Researcher :	Dr. Kevin Low Lock Teng
Designation :	Associate Professor, Deputy Dean
Faculty:	Accountancy and Management
Department:	Accountancy
Email Address:	lowlt@utar.edu.my

Background Information

Many corporate companies have been practising corporate transparency (CT). However, good corporate governance (CG) rather than corporate transparency should be a greater concern. To oversee companies practising governance, it is important to determine rankings in corporate governance. The tools of fuzzy logic can be used to design an approach that will rate the level of application of CG.

Failures of business in which deficiencies of financial reporting and corporate disclosures have figured prominently are not new phenomena. In the wake of high-profile scandals such as Enron, WorldCom, Global Crossing, Adelphia Communications, Tyco and Xerox have clearly demonstrated that current financial reporting standards and corporate governance (CG) are either inadequate or have not been fully complied with.

Scope of Research

As there are many aspects where the shareholders can look into a company before, during and after their investment decision. Therefore, companies will need to practice sound corporate governance in order to gain public confidence. As such, fuzzy logic (FL) may be utilized as a tool to enhance the corporate governance ratings among the industry, to justify whether good corporate governance is being practiced and enhanced even though there is no proper statutory control over certain areas. In this paper, two major factors, namely Financial Transparency and Disclosure and Board Structure, are examined.

Research Methodology

FL has been extensively applied in engineering and science fields; especially in the application of FL in breast cancer diagnosis (Kovalerchuk, Triantaphyllou, Ruiz, and Clayton, 1997). In business arena, the typical business valuation has a significant limitation: the failure to recognise uncertainty; fortunately, 'fuzzy math' functions in spreadsheets can formally incorporate significant additional information into valuation reports and help mitigate the limitations of the traditional valuation approach (McKee, 2004). Thus, FL could be an effective tool to solve many elusiveness problems in CGR.

Outcome of Research - Fuzzy Corporate Governance

	(1)
	(2)
	(3)
	(4)

denotes FTD; denotes BS; and is CGR. is the element of disclosure in the universe of in the fuzzy set in Equation (1). It has an interval [0,1] including infinite degrees from 0 to 1 in Equation (2). The relationship between fuzzy set and crisp value can be explained in Equation (3), where is the degree of membership for corresponding crisp value . The summation as shown in Equation (4) denotes a combination of all different degrees of membership for corresponding crisp values.

From Equation (2), the membership degree has infinite values from 0 to 1. Triangular shape of membership functions is used as depicted in Figure 1. For any given input, crisp value within the boundary between and , the degree of membership could be obtained from Equation (5) with appropriate substitution. The same explanation is applied to Equation (6).

The two input variables and would affect the crisp output value . Each variable that would associate with fuzzy set is represented in difference shapes and levels. As for , fuzzy set is represented by different levels of CG such as 'Very Good' (VG), 'Good' (G), 'Average' (A), 'Poor' (P) and 'Very Poor' (VP) as shown in Figure 2. The Membership functions have infinite interval from 0 to 1. The crisp value of individual variable could range from 1 to 10. The degree of how good the corporate governance would be associated with the crisp output from 1 to 10. Higher score shows the company with higher degree of good corporate governance practice.

Recommendations for future research

Fuzzy logic rating process might require a comprehensive system to enter input variable and obtain the best figure for the rating, which might be time-consuming initially. After the system has been created, the rate can be automated through the system program. The corporate will not only be benefited from knowing the rate of their corporate governance, an improvement program could also be suggested if the corporate has a low rating in certain variable. It can be used to enhance and modernise the existing foundation on which the existing corporate governance framework is premised.

Conclusion

This paper discusses how fuzzy logic can be a useful tool in solving the 'ambiguous' scenario in corporate governance rating. Here, two important variables such as Financial Transparency and Disclosure, and Board Structure have been integrated to get a plausible rate for the corporate governance rating.