Stock Market Volatility Analysis using Fuzzy Logic: Fuzzy AHP and TOPSIS

Accurately measuring stock market volatility is essential for making well-informed decisions in the field of financial analysis. By combining fuzzy logic with the Analytic Hierarchy Process (AHP) and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), this study offers a novel method for analyzing stock market volatility. To deal with the inherent imprecision and uncertainty related to volatility assessments, fuzzy logic is used. Fuzzy membership functions, which quantify the degree of membership in preset categories like Low, Medium, and High, are used to represent each volatility level. Economic indicators, market trends, historical data, and other factors that affect stock market volatility are all methodically ranked and evaluated using the AHP technique. AHP offers a formal framework for evaluating the relative importance of these criteria by creating pairwise comparison matrices and determining priority weights. After that, stocks are ranked and chosen according to how well they perform in comparison to ideal and non-ideal solutions using the TOPSIS approach. With this approach, one may determine which stocks are most suitable and least ideal, as well as get a thorough evaluation of their volatility. This method takes into account the intricacies of financial markets while simultaneously improving the accuracy of volatility analysis by fusing fuzzy logic with AHP and TOPSIS. The suggested methodology provides analysts and investors with a strong tool that helps them control risk and make better stock market investments.