Issue:Clustering stock price volatility using intuitionistic fuzzy sets

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Title of paper: Clustering stock price volatility using intuitionistic fuzzy sets Author(s): Georgy Urumov School of Computer Science and Engineering, University of Westminster, 115 New Cavendish Street, London W1W 6UW w1767944@westminster.ac.uk Panagiotis Chountas School of Computer Science and Engineering, University of Westminster, 115 New Cavendish Street, London W1W 6UW p.i.chountas@westminster.ac.uk Presented at: 25th ICIFS, Sofia, 9—10 September 2022 Published in: Notes on Intuitionistic Fuzzy Sets, Volume 28 (2022), Number 3, pages 343–352 DOI: https://doi.org/10.7546/nifs.2022.28.3.343-352 Download:  PDF (914  Kb, File info) Abstract: Clustering involves gathering a collection of objects into homogeneous groups or clusters, such that objects in the same cluster are more similar when compared to objects present in other groups. Clustering algorithms that generate a tree of clusters called dendrogram which can be either divisive or agglomerative. The partitional clustering gives a single partition of objects, with a predefined K number of clusters. The most popular partition clustering approaches are: k-means and fuzzy C-means (FCM). In k-means clustering, data are divided into a number of clusters where data elements belong to exactly one cluster. The k-means clustering works well when data elements are well separable. To overcome the problem of non-separability, FCM and IFCM clustering algorithm were proposed. Here we review the use of FCM/IFCM with reference to the problem of market volatility. Keywords: K-Means, FCM, IFCM, Intuitionistic fuzzy sets, Volatility of Volatility. AMS Classification: 03E72, 68T20. References: Atanassov, K. T. (1983). Intuitionistic fuzzy sets, VII ITKR’s Session, Sofia, 1983 (Deposed in Central Science–Technology Library of Bulgaria Academy of Science –1697/84). Atanassov, K. T. (1999). Intuitionistic Fuzzy Sets: Past, Present and Future. In: Wagenknecht, M., & Hampel, R. (eds) 3rd Conference of the European Society for Fuzzy Logic and Technology, Zittau, Germany (10.09.2013–12.09.2003), 12–19. Atanassov, K. T. (1999). Intuitionistic Fuzzy Sets: Theory and Applications. PhysicaVerlag, New York.352 Bucci, A. (2020). Realized Volatility Forecasting with Neural Networks. Journal of Financial Econometrics, 18(3), 502–531. Demeterfi, K., Derman, E., Kamal, M., & Zou, J. (1999). More Than You Ever Wanted to Know about Volatility Swaps. Goldman Sachs Quantitative Strategies Research Notes, March 1999. Francq, C., & Zakoian, J.-M. (2010). GARCH Models: Structure, Statistical Inference and Financial Applications. 1st edition ed. Chichester: John Wiley & Sons. Poon, S.-H., & Granger, C. W. J. (2003). Forecasting Volatility in Financial Markets: A Review. Journal of Economic Literature, 41(2), 478–539. Pradeepkumar, D., & Ravi, V. (2017). Forecasting financial time series volatility using Particle Swarm Optimisation trained Quantile Regression Neural Network. Applied Soft Computing, 58, 35–52. Site, A., Birant, D., & Isik, Z. (2019). Stock Market Forecasting Using Machine Learning Models. IEEE 2019 Innovations in Intelligent Systems and Applications Conference (ASYU) - Izmir, Turkey (31.10.2019 – 02.11.2019), 318–323. Sugeno, M., & Terano, T. (1977). A model of learning based on fuzzy information, Kybernetes, 6, 157–166. Szmidt, E., & Kacprzyk, J. (1997). On measuring distances between intuitionistic fuzzy sets. Notes on Intuitionistic Fuzzy Sets, 3(4), 1–3. Szmidt, E., & Kacprzyk, J. (2000). Distances between intuitionistic fuzzy sets, Fuzzy Sets and Systems, 114(3), 505–518. Szmidt, E., & Kacprzyk, J. (2001). Intuitionistic fuzzy sets in some medical applications. Notes on Intuitionistic Fuzzy Sets, 7(4), 58–64. Van Lung, H., & Kim, J.-M. (2009). A generalized spatial fuzzy C-means algorithm for medical image segmentation. In: FUZZ-IEEE'09: Proceedings of the 18th international conference on Fuzzy Systems, Jeju Island, Korea, 20.08.2009 – 24.08.2009, 409–414. Wang, W., & Zhang, Y. (2007). On fuzzy cluster validity indices. Fuzzy Sets and Systems, 158(19), 2095–2117. Wang, Z., Xu, Z., Liu, S., & Tang, J. (2011). A netting clustering analysis method under intuitionistic fuzzy environment. Applied Soft Computing, 11(8), 5558–5564. Wang, Z., Xu, Z., Liu, S., & Yao, Z. (2014). Direct clustering analysis based on intuitionistic fuzzy implication. Applied Soft Computing, 23, 1–8. Citations: The list of publications, citing this article may be empty or incomplete. If you can provide relevant data, please, write on the talk page.