Application of fuzzy multiple attribute decision making on company analysis for stock selection

Abstract

The investor has to consider many factors when making a decision on which stocks to buy. However, judgements on these factors are usually linguistic, fuzzy, and conflicting. Therefore, selection of stocks is a fuzzy multiple attribute decision making (FMADM) problems. A hierarchical composite structure for factors and subfactors is developed for company analysis. A weight model is presented. Values of each subfactor are assumed to have normal distribution in order to build up the membership function of the ascending half-trapezoid. By multiplying the weight matrix with the corresponding fuzzy judgement matrix for each factor and calculating the weighted summation of weighted matrices, the authors make the fuzzy decision by grades. A numerical example of selecting the first priority stock among seven listed companies of the cement industry in Taiwan's stock market is applied to verify this model.