THE TEXTUAL DATA ANALYSIS FOR EXPLORING THE NARRATIVE CONTENT OF CORPORATE REPORTING

DOCUMENTS

Gianluca GinestiDepartment of Economics, Management, Institutions

University of Naples "Federico II" (Italy)

Riccardo MacchioniDepartment of Economics

Second University of Naples (Italy)

Marco MaffeiDepartment of Economics, Management, Institutions

University of Naples "Federico II" (Italy)

ABSTRACTCore (2001) and Berger (2011) call for new techniques in natural language approach to examine corporate documents in order to extend the boundaries of the empirical literature on disclosure. We respond to the call for new researches on natural language approach, by testing a technique which enables to provide of a synthetic and graphical representation, through the data textual reduction of the characterization of the entire text used in corporate narrative documents, such as the Management Commentary. We test our analysis on the outlook section of the Management Commentary using a sample of 50 Italian listed firms. Our first results suggest that only few firms provide textual information useful for external users in order to understand the firm outlook.

___________*Although this study is the joint result of the discussion among all authors, the paragraph 2 (the textual data analysis) and the related graphics are developed by Maria Spano and Nicole Triunfo - Department of Economics and Statistics, University of Naples "Federico II".** Corresponding author: Gianluca Ginesti. Email: gianluca.ginesti@unina.it, g.ginesti@libero.it.

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1. INTRODUCTION

Textual data analysis is a statistical methodology, which allows the examination of large

amounts of information contained in a text. To this purpose, textual data analysis carries

out a set of operations on the corpus of a text and provides a syntagmatic representation

of the words, i.e., the words on the syntagmatic axis are considered in connection with

all other words in the same text (Bolasco, 2005).

In this paper, we analyze the text of narrative corporate disclosure.

It is well argued that narrative corporate disclosure is fundamental to drive investors’

decision-making (Shi Yun Seah and Tarc, 2006). However literature also emphasizes that

narrative disclosure might leave room for discretion to firms, in terms of what

information they provide and how these are reported. With reference to the latter, over

the years an increasing number of studies have paid attention to the narrative sections of

corporate disclosure by examining the text (Leavy et al., 2010). Some studies examined

either the readability (Courtis, 1995; Clatworthy and Jones, 2001; Li, 2008) or the tone

of the corporate disclosure (Davis and Tama-Sweet, 2012). With specific regard to

accounting literature, Li (2008) introduced the natural language processing approaches

by investigating the relatively simple issues of how annual report readability is

associated with firm performance and earnings persistence. This paper provided crucial

evidences about the relevance of computational linguistics to assess the basic aspects of

disclosure in relation to the manager’s motives to obfuscate (negative) performance.

This field of research employs the natural language processing techniques to capture

aspects of disclosure not readily measured by other means. Thus, focusing on the

natural language approach should be intended as a way to analyze the reporting quality

(Berger, 2011). Berger (2011) enlightens that prior researches have some limitations, in

terms of multiple ways to measure the disclosure readability and tone, and lack of

agreement in relation to the text that would be more useful to extract from corporate

narrative reports, in order to assess something of interest. Therefore, Berger (2011) is

agreed with Core (2001) to call for new techniques, coming from other fields of

research, to extend the boundaries of the existing empirical literature on corporate

narrative disclosure.

This study responds to the above-mentioned call for new research, by testing a

technique to examine the text of narrative corporate disclosures, which to the extant of

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authors’ knowledge, has not yet been used in previous empirical studies. This study

carries out an explanatory empirical research for a sample of Italian listed companies.

We focus on the Management Commentary, because this report is mainly narrative, as it

should display the objectives and strategies of the firms (IFRS, Practice statement –

Management Commentary, 2010). To explore a new technique, we examined the

section of the Management Commentary that provides outlook information. Indeed, this

section offers a unique setting to examine forward-looking information, which are

mainly qualitative in nature (Lajili and Zéghal, 2005). Second, it is worth noting that

there are a number of reasons that justify our choice to examine the outlook section of

the Management Commentary. In this regard, it is well acknowledge that the forward-

looking information is one of the most important sections of financial reporting.

However, several questions still persist as to whether this disclosure is truly informative

(Verrecchia, 2001; Li, 2010).

Moreover, what should be highlighted is that despite such forward-looking information

are mandatory in Italy, the requirements are in practice very vague and not really

detailed. Thus, the preparers have a certain degree of discretion in terms of information

to provide (Quagli, 2004; Beretta and Bozzolan, 2004) and this also affects the semantic

dimensions of a text, which can have latent or hidden meanings. Our technique allows

detecting the latent semantic dimensions of the text of the outlook section, through a

reduction of the dimensionality of the representational space. The latent semantic

dimensions of the texts are subsequently interpreted through the reference to control

variables, either financial or non-financial.

The remainder of this paper is organized as follows. Section 2 explains the textual data

analysis used in this study. Section 3 reviews the existing literature. Section 4 describes

the sample and the variables. Section 5 shows the results. Section 6 concludes the study.

2. THE TEXTUAL DATA ANALYSIS

2.1 METHODOLOGY

This paper uses the Canonical Correspondence Analysis (CCA) of Ter Braak (1986),

which is a development of the Correspondence Analysis (CA) of Benzécri (1973).

The main difference between the CCA and the CA concerns the determination of the

factorial axes. CA is an unconstrained ordering-based method and the factorial axes are

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examined in the following way: i) the researcher needs an external knowledge to

understand the factorial axes; ii) the researcher might perform a multiple regression

analysis to understand the factorial axes; iii) the researcher might calculate the

correlation coefficients between the factors and further variables not used in the creation

of the factorial axes. Instead, CCA imposes as restriction that factorial axes are a linear

combination of the variables chosen by the researchers, which contribute to the creation

of the factorial axes.

Therefore the CCA can be defined as a constrained correspondence analysis to the

subspace generated by variables, where texts and words are projected and the maximum

number of dimensions (factors) that can be represented is at most equal to the number of

variables used in the analysis, whether these are quantitative and/or categorical. For

example, considering n firms, if the number of variables increases the correspondence

analysis is progressively less constrained, up to the limit case in which the number of

variables p ≥ n-1 and the CCA is consequently nothing more than a CA.

CCA is based on the analysis of a quantitative matrix containing the variables and

another matrix known as lexical matrix. This latter is a rectangular matrix of size nxp,

where n are the reports and p are the words in the same reports.

2.2. THE ALGORITHM

Considering a sample of n reports that relates to the same number of firms, the

frequency or the presence/absence (presence = 1, absence = 0) of m words and the

values of q variables (q <n) are quantified. Yik is the frequency or the presence-absence

of the word k in report i and zij is the value of variables j for the company i.

The first step of the analysis of the gradient is to summarise the most of the variability

of the words by ordination. Starting from the assumption that the relationship between

the words and the performance indicators follows a Gaussian curve of response, Gauch

et al. (1974) propose a technique called Gaussian Ordination. Therefore the model

response to words is represented by the bell function:

(1)]/)(2/1exp[)( 22kkikik tuxcyE

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Where E(yik) represents the expected value of the yik to the report i, whose score on the

ordination axis is xi. The parameters for the k words are: ck, maximum of the response

curve of the words; uk, fashion, or the value of x for which is maximum.

The next step is to perform a multiple regression analysis, linking the same axis with the

indicators:

q

jijji zbbx

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(2)

Where b0 is the intercept, bj is the regression coefficient of the j-th performance

indicator and xi is the score on the ordination axis of yik. Note that in the first phase the

scores on the ordination axis are obtained from the matrix containing the data about the

frequency of words in the reports; subsequently the regression coefficients bj are

estimated keeping fixed the values xi.

Therefore, the words are indirectly linked to variables through the ordination axes.

Although this two-steps technique, called by ter Braak (1985) Gaussian canonical

ordination, is statistically more rigorous, from a computational point of view it is also

very expensive. For this reason Ter Braak, by showing that the correspondence analysis

approximates the maximum likelihood solution of the Gaussian ordination, introduces

the canonical correspondence analysis, as a heuristic approximation of the Gaussian

canonical ordination.

The considerations, leading to this approximation, are realized in the transition formulas

of the ACC (ter Braak, 1986):

(3)

(4)

(5)

(6)

Where y.k and yi. are respectively the marginal column and row of the matrix on the

composition of the words in the reports, R is a diagonal matrix with generic element yi.

n

ikiikk yxyu

1./

m

kikiki yuyx

1.

* /

*1 ')'( RxZRZZb

Zbx

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of size n n; Z={zij} is a matrix of size n (q+1) containing the values of the

performance indicators and a column of 1, b, x, x* are three column vectors: b=(b0,b1,

…..,bq)’, x=(x1,…...xn)’ e x*=(x1*,……xn

*)’. The transition formulas define a vector

problem similar to that in the analysis of correspondences where λ is the eigenvalue.

This issue can be solved by using the following iterative algorithm:

- Step 1: Assign arbitrarily the initial scores to reports;

- Step 2: Calculate the scores of the words as weighted averages of the scores of

reports (Eq. 3 con λ = 1);

- Step 3: Calculate the scores of new reports as weighted averages of the

scores of words (Eq.4);

- Step 4: Obtain regression coefficients by a weighted multiple regression of

report scores on performance indicators (Eq. 5), where the weights are the

marginal totals of reports yi.;

- Step 5: Calculate new report scores using Eq. 6. The new scores are the fitted

values of the regression of the previous step;

- Step 6: Standardize new scores: e

- Step7: Stop on convergence, for example, when the new scores of the reports are

sufficiently close to those of the previous iteration, otherwise proceeds to Step 2.

The algorithm is analogous to that of the correspondence analysis, but steps 4 and 5 are

additional. The second and subsequent axes of the CCA are also linear combinations of

quantitative and categorical variables, which maximize the dispersion of words but are

constrained to be uncorrelated (orthogonal) with the previous axes.

The final regression coefficients are called canonical coefficients, and the multiple

correlation coefficient of the final regression is defined as the words-indicators

correlation, and measures how much of the variability in the lexical structure can be

explained by the variables. Looking at the signs and the values of canonical coefficients

we can determine the importance of each variable in predicting the lexical structure.

2.3 GRAPHICAL REPRESENTATION

*ix

0. i

ii xy 12.

iii xy

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The graphical representation of CCA is a tri-plot, which allows simultaneously

displaying text, words and quantitative and/or categorical variables. Graphically, the

points represent words and texts, meanwhile the arrows represent quantitative variables.

The similarity (or dissimilarity) between points-word, points-text, or both between

points-word and points-text should be evaluated in terms of distance among the same;

hence the smaller the distance between two projected points, the more the two words,

reports and therefore firms to which they refer are “similar”.

The correlation between two quantitative variables coincides with the cosine of the

angle formed by the two vectors that represent them. The smaller the angle between the

two vectors, the more the variables are related.

3. ASSESSMENTS OF PRIOR LITERATURE

Previous researches have addressed the issues of corporate disclosure through different

approaches and method: i) content analysis (Bryan, 1997; Beattie et al., 2004); ii)

disclosure indexes (Cooke, 1989 and 1992; Wallace et al. 1994; Haniffa and Cooke,

2002); iii) survey ranking (Clarkson et al., 1999); iv) textual analysis (Shroeder and

Gibson, 1990; Courtis, 1995; Li, 2008; Davis and Tama-Sweet, 2012).

Berger (2011) reviews recent literature on corporate disclosure, recognising the

increasing importance of natural language approach, which aims at examining the

information provided by firms.

Many studies analyzed the readability of the annual report and/or its components (Soper

and Dolphin, 1964; Smith and Smith, 1971; Jones, 1998; Clathworthy and Jones, 2001).

In this regard, it is worth highlighting that a major issue is related to the difficulties in

defining the concept of readability. Some authors have argued that readability refers to

the ease of understanding of a message due to the style of writing used by the preparers

of reports (Barnett and Leoffer, 1979), whereas the understandability refers to the

capability of the reader to comprehend the adequate meaning (Smith and Taffler, 1992).

However, readability of a report is often associated to its understandability, and it has

been considered as an indicator of the understandability (Adelberg and Razek, 1984;

Curtis, 1986). Then, the readability could reflect the understandability because if a text

is simple to read, it will arguably be easier to be understood by readers (Smith and

Smith, 1971).

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A variety of techniques to analyze the readability of a text have been developed over the

years. Many of these consider the readability formulas (or indexes), which are

constructed using language style elements, such as sentence length, word length,

syllables and other vocabulary variables (Klare, 1974). According to Courtis (1987, p.

20) “the success of a formula in providing suitable direction depends on its ability to

measure elements in the writing that are related to readers’ comprehension”. The most

popular readability formulas within accounting studies are the Flesch reading ease

(Barnett and Leoffler, 1979), the LIX and the Fog (Courtis, 1987).

In this section we analyze some relevant studies, which investigated the textual

properties of accounting narrative disclosure. Notably, we mainly enlighten the

following information, if available in the papers: i) the methodology; ii) the examined

country; iii) the results and the main concerns on methodology.

Shroeder and Gibson (1990) examined three different aspects of readability (e.g., use of

the passive voice, word length, sentences length) of the Management Commentary,

analyzing a sample of 40 USA firms. They compared the readability of Management

Commentary to the readability of Chairman’s letter and financial statements footnotes.

The authors found that the Management Commentary is significantly less readable than

the Chairman’s letter.

Subramanian et al. (1993) analyzed the writing style of annual reports of 60 USA listed

firms with a software program and using a Flesch readability formula. The authors

tested the relationship between the readability of annual report and the performance of

firms. The findings suggested that the average readability level of the annual reports of

profitable firms was higher than that of the unprofitable firms.

Courtis (1995) used various indexes (Flesch, Fog and Lix) to examine the readability of

annual reports prepared in English language of 32 Hong Kong public firms. The results

provide evidence that the annual reports prepared by Hong Kong public firms are very

difficult to read for the majority of the adult population living in Hong Kong.

Clathworthy and Jones (2001) analyzed the text used in 60 UK Chairman statements.

They showed that the introduction to the Chairman statement is systematically easier to

read than other parts of the same document. Furthermore, the results suggested that the

thematic structure of this document is a key driver of the variability of the annual report

readability.

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Rutherford (2003) analyzed whether the poorly performing UK firms use textual

complexity in the Management Commentary to obfuscate and thus undermine effective

communication and good governance. The main results suggest that poorly performing

firms do not obfuscate their Management Commentary by resorting to textual

complexity and there is no way to attribute this complexity to that of the underlying

activities described.

Li (2008) examined the determinants and implications of the lexical properties of

corporate disclosures. The author used two measures of readability: the fog index and

the length of the report. The findings provide evidence that the annual reports of firms

with poor performance are harder to read, and firms with annual reports that are easier

to read have more persistent positive earnings.

Leavy et al. (2010) investigated the effects of the readability of corporate 10-K filings

on the behavior of financial analysts. The authors calculated the overall readability of

corporate 10-K filings using the Fog index. The authors provided evidence that analysts

who follow the firms with less readable disclosure need on average longer time to issue

the reports in response to 10-K reports. Furthermore they found that less readable 10-K

reports are associated with greater dispersion and lower accuracy in analyst earnings

forecasts.

Goel et al. (2010), using a software style, examined the readability of annual reports of

fraudulent and non-fraudulent firms. They found systematic differences in

communication and writing style of the annual reports between fraudulent and non-

fraudulent firms. The results show that reports of fraudulent companies are much harder

to read and comprehend than those of non-fraudulent firms. Their analysis suggests that

the linguistic features can be used as an effective means for detecting fraud.

Davis and Tama-Sweet (2012), referring to a large sample of firms, investigated the

managers’ languages across alternative communications outlets. The authors used a

textual-analysis approach to analyze the content of earnings press releases and the

corresponding content of Management Commentary. They found that firms exhibit

lower levels of pessimistic language and higher levels of optimistic language in earnings

press releases rather than in the Management Commentary. They also found negative

association between the level of pessimistic language in the Management Commentary

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and future firm performance, controlling for pessimistic language in the correspondent

earnings press release.

Many of the previous studies investigated the textual properties of accounting narrative

disclosure using a multiple approaches, i.e. applying the readability formula or

examining the pessimistic or optimistic language tone.

The readability formulas have different limitations: they use a simplistic assumption a

text without considering the multiple elements involve in reading comprehension

(Stevens et al., 1993) and they do not consider the coherence and logical presentation of

ideas (Courtis, 1994).

The language tone analysis does not have a unique way for evaluating pessimistic or

optimistic words used in accounting narrative disclosure.

Furthermore, there are studies that examine the textual properties of corporate

disclosure with reference to the linguistic and stylistic structure of the financial reports

(Jameson, 2000; Beattie and Jones, 2002; Yeung, 2007).

In this study we extend the prior empirical accounting literature by using a technique of

textual analysis, which enables the provision of a synthetic and graphical representation,

through the data textual reduction of the meaning, and the characterization of the entire

text used in the same section of Management Commentary provided by different firms.

This technique takes advantage from a computer based-approach to understand the text

of narrative disclosure that can improve the generalizability of results and it is able to

replicate in other researches (Li, 2010).

4. OBJECT OF THE ANALYSIS, SAMPLE AND VARIABLES

This section shows the object of the analysis, the sample and the variables used for the

Canonical Correspondence Analysis.

4.1. OBJECT OF THE ANALYSIS

We analyze the text of the forward-looking information contained in the outlook section

of the Management Commentary.

The Italian Management Commentary (Relazione sulla Gestione) is a mandatory

narrative report, intended to complementing and supplementing the financial statements

(Viganò, 2008; Caldarelli, 2010), whose content is regulated according to the article

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2428 of the Civil Code. To integrate the information included in the financial

statements, the Management Commentary should provide analysis of firm’s business

activities, strategy and performance (art. 2428, paragraph 1). The preparers have to

provide a clear description of different topics, such as: i) characteristics of the

regulatory and macro-economic environments, and of the markets in which the firm

operates; ii) review of the results of the firm during the period and its position at the end

of that period; iii) revenues and capital expenditures; iv) research and development

activities; v) number and nominal value of the shares, and the shares of parent

companies held by firm, including trust companies, inter-firm relationships; vi) the

significant events occurring after the year-end close; vii) ownership structure; viii)

financial instruments; ix) list of the subsidiaries firms and their locations; x) important

events occurring between the end of the fiscal year and the financial reporting

publication date; xi) outlook information.

The traditional reporting sections to be included in Italian Management Commentary

have been further extended by the European Directive 51/2003.

In 2007, the transposition of the European Directive 51/2003 has modified the article

2428 of the Civil Code, in order to introduce a new type of information in Management

Commentary. The new requirements are effective for all Italian firms since the fiscal

year 2008.

The current regulation of Italian Management Commentary requires adding disclosure

concerning the following topics: i) risks and uncertainties; ii) financial and non-

financial performance indicators; iii) environment and labour information.

Despite the continuous raising of requirements for the preparation of the Management

Commentary, the regulations are still not detailed, so that the Italian Management

Commentary is perceived as a quasi-voluntary report (Quagli 2004; Beretta and

Bozzolan, 2004). This means that the preparers have great discretion in choosing the

format for the presentation and the level detail of the information included for each

reporting section.

We specifically focus on the outlook section. This section includes forward-looking

information, i.e. programs, actions, results and risks that the management aims to

achieve and monitoring in the future. Notably, in this section the preparers should

disclose the main trends of business and explain the specific factors affecting it. The

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information to provide has to be relevant for the investor’s assessment on future firm's

objectives and performance. This information is based on the managers' assumptions

and might possibly be not accurate or wrong.

4.2. SAMPLE

Our data set is made of a sample of 50 non-financial Italian firms listed on the Italian

stock exchange in 2010 (appendix A). We focused on the Italian listed firms because we

had the chance to get access to data, as they are available to public. We downloaded

from the Italian stock exchange website the Management Commentary for the fiscal

year 2010, written in the Italian language.

We have chosen the firms by using a technique of quota sampling, to ensure the

inclusion of firms from all the non-financial sectors. The sample represents the 24% of

the total population of the non-financial listed firms. In line with Beretta and Bozzolan

(2008) we have excluded the financial listed firms because they are subject to specific

and detailed disclosure requirements.

4.3. LEXICAL MATRIX

The analyzed corpus consists of 20.529 Tokens and of 4.262 Types.

The pretreatment of the corpus was performed using the software TalTac 2.0. The

pretreatment is required to clean up the text from empty words (conjunction, articles,

adverbs, etc.) and words with frequency <5 (rare words). Following this procedure, we

managed to get the lexical matrix of size 50x634.

4.4. VARIABLES

In order to provide an in-depth explanation of the results of the Canonical

Correspondence Analysis, control variables must be measured. To this end, we selected

several variables by relying on the existing literature and taking into account the

specificities of the country setting. Specifically, we use the following firm

characteristics as variables (Wallace et al., 1994):

i) structure-related variables (firm size and leverage), useful to describe a firm on the

basis of its underlying structure;

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ii) performance-related variables (profitability), useful to identify a measure of firm's

market success;

iii) market-related variables, useful to classify the firm in relating to other firms and to

the external operational environment.

Furthermore, we consider the readability formula, which is useful to examine the degree

of understanding of a text. A brief description of the above-cited variables follows.

Firm size

Firm size is a variable used in analyzing the amount and the readability of corporate

disclosure (Li, 2008; Leavy et al., 2010). We measure firm size in terms of market

capitalization.

Leverage

Leverage is a relevant explanatory variable in corporate disclosure. The existence of a

significant relationship between leverage and corporate disclosure has been investigated

in prior studies (Wallace et al., 1994; Hanniffa and Cooke, 2002). We measure leverage

as the ratio of a firm total long-term debt to its equity at the end of the reporting year

(gearing).

Profitability

A number of studies have also examined the relation between corporate profitability and

some disclosure aspects (Courtis, 1986; Cooke, 1989). Profitability has been calculated

in different ways, such as net income to total owners’ equity (Haniffa and Cooke, 2002)

and the ratio of earnings before tax to total sales in the reporting year (Wallace et al.,

1994). We measure profitability as the ratio of earnings before tax to total sales in the

reporting year.

Audit firm size

The auditor’s firm size has been considered in prior accounting studies, relating to

corporate disclosure (Wallace at al., 1994; Haniffa and Cooke, 2002).

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We use a common technique to identify audit firm size, by dichotomizing Big four

firms as large, and non-big four as small. In this regard, if the firm is audited by one of

the Big four auditors, then it was scored “1”, otherwise it was score “0”.

Readability formula

We measure the readability of the text of outlook section, using the Gulpease index

(Lucisano and Piemontese, 1988). This is a careful and thoughtful review of earlier

indices, the one drawn up for American English by Rudolf Flesch in the fourties of the

last century, and the Gunning Fog Index, known as Gunning (Gunning, 1952). The

Flesch index considers the average number of words per sentence and average number

of syllables.

The formula for the Flesch index is: 206.835 – 0.846ASW – 1.015ASL,

where:

ASL = average sentence length (the number of words divided by the number of

sentences);

and ASW = average number of syllables per word (the number of syllables divided by

the number of words).

Instead, the Gunning Fog Index formula is: 0.4 (A + T )

where:

A = average number of words per sentence;

and T = the percentage of hard words in the passage.

We use a Gulpease index because is more suitable for Italian language.

Gulpease index is calculated applying the following formula:

FrLp

3

1089

Where:

Lp = (number of letters in the text 100) / number of words in text

Fr = (number of sentences in the text 100) / number of words of text.

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The index values vary in a range 0-100. For readers with an elementary education it is

easy to read texts with an index above 80, for those with a middle education it is easy to

read texts with an index above 60, and finally, readers with higher education, easily read

texts with an index above 40.

5. RESULTS

The CCA analysis identified two dimensions represented by the first and the second

axis of the graph 1, respectively.

Graph 1 - Distribution of text words and variables on the factorial axis.

The graph 1 provides the distribution on the factorial axis of the variables and the text

words use in the outlook section provided by the Italian listed firms.

The analysis shows that the first latent factor (1) – horizontal axis – is positively

affected by profitability and it is negatively affected by gearing. Thus, there is negative

correlation between profitability and gearing.

The second latent factor (2) – vertical axis – is positively affected by readability and

size. Thus, there is a positive correlation between size and readability. Furthermore, the

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analysis shows that readability is positively correlated with profitability and size;

meanwhile it is negatively correlated with gearing.

In the right area of factor 1 (1st and 4th quadrants of the graph 1) are placed those words

whose firms that are characterized by high profitability and low gearing, meanwhile in

the left area of factor 1 (2nd and 3rd quadrants of the graph 1) are placed those words

whose firms present high gearing and low profitability. In the upper area of factor 2 (1st

and 2nd quadrants of the graph 1) are placed those words whose firms have high

readability and size, meanwhile in the lower area of factor 2 (3rd and 4th quadrants of

the graph 1) are placed those words whose firms have low size and readability.

The words placed in the center of the factorial axis graph are very similar one another,

in terms of words, firm’s characteristics, and readability. The firms placed in the

extreme parts of the factorial axis graph have much more different characteristics

(words, readability scores, and firm’s characteristics) compared to the others.

Furthermore, the analysis provides evidence that the firms with high level of readability

have their financial statements audited by big four audit firms.

We analyze the results by examining each quadrant of graph 1, separately.

In the first quadrant reported in graph 1.A, there are firms with higher profitability, low

gearing, high size and high readability score. These firms use similar words, which are

mainly “forward-looking”, emphasizing the growing of their business, the international

activity, the management objectives and its strategies for achieving those objectives.

These kinds firm provide tend to provide information more in compliance with the

requirements of Italian law for the outlook section.

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Graph 1.A - Distribution of text words and variables on the factorial axis: first quadrant.

In the second quadrant reported in the graph 1.B, there are firms with low profitability,

high leverage, high size and high readability. These firms use similar words, which are

mainly “forward-looking”, but in this case, the management emphasizes the aspects of

financial, liquidity condition and performance, new agreements and investments.

Graph 1.B - Distribution of text words and variables on the factorial axis: second quadrant.

In the third quadrant reported in the graph 1.C, there are firms with low profitability,

high leverage, low size and low readability score. These firms use similar text words,

which are not “forward-looking”, by emphasizing inter-temporal information, such as

capital, past results, environmental situation, current revenues and reorganization

activities. In this case, the management does not explain its outlook vision. These results

suggest that these kinds of firms could be motivated to obfuscate or manipulate the

perceptions of stakeholders on the firm’s results and strategies (Merkl-Davies and

Brennan, 2007).

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Graph 1.C - Distribution of text words and variables on the factorial axis: third quadrant.

In the fourth quadrant reported in the graph 1.D, there are firms with high profitability,

low leverage, low size and low readability score. These firms use similar text words,

which emphasize inter-temporal information, such as efficiency, investments, capital

maintenance, management, and leadership.

Graph 1.D - Distribution of text words and variables on the factorial axis: fourth quadrant.

6. CONCLUSIONS

This research replies the call of Core (2001) and Berger (2011) for new techniques -

coming from other fields of research - to extend the boundaries of the existing literature

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on corporate disclosure. In this study we used the CCA to examine the text of

accounting narrative disclosure. The aim was to detect the latent semantic dimension of

narrative content, through a reduction of the dimensionality of the space of

representation, by employing financial and non-financial variables.

This study contributes to the literature in several ways. Previous studies have used

different techniques, mainly focusing on the tone and the readability formulas with

reference to Anglo-American context. To our knowledge, this is the first empirical

textual analysis from a computer-based approach, which is aimed to investigate the

relationships among text words and firm’s characteristics, with reference to Italy. Our

study contributes to the previous empirical literature by analyzing the latent semantic

dimension of the text included the outlook section of Management Commentary.

Our study also provides a relevant contribution for practitioners. Indeed, It sheds light

on the need for more effort by prepares and auditors, in make sure that accounting

narrative disclosure effectively reveals the actual firms’ profile, and does not represent

for firms just a tool to use discretion in deciding the issues to be provided to the external

users.

Although the aim of this paper (to verify the potentiality of a new tool for analyzing

narrative corporate disclosure) has been reached, its results have some limitations.

First of all, we examined only one section of Management Commentary, meanwhile it

could be useful to analyze all narrative sections of the mandatory financial reports.

Moreover, our data set consists in a small sample of Italian listed firms, which excludes

financial firms.

Further developments of this study requires a more widely sample and longitudinal

analyses.

Appendix A. List of firms

19

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