Readability of Annual Reports: Evidence from Foreign Firms in the
United States Stock Exchange
PHUONG THI THUY NGUYEN1AKIHISA KIMURA

Business Administration Department

Business Administration Department

Graduate school of International Social Science

YOKOHAMA NATIONAL UNIVERSITYYOKOHAMA NATIONAL UNIVERSITY

ABSTRACTThis study measures the readability of annual reports issued by foreign firms in the UnitedStates stock exchange, using the two most popular measurements: Fog Index and length ofreports. This study proves there is no significant diverse in readability of annual reports under
different accounting standards systems. All IFRS, US-GAAP or national accounting
standards systems lead to difficult-to-read reports. English is considered as the key role indetermining the readability of annual reports. Additionally, this study shows that longer
reports are the easier-to-read ones. This research along with related prior research provides afully complete picture of current readability of annual financial statements.
Key Words: 20-F; Readability; Fog Index; Length of Documents

1. Introduction
Financial statements are the official and primary communication tools of firms to investors,
regulators, and other users. At first, investors did not take enough consideration to understand thosereports (You and Zhang 2009). When the business environment changes and becomes more complicated,the high demand for information leads investors to raise more awareness of annual reports. Moreover,regulators request firms to disclose more valuable information to the public. Therefore, the usefulnessof annual reports has increased significantly in some recent years.
The fact that financial statements have become more complex over time, as the result ofcomplicated business environments along with more strict regulations, raises a question relating to theeffectiveness of annual reports in communicating users. Consequently, we witness a rapid increase in
the research on readability of annual reports. Numerous research has shown that the readability ofannual reports has enormous impacts on investors’ perception, analysts as well as stock market (Li1Corresponding Author. Address Business Administration Department, Yokohama National University, 79-4 Tokiwadai,
Hodogaya-Ku, Yokohama, 240-8501, JAPAN. Telephone: +81-80-4338-0135; E-mail nguyen-phuong-tr@ynu.jp.

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2008; Miller 2010; Lehavy et al 2011; Wayne et al 2016). Additionally, the new measurements forreadability of annual reports have been continuously introduced such as LMPE of Loughran and
McDonald (2014b) or Bog Index of Samuel et al (2017). The research has improved the awareness ofscholars and practitioners on the crucial role of readability of annual reports. Therefore, U.S. Securities
and Exchange Commission (SEC) has promulgated regulations and guidelines for firms to improve thereadability of their annual reports. The readability of filings has, however, not been improved
significantly yet.
Noticeably, the prior research referring to the readability of annual reports mostly concentrates
on the United States firms. There is only the fewer number of research on this topic for other samples
including Lundholm et al (2014) or Lang et al (2015). In this research, we hope to draw a complete
picture about the readability of all annual reports by extending our investigation to foreign firms listedon the US stock exchange. Our considered firms operate in different business and legal environments
and follow different accounting standards systems. Additionally, we expect to provide the primary datafor upcoming research on readability of annual reports provided by foreign firms.
Our research uses two most popular measurements of readability: Fog Index and length of reports.
We find that foreign firms fill annual reports more complex than the fillings of the US firms. Annual
reports of foreign firms are longer and have higher Fog Index than those of the US firms. Regardless offollowing different accounting standards systems, the readability of annual reports filed by foreign firms
is not significantly different. However, firms in English speaking countries offer shorter but moredifficult-to-read annual reports than the reports of other foreign firms due to linguistic advantage. Our
research also displayed that longer reports are easier-to-understand for readers.
The remainder of this paper proceeds as follows. Section 2 summarizes the literature reviews
relating to the readability of annual reports. Section 3 presents the theoretical framework about themeasurements of readability. Section 4 presents the sample selection. Section 5 displays findings anddiscussion. Finally, Section 6 shows concluding remarks.

2. Literature Review
Accounting information plays a vital role in making decisions as well as the efficiency of thestock market, however, not at all users can perfectly understand all information of annual reports since
they have different competencies in processing information. Therefore, SEC along with researchers
pays more attention to the readability of annual reports. In recent years, we witness a dramatic increasein the number of research relating to the readability of financial statements. The pioneering research inthis aspect was implemented in the 1970s by Smith and Smith (1971), focusing on the readability offootnotes in financial statements. Courtis (1986) reported the relationship between the readability andrisk, and Subramanian et al (1993) presented the association between readability and firms’
performance. The earlier conducted research, however, only used the small sample sets. The large
sample set in this aspect was firstly collected by Li (2008) with more than 50,000 observations. Since
2008, there has been a blooming in the number of research on readability of annual reports.

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As the first research using a large sample set, Li (2008) has substantial contributions to theacademic accounting research. He successfully introduced Perl language in measuring the readabilityof annual reports. More importantly, Li confirmed that the readability of 10-K is strongly related to theperformance of firms. Firms with bad performances tend to provide longer annual reports with fewer
self-referential, more causation words, more future tense verbs to hide the adverse information incomparison with reports of firms performing well. As concluded, managers use readability of annual
reports as a useful tool for hiding unfavorable information of firms to users.
In fact, the readability of annual reports also affects the efficiency of stock market throughout the
information that they provide. You and Zhang (2009) recognize that if firms provide longer annual
reports, the investor underreaction is statistically significant. This relationship is not statisticallysignificant in the group of firms providing shorter annual statements. However, You and Zhang justfocus on the length of reports and the tendency of stock prices in the 12-month period after filing. To
improve You and Zhang research, Yen-Jung (2012) adds one more new measurement of readability andtests the speed of price adjustment for a 3-day filing window. In detail, he studies the relationshipbetween the complexity of annual reports and underreaction by using the mandatory quarterly reports(10-Q) of the US firms in the period from 2001 to 2007 and focuses on the speed of price adjustment
for a 3-day filing window. Yen-Jung uses the length of reports and Fog Index for measuring thecomplexity of 10-Q. Finally, Yen-Jung reports that the more complex quarterly reports are, the fewer
market responses during the 3-day filling window. He also realizes that the negative impact of thecomplexity of annual reports on market reaction can be reduced if firms have more analysts andinstitutional ownership who are competent in understanding the complex reports. Miller (2010) and
Lawrence (2013) also record that small investors prefer trading stocks of firms which provide morereadable reports.
On the other hand, there is some research building the relationships between the readability ofannual reports and behaviors of analysts such as Lehavy et al (2011), or Hsieh et al (2016). They reportthat there is an inverse relationship between readability of 10-K and analyst reports. Lehavy et al (2011)
find that less readable annual reports are associated with greater dispersion, lower accuracy and greater
overall uncertainty in analyst earnings forecasts. They also recognize that investors require more analyst
services when firms issue difficult-to-read reports. Hsieh et al (2016) record the negative relationshipbetween the volatility of stock prices and analyst readability. Like Lehavy et al (2011), De Franco et al(2015) conclude that analysts, even experienced analysts, spend more time to issue analyst reports forcomplicated annual reports and the readability of analyst reports also affects the trading volume ofstocks. However, the more experienced analysts are, the less complicated analyst reports will be. Not
only affecting behaviors of analysts, Rennekamp (2011) realizes the significant indirect effect of
readability of disclosure on credibility perception of management.
Regardless of the negative effects on both users and the stock market, almost all research presents
the dramatic increase in complexity of annual reports or the significant decrease in readability.
Interestingly, to reduce the adverse impacts of the complexity of annual reports on users, firms increase3the number of voluntary disclosure. Wayne et al (2016) present that firms issue more voluntary
disclosures when they increase the financial statement complexity and have more outside monitors.
Users increase searching when the complexity of annual reports increases. Joshua et al (2017) record
the increase in internet searching during the filing date for less readable 10-K reports. SEC provides
guidelines for firms to disclose the information such as suggesting using plain English or writing styles.
According to Cheung et al (2016), Australia chooses to adopt IFRS with the hope of increasing thereadability of annual report. Consequently, IFRS declines the Fog Index of annual reports from 17.87
before IFRS adoption to 17.47 after such adoption. However, annual reports are still assessed as difficult-to read.
Most of the above research just focuses on the US firms or 10-K or 10-Q reports. The number of
research using broad empirical evidence outside of the US firms is limited. To our knowledge,
Lundholm et al (2014) are the pioneers in studying the readability of annual reports of foreign firms in
the US stock exchange. They concentrate on the readability of MD&A in 20-F and make the comparison
with the readability of this part in 10-K. They report that foreign firms provide more numbers and morereadable text in the MD&A part than US firms do. In 2015, Lang et al collect more annual reports in
English from 42 countries around the world. They conclude that the complexity, as well as thecomparability of annual reports, increases over time especially in countries adopting IFRS or US-GAAP.
As opposed to Lundholm (2014), our research studies the complexity of the whole annual reports issued
by foreign firms in 45 countries listed on the US stock exchange. We also focus on reports called 20-F
which must follow the standardized presentation rather than annual reports based on different formats
like Lang et al (2015) with the hope of providing a complete view on the readability of annual reports
over time and cross-sections.

3. Theoretical Framework
3.1. Definition of Readability
Readability is a concept used in various sectors such as linguistic, healthcare and law; however,there is no sole and precise definition of readability. Some authors use the style of writing, coherence,and organization of reports to identify the readability (Klare (1963) in Loughran and McDonald (2014a)).
Some authors mention the target readers of reports to decide the writing style and vocabulary. Others
suppose that readability requires a mixture of different elements from writing styles to vocabulary andauthors (Dubay, 2007 mentioned in Loughran and McDonald (2014a)). We highly appreciate thedefinition of Loughran and McDonald (2014a) since it focuses on the business context which has theidentified users with acceptable knowledge about business. They define “readability as the ability of
individual investors and analysts to assimilate valuation-relevant information from a financial
disclosure”. SEC also addresses the vital role of readability to users and the stock market. Since 1998,
SEC required issuers to adhere to plain English in the communication with users as shown in Rule 421
in SEC’s Plain English with the hope of improving the readability of reports. SEC continued to expand
the Rules about plain English in 2006 and then widened the scope of application to mutual funds and4analysts in 2009 and 2010. In 2013, AICPA (American Institute of Certified Public Accountants)
established the Center for Plain English Accounting for aiming the auditors and practitioners to fluently
use plain English in communication.

3.2. Measurement of Readability
There are several ways to measure the readability of annual reports; however, no measurement
has been assessed as the perfect way. In the following, we present two most common measurements ofreadability used in prior research.

3.2.1 Length of Reports
The length of reports is identified by the number of words in the reports. It supposes that thelonger reports are, the lower readability is due to containing more information and more detailed, so
investors take more time and costs for processing. This measurement is one of the easiest to identify
since there are several ways to get the results. The simplest way is using Microsoft Office software, thealternative is using a text manipulation programming language like Perl. One of the previous researchuses the package Lingua:: EN:: Fathom of Perl to identify how many words are in the reports because
it focuses only on the text of reports, excluding tables, figures which the Microsoft Office counts.
Additionally, the Perl package can easily be applied to large sample sets as opposed to Microsoft Office.
Despite its simplicity, this measurement is strongly criticized that it excessively concentrates into theconstructs rather than readability. Currently, regulations and business environments become socomplicated that it is difficult to present in the short reports. Otherwise, short reports could lead tomisleading information or lack of transparency which users are not willing to accept. According to
Cheung et al (2016), IFRS makes annual reports of Australian firms to be longer but easier-to-read. It
is obvious that the length of the document does not entirely mean readability.

3.2.2 Fog Index
Fog Index, firstly introduced by Robert Gunning in 1952, has been used more popular in different
research relating to readability. Likewise, previous research shows that Fog Index is the most in-usedmeasurement. To our knowledge, most of the previous research shows that Fog Index is considered as
the main proxy for measuring readability or comparing if using different readability measurements. The
index is based on the numbers of education years that readers need to understand the texts. It contains
two components: the number of words per sentence and the percentage of complex words in a document,
as follows:
Fog Index = 0.4 * (words_per_sentence + percentage_complex_words).
From the formula, complex words mean multi-syllable words or pronunciation with two moresyllables. Fog Index is classified into 5 levels: Fog Index is more than 18 classified as unreadability, inbetween 14 to 18 meant difficult-to-read, from 12 to 14 sorted as ideal for reading, from 10 to 12
considered as acceptable readability, and 8 to 10 arranged as childish.

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So far, Fog Index is assessed as the best way to identify the readability of reports since it starts
focusing on the writing styles of reports rather than simply the length of reports. Moreover, with thesupport of packages of Perl, it is easy to calculate Fog Index for large samples in an automatic manner.
Most importantly, Fog Index is considered as the objective measurement since it does not suffer the
subjective opinions from readers. Additionally, Fog Index is widely used in research relating toreadability, therefore its accuracy and validity were tested and accepted by a wide range of users.
However, Fog Index also receives a lot of criticism from researchers. Loughran and McDonalda(2014 ) criticize that Fog Index is not precise in the business context since the second component of
Fog Index performs poorly. For Fog Index, each word pronouncing with two more syllables is
considered as the complex word. In the business context, the usage of multi-syllable words which leads
to high Fog Index is very popular, and those multi-syllable words are normally easy to understand toinvestors. Therefore, they complain that high Fog Index sometimes does not mean difficult-to-read in
the business context. In addition, some empirical research shows that Fog Index is not the good variable
in explaining the relationships. For example, Lehavy et al (2011) build the relationship between
readability and analyst dispersion or Loughran and McDonald (2014a) find that Fog Index does not havepredictive power in explaining the unexpected earnings and analyst dispersion.

3.2.3 Other Measurements of Readability
File size. Loughran and McDonald (2014a) supposed to use File size rather than the length of the
document. They argue that File size is easier to identify and strongly correlated with othermeasurements of readability and offers fewer measurement errors. However, in our opinion, file size
does not mean readability and is similar to the length of the document since the longer document means
larger file size. Moreover, the development of technology such as images or HTML or PDF format
leads to the dramatic increases in file size, which does not mean the decreases in the level of readability.
Flesh-Kincaid and Flesh Reading Ease. Flesh-Kincaid and Flesh Reading Ease are based on two
similar components of Fog Index: word per sentences and syllable per word, however, they use different
formulas to calculate:
Flesh-Kincaid = (11.8 x syllables per word) + (0.39 x words per sentence) – 15.59
Flesh Reading Ease = 206.8 – (1.015 x words per sentences) – (84.6 x syllables per word)
Those measurements are used in research of Franco et al (2015) and Wayne et al (2016); however,they modified the formulas in different ways by connecting with Fog Index.
Plain English and Bog Index. Since Fog Index receives criticism and SEC also highly
recommends using plain English in communication with users. Some researchers suggest using plain
English to measure the readability of annual reports rather than Fog Index. The pioneer of thissuggestion is Miller in 2010. He used StyleWriter-Plain English Editor software which is based on plain
English to measure the readability of annual reports. Loughran and McDonald in 2014b introduced

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another measurement relying on plain English called LM PE index. LM PE index is measured bysentence length, word length, passive voice, legalese, subtract personal pronouns and other items.
Equally, this measurement is personally assessed as the better method than Fog Index since it is basedon business regulations. The drawback of LM PE Index is time-consuming. More currently, Samuel etal (2017) introduced Bog Index which also relies on plain English but is more easily and quickly
identified, as follows.
Bog Index = Sentence Bog + Word Bog – Pep
In the definition of Bog Index: Sentence Bog means sentence length; Word Bog is calculated by(plain English + word difficulty) x 250 / the numbers of words; Pep means writing attributes.
Fortunately, Bog Index is easily acquired by using the StyleWriter Professional software. Due to time
constraint, this research will not mention the results of Bog Index. However, we highly appreciate thisnew measurement for readability of annual reports since Bog Index provides a complete view about thereadability of reports, and it combines different components into one measurement from word usage towriting styles especially emphasizing the plain English, as highly recommended by SEC.
In this research, we focus on the widest measurements in research relating to the readability ofannual reports, which are Fog Index and Length of Reports.

4. Sample Selection
This research focuses on foreign firms listed on the US stock exchange, normally called as“foreign private issuers” having less than 50% on the shares traded on the US stock exchange. As therequirement of SEC, those firms must annually fill the 20-F form which provides standardized financial
and non-financial information to users for evaluating and making decisions. We use Python to download
all 20-F filings of foreign firms in 10 years from 2004 to 2013 from the website of SEC, called EDGAR,
and get 7,588 filings of 1,475 firms in 10 years. Since those are foreign firms, they have different fiscal
years, therefore we choose only firms as well as their annual filings with fiscal year on December 31st.
We also remove all firms in Finance and Insurance from our sample. Finally, we collect the sample
with 5,014 observations shown in Table 1. The number of observations over 10 years from 2004 to2013 is reported in Appendix 1. Overall, the number of observations fluctuates from 446 in 2013 to 616
in 2004 for a period of 10 years. Each year, the number of observations occupies around 10% of total
observations in the sample.
SEC sorts listed foreign firms into 9 industrial sectors based on their business operating activities:(1) agriculture, forestry, fishing; (2) Mining; (3) Construction; (4) Manufacturing; (5) Transportation,communication, electric, gas and sanitary services; (6) Wholesale Trade; (7) Retail Trade; (8) Financeand Insurance; and (9) Services. As mentioned above, we remove all firms in Finance and Insurance
from our sample. We use SEC’s classification to sort our filings into 8 industrial sectors. The
distribution of filings in 8 industrial sectors is mentioned in Table 1. Approximately 63% of filings(3144 observations over 5014) operate in Manufacturing, and group of Transportation, communication,

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TABLE 1. SAMPLE SELECTION
Total of observations collected from EDGAR

7,588

Number of firms

1,475
st

Number of firms have fiscal year on December 31
Number of observations selected

966
5,014

In which:
Agriculture, Forestry, Fishing

17

Mining850

Construction

29

Manufacturing

1,828Transportation, communication, electric, Gas and Sanitary Services

1,316

Wholesale Trade

100

Retail Trade74

Services

800

electric, gas and sanitary services. The number of observations in mining and services occupies 33%.
The number of filings in other industrial sectors is at 4%.
To identify the readability of 20-F, we use the two most popular measurements which are Fog
Index and length of reports. It is necessary to clean the raw text files downloaded from EDGAR before
measuring the readability of annual reports. To do so, we use packages of Perl and follow the guidelinesof Li (2008), Loughran and McDonald (2014a; 2014b) and Bonsall et al (2017) to acquiring the cleaning
text of 20-F. Details of cleaning the 20-F are presented in Appendix 2. To acquire the Fog Index andthe number of words of each 20-F, we use package Lingua:: EN:: Fathom of Perl and other packages
such as File:: Slurp; Excel:: Writer:: XLSX; File:: Basename for calculation.
Li (2008) proved the validity of the program in measuring the readability of annual reports. The
package Lingua:: EN:: Fathom allows for calculating the number of words, the number of characters,
the percentage of complex words, number of sentences, number of text lines, number of paragraphs,syllables per words, words per sentence, Fog Index, Flesh Index and Flesh Kincaid Index. This research,however, focuses on Fog Index and the number of words only. Other Perl packages provide the Excel
file for statistical analysis.

5. Findings and Discussion
5.1. Descriptive Analysis
Table 2 shows the mean of Fog Index over 10 years from 2004 to 2013 at 19.77. It means thatreaders need more than 19 years of education for understanding annual reports issued by foreign firms
in the US stock exchange. 20-F filings are concluded as the so difficult-to-read for users. This result is8the coincidence with the results mentioned by Lang et al (2015). They show the average Fog Index ofannual reports in English in 42 countries over the world at 19.52 during 14 years from 1998 to 2011.
Lundholm et al (2014) record the average Fog index of MD&A part in 20-F is 17.54 in the period 20002012. However, Li (2008) shows that the readability of MD&A part is extremely easier to read than thewhole of filings. In comparison with the readability of 10-K, Fog Index of 20-F is not significantly
different. Li (2008) reports the Fog Index of 10-K in the period from 1993 to 2003 at 19.4, or Lehavy
et al (2011) record the average Fog Index of 10-K in 10 years from 1995 to 2006 at 19.52, or Bonsall
et al (2017) showed that the Fog Index of 10-K in the period between 1994 to 2011 is 19.60. Moreinterestingly, the average number of words of 20-F is 66,063 which is double the 10-K as mentioned
around 37,000 words in previous research, however, Fog Index is not so substantially different between
10-K and 20-F.
TABLE 2. OVERALL THE READABILITY OF 20-F OVER 10 YEARS FROM 2004 TO 2013
N

Mean

Std.Dev

Q1

MedianQ3

Number of words

5,014

66,06331,20145,59662,53981,737

Words per sentence

5,014

24.08

5.19

20.70

23.06

26.37

Percentage

5,014

25.34

1.24

24.62

25.36

26.12

5,014

19.77

2.11

18.39

19.42

20.69

of

complexwords
Fog Index

As shown in Panel A of Table 3, there is a dramatic increase in Fog Index over time due to theincrease in the complexity of regulations and the business environment. Fog index is recorded at 18.9
in 2004, significantly increasing in the next 5 years to the peak at 20.45 in 2009 where remains in one
year later. However, Fog Index slightly decreases in the last 3 years from 2011 to 2013, the approval of
IASB for Conceptual Framework for Financial Reporting in 2010 could be the reason for this decrease.
The last 3 years witness the approximate stability in Fog Index at around 19.8. Despite the decline in
Fog Index, 20-F is still concluded as the difficult-to-understand.
Conversely, the number of words in 20-F increases substantially during the 10-year period from2004 to 2013, especially after 2010, which is displayed in Panel B of Table 3. The number of words
rises from 57,127 in 2004 to 69,518 in 2010 and reaches to 77,881 in 2013. Obviously, the last three
years from 2010 to 2013 witnessed the decrease in Fog Index but the increase in the number of words.
Foreign firms perhaps write longer reports but shorter sentences since the average words per sentences
reduce from 25.74 in 2010 to 24.23 in 2013 (referred to Appendix 3). The drop in the number of words
per sentence causes the decline in Fog Index or improving the readability of 20-F. Most noticeably, thepercentage of complex words remains stable over 10 years at 25%, since they are standardized financial
reports which use similar professional words in accounting.

9TABLE 3. READABILITY OF 20-F BY FISCAL YEARSPANEL A: NUMBER OF WORDS OVER 10 YEARS
Year

N

Mean

Std Dev

Q1

Median

Q3

2004

616

57,12730,83038,12850,677

69,293

2005

574

58,661

29,02940,58554,14770,914

2006

543

60,48229,00541,921

55,91474,9502007

503

62,744

28,61943,972

58,841

77,9402008

478

66,701

29,104

48,82563,416

79,976

2009

462

67,05830,104

48,63964,61882,0252010

476

69,51830,39150,90066,59083,765

2011

464

72,55331,40453,23071,45586,942

2012

452

74,33833,24153,87471,88189,7642013

446

77,881

33,77556,24775,60994,175PANEL B: FOG INDEX OVER 10 YEARS
Year

N

Mean

Std Dev

Q1

Median

Q3

2004

616

18.90

1.86

17.69

18.56

19.75

2005

574

19.19

1.96

17.94

18.86

19.98

2006

543

19.42

1.92

18.17

19.11

20.08

2007

503

19.97

2.40

18.53

19.49

20.71

2008

478

20.01

2.05

18.63

19.57

21.06

2009

462

20.45

2.23

18.96

20.16

21.64

2010

476

20.45

2.17

18.99

20.10

21.6

2011

464

20.05

2.23

18.67

19.62

20.94

2012

452

19.87

1.86

18.63

19.61

20.65

2013

446

19.83

1.76

18.66

19.60

20.74

FIGURE 1. PEARSON CORRELATION
Number of words
Number of words

1

Fog Index-0.1077***

Fog Index
1

Note: *** denote 1% significance level.

10Based on the classification of SEC, we sort observations in our sample into 8 industrial sectors
as mentioned above. The readability of 8 industrial sectors is presented in Table 4. As seen fromstatistical results, Mining, Wholesales and Services provide the most difficult-to-read annual reports ataround 20 for Fog Index, however, the length of 20-F of those industrial sectors are the shortest ataround 57,000 words in comparison with others. Lehavy et al (2011) and Joshua et al (2017) also reportmining and trading sector issuing the most complicated reports. On the contrary, Construction, and thegroup of Transportation, communication, electric, gas and sanitary services annually issue the least
complicated but the longest reports with more than 78,000 words.
From the descriptive analysis, the increase in Fog Index is accompanied by the decrease in thenumber of words. Figure 1 shows that the Pearson correlation between Fog Index and a number ofwords is significantly negative at the 0.01 levels. Cheung and Lau (2016) also record a similar resultwith us relating to the negative correlation between Fog Index and a number of words. They conclude
that IFRS makes Australian annual reports become longer but have lower Fog Index. Lundholm et al(2014) compared foreign firms and US firms in readability of MD&A. They also present that MD&A
of foreign firms are significantly longer, but have lower Fog Index as compared to US firms. This result
could be explained by changes in writing styles. Those reports with lower Fog Index but the higher
number of words are possible to use single sentences rather than complex sentences, and as a result, thenumber of sentences increases but the average word per sentences decreases. This writing style leads toeasily understanding reports for readers, displayed by the lower Fog Index. Such writing style also
complies with Rule 421(d) on plain English promulgated by SEC.

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TABLE 4. READABILITY BY INDUSTRIAL SECTORSPANEL A: LENGTH OF 20-F OVER INDUSTRIAL SECTORSN

Mean

Std Dev

Q1

MedianQ3Agriculture, Forestry, Fishing

17

63,66737,53432,15352,72790,042Mining850

55,01634,89530,43944,65670,615Construction

29

87,45024,52571,32081,245106,233

Manufacturing

1,828

65,87427,292

49,731

62,918

77,133

Transportation, communication,

1,316

78,11533,67057,29373,376

99,848Wholesale Trade100

57,39424,18941,01857,52373,642Retail Trade74

77,086

26,81764,801

71,65888,204Services

800

57,75824,13541,71654,98075,005Mean

Std Dev

Q1

MedianQ3electric,

Gas

and

Sanitary

Services

PANEL B: FOG INDEX OF 20-F OVER INDUSTRIAL SECTORSN
Agriculture, Forestry, Fishing

17

19.57

3.04

17.46

18.54

20.12

Mining

850

20.07

2.19

18.52

19.66

21.22

Construction

29

19.24

1.63

17.99

18.65

21.22

Manufacturing

1,828

19.67

2.11

18.32

19.28

20.62

Transportation, communication,

1,316

19.54

1.97

18.23

19.36

20.4

Wholesale Trade

100

20.27

2.60

18.48

19.92

21.44

Retail Trade

74

19.52

2.03

18.33

19.26

20.22

Services

800

20.04

2.08

18.67

19.58

20.89

electric,

Gas

and

Sanitary

Services

5.2. Readability of Accounting Standards Systems
As the requirement of SEC, foreign firms listed in the US stock market can choose to adopt
International Financial Reporting Standards (IFRS), or United State General Accounting Accepted
Principle (US-GAAP) or national financial accounting standards, provided that the firms must declarewhich accounting standards systems are adopted for preparing 20-F. As firms' declaration, we sort theminto 3 groups: IFRS, US-GAAP or national accounting standards. The number of observations sorted
into 3 different accounting standards systems is mentioned in Figure 2.

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FIGURE 2: FILLINGS FOLLOWING DIFFERENT ACCOUNTING STANDARDSSYSTEMS

9%
IFRS

47%

US-GAAP
National Accounting Standards

44%

More than 90% observations adopt IFRS or US-GAAP for filings, the number of filings following
IFRS is slightly larger than that of filings using US-GAAP, 2349 versus 2201. Such adoption is expected
to improve the comparability in reporting between foreign firms and the US firms. The readability ofannual reports following different accounting standards systems is mentioned in Table 5.
As shown in Table 5, firms following US-GAAP provide the most complicated reports at 19.84
of Fog Index as compared to 19.71 of Fog Index of 20-F issued by firms adopted IFRS. However, 20F reports prepared under IFRS are longer but have lower Fog Index than reports followed US-GAAP.
Readability of annual reports adhered to national accounting standards has Fog Index in the middle of
US-GAAP and IFRS. Most importantly, Tukey test and ANOVA test in Panel A of Figure 2 show thatthe differences in Fog Index among 3 groups are not significantly statistical even at 10% level except
the difference between group IFRS and US-GAAP which is significant at 10%. The convergence of
IFRS, US-GAAP and national accounting standards is supposed to explain this result. However, thedifference in the length of 20-F among IFRS, US-GAAP, and national accounting standards systems issignificant at 0.01 level. Overall, foreign firms using the same format of annual reports identified 20-F
issue reports with significantly different length but insignificantly diverse of Fog Index or level ofreadability.

13

TABLE 5. READABILITY OF 20-F UNDER IFRS, US-GAAP AND NATIONAL ACCOUNTINGSTANDARDSIFRS

US-GAAP

National
Accounting
standards

Number of words

75,984

58,63451,074

Fog Index

19.71

19.84

19.76

TABLE 6. TUKEY TEST AND ANOVA TEST AMONG IFRS, US-GAAP, NATIONAL ACCOUNTINGSTANDARDS
PANEL A: TUKEY TEST AND ANOVA FOR FOG INDEX AMONG 3 ACCOUNTING STANDARDS SYSTEMS
Difference Lower

Upperp-value

US-GAAP vs IFRS

0.134

-0.012

0.28

0.08*

National Accounting systems vs IFRS

0.054

-0.1970.31

0.86

National Accounting systems vs US-GAAP-0.079-0.3320.17

0.74

P-value for differences among 3 groups

0.101

PANEL B: TUKEY TEST AND ANOVA FOR NUMBER OF WORDS AMONG 3 ACCOUNTING STANDARDSSYSTEMS
Difference

Lower

Upper

p-value

US-GAAP vs IFRS

-17,345-19,416-15,283

0.0e+00***

National Accounting systems vs IFRS

-24,910-28,449-21,371

0.0e+00***

National Accounting systems vs US-GAAP-7,561-11,119-4,003

1.9e-06***

P-value for different among 3 groups<2e-16 ***Note: ***, **, and * denote 1%, 5% and 10% significance levels, respectively.

14

5.3. An Important Role of Native Language in Providing the Annual Reports.
According to Lundholm et al (2014), the important role of English in deciding the readability ofannual reports is not evident since they found that annual reports of foreign firms in English speaking
countries provide shorter MD&A section at higher Fog Index than firms in non-English speaking
countries, but foreign firms in English speaking countries provides easier-to-read MD&A than US firms.
Based on the declaration of firms in 20-F, we identify the executive locations of foreign firms.
According to Lundholm et al (2014), we divide the observations into two groups: filings of firms locatedin countries whose native language is English and filings of firms located in countries not speaking
English. Figure 3 shows that the number of filings issued by firms located in English speaking countries
is approximately equal with its filings submitted by firms cited in non-speaking English countries.
FIGURE 3: 20-F FILINGS ISSUED BY FIRMS LOCATED IN ENGLISH SPEAKING
COUNTRIES AND NON-SPEAKING ENGLISH COUNTRIES

50%
2509

50%
2505

English
Non-English

As shown in Table 7, firms located in countries speaking English provide shorter but higher Fog
Index (i.e., more difficult-to-read) reports than those of firms in non-English speaking countries, i.e.,
53,477 versus 78,629 and 20.25 versus 19.29. Unlikely with differences among 3 accounting standards
systems, the differences in a number of words and in Fog Index between those 2 groups are statisticallysignificant at 1% level as shown in the t-test and Wilcoxon test in Table 7. This result is similar tofindings of Lundholm et al (2014) who show that MD&A of foreign firms in non-speaking English
countries has significantly lower Fog Index than its foreign firms located in English speaking countries.
As concluded, firms in countries speaking English, thanks to native language advantage, are able toshorten annual reports by increasing the length of sentences (22.89 words per sentence of non-English
group versus 25.28 words per sentence of English group), however, such shorter reports lead to theincrease in Fog Index or readability of annual reports. In other words, language plays a key role indeciding the length of reports and the readability of annual reports.

15

TABLE 7. READABILITY OF ANNUAL REPORTS ISSUED BY FIRMS IN ENGLISH SPEAKING
COUNTRIES AND NON-SPEAKING ENGLISH COUNTRIESPANEL A: WILCOXON AND T-TEST FOR FOG INDEX OF ANNUAL REPORTS IN ENGLISH SPEAKING
COUNTRIES AND NON-ENGLISH SPEAKING COUNTRIESEnglish

Non-English

Fog Index

20.25

19.29

Difference-0.29

p-value of Wilcoxon<2.2e-16***t-value-16.55p-value of t-value<2.2e-16***PANEL B: WILCOXON AND T-TEST FOR NUMBER OF WORDS BETWEEN ENGLISH AND NON-ENGLISHGROUPS
English

Non-English

Number of words

53,47778,629Difference

25,152p-value of Wilcoxon<2.2e-16***t-value

31.19

p-value of t-value<2.2e-16***

Note: ***, **, and * denote 1%, 5% and 10% significance levels, respectively

166. Conclusion
Undeniably, the crucial role of accounting information in making decisions of investors and theefficiency of the stock market leads to the increase in concerns about the readability of annual reports
in some recent years. Noticeably, most of the research concentrates on the US firms. Our research alongwith other related research provide complete views about the readability of all annual reports, not justof 10-K or 10-Q. In response, we find that 10-K and 20-F are still difficult-to-read for users. It requires
users to have good background and experience when reading firms’ annual reports. Even SEC and IASB
have requirements to improve the readability of annual reports, however, those reports are still socomplicated with high Fog Index and numerous words to understand due to the complex businessenvironment and requirements for preventing information asymmetry. Foreign firms located in othercountries rather than the US with the different legal environment and different accounting systems
mitigate the negative impacts of annual reports on the US investor by issuing extremely longer reports
as compared to those of US firms. This perhaps helps to improve the readability of annual reports;
therefore, the Fog Index of 20-F is just slightly higher than 10-K. Interestingly, regardless of dramaticdifferences in the length of reports issued by firms following different accounting standards systems:
IFRS, US-GAAP or national accounting standards, the differences in readability of annual reports are
insignificant. Meanwhile, native language is an important determinant of readability. Firms in English
speaking countries with linguistic advantage issue shorter but higher Fog Index reports in comparison
with reports of firms in other countries. Most importantly, firms try to reduce the Fog Index of annual
reports by writing longer reports but using shorter sentences. However, the annual reports of foreign
firms are still considered as complicated for understanding.
Despite identifying the readability of annual reports issued by foreign firms, our research hassome limitations and need further in-depth studies in the future. In terms of measurement, this researchuses two most popular measurements: Fog Index and the length of documents, however, using plain
English such as Bog Index or LM PE index should be added since this is the new tendency and receives
the higher appreciation of SEC. Secondly, this research uses the indirect comparison with 10-K via priorresearch. It will be more valuable if using the direct comparison with annual reports of US firms. Finally,
it is necessary to do further research about the relationship between the readability of 20-F and othervariables such as stock reactions or analysts’ perceptions or management’s behaviors.

17

APPENDIX 1: THE NUMBER OF OBSERVATIONS OVER 10 YEARS FROM 2004 TO 2013
Year

Number of observations

Percentage

2004

616

0.123

2005

574

0.114

2006

543

0.108

2007

503

0.100

2008

478

0.095

2009

462

0.092

2010

476

0.095

2011

464

0.093

2012

452

0.090

2013

446

0.089

APPENDIX 2: CLEANING THE RAW TEXT FILES OF 20-F
To clean the raw text files downloaded from EDGAR, we follow the instructions mentioned by Li(2008), Loughran and McDonald (2014a; 2014b) and Bonsall et al (2017). We use packages File::Slurp;
HTML::format text; HTML::TreeBuilder; HTML::Entities;Text::Unidecode to clean the raw 20-F files:
1. Removing format design in the raw files.
2. Removing all content between <XBRL> and </XBRL> tags.
3. Removing all tables with more than 15% numeric characters.
4. Removing all markup tags
5. Removing other textual expressions such as newline characters, the token or underscorecharacters…

18

APPENDIX 3: AVERAGE WORDS PER SENTENCE AND PERCENTAGE OF COMPLEX WORDS BYFISCAL YEAR
Year

Average words per sentence

Percentage of complex words

2004

22.04

25.21

2005

22.72

25.25

2006

23.25

25.29

2007

24.63

25.28

2008

24.55

25.46

2009

25.69

25.44

2010

25.74

25.39

2011

24.72

25.41

2012

24.29

25.38

2013

24.23

25.36

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