Citation

Bhuvaneswari, S. and Kavitha, G. and Nagarajan, D. (2025) Rule-Based Neutrosophic Triplet Refined Interval-Valued Data Partition Analyzing The Movement of Stock Market Price. Neutrosophic Sets and Systems, 82. ISSN 2331-6055

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Abstract

This study introduces an innovative approach to analysing stock market index movements through a Rule-Based 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.

Item Type:	Article
Uncontrolled Keywords:	Neutrosophic Triplet, Rule-base, interval valued data partition, Mean Absolute Percent Error, Mean Squared Error
Subjects:	H Social Sciences > HG Finance > HG4501-6051 Investment, capital formation, speculation > HG4551-4598 Stock exchanges
Divisions:	Faculty of Engineering and Technology (FET)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	03 Jun 2025 02:38
Last Modified:	03 Jun 2025 02:38
URII:	http://shdl.mmu.edu.my/id/eprint/13913

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