The use of graphs in annual reports. Evidence from European listed banks.
Abstract
Graphs are important tools in a firm’s overall disclosure strategy. They can be used in order to communicate
information in a concise and eye-catching way to serve users’ interest. However, graphs have also been used in a
selective and distorted way, to give a more favourable impression about the firm performance to the reader, and serve
the preparer’s interest. Using a sample of 47 major European listed commercial banks, this paper studies the nature and
differences in graph usage in major European listed banks, and whether institutional settings and banks’ financial
performance had any influence on the way banks used graphs. We found a significant and positive relationship between
the use of financial performance graphs and the increase in banks’ overall financial performance as well as significant
positive association between the increase of some specific performance variables and the number of their graph
representations. We also found significant differences among the countries studied in term of graphs’ use and design.
Anecdotal evidence of measurement distortion of financial performance graphs has been found. Such findings support
impression management and institutional theories.
1. Introduction.
During the past few decades, the annual report of large listed companies has been transformed from
a primarily formal, legal document into a public relations document in which the financial
statements are almost relegated to a ‘technical appendix’ (Lee, 1994; Hopwood, 1996; Beattie et al,
2008). Listed companies use visual representation in their annual reports to communicate financial
and non-financial information, augmenting the financial statements and related notes with a variety
of additional material, like graphs.
The use of graphs has historically functioned for data representation to facilitate managerial
decision-making (Masini, 1947). Beattie and Jones (2008) illustrate the main motivations for graph
use and design choices by annual reports’ preparers to serve the interests of the annual reports’
users. Graphs allow management to present information in a flexible way as they usually fall
outside the framework of accounting regulation 1 . In addition, they are an ‘eye-catching’
presentational format. Not only do they attract the reader’s attention, especially when in colour
and/or highlighted, but also facilitate comparisons and the identification of trends. Graphs also
synthesize key performance indicators and enable management to present key financial and nonfinancial information in a readily accessible form, even for the less expert users. In addition, being
visual, graphs allow ‘spatial’ rather than ‘linguistic’ decoding. The reader can, therefore, use ‘sight’
(the dominant visual sense) to ‘see’ the data more directly and clearly. Graphs are ‘memorable’ as
human beings tend to retain pictorial and graphical representations better than numbers (DeSanctis,
Jarvenpaa, 1989). Last but not least, graphs are ‘international’ as they tend to be independent of
language.
Besides the above-mentioned ‘altruistic’ motivations (Merkl-Davies and Brennan, 2007) for graph
use and design choices, a major concern is the use of graphs to serve managerial interests rather
than users’ interests with the potential outcome that the message conveyed is no longer neutral and
1
To our knowledge, in Europe the only exception to graphs being voluntary, unregulated, disclosure is the performance
graph (i.e. the historical time series of company’s total shareholder return) which UK FTSE-listed companies have been
required by schedule 7A of the Companies Act to display in their Director Remuneration report section of their annual
report.
unbiased (e.g., Beattie and Jones, 1992; 2008; Dilla and Janvrin, 2010). Whether graphs are used to
pursue the informational needs of the users of the annual report, rather than the preparer’s own
interest is still an unsolved question.
The relevance and role of graphs have been documented in previous studies. Several single-country
studies have been conducted (e.g., Johnson et al, 1980; Beattie and Jones, 1992, 1999; Courtis,
1997; Mather et al, 2000; Godfrey et al, 2003; Ianniello, 2009), mostly on Anglo-American firms
(see Beattie and Jones, 2008 for a review), however the disclosure of financial information using
graphs has been the subject of few comparative studies (Beattie and Jones, 1997; 2001; Frownfelter
and Fulkerson, 1998). In addition, no study has yet been focused on the use of graphs in banks, as
the latter have often been excluded as they were expected to have different graphical reporting
practices from non-financial companies (e.g., Beattie and Jones, 2001).
Therefore, the main purpose of this study is to investigate the nature and differences in graph usage
in major European banks, comparing the way variables are graphed with the findings of previous
studies that focused on non-financial companies.
The remainder of this paper is structured as follows. In the next section, the prior research on the
use of graphs in non-financial companies is discussed. Hypotheses will be developed accordingly.
In section three, we present our research methodology, including sample selection and classification
of graphs. Our findings follow in Section four. In section five we discuss our results and conclude.
2. Literature review and hypotheses’ development.
The primary focus of financial report should be to provide a true and fair view about a company’s
performance (IASB, 1989) in order to serve users’ interest, however academic literature has
documented the incentives for, and ways in which, management seeks to create a more favorable
view of the company’s performance than is warranted. Examples of such practices include earnings
management (for a review, see Healy and Wahlen, 1999), accounting narratives (for a review, see
Merkl-Davies and Brennan 2007), the misuse of photographs (e.g., Graves et al, 1996), as well as
graphs.
Previous literature has found that the self-serving motivation is likely to arise and determine a
selective use of graphs (e.g., Beattie and Jones, 1992; 2000a). Companies’ managements may have
incentives to represent their companies’ performance in the best possible light, potentially resulting
in selective financial misrepresentation (Tweedie and Whittington, 1990; Revsine, 1991), with
graphs being adopted and designed specifically to manipulate the financial signals sent to annual
reports’ users, enhance their perception of corporate performance, and lead users of financial
information to sub-optimal decisions (e.g., Beattie and Jones, 1992; 1997; Dilla and Janvrin, 2010).
In financial graphs, selectivity is likely to occur when a company graphs variables when there is a
favorable trend (e.g., rising operating profit) and elects not to graph variables with unfavorable
trends (e.g., declining operating profit). The absence of graphs tends to conceal poor performance,
while companies use graphs to make good performance more salient to the users (Beattie and Jones,
1992; Dilla and Janvrin, 2010). The outcome of such impression management behavior is that the
information conveyed with graphs is no longer neutral (Beattie and Jones, 2008). Previous studies
have found that selectivity has been used to highlight financial performance variables through the
use of graph (Beattie and Jones, 1992; 2000). Hence, we expect that:
Hypothesis 1a: Financial performance indicators are more likely to be graphed in annual reports
of banks with good, rather than bad, financial overall performance.
Hypothesis 1b: Financial performance indicators are more likely to be graphed in the annual
reports of banks with good, rather than bad, performance in terms of the variable graphed.
Positive accounting theory (Watts and Zimmerman, 1986) predicts that managers of highly visible
firms, exposed to public scrutiny and media and regulator’s attention (such as oil and gas industry,
Watts and Zimmerman, 1978), may deem it undesirable to make large increases in firm
performance salient to annual reports’ users, as among those users there are the regulators. As
regulators are not fully informed as it is costly for them to become informed about whether firm
performance represents monopoly profits or not, drawing attention to high profits is more likely to
increase political costs, and an highly visible firm may want to avoid changes in regulations that
would either constrain their activities and/or impose more taxes on them. Along this line, Dilla and
Jarvin (2010) found that large non-financial companies with greater performance increases are less
likely to voluntarily graph key financial indicators.
Banks operate in a highly regulated industry that is under the attention of media and regulators.
Therefore, their potentially selectiveness on financial performance graphs could be driven by the
potential political costs that they could incur by drawing attention to their high performance. Hence,
we expect that:
Hypothesis 2a: Financial performance indicators are less likely to be graphed in the annual reports
of companies with good, rather than bad, financial performance.
Hypothesis 2b: Financial performance indicators are less likely to be graphed in the annual reports
of banks with good, rather than bad, performance in terms of the variable graphed.
As any other organizational practice, financial reporting practices do not develop in a vacuum, due
to the firms’ embeddeness in a nexus of formal and informal rules, rather they are the result of
macro social processes (e.g., DiMaggio & Powell, 1983) and are likely to reflect the underlying
environmental influences that affect firms in different countries (e.g, Ball et al., 2000; Haniffa &
Cooke, 2002; Archambault & Archambault, 2003). National accounting practices vary because of
environmental and cultural factors. Thus, the adoption of graphs, as well as the type of variable
graphed is likely to vary across countries (Beatty and Jones, 2000b, Ianniello, 2009).
Hypothesis 3a: Graphs usage and the variables graphed will differ among banks belonging to
different countries.
Nobes (1983, 1998) classified international accounting systems into micro, Anglo-Saxon practices,
and macro, continental European practices. Micro-based accounting practices are typified by
comparatively weak governmental influence, strong accounting professions and comparatively
active equity markets. The focus is on the provision of a ‘fair’ presentation of the accounts and the
portrayal of economic reality for the benefit of investors. By contrast, macro accounting practices
are typically characterized by strong governmental influence on accounting, relatively weak
accounting professions, and less active equity markets. In micro-based countries, financial reporting
is geared up to satisfying investors comparatively more than in macro-based countries where the
needs of alternative annual reports’ users, such as other stakeholders, tend to dominate. These
pressures may make the financial performance representation in the annual report relatively more
important in micro-based, rather than macro-based, countries. Based on this classification, the U.K.
may be classified as micro-based country, whereas France, Germany, Italy and Spain have a macro
orientation (Nobes, 1983). Hence, we expect that:
Hypothesis 3b: Financial performance indicators are more likely to be graphed in the annual
reports of UK banks than in those of continental European banks.
3. Research method
3.1 Sample and data gathering
Using the database Bankscope, we selected the European financial banks based in the largest five
European economies and listed during the whole 2006 year. Listed subsidiaries of a holding bank
that was already in the sample, financial firms that were not commercial banks and firms whose
annual report was no publicly available were dropped. The final sample comprises 47 commercial
banks, listed in the top five European countries: Germany, France, UK, Italy and Spain. Among
these 47 commercial banks, there are also 11 banks now considered, after the global financial crisis,
systemically important financial institutions2.
We gathered consolidated annual reports from the firms’ websites and collected data about the
graph title, the graph location in the annual report (page and presence in the highlights section), the
graph category-topic, the graph type (e.g., column), and whether the graph refers to the whole group
or to segmental areas, such as specific business divisions, countries and/or subsidiaries The data
checklist was first pilot-tested on 9 banks to ensure clarity and completeness in the data collection
among the authors.
After the data collection process, in order to group graphs into main categories, we first identified
general titles of the graphs, then broader keywords and, at the end, the main topic/category each
graph belongs to.
Banks’s market and accounting performances were collected from Bankscope database.
3.2 Method
Our main variable is represented by the number of specific variables graphed.
Following previous literature (Beattie, Jones, 1992; 2001), selectivity was investigated by testing
the association between the number of the financial performance variables graphed and the increase
in bank’s overall financial performance (Hypotheses 1a and 2a), as well as by testing, for each
financial performance variable, the association between the number of times a specific variable
graphed and the increase in the specific financial performance variable (Hypotheses 1b and 2b).
Hypotheses 3a and 3b were tested by analyzing the association between the country a bank belong
to with the total number of variables graphed, as well as with the topic of variables graphed.
Moreover, we have investigated impression management practices by providing some anecdotal
evidence of key financial variables’ graphs that present material measurement distortion.
Systemically important Financial Institutions (SIFIs) are “financial institutions whose distress or failure, because of their size,
complexity and systemic interconnectedness, would cause significant disruption to the wider financial system and economic
activity”. This definition comes from: Financial Stability Board, 2011. The methodology to identify the requirements to be part of
SIFIs has been developed by the Basel Committee on Banking Supervision and by the Financial Stability Board.
2
Measurement distortion occurs when the variations of the measures depicted in the graph are not
proportional to the variations of the real data and, thus, there is a violation of the fundamental
principle of the graph construction (Tufte, 1983). Based on previous literature (Beattie and Jones,
2008), we have measured graph distortion with the graph discrepancy index (GDI) developed by
Taylor and Anderson (1986), which is a variation of Tufte’s (1983) lie factor.
𝐴
𝐺𝐷𝐼 = [ − 1] × 100
𝐵
Where:
GDI = Graph Discrepancy Index
A=
ℎ𝑒𝑖𝑔ℎ𝑡 𝑜𝑓 𝑙𝑎𝑠𝑡 𝑐𝑜𝑙𝑢𝑚𝑛−ℎ𝑒𝑖𝑔ℎ𝑡 𝑜𝑓 𝑓𝑖𝑟𝑠𝑡 𝑐𝑜𝑙𝑢𝑚𝑛
ℎ𝑒𝑖𝑔ℎ𝑡 𝑜𝑓 𝑓𝑖𝑟𝑠𝑡 𝑐𝑜𝑙𝑢𝑚𝑛
× 100
B = percentage variation of the performance measure
Positive values of the GDI express an exaggeration of a trend while negative values an
understatement3. However, to understand whether the distortion has been favorable or unfavorable
for the firm, we have to analyze the nature of the variable and its trend. In general, the distortion is
favorable for the firm when the GDI is higher than 0 and there has been an upward trend of the
variable graphed or when the GDI is lower than 0 and there has been a declining trend of the
variable graphed. On the contrary, the distortion is unfavorable for the firm when the GDI is lower
than 0 and there has been an increase in the value of the variable graphed or when the GDI is higher
than 0 and there has been a decrease in this value.
As a GDI within the values of +/-10% does not seem to affect the audience perception of a variable
trend depicted in a graph (Beattie, Jones, 2002), we have considered this level as the cut-off level to
split between material and not material distortion. We have also taken into account a +/-5% GDI
level as our threshold for material distortion, following other previous papers (Beattie, Jones, 1992;
Mather et al., 2000).
3
An example could help to better understand the way distortion has been calculated. If income shifted from € 1000 to € 2000 over
three years, the variation of the performance measure (B) is equal to (2000 − 1000)/2000 × 100 = 100 . If in the graph
representation the first column’s height is 10 cms and the last 40 cms, the percentage change depicted in the graph (A) is equal(40 −
10)/40 × 100 = 300 . Thus, the graph discrepancy index is equal to (300/100 − 1) × 100% = 200%
4. Key findings
Descriptive Statistics
Across the five European countries analyzed, we identified 42 banks out of 47 (89.86%) that
inserted graphs in their 2006 annual reports. Graphs adoption in banks is in line with what found by
previous studies in non-financial firms (Beattie et al, 2008.). We identified a total number of 1,050
graphs and 1,243 variables graphed. This misalignment is due to the fact that 12% of the total
graphs reported more than one variable (details are reported in Table 1). By comparing and
contrasting the five European countries analyzed, we found that the number of graphs, as well as the
number of variables graphed is higher in Spain and the UK. In such countries, banks graphed on
average, respectively, 78 and 28 variables; whilst Italian, German and French banks represented
graphically less than 20 variables (see table 1). Hereafter the focus of the paper will be on the
variables graphed.
INSERT TABLE 1
Table 2 reports the total number of variables graphed by type of graph. Consistently with Beatty
and Jones (1992), column graphs were the most popular in banks’ annual reports in all countries.
However, their usage varied from 51% in German banks to the 71% in UK banks.
INSERT TABLE 2
We classified all the variables graphed, but segmental variables (4), into main specific categories
according to their topic. Such categories have then been grouped into four macro-categories:
financial performance, other financial information, risk and non-financial information. Such
classification is reported in table 3. In particular, for each category and for each country, table 3
reports the total number of variables graphed (first column), the average number of variables
graphed by a single bank (second columns) and the number of banks that graphed at least one
variable (third column).
We exclude from this classification 304 segmental variables. They refer to specific business divisions, countries and/or subsidiaries and,
therefore, do not provide a picture of the whole banks’ activity
4
INSERT TABLE 3
As shown in table 3, financial performance variables were the most graphed in the European banks
annual reports (268 times in total, and, on average, 5.7 times per bank), followed by other financial
information (240 times in total, and, on average, 5.1 times per bank.), risk (198 times in total, and,
on average, 4.2 times per bank), and non-financial information variables (176 times in total, and, on
average, 3.7 times per bank). Financial performance variables were also those graphed by the
highest number of banks (37), followed by other financial information variables, graphed by 34
banks. Differently from the previous rank, non-financial variables graphs were more widespread
than risk graphs. Indeed only 24 banks adopted graphs on risk variables, whilst 32 graphed at least
one non-financial variable.
Relevant differences emerged across the five countries. In particular, whilst Spanish and UK banks
used on average more financial performance graphs, Italian and French banks provided more graphs
on other financial variables, such as shareholder equity, loans and deposits. In line with by Beattie
and Jones (2001), we found that German banks are apt to graph more non-financial issues, such as
those related to the macroeconomic situation or to employees. However, when we just considered
the adoption ofat least one graph about a specific category, we found that in each country, graphs on
financial performance variables are still those adopted by the highest number of banks.
By focusing only on financial performance variables, we found that, consistently with general
findings, Spanish banks are those that inserted the major number of graphs, followed by UK,
French, Italian and German banks. We also found that, in each country, share price performance
was the most graphed variable, as well as the one graphed by the highest number of banks. This
findings differs from what has been found by previous studies in non-financial companies (Beattie,
Jones, 1992; 1997; 2000b), where share price was less graphed than income and sales. Nonperforming loans was the second most graphed variable in the sample. That is another difference
with what has been found in non-financial firms, but in such case it is due to the specific industry in
which banks operate. Non-performing loans, although widespread, were graphed less than share
price and most of the time in Spain. Among income variables, the most graphed were operating
income and net income in each country, but in the UK where banks adopted more graphs on profit
before taxes, whilst net income is never graphed.
We also found that ROE, ROA and Cost to income ratios are mainly graphed by Spanish banks, and
that dividends and Earning per share are mainly graphed by Spanish and UK banks. Such result is
consistent with previous studies on non-financial firms, which found that UK firms use more EPS
and DPS graphs than French and German firms (Beattie & Jones, 2001) and that Italian firms rarely
graphed such variables (Ianniello, 2009).
The analysis of the graph location in the annual report provides findings consistent with those
reported in table 3. Financial performance graphs are not only the most frequent graphs, but also
those most frequently inserted in the “highlights section” of the annual report. Table 4 reports the
variables graphed ranked by the most frequently highlighted.
INSERT TABLE 4
Selectivity analysis
Results from the correlation tests provide support to the view that graphs are more likely used in
line with impression management theory, rather than with positive accounting theory. Indeed, we
found a positive association between the total number of financial performance variables graphed
and the increase in banks’ overall financial performance.
INSERT TABLE 5
In particular, as reported in table 5, we found that the number of financial performance graphs is
significantly higher in banks in which there was an increase in net profit (p < 0.01), profit before
taxes (p < 0.01) and gross profit (p < 0.05). In contrast, no significant association has been found
between the number of financial performance graphs and the market performance increase. Thus,
we provide support for hypothesis 1a, whilst hypothesis 2a is not supported. Indeed, when we
analysed the relation between the number of specific financial performance indicators graphed and
the presence of a positive performance in terms of the variable graphed, we found that banks that
performed better in terms of net profit (p < 0.05), income before taxes (p < 0.10), and cost to
income ratio (p < 0.10) were more likely to insert graphs about such financial variables. No
significant association has been found between the number of market performance, Non-performing
loans and gross income’s graphs and the presence of a positive/negative performance of these
variables. We were not able to test any association for ROE/ROA graphs as all the 47 banks
registered an increase in such values. Such results provide some support for hypothesis 1b. No
evidence has been found for hypothesis 2b.
INSERT TABLE 6
Table 6 reports the correlation test used for investigating hypotheses 3a and 3b. Our results provide
supports to both hypotheses. Inter-country differences emerged both in terms of graphs diffusion
and topics graphed. Spanish banks adopted significantly more graphs than other European banks
(p<0.01). In line with hypothesis 3b, UK banks, which belong to a micro-based country, are likely
to graph more financial performance variables (p < 0.01) than banks that operate in continental
Europe. Such variables are graphed significantly less by Italian banks. We also found that French
banks graph significantly more other financial information, whilst UK banks significantly less.
Finally, risk issues are significantly less graphed by French banks.
Measurement Distortion
The last part of our results reports anecdotal evidence of measurement distortion in the variables
graphed and whether such distortion is favourable or not for the bank. We provide some examples
of materially distorted graphs, focusing on financial performance variables graphed. All the four
examples express measurement distortion in excess of 10%, i.e. are cases of distortion that may
cause users’ perceptions of financial graphs to be altered (Beattie, Jones, 2002). These four
examples are chosen in line with previous studies’ results, where measurement distortion is likely to
give a more, rather than less, favorable portrayal of the firm’s performance (Beattie, Jones, 2008).
The first example refers to a share price graph (see figure 1). The graph on the left side is the annual
report bank’s graph, while the graph on the right side has been reproduced by the authors respecting
the correct principles of graph construction The graph discrepancy index (GDI) value of the left
hand side graph is +1596% and is due to the presence of a non zero axis. Instead of depicting a
+34.9% increase of the share price during 2006, the graph has reported a +592% increase, with an
exaggeration of an upward trend.
INSERT FIGURE 1
The second example refers to the distortion of a cost-to-income ratio graph (see figure 2). In this
case, there has been a decrease of the cost-to-income ratio of 0.5% (favorable for the bank) from
2004 to 2006. However, the declining trend depicted in the graph is much higher (-20%), therefore
highlighting a more favorable picture than the real one. The GDI is +3279%. There is also a lack of
the horizontal scale.
INSERT FIGURE 2
In the third example of an operating income graph, the distortion is mainly due to the tridimensional
view (see figure 3). The 2006 column does not follow, due to the tridimensional view, the same
horizontal axis of the first one (2003 year). Therefore, the increase seems higher (+57%) than the
real one (+7%) with a GDI of +728% and with a relevant exaggeration of an upward trend.
INSERT FIGURE 3
The last example is of a non-performing loans graph (see figure 4). There has been a decrease of the
non-performing loans ratio of -22% (favorable for the bank) from 2004 to 2006. However, the
decrease depicted in the graph is higher (-44%), therefore highlighting a more favorable picture
than the real one. The GDI is +98%.
INSERT FIGURE 4
All the four examples represent distorted graphs that could be used to strengthen good news by the
banks in 2006, with an overstatement of a trend. Measurement distortion could be a part of the
impression management strategy, with the aim of affecting the users’ perceptions by portraying a
positive image of the firm’s performance. All the previous studies that investigate, in a systematic
way, graphs’ distortion, have focused on a deliberate manipulation made by the management (for a
review, see Beattie and Jones, 2008a). On the other hand, measurement distortion could be
explained by a lack of competency of the graphs’ designers.
5. Conclusions
This study has investigated banks reporting practices in listed European commercial banks that
operate in the main five European countries (Germany, France, the UK, Italy and Spain), by
analyzing the nature and use of graphs reported in the annual report. We analyzed the main topics
graphed as well as the influence played by impression management, positive accounting and
institutional theories in explaining the use and design of graphs.
By comparing our findings with those from previous studies on non-financial firms (Beattie and
Jones 1992, 2001; Beattie et al, 2008), we report a similar level of graphs’ diffusion, as well as a
tendency on adopting more graphs on financial topics. We also found some relevant differences in
graph patterns between banks and non-financial firms, in particular in terms of variables graphed.
Share price related variables are those graphed by the highest number of banks, whilst they are
relatively less graphed by non-financial firms (Beattie and Jones 1992, 2001; Beattie et al, 2008).
Compared to previous studies (Beattie and Jones 1992, 2001; Beattie et al, 2008), banks are more
likely than non-financial firms to graph industry-specific variables. Non-performing loans, Cost to
Income ratio and Risk-related issues are among the most frequently graphed variables. Moreover,
differently from previous studies (e.g., Beattie and Jones, 1992, 2001), we do not find EPS, DPS
and Income before taxes among the most graphed variables. Such difference is probably due to the
inclusion of non-Anglo-American firms in the study, rather than to the focus on banks. Such graphs
are, together with market performance ones, the most graphed in UK banks’ annual reports, whilst
they are less graphed in other countries, consistently with the findings in non-Anglo-American nonfinancial firms (Beattie and Jones, 2001; Ianniello, 2009).
Our findings provide also support to institutional theory, as they show the presence of national
patterns in the diffusion of graphs, as well as in the topic graphed. The diffusion of graphs in banks
was significantly higher in Spain. UK banks were also apt to use graphs, but not significantly more
than banks of other countries. Moreover, financial performance variables were likely to be graphed
significantly more in UK banks, which operate in a ‘micro-based’ country, than in the banks
operating in ‘macro-based’ countries. Among the latter, Italian banks were those that used
significantly less graphs on such topic. Risk-related issues were less likely to be graphed in French
banks, which were, instead, more likely to graph other financial information. We also found that the
use of a ‘highlights section’ was more frequent in Spain and UK, whilst never adopted by German
banks.
The selectivity analysis has provided some support to the impression management theory, whilst
positive accounting theory is not supported. Indeed, we found a significant and positive correlation
between the use of financial performance graphs and the increase in banks’ overall financial
performance (measured in terms of net/gross income). We also found a significant and positive
correlation between the number of net profit and profit before taxes graphs and the increase in the
variable graphed, and a positive and significant correlation between the number of cost to income
graphs and the decrease of this variable. Anecdotal evidence of other impression management
techniques has also been found by analyzing the distortion of some financial performance graphs.
Our study has several limitations that can be turned as opportunities for future researches. Our
sample examines the nature and use of graphs for a single year. This choice enhances internal
validity, but limits our study to consider the use of graphs as a static concept. Future studies could
encompass a longitudinal dynamic model that account for variations in graph usage. The sample is
relatively small, although it does cover most of the major European banks, and include almost all
the current European systemically important financial institutions. Future studies might enlarge the
overall sample by considering non-European banks. Measurement distortion could be studied in a
more systematic way, with an analysis of its association with performance trends. Future studies
could also investigate whether impression management techniques with narratives, pictures and
other presentational formats are a substitute or a complement of the impression management
graphical choices.
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Table 1 – Descriptive statistics on the use of graphs
1 variable
2 variables
3 variables
More than 3 variables
No graphs
No variables graphed
No Banks
No Banks using graphs
Mean graphs
- per company
- per company using
graphsvariable graphed
Mean
- per company
- per company using
graphs
France
No
%
139
89
12
8
1
1
4
3
156
189
10
9
Germany
No
%
95
84
12
11
5
4
8
1
113
142
9
8
Italy
No
242
12
10
1
265
300
17
14
Spain
No
%
326
85
36
9
13
3
8
2
383
470
6
6
UK
No
125
7
1
0
133
142
5
5
15.6
17.3
12.6
14.1
15.6
18.9
63.8
63.8
26.6
26.6
25.0
22.3
18.9
21.0
14.8
16.6
17.6
21.4
78.3
78.3
28.4
28.4
26.3
29.4
%
91
5
4
0
%
94
5
1
0
Total
No
927
79
30
14
1050
1243
47
42
%
88
8
3
1
Table 2 – Distribution of graphs by type
Column
Line
Pie chart
Other
France
No
123
21
41
4
189
%
65
11
22
2
Germany
No
%
79
56
26
18
37
26
0
0
142
Italy
No
152
72
75
1
300
%
51
24
25
0
Spain
No
%
295
63
113
24
55
12
7
1
470
UK
No
101
16
24
1
142
%
71
11
17
1
Total
No
750
248
232
13
1243
%
60
20
19
1
Table 3 – Aggregate variables classification by topic.
No of graphs
Fr Ger
Share price
12
7
Non performing Loans
5
7
Operating income
11
3
Net income
9
2
ROE/ROA
4
0
Cost to income ratio
0
1
Eps
2
2
Income before taxes
0
0
Dividends
1
1
Net interest income
1
0
Non interest income
1
0
FINANCIAL PERFORMANCE 46 23
Loans
12
6
Deposits
3
0
Shareholders' equity
15
1
Tier capital
3
8
Assets under admin/mgmt
2
0
Assets (other)
5
0
Revenues
1
1
Costs
1
1
Shares other
5
2
Borrowings
0
1
Operating information
1
0
Interest
0
1
OTHER FINANCIAL
INFORMATION
48 21
VaR
3 10
Credit Risk
5
2
Market risk (other)
1
3
Operational Risk
0
1
Interest Risk
0
0
Counterparty Risk
0
2
Other Risk
0
5
RISK
9 23
Employees
22
9
Global
0 17
Governance
3
6
Customers
4
1
Social
5
1
Branches
0
0
NON-FINANCIAL
INFORMATION
34 34
Other
16
7
Total
153 108
It
Sp Uk Tot
15 14 14 62
4 24 8 48
12 10 5 41
2 12 0 25
2 11 2 19
2 13 2 18
2
6 4 16
4
4 5 13
0
5 4 11
2
7 1 11
1
2 0
4
46 108 45 268
17 18 2 55
21
4 0 28
3
4 0 23
1 21 1 34
11
4 0 17
5
3 3 16
3
7 3 15
5
5 2 14
3
2 0 12
0 10 0 11
1
6 2 10
4
0 0
5
74
31
21
1
2
7
1
0
63
18
11
5
8
7
7
Average no of graphs per bank
Fr Ger
1.2 0.8
0.5 0.8
1.1 0.3
0.9 0.2
0.4 0.0
0.0 0.1
0.2 0.2
0.0 0.0
0.1 0.1
0.1 0.0
0.1 0.0
4.6 2.6
1.2 0.7
0.3 0.0
1.5 0.1
0.3 0.9
0.2 0.0
0.5 0.0
0.1 0.1
0.1 0.1
0.5 0.2
0.0 0.1
0.1 0.0
0.0 0.1
84 13 240 4.8
22 3 69 0.3
32 6 66 0.5
7 3 15 0.1
11 0 14 0.0
6 0 13 0.0
5 0
8 0.0
4 4 13 0.0
87 16 198 0.9
12 3 64 2.2
4 5 37 0.0
4 14 32 0.3
5 2 20 0.4
1 1 15 0.5
1 0
8 0.0
2.3
1.1
0.2
0.3
0.1
0.0
0.2
0.6
2.6
1.0
1.9
0.7
0.1
0.1
0.0
Ita Spa
0.9
2.3
0.2
4.0
0.7
1.7
0.1
2.0
0.1
1.8
0.1
2.2
0.1
1.0
0.2
0.7
0.0
0.8
0.1
1.2
0.1
0.3
2.7 18.0
1.0
3.0
1.2
0.7
0.2
0.7
0.1
3.5
0.6
0.7
0.3
0.5
0.2
1.2
0.3
0.8
0.2
0.3
0.0
1.7
0.1
1.0
0.2
0.0
4.4
1.8
1.2
0.1
0.1
0.4
0.1
0.0
3.7
1.1
0.6
0.3
0.5
0.4
0.4
56 27 25 176 3.4 3.8 3.3
13 19 2 57 1.6 0.8 0.8
252 325 101 939 15.3 12.0 14.8
14.0
3.7
5.3
1.2
1.8
1.0
0.8
0.7
14.5
2.0
0.7
0.7
0.8
0.2
0.2
Uk Tot
2.8 1.3
1.6 1.0
1.0 0.9
0.0 0.5
0.4 0.4
0.4 0.4
0.8 0.3
1.0 0.3
0.8 0.2
0.2 0.2
0.0 0.1
9.0 5.7
0.4 1.2
0.0 0.6
0.0 0.5
0.2 0.7
0.0 0.4
0.6 0.3
0.6 0.3
0.4 0.3
0.0 0.3
0.0 0.2
0.4 0.2
0.0 0.1
2.6
0.6
1.2
0.6
0.0
0.0
0.0
0.8
3.2
0.6
1.0
2.8
0.4
0.2
0.0
No Banks using at least
one graph
Fr Ger It Sp Uk Tot
5
7 10
6 5 33
2
1 1
4 1 9
5
3 6
4 2 20
5
2 2
3 0 12
4
0 2
5 2 13
0
1 2
5 2 10
2
2 1
4 4 13
0
0 2
3 4 9
1
1 0
3 4 9
1
0 2
4 1 8
1
0 1
2 0 4
7
8 11
6 5 37
3
4 6
6 1 20
2
0 7
2 0 11
4
1 3
3 0 11
3
1 1
5 1 11
2
0 6
2 0 10
4
0 5
2 2 13
1
1 1
3 3 9
1
1 3
3 2 10
4
2 3
2 0 11
0
1 0
4 0 5
1
0 1
2 1 5
0
1 3
0 0 4
5.1
1.5
1.4
0.3
0.3
0.3
0.2
0.3
4.2
1.4
0.8
0.7
0.4
0.3
0.2
7
1
1
1
0
0
0
0
1
4
0
2
1
2
0
7 11
5 7
1 2
3 1
1 1
0 1
2 1
0
7 7
4 5
2 6
4 3
1 4
1 1
0 4
6
5
3
3
5
2
2
6
5
2
4
2
1
1
3
2
2
3
0
0
0
1
3
3
1
4
1
1
0
34
20
9
11
7
3
5
4.5 5.0 3.7
3.2 0.4 1.2
54.2 20.2 20.0
6
5 10
6
5 32
24
21
11
17
9
6
5
Table 4 – Graphed variables reported as highlights
No of graphs
Banks with at least a highlighted graph
Fr Ger It Sp Uk Tot Fr
Ger
It Sp
Uk
Tot
Operating income
8
0 0 3
3 14
4
0
0
2
2
8
Net income
8
0 0 6
0 14
4
0
0
3
0
7
ROE/ROA
4
0 0 6
2 12
4
0
0
4
2
10
Eps
2
0 1 3
4 10
2
0
1
1
4
8
Share price
2
0 1 3
3
9
2
0
1
2
2
7
Income before taxes
0
0 0 2
5
7
0
0
0
2
4
6
Dividends
1
0 0 1
4
6
1
0
0
1
4
6
Cost to income ratio
0
0 0 4
2
6
0
0
0
4
2
6
Assets (other)
3
0 1 0
1
5
3
0
1
0
1
5
Loans
3
0 0 2
0
5
2
0
0
2
0
4
Shareholders' equity
4
0 0 0
0
4
4
0
0
0
0
4
Revenues
0
0 3 0
1
4
0
0
1
0
1
2
Tier capital
3
0 0 0
0
3
3
0
0
0
0
3
Employees
1
0 1 0
1
3
1
0
1
0
1
3
Assets under admin/mgmt 2
0 0 0
0
2
2
0
0
0
0
2
Deposits
1
0 0 1
0
2
1
0
0
1
0
2
Non-performing Loans
0
0 0 2
0
2
0
0
0
1
0
1
Customers
0
0 0 0
2
2
0
0
0
0
1
1
Governance
0
0 0 1
0
1
0
0
0
1
0
1
Borrowings
0
0 0 1
0
1
0
0
0
1
0
1
Branches
0
0 1 0
0
1
0
0
1
0
0
1
Costs
0
0 0 0
1
1
0
0
0
0
1
1
Global
0
0 1 0
0
1
0
0
1
0
0
1
Net interest income
0
0 0 1
0
1
0
0
0
1
0
1
Graphs macro-category
Financial performance
Financial performance
Financial performance
Financial performance
Financial performance
Financial performance
Financial performance
Financial performance
Other financial information
Other financial information
Other financial information
Other financial information
Other financial information
Non financial
Other financial information
Other financial information
Financial performance
Non financial
Non financial
Other financial information
Non financial
Other financial information
Non financial
Financial performance
Table 5 – Pairwise correlation for testing hypotheses 1 and 2
H1a/2a Changes in net profit
H1a/2a Changes in profit
before taxes
H1a/2a Changes in gross profit
H1a/2a Changes in market
performance
H1b/2b Changes in the variable
graphed
Financial
performance
0.427 ***
Share
Price
Nonperforming
loans
-0.1293
-0.1152
Operating
income
Net
income
Cost to
income
ratio
Eps
Income
before taxes
0.4013 ***
0.3244 **
-0.0571
Levels of significance: *** p < 0.01; ** p. < 0.05; * p < 0.10.
0.1826 0.3325 **
-0.2725 * 0.1841
0.2489 *
Table 6 – Pairwise correlation for testing hypothesis3
France
Germany
Italy
Spain
UK
Total Variables
-0.083
-0.0982
-0.239
0.450 ***
0.122
% Financial performance
0.143
-0.2113
-0.317 *
0.093
0.436 **
Levels of significance: *** p < 0.01; ** p. < 0.05; * p < 0.10.
% Other
financial information
0.297 *
-0.2376
0.159
-0.049
-0.266 *
% Risk
-0.393 ***
0.2462
0.063
0.170
-0.077
% Non-financial
information
-0.095
0.1319
0.066
-0.191
0.071
Figure 1 – Non zero-axes distortion in a share price graph
DISTORTED GRAPH
CORRECT GRAPH
share price trend
share price trend
10
8
share price
trend
7
6
5
share price
trend
0
Year 2006
Year 2006
Figure 2 – Cost to income ratio graph distortion
COST TO INCOME RATIO (%)
Figure 3 – EBITDA graph distortion due to a tridimensional view
EBITDA
(In millions of Euro)
Figure 4 – Non-performing loans ratio graph distortion
NPL RATIO