family ownership structure and non-gaap disclosures
TRANSCRIPT
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Family ownership structure and non-GAAP disclosures
The effect of family ownership structure on the quality of non-GAAP disclosures
Name: Lynn Zhong
Student number: 11417382
Thesis supervisor: David Veenman
Date: 25 June 2018
Word count: 19,476
MSc Accountancy & Control, specialization Accountancy
Faculty of Economics and Business, University of Amsterdam
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Statement of Originality
This document is written by student Lynn Zhong who declares to take full responsibility for
the contents of this document.
I declare that the text and the work presented in this document is original and that no sources
other than those mentioned in the text and its references have been used in creating it.
The Faculty of Economics and Business is responsible solely for the supervision of completion
of the work, not for the contents.
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Abstract
This study examines the relation between family ownership structure and the quality of non-
GAAP disclosures. The largest and most recent hand collected dataset of Bentley et al. (2018),
key variables data collected from Compustat and the family ownership dataset from
Anderson’s website were used. From the 2004 to 2009 sample, I find that the overall quality
of the total exclusions of non-family firms are of relatively high quality. The total exclusions
of family firms appear to be of lower quality as the total exclusions are associated with future
operating cash flows. When I decompose total exclusions into special item exclusions and other
item exclusions, I find that the special item exclusions of non-family firms appear to have
significant predictive power for future operating earnings and cash flows. Also, the special
items exclusions of family firms are significantly associated with future operating cash flows.
Thus, these exclusions of both family and non-family firms are of very low quality. This
implies that managers have adapted their earnings management mechanisms by shifting
recurring transactions (i.e. from other item exclusions) into special item exclusions. After when
the SEC updated non-GAAP reporting regulation in 2010, I find that family firms provide high
quality total exclusions, special item exclusions and other item exclusions. However, now, the
other item exclusions of non-family firm have significant predictive power for future operating
earnings and cash flows. Furthermore, I do not find significant difference in the magnitude of
other item exclusions between family firms and non-family firms. In summary, I conclude that
the overall quality of non-GAAP disclosures of family firms are higher compared to those of
non-family firms. In addition, I conclude that non-family firms are more likely to engage in
aggressive non-GAAP reporting compared to family firms.
Keywords: Family ownership structure; non-GAAP earnings; non-GAAP exclusions; Special
items; Other exclusions.
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Contents
1 Introduction ........................................................................................................................................ 4
2 Literature review ............................................................................................................................... 9
2.1 Capital market, information asymmetry and corporate disclosure ..................................... 9
2.2 Non-GAAP disclosures .......................................................................................................... 11
2.3 Types of exclusions .................................................................................................................. 13
2.4 Non-GAAP disclosure quality ............................................................................................... 15
2.5 Motives for non-GAAP exclusions ....................................................................................... 16
2.6 Investors reactions to non-GAAP earnings ......................................................................... 17
2.7 Regulation G ............................................................................................................................. 18
2.8 Family ownership structure .................................................................................................... 19
2.9 Agency problems and family ownership ............................................................................... 20
3 Hypothesis development ............................................................................................................... 23
3.1 Family ownership structure and non-GAAP disclosure quality ........................................ 23
3.2 Magnitude of non-GAAP exclusions .................................................................................... 24
4 Research methodology .................................................................................................................. 26
4.1 Data and sample ....................................................................................................................... 26
4.2 Data collection Compustat ..................................................................................................... 27
4.3 Family ownership dataset ........................................................................................................ 28
4.4 Libby boxes ............................................................................................................................... 29
4.5 Constructs and variables ......................................................................................................... 30
4.5.1 Independent variables, measure of family ownership ................................................ 30
4.5.2 Dependent variables, measures of non-GAAP disclosures quality .......................... 30
4.5.3 Control variables .............................................................................................................. 31
5 Results ............................................................................................................................................... 34
5.1 Descriptive statistics ................................................................................................................ 34
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5.2 Pearson correlation matrix ...................................................................................................... 36
5.3 Main tests hypothesis 1 OLS models .................................................................................... 38
5.4 Results hypothesis 2 ................................................................................................................. 42
5.5 Summary of results................................................................................................................... 43
6 Conclusions ...................................................................................................................................... 47
6.1. Limitations ..................................................................................................................................... 49
References ................................................................................................................................................. 50
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1 Introduction
Accounting information is important for firms in acquiring resources on both equity and debt
markets. Moreover, financial statement users appreciate high quality accounting information
because it provides them with insider information and consequently reduces information
asymmetry, increases transparency, and provides better contracting devices (Watts and
Zimmerman, 1986). Financial statement users are able to make better economic decisions
because high quality financial statements contain more reliable and useful information which
give a better reflection of the underlying economic performance of a firm.
The aim of my thesis is to investigate whether family ownership structure affects the
quality of non-GAAP earnings measures disclosed by firms. More specifically I will investigate
whether family ownership structure leads to a higher non-GAAP disclosure quality. This leads
to the following research question: Does family ownership structure have a positive effect on
the quality of non-GAAP disclosures in the United States?
Healy and Palepu (2001) argue that managers have superior information about their
firms’ performance relative to outside investors. Information about a firm’s current and future
performance is communicated through the financial statement of a firm. In addition to the
required GAAP disclosures, Securities and Exchange Commission allows managers to
voluntarily disclose additional useful information through, for example, non-GAAP
disclosures. Non-GAAP numbers are derived from adjustments to GAAP earnings numbers by
managers and are not subject to mandatory audit. The number of firms which report non-GAAP
earnings in addition to the required GAAP earnings are increasing and has become a common
practice (Black and Christensen, 2009; Choi et al., 2007; Hitz, 2010). In the early 2000s around
300 firms in the S&P 500 report non-GAAP earnings (Baumker et al., 2014).
Whether non-GAAP disclosures are informative depends on the motives of the manager
who reports the numbers. There is an ongoing debate in the literature about the added value of
non-GAAP earnings. On the one hand, managers can use non-GAAP earnings to convey
relevant information to users. On the other hand, managers can misuse the discretion in
calculating non-GAAP earnings by creating more positive perception of their firms’ current
and future performance. This could be a way to strategically meet earnings forecasts or to
mislead investors by manipulating their perception of the firms’ performance. (Bhattacharya et
al., 2004; Black and Christensen, 2009; Chen et al., 2012; Lougee and Marquardt, 2004;
Walker and Louvari, 2003). Also, non-GAAP reporting is considered a relatively low-cost form
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of perception management compared to accruals or real earnings management (Black and
Christensen, 2009).
According to Doyle et al. (2003), non-GAAP disclosures are of high quality when the
items excluded do not have predictive power for future firm performance. Existing research
suggests that some stakeholders rely on non-GAAP earnings more than GAAP earnings for
economic decision making (Bhattacharya et al., 2004; Doyle et al., 2003; Lougee and
Marquardt 2004; Bowen et al., 2005; Bradshaw et al., 2017). Andersson and Hellman (2007)
find strong reactions from unsophisticated investors and analysts relative to sophisticated ones.
So, investigating the quality of non-GAAP earnings is relevant because such disclosures have
direct impact on different stakeholders and non-GAAP reporting is at an all-time high.
Furthermore, family ownership plays a big role in the financial market. Family
ownership is a common ownership structure in firms all over the world. For example,
companies such as Nike, Volkswagen, Samsung, Oracle, Facebook and Walmart are only a
few of the many family owned firms around the world (Stern, 2015). According to Anderson
and Reeb (2003), about one-third of the S&P 500 are family owned. Prior studies show that the
ownership structure of a firm have an effect on corporate disclosure quality. Therefore, I expect
family ownership structure to have an effect on non-GAAP disclosure quality.
However, findings about the effect of family ownership on corporate disclosure quality
are controversial (Fan and Wong, 2002; Sánchez-Ballesta and García-Meca, 2007). This
controversy is caused by Type I and Type II agency conflicts. Type I agency conflict refers to
the problem caused by separation of ownership and management and Type II agency conflict
is the conflict between controlling and non-controlling shareholders (Gilson and Gordon,
2003). Thus, empirical studies provide inconsistent results about whether family ownership has
a positive effect on financial reporting quality. Therefore, I find it interesting to investigate the
extent to which family ownership structure affects non-GAAP disclosure quality.
By researching the relation between family ownership structure and the quality of non-
GAAP disclosures, I add to the literature of family ownership and the discussion about non-
GAAP reporting. Investigating the quality of non-GAAP disclosures is relevant because
current literature shows that there is a difference between the financial reporting quality of
family owned firms and non-family owned firms. Several studies examine the linkage between
family ownership and financial reporting quality (e.g., Ali et al., 2007; Ghosh and Tang, 2015;
Wang, 2006), but the results are inconclusive and controversial. There are several studies that
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investigate corporate disclosure quality between family and non-family owned firms (Jiraporn
and Dadalt, 2009; Wang, 2006; Zhao and Millet- Reyes, 2007). However, there is no existing
study which explores the relation between family ownership structure and non-GAAP
disclosure quality. Thus, my thesis will be the first study to examine this relation.
Following Whippel (2015) and similar to Doyle et al. (2003) and Kolev et al. (2008), I
examine the quality of non-GAAP disclosures by using a dual approach. I regress the total
exclusions, special item exclusions and other item exclusions with future operating earnings
and cash flows. The dataset used for the regression analyses comprises of three different
datasets. The first database is the largest and most recent hand collected dataset of Bentley et
al. (2018) which identifies GAAP and non-GAAP reporting with at least 95% accuracy. The
second dataset is collected from Compustat. Key variables such as total assets,
Common/Ordinary Equity, Earnings Per Share (Diluted) were extracted from Compustat
Fundamentals Quarterly database based on the unique GVKEYS from Bentley et al. (2018)
dataset. Lastly, to proxy for family ownership, the hand collected dataset from Anderson’s
website was used. After merging these datasets in Stata, a final sample of 9,721 firm quarters
observations was identified.
The descriptive statistics show that the non-GAAP earnings per share of firms are more
positive than their GAAP earnings per share on average. This confirms the finding of Baumker
et al. (2014) and implies that the exclusions made by managers commonly result in a more
positive non-GAAP earning compared to GAAP earnings, which could be a sign of aggressive
non-GAAP reporting. Next, the results from the Pearson correlation matrix show that non-
GAAP earnings are more permanent and more value-relevant than GAAP earnings. This
finding is similar to that of Kolev et al. (2008) where they find that non-GAAP earnings are
more positively associated with future firm performance than GAAP earnings. In addition, I
find that non-GAAP exclusions have predictive power for future firm performance. In line with
Doyle et al. (2003) the Pearson matrix shows significant positive correlation between total
exclusions and future operating earnings and cash flows.
I examine the relation between family ownership structure and non-GAAP disclosure
quality in two separate timelines. The first period is from 2004 to 2009, which is the time after
the initial implementation of Regulation G. The second period ranges from 2010 to 2011, which
is the time when the SEC updated non-GAAP reporting regulation. The reason why I look at
these two timeframes separately is because the extant literature find that quality of non-GAAP
disclosures has improved overall after Regulation G. However, firms were allowed to exclude
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recurring items (i.e., other item exclusions) the update in 2010, even if they do not meet the
previous requirements of “non-recurring, infrequent or unusual” (Webber et al., 2013). Other
item exclusions are considered low quality in the existing literature. Therefore, I want to
examine whether family firms and non-family firms report more other item exclusions after the
update.
First, from the regression results of the period 2004 to 2009, I do not find conclusive
evidence that family firms provide higher quality non-GAAP disclosures compared to non-
family firms. While the total exclusions of family firms do not appear to have significant
predictive power for future operating earnings, these exclusions have significant predictive
power for future operating cash flows. In addition, the special items exclusions of family firms
are significantly associated with future cash flows.
Next, examining the non-GAAP reporting behavior of non-family firms from the period
2004 to 2009, I find that the total exclusions of non-family firms are in overall of relatively
high quality. These exclusions do not have significant predictive power for future operating
earnings and cash flows. However, while the other item exclusions are not associated with
future firm performance, I consistently find that the special item exclusions of non-family firms
have significant predictive power for future operating earnings and cash flows.
In the 2010 to 2011 period, it appears that none of the exclusions made by family firms
have significant predictive power for future operating earnings and cash flows. Thus, it can be
concluded that the overall quality of family firm’s non-GAAP disclosures is of high quality
and the transactions excluded are mainly of transitory and non-cash in nature. However, in
contrast to the findings of the 2004 to 2009 period, now the other item exclusions of non-family
firms are significantly associated with future operating earnings and cash flows. In addition,
the total exclusions of non-family firms have predictive power for future operating cash flows
as well. This indicates that non-family firms are more likely to engage in aggressive non-GAAP
reporting.
Because other item exclusions are considered to be of low quality (Doyle et al,. 2003),
I examined whether the magnitude of these exclusions is higher for non-family firms compared
to family firms. Surprisingly, I do not find significant difference in the magnitude of other item
exclusions between family and non-family firms.
In summary, I find that only in 1 of the total of 4 regressions for family firms, the total
and special exclusions have predictive power for future firm performance. In contrast, in all 4
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regressions for non-family firms, either total exclusions, special item exclusions or other item
exclusions have predictive power for future earnings. Thus, it can be concluded that the overall
quality of non-GAAP disclosures is higher for family firms compared to non-family firms. This
evidence supports the findings of Wang (2006), which imply that family firms have stronger
incentive to provide higher quality corporate disclosures as an effort in preventing litigations
or damage to their firm’s reputation.
The results from this thesis contribute to the extant family ownership and non-GAAP
literature in several ways. First, I am the first to examine the relation between family ownership
structure and non-GAAP earnings. By providing new insight, I contribute to the extant family
ownership literature and non-GAAP literature. I find that family firms provide higher quality
non-GAAP disclosures compared to non-family firms. This finding is in line with the finding
of Gilson and Gordon (2003), that the type I agency problem (i.e., alignment effect) is more
severe in non-family firms in the United States. Furthermore, unlike prior research, my findings
also imply that family ownership does not lead to a more severe type II agency problem (i.e.,
the entrenchment effect). The evidence of family firms providing higher quality non-GAAP
disclosures than non-family firms is in line with family firms being more risk-adverse,
conservative and long-term oriented (Lins et al., 2013).
Furthermore, my findings have implications for regulators. Although the overall quality
of non-GAAP disclosures improved after the implementation of Regulation G in 2003, I find
that after the update of non-GAAP reporting regulation in 2010, non-family firms demonstrate
aggressive non-GAAP behavior. Hence, the concerns of financial regulators about the possible
misleading motives of non-GAAP disclosures may be justified.
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2 Literature review
2.1 Capital market, information asymmetry and corporate disclosure
Capital market
An ideal capital market is one where all available information for economic decision making
is fully reflected in prices. Capital market refers to a financial market where debt and equity
are bought or sold. For example, entities that need funding can acquire funding from investors.
The allocation of ownership of the economy’s capital stock is considered the main role of the
capital market. Improving the efficiency of transactions and decreasing the work that an entity
has to do, such as searching, analyzing, making legal contracts and completing transfers is the
fundamental role of an efficient capital market (Fama, 1970).
Information asymmetry
The demand for financial reporting originates from the information asymmetry problem.
Information asymmetry occurs when the amount of information is unequally distributed
between two parties in a business transaction (Chang et al., 2008). This leads to one party
having more or better information, which creates an imbalance of power in business
transactions.
In theory there are two types of information asymmetry: (1) adverse selection and (2)
moral hazard. Adverse selection occurs when one party in a business transaction has an
information advantage over the other parties (Scott, 2012). In this situation, the party with
superior information could exploit their advantage for their own gain at the expense of other
parties. Consequently, the party with less information will realize their disadvantage and thus
invest less. However, the problem of adverse selection can be reduced by financial reporting,
as this is a way to transfer private information to outsiders which in turn reduces information
asymmetry (Scott, 2012). The second type of information asymmetry, moral hazard, occurs for
example, when there is a separation between ownership and control between management and
shareholders. In this instance, management is supposed to do its best to act in the best interest
of the shareholders. However, as the shareholders cannot perfectly monitor the actions of the
management, the management could use this as an opportunity to demonstrate shirking
behavior.
In summary, the higher the degree of information asymmetry between a firm and the
market is, the more difficult it becomes for investors to evaluate and predict the performance
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of the firm (Chang et al., 2007). Moreover, Francis et al. (2005) find that information
asymmetry lowers the transparency of accounting disclosures. In accordance, when
information asymmetry is reduced, it results in better market efficiency and lower cost of
capital (Bleck and Liu, 2007). These findings indicate that the quality of corporate disclosures
is influenced by information asymmetry.
Corporate disclosures
Financial regulators like the International Accounting Standards Board (hereafter IASB),
Financial Accounting Standards Board (FASB) and the Securities and Exchange Commission
(SEC) oversee capital markets to protect stakeholders and investors against non-transparent or
misleading disclosures resulting from information asymmetry and agency conflicts. The FASB
enforces a set of rules referred to as the Generally Accepted Accounting Principles (GAAP) to
ensure consistency in accounting practices and methods. In the United States the SEC requires
publicly-traded company to comply with the GAAP guidelines when disclosing financial
reports. These guidelines encompass the details, complexities, and legalities of business and
corporate accounting. To ensure that firms comply with the GAAP rules, their GAAP numbers
are audited (O'Sullivan and Sheffrin, 2003). The main aim of GAAP is to safeguard the
consistency and quality in accounting practices across firms so that the users of the financial
statements can easily compare the financial performance of firms.
In addition to mandatory disclosures, firms can publish voluntary information such as
non-GAAP earnings, press releases, sustainability reports and management forecasts to
communicate alternative information to stakeholders. Moreover, intermediaries such as
financial analysts and the financial press also provide their own adjusted measures about a
firm’s financial performance. These alternative measures are derived from GAAP earnings and
adjusted to better value the ‘core’ earnings of a firm (Bradshaw and Sloan, 2002). Manager
adjusted GAAP earnings are referred to as non-GAAP earnings in the literature while analyst
adjusted earnings (i.e., analyst earnings forecast) are called “street” earnings. The most
commonly used forecast data providers (FDP) earnings metric is earnings per share which can
be on GAAP basis or non-GAAP basis. These earnings metrics can be extracted in databases
like I/B/E/S and Compustat and are widely used to calculate a firm’s earnings surprise.
Earnings surprise occurs when the realized earnings of a firm differs from the earnings forecasts
made by analysts. It essentially estimates the ‘news’ at earnings announcements.
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In the last decades it has become commonplace for firms to disclose non-GAAP
earnings as a substitute of GAAP earnings. This raised concerns for the FASB because non-
GAAP disclosures are not subject to mandatory audit and therefore its information content is
not validated. According to Collins et al. (1997), GAAP earnings have become noisier and less
informative about a firm’s underlying economic performance as the frequency of one-time
items included by firms have increased. In the extant non-GAAP theory there are two main
categories of items which managers and analysts exclude from GAAP earnings to arrive at their
alternative earnings. First there are special items exclusions, which are one-time items such as
litigation charges. Then there are other item exclusions, which relate to recurring expenses such
as amortization. Other item exclusions are the remaining exclusions after special item
exclusions (Whipple, 2015).
According to managers and analysts, the aim of providing non-GAAP earnings is to
emphasize cash flows and to provide numbers which will help investors better understand a
firm’s true performance (Doyle et al., 2003). Stakeholders such as analysts and investors often
use other information than GAAP earnings for evaluation purposes. Usually they use GAAP
earnings as a basis and exclude items which they consider to be transitory or non-cash.
Managers and analysts claim that those items excluded do not reflect the firm’s ‘core’
performance properly (Doyle et al., 2003). The steady growth of non-GAAP metrics is a direct
reflection of the demand for metrics other than GAAP-earnings. It also reflects that GAAP
earnings are becoming less value relevant as investors consider these numbers to be more noisy
than non-GAAP disclosures (Ribeiro, 2016). Thus, it is of importance to gain further
knowledge about non-GAAP reporting as they are valued by investors and are used in valuation
and investment decisions.
2.2 Non-GAAP disclosures
Non-GAAP earnings are an alternative measure of firm performance which is often used by
managers in addition to GAAP earnings. Non-GAAP earnings disclosures have been ranked
by managers as the most important voluntarily disclosed financial metric for external
shareholders (Graham et al., 2005). Because managers have discretion in disclosing non-GAAP
earnings, these earnings have a low comparability across firms (Bhattacharya et al., 2004;
Marques, 2006). Typically, non-GAAP earnings depict adjusted-GAAP measure such as
earnings before metric (EB). Earnings before interest and taxes is the most common metric of
EB used.
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Alternative financial measures have become common among large companies.
According to Jagannath and Koller (2013), the 25 largest non-financial companies in the United
States all reported non-GAAP earnings. Similarly, in the S&P 500 almost 90% of the firms
report at least one or more non-GAAP earnings measures along with GAAP numbers. Firms
often present non-GAAP earnings in addition to GAAP earnings to make their firm’s financial
performance better understandable for stakeholders.
An example of non-GAAP earnings is Earnings before interest, taxes, depreciation and
amortization (EBITDA). The formula for EBITDA is earnings before interest and tax +
depreciation + amortization. EBITDA measures a firm’s performance independently of its tax
environments and financing- and accounting decisions. It is calculated by adding back the non-
cash expenses of depreciation and amortization to a firm's operating income. By doing this, the
impact of non-operating decisions are excluded and the outcome of operating decisions are
better revealed (Bhattacharya et al., 2004; Marques, 2006).
Existing literature shows contradicting evidence of a manager’s motives to disclose
non-GAAP earnings. It appears that the motive can be informative or opportunistic (Bradshaw
and Soliman, 2007; Beyer et al., 2010; Young, 2014). There is a debate about whether non-
GAAP earnings add value (Bradshaw and Sloan, 2002; Hirshleifer and Teoh, 2003). Managers
have superior insider information about the firm’s current and future performance which
outsiders do not have (Healy and Palepu, 2001). Therefore, managers can voluntarily choose
to communicate this information through non-GAAP earnings disclosures. But on the other
hand, managers can also misuse this discretion by communicating information which are self-
serving in nature (e.g., earnings-based compensation).
Moreover, Bhattacharya et al. (2004) claim that non-GAAP earnings are valued by
investors. Expenses like stock-based compensation, amortization of intangible assets are
aggregated into R&D expenses or cost of goods sold instead of separate line items. This makes
it hard for investors to disaggregate those items. In this case non-GAAP disclosures can be
helpful as it makes these items more salient. Consequently, investors can better disaggregate
earnings into components which will help them value a firm’s future performance (Leung and
Veenman, 2018). Lougee and Marquardt (2004) find that non-GAAP earnings are especially
informative about future firm performance when GAAP earnings are less informative.
According to Johnson and Schwarts (2005), non-GAAP disclosures are informative and
lead to improvement of the non-GAAP disclosure quality. This is because items considered to
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be transitory or non-recurring are excluded, given that they do not accurately reflect a firms’
financial health or future performance. Bradshaw and Sloan (2002) and Bhattacharya et al.
(2004) find the same results. By separating expenses like stock-based compensation and the
amortization of acquired intangible assets, non-GAAP disclosures can be informative and
useful for investors (Hirshleifer and Teoh, 2003).
However, Bhattacharya et al. (2004) and Bowen et al. (2005) argue that non-GAAP
earnings disclosures are often used to manipulate the capital market. Prior financial research
finds evidence that when the GAAP earnings of firms fail to meet forecast earnings
benchmarks, these firms are more motivated to use non-GAAP earnings as a substitute of
earnings management to meet the forecasted earnings (Dechow et al., 2003). As a result, the
quality of non-GAAP disclosures could be negatively impacted by such motives.
2.3 Types of exclusions
Whether non-GAAP earnings are useful to stakeholders is debated in the non-GAAP literature.
Adjusted numbers (e.g., non-GAAP earnings and street earnings) can be divided into special
item exclusions and other item exclusions. Together they represent the total exclusions by
managers or analysts.
Special item exclusions are related to non-recurring items which are transitory in nature.
These items are typically one-time expenses such as litigation costs. Studies find that when
non-GAAP earnings are informative, it is because of transitory item exclusions which is
referred to as special item exclusions in the literature (Bradshaw and Sloan, 2002; Bhattacharya
et al., 2004).
Other item exclusions relate to recurring items such as stock-based compensation,
depreciation and amortization of intangible assets (Whipple, 2015). However, other items have
features which are hard to identify. For example, impairment of special assets and recurring
items that do appear frequently. Moreover, the magnitude of other item exclusions has
increased tremendously in recent years. At least 78% firms that report non-GAAP earnings
exclude other items in 2012 (Whipple, 2015). Other item exclusions are viewed as low quality
in the extant non-GAAP studies and are considered as an indication of aggressive non-GAAP
reporting (e.g., Black and Christensen, 2009; Brown et al., 2012; Black et al., 2018). Similarly,
Doyle et al. (2003) find that other item exclusions have significant predictive power for future
firm performance. This means that these exclusions could be based on opportunistic motives
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like meeting strategic benchmarks. In contrast, Whippel (2015) find that recurring items could
be informative because the usefulness of these items differ per firm.
Whipple (2015) examines the motives of other item exclusions by managers and
analysts in the post-Regulation G period. Even though Regulation G seem to have reduced the
opportunistic motives for disclosing non-GAAP earnings, it was not clear which incentives
motivate managers and analysts to exclude other item exclusions. To get a deeper
understanding of other item exclusions, he distinguishes between recurring other items and
transitory other items in his research. Following a broad series of prior non-GAAP studies, he
used I/B/E/S/ to identify non-GAAP earnings. In order to identify other item exclusions, he
compared the difference of non-GAAP earnings between I/B/E/S and Compustat. Whippel
(2015) also used hand collected data which entailed specific detailed transactions of other item
exclusions. In contrast to previous non-GAAP studies he finds that the incentive to exclude
other items is mainly informative as those exclusions relate to non-cash items which investors
heavily discount in valuing firm performance. His results show that over 70% of other item
exclusions can be attributed to one-time items (29%), stock-based compensation (22%) and
amortization (21%) (Whipple, 2015). Thus, other item exclusions comprise of transitory and
recurring items. In line with transitory other item exclusions being non-cash items, these items
are not associated with future cash flows. In contrast, less common exclusions like investment
gains and losses are positively associated with future cash flows.
Moreover, he finds that an important incentive to excluding other items is to have
comparable actual performance calculations to the ex-ante non-GAAP earnings forecasts of
analysts (Whippel, 2015). Thus, investors can compare ex ante performance forecasts to the ex
post ones. Managers and analysts claim that other item exclusions are informative to investors
because they are non-cash in nature and are not so relevant in evaluating firm performance.
Even though the incentive to exclude other items appears to be mainly of informative nature,
Whippel (2015) also find evidence that opportunism can still occur in certain situations where
a negative GAAP surprise is changed to positive by other item exclusions.
Similarly, Black et al. (2018) examining non-GAAP disclosures from the period 2009
to 2014 find that the magnitude of non-GAAP disclosures has increased. Moreover, they find
that this increase is caused by non-recurring exclusions, which almost doubled in size over
their sample period. According to Johnson et al. (2011), the following one-time transactions
can be classified as special item exclusions: merger and acquisition expenses, goodwill
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impairment, extinguished debt charges, asset dispositions gains or losses, restructuring charges
and litigation settlements.
2.4 Non-GAAP disclosure quality
A common way of measuring the quality of non-GAAP disclosures is to examine how non-
GAAP exclusions are associated with future firm performance. For example, Doyle et al.
(2003) argue that this way of measuring non-GAAP disclosure quality is in line with the claim
of managers and analysts that excluded items do not reflect the ‘core’ earnings of a firm.
According to this claim, non-GAAP exclusions should have zero association with future firm
performance. To test this claim Doyle et al. (2003) regressed future cash flows on total non-
GAAP exclusions to examine the quality of these exclusions. In addition, they decomposed
total non-GAAP exclusions into special item exclusions and other item exclusions to examine
the predictive power of these different types of exclusions as well.
However, Easton (2003) questioned whether using future cash flows to assess the
quality of non-GAAP exclusions was desirable because current liabilities could have
implications for future cash flows. Following this claim, Kolev et al. (2008) took a different
approach by regressing future operating earnings on non-GAAP exclusions.
Some researchers criticized using future earnings as the dependent variable in
regressions because it could lead to a mechanical relation between current non-GAAP
exclusions and future firm performance (Black et al., 2018). Consequently, it would result in
no association between current transitory exclusions (i.e., special item exclusions) but there
would be a mechanical association between other item exclusions and future firm earnings.
Several researchers like Whipple (2015) and Leung and Veenman (2018) follow both
the approaches of Doyle et al. (2003) and Kolev et al. (2008) by applying a dual approach.
They test both the association between future cash flows and future earnings on non-GAAP
exclusions. This is in accordance with the claims of managers that non-GAAP exclusions are
non-cash in nature and thus do not reflect the ‘core’ earnings. A dual approach gives more
insight into the different types of exclusions (i.e., special item exclusions and other item
exclusions) made by managers.
Similarly, non-GAAP exclusions and the type of exclusions (i.e., special item
exclusions and other item exclusions) are measures of non-GAAP disclosures quality. Doyle
et al. (2003) and Kolev et al. (2008) find that special item exclusions represent high quality
16
exclusions as they are mostly not predictive of future firm performance. Other item exclusions
are labeled as low quality as these exclusions have significant predictive power regarding future
firm performance. Therefore, examining the type of non-GAAP exclusions can also provide a
better understanding of the motives behind these exclusions.
In addition, the quality of non-GAAP exclusions can also be measured by examining
how investors price non-GAAP earnings and by testing whether the motive of firms to provide
non-GAAP disclosures is to meet strategic benchmarks. According to Black and Christensen
(2009) and Doyle et al. (2013) firms that fail to meet GAAP earning targets can use non-GAAP
earnings as a strategic tool to meet benchmarks. Therefore, such practice is considered low
quality as it is evidence of aggressive non-GAAP reporting.
Most recently, Bradshaw et al. (2017) used a different measure to examine non-GAAP
disclosure quality. They used a ‘consensus’ metric developed by Barron et al. (1998). They
examined whether non-GAAP reporting leads to more common versus idiosyncratic belief in
a firm's information environment.
2.5 Motives for non-GAAP exclusions
The motives for providing non-GAAP disclosures have been extensively researched in the non-
GAAP literature. The motives can be divided in two categories: informative or opportunistic.
There are different factors which could influence the motives of managers to disclose
alternative information such as non-GAAP earnings.
For example, Leung and Veenman (2018) examined whether there is a difference in the
incremental information of non-GAAP disclosures and GAAP earnings in loss firms for
forecasting and valuation purposes. They argue that the demand for supplemental disclosures
is higher for loss firms because there is an increase of uncertainty about the firm’s future and
it is more difficult for investors to comprehend the valuation implications of losses. Loss
components are hard to understand because the variation of the persistence of loss is very wide.
Consequently, managers have the motive to inform investors better by providing additional
information which in turn reduces the uncertainty and helps investors to understand the loss
numbers better.
To examine the informativeness of non-GAAP and GAAP earnings in loss firms, Leung
and Veenman (2018) tested the power of these earnings in predicting future operating earnings
and cash flows. The testing was done for two categories: (1) loss firms that only reported GAAP
17
numbers and (2) loss firms that report non-GAAP numbers in addition to GAAP earnings. They
find that the non-GAAP earnings disclosures in loss firms are highly informative about the
nature of GAAP losses compared to firms that only provide GAAP earnings. This means that
those earnings disclosures are significantly predictive of the future performance of the firm.
Compared to profitable firms, the non-GAAP disclosures of loss firms are less strategic in
nature.
Moreover, they also find that investors perceive these non-GAAP disclosures as
informative compared to GAAP only loss firms (Leung and Veenman, 2018). Loss firms that
disclose non-GAAP earnings have better future performance and are not overvalued by
investors compared to GAAP only loss firms. However, their findings are not fully conclusive
because they also find that non-GAAP disclosures reflect both informative and opportunistic
motives. Leung and Veenman (2018) conclude that managers in loss firms disclose non-GAAP
earnings that are GAAP losses which are converted to a non-GAAP profit, to signal the strength
of cash flows underlying earnings instead of hiding poor firm performance. Overall, they find
that the motive of loss firms to disclose non-GAAP earnings are of informative nature rather
than for strategic reasons.
Furthermore, Leung and Veenman (2018) make a distinction in their research between
non-GAAP earnings and street earnings. Non-GAAP earnings are adjusted GAAP earnings by
managers while street earnings are analyst adjusted earnings for earnings forecasts purposes.
According to Bentley et al. (2018), managers and analysts do not have the same motives for
providing alternative earnings measures such as non-GAAP earnings. Therefore, they could
exclude different items in calculating non-GAAP earnings. A commonly excluded recurring
item is for example stock-based compensation. Managers exclude stock-based compensation
transactions to highlight the differential nature of this item because the predictive ability of this
item varies per firm (Barth et al., 2012). Furthermore, excluding this item is supposed to aid
investors in understanding the implications of such items for forecasting and valuation.
2.6 Investors reactions to non-GAAP earnings
Due to the increasing volume of non-GAAP disclosures and the potential effects it could have
on investor’s reactions, numerous research investigated this potential effect. For example,
Elliott (2006) conducted an experiment examining how the appearance of voluntary disclosures
affects non-professional investors and analyst reliance, judgement and capital allocation
decisions. She finds evidence indicating that non-professional investors rely more on non-
18
GAAP disclosures when these disclosures are more emphasized than GAAP earnings in press
releases. However, Elliott (2006) also find that the before mentioned effect is mitigated when
non-GAAP earnings are reconciled with GAAP earnings. Likewise, Allee et al. (2007) find
that less-sophisticated investor’s decisions are indeed affected by how non-GAAP earnings are
presented in press releases. Unlike Elliott’s finding, they do not find that reconsolidation of
non-GAAP earnings with GAAP earnings mitigates this effect. Furthermore, Frederickson and
Miller (2004) experimental research find that unlike sophisticated investors, less-sophisticated
investors do react to and are affected by non-GAAP earnings. Although sophisticated
stakeholders are less susceptible to the influence of non-GAAP numbers, Andersson and
Hellman (2007) find that analysts’ forecast can be influenced by non-GAAP earnings. Also,
sell-side analysts base their forecast revisions on non-GAAP numbers (Bhattacharya et al.,
2003)
Whether non-GAAP disclosures are informative or misleading is thus inconclusive. On the
one hand, Bhattacharya et al. (2003) claim that investors find non-GAAP earnings more
informative and more useful in reflecting the core business of a firm than GAAP earnings. On
the other hand, Black et al. (2017) find that firms develop non-GAAP earnings measures to
increase the informativeness of the earnings numbers.
2.7 Regulation G
Non-GAAP earnings are voluntary and not formally audited. Consequently, financial statement
users and regulators have expressed concerns about the possible opportunistic motives of
managers in disclosing non-GAAP earnings. In response to the highly publicized alleged
misuse of pro-forma disclosures such as the WorldCom and Enron scandals, the United States
Congress ordered the SEC to issue new guidelines governing the presentation of non-GAAP
metrics. The aim of introducing these guidelines was to improve the quality and transparency
of financial accounting information. Also, an important objective was to limit the opportunistic
use of non-GAAP measures (Section 401(b) of the Sarbanes-Oxley Act of 2002).
As a result, the SEC released Regulation G (SEC 2003) on January 2003. Regulation G
requires firms that disclose non-GAAP earnings in preliminary earnings announcements to (1)
clearly reconcile non-GAAP earnings to GAAP earnings with equal emphasis on both figures,
(2) to adequately label non-GAAP measure, (3) to classify non-recurring items only when it is
characterized as such and (4) to present non-GAAP earnings in a non-misleading way (Heflin
and Hsu, 2008).
19
One stream of literature suggests that Regulation G has had a positive impact on the
quality of non-GAAP exclusions. According to Black et al. (2012), investors show more
interest in non-GAAP earnings than GAAP earnings after Regulation G went into effect. Black
et al. (2012) claim that investors consider non-GAAP disclosures more informative about the
performance of firms than GAAP earnings. Kolev et al. (2008) find that non-GAAP exclusions
are in overall more transitory in nature and are of informative motive. However, their findings
also indicate that Regulation G might have led to unintended consequences because the quality
of non-recurring items (i.e., special item exclusions) appear to have decreased in the post
Regulation G period. In addition, Whipple (2015) find that even though recurring items (i.e.,
other item exclusions) remained common after Regulation G, the quality of these items
increased and were of less misleading nature. This could be a demonstration of how managers
adapted to the new rule. He further concludes that the overall quality of non-GAAP earnings
was higher than before Regulation G was implemented.
In addition, Heflin and Hsu (2008) find that the number of managers using non-GAAP
earnings to meet earnings benchmarks decreased after Regulation G. Furthermore, some
studies show evidence of a decline in non-GAAP disclosures in the post regulation G period
(Heflin and Hsu, 2008; Kolev et al., 2008). Overall, these studies show evidence that
opportunistic motives for non-GAAP reporting have decreased following Regulation G. In
accordance, several studies find that investors’ reaction to non-GAAP disclosures relative to
GAAP disclosures is higher in the post Regulation G period (Black et al., 2012; Marques,
2006). This is an indication of increased confidence in non-GAAP reporting but it also means
that investors are more sensitive to misleading non-GAAP disclosures (Black et al., 2012).
Since 2003 the SEC has updated non-GAAP reporting regulations twice, once in 2010
and another time in 2016. This shows that regulators are still concerned about the strategic use
of non-GAAP disclosures and consider it as a risk to investors. Other studies suggest that the
decline in non-GAAP reporting after the Regulation G implementation in 2003 was only
temporary and that the increase of non-GAAP disclosures have been steady since 1998 (Black
et al., 2012; Bentley et al., 2018).
2.8 Family ownership structure
In theory there are two main firm ownership structures, family ownership structure and non-
family ownership structure (hereafter referred to as family firms and non-family firms
respectively). A majority of firms globally are family owned (Burkart et al., 2003). Family
20
firms are firms that are controlled or managed by founding families or a next of kin. Founding
families can have direct control over a firm through their shares and voting rights. They could
also have indirect control through use of pyramid structures (Sacristán-Navarro and Gómez-
Ansón, 2007).
Founding families are characterized as long-term investors with substantial common
stocks. They have very concentrated but poorly diversified portfolios. Moreover, founding
families typically play an active role in the management or board of directors (Wang, 2006).
Within the S&P 1500 around 62% of founding families hold CEO positions. According to
Cheng (2014), around 98,4% of family firms appoint at least one of their own as a member to
the board of directors. Moreover, more than half of family firms appoint at least two family
members and one fifth appoint three or more family members to a management role. By doing
this, the founding families can ensure that their influence is reflected within the firm.
Publicly traded firms are commonly family owned (Burkart et al., 2003). According to
Anderson and Reeb (2003), about one-third of the S&P 500 firms in the United States are
family owned or family controlled. These families account for approximately 11% of their
firms’ cash flow rights and 18% voting rights (Ali et al., 2007). In Western Europe around 44%
of large firms are family owned (Faccio and Lang, 2002). The highest concentration of family
ownership is in East Asian countries with one-thirds of firms being family owned (Claessens
et al., 2000). Moreover, family owned firms can be found in a broad range of industries such
as high-tech industries, retail, transportation and automobile (Chen et al., 2008).
According to Lins et al. (2013), family firms behave differently compared to non-family
firms. Non-family firms mainly focus on maximizing shareholder value and are more willing
to take risks. In contrast, family firms are mainly focused on the continuality of their firm and
as result are more risk-adverse, more conservative and long-term oriented (Lins et al., 2013).
This could imply that family firms’ non-GAAP disclosures could be of higher quality.
2.9 Agency problems and family ownership
The extant finance literature suggest that firm ownership structure could affect the quality of
financial reporting and voluntary disclosures of firms. According to Ho and Wong (2001),
listed family firms with family members on the board of directors have less transparent
disclosures. Because controlling families are both insiders and shareholders of a firm, they have
unrestrained access to private information and thus have less incentive to disclosure of such
21
information. This suggests that family ownership could impede the quality of financial
reporting.
Studies exploring the effects of family ownership on financial reporting quality have
controversial conclusions due to Type I and Type II agency problems. Type I agency problem
is referred as the alignment effect and is caused by separation of ownership and management.
Type II agency problem is the conflict between controlling and non-controlling shareholders
and is referred to as the entrenchment effect (Gilson and Gordon, 2003).
The alignment effect implies that family firms put a lot of effort in preventing damage
to their firm’s reputation and passing on the business. Therefore, family firms are less likely to
engage in opportunistic behavior in corporate reporting. Subsequently, family firms have
stronger incentive to report higher quality disclosures compared to non-family firms (Wang,
2006). Family firms in which the controlling family have large ownership of the firm, the
managers and management are usually family members which are chosen by the controlling
family (Claessens et al., 2000). Family managed boards in family firms have a long-term
investment perspective and therefore have a strong incentive to monitor managers decisions
(Wang, 2006). Usually family owned firms solve the problem of separation of ownership and
management by placing a family member in a manager position. By doing this they gain a
better control over a manager’s opportunistic behaviors than non-family shareholders are able
to (Anderson et al., 2003). This results in a more aligned interest between the controlling
shareholders and managers and consequently possibly less earnings management. Thus, the
alignment effect could also have implications for the quality of non-GAAP disclosures.
According to Wang (2006), contracting terms for family firms are less sensitive to the quality
of financial information if contracting parties believe that family ownership improves the
corporate governance of the firm. Hence, these parties rely less on financial information to
monitor a firm and thus, in turn the demand for high quality financial information decreases.
The entrenchment effect portrays a negative relation between earnings management and
family ownership (Sánchez-Ballesta and García-Meca, 2007; Wang, 2006). Consistent with the
results of Wang (2006) and Anderson and Reeb (2003), Ali et al. (2007) find that the quality
of financial reports and disclosures of family firms are better than those of non-family firms.
They also find that accounting numbers of family firms contain less errors due to less
managerial distortions due to the higher monitoring and higher management integrity in family
firms. This indicates that family ownership could have a positive effect on the quality of non-
GAAP disclosures as well. Furthermore, family firms have a lower cost of debt and better firm
22
performance than non-family firms (Anderson and Reeb, 2003). Family firms in the United
States have a less severe type I agency problem (i.e., alignment effect) compared to non-family
owned firms (Gilson and Gordon, 2003).
On the other hand, as the level of family shareholdings increases, it could encourage
management entrenchment (Type II agency problem). Type II agency problem occurs when
controlling families demonstrate self-serving behavior where they manipulate earnings
numbers for their own gain at the expense of outside investors (Fan and Wong, 2002). The
entrenchment effect increases information asymmetry and decreases corporate disclosure
quality as the earnings are less informative to outside investors. Type II problem is considered
to be the most serious agency problem in the literature (Claessens et al., 2002; Fan and Wong,
2002).
Fan and Wong (2002) find that the incentive of controlling families to manage earnings
for self-serving purposes increases as the insider ownership of controlling families becomes
larger. This could be due to ineffective monitoring by the board members due to the amount of
family members holding these positions. This could result in inferior corporate governance.
Consequently, earnings informativeness decreases as family ownership increases and
information asymmetry becomes greater between families and outside stakeholders. The
entrenchment effect denotes that family ownership has a positive relation with earnings
management, lower quality and less transparent corporate disclosures (Fan and Wong, 2002;
Sánchez-Ballesta and García-Meca, 2007). Consistent with a body of literature in which
earnings management is used as a measure of opportunistic behavior in financial reporting, a
lower disclosure quality equals a higher degree of earnings management. Thus, there is a
significant difference between the effects of family firms and non-family firms on corporate
disclosure quality. In summary, the results from existing family ownership studies examining
whether family firms produce higher quality financial reports compared to non-family firms is
not conclusive.
23
3 Hypothesis development
3.1 Family ownership structure and non-GAAP disclosure quality
The demand and supply of non-GAAP disclosures are at a growing rate in the United States
and other countries. Consistent with Healy and Palepu (2001) I follow the assumption that
managers have access to superior information relative to outsiders regarding their firms’
current and future performance. Frederickson and Miller (2004), Allee et al. (2007) and
Andersson and Hellman (2007) find empirical evidence that less sophisticated investors and
financial analysts react to non-GAAP earnings. Similarly, Bradshaw and Sloan (2002) argue
that investors prefer to use non-GAAP earnings than GAAP earnings in valuations decisions.
They also find evidence suggesting that stock prices reflect this behavior.
Existing family ownership research finds that family ownership structure affects the
quality of GAAP disclosures. This finding may indicate that family ownership structure could
also have an effect on non-GAAP disclosures. According to Ali et al. (2007), family firms have
higher financial reporting quality compared to non-family firms. However, Chen et al. (2008)
find that corporate disclosures by family firms are less transparent compared to those of non-
family firms. When family members are managers within the family firm, the problem of
separation of ownership and management is largely mitigated (Anderson et al., 2003).
Moreover, because founding families have deep knowledge of their firm and directly monitor
managers, opportunistic behavior is less likely to occur in family firms. This suggests that
family firms’ corporate disclosures are more likely to be of higher quality compared to those
of non-family firms (Ali et al., 2007)
From existing non-GAAP literature, it is not clear what the true intentions of managers
are to disclose non-GAAP earnings. Managers can use the discretion in calculating non-GAAP
numbers to reduce information asymmetry between a firm and its investors by providing
relevant information about the firm’s current and future earnings (Bhattacharya et al., 2003;
Lougee and Marquardt, 2004). However, some studies find that managers show opportunistic
behavior in reporting non-GAAP disclosures to meet earnings benchmarks or to mislead
investors (Black and Christensen, 2009; Doyle et al., 2013).
As discussed before, the quality of corporate disclosures between family firms and non-
family firms depend on the severity of the Type I and Type II agency problems of the firm (Ali
et al., 2007). Thus, whether family firm’s non-GAAP disclosures are of better quality compared
24
to those of non-family firms is an empirical question. It is therefore important and relevant to
investigate the possible effects of the characteristics of family firms on non-GAAP disclosure
practices. My thesis aims to find empirical evidence of whether family ownership structure has
a positive effect on the quality of non-GAAP disclosures. Specifically, based on the
inconclusive and controversial results of existing research, I expect that there will be a relation
between family firms and non-GAAP disclosure quality. Prior family ownership research finds
that the alignment effect is more severe in non-family firms compared to family firms. This is
because the entrenchment effect is largely mitigated in family firms as these firms appoint
family members on the management team to resolve this problem. Therefore, I predict that
family firms are more likely to disclose higher quality non-GAAP earnings metrics than non-
family firms. This leads to the following hypothesis.
H1 Family owned firms provide higher quality non-GAAP earnings compared to non-
family owned firms
3.2 Magnitude of non-GAAP exclusions
The difference between GAAP earnings and pro forma earnings have been steadily increasing
since 1850 according to Bradshaw and Sloan (2002). The magnitude of the difference between
bottom-line GAAP earnings and pro forma earnings is around 20% wherein the pro forma
earnings are more positive.
The Wall Street Journal detected in the second quarter of 2001, that more than half of
the S&P 500 firms reported pro forma earnings which were adjusted from their GAAP
earnings. The magnitude of the exclusions to arrive at these pro forma earnings was so
significant that it led to every 60 cents of each dollar of pro forma earnings being the result of
the exclusions made (Doyle et al., 2003).
Managers claim that by excluding items from GAAP earnings they are able to better
inform investors about their firm’s current and future performance. However, financial
regulators and the financial press remain skeptical and suggests that the motives of managers
to provide using non-GAAP metrics could be to mislead investors. Doyle et al. (2003) find
that when a firm’s difference between GAAP earnings and non-GAAP earnings is large, these
firms are likely to have lower future cash flows and stock returns compared to firms with lower
magnitude exclusions. Consistent with the claim of managers that they exclude items to reflect
the core earnings of their firm, these excluded items should primarily be non-recurring and
25
non-cash in nature. Consequently, the financial markets and its stakeholders do not appreciate
the predictive power of the excluded expenses as they are considered low quality.
Family ownership structure could have unique implications in terms of providing
voluntary disclosures such as non-GAAP earnings. First, family owners typically have a large
but undiversified equity holdings, hence their wealth depends heavily on the performance of
their firm. Subsequently, they are more likely to make an assessment of the benefits and costs
of voluntary disclosures relative to a non-family member.
Family firms have incentive to provide less voluntary disclosures due to their longer
investment horizon, better alignment between managers and family owner, better monitoring
of the manager and less information asymmetry. On the other hand, providing more voluntary
disclosures could lead to lower cost of capital (Bleck and Liu, 2007). The benefits from this
could give family firms incentives to provide more voluntary disclosures.
It is not clear whether the magnitude of non-GAAP disclosures is bigger in family firms
or in non-family firms. Ali et al. (2007) examined the voluntary disclosure practices between
family firms and non-family firms of the Standard and Poor's 500 firms from 1997 to 2002.
They find that family firms provide less voluntary disclosures such as earnings forecasts and
conference calls.
In accordance with family firms providing less voluntary disclosures and family firms
taking much more effort in preventing litigations and reputation damage, I expect that family
firms are less likely to engage in aggressive non-GAAP reporting. Thus, I expect that family
firms provide less other item exclusions. The second hypothesis is as follows:
H2 The magnitude of other item exclusions is smaller for family firms relative to non-
family firms
26
4 Research methodology
4.1 Data and sample
The aim of this thesis is to examine whether family ownership structure has a positive relation
with the quality of non-GAAP disclosures. An overview of this thesis is illustrated in figure 1.
The extant non-GAAP literature often uses hand collected data to examine empirical questions
regarding non-GAAP disclosures. Not only is this very time consuming and costly, it also limits
the size of the datasets.
Following Bentley et al. (2018), the dataset of Bentley et al. (2018) with the proxy for
manager’s non-GAAP disclosures per firm was used to examine hypothesis 1 and 2. This
dataset1 is collected through textual analysis and contains 146,121 quarterly observations of
7,090 firms for fiscal years spanning January 1, 2003 to December 31, 2016. A dummy variable
is equal to 1 if a firm reports non-GAAP numbers in that quarter and otherwise 0. Moreover,
this is the largest and most recent hand collected dataset which identifies GAAP and non-
GAAP reporting with at least 95% accuracy. Thus, this eliminated the need to proxy for
manager’s non-GAAP disclosures using databases such as I/B/E/S.
Although data is available regarding the performance metrics of managers’ non-GAAP
disclosures on analyst forecast data providers (e.g., I/B/E/S). Bentley et al. (2018) empirically
examined the difference between non-GAAP measures defined by managers and street
earnings defined by analysts on the database I/B/E/S. They find that I/B/E/S emphasizes the
higher quality non-GAAP earnings while systematically failing to capture the aggressive
disclosures by managers. Thus, using I/B/E/S to proxy for manager’s aggressive non-GAAP
exclusions will have a biased effect. Logically, the sample from Bentley et al. (2018) is a better
alternative. Moreover, they find that the data of I/B/E/S substantially overlap with their dataset
and that there are systematic differences. For instance, I/B/E/S excludes managers’ lower
quality non-GAAP numbers and sometimes provides higher quality non-GAAP measures that
managers do not explicitly disclose. In addition, Bhattacharya et al. (2003) finds evidence
showing that the expense items excluded by managers are not always excluded by analysts.
There is a significant difference of 4 cents per share between non-GAAP earnings disclosed in
press releases and the numbers reported in I/B/E/S reflecting actual earnings. Thus, the
1 This link contains most recent manager non-GAAP disclosure dataset of Bentley et al. (2018), with data from
2003 through part of 2016. This dataset was made available through https://sites.google.com/view/kurthgee/data
27
numbers in I/B/E/S are not completely reliable as a source of manager-disclosed non-GAAP
earnings per share.
Since the focus of this thesis is on the relation between non-GAAP disclosure quality
and family ownership structure, the dataset from Bentley et al. (2018) is more suitable than
data from I/B/E/S for example. Moreover, this dataset is very recent and one of the largest
hand-collected dataset of non-GAAP disclosures (Bentley et al., 2018). By using more recent
data the empirical results from my research are more valuable and relevant.
Furthermore, the results from existing literature provide evidence that managers use
aggressive non-GAAP reporting as a substitute of accrual and real earnings management
(Black and Christensen, 2009). This could have a negative effect on the quality of non-GAAP
disclosures. Therefore, it is essential that the dataset I will use for my thesis reflects the
aggressiveness of managers’ reporting choices. Bentley et al. (2018) argues that their dataset
captures managers’ reporting choices more accurately. Therefore, I used the dataset of
managers’ non-GAAP earnings disclosures of Bentley et al. (2018).
4.2 Data collection Compustat
The second data sample originates from Compustat. Key variables such as total assets,
common/ordinary equity, earnings per share (diluted) were extracted from Compustat
Fundamentals Quarterly database based on the unique GVKEY’s from Bentley et al. (2018)
dataset. The GVKEY is a unique six-digit number key assigned to each company. These key
variables are then used to calculate the dependent and control variables. The dataset from
Compustat contains 260,831 firm quarter observations of 4,776 firms. All the duplicates in this
sample were dropped. In table 1 is an overview of all the retrieved variables from Compustat.
After all the relevant variables were collected, the non-GAAP dataset was merged with the
dataset from Compustat in Stata.
TABLE 1 Key variables from Compustat
Description Compustat data item
Total assets atq
Common shares for diluted earnings per share cshfdq
Sales/turnover saleq
28
Standard Industry Classification Code sic
Operating Activities - Net Cash Flow oancfy
Capital Expenditures capxy
Earnings Per Share from Operations opepsq
Earnings Per Share (Diluted) Excluding Extraordinary items epsfxq
Income Before Extraordinary Items ibq
Shareholders' Equity - Total seqq
Common Shares for Diluted EPS cshfdq
Price / Close / Quarter prccq
4.3 Family ownership dataset
Lastly, to proxy for family ownership the hand collected dataset from Anderson’s website2 was
used. This dataset contains 16,200 observations of the top 2,000 largest firms in the United
States from the period 2001 through 2010. This dataset contains a dummy variable that equals
1 when the family owns or has voting rights of 5% or larger stake. Furthermore, there is an
indicator variable that equals 1 when the firm has a dual-class share structure.
To match the datasets, the final sample is limited from the year 2004 to 2011. I choose
2004 as the start date of my data sample as it is the year after when Regulation G was
implemented by the SEC. Then I merged the first merged dataset with the family ownership
dataset using Stata. Since the dataset of family ownership is only of 2,000 firms and consist of
fiscal year 2001 to 2011, observations that did not match with the family ownership dataset
were dropped.
The aim of my thesis is to examine family and non-family firms that disclose non-
GAAP exclusions and therefore I dropped the dummy variable for manager non-GAAP
reporting if it was equal to 0. In other words, firm quarters without non-GAAP exclusions were
dropped from the final sample. To have a more homogeneous set of firms I excluded financial
firms because as prior studies (e.g., Marques, 2006; Leung and Veenman, 2018) argue that the
nature of these firms’ non-GAAP disclosures differs systematically from those of nonfinancial
2 http://www.ronandersonprofessionalpage.net/data-sets.html
29
firms due to factors such as different regulations. Table 2 presents the sample selection for my
thesis.
TABLE 2 Sample selection quarterly data
Description
Number of
observations
Bentley et al. (2018) non-GAAP firm quarters 146,121
Number of firms 7,090
Firm quarter observations of key variables from Compustat 260,831
Number of firms 4,776
Family ownership firm-year dataset from Anderson’s website 16,200
Number of firms 2,000
Merge 1 Non-GAAP and Compustat 401,573
Successful match (282,057 observations dropped) 119,516
Merge 2 Merge above dataset with family dataset 119,516
Successful match (89,449 observations dropped) 30,067
Only non-GAAP firms (18,193 observations dropped) 11,874
Dropping financial firms (96 observations dropped)
Cleaning missing values (2.057 observations dropped)
Final dataset firm quarters observations 9,721
4.4 Libby boxes
The Libby boxes in figure 1 give an overview of the first hypothesis. It illustrates the first
hypothesis which examines the effect of family ownership structure on the quality of non-
GAAP disclosures.
FIGURE 1 Research Libby boxes
Independent variable (X) Dependent variable (Y)
Family firm = 1
Non-family firm = 0
Family ownership
Non-GAAP disclosure
quality
Association between non-
GAAP exclusions and
future firm performance
Control variables
• Sales growth
• Size
• Earnings volatility
• Loss
• Book to Market
• Age of the firm
30
4.5 Constructs and variables
To test the first hypothesis, I used the dummy variable for family ownership from the dataset
of Anderson (2015). To test the quality of non-GAAP earnings I follow Doyle et al. (2003),
Kolev et al. (2008) and Whipple (2015). I test whether the non-GAAP disclosures of family
firms are more associated with the future operating cash flow and future operating earnings
compared to non-family firms.
4.5.1 Independent variables, measure of family ownership
Following existing literature, non-family firms will be used as the default comparison variable.
I used the dataset from Anderson’s website to identity family firms. This dataset from 2001 to
2010 contains the 2,000 largest United States companies in terms of total assets including a
dummy variable which indicates whether a firm is family owned. In addition, there is an
indicator variable that equals 1 when the firm has dual-class share structure. Financial firms
are excluded from their sample as those firm’s profile are complex.
The family ownership dummy variable is coded 1 if the family owns 5% or more
common shares and 0 otherwise. The amount of common stock in possession indicates the
interest and reflects the voting rights of the founding family. Controlling families can exert
influence by having voting power through their ownership of common stocks.
4.5.2 Dependent variables, measures of non-GAAP disclosures quality
To examine the quality of non-GAAP disclosures I follow prior research and test for
association between the non-GAAP exclusions and future firm performance (e.g., Doyle et al.,
2003; Kolev et al., 2008). According to managers and analysts, items excluded in calculating
non-GAAP earnings do not represent a firm’s ‘core’ operations. Consequently, there should be
no association between non-GAAP exclusions and future firm earnings and cash flows. The
higher the association between non-GAAP exclusions and future earnings and cash flows, the
lower the quality of the exclusions are. Following prior research (e.g., Doyle et al., 2003; Kolev
et al., 2008; Whipple, 2015), I regressed future operating earnings and future operating cash
flows separately on total exclusions, special item exclusions and other item exclusions.
To test the quality of non-GAAP disclosures, I follow the dual approach by Whipple
(2015) by regressing future cash flows and future operating earnings on total non-GAAP
exclusions, special item exclusions and other item exclusions. The total exclusion is the
difference between GAAP earnings per share and non-GAAP earnings per share. Special item
31
exclusion is the difference between GAAP earnings per share and operating earnings
(Compustat data item “opepsq”). Lastly, other item exclusion is the difference between total
exclusions and special item exclusions. By using a dual approach, it will be more clear whether
the excluded items are truly transitory or truly non-cash in nature. This gives a deeper
understanding of manager’s motives for the different types of exclusions. Following Whippel
(2015), future operating cash flows is defined as the sum of cash flows (Compustat data item
“oancf”) over the four subsequent quarters, starting in q+1. Future operating earnings is defined
as the sum of operating earnings over the four subsequent quarters, starting in q+1. Both
variables are scaled by asset per share.
The aim is to compare the quality of non-GAAP disclosures between family firms and
non-family firms. The following pooled regressions are applied to compare the association
between non-GAAP exclusions and future firm performance between family and non-family
firms. The control variables are defined in table 3.
𝐹𝑢𝑡𝑢𝑟𝑒 𝑂𝑝𝑒𝑟𝑎𝑡𝑖𝑛𝑔 𝐶𝑎𝑠ℎ 𝐹𝑙𝑜𝑤𝑠 𝑞+1,𝑞+4
= 𝛽0 + 𝛽1𝑇𝑜𝑡𝑎𝑙 𝑛𝑜𝑛 − 𝐺𝐴𝐴𝑃 𝑒𝑥𝑐𝑙𝑢𝑠𝑖𝑜𝑛𝑠 + 𝛽2 𝑆𝑝𝑒𝑐𝑖𝑎𝑙 𝐼𝑡𝑒𝑚 𝐸𝑥𝑐𝑙𝑢𝑠𝑖𝑜𝑛𝑠
+ 𝛽3 𝑂𝑡ℎ𝑒𝑟 𝐼𝑡𝑒𝑚 𝐸𝑥𝑐𝑙𝑢𝑠𝑖𝑜𝑛𝑠 + 𝛽𝑛 𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑠 + 𝜀𝑞
𝐹𝑢𝑡𝑢𝑟𝑒 𝑂𝑝𝑒𝑟𝑎𝑡𝑖𝑛𝑔 𝐸𝑎𝑟𝑛𝑖𝑛𝑔𝑠 𝑞+1,𝑞+4
= 𝛽0 + 𝛽1𝑇𝑜𝑡𝑎𝑙 𝑛𝑜𝑛 − 𝐺𝐴𝐴𝑃 𝑒𝑥𝑐𝑙𝑢𝑠𝑖𝑜𝑛𝑠 + 𝛽2 𝑆𝑝𝑒𝑐𝑖𝑎𝑙 𝐼𝑡𝑒𝑚 𝐸𝑥𝑐𝑙𝑢𝑠𝑖𝑜𝑛𝑠
+ 𝛽3 𝑂𝑡ℎ𝑒𝑟 𝐼𝑡𝑒𝑚 𝐸𝑥𝑐𝑙𝑢𝑠𝑖𝑜𝑛𝑠 + 𝛽𝑛 𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑠 + 𝜀𝑞
The results from the OLS regressions are presented in table 6. All continuous variables are
winsorized at the 1st and 99th percentile to avoid undue influence by outliers. Winsorization
replaces the smallest and the largest values (i.e., outliers) of the dataset with the values of the
observation closest to those extreme values. The winsorized sample mean is more robust
compared to the normal sample mean because the influence of outliers is limited.
4.5.3 Control variables
In accordance with Doyle et al. (2003), Kolev et al. (2008) and Whipple (2015) control
variables which are expected to be associated with future performance are included in the
regression analyses. The control variables are calculated from the key quarterly data from
Compustat (see table 3).
32
For the first hypothesis the following control variables are included: sales growth, firm
size, earnings volatility, firm loss, book to market and firm age. Because firm size (Compustat
data item “atq”) and earnings volatility (Compustat data item “ibq” / “atq”) are highly skewed,
a natural log of these two variables are used in the regression analyses. Different from prior
research but consistent with Whipple (2015), accruals are not taken into consideration as a
control variable. Accruals enter the regression once as the actual portion of exclusions and then
again as a control variable. Thus, accruals enter the regression twice. Because of this,
interpreting the exclusions coefficients will be difficult. For this reason, accruals are not
included as a control variable.
According to Lougee and Marquardt (2004), managers could have bigger incentives to
report non-GAAP earnings when their GAAP earnings do not meet benchmarks. Therefore, it
is expected that firms with GAAP losses are more likely to report non-GAAP earnings. In the
same context, when the quarter sales numbers are larger or equal to its sales of prior four
quarters, firms are less likely to report non-GAAP earnings. I expect the signs on these
variables’ coefficients to be positive and negative, respectively. Also, large firms and firms
with higher earnings volatility are more likely to report non-GAAP earnings. Also, large firms
are more likely to have non-recurring transactions relative to smaller firms (Brown et al., 2012).
The age of the firm is also included as a means to control for possible effects of the maturation
effects of a firm.
TABLE 3 Definitions of variables
GAAP earnings per share
=
earnings per share (diluted) excluding extraordinary items (Compustat data item
“epsfxq”).
Non-GAAP earnings per
share =
Manager diluted non-GAAP EPS number from dataset of Bentley et al. (2018).
Future operating earnings
=
the sum of operating earnings (Compustat data item “opepsq”) over the four subsequent
quarters, starting in q+1, scaled by total assets (Compustat data item “atq”.
Future operating cash
flows =
the sum of cash flows (Compustat data item “oancf”’) over the four subsequent quarters,
starting in q+1, scaled by asset per share.
Total Exclusions = GAAP earnings per share less non-GAAP earnings per share.
Special item exclusions = GAAP earnings per share less operating earnings (Compustat data item “opepsq”).
Other exclusions = total exclusions less special item exclusions.
Sales growth = percentage growth in sales (Compustat data item “saleq”) relative to q-4 from the
previous year, scaled by asset per share.
33
Book-to-market = stock holders’ equity divided by market capitalization (Compustat data item
seqq/(cshoq*prccq).
Firm size = total assets (Compustat data item “atq) at the end of the fiscal quarter.
Earnings volatility = firm-specific standard deviation of quarterly earnings (Compustat data item “ibq”)
scaled by total asset, using a time-series of eight prior quarters.
Loss= indicator variable equal to 1 if GAAP earnings (Compustat data item “ibcomq”) in
quarter q is negative, and zero otherwise.
Age = number of years since the company first appeared in Compustat.
First, I calculate total exclusions by deducting non-GAAP earnings from GAAP
earnings using Compustat data. Then I decomposed total exclusions into special item
exclusions and other item exclusions. Special item exclusions are labeled as non-recurring
exclusions in existing literature (e.g., Doyle et al., 2003; Kolev et al., 2008; Whipple, 2015)
while other item exclusions have features which are considered hard to determine.
To control for scale effects in the regressions, future operating earnings, future operating cash
flows, total non-GAAP exclusions, special non-GAAP exclusions, other non-GAAP exclusions
and sales growth are all scaled by total assets per share.
To test for association, pooled OLS regressions are used. The regression analysis includes
fixed industry and year effects and is based on robust standard errors. Industry is defined using
the Fama and French 12 industry classifications (Fama and French ,1997). Following Marques
(2006), financial firms (SIC codes 6000-6999) are excluded because the nature for non-GAAP
disclosures of these firms systematically differs from that of non-financial firm. To avoid
influence by outliers all continuous variables are winsorized at the 1st and 99th percentile. The
statistical significance of 10%, 5% and 1% are denoted as *, **, *** respectively in the tables.
Non-GAAP exclusions are considered to be high-quality when they do not have predictive
power for future earnings or cash flows. To examine the effect of different kinds of exclusions
I decompose the total exclusions into special item exclusions and other item exclusions similar
to Doyle et al. (2003), Kolev et al. (2008) and Whipple (2015). The motive of making a
distinction between special item exclusions and other item exclusions is to examine whether
these two types of exclusions have different predictive abilities for future firm performance.
34
5 Results
5.1 Descriptive statistics
TABLE 4 Descriptive statistics
Panel A 2004-2009 Panel B 2010 - 2011
Variables SD Min Mean Max SD Min Mean Max
GAAP EPS 1.525 -52.32 0.098 10.11 0.907 -17.58 0.344 7.33
Non-GAAP EPS 0.526 -7 0.371 10.11 0.498 -1.96 0.470 5.33
Dummy Firm ownership 0.428 0 0.241 1 0.402 0 0.202 1
Future operating income 0.085 -0.275 0.048 0.295 0.072 -0.275 0.066 0.295
Future operating cash flow 0.221 -0.528 0.227 0.891 0.203 -0.528 0.230 0.891
Total exclusion 0.033 -0.214 -0.012 0.042 0.017 -0.214 -0.005 0.042
Special item exclusions 0.026 -0.174 -0.007 0.035 0.014 -0.174 -0.003 0.035
Other item exclusions 0.011 -0.068 -0.005 0.022 0.008 -0.068 -0.002 0.022
Sales growth 0.061 -0.208 0.012 0.203 0.056 -0.208 0.021 0.203
Book-to-Market 0.409 -0.171 0.551 2.297 0.333 -0.171 0.544 2.298
Log size 1.435 3.284 7.510 12.553 1.499 3.825 7.824 12.181
Log Earnings volatility 1.087 -9.174 -4.467 -0.823 1.159 -7.549 -4.216 -0.827
Dummy Firm Loss 0.438 0 0.259 1 0.374 0 0.168 1
Age 14.347 1 22.607 60 14.596 2 26.912 62
N= 7625 N= 1533
The sample consist of a total of 9.158 firm quarter observations ranging from the years 2004 to 2011.
GAAP earnings per share = earnings per share (diluted) excluding extraordinary items (Compustat data item
“epsfxq”)
Non-GAAP earnings per share = Manager diluted non-GAAP EPS number from dataset of Bentley et al. (2018)
Future operating earnings = the sum of operating earnings (Compustat data item “opepsq”) over the four
subsequent quarters, starting in q+1.
Future operating cash flows = the sum of cash flows (Compustat data item “oancf”) over the four subsequent
quarters, starting in q+1
Total Exclusions = GAAP earnings per share less non-GAAP earnings per share
Special item exclusions = GAAP earnings per share less operating earnings (Compustat data item “opepsq”)
Other exclusions = total exclusions less special item exclusions
Sales growth = percentage growth in sales (Compustat data item “saleq”) relative to q-4 from the previous year
Book-to-market = stock holders’ equity divided by market capitalization (Compustat data item
seqq/(cshoq*prccq)
Firm size = total assets (Compustat data item “atq) at the end of the fiscal quarter
Earnings volatility = firm-specific standard deviation of quarterly earnings (Compustat data item ibq) scaled by
total asset, using a time-series of eight prior quarters.
Loss= indicator variable equal to 1 if GAAP earnings (Compustat data item “ibcomq”) in quarter q is negative,
and zero otherwise.
Age = number of years since the company first appeared in Compustat.
All continuous variables and are winsorized at the 1st and 99th percentile 1% and 99% to limit the influence of
outliers. Future operating earnings, Future operating cash flows, Total Exclusions, Special item exclusions, Other
item exclusions, and sales growth are all scaled by assets per share in the descriptive statistics, correlation matrix
and regression results.
35
Table 4 gives a summary of the key variables used in the regression analyses. The descriptive
statistics include the independent, dependent and control variables. These variables help to
determine the motives of family and non-family firms to report non-GAAP earnings. All
continuous variables are winsorized at the 1st and 99th percentile to limit the influence of
outliers.
Panel A contains the variables for the years after Regulation G was introduced, ranging
from 2004 to 2009. In 2010 there was an update for Regulation G which loosened up the rules
for classifying recurring items. Panel B contains the years 2010 and 2011. In 2010 the SEC
updated non-GAAP reporting regulation. The aim of making a distinction between the period
when Regulation G was implemented (i.e., 2003) and after the update (i.e., 2010) is to examine
the different disclosure behaviors of family and non-family firms when it comes to
opportunistic behavior. According to Baumker et al. (2014), non-GAAP exclusions are
typically more positive than GAAP earnings. Table 4 illustrates a higher mean of 0.371 for
non-GAAP earnings per share compared to the mean of 0.098 for GAAP earnings per share.
Therefore, it can be concluded that the non-GAAP exclusions by managers indeed result in a
more positive non-GAAP earning. This may be an indication of aggressive non-GAAP
reporting, although this could be debated. After the update in 2010, non-GAAP earnings per
share still remained more positive than GAAP earnings per share.
The final sample of 2004 to 2009 contains 7.625 firms. The dummy variable to proxy
for firm ownership structure is coded 1 if the firm is a family firm and 0 if it is a non-family
firm. The mean of 0.241 means that only 24.1% of the 7.625 are family firms in the 2004 to
2009 sample. In the 2010 to 2011 sample family firms are 20.2% of the firms. Overall the
number of non-family firms are significantly larger than family firms in both samples. The
dependent variables which are measures of non-GAAP disclosure quality have a quite normal
distribution.
Furthermore, comparing the means of total exclusions, special item exclusions and other
item exclusions, there is no significant difference in the exclusions magnitude. In both samples
special item exclusions and other item exclusions are approximately 60% and 40% of the total
exclusions respectively. Firm size and earnings volatility were highly skewed, therefore a
natural log is used for these variables in the regression analyses. Log of size is significantly
large compared to all the other variables. This is caused by the family firm dataset of
Anderson’s website, which contains the 2,000 largest firms in the United States.
36
5.2 Pearson correlation matrix
Table 5 presents a correlation matrix between the key variables. The Pearson correlation
investigates the linear relation between variables and controls for multicollinearity. When the
correlation between two variables is greater than 0.8 then it is very likely that these variables
are multicollinear. When the correlation is below 0.8 then there is a chance that collinearity
exists. Also, just like in the descriptive statistics, all the continuous variables are winsorized at
the 1st and 99th percentile as the Pearson's r is sensitive to outliers.
The correlation matrix illustrates that total exclusions are more correlated with GAAP
earnings per share than non-GAAP earnings per share. Opposite to Kolev et al. (2008), I find
that total exclusions are significantly more positively correlated (0.637) with GAAP earnings
per share than non-GAAP earnings per share (0,146). Kolev et al. (2008) find that in their
sample, total exclusions are negatively correlated with GAAP earnings, which indicates that
firms with poor performance are more likely to report non-GAAP. However, the non-GAAP
reporting environment has dramatically changed dramatically since 2007 according to Black
et al. (2017). This could be the cause of the difference and an indication that the firms provide
more non-GAAP earnings irrespective of poor performance. Similarly, Bentley et al. (2018)
find that managers’ reporting of non‐GAAP metrics has increased by more than half in 2013
compared to 2007.
Similar to Kolev et al. (2008), I find that future operating income is more positively
correlated with non-GAAP earnings (0.387) than GAAP earnings per share (0.206), this is also
the case for future operating cash flows, with correlation coefficients of 0.287 and 0.135
respectively. This implies that non-GAAP earnings are more permanent, and thus also more
value-relevant than GAAP earnings (Kolev et al., 2008). In addition, I also observe that total
exclusions are positively correlated with future operating earnings (0.205) and future operating
cash flow (0.119). So, when total exclusions increase, future operating earnings and cash flow
also increases positively. This is consistent with the findings of Doyle et al. (2003) that non-
GAAP exclusions could have predictive power for future firm performance.
In this dataset, the only two variables that have collinearity are special item exclusions
with a correlation coefficient of 0.884 on total exclusions. However, this makes sense because
total exclusions and special item exclusions are calculated similarly. Both these calculations
start with GAAP earnings per share minus a different key variable from Compustat.
37
TABLE 5 Pearson
correlation matrix
GAAP
EPS
Non-
GAAP
EPS
Future
operating
earnings
Future
operating
cash
flows
Firm
ownership
structure
Total
exclusions
Special
item
exclusions
Other
item
exclusions
Sales
growth
Book-
to-
Market
Log
firm
size
Log
earnings
volatility
Dummy
Firm
loss
Firm age
GAAP EPS 1.000
Non-GAAP EPS 0.397 1.000
*** Future operating earnings 0.206 0.387 1.000 *** *** Future operating cash flows 0.135 0.287 0.711 1.000 *** *** *** Firm ownership structure -0.009 -0.027 -0.007 -0.033 1.000
*** *** Total exclusions 0.637 0.146 0.205 0.119 0.014 1.000 *** *** *** *** Special item exclusions 0.605 0.133 0.126 0.104 0.005 0.884 1.000 *** *** *** *** *** Other item exclusions 0.314 0.134 0.251 0.093 0.032 0.574 0.208 1.000 *** *** *** *** ** *** *** Sales growth 0.172 0.264 0.204 0.136 0.016 0.166 0.143 0.122 1.000 *** *** *** *** *** *** *** Book-to-Market -0.164 -0.174 -0.371 -0.250 0.075 -0.160 -0.161 -0.079 -0.254 1.000 *** *** *** *** *** *** *** *** *** Log firm size 0.075 0.322 0.264 0.217 -0.118 0.115 0.066 0.159 0.041 -0.167 1.000 *** *** *** *** *** *** *** *** *** *** Log earnings volatility -0.291 -0.251 -0.226 -0.150 -0.053 -0.386 -0.327 -0.304 -0.241 0.141 -0.231 1.000 *** *** *** *** *** *** *** *** *** *** *** loss -0.416 -0.441 -0.486 -0.379 -0.015 -0.464 -0.396 -0.360 -0.301 0.293 -0.227 0.443 1.000 *** *** *** *** *** *** *** *** *** *** *** age 0.018 0.177 0.150 0.046 -0.121 0.054 -0.001 0.150 -0.067 -0.021 0.484 -0.122 -0.117 1.000
*** *** *** *** *** *** *** * *** ***
*** Significant at 0.01 level, ** Significant at 0.05 level, * Significant at 0.10 level (two-tailed)
Table 3 provides the definitions of variables. All continuous variables are winsorized at the 1st and 99th percentile to avoid undue influence by outliers. Future operating earnings, Future operating
cash flows, Total Exclusions, Special item exclusions, Other item exclusions, and sales growth are all scaled by assets per share.
38
5.3 Main tests hypothesis 1 OLS models
The first hypothesis is as follows: Family owned firms provide higher quality non-GAAP
earnings compared to non-family owned firms. The results of the first hypothesis are illustrated
in Table 6 and are discussed in the sections hereafter.
Sample 2004 to 2009
My expectation was that family firms are more likely to provide higher quality non-GAAP
disclosures in line with the alignment effect being less severe for family firms in the United
States compared to non-family firms (Gilson and Gordon, 2003). Type I agency problem is
referred to as the alignment effect and is caused by separation of ownership and management.
However, family firms largely can mitigate this problem by placing one of their own on the
management board and as managers. This results in a more aligned interest between the
controlling shareholders and managers and consequently possibly higher quality non-GAAP
disclosures.
According to Doyle et al. (2003) and Kolev et al. (2008), high quality exclusions should have
zero association with future firm performance. The first column of table 6 presents the
association between non-GAAP exclusions and future operating earnings of family and non-
family firms. Looking at total exclusions, I find no significant effect on operating earnings for
both family firms and non-family firms. This implies that the overall quality of the total
exclusions made are of relatively high quality.
Furthermore, special item exclusions and other item exclusions of family firms do not
appear to be significantly associated with future operating earnings either. Overall, the
exclusions made by family firm managers seem to be of informative nature and consist mostly
of non-cash and non-recurring transactions. However, specials item exclusions of non-family
firms do appear to have significant predictive power for future operating earnings. In line with
the findings of Kolev et al. (2008), I find that the low-quality exclusions seem to be
concentrated in special item exclusions. When the special item exclusions of non-family firm
go up by $1, future operating earnings increases with $1.662. This means that the quality of
special item exclusions of non-family firms seems to be of low quality and consists of
transactions with recurring nature. The control variables book to market, firm size and firm
loss are all significantly associated with future operating earnings for both family and non-
family firms. However, the magnitude of the association is relatively small.
39
The second column presents the association between non-GAAP exclusions and future
operating cash flows. In this instance, the results are different of that from future operating
earnings. The total and special item exclusions by family firms now have significant predictive
power for future operating cash flows. It appears that when the total exclusions go up by $1,
future operating cash flows decreases with $1.941. Unexpectedly, this leads to contradicting
results as now it appears that the total and special item exclusions by family firms are of low
quality and could mislead investors.
Furthermore, the total and other item exclusions of non-family firm do not appear to
have significant predictive power over future operating cash flows. However, the significant
negative effect of special item exclusions has more than doubled compared to column 1. Now
a $1 increase in special item exclusions leads to $1.461 decrease in future cash flows compared
to a decrease of $0.660 for future operating earnings in column 1. Special item exclusions seem
to be far from non-recurring, unlike the claims of managers that these items should be. This
implies that while the overall non-GAAP disclosures appear to be of relatively high quality,
non-family firms provide very low quality special item exclusions. According to the extant
literature, special item exclusions consist of items which are non-recurring. Hence there should
be zero association with future firm performance. Thus, the special items excluded by non-
family firms appear to be from a misleading motive and consequently less useful for investors.
The impact of the control variables is similar to that of column 1, except for firm loss
where the significant impact has more than doubled from -0.075 in column 1 to -0.182 in
column 2. Also, the age of the firm has significant and negative effect on future operating cash
flows. However, the magnitude of the effect is very small (-0.001).
Sample of when non-GAAP reporting regulation were updated in 2010
It is notable that after the non-GAAP reporting regulation update in 2010, non-family firms
appear to engage significantly more in aggressive non-GAAP reporting compared to family
firms. Before the regulatory update, the total exclusions made by non-family firms did not have
any significant predictive power for future firm performance. However, after the update the
total exclusions appear to have very powerful predictive power of negative future operating
cash flow. An increase of $1 in other item exclusions have highly significant predictive power
for both future operating earnings and cash flows, $1.949 and $5.629 respectively. This means
that other item exclusions items are not only highly recurring but these items consume cash as
well.
40
Before the non-GAAP reporting regulation update in 2010, other item exclusions did
not have any significant predictive power. The change in exclusions behavior is therefore very
extreme. In contrast, none of the exclusions made by family firms appears to have any
significant predictive power for both future operating earnings and cash flows. This means that
the non-GAAP exclusions made by family firms are of higher quality than that of the 2004 to
2009 sample. Overall, in three of the four regression analysis, family firms provide high quality
non-GAAP earnings. In comparison, 3 out of 4 regressions show that the exclusions by non-
family firms have predictive power for future firm performance. Thus, H0 is rejected as there
appears to be association between family ownership structure and non-GAAP disclosure
quality. Consequently, H1 is supported as the regression analysis show that family firms indeed
do provide overall higher quality non-GAAP exclusions compared to non-family firms.
41
TABLE 6 Results Hypothesis 1, the quality of non-GAAP disclosures of family firms versus non-family firms
Sample 2004 – 2009 Sample 2010- 2011
Column (1) (2) (3) (4)
Family
firms
Non-
family
Firms
Family
firms
Non-
family
Firms
Family
firms
Non-
family
Firms
Family
firms
Non-
family
Firms
Dependent
variable
Future
operating
earnings
Future
operating
earnings
Future
operating
cash
flows
Future
operating
cash
flows
Future
operating
earnings
Future
operating
earnings
Future
operating
cash
flows
Future
operating
cash
flows
Total exclusions -0.175 0.275 -1.941** 0.690
-1.682 -0.806 3.526 -2.987**
(0.257) (0.257) (0.837) (0.523)
(2.201) (0.556) (8.340) (1.395)
Special item
exclusions -0.118 -0.660** 1.662* -1.461*** 1.517 0.175 -3.430 1.536
(0.305) (0.264) (0.924) (0.543)
(2.158) (0.641) (7.964) (1.641)
Other item
exclusions 0.305 0.407 1.141 -0.577 3.939 1.949** -0.110 5.629***
(0.488) (0.306) (1.156) (0.681)
(3.223) (0.879) (9.898) (2.082)
Sales growth 0.064 0.107*** -0.029 0.145* 0.070 0.205*** 0.359 0.172
(0.039) (0.029) (0.129) (0.083)
(0.056) (0.052) (0.283) (0.182)
Book-to-Market -0.051*** -0.052*** -0.110*** -0.089*** -0.068*** -0.054*** -0.175*** -0.151***
(0.008) (0.007) (0.023) (0.018)
(0.014) (0.009) (0.044) (0.026)
Log size 0.006** 0.007*** 0.013 0.020*** 0.001 0.004* -0.006 0.011*
(0.003) (0.002) (0.008) (0.005)
(0.003) (0.003) (0.011) (0.007)
Log earnings
volatility 0.000 -0.001 -0.010 -0.003 0.003 0.001 -0.016 0.015**
(0.004) (0.002) (0.013) (0.006)
(0.006) (0.002) (0.016) (0.007)
Firm loss -0.063*** -0.075*** -0.144*** -0.182*** -0.035** -0.066*** -0.099** -0.181***
(0.010) (0.007) (0.028) (0.017)
(0.015) (0.008) (0.041) (0.025)
Firm age 0.000 0.000 -0.001 -0.001* 0.000 -0.000 -0.000 -0.001**
(0.000) (0.000) (0.001) (0.000)
(0.000) (0.000) (0.002) (0.001)
Year fixed
effects Included Included Included Included Included Included Included Included
Industry fixed
effects
Included Included Included Included
Included Included Included Included
Constant 0.075*** 0.024 0.163* 0.093
0.091*** 0.067*** 0.261** 0.286***
(0.027) (0.029) (0.097) (0.076)
(0.035) (0.026) (0.104) (0.067)
Observations 1,897 5,944 1,853 5,772
321 1,242 318 1,215
R-squared 0.329 0.363 0.251 0.254
0.464 0.324 0.332 0.291
This table illustrates the results of the OLS regressions examining the predictive power of the exclusions made by
managers of family firms and non-family firms. The dependent variables used are future operating earnings and
future operating cash flows. Table 3 provides the definitions of variables. All continuous variables are winsorized
at the 1st and 99th percentile to avoid undue influence by outliers. Time fixed effects are based on firm fiscal
years. Industry fixed effects are based on four-digit SIC codes and defined using the Fama and French 12 industry
classifications. Robust standard errors are reported in parentheses below the coefficient estimates and are adjusted
for clustering by firm. Statistical significance are denoted as *** p<0.01, ** p<0.05, * <0.10 (two-tailed).
42
5.4 Results hypothesis 2
The second hypothesis examines the magnitude of the total, special and other item exclusions
by family firms and non-family firms. According to Black and Christensen (2009), one of the
best indicator of aggressive non-GAAP reporting is the exclusions of recurring items (i.e., other
item exclusions). Managers claim that by excluding non-recurring items, the ‘core’ earnings of
the firm are better reflected. However, excluding recurring items are harder to justify than the
exclusion of transitory items. Therefore, I examine whether family firms provide lower
magnitude of other item exclusions compared to non-family firms. A lower magnitude of other
item exclusions is an indication that family firms are less likely to engage in aggressive non-
GAAP reporting and in turn report higher quality non-GAAP earnings.
Table 7 shows the total, special and other item exclusions of family and non-family
firms. The magnitude of other item exclusions relative to total exclusions are expressed in
percentages. Furthermore, all exclusions are scaled by the number of family and non-family
firm in each fiscal year.
Surprisingly, there is no significant difference between the magnitude of other item
exclusions between family firms and non-family firms over the years 2004 to 2011. On average
the other item exclusions of family firms are 29% of the total exclusions, while there is only
1% increase on average for non-family firms. In line with the results of the first pooled OLS
regression in table 6 which examined whether the exclusions of family and non-family firms
have predictive power for future firm performance. It can be concluded that indeed the overall
motives for both family and non-family firms to report non-GAAP earnings appear to be of
informative nature and of relatively high quality. In this instance, H2 is not supported.
43
TABLE 7 Magnitude of exclusions by family and non-family firms
Family firms Family firms Family firms
Non-family
firms
Non-family
firms
Non-family
firms
Year
Total
exclusions
Special item
exclusions
Other item
exclusions Total exclusions
Special item
exclusions
Other item
exclusions
2004 -0,13 -0,10 -0,03 -0,19 -0,16 -0,03
2005 -0,09 -0,07 -0,01 -0,16 -0,13 -0,03
2006 -0,14 -0,09 -0,04 -0,12 -0,08 -0,04
2007 -0,13 -0,07 -0,07 -0,16 -0,10 -0,06
2008 -0,71 -0,55 -0,17 -0,64 -0,47 -0,17
2009 -0,32 -0,22 -0,10 -0,28 -0,18 -0,10
2010 -0,08 -0,05 -0,02 -0,14 -0,10 -0,05
2011 -0,03 0,01 -0,04 -0,08 -0,03 -0,05
Total -1,621 -1,147 -0,474 -1,760 -1,234 -0,526
in % 100% 71% 29% 100% 70% 30%
This table illustrates the results of the calculations which examines the second hypothesis. Total exclusions are defined as
GAAP earnings per share (Compustat data item “epsfxq”) less non-GAAP earnings per share (Data item
“MGR_NG_EPS” from dataset of Bentley et al., 2018). Special item exclusions are defined as GAAP earnings per share
(Compustat data item “epsfxq”) less operating income (Compustat data item “opepsq”). Other item exclusions are defined
as total exclusions less special item exclusions. The exclusion variables are all scaled by the number of family firms or
non-family firms per year. The magnitude of other item exclusions relative to total exclusions are expressed in
percentages. All continuous variables are winsorized at the 1st and 99th percentile to avoid undue influence by outliers.
5.5 Summary of results
The aim of this thesis is to examine the relation between family ownership structure and the
quality of non-GAAP disclosures. The following two hypotheses are formulated to examine
this relation.
H1 Family owned firms provide higher quality non-GAAP earnings compared to non-
family owned firms.
H2 The magnitude of other item exclusions is smaller for family firms relative to non-
family firms.
The first hypothesis is examined with OLS regressions in table 6. The second
hypothesis is tested by calculating the magnitude of other item exclusions relative to total
exclusions to proxy for aggressive non-GAAP reporting behavior and the quality of the
exclusions by family and non-family firms (see table 7).
A distinction is made between the period after when Regulation G was first
implemented (i.e., 2003) and the period when the SEC updated non-GAAP regulation (i.e.,
44
2010). The motive for this is because prior research finds that the overall quality of non-GAAP
disclosures increased after Regulation G went into effect in 2003. However, firms were allowed
to exclude recurring items (i.e., other item exclusions) the update in 2010, even if they do not
meet the previous requirements of “non-recurring, infrequent or unusual” (Webber et al.,
2013). Other item exclusions are considered low quality in the existing literature. Therefore, I
examine whether family firms and non-family firms report more other item exclusions after the
update.
Overall, only in 1 of the total of 4 regressions for family firms, the total and special
exclusions have predictive power for future firm performance. In contrast, in all 4 regressions
for non-family firms, either total exclusions, special item exclusions or other item exclusions
have predictive power for future earnings. Thus, it can be concluded that the overall quality of
non-GAAP disclosures is higher for family firms compared to non-family firms. H1 is
supported in this case. This evidence supports the findings of Wang (2006), which imply that
family firms have stronger objective to provide higher quality corporate disclosures as an effort
in preventing litigations or damage to their firm’s reputation. Thus, it can be concluded that the
alignment effect is indeed more severe for non-family firms in the United States (Gilson and
Gordon, 2003). In accordance with family firms being more risk-adverse and long-term
oriented (Lins et al., 2013), I find that family firms are less likely to engage in aggressive non-
GAAP reporting compared to non-family firms. Lastly, in contrast to Fan and Wong (2002)
and Sánchez-Ballesta and García-Meca (2007), my findings indicate that the entrenchment
effect in family firms (i.e., type II agency problem) does impede the quality of corporate
disclosures.
The goal of Regulation G was to improve the quality and transparency of financial
accounting information. Also, an important objective was to limit the opportunistic use of non-
GAAP measures (Section 401(b) of the Sarbanes-Oxley Act of 2002). Consistent with Yi
(2007) and Kolev et al. (2008), I find that the overall quality of the total exclusions of both
family and non-family firms in the post Regulation G period are of relatively high quality. The
total exclusions are not significantly associated with future firm performance (i.e., future
operating earnings and cash flows). However, the total exclusions of family firms appear to
have significant predictive power for future operating cash flows. Thus, they are of low quality.
According to Easton (2003), current liabilities could have implications for future cash flows.
This could be an alternative explanation of the association between total exclusions and future
operating cash flows.
45
Kolev et al. (2008) find that after the Regulation G period, the quality of special item
exclusions has decreased. I find evidence that the special item exclusions of non-family firms
are indeed of low quality as these items are highly powerful in predicting future operating
earnings and income. Therefore, my findings support that of Kolev et al. (2008). Surprisingly,
the special exclusions of family firms also appear to be significantly predictive of future cash
flows. In accordance with the findings of Kolev et al. (2008), this implies that managers have
adapted their earnings management mechanisms by shifting recurring transactions (i.e. from
other item exclusions) into special item exclusions. This could be considered an unintended
consequence of Regulation G. Hence, the concerns of financial regulators about the possible
misleading motives of non-GAAP disclosures may be justified.
Whippel (2015) who examined the motives of non-GAAP exclusions in the current
non-GAAP reporting environment find that the motive of other item exclusions is mainly to
inform investors. He further finds that these items are mostly non-cash in nature, and excluding
these items increases the usefulness of non-GAAP earnings for valuation purposes. I find
similar evidence to Whippel’s (2005). Other item exclusions between 2004 and 2009 for both
family and non-family firms do not appear to have predictive power for future operating
earnings or cash flows. Therefore, it can be concluded that these items are non-cash and thus
of high quality. This implies that these exclusions are not mispriced by investors. In line with
Heflin and Hsu (2008), my evidence also indicates that the opportunistic motive for other
exclusions have decreased after the implementation of Regulation G. However, my evidence
contradicts with the common view in the extant non-GAAP literature that other item exclusions
are of low quality and a signal of aggressive non-GAAP reporting (e.g., Doyle et al., (2003);
Black and Christensen, 2009; Brown et al., 2012; Christensen et al., 2014).
Next, I find that after the non-GAAP reporting regulation update in 2010, the other item
exclusions of non-family firms are significantly predictive of future operating earnings and
cash flows. This indicates that the motive of these exclusions are opportunistic and misleading
in nature. My evidence challenges the finding of Black et al. (2017) which imply that managers
use the discretion in providing non-GAAP earnings to inform stakeholders.
The second hypothesis is examined by comparing the magnitude of other item
exclusions made by family and non-family firms from 2004 to 2011. Unlike expected, the other
exclusions by family and non-family firms do not differ significantly. On average the other
item exclusions of family firms are 29% of the total exclusions, while there is only 1% increase
on average for non-family firms. Thus, it can be concluded that non-family firms do not
46
participate more in aggressive non-GAAP reporting behavior than family firms based on this
evidence. In this instance, H2 is not supported.
In line with Kolev et al. (2008) and Entwistle et al. (2006), I find that the magnitude of
special item exclusions is indeed much larger than that of other item exclusions. On average,
special item exclusions are approximately 70% of the total exclusions. While Doyle et al.
(2003) argue that special items do not have predictive power for future firm performance (i.e.,
they are of high-quality), Kolev et al. (2008) find that the increase in special item exclusions
in the post Regulation G period are a means of earnings management. However, I do not find
such evidence. In summary, based on the findings of the first and second hypothesis, it can be
concluded ownership structure does have an impact on the quality of non-GAAP disclosures.
47
6 Conclusions
Corporate disclosures such as the annual financial statement are one of the most important
information sources in the capital market for valuation and investment decisions. Managers
have superior information regarding their firms’ performance, which results in information
asymmetry between insiders (i.e., managers) and outsiders (i.e., investors). Besides mandatory
corporate disclosures, firms can also provide voluntary supplemental disclosures such as non-
GAAP earnings. In the non-GAAP literature there are two main motives for managers to
provide non-GAAP disclosures: (1) to inform investors or, (2) to mislead investors. The
motives of managers are heavily debated in the existent non-GAAP literature (e.g.,
Bhattacharya et al., 2004; Black and Christensen, 2009; Chen et al., 2012; Lougee and
Marquardt, 2004).
Furthermore, there are many factors which could affect non-GAAP disclosure
practices. I examine the impact of family ownership on the quality of non-GAAP disclosures.
Family ownership is a common ownership structure around the world, approximately one-third
of the S&P 500 firms are family owned (Anderson and Reeb, 2003).
The first hypothesis examines the quality of non-GAAP exclusions based on the extent
to which these exclusions have predictive power for future firm performance. According to
Doyle et al. (2003), exclusions are of high quality when they have zero predictive power for
future firm performance. This is in line with the claim of managers and analysts that the items
excluded are non-recurring and non-cash in nature. Consequently, by excluding such items the
non-GAAP earnings should reflect the ‘core’ earnings of a firm better since the items that
caused noise in the GAAP earnings are now excluded. Following Whipple (2015) a dual
approach was implemented which examines future operating earnings and cash flows.
First, the descriptive statistics in table 4 show that firm’s non-GAAP earnings per share
are more positive than their GAAP earnings per share on average. This finding is consistent
with that of Baumker et al. (2014). This indicates that the exclusions made by managers
typically result in a more positive non-GAAP earning and could be a sign of aggressive non-
GAAP reporting.
Furthermore, the results from the Pearson correlation matrix show that non-GAAP
earnings are more permanent and more value-relevant than GAAP earnings. This finding is
similar to that of Kolev et al. (2008) where they find that non-GAAP earnings are more
positively correlated with future firm performance than GAAP earnings. In addition, I find that
48
non-GAAP exclusions have predictive power for future firm performance. In line with Doyle
et al. (2003) the Pearson matrix shows significant positive correlation between total exclusions
and future operating earnings and cash flows.
In accordance with Type I agency conflict being less severe in family firms, I expected
family firms to provide higher quality non-GAAP earnings. The results from the pooled OLS
regressions show that in both time series (i.e., 2004 to 2009 and 2010 to 2011) the exclusions
of family firms are less significantly associated with future firm performance than that of non-
family firms. Thus, H1, which predicted that family firms provided higher non-GAAP
disclosures compared to non-family firms is supported. Furthermore, I tested whether the
magnitude of other item exclusions relative to total exclusions are larger for non-family firms
than family firms. I examined this for the period of 2004 to 2011 and find that there is no
significant difference in the magnitude of other item exclusions between family firms and non-
family firms. On average, other item exclusions are 30% of total exclusions. This means that
the motive of both family and non-family firms to provide non-GAAP earnings is of
informative nature. Overall, there is significant evidence that family firms provide higher
quality non-GAAP earnings and are less likely to engage in aggressive non-GAAP reporting.
The results from this thesis contribute to the extant family ownership and non-GAAP
literature in several ways. First, I am the first to examine the relation between family ownership
structure and non-GAAP earnings. By providing new insight, I contribute to the extant family
ownership literature and non-GAAP literature. I find that family firms provide higher quality
non-GAAP disclosures compared to non-family firms. This finding is in line with the finding
of Gilson and Gordon (2003) that the type I agency problem (i.e., alignment effect) is more
severe in non-family firms in the United States. Furthermore, unlike prior research, my findings
also imply that family ownership does not lead to a more severe type II agency problem (i.e.,
the entrenchment effect). The evidence of family firms providing higher quality non-GAAP
disclosures is in line with family firms being more risk-adverse, conservative and long-term
oriented (Lins et al., 2013). In summary, I find that family ownership structure does have a
positive effect on the quality of non-GAAP disclosures. Therefore, the research question of this
thesis is answered.
Furthermore, my findings have implications for regulators. Although the overall quality
of non-GAAP disclosures appears to be relatively high in the period after the initial
implementation (i.e., 2004 to 2009) of Regulation G in 2003, I find that after the update in
2010, non-family firms demonstrate aggressive non-GAAP behavior. Their other item
49
exclusions now have strong significant predictive power for future operating earnings and cash
flows. It appears that the concerns of financial regulators about the misleading nature of non-
GAAP earnings are justified in this case.
6.1. Limitations
The first limitation of this study is that the results are not generalizable because the data sample
is based on the 2,000 largest firms in the United States. Also, this is the first research that
examines the effect of corporate ownership, specifically, family ownership and non-family
ownership on the quality of non-GAAP disclosures. This makes it difficult to directly compare
the results to prior non-GAAP research.
Moreover, even though family ownership is common throughout the world, the results
of the comparison between non-GAAP disclosure practices between family firms and non-
family firms may not be applicable in different countries. The institutional difference between
countries can differ significantly and should be taken into account when comparing results.
Another limitation is that no distinction was made between firms with founding members and
descending members in control. These characteristics could have different impact on the non-
GAAP reporting environment of family firms.
Future research should take into account the characteristics of founding family
members and descendant family members. According to Cheng (2014), there is a difference
between founder CEO’s and descendant CEO’s. Founding CEOs are characterized as great
leader with excellent management skills and charisma while descendants are less skilled and
are considered spoiled. This difference plays an important role in understanding the agency
problems in family firms and could have different unique implications for non-GAAP reporting
practices. Therefore, examining these different characteristics could shed more light on the
relation between family firms and non-GAAP disclosure practices.
50
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