Stock Market Prediction Based Multi-Attribute Decision Making Model Using Picture Fuzzy $${\hat{Z}}$$-Information

As demonstrated in the section above, the stock market place is a dynamic factor, which makes it possible for traders and investors to make good decisions based on the information acquired through accurate prediction. This research aims at improving the prediction of stock market by applying a new method to Multi-attribute Group Decision making (MAGDM). MAGDM goes through a cycle of evaluating and ranking several criteria hence enhancing the decision-making aspects further. To overcome the shortcomings of prior models, some EU and FU is incorporated by combining Zadeh’s $${\hat{Z}}$$ -numbers with Picture Fuzzy Sets (PFSs). This integration is to enhance the ability of the model to address completely unclear decisions utilizing the peculiarities of $${\hat{Z}}$$ -numbers. To compare decisions between decision-makers, we proposed picture fuzzy $${\hat{Z}}$$ -numbers (PF $${\hat{Z}}$$ N) and for their aggregation, introduced picture fuzzy weighted averaging, picture fuzzy ordered weighted averaging, picture fuzzy hybrid averaging, picture fuzzy weighted geometric, picture fuzzy ordered weighted geometric and picture fuzzy hybrid geometric operators based algebraic $${\mathfrak {T}}$$ -norm ( $${\mathfrak {T}}-N$$ ) and $${\mathfrak {T}}$$ -conorm ( $${\mathfrak {T}}-CNs$$ ) To verify the efficiency of our suggested technique, we compare these operators with the Combined Compromised Solution (CoCoSo) model focusing on the stock market analysis. Our results, therefore, show how these operators are important in improving decision making accuracy and precision in conditions of risk. This research laid down the basis for enhancing decision-making and dealing with uncertainty in different fields especially in the application of stock market prediction. The proposed methodology can be attributed to providing a systematic and a more efficient way of dealing with uncertainty which in one way or the other has an outcome of enhancing the credibility of the decision making process in the financial sector.