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Amity Journal of Management Research 2(1), (10-19) ©2017 ADMAA Accounting for Derivative Financial Instruments: An Analysis of Disclosure Determinants Arjun Gope Iswar Chandra Vidyasagar College, Tripura, India Abstract Interpretation of financial statement becomes a challenging one when a firm uses derivative financial instruments because most derivative contracts are off-balance-sheet items, lacking in transparency and inconsistent accounting treatment.
International Financial Reporting Standards (IFRS) user countries follow different accounting standards like International Accounting Standards 32 & 39, and IFRS 7 & 13 for reporting and disclosing derivative transactions.

But the reporting and disclosure level vary from business to business.

The study reveals that the conformity level of disclosure on DFIs is related to the firm’s volume and the listing status of the said company.
The study has important implications to the management and the stakeholders of corporate firms.

Keywords: Derivative, Accounting Standard, IFRS, Disclosure JEL Classification: M4 Paper Classification: Research Paper Introduction Financial reporting is the medium through which accounting language is divulged.

Financial reporting of the business organization plays a significant function in the economic development of a nation.
This reporting of financial transactions many a times does not reveal the exact financial scenario that happens.

Lack of transparency due to loop holes of accounting guidelines and management’s intention mislead the investors’ interpretation.

Moreover some accounting areas have remained hazy for a long period of time.

Disclosure of Derivative Financial Instrument (DFIs) is one of the most important fields of financial reporting practice.
A derivative is simply a contract of a future transaction on an underlying asset at a present time.

On the other, an instrument whose value gets changed with the value of other asset is to be considered as a derivative instrument.

If the price of an underlying asset changes the value of derivative may also change.

That underlying can either be a financial or a non-financial item.

On the basis of underlying item the derivative can be termed as commodity derivative or financial derivative instrument.
But the type of derivative is not finite; the commonly used derivative instruments are Futures, Options and Swaps.

IFRS adopted companies follow the accounting standards IAS 32, IAS 39, IFRS 7 & IFRS 13 for the purpose of reporting and disclosing the transactions on DFIs.

In this regard, this 10 Volume 2 Issue 1 2017AJMRAmity Journal of Management ResearchADMAA paper tends to find whether the disclosure level of DFIs is influenced beyond the adoption of accounting standards.
Review of Literature Derivatives Derivative is used to manage and effectively transfer the risk arising in business firm.

According to Varma (2008), the dependence of the derivative’s value on other prices or variables makes it an excellent vehicle for transferring risk.

Vashishtha & Kumar (2010) pointed out that “derivatives provide an effective solution to the problem of risk caused by uncertainty and volatility in underlying asset”.
According to Stulz (2004) and Verma (2008), derivatives allow firms and individuals to hedge risks and to handle risks efficiently.

Bhuvaneswari et.

al.

(2007), Vashishtha & Kumar (2010), Prabhakara (2013) also considered derivative as a very important tool of risk management provided (Selvam and Rita, 2011) the derivative users understand the complexity of financial derivative instrument contracts.
The findings of Hwang (2002), Prabhakara (2013) & Gope (2014) reported that financial derivative instruments as risk management tools have gained an enormous position among all the financial instruments in many cases and use of derivatives to curve financial risks are increasing gradually in India both in terms of volume and number of contracts traded.
Greenspan (1997) had reassured that use of financial derivatives was expanding extraordinarily in many countries like India due to innovation and hence it could revolutionize the accounting landscape.

According to Ernst & Young (2006) financial transparency is the most important aspect of the initial stage when an investor considers an investment option.
Woods & Marginson (2004) opined that an institution’s financial risk exposure is hardly identified to prospective and existing investors due to poor financial reporting practices.

Strouhal, Bonaci & Matis (2010) are also in favour of saying that somehow financial reporting practices fail to provide a uniform and high level of information on DFI for all investors.

Leuz & Verrecchia (2000) reported that incomplete supply of information causes information asymmetry.
Mitra & Gope (2013) also pointed out that management requires to disclose whether the management uses the Derivative financial instruments to tackle the risk arises in the business or not.

Disclosure of Derivative Financial Instruments Welker (1995) & Leuz and Verrecchia (2000) opined high disclosure level reduces information asymmetry and becomes more cost effective.
Levine, Loayza & Beck (2000) stated that the disclosure of accounting information, for instance, helps reduce information asymmetry, sheds light on the volatility of stock returns and can also be an indicator for both domestic and foreign investors in making their choices.

Verechia (2001) reported that minimization of information asymmetry between stakeholders and management is the root of comprehensive theory of disclosure.
After adoption of SFAS 119, Edwards Jr. and Eller (1996) had analysed the selected US dealer banks’ annual reports and concluded that the depth of both the qualitative and the quantitative disclosures had improved.

According to Malaquias, Lemes (2013), due to lack of economic incentives in Brazil for firms to provide more disclosure in financial statements, the low level of disclosure is found in accounting reports.
Disclosure practices on the basis of IFRS 7 is analysed by Bischof (2009) from a sample of 171 banks of 28 different countries of Europe.

He found that the level of disclosure has significantly 11 Volume 2 Issue 1 2017AJMRAmity Journal of Management ResearchADMAA increased during the year of the standard’s first-time adoption.

This is due to both a more extensive description of accounting policies and a more elaborate disclosure of information about exposures to significant risks.
Chalmers and Godfrey (2000) concluded that to make the accounting information comparable and useful the firms need to disclose a detail report on DFIs where the companies are not in same line in terms of disclosure.

According to Strouhal, Bonaci & Matis (2010) the very low disclosure level of accounting information reported on derivatives is a cause of current financial crisis.
According to Lopes & Rodrigues (2007), some economic sectors can have greater institutional pressures for disclosure of information than others.

Hunziker (2013) revealed that in Switzerland, significant associations are found between the number/amount of market risk disclosures and company size.

Likewise a significant association is found between the number/amount of risk disclosures and the company’s risk proxies by the gearing ratio.
No association is found between the number/amount of risk disclosures and the company’s performance, however.

Lopes & Rodrigues (2007) identified four factors namely size of the firm, type of auditor, listing status of stock exchange and financial sector which accelerates the degree of disclosure.

Hypothesis Development Review of literature reveals asymmetrical views regarding the disclosure practices of DFIs in case of IFRS user companies.
Thus the hypotheses are developed as follows: H01: Larger listed companies have low level of disclosures on DFIs in comparison to smaller listed companies.

H02: Information on DFIs disclosure practices is not related to the type of industry the company belongs to.

H03: Information on DFIs disclosure practices is not related with firm’s leverage.

H04: Disclosure requirements on DFIs are not positively associated with firm’s profitability.
H05: The degree of disclosure on DFIs does not depend on the type of stock exchange where the company is listed.

Research Methodology Population and Sample Selection All disclosure data has been retrieved from a group of countries comprising United Kingdom, European Union, Canada, Germany and Switzerland.

The population of the study has been taken from the respective stock exchange websites.

Annual reports of 2,182 eligible companies are considered.
Some companies are eliminated that did not upload latest annual reports or not reported in English language or because of the partial uploading of the annual reports or the companies did not use Derivative financial instruments during the period.

Simple random sampling has been used to select a sample of 75 annual reports of companies.

Data Collection Data of the study has been sourced from the annual reports of selected companies.
With the objective of identifying level of disclosure regarding financial derivative instruments, content analysis technique is used.

This is done identifying the existence of disclosures required by IAS 32, IAS 39, IFRS 7 and IFRS 13 for DFIs.

12 Volume 2 Issue 1 2017AJMRAmity Journal of Management ResearchADMAA Disclosure Index According to Htay et al.

(2011) disclosure of information in the annual reports is highlighted as one of the important aspects of the good corporate governance.
Hassan & Marston (2010) opines that “a disclosure index could include mandatory items of information and/or voluntary items of information.

It can cover information reported in one or more disclosure vehicles such as corporate annual reports, interim reports, investor relation etc”.

The score of each selected company is calculated following the score index (Table-1) where total score is 19.
The score index is formed on the basis of existing accounting literature, and has two important features: Dichotomous: The score measured for each firm as: Where for each item the company would be allotted 1 if it is disclosed and 0 otherwise, so it is dichotomous in nature.

Unweighted: The total score is computed without allotting any over and underweight.
Then the disclosure score index is calculated for each company following the mentioned ‘financial derivative instruments disclosure score index table’ using content analysis technique: Table 1: Financial Derivative Instruments Disclosure Score Index Basic information: Types of DFI used Purposes of issuing or holding derivatives Categories of DF Asset/Liability Recognised and derecognized information: DFI recognised when and only when the entity becomes a party Derecognition (Removal) of DFI when part or all the asset do not qualify Measurement information: Initial measurement of DFI Subsequent measurement of DFI Fair value hierarchy and Valuation technique information: Fair value hierarchy used Valuation techniques used Hedge accounting information: Hedge effectiveness Type of Hedge Accounting adopted Qualitative & quantitative risk information: Firm’s financial risk management objective & policies Summary of quantitative data at the end of the reporting period Risk information Market risk Liquidity risk Credit risk Other Disclosures: Gains of DFI in income statement Gains of DFI in notes DFI in Balance Sheet Total Score 13 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 19 Volume 2 Issue 1 2017AJMRAmity Journal of Management ResearchADMAA Statistical Tool Finally the linear regression model is used to test whether the Disclosure Index is dependent on some other variables.
The model is used in which the dependent variable is the disclosure index (Disclosure Index) and the independent variables are Size of the company (Size), type of Industry (Ind), leverage of the company (Leverage), profitability (Profit) and listed stock exchange (Exch).

Auditing firm is not considered as another independent variable as almost all the select companies’ annual reports are audited by top most world audit firms like Deloitte, PwC, Ernst & Young, KPMG and Grant Thornton.
The regression model is as follows: Disclosure Index (DI) = a+ b1 Size + b2 Ind + b3 Lev + b4 Profit + b5 Exch.

Where- Size represents the log value of total assets of each selected company.

Ind represents the type of industry in which the company belongs to.

This is a dummy variable belonging to financial companies or non-financial companies and allots 1 and 0 respectively.

Lev indicates the ratio of total debt to total equity.

Profit represents the ratio of net income on capital employed.
Stock Exchange is another dummy variable which is used to represent the companies listed in different select stock exchanges; 1 for London Stock Exchange (LSE); 2 for Six Swiss Exchange (Switzerland); 3 for TMX Group (Canada); 4 for Euronext (European Union) and 5 for Frankfurt (Germany).

Data Analysis & Findings The data is derived from the annual reports of the select companies.
Descriptive Statistics (Table-2) shows minimum, maximum and mean score of different variables which might enlighten the nature of the collected data.

Disclosure index shows the minimum and maximum scores of 7 and 19 respectively.

Most of the companies (81%) of the study are non-financial in nature.

The table also shows the number of selected companies belonging to respective stock exchanges.
Table 2: Descriptive statistics Disclosure Index Size of the firm (Log value) Debt equity ratio Earning capacity Type of Industry Financial Non financial Total Listed in Stock Exchange LSE SWISS TMX EURONEXT FRANKFURT Total N Minimum Maximum Mean 75 75 75 75 7 1.28 .00 .00 19 5.28 78.00 .89 13.92 3.2739 4.6809 .1064 Frequency Percent 14 61 75 18 8 15 23 11 75 18.6 81.3 100.0 24.0 10.7 20.0 30.6 14.7 100.0 14 Volume 2 Issue 1 2017AJMRAmity Journal of Management ResearchADMAA According to Swain (2008) multiple regression explains variation in one dependent variable in terms of other independent variables.
It is inherently a co- relational technique and cannot tell anything about underlying casual relationship.

The multiple regression analysis was run to test all the formulated hypotheses simultaneously where all the mentioned independent variables were considered in the model.

Table 3: Model Summary Model 1 R .462a R Square Adjusted R Square Std.

Error of the Estimate .213 .156 2.299 Table 3 provides R value which represents the degree of correlation (0.462).
The R2 value measures the total variation in the dependent variable caused by the independent variables.

Table 4 indicates that the regression model predicts the dependent variable significantly well.

Here, p=0.00, which is less than 0.05, and indicates that, overall, the regression model statistically significantly predicts the outcome variable (i.e., it is a good fit for the data).

Table-4: ANOVA Model Regression Residual Total Sum of Squares df Mean Square F Sig.
98.864 364.656 463.520 5 69 74 19.773 5.285 3.741 .00 From the analysis result (Table 5) it is observed that H01 and H05 are rejected at 1% and 7% level of significance and the hypotheses H02, H03 and H04 are accepted.

Thus the statistical result implies that larger companies (size of the company) will have high levels of disclosures on DFIs in comparison to smaller size of companies.

Table 5: Regression Results Independent variables Unstandardized Coefficients (B) Unstandardized Coefficients (Std.
Error) Standardized Coefficients (Beta) t Sig.

(Constant) Size of the firm Type of Industry Debt equity ratio Earning capacity Listed in Stock Exchange 8.858 1.212 -.270 .007 .136 .365 1.375 .356 .738 .028 1.689 .197 .409 -.042 .028 .009 .206 6.442 3.401 -.366 .238 .080 1.852 .000 .001 .716 .813 .936 .068 The level of compliance with DFIs disclosure also depends on the type of stock exchanges where the company is listed.
On the other the type of industry, firm’s leverage and firm’s profitability do not have positive association with disclosure level of DFIs in the annual reports of the company.

Although adoption of IFRS for DFIs brings uniformity in case of reporting and disclosing of DFIs among the countries but level of disclosure varies from company to company (Gope & Mitra, Conclusion 15 Volume 2 Issue 1 2017AJMRAmity Journal of Management ResearchADMAA 2015).
The present study focuses on the variables affecting the degree of compliance with the select IFRSs specified for disclosure of DFIs.

Through content analysis the disclosure index score is measured for the independent variables of regression model - size of the firm, nature of the industry, leverage, earning capacity and listed stock exchange.

The findings of the study suggest that disclosure level on DFIs is related to firm’s size and listing status of the said firm.
However, the type of industry, leverage of the firm, and profitability don’t influence the level of disclosure for DFIs.

However, the study is not free from a few limitations as well.

Though the disclosure index score is calculated very carefully but errors might occur in case of construction of Index score card and concerning identification of disclosed information in the annual reports because of different ways of qualitative information disclosure.

References Bischof, J.

(2009).


The effects of IFRS 7 adoption on bank disclosure in Europe.

Accounting in Europe, 6 (2), 167-194.

Buvaneswari, R.P.

; Ragupathi, M. ;Mani, A.

& Kirubakaran, M. (2007).

Indian Stock Market - Hedging of Index Future is a boon or bane.

Indian Journal of Finance , I (3), 25-23.

Chalmers, K., & Godfrey, J.

(2000).

Practice versus Prescription in the Disclosure and Recognition of Derivatives.

Australian Accounting Review , 11 (2), 40-50.


Edwards Jr. and Eller, G. (1996): Derivatives Disclosures by Major U.S. Banks, 1995, Federal Reserve Bulletin, September, 792-801.

Ernst & Young (2006).

Risk Investor Survey Report.

Retrieved from http://www.ey.com/global/ssets.nsf/ international/Global_Risk_investor_Survey_Report/$file; on 29.03.2008 Gope, A.

(2014).

Indian Equity Derivative Market.

International Journal of Business and Management, 2 (6), 115- 124.

Gope, A., & Mitra, G. (2015).


IFRS for Derivative Financial Instruments: Disclosure Level and Uniformity.

Journal of Advanced Research in Accounting & Finance Management , 1 (2), 11-16.

Greenspan, J.

(1997).

Financial Futures and Options in Indian Perspective.

Jaico Publishing House.

Hassan , O; Marston , C (2010) Disclosure measurement in the empirical accounting literature - a review article.
Retrieved from http://ssrn.com/abstract=1640598 on 05/06/2015 Htay, S. N., Rashid, H. M., Adnan, M. A., & Meera, A. K. (2011).

Corporate Governance and Risk Management Information Disclosure in Malaysian Listed Banks: Panel Data Analysis.

International Review of Business Research Papers , 7 (4), 159-176.

Hunziker, S. (2013).

The disclosure of market risk information under IFRS 7 : Evidence from Swiss listed non- financial companies (IFZ Working Paper No.
0020/2013) Lucerne University of Applied Sciences and Arts,Switzerland 16 Volume 2 Issue 1 2017AJMRAmity Journal of Management ResearchADMAA Hwang, A.

(2002).

Comparative analysis of accounting treatments for derivatives.

Journal of Accounting Education, 20 (3), 205-233.

Jones, M. J., & Shoemaker, P. A.

(1994).

Accounting narratives: A review of empirical studies of content and readability.

Journal of Accounting Literature , 13, 142-161.

Leuz, C., & Verrecchia, R. (2000).


The Economic Consequences of Increased Disclosure.

Journal of Accounting Research , 38, 91–124.

Levine, R., Loayza, N., & Beck, T. (2000).

Financial intermediation and growth: causality and causes.

Journal of Monetary Economics , 46 (1), 31-77.

Lopes, P. T., & Rodrigues, L. L. (2007).

Accounting for financial instruments: An analysis of the determinants of disclosure in the Portuguese stock exchange.

The International Journal of Accounting , 42, 25-56.
Malaquias, R. F., & Lemes, S. (2013).

Disclosure of financial instruments according to International Accounting Standards: empirical evidence from Brazilian companies.

Brazilian Business Review , 10 (3), 82-107.

Mitra, G., & Gope, A.

(2013).

IFRS on Derivative Financial Instruments.

DBM Social Science Reporter, 1 (1), 12- 19.

Prabhakara, T. (2013).

Derivatives, Hedge Accounting Disclosure and Impact on Indian Financial Market.

Golden Research Thoughts , 3 (3).

Selvam, V., & Rita, S. (2011).


Financial Derivatives - Real Challenges in India.

Advances in Management, 4 (1), 20-23.

Strouhal, J., Bonaci, C., & Matis, D. (2010).

Accounting for Derivatives: Hedging or Trading?

Wseas Transaction on Business and Economics , 7 (3), 242-251.

Stulz, R. M. (2004).

Should We Fear Derivatives?

Journal of Economic Perspectives , 18 (3), 173-192.

Swain, A.

(2008).

Text Book of Research Methodology (2nd ed.).

New Delhi: Kalyani Publishers.

Varma, J. R. (2008).


Derivatives and Risk Management, New Delhi: Tata McGraw-Hill Publishing Company Limited.

Vashishtha, A., & Kumar, S. (2010).

Development of Financial Derivatives Market in India- A Case.

International Research Journal of Finance and Economics, (37), 15-29.

Verrecchia, R. E.,(2001) Essays on Disclosure.

Retrieved from http://papers.ssrn.com/sol3/papers.

cfm?abstract_id=276699 on 28.08.2014 Welker, M. (1995).
Disclosure Policy, Information Asymmetry, and Liquidity in Equity Markets.

Contemporary Accounting Research, 11 (2), 801-827.

Woods, M., & Marginson, D. E. (2004).

Accounting for Derivatives: An Evaluation of Reporting Practice by UK Banks.

Accounting in Europe, 13 (2), 373-390.
17 Volume 2 Issue 1 2017AJMRAmity Journal of Management ResearchADMAA Appendix: Score of Dependent and Independent variables CO CODE DINDEX SIZE IND LEV PROFIT EXCH Volume 2 Issue 1 2017AJMRAmity Journal of Management ResearchADMAA Author’s Profile Arjun Gope
5 5 5 5 5 5 5 5 5 5 5 Author’s Profile Arjun Gope is working as Assistant Professor, Department of Commerce, Iswar Chandra Vidyasagar College, Belonia, South Tripura, India.
He has worked for four years as a contractual Lecturer in Tripura Institute of Technology, erst-while Polytechnic Institute, Narsingarh, Tripura (West).

He has actively networked with BBA Department, Tripura University; Management Department, ICFAI University and MBA Department, The NIS Academy, Agartala, Tripura, India.

19 Volume 2 Issue 1 2017AJMRAmity Journal of Management ResearchADMAA