Impression Management in Financial Graphs

Tofig Aliyev

A dissertation submitted to the University of Bristol in accordance with the requirements of

the degree of Master of Science by advanced study in Accounting, Finance and Management

in the Faculty of Social Sciences and Law.

School of Economics, Finance and Management

Date: 4 September 2014

Word Count: Fourteen thousand and three hundred ninety two (14,392)

Abstract

Recently, researchers have shown an increased interest in investigation of impression

management in financial graphs. Prior literature shows evidence of impression management

techniques (selectivity, graph distortion and presentational enhancement) applied in graph

construction and design. Given the fact, that impression management serves the interests and

motives of the management, and no longer gives an “unbiased, true and fair” image of the

company, then the importance of this problem increases. It is essential to investigate this issue and

develop generally accepted rules and regulations to avoid the usage of impression management

techniques in financial graphs.

This dissertation investigates the existence of impression management techniques in annual

reports of 60 Russian Stock Exchange Listed companies, ranked based on the alteration of their

EBIT figures from 2012 to 2013. Annual reports for the year 2013 are analysed and the obtained

data is examined based on statistical tests. The average number of graphs in annual reports is 20.68,

which is quite high compared to prior literature. Moreover, the dissertation finds that graph usage is

approx. 95 %, while level of distortion of key financial variable graphs is equal to 44 %. Sales,

earnings, EPS and DPS figures are chosen as KFV graphs for the analysis. Results of the

dissertation suggest that, top profitable companies include more KFV graphs than the least

profitable companies, who have nothing to share due to poor performance. Moreover, there is a

strong association between “sales” performance and inclusion of KFV graph, which has not been

found before. Analysis of graph distortion reveals the existence of distortion and existence of non-

compliance with graph design, however, the test of differences between top and bottom profitable

companies demonstrates insignificant results. In addition, specific analysis demonstrate that graphs

are distorted in favourable trend.

Nevertheless, results of the research are reliable and can be used for further investigation, or

may assist investors and shareholders in their investment decisions. Moreover, no research in the

area of impression management in financial graphs of annual reports of Russian companies has

been carried out before.

Dedication and Acknowledgements

First and foremost, I would like to express my gratitude to my supervisor Professor Mike

Jones, for his helpful feedback, kind assistance and priceless support. I would also like to thank my

family, my friends from Azerbaijan and all the new friends that I have met in United Kingdom for

adding truckloads of happiness, motivation, inspiration and support during this year. These people

had invaluable positive impact on me.

I would also like to thank the University of Bristol, and all the staff from Graduate Studies

Office, Library Service and the School of Economics, Finance and Management, who have assisted

me to overcome any academic difficulties, confusions and timely solved any enquiry in a friendly

and professional way. My special thanks to Accommodation Office who helped me with my issue

at the beginning of the academic year. I appreciate all the motivating, inspiring, supporting and

academic environment that the University of Bristol provided through its facilities and scholastic

staff.

My kind words of gratitude to my personal tutor Stuart Cooper for his help and very interesting

lectures of International Financial Accounting, which created my great interest in this area. Special

thanks to academic staff of the University of Bristol, who lectured me during this year: Professor

Stuart Cooper, Professor Sheila Elwood, Dr. Matt Bamber, Dr. Raul Crespo, Dr. Stephen Lyne,

Professor David Dugdale, Professor Paul Grout, Dr. Brian Dangerfield

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AUTHOR’S DECLARATION

I declare that the work in this dissertation was carried out in accordance with the Regulations of the

University of Bristol. The work is original except where indicated by special reference in the text

and no part of the dissertation has been submitted for any other degree.

Any views expressed in the dissertation are those of the author and in no way represent those of the

University of Bristol.

The dissertation has not been presented to any other University for examination either in the United

Kingdom or overseas.

SIGNED: DATE: 4 September 2014

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TABLE OF CONTENTS

INTRODUCTION 6

LITERATURE REVIEW 9

ENVIRONMENT 19

Hypotheses 21

Methods 24

Findings 30

Discussion 43

Conclusion 49

List of References 51

Appendices 57

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List of Tables

TABLE 1. SUMMARY OF PRIOR FINANCIAL GRAPHS LITERATURE (WITH EXTRA) 17

TABLE 2. SUMMARY OF GRAPH USAGE AMONG RUSSIAN COMPANIES 30

TABLE 3. PERCENTAGE PROPORTION OF KFV GRAPHS OUT OF TOTAL FINANCIAL 31

TABLE 4. SUMMARY OF ANNUAL REPORTS ANALYSIS 32

TABLE 5. SUMMARY OF SIGNIFICANCE ANALYSIS OF MEANS OF NUMBER OF GRAPHS (BY CLASSIFICATION) BASED ON TWO-TAILED T-TEST ANALYSIS 33

TABLE 6. SELECTIVITY BIAS OF FAVOURABLE/UNFAVOURABLE GRAPHS BY EACH CLASS 35

TABLE 7. COMPARISON OF MEANS OF KFV GRAPHED BETWEEN MOST AND LEAST PROFITABLE COMPANIES (BASED ON TWO-TAILED T-TEST ANALYSIS) 36

TABLE 8. NUMBER OF COMPANIES WHICH INCLUDED AT LEAST ONE KFV 36

TABLE 9. TEST OF RELATIONSHIP BETWEEN CHANGE OF AMOUNT OF KFV GRAPHS AND PERFORMANCE OF THE COMPANY (SALES AS PERFORMANCE INDICATOR) 37

TABLE 10. DISTORTION OF KFV GRAPHS BASED ON MEAN DISCREPANCY INDEX 39

TABLE 11. SUMMARY OF TWO-TAILED T-TEST ANALYSIS OF MEAN DISCREPANCY INDEX BETWEEN MOST AND LEAST PROFITABLE COMPANIES BASED ON KFV GRAPHS. OTHER TYPES OF GRAPH DISTORTION AND IMPRESSION MANAGEMENT TECHNIQUES 41

TABLE 12. SUMMARY OF DISCUSSION AND INVESTIGATION OF ORIGINALITY OF THE RESULTS 43

TABLE 13. COMPARISON OF PRIOR LITERATURE WITH THIS DISSERTATION BASED ON MAIN RESULTS 47

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List of figures

FIGURE 1. COLUMN TYPE GRAPH (FAVOURABLE TREND). (AEROFLOT ANNUAL REPORT 2013) 26

FIGURE 2. PIE TYPE GRAPH. (ROSTELECOM ANNUAL REPORT 2013) 26

FIGURE 3. LINE TYPE GRAPH (FAVOURABLE TREND). (LUKOIL ANNUAL REPORT 2013) 26

FIGURE 4. NO SCALED TIME AXIS WITHOUT NUMERIC FINANCIAL LABELS 28

FIGURE 5. NO SCALED FINANCIAL VARIABLE AXIS 28

FIGURE 6. SPECIFIER COLOUR OF LAST YEAR DIFFERENT TO PRIOR YEARS 28

FIGURE 7. UNCONVENTIONAL LOCATION OF NUMBER ATTACHED TO INDIVIDUAL SPECIFIER AND THREE-DIMENSIONAL SPECIFIER 29

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Introduction

In the past two decades a number of researchers have sought to determine impression

management in the annual reports of companies. Beattie and Jones (2000) state that impression

management “…occurs, motivated by management’s desire to dictate the corporate reporting

agenda and present a self-serving view of corporate performance. Thus, impression management

conflicts with a commonly expressed purpose of accounting, which is to present fairly annual

financial performance in a neutral, unbiased manner.”

The importance of annual reports as a communication “bridge” between investors,

shareholders, etc. and managers has increased (Lee, 1994; Hanson, 1989). However, recent research

demonstrates that this “bridge” is subject to impression management and sometimes is fairly biased.

The fact that impression management techniques exist in annual reports of companies, decreases

the level of their assurance for effective investment decisions and can lead to a fake image of the

company. This biasedness is presented either in the narratives part or graphic part, which is part of

voluntary disclosure.

Studies done by Paivio (1974) and Lee (1975) reveal the importance of graphs in annual

reports as the main communicative link and “display information in an alternative format to the

traditional alphanumerical table” (Beattie and Jones, 1997), while quantitative research by

Steinbart, 1989; Beattie and Jones, 1992; 1996; 1997; 2000; 2001a; 2001b, Frownfelter and

Fulkerson, 1998 etc. confirm the existence of impression management techniques in graphs. These

techniques are presented in three types: selectivity, distortion and presentation enhancement.

Selectivity is one of the main techniques used by managers, as it involves inclusion or absence of a

particular graph which can change the overall image of the company. In contrast, distortion and

presentational enhancement are directly linked to graph construction and design.

Distortion involves exaggeration or understatement of the graph, while presentation

enhancement relates to non-compliance with generally accepted graph construction and design

regulations. If these information manipulation methods are applied in annual reports, then they will

affect the effectiveness and accuracy of the investors` decision. Although this voluntary disclosure

is a part of financial statements checked and proved by auditors, there is no standard to deal with

impression management in graphs.

Mather et al. provided text from the Statement of Accounting Concepts SAC 2 “…the

objective of general purpose financial reporting is to “provide information useful to users for

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making and evaluating decisions about the allocation of scarce resources” (paragraph 43)”. He

then continues with SAC 3 “that financial information should be representationally faithful and

unbiased”. Referring to SAC 2 and SAC 3, graph distortion or presentational enhancement are

subject to unfaithful and biased disclosure of information, which violates the principles of

accounting standards. Despite the fact that AUP 19(AUS 212) Other Information in Documents

Containing Audited Financial Statements states the importance of checking that additional or

voluntary disclosure information is materially consistent with financial statements, auditors still

avoid considering them in their “check-list”. Unless, generally accepted rules and regulations of

checking graphs are provided, auditors will still avoid investigating their consistency. Hence,

graphs in annual reports will be subject to impression management.

This dissertation will investigate three types of impression management in graphs:

selectivity, graph distortion and presentational enhancement, in the annual reports of 60 Russian

companies listed in the Russian Stock Exchange. Federal Law (2010) in Russia obligates

companies listed in RSE to provide their financial statements in accordance with IFRS, which is the

main reason why the companies listed in RSE are subject to investigation in this dissertation. It is

beyond the scope of this study to examine accounting narratives. Investigation will be based on the

financial year of 2013, while EBIT rank will be provided based on alteration of the performance of

the company from 2012 to 2013.

There has been no other investigation of Russian companies and impression management in

graphs, which increases the importance of this dissertation. The purpose of this dissertation is to

establish: 1) extent of graph usage in Russian companies in general and specific terms (types,

classes, and amount); 2) analyse KFV graphs and identify degree of selectivity, graph distortion and

presentational enhancement applied in their construction; 3) investigate relationship of KFV graphs

and financial performance of the companies, based on their EBIT and Sales performance.

This dissertation is divided in the following way: literature review, environment,

hypotheses, methods, findings and conclusion. The literature review deals with investigation of

prior literature of impression management, focusing on graph usage and distortion; the environment

will provide a short overview of Russia`s current economic position and issues; the hypotheses will

show the main aim of the research, while the methods part will identify the scope of analysis and

tools used for the examination; the findings will provide quantitative information of results with

statistical tests and tables, while the conclusion part will outline limitations and summarise the

research.

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Literature Review

Over the last few years, an increasing interest in investigating strategy and motives for

impression management in corporate annual reports of companies has been noted. A definition of

impression management from an accounting perspective was given by Clatworthy & Jones (2001),

who explained that “impression management involves controlling and manipulating the impression

presented to accounting information users, with a view to strategically manipulate their

perceptions, and eventually, their decisions”(Merkl-Davies & Brennan, 2007, p.116) and it “occurs

when management selects the information to display and presents information in a manner

intended to distort readers` perceptions of corporate achievement” (Godfrey et al, 2003, p.96).

More specifically it is a tool used by managers to manoeuvre information in financial reports and

change the insight of the user (e.g. potential investor, shareholder etc.) (Barnett, 1979). Beattie &

Jones (2000) classified it as presentational management which involves “manipulation of the

content and syntax of accounting narratives, or of presentational formats, such as graphs or

pictures”. They further divided it into three categories “content analysis studies; understability and

readability study; and alternative presentation studies (e.g. graphs, pictures).” As this dissertation is

focused on abuse of graphs in financial reports, content analysis and understability study will not be

considered further. More specifically, analysis of graphs will be divided into three parts: selectivity,

measurement distortion and presentational enhancement. These three terms have been the main

aims of several studies so investigation of prior literature will be consistent with the framework of

analysis provided above.

Beattie and Jones (2000) stated that graphs “… rely on spatial, rather than linguistic,

intelligence”, while Ackerman (1991) noted the importance of graphs as contact between

shareholder, investor and manager “making the process more direct and immediate” (Beattie &

Jones, 2000). Significance of graphs as contact between manager and user of annual reports was

also demonstrated in the study by Paivio (1974). Paivio (1974) showed the supremacy of graphs

from the ability of human memory to remember visual trends (Yan Huang, 2008). Quantitative

investigation of graph usage in annual reports was also conducted by other researchers (McKinstry,

1996; Lee, 1999). Some studies indicated the significance of graphs from understability terms.

Tufte (1983) and several other researchers (Blocher, Moffie and Zmud, 1986; Courtis, 1997)

concluded that understability level of complicated information is higher in graphs than in numerical

tables or words. These supreme characteristics have made graphs one of the most commonly used

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ways of communicating with users (e.g. potential investor, shareholder, etc.) and one of the most

abused terms.

According to Carswell (1991) and Meyer (1997), selectivity and measurement distortion of

graphs can be used for impression management to deviate the overall picture of the company.

Moreover, Steinbart (1989) stated that a graphical picture is “accurate to the extent they lead

viewers to form conclusions consistent with those that would result from more formal quantitative

analysis of the data”. Earlier, Tufte (1983) developed 6 graphic design principles and asserted that

“surface depiction of the graph, must be directly proportional to the underlying numerical values

represented” in order to be accepted as accurate. The main design principles of Tufte (1983) were:

“the depiction of the graph should be directly proportional to any changes in numerical

values”;

“graphs should be clearly labelled, so as to prevent misunderstandings, and the amount of

space used shall be limited”;

“any alterations to graphic design should be able to reflect changes to the numerical

changes”;

“visual effects should not influence or distract the reader, causing him to pay particular

attention to one aspect”;

“when depicting time series information, standardization of currency weightings often are

superior to weightings of varying currencies”;

“The serial information pattern must provide at least 3 information points and depict

accurate information pertinent to the situation” (Yan Huang, 2008).

These 6 principles are used as fundamentals of accurate graph depiction and violation of these

principles leads to “measurement distortion”, which was developed and described in the work by

Beattie and Jones (1992).

The third way of impression management considered by this dissertation was

“presentational enhancement”. Several uses of this term are available for managers, such as: non-

zero axis, broken axis, non-arithmetic scales (Beattie & Jones, 1992) which can be used either to

exaggerate or minimize the pattern depicted in the graph. Non-zero axis is formed “by the

intersection of two perpendicular lines” which are “scaled in equal units from the origin” and it is

“essential to include a zero referent line, since the origin of the two axes in Cartesian space is

usually at the same point (Kosslyn, 1989)” (Beattie and Jones, 1992). As for broken axes or non-

arithmetic, Beattie and Jones (1992) demonstrated that it “also results in distortion, since equal

distances along the axis do not represent equal amounts”. Further developments in the study of

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presentational enhancement were made by Arunachalam and Steinbart (2002), which determined all

the possible ways of presentational enhancement and classified them into 4 categories:

“exaggerating the magnitude of a trend (by scaling the vertical axis improperly, not starting

at the origin, or indicating a discontinuity in the scale), with inappropriate vertical

representations in measurement criteria, such as not starting from the point of origin or non-

continuous measurements”;

“masking graphs (using only one vertical scale to include two variables that differ greatly

in magnitude on the same graph)—such that it may disperse the reader`s attention, easily

resulting in erroneous judgments”;

“reverse annual sequences—most languages are read from left to right; therefore most

people expect to read a graph of time-series data from left to right (Gillian et al., 1998), when

reversing the sequence of years, mistakes in understanding the information due to

carelessness by the reader are common (misunderstanding annual profit increases, for

instance)”;

“Omitting negative value, such as concealing information that shows the company in a bad

light; if the reader doesn`t review in any great detail, then certain information may be

overlooked.” (Yan Huang, 2008).

All of these violations and distortions have led to the development of quantitative analysis

in annual reports to investigate the existence of impression management among them. Most

quantitative analysis of selectivity and measurement distortion of graphs are done based on the “lie

factor” developed by Tufte (1983), which is also considered in this dissertation:

Lie factor = ¿effect shown∈graphic¿effect∈numeric data

Further, this formula was altered by Taylor (1986) and Steinbart (1989) to more of a statistical tool,

to facilitate calculation of distortion in the graphs. The newly developed term was called Graph

Discrepancy Index and was an easier alternative to the “lie factor”:

Graph Discrepancy Index = 100* ((a/b-1),

where

“a – percentage change depicted in graph”,

“b – percentage change in data”.

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If the result was 0% then it showed that no distortion took place, however deviations greater

than 5% and less than -5% (Mather, 1996) were accepted as materially distorted. GDI was further

used by Steinbart (1989) to analyse 319 companies from Fortune 500 list. He found that “on

average, graphs of the three variables exaggerated the magnitude of change by about 11%”

(Steinbart, 1989). Moreover, it was found that distortion of more than 10% took place in about 26%

of the graphs from annual reports. Furthermore, to analyse reports in selectivity terms, he divided

the companies into two groups “good news companies” and “bad news companies”, from the

change in profit (Steinbart, 1989). His study concluded that “good news companies” had more

intention to add key financial variables in their reports (74% against 53%), while “bad news

companies” had more intention to add distorted graphs— the difference between them was

significant at the 1% and 5% levels, respectively (Steinbart, 1989). As this dissertation applies all

three types of impression management (e.g. selectivity, measurement distortion and presentational

enhancement) to investigate annual reports, relevant prior literature results and conclusions should

be mentioned.

A considerable amount of research has indeed confirmed that corporate annual reports are

manipulated by managers to portray a more favourable view of the company (Beattie & Jones,

1989; 1992; 1997; 2000; 2002; Johnson et al, 1980; Steinbart, 1989; Courtis, 1997; Godfrey et al,

2003; Muino and Trombetta, 2009 and etc.). In general, there were different types of quantitative

analysis: inter-country, two country or single country analysis. Among the inter-country analysis

should be mentioned the studies by Beattie and Jones (1996; 2001a; 2001b), Frownfelter and

Fulkerson (1998).

Beattie and Jones (1996) analysed the top 50 national companies among 6 countries based

on the annual reports of 1991-1992 (Beattie and Jones, 1996). These countries were Australia,

France, Germany, The Netherlands, United Kingdom and United States (Beattie & Jones, 1996).

Findings showed that KFV, which were selected as earnings per share (EPS), dividends per share

(DPS), return on capital employed (ROCE) and cash flow, were graphed by at least 25% of the

companies in each of the countries (Beattie and Jones, 1996). Moreover, results demonstrated a

significant impact on the performance of the companies from the amount and selectivity of KFV

graphed, among the US, UK and Australia (Beattie and Jones, 1996).

A similar sample was used by Beattie and Jones for the (2001a) and (2001b) studies.

Research (2001a) investigated whether each of the countries used different policies in graph

construction, with some focusing more on the German model of construction (Beattie & Jones,

2001a). Across 300 companies examined, only 263 of them (88%) used at least one graph in their

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annual reports, while total graphs were equal to 2364, 515 of which were KFV graphs from 196

countries (Beattie and Jones, 2001a). Statistical analysis showed significant evidence of association

of graphs usage with specific country and with micro/macro variables (Beattie & Jones, 2001a).

Specifically, the study concluded that French companies used the most graphs in their reports (11.0

mean per report), emphasizing more on stocks information and cash flow. Among the micro-

countries, Australia had the greatest number of graphs focusing on prices and raw material products

(Beattie & Jones, 2001a). Overall, the study demonstrated strong results of inter-country variation

in graph usage practices and alteration of graphs usage among micro- and micro-countries. These

conclusions supported hypotheses provided at the beginning of the research and demonstrated

consistency with the previous literature (Beattie & Jones, 2001a).

Following a general analysis, Beattie and Jones (2001b) developed more detailed analysis of

their previous study of inter-country financial graphs. This time Beattie and Jones determined as

their main purpose to investigate selectivity and distortion pattern and examine the possible relation

of graph usage to performance of the company in each of the chosen countries (Beattie & Jones,

2000b). Hypotheses used in this research emphasized widely on investigation of the association

between performance increase/decrease, with KFV graphs included and whether distortion in the

graphs is directed in a favourable pattern (Beattie & Jones, 2001b). Results of graph usage and

means were similar to their previous study: 263 countries (88%) used graphs in their reports, with

graphs totalling 2364 (Beattie & Jones, 2001b). As for conclusions of measurement distortion,

significant evidence was only found in two countries: the US and the Netherlands; also in all of the

countries a favourable trend of graphs was found (Beattie & Jones, 2001b). This supported the

hypothesis of graph distortion in a favourable direction and displayed consistency with prior

literature. In addition, proof of alteration in “measurement distortion” policies across different

countries was also demonstrated.

Contradictory results compared to prior literature were found in the inter-country study by

Frownfelter and Fulkerson (1998). This research was similar to Beattie and Jones’ inter-country

analysis (1997). However, their research classified reports into two major groups U.S and non- U.S

companies listed in the U.S Stock Exchange (Frownfelter & Fulkerson, 1998). Research

investigated 2466 graphs from 74 companies across 12 countries and found that non- U.S

companies had more association of their performance and key financial variables added to the

reports, than U.S companies (Frownfelter & Fulkerson, 1998). This conclusion was contradictory to

Beattie and Jones’ results, where U.S companies showed significant relation to selectivity and

distortion. Moreover, Frownfelter and Fulkerson found materially distorted graphs at the level of

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68% out of which 43% were overstated, which was quite high compared to Beattie and Jones

(1997) (Frownfelter & Fulkerson, 1998).

Two country analysis is not as common in the history of impression management as inter-

country or single country analysis. Among the two country studies is the research by Beattie and

Jones (1997).

This two country study analysed 176 top listed (85 reports) US and (91 reports) UK

companies based on the annual reports of 1990. There had been no inter-country analysis before,

due to difficulties in economic and accounting environments of the countries (Beattie and Jones,

1997). In contrast with previous studies, Beattie and Jones developed a new hypothesis in terms of

“measurement distortion”, which they called “presentational enhancement’. They explained it as a

“departure” from the normative of graph construction, which they listed in the study:

“….the width of specifiers and the spaces between them (interspaces) should be uniform and

neither disproportionately long and narrow nor excessively short and wide; three-dimensional

specifiers should be avoided, since they can lead to perceptual ambiguity; specifiers should be

coloured with care since the human perception of colour is very complex …and the numeric labels

should be located close to the axes, be horizontal, and use a consistent typeface (Kosslyn, 1989)”.

(Beattie and Jones, 1997, p.38)

Findings showed a strong association of company performance in terms of EPS and usage

of at least one key financial variable in the reports (Beattie and Jones, 1997). Similar significance

was found in the association of favourable trend with presence of at least one key financial variable

(e.g. sales, earnings, dividends per share or earnings per share) (Beattie and Jones, 1997). The

results were quite consistent with prior research in terms of selectivity and showed quite a high

outcome for mean graph usage (13% for the US), which was in comparison with research the

highest mean frequency. In terms of “measurement distortion”, the paper found that 24% of KFV

were materially distorted: the highest distortion was in the US with 16% against 7% in the UK

(Beattie and Jones, 1997). These findings contradicted the expectations of Beattie and Jones,

because based on their hypothesis for the research:

“…increased regulatory stringency and more conservative GAAP may “spill over” to

graphical reporting practices, leading U.S companies to undertake less interpretative shading than

U.K companies. This difference may be exacerbated by the more litigious U.S financial reporting

environment.” (Beattie and Jones, 1997, p.41).

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However, results showed a significantly high level of distortion and manipulation of corporate

reports in the U.S compared to the U.K. Nevertheless, this dissertation is a single country (Russia

focused) study, therefore inter-country analysis won`t be considered further. In contrast, single

country analysis has the most number of studies and is methodologically closer to this dissertation.

One of the first single country analyses was done by Johnson, Rice and Roemmich, who

analysed 50 annual reports over the period 1977-1978 of US companies, which were selected

randomly from Fortune 500 list. Results of their research showed that at least one incorrect graph

was depicted in 42% of the annual reports, while 29, 5% of all graphs (423 graphs in total) were

constructed incorrectly (Johnson et al, 1980). However, to what extent the graphs were distorted or

level of selectivity were not considered in their work.

Beattie and Jones (1992) analysed “use and abuse” of graphs in the 1989 annual reports of

240 UK listed companies. Hypotheses were developed in two directions: selectivity and

measurement distortion of graphs (Beattie & Jones, 1992). For the analysis of selectivity,

companies were divided into two groups: ones with good performance and with bad performance,

in terms of Earnings per share (EPS) and change in Key Financial Variable (KFV). Results showed

a significant relation between companies with good performance and amount of KFV graphed in

the reports (Beattie & Jones, 1992). The second part of the study concerned “measurement

distortion” analysis with classification of favourable and unfavourable trends in graphs. Findings

were that 76% of graphs were distorted for a favourable trend, and only 24% an unfavourable trend

(Beattie & Jones, 1992). This research indicated the vast spread of graph usage among companies

and significant difference in graph distortion level between “good and bad” performing companies

with a mean discrepancy index of 10.7% (Beattie & Jones, 1992).

According to CICA, which studied 200 annual reports of Canadian listed companies for

1991, 83% of them used graphs, with “sales, earnings” most frequently used (CICA, 1993). This

study didn`t consider any analysis in terms of distortion or selectivity level (CICA, 1993). In

contrast, research by Mather et al (1996) found no relationship between companies’ performance

and graphs used in annual reports. Although the research concluded that 80% of companies used

graphs in annual reports, evidence of selectivity was not found among the main sample of the top

50 Australian companies (Mather et al, 1996). Nevertheless, analysis of 100 more Australian listed

small companies in 1 year term target found evidence of a relationship with distortion level at 16.4

%. In addition, graph usage of small companies was 73% compared to 80% in big companies.

Overall, results of the study led to contrary conclusions compared with the prior literature:

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“The relationship between distortion of graphs and company performance was also

addressed. Using either the year-to-year change in operating profit before tax or the change in the

variable being graphed as measures of performance, no significant overall relationship between

distortion and company performance was found. This contrasts with both UK and US studies.”

(Mather et al, 1996, p.62).

Relationship of corporate performance and usage of graphs was also studied in the research

by Beattie and Jones (2000). No work has been done before with time-series analysis in order to

follow changes in graph construction policy of the companies based on their performance results.

Beattie and Jones (2000) used in their research annual reports of 137 UK companies that were

performing in the 1988-1992 time period (Beattie & Jones, 2000). The companies were selected

from the top 500 UK companies by using a sampling method (each 2nd in the list) and were asked to

send their reports annually (Beattie and Jones, 2000). Results demonstrated strong support of the

existence of graph selectivity manipulation. Specifically, it was noted in two (EPS and Income) out

of four KFVs used in the reports which are assumed as the main determinants of decisions (Beattie

& Jones, 2000). Beattie and Jones asserted that the increase in performance of EPS and Income

influenced the decision of management to add graphs of EPS and Income, while a decrease led to

the absence of these graphs in annual reports (Beattie and Jones, 2000). Considering that among

prior literature all the analysis was done in cross-sectional data format, time-series analysis gave a

new approach to analyse the impression management problem. Moreover, results showed

significant evidence of selectivity manipulation, which again confirmed the existence of impression

management in the annual reports of the companies.

An alternative type of impression management analysis was done by Shaio Yan Huang

(2008). Huang (2008) stated that graphs are mainly used to show comparative information rather

than numerical information. His study investigated direction of usage of graphs, impression

management techniques and how this all can be used to manipulate investors’ decision (Huang,

2008). Methods used by Huang are quite different from prior literature, because he simulated and

created a fictional company, the annual reports of which were given to participants of the

experiment, who were assumed as potential investors. Simulation consisted of three different

scenarios: information of numerical table, non-distorted graphs information, distorted graphs

information (Huang, 2008). His results on sources of investment decisions demonstrated that,

investors mostly used newspaper information (over 65%), while website and magazine information

took 2nd and 3rd places (Huang, 2008). Moreover, this research gave quite significant results for the

potential power of graphs in impression management:

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“In addition, we found that participants given numeric tabulations of financial information

in experiment 1 made more conservative investment decisions than those given non-distorted

graphs of financial information. This is in accordance with the predication of the hypothesis and

consistent with prior research (Tufte, 1983; Anderson, 1983; Jarett, 1993).These results also

indicate that using graphs to express financial information certainly creates a different

impression.” (Huang, 2008, p.52)

Huang (2008) also found that 25.68% of websites already used distorted graphs and therefore,

suggested developing regulations over graph construction and disclosure. Together these studies

provide important insights into the concept of impression management. Research from 1980 to

1999 was summarized in a table provided by Beattie and Jones (2001); this table was expanded by

adding recently published studies which were also considered above in the literature review for

some general analysis:

Table 1. Summary of prior financial graphs literature (with extra)

Country Companies studied

Graph usage (%)

Mean number

of graphs

Most frequently graphed variables

Johnson et al.

(1980) US

125 graphs from 50 Fortune 500

annual reports for 1977 and 1978

Not reported 8.5 Not given

Steinbart (1989)

US

319 Fortune 500 annual reports for

1986 79 8.0Sales, net income,

dividendsBeattie and

Jones

(1992)

UK240 large

companies in 1989 79 5.9Sales, earnings before tax, EPS,

DPS

Green et al.

(1992)Republic of

Ireland

117 semi state sector and public

limited companies 54 6.0 Not givenCICA

(1993)Canada

200 companies in 1991 83 8.4

Sales, earnings, shareholders` equity, assets

Mather et al.

(1996) Australia

a) 43 top Australian listed

and b) 44 not-for-profit entities in 1991 and 1992

a)83b)73

Not given

Sales, earnings, EPS, dividends

Beattie and Jones

(1997)UK/US

176 leading companies, 1990

annual reports UK 80US 92

UK 7.7US 13.0

Sales, earnings, EPS, DPS

17

Frownfelter and Fulkerson

(1998)

US/Non-US 74 companies Not reported Not reported

Key financial variable

Beattie and Jones

(1999)

Australia 89 leading Australian listed

companies

89 9.4Sales, earnings,

EPS, DPS

Beattie and Jones

(2000)

UK137 top leading UK companies Not reported

Varies across time-series

EPS, Income, Sales, DPS

Beattie and Jones

(2001a)

Australia, France,

Germany Netherlands,

UK, US

50 top companies from each country,

300 overallMore than

80%Varies

from 4.0 to 14.0

Sales, Income, EPS

Beattie and Jones

(2001b)

Australia, France,

Germany Netherlands,

UK, US

50 top companies from each country,

300 overall88%

Varies across

countries

Sales, Income, EPS

Shaio Yan Huang

(2008)

TaiwanFictional company

created as case study

Not applicable

Not applicab

le

Not applicable

Source: “A six country comparison of the use of graphs in annual reports” (Beattie and Jones, 2001, p.198)

Prior literature shows that “impression management” techniques, like “selectivity” and

“measurement distortion” in graphs are widely used in companies` annual reports to show poor

performance in a better way (Johnson et al, 1980; Steinbart, 1989; Beattie and Jones, 1992, 1999,

2000, 2001a, 2001b; Green et al, 1992; CICA, 1993; Mather et al, 1996; Huang, 2008). Graphs

have potential power to influence the decision of investors and can easily lead to an untrue vision of

the company (Huang, 2008) through manipulating techniques. It was also concluded that this

manipulation was directed towards a favourable trend. This dissertation will focus on the

construction, usage, type (pie, bar, line) preference, relation of selectivity level to overall

performance of the company and distortion of graphs in annual reports of 30 most profitable and 30

least profitable Russian stock exchange listed companies, ranked by Earnings before Tax and

Interest (EBIT). Prior literature investigations will be used as a base for the methodology and for

comparison of final outcome.

18

Environment

The economic environment of Russia for 2013 can be characterised as unstable. Although

ranking of the companies for the year 2013 shows that there are still well performing ones, the main

big industrial companies are mainly placed in the least profitable list. Such companies as Gazprom,

Vimpelcom, and Severstal were the main victims of macroeconomic slowdown. Moreover, even

companies listed in profitable ones such as Gazprom Neft, VTB Bank, Rostelecom, Tatneft did not

have any notable performances either (growth of EBIT was lower than 10%). Government

forecasted growth for the end of the year was 3%, while President Vladimir Putin pointed out that

the economy would reach 5 % growth (Ministry of Economic Development, 2013). However,

neither of the government forecasts matched the predictions. In reality, the economy went down by

approx. 2 % from 2012 performance, which resulted in many cases of stagnation among the biggest

industrial companies. Even oil and gas companies became victims of the overall slowdown.

Analysts point out political and economic explanations of these issues; however, research of

profitable companies was the main problem of this dissertation data analysis.

A future decreasing trend of the economy is forecasted due to sanctions from EU countries

and the U.S, which can have disastrous outcomes for companies who are mainly focused on export

of industrial products (such as Rosneft, Novatek, Gazprom, etc.). Extracts from economic analyst

journals are provided for some clarification of issues faced by the Russian economy and companies

as well (World Bank, Bloomberg):

“Russia’s economy is navigating an economic downturn with real GDP growth slowing to

an estimated 1.3 percent in 2013 from 3.4 percent of 2012. In the past, the lack of comprehensive

structural reforms was masked by a growth model based on large investment projects, continued

increases in public wages, and transfers – all fuelled by sizeable oil revenues. Recent events around

the Crimea crisis have compounded the lingering confidence problem into a confidence crisis and

more clearly exposed the economic weakness of this growth model.”

(World Bank, Russia Overview)

19

“Russia’s economy grew at less than half the previous year’s pace in 2013, missing

economist forecasts as investment fell amid a record slump in Europe. Officials warned the outlook

remains weak for this quarter. Russia’s dollar-denominated RTS stock index has lost 9.7 percent

this year, underperforming the MSCI Emerging Market Index, which has dropped 7.1 percent, data

compiled by Bloomberg show. The ruble has lost 7 percent against the dollar, making it the third-

worst performer this year among 24 emerging-market currencies tracked by Bloomberg.”

(Bloomberg, Olga Tanas, “Russian Economic Growth Slows than Estimated in 2013”)

Those political issues and forecasted slowdown in GDP growth of external analysts had a harmful

impact on the performance of Russian companies. Most of the companies used the policy of

reduction of workers and some of them borrowed huge loans for further development (Bloomberg,

2013). The issues described above are the main obstacles that Russian companies faced and which

led to enormous numbers of losses as seen in the tables (See Appendix 2).

20

Hypotheses

The vast majority of prior literature proved the existence of “impression management” in

corporate annual reports of companies. As annual reports are assumed to be the main

“communication device” and graphs are used for portraying the image of the company (Kosslyn,

1989), then the significance of analysing the problem increases. Till Steinbart`s (1989) study, all

previous single-country analyses investigated the usage of graphs in annual reports with some

extension on graph construction (Jarett, 1983; Taylor and Anderson, 1983 etc.). However, to

identify “impressions management” in annual reports, those studies had many limitations. In order

to identify “impressions management” Beattie and Jones (1992) developed “measurement

distortion” and analysed corporate annual reports from a selectivity perspective, by using Tufte`s

“lie factor” model called “Graph Discrepancy Index”. Following this research, many other

researchers used similar methodology in investigating “impressions management” and got very

significant results. By examining the association between good performance and increase/decrease

of graphs, researchers found quite important proof of graph manipulations in annual reports. For

example, Steinbart (1989) found that “good news” companies had more intention to add (74%)

KFV graphs in annual report than companies with “bad news” (53%). Later studies show a similar

positive relation of companies’ good performance with KFV graphs added in annual reports

(Beattie and Jones, 1992; 1997; Mather et al, 1996 etc.). Moreover, Beattie and Jones (2000)

identified a strong association of changes in certain performance variables with inclusion of graphs

in the annual report. This dissertation will examine the annual reports of Russian Stock Exchange

listed companies by known methods described earlier in prior literature. Therefore, the first two

hypotheses will be developed from a “selectivity” analysis perspective:

Hypothesis 1

Number of key financial variable graphs will be higher in annual reports of “most

profitable” companies, rather than “least profitable”, in terms of earnings before interest and tax,

as a profit indicator.

The first hypothesis as mentioned before deals with the “selectivity” problem in corporate

annual reports. This hypothesis assumes that “most profitable” companies which were identified

21

and ranked (see Appendix 1,2) by earnings before tax and interest, will have more intention and

motive to show their good performance by key financial variables, rather than “least profitable”

companies which will prefer using a “concealment” (Merkl-Davies & Brennan, 2007) technique

and focus on other indicators. This is consistent with the hypothesis developed earlier and will

assist showing the “selectivity” level in annual reports.

Hypothesis 2

“Most profitable” companies will have more intention to include graphs of key financial

variables in annual reports, rather than “least profitable” companies, in terms of the sales variable

graphed.

The second hypothesis deals with the “selectivity” issue as well, but has a more specific

purpose. While the first hypothesis analyses “selectivity” by companies` rank in terms of EBIT, this

hypothesis examines whether increase/decrease in sales has an association with increase/decrease of

KFV in total or separately (earnings, sales, earnings per share and dividends per share). In other

words, this hypothesis will assist in investigating the possible association of inclusion “sales” graph

with other key financial variables. This is also consistent with prior literature. Moreover, this type

of analysis was provided by Beattie and Jones (1992) when they determined the association of EPS

with other key financial variables. This dissertation will follow the prior method but will focus on

the association of “sales” variable with other key financial variables.

Furthermore, prior studies also investigated the existence of “measurement distortion” based

on Tufte`s “lie factor”, developed later by Steinbart et al. as the “Graph Discrepancy Index”. His

study identified that 24 % of graphs were materially distorted by over 10 %, while most materially

distorted graphs (76%) were distorted in a favourable trend (Steinbart, 1989). Similarly, Beattie and

Jones (1997) found graph distortion of 24 % of graphs, Frownfelter and Fulkerson found graph

distortion of 68 % of graphs, while Mather et al. found 51 % of graphs distorted with “32 %

exaggerated and 19 % understated” (Mather et al, 1996, p.61). All of the prior literature used the

new “Graph Discrepancy Index”. This dissertation will use the same method of identification, by

following the analyses done earlier. Although most of the studies took a 10 % cut-off as material

distortion normative (Steinbart, 1989) or didn`t give any normal cut-off at all (Beattie and Jones,

1997), this dissertation will follow the value of 5 % given in the study by Mather et al (1996).

Therefore, +5 % will be assumed as exaggeration and -5 % as understatement, which “… follows

Tufte and is consistent with Beattie and Jones” (Mather et al, 1996, p.58).

22

Hypothesis 3

Measurement distortion will be used to give a more favourable image of the company,

rather than unfavourable.

As mentioned earlier, “impression management” should be analysed also in terms of

“measurement distortion”. Beattie and Jones (1992) developed this type of hypothesis to examine

UK companies` annual reports. The hypothesis itself is consistent with prior literature (Steinbart,

1989) and assumes that companies will have more motive to add a favourable graph in annual

reports rather than an unfavourable, or the graph discrepancy index will be directed in a favourable

trend. This means that companies will try either to exaggerate a favourable trend or understate an

unfavourable, which both support hypothesis 3.

Hypothesis 4

Measurement distortion of key financial variables is more likely to be found in “least

profitable” companies, rather than “most profitable”, in terms of mean discrepancy index.

This hypothesis has not been investigated before, however this can be matched with

research of Beattie and Jones (1997) where they assumed that UK companies will have more

distortion in graphs than US due to lack of regulatory stringency. It is more or less similar, however

this dissertation is going to investigate the intention of companies to distort graphs. In other words,

hypothesis 4 supposes that the “most profitable” companies due to better performance will have

less intention to distort graphs than “least profitable” who, due to worse performance, have to

understate decreasing graph or use other techniques to manipulate visually the readers of annual

reports.

23

Methods

The top 30 and bottom 30 most profitable Russian Trading System listed companies were

chosen for this dissertation (see Appendix 1 and 2). All of them were chosen from the list of Stock

Exchange Daily Official List (SEDOL) using Thomson One Banker financial software. Firstly, they

were sorted by market capitalization, so only companies with quite big market capitalization were

chosen. In practice only companies with big capitalization and shareholders use assistance of

international auditors and produce their annual reports based on international standards. Secondly,

companies were sorted by industries where they function. As mentioned before, companies are

listed in the Stock Exchange Daily Official List (SEDOL) and their identification codes were

obtained from Thomson One Banker software, included in column 2 of both tables.

For the first part of the analysis, ranking of the companies by earnings before interest and

tax was needed. Therefore, after listing the companies, all the financial figures (EBIT) were

collected from Thomson One Banker software for financial years 2012 and 2013. They are all

presented in US dollars ($) and were listed in columns 3 and 4 of the tables, respectively. In order

to rank the companies by earnings before interest and tax, it was best to alter this figure from 2012

to 2013. That means, companies with positive outcome were listed in 30 most profitable, while

companies with negative outcome in least 30. For this purpose, 2012 was chosen as the base year,

after that change in figures from 2012 to 2013 was divided by earnings before tax and interest of

2012, which gave the final outcome. Afterwards, companies were ranked and listed by EBIT

change, presented in column 5 of the tables. List of revenues of companies in 2013 presented in

column 6 is to show that neither of the companies made negative revenue in 2013, though they are

divided into two groups based on change of EBIT figure. In addition, all the figures presented in the

tables were sorted into 000 $ term, for easiness and normal presentation.

From the table of most profitable companies it can be seen that the most profitable company

in terms of EBIT was electricity company RAO Energy System of East which had a 192% change

in EBIT figure. This is due to the negative EBIT figure in 2012, which changed to positive in 2013.

This is similar to the company ranked second in the table AFI development from the real estate

industry. The company changed its EBIT figure from negative -7,284 $ to positive 6,470, which in

the end gave a 188 % alteration in figures. Phosagro is the company listed third in the list of most

24

profitable companies and is the only one with both positive figures of EBIT for 2012 and 2013 but

with more than a 100% change in EBIT figure (169 %). Although the company has quite a low

market capitalization compared with other top 30 companies, the figures of the company show

quite a strong performance. As for the bottom 3 companies from the list of most profitable, the

worst performer is Gazprom Neft; being part of the huge Gazprom holding didn`t help the company

to overcome their issues, which led to a 1% increase in EBIT figure for the year 2013. The second

worst performer in the list is also an oil company, known as Tatneft. Although the company has

market capitalization less than Gazprom Neft, for the year 2013 it had a 3 % increase in EBIT

figure. Third place is the only representative of the finance sector in the list, VTB Bank which had a

5 % increase in EBIT figure.

The table of least profitable companies has quite high figures of change compared to most

profitable, where percentage of change didn`t exceed a level of 192 %. The company with the worst

performance and therefore with the highest loss in the list is Federal Grid Company of Unified

Energy. Although the company had 9, 479 million in earnings before interest and tax in 2012, it

posted a loss of -287,881 million for the financial year 2013. The change in percentage is quite

enormous (equal to -3137 %) for a company with just 74 million in capitalization. Second in the list

of worst performers is a well-known Russian company in the metal industry UC Rusal. It can be

seen that the company had a negative EBIT figure in 2012, but it couldn`t overcome this issue in

2013 which led to higher loss resulting in a -1364 % change in EBIT figure. The third company in

the rank of worst performers is Raspadskaya Coal, which is a representative of the coal industry in

Russia. The company, with only 445 million earnings before interest and tax, posted a loss of -3387

million in 2013 which led to a change in EBIT of -761 %. Companies with quite a small percentage

decrease in the list were Alrosa with -2 %, Territorial Generating Company with -3% and the whole

holding Gazprom -4 %.

After finishing sorting and ranking the companies in the tables (see Appendix 1 and 2), the

next stage was downloading their annual reports for the financial year 2013. All the reports were

accessed through their websites and sorted in the folders manually. As the dissertation investigated

annual reports for two purposes: selectivity and graph distortion, overall manual work with annual

reports was also divided into two stages.

The first stage consisted of manual analysis of the annual reports in terms of “selectivity”

distortion. For this stage, it was chosen to classify graphs by their main functional purposes:

operating, financial, market, environment, social and then by types of graphs used in annual reports:

bar/column, pie, line which is consistent with prior literature (Beattie and Jones, 1992; 1997).

25

Operating graphs consisted of non-financial performance graphs like efficiency, productivity,

number of sales, expenses etc. provided in non-financial figures (tonne, kilogram etc.); financial

graphs like key financial variables (sales, earnings, earnings per share, dividends per share) and

other financial graphs (expenses etc.) were considered in “financial” type; market type was added

because the vast majority of Russian companies prefer comparing their performance with the

performance of other companies in the same industry (especially most profitable companies);

environment and social types were added as they always exist in worldwide practice showing high

quality of safety without any harm to the environment. In addition, graphs were also sorted by the

pattern they are showing: favourable or unfavourable. Favourable trend is assumed as increasing

pattern (for example sales, earnings) or decreasing pattern of bad performance (number of workers

died, accidents etc.), while unfavourable trend is assumed as decreasing pattern (for example sales,

earnings etc.) or increasing pattern of bad performance (number of workers died, accidents etc.).

This is consistent with prior literature, which also sorted favourable and unfavourable trend graphs

for the purposes of analysis in selectivity level. Simultaneously, graphs were also sorted by their

generic type being divided into three groups: bar, type and line (examples provided below):

Figure 1. Column type graph (Favourable Trend). (Aeroflot Annual Report 2013)

Figure 2. Pie type graph. (Rostelecom Annual Report 2013)

26

Figure 3. Line type graph (Favourable Trend). (Lukoil Annual Report 2013)

A similar method was used to analyse specifically KFV graphs in the top 30 profitable and bottom

30 profitable companies. Moreover, analysis described above was applied separately for the

purposes of comparison and the final outcome is presented in tables (see Appendix 3 and 4).

The second stage of analysis investigated “measurement distortion” in graphs sorted earlier.

For this purpose Tufte`s “Lie Factor” formula, which was then developed by Taylor and Anderson

(1986) into the Graph Discrepancy Index was used:

Graph Discrepancy Index = ( ab

- 1) x 100 %,

where

a = percentage change (in cms) depicted in graph, i.e.

height of last column−height of first columnheight of first column

x 100 %

b = percentage change in data

(Beattie and Jones, 1992, p.295)

As seen from the formula, method is only able to analyse bar/column type graphs, therefore

all other type of graphs were ignored. Moreover, investigation was aimed specifically at KFV

graphs, therefore bar/column type graphs portraying KFV were selected. Afterwards, using a virtual

tool “ruler” graphs were analysed manually and their heights were sorted. Using the formula

suggested by Taylor and Anderson (1986) the percentage of distortion was calculated for each of

the graphs and choosing normative of 5 or -5 % distortion (suggested by Tufte 1983), the total

number of distorted graphs was sorted out. Simultaneously, graphs were also sorted by “non-

compliance with normative graph construction and design principles” (Beattie and Jones, 1997).

27

Following the research by Beattie and Jones (1997), where graphs at the end of the analysis

were investigated in terms of non-compliance with graph design principles, this dissertation chose 6

main components from that research and analysed the graphs of Russian companies. Those 6

chosen components were the main problems of Russian companies (from observation) and were as

follows:

Figure 4. No scaled time axis without numeric financial labels

Magnit`s sales performance graph. (Magnit Annual Report 2013)

Figure 5. No scaled financial variable axis

Gazpromneft`s revenue graph. (Gazpromneft Annual Report 2013)

Figure 6. Specifier colour of last year different to prior years

CTC`s group revenue graph. (CTC Annual Report 2013)

28

Figure 7. Unconventional location of number attached to individual specifier and three-dimensional specifier

Arzamas`s revenue graph. (Arzamas Annual Report 2013)

Analysis described was presented in table format (see Tables list) and differences were examined

further on two-tailed t-test, also presented afterwards in table format. Methods used for analysis of

both hypotheses: “selectivity” and “graph distortion” are consistent with prior literature;

furthermore, the number of graphs and their final outcome will be compared to prior literature

(given in Discussions).

29

Findings

Quantitative analysis of the hypothesis will be presented in the Findings part by tables of

comparison between the top 30 most profitable and least profitable companies. Moreover, for the

purpose of the investigation whether the outcome has a significant impact either on the topic or the

difference between the two types of company, numerical tables with t-test will be provided. Both

one tailed and two tailed analysis were used in this dissertation: if analysis of effect was important

then one tailed; if analysis of effect and significance in both directions was important then two-

tailed t-test was applied. As the hypotheses for this dissertation investigate two aspects:

“selectivity” and “measurement distortion”, then the findings part will be divided into two parts as

well.

Table 2. Summary of graph usage among Russian companies

Panel A

Reports with at least one graph and total number of graphs

Operational Financial Market Environment Social TotalReports with graph (at least one graph) 57 57 46 37 44 57Total number of graphs 360 457 117 100 168 1241

Panel B

Type of graphs preference by Russian companies

Type Number of graphs Percentage Total

30

Bar/Column 917 74% 1241

Pie 181 15% 1241

Line 104 8% 1241

Table 2 provides general information about graph usage in 60 annual reports of Russian

companies. In total 1241 graphs were analysed and sorted out from 60 annual reports, which gave a

mean usage of graphs per annual report equal to 20.683. This is quite a high mean number

compared to prior literature which shows that graph usage among companies has rapidly increased

in the last decade. The highest mean graph usage was obtained in the research by Beattie and Jones

(2001a) equal to 14.0.

High mean demonstrates that Russian companies have intention and quite developed

practice in graph usage. Moreover, Table 2 provides us with some detailed analysis by each

classification of graph. Financial and operational graphs dominate among other classes by 4 and 3

times respectively, which shows high interest of Russian companies in portraying their performance

either by product or financially than focusing on market comparison, environment problems or

social issues.

The first line of Table 2 also provides us with aggregate information of graph usage per 60

annual reports, which demonstrates that 57 companies out of 60 investigated included at least 1

graph in their annual report. Moreover, from the analysis of Appendix 3 and 4, it can be seen that

those 3 companies which avoided the usage of graphs in their annual reports are listed among the

30 least profitable companies. The smallest number is the “environment” class of graphs: 23

companies out of 60 had absence of graphs on environment problems and development;

interestingly, 15 companies out of those 23 are also listed among the least profitable companies.

Identical pattern was noted among “market” and “social” classes of graphs; all of them have most

graph absence from the bottom listed companies. Moreover for general analysis, graphs were

divided by type (e.g. bar, pie, line) which can be seen in Table 2.

Results show that the vast majority of companies prefer using bar/column type graphs, as 74

% of the total amount of graphs were bar/column type. Further analysis will show association of

bar/column type graphs to most profitable companies and link to KFV graphs, which are the main

reasons for such an excessive result.

This is also consistent with Beattie and Jones (1992) equal to 63%, Beattie and Jones (1997)

equal to 79 % and 62.4 % for U.S and U.K, respectively. As our dissertation is focused mainly on

31

investigation of KFV graphs, general analysis of KFV was also conducted. Summary of the

analysis given in the table below will be useful for future investigation of hypotheses. Table 3

represents percentage proportion of total KFV graphs in each type of company out of the total

amount of financial graphs:

Table 3. Percentage proportion of KFV graphs out of total financial

Total KFV Total Financial Percentage out of total

Top Profitable 104 324 32.10%

Bottom Profitable 27 172 15.70%

As can be seen, the proportion of total KFV graphed is two times the difference between top and

bottom companies.

This is consistent with “selectivity” level hypothesis, that profitable companies will prefer

showing their good financial performance, while bottom companies will prefer concealing it from

the reader. This is consistent also with prior literature and significant at the 1% level based on two

tailed T-test analysis. For more specific analysis, from Appendix 1 and Appendix 2 the reports are

provided below (Table 4):

Table 4. Summary of annual reports analysis

Panel A

Most Profitable Companies

Operational

Financial

Market

Environment Social Total

Favourable

Unfavourable

Bar/Column Pie Line

Total 153 311 79 52 81 702 616 86 533 108 61

Mean 5.1 10.3 2.6 1.73 2.7 23.4 20.5 2.9 18.9 3.6 2.0St. Deviation 3.2 4.1 2.3 1.9 2.3 14.4 12.5 2.8 11.7 2.4 2.9

Panel B

Least Profitable Companies

Operational

Financial

Market

Environment Social Total

Favourable

Unfavourable

Bar/Column Pie Line

Total 194 172 38 48 87 539 329 210 423 73 43

Mean 6.4 5.7 1.2 1.6 2.9 17.9 10.9 7 13.1 2.4 1.4

32

St. Deviation 3.7 3.3 1.6 2.3 3.2 9.6 7.2 4.9 9.1 2.2 1.4

It is apparent from these two tables that “operational” graphs which present performance of

the company, dominate in least profitable companies. It can be assumed that least profitable

companies try to “conceal” their bad financial performance and enhance the attention of the reader

on performance indicators. With a high number of “operational” graphs a company can also show

that they did well, however some external factors had a negative impact on financial performance.

Difference between means of “operational” graphs is not significant in two-tailed t-test, which

again demonstrates the intention of Russian companies to portray their performance, even if

financially they are weaker. In contrast, financial graphs which also contain KFV graphs, have

quite notable difference in numbers.

It was anticipated before when Hypothesis one was developed, and it can be seen even

without specific KFV analysis that the most profitable companies prefer demonstrating their good

financial performance than least profitable, who have nothing financially strong to show.

Table 5. Summary of significance analysis of means of number of graphs (by classification) based on two-tailed T-test analysis

Top Profitable Companies

Bottom Profitable Companies

T-test (two-tailed)

Probability Level

Operational 5.1 6.46 0.8989 0.3724

Financial 10.36 5.73 4.3495 0.0001***

Market 2.63 1.26 4.4334 0.0001***

Environment 1.73 1.6 0.9996 0.3217

Social 2.7 2.9 0.1376 0.891

Total 23.41 17.96 2.9842 0.0042***

Favourable 20.5 10.96 5.1283 0.0001***

Unfavourable 2.91 7 3.9518 0.0002***

Bar/Column 17.9 14.1 1.7737 0.0814*

Pie 3.6 2.43 1.1973 0.2361

Line 2.08 1.43 1.085 0.2824Notes: * Significant at 1% level

** Significant at 5% level

*** Significant at 10% level

Analysis of difference based on two tailed t-test also supports this expectation at the 1%

level, with t-test value of 4.3495. Moreover, this is consistent with prior literature analysis of

financial graphs, where selectivity level of portraying financial graphs in profit (so called “good

news” companies) was high.

33

Another significant difference in analysis was obtained from number of “market” graphs

between top and bottom companies. As defined before, “market” graphs in this dissertation show

the number of graphs used by companies to compare their performance either financial or non-

financial with other companies` performance in the same industry. T-test value of difference is

4.4334 (1 % level) based on two tailed analysis, which can be linked to the absence of something to

compare.

In other words, the least profitable companies know that their bad performance cannot be

compared to others in the same industry; in order to avoid comparison with potential competitors,

they choose not to include such graphs. However, most profitable companies in order to strengthen

their position for the reader of annual reports, demonstrate their leading position in the industry by

such graphs.

As for the other two classes of graphs: “environment” and “social”, analysis of t test shows

absence of significance in difference of means, which supports the preliminary supposition that

neither top nor bottom companies focus much on portraying these type of graphs. Both of them

failed significance analysis based on two tailed t-test.

Nevertheless, total mean usage of graphs has a significant difference between most and least

profitable companies; t-test shows strong value of 2.9843 with significance at 1% level, based on

two tailed t-test. This outcome confirms again, that quite a high majority of bottom companies

refuse to portray their poor performance. This issue was also met before in analysis by Beattie and

Jones (1992), where lack of significance in “measurement distortion” analysis was associated with

low number of graphs in the annual reports of “bad news” companies.

For the specific analysis of “selectivity” level and support of Hypothesis 1, investigation of

KFV graphs and favourable/unfavourable trend used in graphs was conducted (Table 5). As seen

from the table, t-test analysis of trends classification of graphs (e.g. favourable and unfavourable)

has strong values. Both of them have quite a large difference in total amount (see Table 5), which

was expected due to results of prior literature. Favourable graphs have a t-test value of 5.1283 with

significance at the 1% level, which is due to an excess of favourable graphs in the profitable

companies approximately two times compared to least profitable companies. This is due to the

intention of profitable companies to use graphs to show good financial and non-financial

performance, while most favourable graphs of the bottom companies are associated with other

types of operational performance or a little of financial performance. This can be seen from the two

tables, presenting analysis of selectivity bias of favourable/unfavourable graphs by each class of

graphs (Table 6).

34

Although unfavourable graphs also have a strong value of t-test equal to 3.9518 with

significance at the 1% level, this time the vast majority of them are associated with graphs

portrayed in annual reports of the least profitable companies (see Table 5). It can be also associated

with the “honesty” of companies portraying unfavourable trends for the readers of annual reports,

but is also associated with understatement of graphs in “measurement distortion”, which will be

seen further.

Analysis by types of graphs (bar, pie, line) is linked to total amount of KFV graphed in

annual reports. Most financial graphs and all of the KFV graphs were portrayed by bar graphs with

time-series analysis. Considering that financial graphs in total were significantly higher in most

profitable companies` annual reports, then this outcome was quite anticipated, though significant at

10% level. Pie and Line graphs are preferable in the graphing environment and social graphs, or

other types of financial graphs (see e.g. in Methods) which is the main reason for the absence of

significance between means of most profitable and least profitable companies.

Table 6. Selectivity bias of favourable/unfavourable graphs by each class

Panel A

Most Profitable Companies

Operational Financial Market Environment Social Total

Favourable 140(23%) 293(46%) 69(11%) 44(7%) 70(13%) 616Unfavourable 26(38%) 31(19%) 10(15%) 8(12%) 10(16%) 86

Panel B

Least Profitable Companies

Operational Financial Market Environment Social Total

Favourable 130(41%) 96(29%) 21(6%) 22(6%) 60(18%) 329Unfavourable 64(30%) 76(36%) 17(8%) 26(12%) 27(14%) 210

Table 6 again confirms that the most profitable companies use favourable graphs to

demonstrate their financial (46%) and operational (23%) performances, while unfavourable graphs

are mainly met among operational (38%) ones.

35

This table confirms the hypothesis that due to better results operationally and financially

profitable companies will concentrate the attention of the reader on operational and financial

graphs. In contrast, due to poor financial performance, least profitable companies concentrate the

attention of the reader on how they performed well operationally. The fact that unfavourable graphs

are mainly focused on in operational and financial classes, can be assumed as honesty of the

companies as mentioned earlier. However, further GDI analysis shows that the most understated

graphs are used by the least profitable companies. Analysis of trend usage between most and least

profitable companies demonstrated the existence of “selectivity” in annual reports. However, for

the support of Hypothesis 1 which is focused on “selectivity” in KFV graphs, more specific

analysis was conducted, with the results summarized in Table 7:

Table 7. Comparison of means of KFV graphed between most and least profitable companies (based on two-tailed T-test analysis)

Top Profitable

CompaniesBottom Profitable

CompaniesT-test (two-

tailed)Probability

Level

KFV 3.46 0.89 7.8505 0.0001***

Sales 1.5 0.29 7.8195 0.0001***

Earnings 1.06 0.26 6.6665 0.0001***

EPS 0.56 0.19 5.6531 0.0001***

DPS 0.3 0.13 3.7202 0.0005***Notes: * Significant at 1% level

** Significant at 5% level

*** Significant at 10% level

Results of Table 7 were expected and unsurprisingly all of the types of KFV and totals have

significant difference. Expectancy of these results was linked to t-test analysis of financial graphs

(Table 5) which showed quite strong significance at the 1% level. Considering that one third of

those financial graphs were KFV (Table 3) while only 15% represented outcomes for similar

analysis in the least profitable, such a strong difference is quite normal. Based on the significance

of difference between means of graphs (two-tailed T-test equal to 3.46 at 1% level) Hypothesis 1 is

accepted.

The second part of “selectivity” analysis is linked to investigation of possible relation of

performance by company to graphing key financial variables. This type of investigation was done

before by Beattie and Jones (1992; 1997; 2001b) and gave quite strong values to support the

hypothesis of selectivity in each research. Therefore, chi-square test statistic was applied on each

sample to analyse this relationship. Moreover, investigation of inclusion of key financial variable

by companies was also done, to show selectivity level by companies:

36

Table 8. Number of companies which included at least one KFV

Most Profitable Companies

Total Companies KFV No KFV Percentage

30 30 0 100.00%

Least Profitable Companies

Total Companies KFV No KFV Percentage

30 13 17 43.33%

It was quite expected based on primary analysis that least profitable companies would prefer

not to show their poor financial performance by graphs or would try to focus on other financial

indicators, which can be shown in a favourable light. Mean differences presented in Table 7 also

demonstrate that proportion of key financial variable graphs in their annual reports varies between

top and bottom companies. Therefore, investigation of whether this variation is associated with

performance of the company (e.g. performance indicator) is important.

The research of Beattie and Jones (1997) analysed this issue in terms of EPS as a

performance indicator; this dissertation investigated this relationship based on Sales as a

performance indicator. For this purpose, statistical analysis based on chi-square test (one tailed)

was applied to the sample of top and bottom companies. Table 9 presents the outcome of this

analysis for both types of company. Results of this test are quite interesting because most profitable

companies have only an association of two out of four key financial variables (with sales itself)

while least profitable companies have three out of 4 KFV strongly associated with Sales as a

performance indicator. However, association of performance indicator with inclusion of at least one

key financial variable is stronger in the most profitable companies at the 1% level versus least

profitable companies at the 10% level. Earnings significance level also differs between the two

samples: 1 % level versus 5% level of significance for the most and least profitable companies,

respectively. In contrast, least profitable companies have also relation of Earnings per share with

sales at the 5% level, while for top companies the relation with EPS and DPS was not found. These

results may have some limitations due to fewer numbers of graphs among the least profitable

companies, or it may be assumed that least profitable companies might use the tool of “selectivity”

more than top companies.

37

Table 9. Test of relationship between change of amount of KFV graphs and performance of the company (sales as performance indicator)

Chi-square t-statistic (one tailed probability in parentheses)

Panel A

Most Profitable Companies

Performance Indicator

At least one KFV Sales Earnings EPS DPS

Sales ↑↓ (2012-2013) 67.1194 97.82608 34.744 15.408 8.9055

(0.002)*** (0.000)*** (0.003)*** (0.2) (0.127)

Panel B

Least Profitable Companies

Performance Indicator At least one KFV Sales

Earnings EPS DPS

Sales↑↓(2012-2013) 9.7848 30 5.4545 3.75 2.3077

(0.080)* (0.000)***(0.020)*

*(0.053)

*(0.129

)Notes: * Significant at 1% level

** Significant at 5% level *** Significant at 10% level

Values shown in the tables above, support Hypothesis 2, stating that most profitable

companies will have more intention, and therefore stronger relation between inclusion of key

financial variable graphs and performance indicator. Even though figures show that an

increase/decrease in performance will have an impact on 3 out of 4 KFV while in most profitable

companies it is only 2 out of 4, the association with KFV, which was stated in Hypothesis 2, is

stronger among profitable companies. Therefore, Hypothesis 2 is supported.

The next section of the “impression management” analysis is concerned with “measurement

distortion” developed by Beattie and Jones (1992). It emphasizes the degree of graph distortion

among top and bottom companies. Investigation of “measurement distortion” was based on manual

examination of each bar/column graph portraying KFV in annual reports, and then by using Tufte`s

“lie factor” formula calculating the degree of discrepancy (%). All of the reports containing KFV

graphs were represented by bar/column type graphs which made analysis easier, and gave stronger

results for the hypothesis evaluation.

38

Although most of the research exploring Graph Discrepancy Index ranked the sample by

degree of distortion, this dissertation will focus on the method used by Mather et al (1996) and

Tufte (1983). This method considered only graphs distorted more than +5 or -5%, which is assumed

as excessive. Hypothesis 3 regarding “measurement distortion” assumed that companies will use

graphs to demonstrate better company performance rather than worse.

Previous analysis (Table 4) showed that although companies had significant difference in

terms of top and bottom profitability, both of them had more favourable graphs(top profitable=616,

bottom profitable= 329) than unfavourable (top profitable=86, bottom profitable 329). These values

are consistent also with the hypothesis developed, that companies will try to show their best

performance by graphs than worst. However, for stronger support of Hypothesis 3, GDI analysis

was done.

As mentioned in prior literature (Mather et al, 1996) favourable distortion is exaggeration of

increasing trend or understatement of decreasing, while unfavourable distortion is exaggeration of

decreasing and understatement of decreasing. Results of graph discrepancy index analysis are given

in the tables below for each of the samples:

Table 10. Distortion of KFV graphs based on mean discrepancy index

Panel A

Most Profitable Companies (sample=30)

Graph Discrepancy Index Sales Earnings EPS DPS Total

Exaggeration of upward trend, GDI ≥5% 9 7 2 2 20Exaggeration of downward trend, GDI≥5% 2 1 2 0 5Understatement of upward trend, GDI≤ -5% 1 2 1 0 4Understatement of downward trend, GDI≤ -5% 1 5 2 2 10

Absence of Discrepancy 32 17 10 6 65

Total 45 32 17 10 104

Discrepancy percentage out of total 28.89% 46.88% 41.18% 40.00% 37.5%

Mean Discrepancy Index 0.43333 0.5 0.23333 0.133333 1.3

Standard Deviation 0.4955 0.5627 0.4955 0.3399 1.1

Panel B

39

Least Profitable Companies (sample=13)

Graph Discrepancy Index SalesEarnin

gs EPS DPS Total

Exaggeration of upward trend, GDI ≥5% 2 1 1 0 4

Exaggeration of downward trend, GDI≥5% 0 0 0 1 1Understatement of upward trend, GDI≤ -5% 1 1 2 1 5Understatement of downward trend, GDI≤ -5% 4 3 1 1 9

Absence of Discrepancy 2 3 2 1 8

Total 9 8 6 4 27

Discrepancy percentage out of total 77.78% 62.50% 66.6% 75% 70.37%

Mean Discrepancy Index 0.5384 0.384 0.307 0.23 1.4615

Standard Deviation 0.4985 0.4865 0.461 0.421 0.74757From the tables above it can be seen that 39 out of 104 and 19 out of 27 graphs were

distorted in the most and least profitable companies, respectively. With specific analysis: in most

profitable companies 20 graphs out of 25 exaggerated upward trend and 10 out of 14 understated

decreasing graphs; in least profitable companies 4 out of 5 graphs exaggerated upward trend and 9

out of 14 understated decreasing graph, overall favourable distorted graphs were equal to 45 out of

58 materially distorted. Among the 13 graphs left: 3 out of 25 exaggerated decreasing trend and 4

out of 14 understated increasing trend in most profitable companies, while in least profitable 1 out

of 5 exaggerated decreasing graph and 5 out of 14 understated increasing graph. This results in

77.5% of materially distorted graphs showing favourable portrayal of the company while only

22.5% the opposite. Moreover, specific investigation showed that favourably distorted graphs were

equal to 28% in most profitable, while this number was equal to 48% in least profitable companies,

which again confirms Hypothesis 3.

Furthermore, difference was analysed based on two-tailed t-statistic of their means, which

gave t=7.1156 with significance at 1% level. Significance level shows quite strong values for

hypothesis 3 which stated that companies will prefer showing favourable view of the company,

rather than unfavourable. Analysis of this hypothesis means that it cannot be rejected with 99%

confidence.

The last hypothesis of this dissertation deals with the problem of, whether proportion of

distorted graphs will be higher in least profitable companies rather than most or opposite.

Hypothesis 4 supposed that this proportion would be higher among least profitable companies due

to their poor performance. Quantitative analysis of this hypothesis is represented also in Table 10.

40

It can be seen that the percentage of distorted graphs is higher in least profitable companies

than most (70.37% against 37.50%) and this pattern is preserved in each class of the graphs (e.g.

sales, earnings, EPS, DPS). Moreover, their mean discrepancy indexes also showed that the

proportion is higher in the least profitable companies. Furthermore, each class of graphs was

analysed apart (sales, earnings, DPS, EPS) and the results also demonstrated that except earnings,

all other graphs were distorted higher in the least profitable companies.

However, two tailed t-test analysis of total amount of materially distorted graphs (top=39

and bottom=19), showed low significance with t=0.4817 and p=0.6326; moreover, specific analysis

of each graph (sales, earnings, EPS and DPS) didn`t show any significance, which can be seen from

Table 11 below. Although the existence of material distortion can be supported, the proportion

cannot be confirmed quantitatively. Existence of material distortion was also analysed based on

non-compliance with graph design and construction, developed by Beattie and Jones (1997).

Table 11. Summary of two-tailed t-test analysis of mean discrepancy index between most and least profitable companies based on KFV graphs. Other types of graph distortion and impression management techniques

Panel A

Summary of mean discrepancy index analysis

Top

ProfitableBottom

Profitable T-test Probabilit

y

n=30 n=13 (two-tailed)

Sales 0.4333 0.5384 0.6376 0.5273

Earnings 0.5 0.384 0.6451 0.5225

EPS 0.2333 0.307 0.4567 0.6503

DPS 0.1333 0.23 0.7962 0.4305

Total KFV 1.3 1.4615 0.4817 0.6326

Panel B

Other types of graph distortion and impression management techniques

Top

ProfitablePercen

tageBottom

ProfitablePercent

age

T-test of

means Probability

Techniques n=30 % n=13 %(two-tailed)

Time axis without scale 6 20% 4 30.7% 0.77 0.4433Financial axis without scale 18 60% 10 76.9% 0.98 0.3314

41

3D graph type 2 6.6% 5 38.4% 2.84 0.0069***Change of the colour by years (darker) 16 53.3% 11 84.6% 2.03 0.0483**Absence of numeric labels on fin variable axis 9 30% 10 76.9% 3.1 0.003***Absence of numeric labels on specifier 10 33.3% 9 69.2% 2.30 0.0262**

Notes: * Significant at 1% level

** Significant at 5% level

*** Significant at 10% level

Results showed that graph construction and design failures were mainly found in the 27

KFV graphs of the 13 least profitable companies. Each of the non-compliances were found to be

higher in the least profitable companies than most. Differences of their means were analysed based

on two tailed t-test analysis, which resulted in two failures significant at the 1% level (e.g. three

dimensional graphs, absence of numeric label on financial variable axis), two failures significant at

the 5% level (e.g. darkening columns by years, absence of numeric labels on specifier) and two not

significant failures (e.g. time axis without scale, financial axis without scale). It can be assumed

that existence of “material distortion” was confirmed based on the results of Table 11. However

analysis lacks confidence to accept Hypothesis 4, therefore it is rejected.

42

Discussion

Overall, results obtained from quantitative analysis confirmed the existence of impression

management techniques in the annual reports of Russian companies. Hypotheses developed initially

were supported during the investigation except one (Hypothesis 4).

Despite the fact that Russian companies have never been investigated in terms of impression

management in graphs, results obtained still can be compared to prior literature. For this purpose

Table 13 (at the end of the Discussion Chapter) was developed, which has all the prior literature

included in the literature review with comparison to the results of this dissertation.

It can be seen that proportion of graphs usage found in Russian companies is the highest,

compared to others. Although most of the research done earlier deviated between approx. 79 % - 92

%, none was higher than the graph proportion in Russian companies.

This shows that Russian companies have experience in constructing and using graphs in

their annual reports, which assumes that the results of this dissertation can be compared to prior

literature, which analysed mostly European countries and the US. Size of the sample used is the

smallest compared to prior literature (except Johnson et al., 1980), however mean number of graphs

is the highest (except Frownfelter and Fulkerson, 1998). Similar results are shown for graph

distortion, which except for Frownfelter and Fulkerson (1998) is the highest. Moreover, approx.

one third of the total financial graphs are taken by the KFV topic, while bar/column graphs are

43

highest among all the research (74 %). This indicates the originality of the Russian accounting

environment, which involves wide usage of graphs in annual reports and has the opportunity for

impression management.

For the originality analysis of each hypothesis, Table 12 was developed:

Table 12. Summary of discussion and investigation of originality of the results

Hypothesis Findings Originality

Hypothesis 1

Number of key

financial variable graphs will

be higher in annual reports of

“most profitable” companies,

rather than “least profitable”,

in terms of earnings before

interest and tax, as profit

indicator.

Results obtained demonstrated

significant support of

Hypothesis 1. Two-tailed t test

analysis of graph means were

found at the level of 3.46

which is significant at 1%

level. Therefore, Hypothesis 1

is accepted.

Similar hypothesis was

developed in two prior

research studies included in

literature review (e.g. Beattie

and Jones, 1992; Steinbart,

1989). Unsurprisingly, in both

of them analysis led to

significant support of

Hypothesis at 1 % level. It can

be assumed that inclusion of

KFV in annual reports of

“good news” companies is

commonly accepted.

Hypothesis 2

“Most profitable”

companies will have more

intention to include graphs of

key financial variables in

annual reports, rather than

“least profitable” companies,

in terms of the sales variable

graphed.

Chi-square test of relationship

between sales performance

and KFV graphs,

demonstrated strong

association at 1% level for

most and 10% level for least

profitable companies.

Simultaneously, strong

association was found

individually in each of the

This unique way of selectivity

analysis was used in the

research by Beattie and Jones;

1992; 1997; 2000; 2001;

Frownfelter and Fulkerson,

1998. However, neither of

them investigated this

hypothesis in Russian

companies and analysed it

based on “sales” variable.

44

KFV deviating from 10% to

1%. Therefore, Hypothesis 2

was not rejected.

Most of the research done

earlier, focused on association

with KFV graphs or EPS,

Income graphs, while other

variables were analysed in

general terms. However, this

dissertation emphasized the

association of performance

with sales variable and then

analysed it with other KFV in

general terms. This had not

been done before.

Hypothesis 3

Measurement distortion will

be used to give more

favourable image of the

company, rather than

unfavourable.

This hypothesis was initially

developed for the analysis of

graphs distortion in terms of

favourable or unfavourable

trend usage. Results of the

analysis were more than

enough to confirm favourable

distortion of graphs in annual

reports. T-test analysis was

equal to 7.1156 which was

significant at 1% level.

Hypothesis 3 is accepted.

Hypothesis was investigated in

the research by Steinbart 1989;

Beattie and Jones, 1992.

Although, Beattie and Jones

investigated this hypothesis

only in general terms, finding

favourably distorted graphs

out of total KFV graphs,

Steinbart did more specific

analysis to find out the

proportion of favourable

graphs in “good” and “bad”

news companies. Originality

of this dissertation is based on

the fact, that it mixed both

types of investigations and

applied them. It was found that

favourable distorted graphs

were equal to 28% and 48% in

most and least profitable

45

companies, respectively.

Further, Hypothesis was

investigated on the difference

in total amount of favourably

and unfavourably distorted,

which gave significant

outcome at 1% level.

Hypothesis 4

Measurement distortion of key

financial variables is more

likely to be found in “least

profitable” companies, rather

than “most profitable”, in

terms of mean discrepancy

index.

In contrast to previous

hypotheses, no significant

support was found. Although

existence of material distortion

was confirmed in both types of

company, significance of their

difference was not obtained.

Therefore, this hypothesis

failed to be accepted at any

high level of significance.

Originality of this hypothesis

is due to absence of this type

of investigation in prior

literature. Some kind of link

could be matched to the

research by Beattie and Jones

(1992), but their research did

not analyse companies based

on their performance.

Therefore, this hypothesis is

totally original, however

significant support for the

hypothesis was not found.

This could be linked to huge

difference of sample by types

of company (top=30 and

bottom=13) or to some other

limitations. Nevertheless,

originally developed

hypothesis found no support

from investigations.

In all, the existence of impression management techniques was found in annual reports of

Russian companies. High percentage of graph usage and mean number of graphs lead to possibility

46

of comparison with other countries investigated previously. However, Russian companies should be

assumed as newbies in applying International Accounting Standards for the preparation of annual

reports. This fact makes the outcome of analysis doubtful in terms of whether the distortion and

selectivity was done incidentally or expressly. Although this fact cannot be analysed quantitatively

it should be taken into account. Hence, the level of distortion and selectivity found in this

dissertation could be judged only if it was not done incidentally by the managers of the companies

investigated.

Another point to be mentioned is the size of the sample used, which is not as big as prior

research and therefore, could have some limitations. Moreover, this dissertation considered only

cross-sectional type of analysis based on the financial performance of 2013. Time series analysis is

essential to find out alteration of annual reports of each company based on several years of market

performance (Godfrey, Mather & Ramsey, 2003). This could lead to more accurate conclusions.

Nevertheless, based on the methods applied and results obtained, the existence of impression

management and selectivity in annual reports of Russian companies was confirmed.

47

Table 13. Comparison of prior literature with this dissertation based on main results

John

son et

al.

Stein

bart

Beattie

and

Jones

Green

et al.

CICA Mather

et al.

Beattie

and

Jones

Frownfelter

and

Fulkerson

Beattie

and

Jones

Beattie

and

Jones

Beattie and

Jones

Beattie

and

Jones

Disser

tation

Year 1980 1989 1992 1992 1993 1996 1997 1998 1999 2000 2001a 2001b 2014

Countries

observed US US UK

Republ

ic of

Ireland

Canad

a

Australi

a

UK/US US/non-US Australia UK

Australia,

France,

Germany,

Nether

lands,

UK, US

Australia

, France,

Germany

Nether

lands,

UK, US

Russia

Companie

s

observed

50 319 240 117 200

a)43

b)44 176 74 89 137 300 300 60

Graph

usage (%)

N/A 79 79 54 83 a)83

b) 73

UK 80

US 92

N/A 89 N/A 80 88 95

48

Mean

number of

graphs

8.5 8.0 5.9 6.0 8.4 N/A

UK 7.7

US 13 33 9.4 N/A 4 - 14 N/A 20.68

Graph

Distortion

of Graphs

(%)

42 26 10.7 N/A N/A 16.4 24 68 24 N/A N/A N/A 44

49

Conclusion

This dissertation investigated the existence of impression management in financial graphs of

60 Russian Stock Exchange listed companies based on three techniques: selectivity, distortion and

presentational enhancement. Companies were ranked based on the alteration of their EBIT figures

from the financial year 2012 to 2013. Obtained data from annual reports for the year 2013 was

examined and statistical tests were applied. Results indicated existence of impression management

techniques (selectivity, distortion and presentational enhancement) in the companies` annual

reports.

The outcome of the research is consistent with prior literature and gave unsurprising

evidence of the difference in inclusion of KFV graphs in the most and least profitable companies.

The first two hypotheses about selectivity level in the companies were confirmed at 1%

significance level. It was found that the most profitable companies really have intention to add

more financial graphs; while the least profitable companies have nothing to show by KFV graphs.

Even though this hypothesis was in line with expectations based on previous studies, none of the

prior literature investigated selectivity level based on “sales” variable graph.

The association found with “sales” performance and inclusion of financial graphs indicates

a high level of selectivity and bias of presented information. It demonstrates that companies’ KFV

graph inclusion decision is strongly dependent upon its financial performance, where most

profitable companies add more KFV graphs compared to least profitable ones. In contrast,

distortion and presentational enhancement found in financial graphs were not as significant based

on statistical tests. Although the existence of distortion was confirmed and favourable trend usage

was proven at 1% significance based on statistical tests, confirmation of strong difference in graph

distortion between most and least profitable companies was not found. Existence of difference was

confirmed, with 48% distorted graphs in least profitable and 28% in most profitable; however, these

results failed at significance tests.

This is quite interesting, because the difference between samples was quite big (top=30 and

bottom=13); however, the difference between them was not significant. This can be linked to lack

of sample for least profitable companies, which could be higher if research considered more than 60

companies.

“Graphs enhance the potential to communicate, rather than merely to report financial

information and represent an aspect of corporate financial reporting…Moreover, graphs provide

opportunities for managements to manipulate the financial signals sent to users” (Beattie and

50

Jones, 1997). The importance of graphs is suggested by a number of researchers (Hanson, 1989;

Paivio, 1974; Lee, 1999). However, existence of impression management in financial graphs

decreases the level of fair presentation of the position of the company. Considering the growing

tendency of impression management and graph usage in the companies (Table 13), it should be put

under control. Existence of impression management in annual reports already contradicts with AUP

19 “Other Information in Documents Containing Audited Financial Statements”, which states the

importance of other information contained in annual reports is consistent with financial statements

without bias and fraud. However, graphs in annual reports are still not considered essential

information and not audited yet, which increases their potential to misrepresent financial data.

After all, preparation and organization of annual reports is on the behalf of managers, who

have their own motives to mislead investors, shareholders, etc. This can be overcome if appropriate

rules and regulations are developed and applied in auditing annual reports. Stringent regulatory

framework can clearly blur the image of the company and lead to effective decision making.

Limitations related to this dissertation are mainly linked to the sample size. Considering the

size of the sample used in previous studies, a more expanded sample is needed for further analysis.

Moreover, companies were analysed based on cross-sectional statistical data analysis, and for more

robust results investigation of time series is needed. If cross sectional analysis can confirm the

existence of impression management in annual reports, then time series analysis can show the

alteration of annual reports and behaviour of managers based on different performances in different

years. Therefore, more expanded analysis in a time framework is needed. In addition, the rank of

the companies was based on EBIT figures, which can be changed in further studies based on other

KFV variables, which might gave more clarification. Specific analysis by sectors might also show

the degree of distortion in each sector and might show outliers of the data.

Nevertheless, results obtained in this dissertation are consistent with prior literature and are reliable,

based on statistically significant evidence. It can be assumed that existence of impression

management was confirmed in annual reports of Russian Stock Exchange listed companies and

general expectations based on prior literature were met. This means that a stringent regulatory

framework is needed for the Russian Accounting System, in order to overcome current issues.

Although, degree of distortion is not so big, it might lead to wrong decisions and have a devastating

impact on the foreign investment in the economy of the country, which is currently in an unstable

position.

51

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Appendices

Appendix 1. List of Top Profitable Companies

Company SEDOL EBIT 2013 EBIT 2012 Change Revenue 2013 Market Cap. SectorAeroflot B58X588 17,107 14,462 18% 292,342 2,037 Air TransportationAFI Development B1VZKH7 6,470 -7,284 188% 11,795 378,000 Real Estate InvestmentARZAMASSKIY PRIB B232SQ8 474,450 412,016 15% 5,038 6,000 IndustrialsCTC Media Inc B142B27 7,112 5,123 38% 26,627 1,650 TelecommunicationsDIXY Group B596T06 7,507 4,513 66% 180,504 51,285 RetailEurasia Drilling Company B289L09 20,793 17,265 20% 111,613 4,266 Oil Equipment and ServicesGazprom Neft B11XHC5 7,451 7,369 1% 39,612 642,889 Oil and GasIDGC of Center and Volga B5B2VW8 4,452 3,769 18% 77,554 12,033 ElectricityInter Rao UES JSC B5B2VL7 55,048 -23,064 139% 662,321 98,867 UtilitiesJSC VTB Bank B5B1TP4 141,300 134,700 5% 784,800 540,454 FinanceLENENERGO JSC B59BHS5 6,972 2,725 155% 37,323 6,895 ElectricityLukoil B59SNS8 350,272 259,554 35% 3,811,776 52,349,000 Oil and GasM.VIDEO B59SPB5 6,906 5,667 22% 148,042 44,800 General RetailersMagnit OJSC B59GLW2 51,322 36,223 42% 528,461 923,409 Grocery StoresMEGAFON B8PR8P2 79,370 56,793 40% 297,229 651,806 Telephone and CommunicationsMMK B5B1RP0 91,700 81,536 12% 262,080 2,199 IndustrialsMobile Telesystems OJSC B59FPS3 112,186 100,515 12% 398,443 604,446 Telephone and CommunicationsNizhnekamskneftekhim B59LYW8 9,382 5,192 81% 126,043 38,645 Oil and GasNOVATEK OJSC B59HPK1 66,396 30,582 117% 83,915 1,215,220 GasPhosagro OJSC B3ZQM29 32,407 12,069 169% 104,566 169,593 IndustrialsPIK Group B59Q6G1 13,121 10,323 27% 62,543 52,615 ConstructionRAO Energy System of East B585368 6,978 -2,387 192% 139,596 7,588 ElectricityRosneft Oil B59SS16 676,000 479,000 41% 4,546,000 2,596,022 Oil and GasRostelecom B58ZLT7 53,831 49,909 8% 290,736 258,675 Telephone and CommunicationsRushydro JSC B450L21 1,013 868 17% 9,801 290,494 IndustrialsSurgutneftegaz B01WHG9 10,919 7,477 46% 26,179 1,111,576 Oil and GasTATNEFT B59BXN2 108,215 104,637 3% 454,983 512,212 Oil and GasTRANSAERO B5W3KT0 6,199 4,793 29% 110,150 31,231 Air TransportationTranscontainer B6694M2 8,131 5,834 39% 39,164 33,001 Transportation

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Yaroslavnefteorgsintez B5B1PS9 5,187 4,387 18% 21,311 24,389 Oil and Gas

Appendix 2. List of Least Profitable Companies

Company SEDOL EBIT 2013 EBIT 2012 Change % Revenue Market Cap. SectorAcron JSC B5902M1 16,834 20,111 -16% 67,904 49,573 IndustrialsAlrosa B6QPBP2 51,210 52,363 -2% 138,224 329,229 IndustrialsAvtovaz B5B8KK7 -7,726 38,772 -120% 177,049 19,124 Automobiles and PartsBASHNEFT 7129340 71,610 83,927 -15% 403,041 535,229 Oil and GasChelyabinsk Zink Zavod B59PZ27 -209,000 694,200 -130% 13,062 7,278 IndustrialsCHERKIZOVO B5B9CR1 2,927 7,297 -60% 52,957 820,000 Consumer ServicesDorogobuzh B596TG2 5,532 6,226 -11% 17,497 11,805 ChemicalsEvraz B71N6K8 8,900 252,400 -96% 9,198 2,370 IndustrialsFederal Grid Company of Unified Energy B59RSV3 -287,881 9,479 -3137% 157,970 74,402 ElectricityGazprom B59L4L7 1,528,851 1,594,758 -4% 5,255,815 3,522,382 Oil and GasIrkutstk Energetics JSCO B56GLL4 12,383 14,280 -13% 89,908 32,486 UtilitiesKrasnoyarskenergosbyt B56KKX1 12,500 543,200 -98% 25,388 2,234 IndustrialsKuybishevAzot B59CVN1 2,475 3,425 -28% 31,009 20,524 ChemicalsMechel B5960H0 -24,598 -6,613 -372% 274,445 617,000 IndustrialsMOSTOREST B59MBM 5,730 7,356 -22% 116,714 32,294 ConstructionNorilsk Nikel B114RK6 1,705 3,431 -50% 11,115 33,547 IndustrialsNovolipec Steel B2R9QD8 19,115 30,453 -37% 349,102 8,529 Steel IndustryPolymetal International- B6T5S47 -3,794 20,737 -118% 53,933 3,872 IndustrialsProtek- B3VLDC5 3,614 -2,556 41% 139,310 21,745 PharmateuticalsRaspadskaya Coal OJSC B5800Q2 -3,387 445 -761% 17,453 395,000 IndustrialsRos Agro PLC B5MTFN7 4,969 6,288 -21% 36,489 6,250 Consumer GoodsRussian Sea Group OJSC B3LNC13 117,500 362,800 -32% 16,008 4,772 Consumer GoodsSamarenergo B5B8MD4 -135,900 -66,300 -205% 40,197 1,091 ElectricitySeverstal B5B9C59 16,761 45,768 -74% 425,971 7,484 IndustrialsSollers OJSC 7425305 5,674 9,078 -37% 61,317 21,080 Automobiles and PartsTerritorial Generating Company B58H8C5 9,628 9,910 -3% 69,853 26,210 IndustrialsTube Metallurgical Company OJSC B590TG4 18,068 21,593 -16% 205,820 84,147 IndustrialsUC Rusal B5BCW81 -64,640 -4,738 -1364% 312,320 7,175 Metal IndustryUralkali OJSC B59MFL7 29,432 60,678 -51% 106,323 14,117 IndustrialsVimpelcom B62HR76 -2,024 4,311 -147% 17,497 15,248,000 Communications

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Appendix 3. Summary of financial report analysis (Top Profitable Companies)

Company Operational Financial Market Environment Social Total Favourable Unfavourable Bar Pie LineAeroflot 13 12 7 3 4 39 29 10 23 3 13Afi Development 1 2 5 0 0 8 5 3 3 5 0Arzamas 11 11 0 1 4 27 24 3 20 3 4CTC Media 6 8 4 0 0 18 18 0 14 4 0Dixy 4 9 2 0 0 15 15 0 13 2 0Energy System 5 9 1 1 1 17 14 3 13 4 0Eurasia 1 4 1 0 0 6 6 0 6 0 0Gazprom 9 10 5 5 4 33 33 0 27 3 3IDGC 7 10 3 3 6 29 25 4 27 2 0Inter Rao 2 6 1 3 3 15 15 0 14 1 0Lenenergo 9 19 9 2 7 46 37 9 36 7 3Lukoil 9 14 5 8 7 43 40 3 37 4 2Magnit 4 9 4 0 0 17 16 1 12 2 3Megafon 5 9 6 1 3 24 24 0 18 3 3MMK 4 13 3 3 1 24 18 6 10 8 6MTS 10 15 2 3 3 33 32 1 26 3 4Mvideo 2 6 1 0 0 9 9 0 9 0 0Nizhnekamsk 5 10 1 4 6 28 27 1 21 7 0Novatek 4 5 1 2 2 14 14 0 11 3 0Phosagro 7 15 1 2 5 28 23 5 18 8 2Pik Group 3 13 2 0 3 21 18 3 12 5 4Rosneft 7 16 1 3 3 30 27 3 19 9 2Rostelekom 4 12 2 1 2 21 19 2 16 5 0Rushydro 8 22 2 4 11 47 41 6 29 9 9Surgutneftgaz 8 9 3 1 4 25 21 4 18 5 2Tatneft 5 4 0 2 2 13 13 0 9 3 1Transcontainer 4 9 5 3 2 23 23 0 14 4 5VTB 6 13 9 0 4 32 30 2 28 4 0X5 12 15 2 2 5 37 37 0 26 9 2Yaroslavlneft 1 2 1 1 1 6 6 0 4 2 0Total 153 311 79 52 81 702 616 86 533 108 61

Mean 5.1 10.3 2.6 1.73 2.7 23.4 20.5 2.9 18.9 3.6 2

St.Deviation 3.2 4.1 2.3 1.9 2.3 14.4 12.5 2.8 11.7 2.4 2.9

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Appendix 4. Summary of financial report analysis (Least Profitable Companies)

Company Operational Financial Market Environment Social Total Favourable Unfavourable Bar Pie LineAkron 5 3 4 2 3 17 14 3 6 7 4Alrosa 9 1 0 0 1 11 8 3 9 2 0Avtovaz 4 2 2 1 4 13 11 2 5 6 2Bashneft 5 6 1 1 2 15 12 3 10 2 3Chelyabinsk 5 9 0 0 0 14 5 9 9 4 1Cherkizovo 13 12 2 0 0 27 11 16 22 4 1Dorogobuzh 3 5 0 3 0 11 8 3 6 3 4Energy Unified 9 7 0 5 12 33 26 7 31 2 0Evraz 10 9 2 4 5 30 18 12 26 4 1Gazprom 8 6 1 3 2 20 12 8 17 1 2Irkutsenergo 0 0 0 0 0 0 0 0 0 0 0Krasnoyarskenergo 7 6 1 0 0 14 6 8 8 1 5Kuybishevazot 8 6 1 0 1 16 10 6 12 2 2Mechel 11 9 0 0 0 20 7 13 19 1 0Mostotrest 7 9 3 1 5 25 17 8 19 4 2Norilsk Nikel 13 3 6 3 10 35 31 4 27 7 1Novolipeck Steel 6 8 0 10 8 32 18 14 28 1 3Polymetal 12 11 1 1 4 29 10 19 27 1 1Protek 12 10 3 0 3 28 22 6 24 2 2Raspadskaya 6 5 0 0 3 14 5 9 13 1 0RusAgro 4 4 5 1 2 16 13 3 8 8 0Rusal 0 0 0 0 0 0 0 0 0 0 0Russian Sea Group 4 9 0 0 0 13 6 7 9 1 3Samarenergo 2 4 1 0 4 11 7 4 5 3 3Severstal 9 10 4 0 2 25 10 15 17 4 2Sollers 7 5 0 6 8 26 17 9 25 0 1TGK-1 0 0 0 0 0 0 0 0 0 0 0TM Company 7 4 1 5 1 18 13 5 16 2 0Uralkali 7 6 0 2 7 22 11 11 21 0 0Vimpelcom 1 3 0 0 0 4 1 3 4 0 0Total 194 172 38 48 87 539 329 210 423 73 43Mean 6.4 5.7 1.2 1.6 2.9 17.9 10.9 7 13.1 2.4 1.4St.Deviation 3.7 3.3 1.6 2.3 3.2 9.6 7.2 4.9 9.1 2.2 1.4

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