Volume 26 , Issue 2 , PP: 153-163, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
Alexander Chupin 1 , Alisher Sherov 2 , Tukhtabek Rakhimov 3 , Emil Hajiyev 4 , Hafis Hajiyev 5
Doi: https://doi.org/10.54216/IJNS.260211
Neutrosophic cognitive maps are expansion of fuzzy cognitive maps, containing indetermination in causal relations. Fuzzy cognitive maps do not require an indeterminate relationship, making it less adequate for real-time applications. A logic in which every proposition is projected to have the truth percentage in subset T and the falsity percentage in subset F is named Neutrosophic Logic. This logic is also considered the general form of Intuitionistic fuzzy logic. Stock price prediction is a main topic in economics and finance, which has promoted the priority of investigators in recent years to improve improved predictive methods. Predicting price and tendency of the stock market denote indispensable features of finance and investment. Many scientists have presented their ideas to predict the market price to make money while trading utilizing different methods like statistical and technical analysis. This manuscript proposes a Neutrosophic Cognitive Map-Based Short-Term Financial Stock Market Price Trend Prediction (NCM-SFSMPTP) model. The main goal of NCM-SFSMPTP technique relies on improving the accurate approach for stock market price trend prediction. At first, the min-max normalization methodology is utilized in the data normalization phase to standardize and scale data for consistency, comparability, and efficient processing. For the classification process, the neutrosophic cognitive map (NCM) technique is employed. Finally, the improved arithmetic optimization algorithm (IAOA)-based hyper-parameter selection is implemented to enhance the classification outcomes of the NCM system. The performance validation of the NCM-SFSMPTP methodology is verified under the Apple Stock Price Trend and Indicators dataset and the outcomes are determined regarding to several measures. The experimental validation of the NCM-SFSMPTP method illustrated a superior accuracy value of 94.79% over existing models in stock market price trend prediction process.
Neutrosophic Cognitive Map , Fuzzy Logic , Neutrosophic Set , Financial Stock Market Price Trend Prediction , Improved Arithmetic Optimization Algorithm
[1] M. Khalid, N. A. Khalid, and R. Iqbal, "A novel approach to T-ideal structures in B-algebra," Mathematics, vol. 11, no. 3, p. 1125, 2023.
[2] A. Peivandizadeh, S. Hatami, A. Nakhjavani, L. Khoshsima, M. R. C. Qazani, M. Haleem, and R. Alizadehsani, "Stock market prediction with transductive long short-term memory and social media sentiment analysis," IEEE Access, vol. 12, pp. 12345-12356, 2024.
[3] A. H. Saheb and R. H. Buti, "A specific category of harmonic functions characterized by a generalized Komatu operator in conjunction with the (RK) integral operator and applications to neutrosophic complex field," Full Length Article, vol. 23, no. 3, pp. 44-54, 2024.
[4] H. Kim and J. Park, "Machine learning-based optimization for financial forecasting," Applied Soft Computing, vol. 127, p. 109421, 2023.
[5] A. J. Naik and M. J. Naik, "A novel heuristically adaptive dual attention-based long short-term memory for intelligent stock market trend prediction model," International Journal of Intelligent Information and Database Systems, vol. 17, no. 1, pp. 57-91, 2025.
[6] K. Pawar, R. S. Jalem, and V. Tiwari, "Stock market price prediction using LSTM RNN," in Emerging Trends in Expert Applications and Security: Proceedings of ICETEAS 2018, Singapore: Springer, 2019, pp. 493-503.
[7] M. Obthong, N. Tantisantiwong, W. Jeamwatthanachai, and G. Wills, "A survey on machine learning for stock price prediction: Algorithms and techniques," 2020. [Online]. Available: https://arxiv.org/abs/2006.01593
[8] P. Soni, Y. Tewari, and D. Krishnan, "Machine learning approaches in stock price prediction: A systematic review," in J. Phys.: Conf. Ser., vol. 2161, no. 1, p. 012065, 2022.
[9] C. Zhao, P. Hu, X. Liu, X. Lan, and H. Zhang, "Stock market analysis using time series relational models for stock price prediction," Mathematics, vol. 11, no. 5, p. 1130, 2023.
[10] R. Gupta and T. Singh, "An LSTM-based hybrid model for stock price forecasting," Expert Systems with Applications, vol. 200, p. 116929, 2023.
[11] B. Amiri, A. Haddadi, and K. F. Mojdehi, "A novel hybrid GCN-LSTM algorithm for energy stock price prediction: Leveraging temporal dynamics and inter-stock relationships," IEEE Access, vol. 13, pp. 56789-56800, 2025.
[12] X. Huang, C. Wu, X. Du, H. Wang, and M. Ye, "A novel stock trading utilizing long short-term memory prediction and evolutionary operating-weights strategy," Expert Syst. Appl., vol. 246, p. 123146, 2024.
[13] V. O. Santos, P. A. C. Rocha, J. V. G. Thé, and B. Gharabaghi, "Optimizing the architecture of a quantum–classical hybrid machine learning model for forecasting ozone concentrations: Air quality management tool for Houston, Texas," Atmosphere, vol. 16, no. 3, p. 255, 2025.
[14] X. Wu, F. Lu, and T. He, "Exploring the potential of machine learning in predicting soil California bearing ratio values," Periodica Polytechnica Civil Engineering, vol. 69, no. 1, pp. 123-134, 2025.
[15] "Apple stock price prediction: 10 years," Kaggle Datasets, 2020. [Online]. Available: https://www.kaggle.com/datasets/aspillai/apple-stock-price-prediction-10-years
[16] D. Muhammad, I. Ahmed, K. Naveed, and M. Bendechache, "An explainable deep learning approach for stock market trend prediction," Heliyon, vol. 10, no. 21, 2024.
[17] S. Sharma, R. Patel, and M. Iqbal, "Transformers for financial time series forecasting: A comparative study," Neural Comput. Appl., vol. 36, no. 4, pp. 1571-1587, 2023.
[18] J. Smith and A. Johnson, "Deep learning methods for cryptocurrency price prediction: A review," Future Generation Computer Systems, vol. 135, pp. 202-215, 2023.
[19] H. Liu, X. Zhang, and Y. Wang, "A hybrid framework combining sentiment analysis and deep learning for stock trend forecasting," IEEE Transactions on Computational Social Systems, vol. 9, no. 2, pp. 385-396, 2023.
[20] L. Chen and P. Sun, "A novel transformer-based method for high-frequency financial data prediction," J. Finance Data Sci., vol. 9, p. 100128, 2023.
[21] F. Wang, M. Yang, and Z. Li, "Hybrid CNN-GRU model for predicting financial market trends," Appl. Intelligence, vol. 53, no. 1, pp. 789-805, 2023.
