RANKING STOCKS USING THE FUZZY MULTIPLE CRITERIA DECISION MAKING APPROACH

Abstract

This work applies the Fuzzy Multiple Criteria Decision Making (FMCDM) approach to assist in making stock investment decisions. The proposed approach applies to quantitative data and can accommodate qualitative information which is normally difficult to be integrated by traditional finance and accounting methods. A major-sub-criteria hierarchy is established to reduce the possibility of over-weighing some dependent criteria existing in a single-level structure. The ratings of stocks versus qualitative sub-criteria and the weights of major and sub-criteria are assessed in linguistic terms represented by fuzzy numbers. Each sub-criterion is in a benefit, cost, or balanced nature. New standardization methods for cost-nature and balanced-nature criteria are presented. The algorithms of membership functions of the final aggregation are derived from the roots of cubic equations of multiplications of triple fuzzy numbers. Since these algorithms are clearly developed, the investor can easily calculate the fuzzy aggregation values. The defuzzified final aggregation judges the performance of alternative stocks. Moreover, the ratio of the market price to performance (PP) is suggested to filter the over and under-pricing of alternative stocks. A set of buying and selling rules are recommended based on the performance and PP ratio. Finally, an empirical example of evaluating a set of TSE-listed stocks tests the proposed approach.