Rule-Based Neutrosophic Triplet Refined Interval-Valued Data Partition Analyzing The Movement of Stock Market Price

Abstract

his study introduces an innovative approach to analysing stock market index movements through a RuleBased Neutrosophic Triplet Refined Interval-Valued Data Partition (NTRIVDP) model. This method segments 
data into intervals and applies Neutrosophic statistical measures to convert it into Neutrosophic triplets. These 
triplets are then processed through a defined rule-base, calculating the Neutrosophic triplet count to derive the 
average predicted output. The stock market poses significant challenges for accurate prediction, as traditional 
methods often fall short in capturing the intricacies of market behaviour. The proposed NTRIVDP model 
leverages Neutrosophic logic, effectively handling uncertain and vague information to offer a more nuanced 
understanding of market dynamics. This provides valuable insights for investors and financial analysts aiming to 
analyse stock market index behaviour more accurately. To assess the efficacy of the proposed model, performance 
metrics such as Mean Squared Error (MSE) and Mean Absolute Percent Error (MAPE) are utilized. For the 
parameter ‘α = 0.1, 0.2, 0.3’, the model yields promising results, with an MSE of 25,969.68 and a MAPE of 
6.570417. These findings indicate that the proposed model surpasses existing methods, demonstrating its 
effectiveness in forecasting stock market movements. This research highlights the potential of Neutrosophic logic 
in enhancing the accuracy of stock market predictions, presenting a significant advancement in financial 
forecasting methodologies.

DOI: 10.5281/zenodo.14996679