This paper is in the appreciation of a need to develop a grading model for the equity in Kuwait since the volumes of the stocks being traded on Kuwait Stock Exchange is rising tremendously. Hence, the existence of a sound recommendatory grading scale can not be overemphasized. And this paper is an effort in the same direction. This paper attempts to develop a grading model for the equity which is traded on various exchanges. The model takes into account all the major parameters that can affect the performance of the stocks. However, the list is not exhaustive and more of such parameters can be included if need be. The development of model has been effected using Fuzzy logic which is essentially a concept borrowed from the physical sciences. This concept is used for problem-solving when the parameters chosen do not conform to the requirements of traditional methods of analysis like, having precisely defined domain and range, and having specific quantitative values for all the parameters in their respective domains. In such cases the application of the fuzzy logic concept is done and it has proved to be of great significance. In this model, there are five modules, each encompassing three parameters. The economy module has the GDP Growth rate, the rate of interest and the inflation rates as the governing factors. The return module has total return, the residual return over CAPM and the intrinsic valuation of stock vis-a-vis market prices as parameters. The risk module has the business risk, absolute risk and market risk as parameters. The liquidity module has market trend, the trading volumes and the impact costs as important factors. And the last module, that is, the management module comprises the management quality, the growth and the ratio analysis. Based on the nature of these parameters and their variance with respect to the environmental aspects, these parameters have been graded as good, fair or bad, which is the methodology of the fuzzy logic. Once such grading is done for all the parameters, these are traced on the fuzzy logic network that is specific for all the modules. The network has 27 points on the scale, each corresponding to different situations. After the cumulative score for the modules have been reached through fuzzy network, the modules are given relative weights according to their importance and the objective of rating. The weights multiplied with respective scores sum up to give the final score for the derivative instrument being considered and that gives the rating assigned to that instrument from this model. The rating scale ranges from AAA++ to E and there are 27 possible grades. The best possible grade is the AAA++ and the worst is E.