism. European Accounting Review 20: 57–80.
Boubaker, S., H. Mansali, and H. Rjiba. 2014. Large controlling shareholders and stock price
synchronicity. Journal of Banking & Finance 40: 80-96.
Brown, L, G. Richardson, and S. Schwager. 1987. An information interpretation of financial analyst
superiority in forecasting earnings. Journal of Accounting Research 25: 49–67.
Brown, L. 2001a. How important is past analyst forecast accuracy? Financial Analyst Journal
(November/December): 44–49.
41
Brown, L. 2001b. A temporal analysis of earnings surprises: Profits versus losses. Journal of
Accounting Research 39 (September): 221-241.
Bushman, R., Q. Chen, E. Engel, and Smith, A. 2004. Financial accounting information, organizational
complexity and corporate governance systems. Journal of Accounting and Economics 37: 167201.
Byard, D., Y. Li, and J. Weintrop. 2006 Corporate governance and the quality of financial analysts’
information. Journal of Accounting and Public Policy 25: 609-625.
Chan, K., and A. Hameed. 2006. Stock price synchronicity and analyst coverage in
emerging markets. Journal of Financial Economics 80: 115–147.
Chan, L. C., and A. W. Hsu. 2013. Corporate pyramids, conservatism and cost of debt: Evidence from
Taiwan. International Journal of Accounting 48 (3): 390-413.
Chen, K. P., and C. Chu. 2005. Internal control vs. external manipulation: a model of corporate income
tax evasion. RAND Journal of Economics 36: 151-164.
Chen, Y., S. Huang, R., and J. Wang. 2011. Corporate tax avoidance and firm opacity. Working paper,
Florida International University.
Cheng, S. 2008. Board size and the variability of corporate performance. Journal of Financial
Economics 87:157-176.
Chin, C.L., K, Gary, P. P. Lee., and M. F. Lin. 2006. Corporate ownership structure and accuracy and
bias of mandatory earnings forecast: Evidence from Taiwan. Journal of International
Accounting Research 5(2): 41-62
Claessens, S., S. Djankov, and L.H.P. Lang. 2000. The separation of ownership and control in East
Asian corporations. Journal of Financial Economics 58: 81-112.
Claessens, S., S. Djankov, and L.H.P. Lang. 2002. Disentangling the incentive and entrenchment
effects of large shareholdings. The Journal of Finance 57: 2741-2771.
Crocker, K. and J. Slemrod. 2005. Corporate tax evasion with agency costs. Journal of Public
Economics 89: 1593-1610.
Demirkan, S., Radhakrishnan, S., Urcan, O., 2011. Discretionary accrual quality, cost of capital, and
diversification. Journal of Accounting, Auditing, and Finance 27: 496-526.
Desai, M. and Dharmapala, D. 2006. Corporate tax avoidance and high-powered incentives. Journal
of Financial Economics 79: 145-179.
Desai, M., and D. Dharmapala. 2008. Tax and Corporate Governance: an economic approach. Tax and
Corporate Governance MPI Studies on Intellectual Property, Competition and Tax Law 3:13-30.
Desai, M., and Dharmapala, D. 2009. Earnings management, corporate tax shelters, and book-tax
alignment. National Tax Journal 62: 169–186.
Dhaliwal, D., S. Huang, W. Moser, and R. Pereira. 2011. Corporate tax avoidance and level and
valuation of firm cash holdings. Working paper, University of Arizona, Arizona State University,
Miami University, and University of Missouri at Columbia
Dhaliwal, D.S., S. Radhakrishnan, A. Tsang, and Y.G. Yang. 2012. Nonfinancial disclosure and
analyst forecast accuracy: International evidence on corporate social responsibility disclosure.
The Accounting Review 87(3): 723–759.
Dichev, I. D., and V. W. Tang. 2009. Earnings volatility and earnings predictability. Journal of
Accounting and Economics 47 (1-2): 160–181.
Dimson, E., 1979 Risk measurement when shares are subject to infrequent trading. Journal of
Financial Economics 7:, 197–227.
Drucker, J. 2010. Google 2.4% Rate Shows How $60 Billions Lost to Tax Loopholes, Bloomberg
News, 21 October.
Dunn, K., and S. Nathan, “Diversified Companies and Analysts’ Earnings Forecasts.” Journal of
Investing (Summer 2009): 33-41.
42
Durnev, A., T. Li and M. Magnan .2013. Beyond tax avoidance: Offshore firms’ institutional
environment and financial reporting. Working paper. University of Iowa, University of Ottawa
and Concordia University.
Durnev, A., Morck, R., Yeung, B., and P. Zarowin, P. 2003. Does greater firm-specific return
variation mean more or less informed stock pricing? Journal of Accounting Research: 797-836.
Duru, A., and D. Reeb. 2002. International diversification and analysts’ forecast accuracy and bias.
The Accounting Review 77: 415–433.
Dyreng, S. D., and B. P. Lindsey .2009. Using financial accounting data to examine the effect of foreign
operations located in tax havens and other countries on U.S. multinational firms tax rates. Journal
of Accounting Research 47(5):1283-1316.
Dyreng, S., M. Hanlon, and E. Maydew. 2012. Where do firms manage earnings? Review of
Accounting Studies 17: 649-687.
Eames, M., and S. Glover. 2003. Earnings predictability and the directing of analysts’ earnings
forecast errors. The Accounting Review 78 (July): 707–724.
Erickson, M., M. Hanlon, and E. Maydew. 2004. How much will firms pay for earnings that do not
exist? Evidence of taxes paid on allegedly fraudulent earnings. The Accounting Review 79: 387–
408.
Faccio, M., and L. Lang. 2002. The ultimate ownership of western European corporations. Journal of
Finance and Economics 65 (3): 365–395.
Fan, P. H., and T. J. Wong. 2002. Corporate ownership structure and the informativeness of accounting
earnings in East Asia. Journal of Accounting and Economics 33: 401-425.
Feng, M., and S. McVay. 2010. Analysts’ incentives to overweight management guidance when
revising their short-term earnings forecasts. The Accounting Review 85 (5): 1617-1646.
Fernandes, N., and M. A. Ferreira. 2008. Does international cross-listing improve the
information environment. Journal of Financial Economics 88: 216–244.
Financial Analysts Federation. 1984-89. Report of the Financial Analysts Federation Corporate
Iinforma-tion Committee. New York, NY: FAF
Francis, J., K. Schipper, and L. Vincent. 2005. Earnings and dividend informativeness when cash
flow rights are separated from voting rights. Journal of Accounting and Economics 39 (June):
329-360.
Frank, M.M., L Lynch, and S. Rego. 2009. Tax reporting aggressiveness and its relation to aggressive
financial reporting. The Accounting Review 84: 467-496.
Gilson, S., Healy, P., Noe, C., Palepu, K., Analyst specialization and conglomerate breakups. Journal
of Accounting Research 39: 565-582.
Gopalan, R., V. Nanda, and A. Seru. 2007. Affiliated firms and financial support: Evidence from
Indian business groups. Journal of Financial Economics 86 (3): 759-795
Graham, J., M. Hanlon, T. Shevlin and N. Shroff . 2014. Incentives for tax planning and avoidance:
Evidence from the field. The Accounting Review 89 (3): 991-1023.
Graham, J., Harvey, C., Rajgopal, S., 2005. The economic implications of corporate financial
reporting. Journal of Accounting and Economics 40: 3-73.
Grubert, H. and J. Slemrod. 1998. The effect of taxes on investment and income shifting to Puerto
Rico. Review of Economics and Statistics, 80 (3): 365–373.
Gul, F., M. Hutchinson, and K. Lai. 2013. Gender-diverse boards and properties of analyst earnings
forecasts. Accounting Horizon 27(3): 511-538.
Gul, F.A., Kim, J.B., and A. Qiu. 2010. Ownership concentration, foreign shareholding,
audit quality, and stock price synchronicity: evidence from China. Journal of
Financial Economics 95: 425–442.
Haggard, K., Martin, X., and R. Perreira. 2008. Does voluntary disclosure improve stock
price informativeness? Financial Management 37: 747–768.
43
Harris, D., 1993. The impact of U.S. Tax law revisions on multi-national corporations’ capital location
and income-shifting decisions. Journal of Accounting Research 31 (Suppl.): 111–140.
Haw, I. M., B. B. Hu, L. S. Hwang, and W. Wu. 2004. Ultimate ownership, income management
and legal and extra-legal institutions. Journal of Accounting Research 42 (May): 423-462.
Hermann, D., and W.B. Thomas. 2005. Rounding of analyst forecasts. The Accounting
Review 80 (July): 805-823.
Hines, J. R., Jr. and Rice, E. M. 1994. Fiscal paradise: Foreign tax havens and American business.
Quarterly Journal of Economics, 109: 149-182.
Hope, O.-K. 2003a. Accounting policy disclosures and analysts’ forecasts. Contemporary
Accounting Research 20: 295–321.
Hope, O. 2003b. Disclosure practices, enforcement of accounting standards, and analysts’ forecast
accuracy: An international study. Journal of Accounting Research 41 (May): 235–272.
Hope, O-K., and W.B. Thomas. 2008. Managerial empire building and firm disclosure. Journal of
Accounting Research 46: 591-626.
Hope, O-K., M. Ma, and W.B. Thomas. 2013. Tax avoidance and geographic earnings disclosures.
Journal of Accounting and Economics 56 (2): 170-189.
Houlder, V 2010.Tax Claims Hit Reputation as Well as Coffers’, The Financial Time, 18 November.
Hsu, A., C.Huang and S. Liu. 2015. Investment layers, regional environments, and investment
efficiency: evidence from FDI in China. Asia-Pacific Journal of Accounting & Economics 22
(3): 291-310 .
Hutton, A.P., Marcus, A.J., Tehranian, H., 2009. Opaque Financial Reports, R2, and Crash Risk.
Journal of Financial Economics 94: 67-86
Hwang, L., C. Jan, and S. Basu. 1996. Loss firms and analysts’ earnings forecast errors. Journal of
Financial Statement Analysis 1 (Winter): 18–31.
Imhoff, E. A., and G.J. Lobo. 1992. The effect of ex ante earnings uncertainty on earnings response
coefficient. The Accounting Review 67 (April): 427-439.
Jacob, J., 1996. Taxes and transfer pricing: Income shifting and the volume of intrafirm transfers.
Journal of Accounting Research. 34 (2), 301–312.
Jin, L., Myers, S.C.,2006. R2 around the world: New theory and new tests. Journal of Financial
Economics 79, 257–292.
Johnson, S., R. La Porta, F. Lopez-de-Silanes, and A. Shleifer. 2000. Tunneling. American Economic
Review 90: 22-27.
Kim, J., Y. Li, and L. Zhang. 2011. Corporate tax avoidance and stock price crash risk: firm-level
analysis. Journal of Financial Economics 100 (3): 639-662.
Kim, J.B., Shi, H., 2012. Voluntary IFRS adoption, analyst coverage, and information
quality: international evidence. Journal of International Accounting Research
11: 45–76.
Kim, J., B. Song, and L. Zhang. 2009. Internal control quality and analyst forecast behavior: Evidence
from SOX section 404 disclosure. Working paper.
Kim, J., H. Zhang, L. Li., and G. Tian. 2014. Press freedom, externally-generated transparency, and
stock price informativeness: International evidence. Journal of Banking & Finance 46: 299-310.
Kimbrough, M. D. 2005. The effect of conference calls on analyst and market underreaction to
earnings announcements. The Accounting Review 80 (1): 189-219.
Krishnaswamy, S. and V. Subramaniam. 1999. Information Asymmetry, Valuation and the Corporate
Spin-off Decision. Journal of Financial Economics 53: 73-112
Kross, W., B. T. Ro, and D. Schroeder. 1990. Earnings expectations: the analysts’ information
advantage. The Accounting Review 65 (April): 461–476.
44
Lang, M., K. Lins, and D. Miller. 2003. ADRS, analysts, and accuracy: Does cross-listing in the United
States improve a firm’s information environment and increase market value? Journal of
Accounting Research 41 (2): 317–345.
Lang, M., and R. Lundholm. 1996. Corporate disclosure policy and analyst behavior. The Accounting
Review 71 (4): 467–493.
La Porta, R., Lopez-de-Silanes, and A. Shleifer. 1999. Corporate ownership around the world. The
Journal of Finance 54 (2): 471-517.
Lee, K.W. 2007. Corporate voluntary disclosure and the separation of cash flow rights from control
rights. Review of Quantitative Finance and Accounting 28: 393-416
Lehavy, R., Feng L., and K. Merkley, 2011, The effect of annual report readability on analyst
following and the properties of their earnings forecasts. The Accounting Review 86: 10871115.
Leuz, C., D. Nanda, and P. Wysocki. 2003. Earnings management and investor protection: An
international comparison. Journal of Financial Economics 69: 505–527.
Lim, T. 2001. Rationality and analysts’ forecast bias. Journal of Finance 56: 369-385.
Lys, T., and L. Soo. 1995. Analysts’ forecast precision as a response to competition. Journal of
Accounting, Auditing and Finance 10 (4): 751–765.
Manconi, A., and M. Massa. 2009. Modigliani and Miller meet Chandler: Organizational complexity,
capital structure, and firm value. Working paper.
Morck, R., 2005. How to eliminate pyramidal business groups-the double taxation of inter-corporate
dividends and other incisive uses of tax policy. NBER Working paper.
Morck, R., D. Wolfenzon, and B. Yeung. 2005. Corporate governance, economic entrenchment, and
growth. Journal of Economic Literature 43 (3): 655-720.
Morck, R., Yeung, B., and W. Yu. 2000. The information content of stock markets: why
do emerging markets have synchronous stock price movements? Journal of
Financial Economics 58: 215–260.
Nowland, J. 2008. The effect of national governance codes on firm disclosure practices:
Evidence from analyst earnings forecasts. Corporate Governance: An International
Review 16(6): 475-491.
Okuda, S, and A. Shiiba. 2010. An evaluation of relative importance of parent-only and subsidiary
earnings in Japan: A variance decomposition approach. Journal of International Accounting
Research 9(1): 39-54.
Parkash, M., D. S. Dhaliwal, and W. K. Salatka. 1995. How certain firm-specific characteristics
affect the accuracy and dispersion of analysts’ forecasts. Journal of Business Research 34: 161–
169.
Pindyck, R.S., and J. Rotemberg. 1993. The comovement of stock prices. The Quarterly
Journal of Economics 108: 1073–1104.
Piotroski, J., and D. Roulstone. 2004. The influence of analysts, institutional investors,
and insiders on the incorporation of market, industry, and firm-specific
information into stock prices. Accounting Review 79: 1119–1151.
Rego, S., 2003. Tax-avoidance activities of U.S. multinational corporations. Contemporary Accounting
Research 20 (4): 805–833.
Riyanto, Y., and L. Toolsema. 2008. Tunneling and propping: A justification for pyramidal
ownership. Journal of Banking and Finance 32, 2178-2187.
Roll, R., 1988. R-squared. Journal of Finance 43: 541–566.
Rosenbaum P.R. 2002. Observational Studies. 2nd ed. New York, NY: Springer-Verlag.
Rosenbaum,P., and D. Rubin.1983.The central role of the propensity score in observational studies
for causal effects. Biometrika 70 (1):41–55.
45
Shipman, J.E., Q.T. Swanquist and R.L. Whited. 2016. Propensity Score Matching in Accounting
Research. Forthcoming, The Accounting Review.
Slemrod, J. 2004. The economics of corporate tax selfishness. National Tax Journal 57:877–899.
Stein, J., 1997. Internal capital markets and the competition for corporate resources. The Journal of
Finance52, 111–133.
Wang, X., 2011. Tax avoidance, corporate transparency, and firm value. Working paper, University of
Texas at Austin.
Watkins, A. L., W. Hillison, and S. E. Morecroft. 2004. Audit quality: A synthesis of theory and
empirical evidence. Journal of Accounting Literature 23: 153–193.
Wagener, T. and C. Watrin. 2014. The relevance of complex group structures for income shifting and
investors’ valuation of tax avoidance. Working paper.
Warfield, T., Wild, J., and K. Wild. Managerial ownership, accounting choices, and informativeness
of earnings. Journal of Accounting and Economics 20 (1): 61-91.
Waymire, G. 1986. Additional evidence on the accuracy of analyst forecasts before and after
voluntary management earnings forecasts. The Accounting Review 41 (1): 129-142.
Weiss, D. 2010. Cost behavior and analysts’ earnings forecasts. The Accounting Review 85 (4):
1441-1471.
Wurgler, J., 2000. Financial markets and the allocation of capital. Journal of Financial
Economics 58: 187-214.
Xu, L. and A. Tang. 2014. Internal control material weakness, analyst accuracy and bias, and
brokerage reputation. Handbook of Financial Econometrics and Statistics: 1719-1751.
Yu, M.2010. Analyst forecast properties, analyst following and governance disclosures:
A global perspective. Journal of International Accounting, Auditing and Taxation
19 (1):1–15.
Zhang, Y. 2008. Analyst responsiveness and the post-earnings-announcement drift. Journal of
Accounting and Economics 46: 201-215.
Zmijewski, M. E. 1984. Methodological issues related to the estimation of financial distress
prediction models. Journal of Accounting Research 22 (Supplement): 59–82.
46
Figure 1. ASUS Corporation
Parent
Company
ASUS
100%
Layer 1
8.64%
Pegatron
Corp.
HuiHai
Technology
100%
Layer 2
Pegatron
Holding Ltd
Kinsus
Interconnect
Technology
Corp.
Aslink
Precision
Co., Ltd.
(Cayman).
100%
Layer5
Kinsus
Holding
(Cayman)
Ltd.
100%
Pegavision
Investing
Corp.
Others
100%
100%
100%
Kinsus
Holding
(Samoa)
100%
Azwave
Holding
(Samoa)
13.48%
Layer 4
Asus
Holland
B.V.
100%
HuaWei
Investing
Corp.
Layer 3
100%
Aslink
(HK)
Precision
Co.,
100%
Azwave
Technology
(China)
100%
Casetek
(BVI)
ASUS
Tech
(HK)
Boardtek
(Cayman)
Holdings
Limited
100%
Ability
Enterprise
(BVI)
Co.,Ltd
56.6%
Boardtek
(HK)
Holdings
Limited
56.67%
Pegavision
Corp.
Lian
Shih
Corp.,
(China)
100%
Layer6
Tubesonic
Technology
Ltd.
(China)
The figure is based on ASUS annual
financial report 2009.
47
Appendix 1 Variable definitions
(1) Properties of Analyst Earnings Forecasts
Analyst forecast accuracy defined as the negative of the absolute value of
ACCY
forecast error, scaled by the year end stock price of year end t-1:
Dispersion of individual forecasts measured as the standard deviation of
DISP
individual forecasts, scaled by stock price of year end t-1.
(2) Variables of Interest
The natural logarithm of the number of investment layers of the longest
LAYER
investment chain in firm’s investment structure.
The number of subsidiaries in tax havens divided by the total number of
TAXH
subsidiaries, based on the list of tax haven in Durnev et al. (2013).
An indicator which equals one for high deviation group and zero for low
DEV
deviation group where we define high deviation group if the parent firms
have any subsidiary within the long chain of investment structure with
ownership less than 50%.
(3) Control Variables in the Main Tests
The natural logarithm of the number of the widest investment chain in firm’s
WIDTH
pyramidal structure
The natural logarithm of total assets in year t.
SIZE
The natural logarithm of one plus the number of analysts following the firm
NANA
through the year.
The ratio of the book value of debt plus the market value of equity scaled by
MB
total assets.
An indicator variable that equals 1 if a Taiwanese company also trades in the
U.S. markets through American Depositary Receipts (ADR) programs during
ADR
the year, and 0 otherwise.
The standard deviation of earnings per share (EPS) over the preceding five
STD_EPS
years.
The standard deviation of return on equity computed over the preceding five
STD_ROE
years.
Change in sales from the prior year.
∆SALES
An indicator variable that equals 1 if pre-tax income from continuing
LOSS
operations in year t is smaller than zero and 0 otherwise.
ZMIJ=-4.3-4.5X1+5.7X2+0.004X3
Where X1= net income/ total assets
ZMIJ
X2=total liabilities/total assets
X3=current assets/current liabilities
The natural logarithm of the average number of days between each forecast
announcement date used to calculate ACCY (DISP) and the actual earnings
HORIZON
announcement date of each forecast for firm i.
An indicator variable which equals 1 if firms hire Big4 audit firm and 0
BIGN
otherwise.
The level of earnings per share for firm i in year t.
EPS
Absolute value of this year’s EPS minus last years’ EPS, scaled by the stock
SURPIRSE
price of year end t-1.
An indicator variable that equals one if the CEO also serves as chairman of
DUALITY
the board and zero otherwise.
48
Director ownership.
INSIDEB
Management ownership.
INSIDEM
The percentage of common stocks held by foreign institutional investors.
FOREIGN_INST
The proportion of independent directors.
INDE
The natural logarithm of the number of directors sitting on the board.
BOARDSIZE
Variables for Price Synchronicity Test
average monthly share turnover over the current bank-year t minus the
DTURN
average monthly share turnover over the previous bank-year t-1, where
monthly share turnover is calculated as the monthly trading volume divided
by the total number of shares outstanding during the month.
monthly share turnover is calculated as the monthly trading volume divided
TURNOVER
by the total number of shares outstanding during the month and yearly
turnover is average monthly turnover over the year t.
the standard deviation of firm-specific weekly returns over the firm-year t
STD_RET
mean of firm-specific weekly returns over the over the firm-year t
AVE_RET
total long-term liabilities divided by total assets at the beginning of year t.
LEV_LT
the net income over year t scaled by total assets at the beginning of year t
ROA
the absolute abnormal accruals in year t, where abnormal accruals are
ABACC
estimated based on the cross-sectional modified Jones model (Dechow et
al., 1995) by each year and each 2-digit TEJ industry classification.
(4) Other Variables used in Propensity Score Matching
An indicator variable that equals one if the number of layers >=3 and zero
DLARGE
otherwise
The natural logarithm of the number of investees
INVESTEE
The natural logarithm of the number of investees in tax havens, based on the
TAXHAVEN
list of tax havens in Durnev et al.(2013).
The percentage of equity shares used by the blockholders as the pledge for
PLEDGE
financing.
49
Appendix 2 List of tax havens as in Durnev et al. (2013)
Tax Havens
Andorra
Liechtenstein
Anguilla
Luxembourg
Antigua and Barbuda
Macao SAR
Aruba
Malaysia (Labuan)
Bahamas
Malta
Bahrain
Marshall Islands
Barbados
Mauritius
Belize
Monaco
Bermuda
Montserrat
British Virgin Islands
Nauru
Cayman Islands
Netherlands Antilles
Cook Islands
Niue
Costa Rica
Panama
Cyprus
Palau
Dominica
Samoa
Gibraltar
Seychelles
Grenada
Singapore
Guernsey and Alderney
St. Kitts and Nevis
Hong Kong
St. Lucia
Ireland
St. Vincent and the Grenadines
Isle of Man
Switzerland
Jersey
Turks and Caicos Islands
Lebanon
Vanuatu
Following Durnev et al. (2013), the list of tax haven countries comes from the International Monetary
Fund (IMF) and Financial Stability Forum (2000).
50
Table 1 Descriptive statistics (N=2,514)
Variable
Mean
-0.036
ACCY
0.025
DISP
3.443
LAYER (number)
1.036
LAYER(log)
Number of Subsidiaries in
Tax Havens (based on
3.75
Durnev et al. 2013)
0.312
TAXH
16.752
SIZE
6.031
NANA(number)
1.557
NANA(log)
1.980
MB
0.048
ADR
1.740
STD_EPS
0.075
STD_ROE
0.174
∆SALES
LOSS
0.105
-2.119
ZMIJ
5.149
HORIZON_ACCY(days)
0.863
BIGN
3.381
EPS
0.080
SURPIRSE
0.245
DUALITY
0.194
INSIDEB
0.015
INSIDEM
0.158
FOREIGN_INST
0.127
INDE
7.596
BOARDSIZE(number)
1.968
BOARDSIZE(log)
Std.
0.086
0.040
1.121
0.452
Q1
-0.033
0.009
2.000
0.693
Median
-0.015
0.015
3.000
1.099
Q3
-0.006
0.028
4.000
1.386
6.31
1.00
2.00
4.00
0.185
1.443
6.710
0.814
1.363
0.215
1.492
0.067
0.285
0.307
1.083
0.489
0.344
3.872
0.126
0.430
0.127
0.022
0.155
0.157
0.310
0.317
0.182
15.664
1.000
0.693
1.071
0.000
0.805
0.034
0.005
0.000
-2.830
5.056
1.000
1.140
0.018
0.000
0.099
0.001
0.031
0.000
6.000
1.609
0.300
16.563
3.000
1.386
1.646
0.000
1.280
0.057
0.127
0.000
-2.047
5.262
1.000
2.810
0.043
0.000
0.162
0.006
0.102
0.000
7.000
1.946
0.417
17.733
8.000
2.079
2.462
0.000
2.141
0.091
0.295
0.000
-1.367
5.403
1.000
4.850
0.097
0.000
0.252
0.021
0.248
0.286
10.000
2.197
51
Table 2 Pearson (Above) and Spearman (Below) correlation table
1.ACCY
ACCY
DISP
LAYER
TAXH
SIZE
1.00
-0.57
(0.00)
1.00
0.08
(0.00)
-0.02
(0.48)
0.06
(0.01)
-0.06
(0.01)
-0.02
(0.43)
0.14
(0.00)
-0.05
1.00
0.20
0.36
(0.00)
1.00
(0.00)
-0.19
(0.00)
1.00
(0.00)
0.03
(0.18)
0.51
(0.00)
1.00
-0.06
.
2.DISP
3.LAYER(lo
g)
-0.55
(0.00) .
0.11
(0.00)
0.06
(0.01)
0.01
(0.67)
0.15
(0.00)
0.18
(0.00)
0.01
(0.72)
0.03
(0.17)
(0.03) .
-0.07
(0.00)
0.06
(0.01)
-0.03
(0.27)
-0.11
(0.00)
0.05
(0.05)
-0.01
(0.70)
10.STD_RO
A
-0.07
0.12
-0.11
0.05
-0.04
11.∆SALES
(0.01)
0.14
(0.00)
(0.00)
-0.05
(0.02)
(0.00)
-0.04
(0.09)
(0.04)
0.05
(0.02)
(0.07)
-0.03
(0.21)
4.TAXH
5.SIZE
6.NANA
7.MB
8.ADR
9.STD_EPS
NANA
MB
ADR STD_EPS
0.15 0.34 0.01
(0.00) (0.00) (0.54)
0.08 -0.23 0.03
(0.00) (0.00) (0.17)
0.26 -0.10
0.19
(0.00) (0.00)
0.20
0.04 0.01
(0.00) .
(0.07) (0.72)
0.33 -0.20
-0.23 0.28
(0.00) (0.00) .
(0.00) (0.00)
0.24
0.03
0.52
0.18 0.18
(0.00) (0.27) (0.00) .
(0.00) (0.00)
-0.07
0.05 -0.20
0.16 1.00 -0.03
(0.00) (0.06) (0.00) (0.00) .
(0.16)
0.18
0.00
0.29
0.19 -0.02 1.00
(0.00) (0.95) (0.00) (0.00) (0.37) .
-0.04
0.09 -0.02
0.10 0.26 -0.04
(0.09) (0.00) (0.50) (0.00) (0.00) (0.09) .
0.08 -0.07
(0.01) (0.00) (0.00)
0.09 0.27 0.08
(0.00) (0.00) (0.00)
52
0.02
(0.43)
0.02
(0.33)
STD_RO
∆SALES LOSS
A
-0.08
0.22 -0.33
(0.00)
(0.00) (0.00)
0.10
-0.13
0.23
(0.00)
(0.00) (0.00)
ZMIJ HORIZON
-0.24
(0.00)
0.20
(0.00)
-0.17
(0.00)
0.16
(0.00)
-0.07
-0.15
-0.03
-0.04
0.22
0.03
(0.00)
0.16
(0.00)
-0.03
(0.23)
0.10
(0.00)
0.21
(0.00)
-0.04
(0.08)
1.00
(0.00)
0.11
(0.00)
-0.10
(0.00)
-0.04
(0.10)
0.11
(0.00)
-0.08
(0.00)
0.80
(0.00)
(0.21)
0.08
(0.00)
-0.04
(0.06)
0.09
(0.00)
0.30
(0.00)
0.08
(0.00)
0.05
(0.04)
(0.11)
-0.04
(0.14)
0.10
(0.00)
-0.05
(0.04)
-0.26
(0.00)
-0.04
(0.08)
0.10
(0.00)
(0.00)
-0.06
(0.01)
0.35
(0.00)
0.02
(0.33)
-0.36
(0.00)
0.10
(0.00)
-0.11
(0.00)
(0.21)
-0.05
(0.04)
0.13
(0.00)
0.09
(0.00)
-0.11
(0.00)
0.04
(0.09)
0.01
(0.60)
0.63
1.00
0.06
0.19
-0.01
-0.01
(0.01)
1.00
(0.00)
-0.28
(0.00)
(0.81)
-0.04
(0.13)
(0.68)
-0.08
(0.00)
(0.00) .
0.09
(0.00)
0.07
(0.00) .
12.LOSS
13.ZMIJ
14.HORIZO
N
15.BIGN
16.EPS
17.SURPRIS
E
-0.35
(0.00)
-0.22
(0.00)
0.20
(0.00)
0.19
(0.00)
-0.05
(0.04)
0.18
(0.00)
-0.03
(0.17)
-0.07
(0.00)
0.09
(0.00)
0.33
(0.00)
0.00
0.08
0.08
-0.08
0.17
(0.92)
0.12
(0.00)
0.25
(0.00)
(0.00)
-0.12
(0.00)
-0.17
(0.00)
(0.00)
0.20
(0.00)
0.02
(0.29)
(0.00)
0.13
(0.00)
0.10
(0.00)
(0.00)
0.07
(0.01)
-0.07
(0.00)
-0.41
0.28
-0.05
0.01
0.02
(0.00)
(0.00)
(0.02)
(0.75)
(0.52)
-0.04
(0.10)
0.01
(0.65)
-0.21 -0.04
(0.00) (0.08)
-0.37 0.09
(0.00) (0.00)
0.08
(0.00)
-0.11
(0.00)
0.20
(0.00)
0.11
(0.00)
0.06
0.03
0.00
-0.08
0.03
(0.00) (0.00) (0.01)
0.08 0.01 0.02
(0.00) (0.61) (0.33)
0.20 0.60 0.03
(0.00) (0.00) (0.19)
(0.28)
0.03
(0.26)
0.37
(0.00)
(0.91)
-0.01
(0.65)
-0.04
(0.13)
(0.00)
-0.04
(0.06)
0.31
(0.00)
(0.15)
-0.01
(0.77)
-0.46
(0.00)
0.02
0.03
0.16
0.03
(0.00) (0.00) (0.45)
(0.22)
(0.00)
(0.26)
0.17 -0.11
-0.09 -0.10
All the variables are defined as in Appendix 1.
53
-0.25
1.00
0.32
(0.00) .
(0.00)
-0.06
0.34
1.00
(0.02) (0.00) .
0.05
0.06
(0.01)
0.07
(0.01)
1.00
(0.03) .
0.00
(0.95)
-0.45
(0.00)
-0.01
(0.54)
-0.02
(0.29)
0.20
0.16
-0.03
(0.00)
(0.00)
(0.14)
Table 3 Analyst forecast and investment layers
(1)
ACCY
-0.051
INTERCEPT
(-1.75)
LAYER
-0.018
(-4.68)***
0.001
SIZE
(0.74)
0.006
NANA
(2.32)*
MB
0.003
(1.77)
-0.005
ADR
(-0.66)
STD_EPS
0.000
(0.31)
0.071
STD_ROE
(2.13)*
0.022
∆SALES
(3.74)***
-0.057
LOSS
(-9.31)***
-0.008
ZMIJ
(-4.37)***
-0.008
HORIZON
(-2.44)*
0.012
BIGN
(2.55)*
-0.001
EPS
(-1.02)
-0.291
SURPIRSE
(-22.87)***
DUALITY
INSIDEB
INSIDEM
FOREIGN_INST
INDE
BOARDSIZE
(2)
ACCY
-0.071
(-2.24)*
-0.018
(-4.74)***
-0.000
(-0.16)
0.007
(2.80)**
0.002
(1.36)
-0.005
(-0.69)
0.000
(0.10)
0.068
(2.01)*
0.025
(4.07)***
-0.056
(-8.95)***
-0.007
(-3.72)***
-0.008
(-2.38)*
0.009
(2.04)*
-0.001
(-0.89)
-0.286
(-22.27)***
0.000
(0.01)
0.015
(1.20)
0.039
(0.50)
-0.000
(-0.02)
0.036
(3.37)***
0.019
(3.37)***
yes
(3)
DISP
-0.011
(-0.56)
0.004
(1.98) **
0.000
(0.17)
0.000
(0.26)
-0.000
(-0.15)
0.008
(1.97)*
-0.002
(-2.06)*
0.059
(3.01)**
-0.005
(-1.40)
0.013
(3.46)***
0.004
(3.77)***
0.009
(3.50)***
-0.012
(-4.36)***
0.000
(0.14)
0.076
(9.69)***
(4)
DISP
-0.015
(-0.77)
0.004
(1.92)**
0.001
(1.39)
0.001
(0.58)
0.001
(0.76)
0.009
(2.25)*
-0.002
(-2.29)*
0.065
(3.28)**
-0.006
(-1.84)
0.012
(3.14)**
0.003
(2.94)**
0.010
(3.74)***
-0.011
(-3.98)***
0.000
(0.52)
0.071
(8.96)***
0.001
(0.68)
-0.010
(-1.34)
-0.108
(-2.45)*
-0.025
(-3.42)***
-0.017
(-2.85)**
-0.008
(-2.37)*
yes
yes
yes
Industry and year
effects
2,514
2,514
2,514
N
adj. R2
0.310
0.326
0.130
All other variables are as defined in Appendix 1. t statistics in parentheses.***, **, * indicate
significance at the 1%, 5% and 10% level, respectively.
54
2,514
0.144
Table 4 Analyst forecast and investment layers for high/low intensity of subsidiaries in tax havens
(1) ACCY
(2) ACCY
(3)DISP
(4)DISP
INTERCEPT
-0.088
-0.076
-0.004
-0.004
(-2.48)*
(-2.29)*
(-0.20)
(-0.18)
LAYER
0.028
0.008
(1.61)
(1.89)*
-0.040
-0.032
0.036
0.023
TAXH
(-1.98)**
(-1.86)*
(2.17)* *
(2.22)**
LAYER*TAXH
-0.038
0.016
(-2.16)**
(1.93) *
0.001
-0.000
0.001
0.001
SIZE
(0.75)
(-0.22)
(0.78)
(1.05)
NANA
0.008
0.007
0.001
0.001
(2.88)**
(2.89)**
(0.78)
(0.71)
MB
0.002
0.002
0.001
0.001
(1.35)
(1.21)
(0.58)
(0.71)
ADR
-0.004
-0.005
0.009
0.009
(-0.52)
(-0.66)
(2.22)*
(2.30)*
0.001
0.000
-0.002
-0.002
STD_EPS
(0.38)
(0.07)
(-2.31)*
(-2.25)*
STD_ROE
0.054
0.069
0.068
0.066
(1.49)
(2.03)*
(3.46)***
(3.34)***
∆SALES
0.023
0.025
-0.006
-0.006
(3.60)***
(4.13)***
(-1.67)
(-1.81)
LOSS
-0.060
-0.056
0.012
0.012
(-8.98)***
(-8.89)***
(3.33)***
(3.16)**
ZMIJ
-0.006
-0.007
0.003
0.003
(-3.06)**
(-3.63)***
(2.71)**
(2.82)**
HORIZON
-0.007
-0.008
0.009
0.010
(-2.03)*
(-2.34)*
(3.55)***
(3.58)***
0.012
0.009
-0.011
-0.010
BIGN
(2.38)*
(1.85)
(-4.08)***
(-3.72)***
EPS
-0.001
-0.000
0.000
0.000
(-0.95)
(-0.70)
(0.59)
(0.43)
SURPIRSE
-0.287
-0.286
0.072
0.072
(-20.80)***
(-22.28)***
(9.04)***
(9.04)***
DUALITY
0.002
0.001
0.002
0.002
(0.50)
(0.15)
(0.81)
(0.76)
INSIDEB
0.009
0.014
-0.009
-0.010
(0.68)
(1.11)
(-1.22)
(-1.33)
INSIDEM
0.046
0.036
-0.111
-0.108
(-2.45)*
(0.56)
(0.47)
(2.51)*
FOREIGN_INST
0.005
-0.000
-0.026
-0.025
(0.38)
(-0.02)
(-3.59)***
(-3.44)***
INDE
0.036
0.036
-0.016
-0.016
(3.16)**
(3.40)***
(-2.61)**
(-2.66)**
BOARDSIZE
0.019
0.018
-0.008
-0.008
(3.13)**
(3.26)**
(-2.50)*
(-2.44)*
Industry and year effects
yes
Yes
yes
yes
N
2,514
2,514
2,514
2,514
adj. R2
0.294
0.327
0.144
0.145
All other variables are as defined in Appendix 1. t statistics in parentheses.***, **, * indicate significance at the
1%, 5% and 10% level, respective.
55
TABLE 5 Analyst forecast and investment layers for high/low deviation
(1) ACCY
(2)DISP
-0.065
-0.016
INTERCEPT
(-1.99)*
(-0.77)
LAYER
0.011
-0.001
(1.61)
(-0.35)
DEV
0.021
-0.008
(1.41)
(-0.86)
LAYER*DEV
-0.048
0.027
(-2.75)**
(2.53)*
0.000
0.001
SIZE
(0.23)
(0.80)
0.007
0.001
NANA
(2.84)**
(0.55)
0.002
0.001
MB
(1.16)
(0.94)
-0.006
0.009
ADR
(-0.76)
(2.25)*
-0.000
-0.002
STD_EPS
(-0.13)
(-1.97)*
0.075
0.058
STD_ROE
(2.20)*
(2.96)**
0.025
-0.007
∆SALES
(4.16)***
(-1.99)*
-0.056
0.012
LOSS
(-9.01)***
(3.26)**
-0.007
0.003
ZMIJ
(-3.89)***
(3.03)**
-0.007
0.010
HORIZON
(-2.22)*
(3.61)***
0.009
-0.011
BIGN
(1.94)
(-4.01)***
-0.001
0.000
EPS
(-1.21)
(0.90)
-0.284
0.069
SURPIRSE
(-22.16)***
(8.77)***
-0.000
0.002
DUALITY
(-0.12)
(0.81)
0.001
0.000
INSIDEB
(0.04)
(0.00)
0.061
-0.124
INSIDEM
(0.80)
(-2.81)**
0.004
-0.029
FOREIGN_INST
(0.29)
(-3.90)***
0.019
-0.006
INDE
(1.64)
(-0.90)
0.019
-0.008
BOARDSIZE
(3.46)***
(-2.44)*
yes
yes
Industry and year effects
2129
2129
N
adj. R2
0.329
0.150
All other variables are as defined in Appendix 1. t statistics in parentheses.***, **, * indicate
significance at the 1%, 5% and 10% level, respectively.
56
TABLE 6 Stock price synchronicity and investment layers with respect to high/low deviation and high/low intensity of
subsidiaries in tax havens
(1)
SYNCH
-7.754
(-17.35)***
0.176
(2.51)**
-0.087
(-1.91)*
0.858
(4.43)***
INTERCEPT
LAYER
DEV
LAYER*DEV
0.471
(3.21)**
-0.209
(-2.09)*
0.497
(1.95)**
0.467
(3.17)**
4.217
(3.68)***
2.575
(1.23)
1.105
(5.38)***
2.686
(7.60)***
-0.091
(-0.24)
0.403
(17.44)***
-0.303
(-9.40)***
0.086
(4.28)***
0.148
(1.47)
-0.211
(-3.55)***
0.036
(0.77)
0.156
(0.87)
-1.107
(-1.16)
-0.792
(-4.69)***
-0.828
(-5.59)***
0.419
(5.86)***
yes
2,514
0.299
3.934
(3.41)***
2.544
(1.21)
1.185
(5.68)***
2.754
(7.75)***
0.022
(0.06)
0.400
(17.25)***
-0.313
(-9.71)***
0.092
(4.58)***
0.203
(2.03)*
-0.180
(-3.01)**
0.038
(0.80)
0.281
(1.65)
-1.156
(-1.21)
-0.776
(-4.63)***
-0.693
(-5.22)***
0.399
(5.55)***
Yes
2,514
0.292
TAXH
LAYER*TAXH
TURNOVER
STD_RET
AVE_RET
SIZE
LEV_LT
ROA
MB
ABACC
NANA
ADR
BIGN
DUALITY
INSIDEB
INSIDEM
FOREIGN_INST
INDE
BOARDSIZE
Industry and year effects
N
adj. R2
t statistics in parentheses
*
p < 0.05, ** p < 0.01, *** p < 0.001
57
(2)
SYNCH
-7.879
(-17.40)***
-0.165
(-2.07)*
Table 7. Panel A: Propensity score regression
Probit model (DLAYER1=1 if the number of layers >=3 and 0 otherwise.)
Coefficient
t-Stat
-0.881
(-1.37)
CONSTANT
-0.148
(-3.15)**
SIZE
-0.024
(-0.66)
MB
0.456
(1.00)
LEV
0.287
(1.85)
∆SALES
1.060
(10.82)***
INVESTEE
0.266
(3.79)***
TAXHAVEN
-0.135
(-1.56)
DUALITY
0.520
(1.51)
INSIDEB
-3.886
(-2.01)*
INSIDEM
0.693
(2.19)*
FOREIGN_INST
-0.720
(-2.70)**
INDE
-0.404
(-2.02)*
PLEDGE
-0.033
(-1.85)
BOARDSIZE
2,129
N
Psedo. R2
37.16%
58
Table 7 Panel B: Association between analyst forecast and long-span firms for the intensity of
subsidiaries in tax havens
(1) ACCY
(2) ACCY
(3)DISP
(4)DISP
-0.078
-0.083
-0.016
-0.002
INTERCEPT
(-2.18)*
(-2.23)*
(-0.69)
(-0.08)
DLARGE
-0.019
-0.030
0.005
0.008
(-4.40)***
(-4.40)***
(1.95)
(2.08)*
0.037
0.017
TAXH
(1.91)
(1.51)
DLARGE*TAXH
-0.043
0.026
(-2.20)*
(2.26)*
SIZE
-0.000
-0.001
0.002
0.001
(-0.22)
(-0.27)
(1.34)
(1.00)
0.008
0.008
0.001
0.001
NANA
(2.82)**
(2.90)**
(0.49)
(0.64)
MB
0.002
0.002
0.001
0.001
(1.36)
(1.22)
(0.73)
(0.66)
-0.009
-0.008
0.010
0.010
ADR
(-1.02)
(-0.99)
(2.24)*
(2.30)*
0.000
0.000
-0.003
-0.003
STD_EPS
(0.08)
(0.03)
(-2.70)**
(-2.63)**
0.064
0.066
0.084
0.085
STD_ROE
(1.63)
(1.67)
(3.66)***
(3.70)***
0.027
0.027
-0.008
-0.008
∆SALES
(4.01)***
(4.09)***
(-2.15)*
(-2.13)*
-0.064
-0.064
0.012
0.012
LOSS
(-9.10)***
(-9.05)***
(2.91)**
(2.93)**
-0.007
-0.007
0.004
0.003
ZMIJ
(-3.53)***
(-3.44)***
(3.05)**
(2.92)**
-0.008
-0.008
0.010
0.010
HORIZON
(-2.09)*
(-2.06)*
(3.44)***
(3.26)**
0.008
0.007
-0.010
-0.009
BIGN
(1.61)
(1.41)
(-3.42)***
(-3.15)**
-0.001
-0.001
0.000
0.000
EPS
(-0.94)
(-0.75)
(1.10)
(1.00)
-0.284
-0.285
0.067
0.067
SURPIRSE
(-20.61)***
(-20.64)***
(7.82)***
(7.91)***
0.001
0.001
0.002
0.002
DUALITY
(0.15)
(0.28)
(0.82)
(0.92)
0.020
0.019
-0.012
-0.012
INSIDEB
(1.41)
(1.31)
(-1.45)
(-1.46)
0.035
0.031
-0.125
-0.125
INSIDEM
(0.41)
(0.36)
(-2.53)*
(-2.53)*
0.000
0.000
-0.029
-0.030
FOREIGN_INST
(0.02)
(0.02)
(-3.52)***
(-3.55)***
0.038
0.039
-0.018
-0.017
INDE
(3.09)**
(3.12)**
(-2.51)*
(-2.31)*
0.021
0.020
-0.008
-0.008
BOARDSIZE
(3.44)***
(3.31)***
(-2.22)*
(-2.32)*
Yes
Yes
Yes
Yes
Industry and year effects
1,340
1,340
1,340
1,340
N
adj. R2
0.331
0.332
0.136
0.138
All other variables are as defined in Appendix 1. t statistics in parentheses.***, **, * indicate
significance at the 1%, 5% and 10% level, respectively
59