Demand for a Financial Information Disclosure Index: Empirical Study for China, Ecuador, Germany and the USA

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The rising demand for a financial information disclosure index: An empirical study for China, Ecuador, Germany and the United States of America

Abstract

This paper uses an empirical study to examine the level of voluntary information disclosure in listed public companies from China, Ecuador, Germany and the United States of America based on a quantitative proxy of financial and non-financial data. The index was built with 61 items, classified in 5 dimensions. Size, profitability, leverage, liquidity and industry as independent variables were evaluated in order to establish the determinants of online financial information disclosure in the selected countries. Mainly, size had a direct positive relationship with online financial disclosure in Germany and China, in line with the majority of previous studies. However, all the variables proved to be insignificant in the case of the United States, given a broad approach on financial disclosure, and Ecuador, given a limited disclosure of information, which can be attributed to a lack of pressure from interest groups.

Key words: Online financial disclosure; determinants; China; Ecuador; Germany; United States

Introduction

The online financial reporting system plays a key role in the process of economic development. It’s low cost, wide reach, high frequency and speed allows to satisfy the increasing demand for timeliness and transparency from stakeholders around the world; however, Internet reporting varies across companies and across countries. Therefore, it is necessary to keep updating the current state of internet financial reporting (IFR) in light of the continuous regulatory changes in the financial reporting environment of every country. Full disclosure about transparency and accountability is always considered pivotal, and during the last twenty years, Internet has been massively used as a tool for disclosing voluntary information (Puspitaningrum & Atmini, 2012). Indeed, IFR emphasizes the disclosure of a company’s financial information and the publication of the latest relevant information in order to improve efficiency and effectiveness for all stakeholders in the market.

Therefore, this study employs quantitative methods to develop an index that will better explain the practices of IFR in the selected countries, as well as the motivations underlying them, with the help of a qualitative approach. This study identifies the following research questions:

  • Are there any significant differences between the level of online financial information disclosure among the countries?
  • What are the motivations for online financial disclosure?
  • What impact does a financial information disclosure index have on the selected companies and countries?

Internet has been pondered crucial for the disclosure of corporate related information, and this agenda is widely discussed across the globe in-order to promote growth and transparency, while technology keeps enhancing the possibility for firms to disseminate their information on their websites. Internet has supplemented and modified the conventional approach for stakeholders, investors and firms to communicate information. Moreover, it offers information accessibility to a wide variety of users (Khan, 2015).

In today’s global society, corporate information is increasingly being disclosed online as most of the Western Nations have initiated the implementation of IFRS. A company website has become a platform or station where most of a firms’ significant information is being disseminated, and not only limited to annual reports and memorandums (Khan, 2015).  The decision of the governments in developing nations to implement IFRS despite the cost can be interpreted as an effort from the governments to increase transparence and information disclosure, with studies documenting information disclosure improvement in Asia and foreign direct investment development in transition economies and European settings, given the reduction of perceived risk  (Houqe & Monem, 2016; Pawsey, 2017).

As mandatory disclosure regulations change, policies and levels of voluntary disclosure also might change. The main research question of this study is whether the recent developments in the financial and regulatory environment of China, Ecuador, Germany and the United States have brought changes regarding the motivations for voluntary financial information disclosure.  This gives an opportunity to implement an empirical comparative analysis of the current determinants for online financial disclosure using selected factors of study.

This study contributes in understanding the situation and the importance of investor relationships in China, Ecuador, Germany and the United States of America. This might help in the analysis and development of investor relationships in these countries and in countries that are experiencing similar economic changes.  These nations not only represent different economic scenarios but also the implementation of the international accounting reporting norms at different stages.

Research Background

Around the world, the adoption of international financial reporting standards has been propagated from one country to another. Following the adoption by December 2015 of IFRS by 131 jurisdictions around the world, including a majority of developing countries, many studies have been developed to determine the possible implications and consequences, especially in developing countries, characterized by widespread corruption (Zehri & Chouaibi, 2013; Hoque & Memon 2014).

Previous research regarding China, Ecuador, Germany and the United States of America accounting systems, corporate and financial reporting online, as well as comparative analysis with other countries has provided information to establish a background on the current situation. Studies on corporate reporting on Internet date back to 1996 and professional bodies, such as the International Accounting Standards Committee (IASC) and the Financial Accounting Standards Board (FASB) have also performed studies on aspects such as the formats used for annual reports online, press releases and the availability of real time quotes and press releases. However, the dimensions used to represent Internet Financial Reporting (IFR) are inconsistent among the studies. These differences contribute to the variations in the findings and thus unable to clearly explain factors influencing the current behavior of IFR in countries.

Corporate scandals as a result of a financial information disclosure problem, such as the Enron Scandal and WorldCom, and the global financial crisis of 2007-2008 have raised the need of increased transparency and financial information disclosure to reduce information asymmetries in the market; as well as the need for updated information regarding transparency and the current regulatory environment in the selected markets.

Many countries seek to attract foreign direct investments (FDI) since they tend to generate net gains and improve economic growth, with benefits including the diffusion of technology in many developing countries.  FDI can be influenced by institutional quality, human and natural resources, capital control liberalization and the local regulations for foreign investment. However, if a country does not have a strong reputation to attract capital, based on the investors’ confidence on the level of disclosure, capital will find a different market (Desbordes & Wei, 2017).

Literature Review

Online Financial Disclosure Information

For the purpose of this research, online information disclosure is referred as the information that listed companies publish on their website for the investors’ knowledge.  Information can be disclosed online in a number of ways by the companies that participate in the stock market, mainly by periodic reports, letters to shareholders, meeting reports, press releases or stock markets announcements.

Transparency and disclosure indices have been analyzed for developed and emerging countries. Regarding emerging countries, (Patel, Balic, & Bwakira, 2002) analyzed transparency and disclosure scores, as a percentage of attributes present in annual reports, for 19 emerging countries. On the topic of Asian markets and Latin America markets, the authors found a significant difference, with the former establishing a widening gap in transparency and disclosure, attributable to currency, banking and equity market crises. However, the results exhibit a low level of information disclosure among all emerging markets.

Empirical tests of the determinants of Financial Disclosure have been carried out in several countries and the results are conflicting.  Most authors have used firm size, profitability, liquidity, leverage, sector, stock market listing, market risk, analysts ratios, forecast ratios, capital ownership, as independent variables  (Debreceny, Gray & Rahman, 2002; Marston and Polei, 2014; De La Bruslerie & Gabteni, 2014; Ali, 2008; Andrikopoulos, Merika, & Merikas, 2016; Kumar, 2013;  Puspitaningrum & Atmini, 2012; Hernández Madrigal, Aibar Guzmán, & Aibar Guzmán, 2015; Gómez Sala, Iñiguez Sánchez, & Poveda Fuentes, 2006; Bolsón, 2004). For the purpose of this study, the variables used in literature as control variables will be used: firm size, profitability, liquidity, leverage and industry.

Firm Size and Financial Information Disclosure

It is naturally assumed that bigger companies will disclose more information than smaller firms. In previous studies company size has been mainly measured as total assets of a company on a given year or as the value for market capitalization on a year. Including some exceptions, overall, authors support a positive relationship between disclosure and size. Among the arguments, larger firms have more complex management information systems than smaller firms to handle their product portfolio and distribution networks, as well as, for internal analysis; therefore, disclosure costs are lower. Larger firms, as well, handle the requirements of a network of suppliers, and analyst, as they tend to use capital markets for external financing, and extensive financial information increases the marketability of their securities. Studies have established an increase in the potential benefits of disclosure with agency costs. Smaller firms, on the other hand, tend to consider information disclosure as a competitive disadvantage (Oyelere, et. al., 2003).

In more recent studies, authors have found a positive relationship between financial information disclosure and size in Kenya (Barako, Hancock, & Izan, 2006), in Slovenia, based on number on employees and income (Dolinšek, Tominc, & Ske, 2014), and in companies in leisure and travel industry listed in the London Stock Exchange (Andrikopoulos, Merika, & Merikas, 2016).

From this information, the first research hypothesis has been developed: H1 There is a direct relationship between the size of the company and online disclosure.

Profitability and Financial Information Disclosure

According to signaling theory, companies that present lower efficiency ratios disclose more information to reduce capital costs. Profitability is the measure of a company’s efficiency in generating profits. It can be measured by (1) gross profit margin; (2) return on assets, and (3) return on equity (Brigham & Houston, 2015).

Corporate profitability has been considered in a number of studies. It has been argued that the disclosure of profitability information has been used as support for the management activities and compensation. However, Oyelere et. al. (2003) has found conflicting results in the range of positive, neutral and no relationship, and a warning that performance serves as a proxy for information asymmetries. Among the reasons for companies to disclose information based on profitability, authors have stated that competitive reasons might lead to a reduced level of disclosure for more profitable companies, and that less profitable companies have a tendency to disclose more to avoid legal suits  (Gómez Sala, Iñiguez Sánchez, & Poveda Fuentes, 2006).

Wang, Sewon, & Claiborne (2008) found the level of disclosure of listed Chinese companies  to be positive related to the firm’s performance measured by return on equity. According to the authors, the firms’ earnings present an incentive to supply more information so managers can signal quality in the market and differenciate their stocks, based on the investor’s fixation on short-term returns. Gómez Sala, Iñiguez Sánchez, & Poveda Fuentes (2006) also found a positive and significant relationship between profitability and information disclosure, measured by return on assets and return on equity.

Authors have found no relatinship between profitability and financial information disclosure in Slovenia (Dolinšek, Tominc, & Ske, 2014) and in Kenya (Barako, Hancock, & Izan, 2006).

From this information, the second research hypothesis has been developed: H2 There is a positive direct relationship between performance and online financial disclosure.

Liquidity and Financial Disclosure

Liquidity is a financial measure of the capacity of an asset to turn into cash or, in other words, the capacity of a firm to fulfill its short-term legal obligations. This indicator has been used as possible determinant for online voluntary information disclosure regarding financial information, as well as environmental and social information. There are several methods to measure a company’s liquidity on a given period of time based on a company’s current assets and current liabilities (Brigham & Houston, 2015).

Regarding financial information disclosure, Barako, Hancock, & Izan (2006) in Kenya and other scholars found no significant relationship between liquidity and information disclosure.

From this information, the third research hypothesis has been developed: H3 There is a positive direct relationship between liquidity and online financial disclosure.

Debt on Financial Disclosure

Financial leverage as a measure of the ability of a company to finance its assets with long-term debt, can be evaluated through different ratios, mainly, through (1) debt ratio, (2) debt-to-equity ratio and (3) interest coverage (Brigham & Houston, 2015).

The relationship between leverage and disclosure has been explained by the agency theory, since as leverage increases there is transference of wealth, resulting in an incentive to increase the level of monitoring and subsequently the level of disclosure. However, most studies have conflicting results, establishing a positive relationship or no relationship at all (Oyelere, et. al., 2003).  Moreover, Wang, Sewon, & Claiborne (2008) found no evidence on the relationship between levels of diclosure and leverage in the Chinese context. For the purpose of this study, the debt ratio has been used to measure the firms’ financial leverage.

From this information, the forth research hypothesis has been developed: H4 There is a positive direct relationship between debt and online financial disclosure.

Industry on Financial Disclosure

Studies have argued the differences in disclosure levels to be related with the use of specific business segments to analyze information disclosure. Some authors construct indices for a specific industry, including related relevant information, and other exclude specific industry sectors, mainly the financial industry, given the fact that these companies are subjected a different set of rules and therefore the results might not reflect the real situation of the market if included.  In some sectors for example, the item “Information regarding research and development (R&D) projects” are not relevant or not generally disclosed. “Advertisement, retailing and entertainment”, as well are not significant or do not have a clear meaning for a specific company (De La Bruslerie & Gabteni, 2014). The results regarding the relationship between industry and financial disclosure of information, therefore, depends on the specificity of the study. This study presents an overview of the current situation of the markets and therefore, a specific sector hasn´t been identified or excluded.

Patel, Balic, & Bwakira (2002) found no significant differences in disclosure scores among economic sectors in emerging markets, and the variation is explained by ownership structures. Gómez Sala, Iñiguez Sánchez, & Poveda Fuentes (2006) on the other hand found no significant relationship between the level of financial disclosure and companies belonging to the manufacturing sector.

Bonsón (2004) found that online information disclosure is positive related to the sector of the company, since companies from the same group tend to adopt similar practices in order to improve its corporate image.

From this information, the fifth research hypothesis has been developed: H5 There is no relationship between debt and online financial disclosure.

Determinants of Corporate Social Responsibility (CSR) Disclosure

Ali, et.al (2017) presented a report on CRS disclosure in developed and developing countries based on 76 empirical research studies and a survey. According to the authors findings, company size, industry sector, profitability and corporate governance mechanisms have a direct effect on CSR disclosure; however, there were differences between developed and developing countries based on the importance placed on the concerns of the stakeholders and the public pressure to disclose information. In the case of developed countries, including the United States, studies have found a positive relationship between size and industry, and CRS disclosure. However, regarding performance, the results were conflicting. According to Ali, Frynas, & Mahmood (2017), the differences between the mentioned variables, among others, resulted from different national contextual factors. Moreover, studies have found substantial differences between the United States and Europe regarding the effects of national institutional context.

Studies in developing countries on the other hand, including China, found that size and industry (excluding a few studies) have a positive relationship with CSR disclosure.  Furthermore, the results regarding corporate financial performance presented significant as well as insignificant positive relationships with disclosure. Additional to national contextual factors, the absence of reporting regulations and implementation has been argued as a main reason for non-disclosure, having it been influenced by external stakeholders and international regulatory institutions.

The authors conclude that highly visible companies attached more importance in CSR disclosure given the concerns of the stakeholder and the pressures from the media, NGOs and regulatory institutions; however, developing countries receive little pressure to disclose information.

The relationship between corporate environmental information disclosure with size and industry has also been assessed in the case of China. Findings include a positive relationship between size and industry, given the fact that some sectors and more environmental sensitive than others (Zeng, Xu, Dong, & Tam, 2010).

Research Methodology

Measures of Online Disclosure Index

Several measures to obtain an online information disclosure index have been developed. This study measure of online financial disclosure indices has been based on the research on by Aly (2008) and considers all the required information for investors and the general public, taking into account that corporate social responsibility and sustainability play a key role in current business development. Discrete values were used in order to determine the index. The method assigns three values ―0, 0.5 or 1― according to the degree of specificity of the information, whether the information is not present, limited or ambiguous, or complete. These variables were organized according to its common characteristics in specific dimensions, as shown in Table 1. The websites of the firms were accessed within the period of July-December 2016 and then the information was corroborated until February 2017.

Table 1. Dimensions and Dependant Variables

Item Accounting and Financial Information Item Corporate Governance Information
1 Quarterly Report of Current Year 1 Background or History of the Organization
2 Quarterly Report of Past Years 2 Ownership structure
3 Semi Annual Report of Current Year 3 Composition of board of directors
4 Semi Annual Report of Past Year 4 Company Objectives
5 Audit Review Report /Report of the Supervisory Board 5 Disclosure of Risk or Risk Management
6 Current Year Financial Statements 6 Press Releases or News
7 Historical Financial Statements 7 Past Year Resolutions of  Shareholders’ Meeting
8 Current Year Annual Reports 8 Financial Calendar
9 Annual Reports of Past Years 9 A code of ethics
10 Management Report for the Current Year 10 Governance Documents
11 CEO’s Letter to Shareholders 11 Committee Charters
12 Auditor’s Report of Current Year 12 Guidelines and By-laws
13 Auditor’s Report of Past Years 13 Public Policy Advocacy
14 Notes to Financial Statements Item Corporate Social Responsibility and Human Resources Information
15 Note on Language Translation and Audit 1 Environmental Awareness Information
16 Summary of Financial Data Over a Period of at Least 4 Years 2 Social Awareness Information
17 Segmental Reporting by Line of Business (Revenue) 3 Product Quality
18 Segmental Reporting by Region (Revenue) 4 Supplier Responsibility
19 Financial Analysis 5 Employee Profile
20 Earnings or Sales Forecast 6 Professional Development and Training Activities
21 Historical Share Prices 7 Sustainability Key Figures
22 Current Share Prices Item Contact Details to Investor Relations
23 Dividend History 1 Mailing List
24 Auditors Name 2 Contact Us
25 Key Financial Ratios 3 E-mail to investor Relations
26 Reconciliation with IFRS 4 One click to Investor Relations
27 Debt Information 5 Investor Relations Postal Address
28 Fixed Income Information 6 Investor Relations Phone Number
29 Credit Rating 7 Frequently Asked Questions
Item Material Processable Formats
1 Financial Data in Processable Format
2 Data in PDF Format
3 Financial Data in HTML
4 Graphics or Diagrams
5 Sound Files
6 Video Files

Additionally to these items, factors unique to the evaluated countries have been taken into account to amount a total of 63 items, as follows:

  • China: Past Year Resolutions of the Board of Directors (Corporate Governance Information).
  • Ecuador: Excerpts of Financial Reports or Statements (Accounting and Financial Information).
  • Germany: Compliance to de German Commercial Code (Corporate Governance Information).
  • United States: SEC Filings (Accounting and Financial Information).

In the case of the item Key Financial Ratios in the Accounting and Financial Information Section, a value of 1 was assigned in case the company presented information for at least 10 relevant financial ratios, including return on assets (ROA) and return on equity (ROE), as well as a leverage ratio. With all the information collected, the determined index represents the company’s degree of financial disclosure. A non-weighted index has been the most used on previous research and seems to be the most objective, since there is no particular focus group in this study.

Sample Size

In order to obtain a general overview of the market, an initial sample of 50 companies per country was reduced to 30 companies per country after eliminating, all those companies that did not have a website or that did not have an English website (except for Ecuador). Market capitalization appeared to be the best objective criterion for the purposes of this study, as it is based on real share prices, and the biggest companies often have the resources to run a dynamic website and are more likely to disclose their financial information on the Internet. The data used to determine the top companies for China, Germany and the USA was obtained from the Financial Times FT500 official report on company ranking based on the market value of the companies for 2013-2015, after eliminating mergers, unlisted and bankrupt companies. The ranking for the companies in Ecuador was determined based on the available information disclosed by the Superintendence of Companies of Ecuador for 2015.

Independent Variables

A series of hypothesis were evaluated in order to understand the factors that determine the companies’ disclosure pattern. Table 2 presents the expected signs of association with the extent of voluntary internet corporate reporting and disclosure.

Table 2. Independent Variables, their symbols, proxies and expected relation with financial disclosure

Variables Symbols Proxies Expected sign
Firm Size Size Log of total assets +
Profitability ROE and ROA H3a: EBIT/Total assets

H3b:EBIT/ Total equity

+
Liquidity CR H4: Current assets/ current liabilties +
Leverage ratio LEV H5: Total Debt/Total Assets +
Industrial Sector INDUSTRY 1: Industrial, 2: Consumer Goods, 3: Financial, 4: Health Care, 5: Basic Materials, 6: Consumer Services, 7: Telecommunications, 8: Technology, 9: Oil &Gas, 10: Utilities. +/-

Econometric Model

 

The following the econometric model which will be used in this study.

FD=β0+β1SIZE+β2ROA+β3jROE+β4jLEV+β5jCR+β6jINDUSTRY   +εt

(1)

In the above equation, FD represents the financial disclosure index, the

β0is the constant term, the SIZE is the size of the firm measured as log of total assets, ROE is the return on equity, ROA is the return on assets, LEV is the leverage and is measured as total liabities divided by total assets, CR represents the current ratio and is measured as total current assets divided by total current liabilities, INDUSTRY is industry dummy variable.

Results and Findings

Descriptive Statistics

Research results for Germany indicate that generally companies that are positioned among the top ranking companies according to market capitalization were financial institutions.  According to the results in Table 3 for descriptive statistics, the mean value 43.3 for financial information disclosure suggests that these companies have mostly disclosed their information, although this is not an indication of quality of information. The mean value for the variable CR was 0.614, for the variable Industry was 3.533, for the variable Lev was 0.721. For the efficiency variables ROA and ROE, the mean values were 14.376 and 0.575, respectively. For the variable TA, the mean value was found to be 14.361, while the Standard deviation value was 5.73 for the financial disclosure index, which suggests volatility in data for all the variables except CR, Lev, ROE and TA in our analysis.

Table 3. Descriptive Statistics. Germany

FD CR INDUSTRY LEV ROA ROE TA
 Mean  43.43333  0.614739  3.533333  0.721993  14.37667  0.575072  14.36133
 Median  43.50000  0.066086  3.000000  0.650000  15.98500  0.000000  14.30656
 Maximum  52.00000  3.240000  9.000000  1.830000  26.42000  3.240000  16.91604
 Minimum  31.50000  0.000000  1.000000  0.000000  0.890000  0.000000  11.36560
 Std. Dev.  5.739658  0.866006  2.208656  0.525124  5.326724  0.866002  1.467656
 Skewness -0.354305  1.670933  1.507496  0.492700 -0.563584  1.787996  0.067440
 Kurtosis  1.900537  5.555556  4.512385  2.178321  3.728251  5.877948  2.113504
 Jarque-Bera  2.138684  22.12366  14.22187  2.057710  2.251071  26.33789  1.005084
 Probability  0.343234  0.000016  0.000816  0.357416  0.324479  0.000002  0.604991
 Sum  1303.000  18.44217  106.0000  21.65980  431.3000  17.25217  430.8398
 Sum Sq. Dev.  955.3667  21.74905  141.4667  7.996900  822.8457  21.74882  62.46639
 Observations  30  30  30  30  30  30  30

Table 4.  Correlation between variables.  Germany

FD CR INDUSTRY LEV ROA ROE TA
FD 1
CR -0.0599 1
INDUSTRY -0.0664 -0.1004 1
LEV -0.06206 -0.2592 -0.3134 1
ROA -0.2532 0.12139 -0.5590 0.2817 1
ROE -0.0067 0.9566 -0.1409 -0.3323 0.1294 1
TA 0.46216 -0.44078 -0.0832 0.2855 0.0718 -0.4453 1

From Table 4 of the results for Germany, we can extract that financial disclosure was found to be inversely associated with the current ratio, industry, leverage, return on assets and return on equity, except for total assets, which has been found to be positively associated with FD; whereas industry was negatively associated with CR as the obtained value was -0.1004. The variable leverage, on the other hand, was found to be negatively associated with industry and CR. According to the results, ROA is positively associated with CR and Lev but shows an inverse relationship with industry. ROE has been found to be negatively associated with Industry and LEV but positively associated with CR and ROA. TA also has a positive association with FD, LEV, ROA and a negative association with CR and ROE.

Table 5. Regression Analysis. Germany

Variable Coefficient Std. Error t-Statistic Prob.
CR -2.835012 3.817672 -0.742602 0.4656
INDUSTRY -0.567389 0.531400 -1.067724 0.2972
LEV -0.814154 2.120955 -0.383862 0.7048
ROA -0.411006 0.228533 -1.798454 0.0858
ROE 4.139623 3.965896 1.043805 0.3079
TA 2.156233 0.767709 2.808658 0.0102
C 20.47472 11.73553 1.744678 0.0950
R-squared 0.344932     Mean dependent var 43.84483
Adjusted R-squared 0.166277     S.D. dependent var 5.372056
S.E. of regression 4.905139     Akaike info criterion 6.224949
Sum squared resid 529.3286     Schwarz criterion 6.554986
Log likelihood -83.26176     Hannan-Quinn criter. 6.328313
F-statistic 1.930719     Durbin-Watson stat 1.257122
Prob(F-statistic) 0.120540

From Table 5, we can conclude that financial disclosure index is found to be negatively related with CR as the value obtained was -2.83, where FD has also an inverse relationship with industry, leverage, ROA. However, FD as the dependant variable is positively related with ROE and TA. Nonetheless, all results are found insignificant except for the relationship between FD with TA at a level of significance of 10%. These results are consistent with previous studies considering German companies. The R squared, coefficient of determination, value is found to be 0.34, which is considered to be a modest value in social science empirical studies and as a result it shows goodness of fit for the regression model.

Regarding Germany, the mean value for FD is 43.3, which is considerably higher than the value obtained for China, which is 26.699.  This indicates that voluntary disclosure of financial information is lower for China in comparison with Germany, where as Standard deviation value is found to be 13.946, which in turn shows more volatility among variables, with the standard deviation value for ROE and ROA being the highest.

Table 6: Descriptive statistics China

FD CR INDUSTRY LEV ROA ROE TA
 Mean  26.69984  1.136667  3.633333  0.646667  3.661333  10.29533  10.95984
 Median  29.35996  1.090000  3.000000  0.490000  3.690000  13.51000  10.58816
 Maximum  43.63928  2.600000  10.00000  2.550000  10.00000  23.25000  14.30356
 Minimum -26.22874  0.000000  1.000000  0.000000 -5.850000 -34.13000  8.835356
 Std. Dev.  13.94630  0.640045  2.385059  0.584698  3.672727  11.23841  1.322163
 Skewness -1.999453  0.070807  1.002844  1.594972 -0.294562 -2.331523  0.671744
 Kurtosis  8.107077  3.114357  3.232564  5.573820  2.963935  9.458473  2.962703
 Jarque-Bera  52.59186  0.041415  5.096088  21.00037  0.435460  79.31984  2.257936
 Probability  0.000000  0.979506  0.078235  0.000028  0.804343  0.000000  0.323367
 Sum  800.9951  34.10000  109.0000  19.40000  109.8400  308.8600  328.7951
 Sum Sq. Dev.  5640.479  11.88007  164.9667  9.914267  391.1787  3662.753  50.69534
 Observations  30  30  30  30  30  30  30

Table 7. Correlation between variables. China

FD CR INDUSTRY LEV ROA ROE TA
FD 1
CR 0.3595 1
INDUSTRY -0.3839 0.2072 1
LEV -0.2384 -0.1639 0.0052 1
ROA 0.8495 0.5995 -0.1888 -0.3508 1
ROE 0.9857 0.2801 -0.4180 -0.2812 0.7720 1
TA -0.2591 -0.6654 -0.0741 0.4864 -0.5146 -0.2583 1

From Table 7, we conclude that FD has a positive association with variables such as CR, ROA and ROE but it has higher correlation with ROA and ROE, where as the values obtained are 0.8495 and 0.9857, respectively. This study has found FD to have inverse association with industry, Lev and TA. Industry is positively associated with CR, and Lev is positively associated with industry, while ROA is positively associated with CR but negatively associated with industry and Lev. ROE is positively associated with CR and ROA, and negatively associated with leverage and industry. Finally, TA is mostly negatively associated with all the variables except Lev and has shown higher correlation with CR.

Table 8. Regression analysis. China

Variable Coefficient Std. Error t-Statistic Prob.
CR 2.067500 6.490014 1.541713 0.0000
INDUSTRY -4.710005 1.243414 -0.137921 0.8915
LEV 2.050000 5.224314 1.923813 0.0000
ROA 2.067000 1.402114 7.172413 0.0000
ROE 1.045000 3.973715 2.525414 0.0000
TA 4.470700 2.945914 3.407813 0.0000
C -1.580882 3.503413 -4.511356 0.0002
R-squared 0.376000     Mean dependent var 26.69984
Adjusted R-squared 0.178000     S.D. dependent var 13.94630
S.E. of regression 4.330013     Sum squared resid 4.066825
F-statistic 5.335628     Durbin-Watson stat 1.271155
Prob(F-statistic) 0.194530

From the results we can conclude that FD has a direct relationship with CR, Lev, ROA, ROE, and TA. The results are all significant. Industry, on the other hand, is found to be negatively related; however, the result is not significant. The R squared value is 0.37, which again shows goodness of fit for the model used in this analysis.

Table 9. Descriptive statistics. USA

FD CR INDUSTRY LEV ROA ROE TA
 Mean  43.10153  1.386897  5.344828  0.893103  7.368966  22.11448  12.23233
 Median  31.27282  1.110000  6.000000  0.540000  7.000000  14.08000  12.02319
 Maximum  151.3275  4.670000  9.000000  6.310000  20.45000  124.4800  14.67065
 Minimum  9.717640  0.000000  1.000000  0.000000 -1.080000 -5.420000  10.43282
 Std. Dev.  31.57093  1.160493  2.496796  1.188880  5.746284  28.18670  1.154391
 Skewness  2.191850  1.192399 -0.092845  3.429584  0.662143  2.530094  0.722385
 Kurtosis  7.345055  4.240118  1.564678  16.10282  2.728987  8.893344  2.883698
 Jarque-Bera  46.03307  8.730397  2.531011  264.3013  2.207844  72.90723  2.538569
 Probability  0.000000  0.012712  0.282097  0.000000  0.331568  0.000000  0.281033
 Sum  1249.944  40.22000  155.0000  25.90000  213.7000  641.3200  354.7376
 Sum Sq. Dev.  27908.26  37.70882  174.5517  39.57622  924.5539  22245.73  37.31331
 Observations  29  29  29  29  29  29  29

China is found to have a lower mean value (26.699) of FD in comparison with other countries, such as USA, which presents a value of 43.1, and Germany, which has a value of 43.3 for the financial information disclosure index. The standard deviation value is 31.57, which shows high FD volatility except for the variables TA, LEV, CR. The results are very elaborative and comprehensive in terms of financial disclosure index, as countries such as Germany and USA, which are developed nations, have higher mean values indicating a higher ratio for volunteer financial information disclosure index. Therefore, we can anticipate that companies in developed nations generally adopt a tendency for higher information disclosure as a trend for various reasons to attract investors by reducing information asymmetries.

Table 10. Correlation between variables. USA

FD CR INDUSTRY LEV ROA ROE TA
FD 1
CR 0.1456 1
INDUSTRY -0.0730 0.4364 1
LEV -0.1711 -0.2572 0.0536 1
ROA 0.3144 0.3788 0.2286 -0.1183 1
ROE -0.0241 -0.0364 0.3279 0.7936 0.3599 1
TA -0.0729 -0.5128 -0.2092 0.1404 -0.6533 -0.1801 1

From the above correlations table (8) for the USA, it is deduced that FD has inverse association with Industry, LEV, ROE and TA, but FD is found to have positive association with CR and ROA. Industry has a positive and mild correlation with CR. LEV presents an inverse association with CR and a positive association with Industry, but the correlation among these two independent variables is not strong. ROA generally enjoys a positive association with CR and Industry except with LEV, with which an inverse association and no multi-colinearity is found in this regard. ROE is found to have a positive association with Industry, LEV and ROA, except CR. The results do not indicate a strong correlation among independent variables with the exception of LEV. Lastly, TA is found to have a negative association with ROE, ROA, Industry, and CR, except LEV. As a result, almost all independent variables present mild or weak relationships among each other.

Table 11. Regression Analysis. USA

Variable Coefficient Std. Error t-Statistic Prob.
CR 5.032071 7.045584 0.714216 0.4826
INDUSTRY -2.607770 3.186008 -0.818507 0.4218
LEV -4.151313 13.01067 -0.319070 0.7527
ROA 2.463723 1.920924 1.282572 0.2130
ROE 0.076180 0.616927 0.123484 0.9028
TA 8.367579 7.536321 1.110300 0.2789
C -68.42658 102.3760 -0.668385 0.5108
R-squared 0.185796     Mean dependent var 43.10153
Adjusted R-squared -0.036260     S.D. dependent var 31.57093
S.E. of regression 32.13821     Akaike info criterion 9.984474
Sum squared resid 22723.02     Schwarz criterion 10.31451
Log likelihood -137.7749     Hannan-Quinn criter. 10.08784
F-statistic 0.836708     Durbin-Watson stat 2.113126
Prob(F-statistic) 0.554815

From Table 11, we conclude that the financial disclosure index has a positive relationship with the independent variables, including CR, ROA, ROE and TA, whereas there is an inverse relationship with industry, and LEV. However, from the probability value, the results are found to be insignificant. Moreover, even though the R squared value is low, the results are justified with previous empirical studies on financial information disclosure indexes.
Table 12: Descriptive statistics Ecuador

FD CR INDUSTRY LEV ROA ROE TA
 Mean  8.767857  16.88926  3.821429  0.352687  0.250828  1.939233  17.29880
 Median  5.250000  1.671698  2.000000  0.209780  0.086874  0.127373  17.48689
 Maximum  33.00000  132.2054  7.000000  1.551246  1.192071  14.59545  21.01817
 Minimum  0.000000  0.000000  1.000000  0.000000  0.000000  0.000000  11.86553
 Std. Dev.  9.570868  34.57112  2.261654  0.397704  0.355717  3.852512  2.155254
 Skewness  1.260454  2.691649  0.342803  1.792032  1.546048  1.998977 -0.545381
 Kurtosis  3.499019  9.301635  1.402896  5.323431  4.078785  5.877963  2.890560
 Jarque-Bera  7.704667  80.13894  3.524265  21.28449  12.51231  28.31069  1.402027
 Probability  0.021230  0.000000  0.171678  0.000024  0.001919  0.000001  0.496082
 Sum  245.5000  472.8994  107.0000  9.875231  7.023196  54.29854  484.3665
 Sum Sq. Dev.  2473.241  32269.37  138.1071  4.270558  3.416435  400.7300  125.4182
 Observations  28  28  28  28  28  28  28

From Table 10 we can retrieve the financial disclosure index information for Ecuador, which has a mean value of 8.767. This is the lowest value obtained in comparison with developed nations, including USA and Germany, and it is even lower than the value obtained for a developing country, such as China. Therefore, we can rationalize from the descriptive analysis that those nations which are neither developed nor developing could have a low tendency for disclosure of financial information on their websites. Regarding the standard deviation value, 9.570 is also on the higher side of the scale, indicating a higher data variation for Ecuador.

Table 13: Correlation Analysis Ecuador

FD CR INDUSTRY LEV ROA ROE TA
FD 1
CR 0.1176 1
INDUSTRY 0.3145 0.4131 1
LEV 0.1322 -0.0222 0.1162 1
ROA 0.6964 0.3298 0.6513 0.3070 1
ROE 0.6743 0.2118 0.6709 0.2859 0.9380 1
TA -0.2547 -0.4819 -0.7305 -0.1700 -0.6084 -0.5778 1

From Table 13, we can extract the results that FD has mostly positive associations with all independent variables, except TA, which is found to be inversely associated with the financial disclosure information index, presenting a value of -0.2547. Industry is also positively associated with CR; whereas LEV is inversely associated with CR, with a value of -0.0222, and directly associated with industry, with a value of 0.1162. ROA has been found to have direct associations with all the independent variables, except TA. ROE also indicates positive associations but an exceptionally stronger relationship with ROA, showing multi-colinearity. TA has shown negative associations with all independent variables and no multi-colinearity is found in this regard, except with the variable industry, where a value of -0.7305 has been found showing high correlation or multi-colinearity.

Table 14. Regression Analysis. Ecuador

Variable Coefficient Std. Error t-Statistic Prob.
CR -0.005388 0.050141 -0.107461 0.9154
INDUSTRY -0.756186 1.033671 -0.731554 0.4725
LEV -2.691513 3.741194 -0.719426 0.4798
ROA 19.27991 12.51084 1.541056 0.1382
ROE 0.638308 1.161675 0.549472 0.5885
TA 0.758662 1.022589 0.741903 0.4664
C -6.499899 20.54225 -0.316416 0.7548
R-squared 0.555529     Mean dependent var 8.767857
Adjusted R-squared 0.428537     S.D. dependent var 9.570868
S.E. of regression 7.235112     Akaike info criterion 7.008087
Sum squared resid 1099.284     Schwarz criterion 7.341138
Log likelihood -91.11321     Hannan-Quinn criter. 7.109904
F-statistic 4.374531     Durbin-Watson stat 1.983084
Prob(F-statistic) 0.005109

In the case of Ecuador, the R-squared measure is 0.55, which discloses goodness of fit for the model and we can conclude that it is appropriate for this analysis. Table 12 indicates that the dependant variable, financial information disclosure index, has an inverse relationship with independent variables such as CR, LEV and Industry, while the rest of the variables, such as ROA, ROE and TA are found to be positively associated. However, all the results as shown in table are insignificant.

Conclusions

The purpose of this study was to provide information regarding the determinants of online financial disclosure online in order to obtain an overview of the differences between  reporting practices in developed, emerging and developing countries.  The results have determined that developed countries such as, Germany and the United States disclose a great amount of voluntary information in their websites, compared to China and Ecuador.

Regarding the presentation of the information, Germany’s companies have a more dynamic approach to present data online and the information available in English is more extensive compared to that of the other countries, especially regarding corporate social responsibility and sustainability. Companies in the USA present financial information mainly as it is required by the regulatory body, SEC. Moreover, the information available in English for Chinese companies is limited and mostly not updated.

This study employed a multivariable analysis to investigate the determinants of online financial information disclosure for the top 30 companies, based on market capitalization, in China, Ecuador, Germany and the United States of America. In particular, the effect of company size, profitability, liquidity, leverage and industry on IFR was investigated.

Regarding the United States, the results of the descriptive statistical analysis showed that all the variables did not have a significant relationship with IFR, similar to the results obtained for Germany, with the exception that company size continues to have a significant positive relationship with IFR, according to previous studies.

The results for China showed that IFR has a direct positive relationship with company size, profitability, leverage and liquidity. Industry proved to be a not significant variable in this study for all the countries.

Regarding Ecuador, all the variables proved to be insignificant given the limited disclosure of financial information online. This can be attributed to the lack of concerns for the stakeholders interest and little to none public pressure to disclose information (Ali, Frynas, & Mahmood, 2017), and exposes the need to reduce information asymmetries in the market by increasing voluntary information disclosure. 

Limitations

The sample size in this study was limited to 30 listed companies per country. Moreover, the inclusion of companies in the financial sector might have impacted the results, since these companies are regulated by different organisms and follow different sets of rules, that those of the remaining sectors. Additionally, data corresponding to only one period is insufficient to obtain conclusive results about the impact of the selected variables on online financial information disclosure. The use of different indices to measure financial disclosure is a barrier for comparison among studies. Most studies are adapted to the needs of the researches and the characteristics of the countries regarding disclosure policies and the level of adoption of IFRS. Among multinational studies, there are limitations associated with language, the format of presentation of the information and the selected items. As this study is mainly based on findings in English and Spanish language, the data collected and the results, therefore, are limited to availability of information in English, which might not be a real measure of all the voluntary information the company is disclosing to the local market; especially in the case of China, where the information available in English was very limited and not updated. 

Future Research

This research has provided a general view on the comparability of financial information disclosure in China, Ecuador, Germany and the United States. There are a number of possibilities for future research. This study can be duplicated for different periods and/or using more extensive data or variables. More exhausting research regarding establishing a marked difference between mandatory and voluntary financial information disclosure can be explored as well. Comparative studies with other countries in Latin America can also provide differences and similarities between countries of the same region. Similarly, focus groups or sectors can be selected in order to develop more specific results or a comparison between specific industries. 

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