=Paper=
{{Paper
|id=Vol-2018/paper-18
|storemode=property
|title=The Application of Methods of Semantic and Correlation Analysis Through Studies of
the Annual Reports of Russian Companies According to the Principle of Integrated Reporting
"Reliability and Completeness"
|pdfUrl=https://ceur-ws.org/Vol-2018/paper-18.pdf
|volume=Vol-2018
|authors=Vera Plotnikova,Maria Shilovskaya,Anton Dvorak
}}
==The Application of Methods of Semantic and Correlation Analysis Through Studies of
the Annual Reports of Russian Companies According to the Principle of Integrated Reporting
"Reliability and Completeness"==
https://ceur-ws.org/Vol-2018/paper-18.pdf
The Application of Methods of Semantic and
Correlation Analysis Through Studies of the
Annual Reports of Russian Companies
According to the Principle of Integrated
Reporting “Reliability and Completeness”
Vera V. Plotnikova1[0000−0002−4661−0942] , Maria S. Shilovskaya2 , and
Anton A. Dvorak3
1
Saratov Socio-Economic Institute of Plekhanov Russian University of Economics,
Saratov, Russia, http://www.seun.ru
2 3
Yuri Gagarin State Technical University of Saratov, Saratov, Russia, Russia,
http://en.sstu.ru
Abstract. The article contains an empirical study of the annual reports
of the largest Russian companies for 2011–2015 according to the principle
of integrated reporting “reliability and completeness”. It is based on a
computer model of the semantic analysis of the report texts and the
correlation analysis of the values of financial ratios and the opinion of
the company’s management about their financial position and financial
results. As part of study, the distribution algorithm of words by semantic
groups is proposed, and software that allows semantic analysis of the
information of annual reports according to the principles of integrated
reporting is developed. The correlation analysis was carried out using
standard Microsoft Excel tools. According to the analysis of the 30 largest
Russian holding entities, the conclusion is that in 70% of the reports
the opinion of the management of Russian companies does not have an
affiliation with the actual values and the dynamics of financial ratios
as well as it does not correspond to the principle of IR “reliability and
completeness”. In the end it was concluded that companies need to use a
standardized system of financial indicators to generate reports that can
provide an objective view of the company’s current situation, and also
to classify analytical interpretations of these indicators.
Keywords: integrated reporting, financial position, semantic analysis,
correlation analysis
1 Introduction
The problem of reliability and quality of the information disclosed in the an-
nual reports of Russian companies continues to be relevant for a long time. This
is due to the uncertainty about the predicted value of such information and
the objectivity of management’s assessment of the company’s financial position.
In other words, there is a situation when management tries to manipulate the
opinion of stakeholders for its own benefit, hiding the real financial situation
of the company. Subjective evaluations of the management of the company do
not always reflect the real picture of doing business [11]. And this is the reason
why over the last years, the attention to the quality of the descriptive infor-
mation disclosed in the annual reports has increased significantly. The research
is conducted on the basis of annual and integrated reports of the largest Rus-
sian companies. Russia participates in the pilot project on the transition to the
international standards of integrated reporting (hereinafter IR) and the GRI
standards. At the present point in time, Russian business is still not ready for
open communication with all stakeholders, since often even financial statements
fail to meet the requirements of reliability and completeness of information dis-
closed. The new model of IR is oriented toward a constructive dialogue between
companies and users of its information. In this case, the requirements to the
quality of financial and non-financial information disclosed, as well as to the
management’s objective overview of their financial position and the prospects
for creating value for a long period, are raised. To a large extent, the form of the
company management’s presentation of information about financial situation in
the content of annual reports depends on the conditions that affect the financial
position of the company at the time of preparation of reporting. The way the
management reacts to these conditions and accordingly offers its opinion about
the current position of the company and the prospects for its development is
the knowledgebase of our research, which allows us to draw a conclusion about
the maturity of Russian business and also its availability to move to a new level
of disclosed corporate information. One of the tools for analyzing the quality
of information contained in the annual reports of Russian companies and the
compliance of this information with the principles of integrated reporting is the
semantic analysis combined with the correlation analysis. Carrying out that sort
of research requires computer modeling of the process of word distribution by
semantic groups according to Loughran-McDonald Word Lists by distinguish-
ing positive, negative, uncertainty, and litigious, constraining, modal words that
make it possible to analyze the entire volume of text information contained in
the annual reports and through the use of correlation analysis to establish a cor-
relation between the management’s subjective opinion and the objective values
of financial ratios. This approach allows determining the quality of financial and
non-financial information for compliance with the principle of IR “reliability and
completeness”.
2 Literature Review
There are many studies related to determining the relationship between the
quality of information disclosed in corporate reports of companies and their fi-
nancial position. Some of them are dedicated to the study of the influence of
the quality of information disclosed in annual reports of companies to the val-
uation of their shares traded on the securities market [5] Tennyson B. M. and
others related the bankruptcies of some American companies to the quality of
managerial descriptive information disclosed in annual reports [13]. One study
showed a positive relation between the quality evaluation of information dis-
closed of the financial condition of Italian companies and the forecast precision
of financial analysts [10]. In all these studies the methods of economic and math-
ematical modeling were used as a methodological tool for analysis. According to
the world practice, lately the interest to evaluation and analysis of the quality
of corporate information disclosed with the use of text or semantic analysis has
increased significantly. Semantic analysis helps to study the linguistic structure
of the text, determine the tone and mood of corporate reports that helps to
assess their impact on the ability to identify situations when there are attempts
to avoid management from disclosing the real financial position of the company.
Text mining analysis works as an additional tool for forecasting the bankruptcy
of the company, also by using this technique it is possible to distinguish manage-
ment’s unintentional signals about the current financial position of the company
and its further development which are hidden in the report texts [8]. As a part of
the text mining analysis of company reports, it is possible to determine whether
certain categories of words are prevalent and to what extent they are used by
management in individual cases. Some authors explicitly specify the presence of
fraudulent application in the annual reports and conduct an appropriate research
on how the linguistic structure of the reported text helps to identify indicators
of true or false statements through textual analysis of emotions [6]. In the works
of Caserio C., Panaro D., Trucco S. [2] there is a study that was conducted on a
sample of US listed financial companies, in which, through the use of semantic
and regression analysis, a relation between the content of MD&A (management’s
analysis of the financial condition and results of operations) and the financial
position of the company was established. Also, it was revealed how much the
content of this report affects the quality of forecasting the financial condition of
the company in the future. Among the Russian studies, the most notable are the
works of Efimova O.V., which details the relation between long-term sustain-
able development of the company and the influence of all stakeholders, and puts
forward the thesis on the importance of qualitative of non-financial information
disclosed to improve the sustainability of the company’s development [4]. In the
work of Malinovskaya N.V. (2015), a general analysis was carried out to ensure
that the corporate reporting of the largest Russian companies was consistent
with the leading principles for the preparation and presentation of integrated
reporting, and concluded that the quality and information transparency of the
reports were gradually improving [9]. It is important to pay attention to the
insufficient study of the chosen research topic in the context of the activities
of Russian companies, meaning to identify the compliance of company reports
with the principles of integrated reporting using semantic analysis tools, since
earlier such studies were conducted only on the basis of foreign companies (in
particular, Italian [10] and American [2]). This places emphasis on our research
in the context of the adaptation of the considered text analysis techniques to
the linguistic features of the Russian language.
3 Research methodology
The basis of this study is the analysis of the quality of information disclosed by
Russian companies on the financial position in accordance to the principle of
IR “reliability and completeness” and the review of financial and non-financial
information for material errors in the reported texts, as well as the inclusion of
all material information in the report (both positive and negative). Based on
this, we proposed two hypotheses of the study: I0 – the quality of information
disclosed in annual reports of Russian companies is consistent with the principle
of IR “reliability and completeness”; I1 – the quality of information disclosed in
annual reports of Russian companies does not meet the principle of IR “relia-
bility and completeness”. Currently, the analysis of the semantic orientation of
company reports is characterized by the presence of a significant variety of text
analysis tools. Among them, a specific place is hold by Fog-index, Naive Bayes
Classifier, Loughran-McDonald Word Lists, and others. At the same time, it
is important to pay special attention to the fact that any of these methods of
analysis are based on linguistic features and textual structure in the native lan-
guage of authors, then there is the English language, this fact is emphasized
by scientists. In our opinion, the most interesting is the method of carrying out
text analysis on Loughran-McDonald Word Lists, according to which there are
several semantic fields or groups that determine the tonality of a word. These
include positive, negative, uncertainty, litigious, constraining, modal strong and
modal weak words [7]. Considering the fact that the native language for the au-
thors of this research is the Russian language which is characterized by certain
expressiveness, instead of the group modal strong and modal weak words the
combined group of modal words is proposed. Modality in the Russian language
is expressed by special forms of inclination, intonation and lexical means. In this
group, in addition to the modal verbs expressing not the action itself, but the
relation to it, it is preferable to include modal particles and words (of course,
unfortunately, probably, etc.). This group also includes words that give a strong
emotional coloring to the events described. For example, impressively, colossal,
cardinal, etc. This category of words is rarely found in annual reports, but by
their number one can estimate the degree of expression of opinion of the authors
in relation to the events described [12]. Examples of words in accordance with
the author’s classification of semantic groups are given in Table 1.
As a rule, positive words are the most common in the texts of reports. De-
pending on the context, an inversion of the value of positive and negative words
can occur. For example, when you use the word “growth” in the phrase “revenue
growth,” it is treated as an unambiguously positive, and in the phrase “costs
growth” – as a negative one. The group of legal words includes legal terms and
words that mark legally binding actions. Also it includes words that do not
necessarily indicate a legal basis, but indicate an attitude toward the judicial
sphere. The word selection for the formation of the dictionary database for each
semantic group was carried out on the basis of the study of typical phrases and
word combinations found in the texts of annual reports, taking into account
the specifics of the financial and economic sphere regarding to the accounting,
Table 1. Examples of words by semantic groups
Semantic group Words
Negative costs, crisis, exacerbate, inefficient, lose, recession, struggle
Positive profit, achievement, prosperity, benefit, leader, effective
Uncertainty risk, forecast, about, almost, guess
Litigious contestation, offering, consolidation, contract, own, become op-
erative
Modal can, to be able to, to want, of course, unfortunately, probably, it
must be, impressively
Constraining impose, determining, stick, require, allowing passage, limit
analysis and audit. In the course of analysis it is important to establish whether
there is a relation between specific groups of words, or rather their share in the
total number of words in the reports, and financial indicators of companies. This
goal can be achieved through a correlation analysis which establishes not only
the dependence by itself, but determines its strength and character.
4 Data Collection and Pre-processing
As the test object, there were selected 30 companies which are included in the
final rating of the quality of information disclosed in the integrated reporting by
Russian companies (http://transparency2015.downstream.ru/#/ru/1410), con-
ducted by the Russian Regional Network on integrated reporting. According
to this rating, the participating companies are divided into 5 groups by trans-
parency of information: I level – “Information disclosed at the level of the best
international practices”; II Level – “Information disclosed on international re-
quirements”; III Level – “Information disclosed in excess of Russian legislative
requirements”; IV level – “Information disclosed in accordance with Russian
legislative requirements”; V (zero) transparency level – “Non-transparent level”.
The criterion for selection of companies for analysis was their rating in the
overall assessment. The priority was given to companies with the highest score,
included in the I, II, III level of transparency. Financial companies and orga-
nizations whose accounting is developed in a foreign language and companies
that are the part of the State Atomic Energy Corporation “Rosatom” and the
holding of Rosseti PJSC have been excluded from this list. In this case it makes
economic sense to use only the reporting of the corporations themselves, since
they also participate in the rating. The texts of the financial performance re-
views for 2011–2015 were extracted (150 texts). The limitation of the analysis
period is connected with the fact that only in 2011 Russian companies whose
securities were admitted to trading at stock exchanges and (or) other organizers
of trade on the securities market have a responsibility to provide consolidated
financial statements in accordance with International Financial Reporting Stan-
dards (IFRS) [1]. The calculation of the total number of words in each text was
initially carried out using the text editor MS Word, in which this function is
performed automatically. But the use of this editor assumes the implementation
of the semantic analysis by the researcher manually, that is, by reading the text,
searching and distributing the necessary words by groups. In connection with
the great labour intensity of this work, we developed a special program “Sema-
nomics – TextAnalyser” which performs semantic analysis automatically with
the output of the number of words by groups and their frequency in the text on
the screen. Further, the company’s financial situation was determined by calcu-
lating financial ratios: Return on sales, ROS; Return on assets, ROA; Return on
investments, ROI; Current ratio, CR; Financial stability, FS; Turnover of cur-
rent assets, TR; Own funds ration, OFR. The reporting data from Consolidated
Statement of Financial Position and Consolidated Statement of Income of the
analyzed companies for the period 2011–2015 is used.
5 Implementation algorithm of the analysis
The basis of the algorithm for implementing semantic analysis is a combination
of several ways of content search. The purpose of the program is to determine
the number and frequency of occurrence of the words of each selected semantic
group in the analyzed text. For this, the words and phrases of each semantic
group are divided into 3 subgroups, and then 2 methods of finding matches in the
text are implemented. Words that do not carry semantic meaning (conjunctions,
prepositions, particles, etc.) are singled out. The scheme of specific cases of
possible interactions between subgroups of words and variants of the search
inside the implementation algorithm is shown in Fig. 1.
Fig. 1. Implementation algorithm of the semantic analysis
Words and phrases from the first and second groups are not included in
other, longer words, so algorithm I is suitable for its search and it is simpler
to implement. Words from the third group, as well as words that do not carry
semantic meaning (“and”, “but”, “not”) can be part of other words, therefore
a more sophisticated search algorithm II is implemented for them. The total
number of words in the text is initially determined by the number of spaces (1):
Nw = Nsp + 1, (1)
where Nw – total number of words; Nsp – the number of spaces. And then
the number of words without semantic meaning is subtracted from it, and re-
calculation is carried out taking into account the word combinations which are
considered as one word (Russian Federation, Ministry of Energy RF, public com-
pany “Company name”, etc.). As a result, only the number of words bearing the
semantic meaning is obtained: Nws ≤ Nw (where Nws is the number of words
with semantic meaning, “semantic” words). The number of words and phrases
of each group Ni consists of searching by subgroups (2):
Ni = Ni1 + Ni2 + Ni3 , (2)
where Ni is the number of words and phrases of each semantic group (positive,
negative, uncertainty, litigious, constraining, modal);Ni1 – the number of words
included in 1 subgroup (a base without a word ending);Ni2 – the number of words
in the 2 subgroup (all forms of the word);Ni3 – the number of words included
in the 3 subgroup (Combinations of words with all forms without spaces). Their
frequencies fi are defined as the ratio of the found quantity to the total number
of “semantic” words (3):
Ni
fi = . (3)
Nws
Then, the program displays the results of calculating the word number and
frequency of each semantic group in the analyzed text. The final stage of the
analysis is the calculation of pair correlation coefficient (Pearson) between the
financial ratios and the frequency of words of each semantic group in the report
for each company separately by formula (4):
¯ − x̄ ∗ ȳ
xy
rxy = , (4)
σx σy
where x – the independent variable which characterizes financial ratios (ROA,
ROS, ROI, CR, FS, TR, OFR);y – the dependent variable which characterizes
the frequency of words of each semantic group (positive, negative, uncertainty,
constraining, litigious, modal);σx – standard error of the mean of the indepen-
dent variable x;σx – standard error of the mean of the dependent variable y [3].
This coefficient makes it possible to estimate the strength of the connection be-
tween the two analyzed parameters. Its value varies from -1 to 1, where 1 means
the exact direct relation, and -1 – the exact inverse relation. Accordingly, if the
ratio value is 0, then there is no relation between the parameters [3]. The con-
nection is considered strong if the value of the coefficient falls within the interval
(0.7;1.0) or (-1.0;-0.7). The value in the intervals (0;0.3) or (-0.3;0) indicates a
weak connection between the parameters.
The calculation of the pair correlation coefficient can be performed using
the statistical analysis package STATISTICA using the Correlation Matrices
function. There is another way – the “Data Analysis” package in MS Excel. We
used a simpler method – selecting the function “CORREL” from the list of the
function wizard, as shown in Fig. 2.
Fig. 2. Calculation of the pairwise Pearson’s correlationcoefficient in MS Excel
For example, in the selected cell C15, the result of calculating the pair corre-
lation coefficient between the return on assets (ROA) and the share of negative
words in the financial survey from the integrated reports of RusHydro PJSC is
displayed.
6 Empirical Findings
A general analysis of the entire sample showed that the financial position and
the frequency of words in different semantic groups are interrelated weakly or
moderately in most companies. Significant influence on this result was provided
by insufficient data due to a thin volume of analysis (only 5 years). In order to
show the results of the correlation analysis visually with the calculation of the
number of companies for which a certain type of correlation was detected (direct
or reverse, strong or moderate), Table 2 is formed. Here the following notation for
the types of dependence is used: Dr.Md. – direct moderate; Dr.St. – direct strong;
Rv.Md. – reverse moderate; Rv.St. – reverse strong. The numbers indicate the
number of companies in the digest that have a dependency of the corresponding
type. In the analysis, we took into account only those types of dependence, in
which the total number of companies with a moderate and strong relation in
total exceeds the value of 8 out of 30 or 25% of the total sample.
Table 2. Results of the correlation analysis of the dataset of firms
ROA ROS ROI CR TR FS OFR
Negative Dr.Md.1 Dr.Md.1 Dr.Md.3 Dr.Md.3 Dr.Md.2 Dr.Md.1 Dr.Md.0
Dr.St.2 Dr.St.2 Dr.St.0 Dr.St.0 Dr.St.2 Dr.St.4 Dr.St.2
Rv.Md.5 Rv.Md.5 Rv.Md.5 Rv.Md.3 Rv.Md.4 Rv.Md.3 Rv.Md.6
Rv.St.8 Rv.St.7 Rv.St.7 Rv.St.6 Rv.St.5 Rv.St.4 Rv.St.8
Positive Dr.Md.6 Dr.Md.5 Dr.Md.6 Dr.Md.3 Dr.Md.3 Dr.Md.5 Dr.Md.6
Dr.St.10 Dr.St.10 Dr.St.10 Dr.St.6 Dr.St.7 Dr.St.6 Dr.St.8
Rv.Md.2 Rv.Md.1 Rv.Md.2 Rv.Md.2 Rv.Md.2 Rv.Md.3 Rv.Md.3
Rv.St.2 Rv.St.3 Rv.St.2 Rv.St.1 Rv.St.4 Rv.St.2 Rv.St.0
Uncer- Dr.Md.2 Dr.Md.4 Dr.Md.2 Dr.Md.4 Dr.Md.4 Dr.Md.3 Dr.Md.6
tainly Dr.St.7 Dr.St.5 Dr.St.6 Dr.St.3 Dr.St.4 Dr.St.5 Dr.St.6
Rv.Md.3 Rv.Md.5 Rv.Md.5 Rv.Md.3 Rv.Md.1 Rv.Md.3 Rv.Md.5
Rv.St.7 Rv.St.6 Rv.St.5 Rv.St.4 Rv.St.5 Rv.St.5 Rv.St.3
Litigious Dr.Md.6 Dr.Md.5 Dr.Md.5 Dr.Md.6 Dr.Md.5 Dr.Md.3 Dr.Md.4
Dr.St.1 Dr.St.2 Dr.St.2 Dr.St.2 Dr.St.2 Dr.St.4 Dr.St.4
Rv.Md.2 Rv.Md.2 Rv.Md.2 Rv.Md.5 Rv.Md.4 Rv.Md.2 Rv.Md.2
Rv.St.4 Rv.St.3 Rv.St.4 Rv.St.4 Rv.St.2 Rv.St.6 Rv.St.5
Modal Dr.Md.0 Dr.Md.0 Dr.Md.0 Dr.Md.2 Dr.Md.3 Dr.Md.2 Dr.Md.3
Dr.St.4 Dr.St.4 Dr.St.4 Dr.St.4 Dr.St.4 Dr.St.4 Dr.St.2
Rv.Md.3 Rv.Md.4 Rv.Md.3 Rv.Md.5 Rv.Md.1 Rv.Md.4 Rv.Md.2
Rv.St.5 Rv.St.4 Rv.St.5 Rv.St.3 Rv.St.5 Rv.St.5 Rv.St.6
Const- Dr.Md.3 Dr.Md.4 Dr.Md.3 Dr.Md.1 Dr.Md.3 Dr.Md.4 Dr.Md.2
raining Dr.St.2 Dr.St.1 Dr.St.1 Dr.St.3 Dr.St.3 Dr.St.2 Dr.St.2
Rv.Md.1 Rv.Md.2 Rv.Md.2 Rv.Md.4 Rv.Md.1 Rv.Md.6 Rv.Md.2
Rv.St.8 Rv.St.7 Rv.St.8 Rv.St.7 Rv.St.5 Rv.St.4 Rv.St.8
According to Table 2, it can be seen that 50% of companies in the digest have
a clear positive relation between all financial ratios and the frequency of positive
words. This situation is considered to be natural for companies, as in the case of
improving financial condition, it is obvious that when reflecting their opinion in
the report, management will use positive words more often. As previously men-
tioned, some companies use positive words to relief negative events, which can
lead to the identification of feedback between these indicators. There is another
obvious correlation between financial ratios and the frequency of negative words.
Approximately 40% of companies have a reverse and strong or moderate rela-
tion between profitability, their own funds ratio and the proportion of negative
words in the reports. For all other financial ratios, a similar relation is observed
only in 30% of companies. This suggests that the better the company’s financial
position, the fewer negative words are used in the reports. This is a controversial
issue, since this dependence is not found by all companies in the digest. The
relation between the frequency of uncertain words and financial indicators is of
particular interest. This dependence is direct strong or moderate for almost all
financial ratios for one third of the companies, and for the other third of the
companies it is inverse. For the remaining 30% of companies this dependence is
not established or is weak. This may indicate that the leadership of the first third
of companies is about expressing their opinion with reserve and making forecasts
using more words indicating uncertainty or uncertainty in the events described,
despite the improving financial condition. It will also be the matter-of-course
that, if there is weakening of financial standing of the company, the number of
insecure words will increase, and vice versa, as is the case for another third of
the companies. The relation between the financial position and the frequency of
restrictive words is reversed and is observed in 35% of companies. This confirms
our view that the increase in the number of verbal constructions indicating the
increased pressure or the emergence of new obligations and the worsening of
the financial situation are interrelated [12]. The frequency of modal words in
the financial results reviews for most of the companies does not have a close
connection with the financial position of the company. But, nevertheless, as the
data in Table 2 show, for those companies where it is found, the direction of this
connection is predominantly the reverse. That is, about 25–30% of companies
with a deteriorating financial situation often use modal words, and vice versa.
In general, the direction of this connection is irrelevant, since both the improve-
ment and deterioration of the financial situation may influence the increase or
decrease in the number of modal words with the same degree. In this case, the
value should have only the quantity, since the more of them, the better reflected
the attitude of the management to the information provided [12]. Depending on
the semantic group, in 40–50% of the companies the profitability ratios (ROA,
ROI, ROS), as well as OFR have a closer connection with virtually all groups
of words. This suggests that the company’s financial result is estimated by the
company’s management better than the financial position. Management pays
more attention to the description of financial results due to the greatest popu-
larity of information on them from stakeholders, since often the success of the
company is primarily measured by profitability and efficiency. The conclusions
reached allow to reject a hypothesis of the investigation of H0 and to accept
the hypothesis H1. The quality of information disclosed in the annual reports of
Russian companies does not correspond to the principle of IR “reliability and
completeness”. In 70% of the reports, the opinion of the management of Russian
companies does not have a clear relation with the actual values and the dynam-
ics of financial ratios. These results confirm the thesis that Russian business is
not ready for open interaction with all stakeholders.
7 Conclusions
The originality of our research is the proposed empiric treatment to the anal-
ysis of financial information disclosed according to the principle of integrated
reporting “reliability and completeness” in integrated or annual reports of Rus-
sian companies, combining elements of semantic and correlation analysis. Based
on the analysis of 30 largest Russian holding entities, the conclusion is that in
70% of the reports the opinion of the management of Russian companies does
not have a significant relation with the actual values and the dynamics of fi-
nancial ratios and does not comply with the principle of IR “reliability and
completeness”. We found for 30% of companies strong or moderate dependence
of financial position with all groups of words, except legal or judicial, which jus-
tifies the conclusion that the number of words of this group is not significant
for the purposes of forecasting the financial position. For 50% of companies, a
strong or moderate correlation between the number of positive words and finan-
cial position is direct, and the number of negative words (40% of companies) is
the reverse. Correlation with restrictive words is two-way, since it can be direct
and inverse in equal proportion for different companies. It was also concluded
that performance measurements are more closely related to all groups of words
than financial indicators. This suggests that management pays more attention to
describing financial results than describing the financial situation. In the future,
this study will be deepened by a comparative analysis of two groups of compa-
nies with low and high scale of bankruptcy. The method of semantic analysis
used in the research by distributing words from the texts of reports on certain
groups that characterize the tonality of the text is not new. But, the possibility
of adapting this methodology for the analysis of annual reports, which in the
end will allow us to assess their compliance with the principles of IR, determines
our contribution to the development of the prospects for open interaction of
Russian business with all interested parties. In our opinion, companies need to
use a single system of financial indicators to generate reports that can provide
an objective view of the current situation of the company, as well as to unify the
analytical interpretations of these indicators. This will eliminate the possibility
of manipulating the opinion of stakeholders by the company management.
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