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Fuzzy Logic in Corporate Governance By Ong Soo Geok Alex See Kok Bin Low Lock Teng Kevin

Fuzzy Logic in Corporate Governance By Ong Soo Geok Alex See Kok Bin Low Lock Teng Kevin Bala Shanmugam. Outline of this presentation. Corporate Governance (CG) Fuzzy? Hybrid fuzzy CG 3D visual graphic conclusion. Corporate Governance. corporate governance

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Fuzzy Logic in Corporate Governance By Ong Soo Geok Alex See Kok Bin Low Lock Teng Kevin

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  1. Fuzzy Logic in Corporate Governance By Ong Soo Geok Alex See Kok Bin Low Lock Teng Kevin Bala Shanmugam

  2. Outline of this presentation • Corporate Governance (CG) • Fuzzy? • Hybrid fuzzy CG • 3D visual graphic • conclusion

  3. Corporate Governance corporate governance “transparency and accountability” It seems simple enough, but it gets more complex when it looks into the corporate related issues (Low et al., 2001)

  4. Corporate governance Limelight especially after failure of • Enron • hidden lost and debts through manipulating of financial report • Worldcom • Hidden bad debt and backdating contract

  5. Corporate governance the growing awareness of corporate governance “more difficult to define good governance” (Vijaya, 2004).

  6. Corporate governance recently floated idea of “governance scoring system for companies to access their governance” So,companies can expect the ratings phenomenon to gain traction (David, 2002).

  7. Recent CGR systems:- • Corporate Governance Score (CGS)from Standard and Poor (S&P) • Governance Metrics International (GMI) system • ISS Corporate Governance Quotient (CGQ)

  8. Fuzzy logic • Coined by Lotfi Zadeh in 1960s • To model human’s sense of words and decision making • Represented based on degree of membership. Not crisp logic. • denotes FTD: It has an interval [0,1] including infinite degrees of membership from 0 to 1. • Can be 0.2, 0.4, 0.6, 0.8, 1. instead of true 1 or false 0. * (Negnevitsky, 2002)

  9. Fuzzy CGR BS code FTD code Defuzzification Fuzzification Rule inference CGR crisp Value

  10. Fuzzification • Linguistic variables (primary stage ) • FTD has a membership terms • very low, low, medium, high and very high • BS (membership terms) • Poor, average and good • equation denotes combination of all different degrees of membership for corresponding crisp values.

  11. very low low Average High Very high Fuzzification M e m b e r s h i p t e r m s Degree of membership Range Linguistic variable : FTD

  12. Rule inference • Non mathematical description • Rule base decision making • CGR strategy can be defined by using “IF-THEN” rules such as: • If FTD is high, and BS is good then Ω is good • If FTD is low, and BS is average then Ω is bad • If FTD is very high, and BS is bad then Ω is average *

  13. Defuzzification Stage of translating linguistic value into real value CGR =

  14. 3D graphical presentation

  15. Conclusion • CG is essential to establish a framework for enhancing investors’ confidence. • Fuzzy logic is a useful tool in solving ambiguous scenarios especially in CGR. • Handle substantially huge amount of different combination (rules) • Can have more than 4 input variables.

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