Application Of Density-Based Clustering Approaches For Stock Market Analysis

References

• Aldershof, B., J. Marron, B. Park, and M. Wand. 1995. Facts about the gaussian probability density function. Applicable Analysis 59 (1–4):289–31. doi:10.1080/00036819508840406.
   Google Scholar
• Alexander, L., S. R. Das, Z. Ives, H. Jagadish, and C. Monteleoni. 2017. Research challenges in financial data modeling and analysis. Big Data 5 (3):177–88. doi:10.1089/big.2016.0074.
   Web of Science ®Google Scholar
• Baser, P., and J. R. Saini. 2015. Agent based stock clustering for efficient portfolio management. International Journal of Computer Applications 975 (3):35–41. doi:10.5120/20317-2381.
   Google Scholar
• Bhattacharjee, P., and P. Mitra. 2021. A survey of density based clustering algorithms. Frontiers of Computer Science 15 (1):1–27. doi:10.1007/s11704-019-9059-3.
   Web of Science ®Google Scholar
• Bini, B., and T. Mathew. 2016. Clustering and regression techniques for stock prediction. Procedia Technology 24:1248–55. doi:10.1016/j.protcy.2016.05.104.
   Google Scholar
• Cai, F., N.-A. Le-Khac, and T. Kechadi. 2016. Clustering approaches for financial data analysis: A survey. arXiv Preprint arXiv 1609:08520.
   Google Scholar
• Campello, R. J. 2007. A fuzzy extension of the rand index and other related indexes for clustering and classification assessment. Pattern Recognition Letters 28 (7):833–41. doi:10.1016/j.patrec.2006.11.010.
   Web of Science ®Google Scholar
• Chandar, S. K. 2019. Stock market prediction using subtractive clustering for a neuro fuzzy hybrid approach. Cluster Computing 22 (6):13159–66. doi:10.1007/s10586-017-1321-6.
   Google Scholar
• Chang, C.-L., J. Ilomäki, H. Laurila, and M. McAleer. 2018. Long run returns predictability and volatility with moving averages. Risks 6 (4):105. doi:10.3390/risks6040105.
   Google Scholar
• Chaudhuri, T. D., and I. Ghosh. 2016. Using clustering method to understand Indian stock market volatility. arXiv Preprint arXiv 1604:05015.
   Google Scholar
• Cheong, D., Y. M. Kim, H. W. Byun, K. J. Oh, and T. Y. Kim. 2017. Using genetic algorithm to support clustering-based portfolio optimization by investor information. Applied Soft Computing 61:593–602. doi:10.1016/j.asoc.2017.08.042.
   Web of Science ®Google Scholar
• Chowdhury, H. A., D. K. Bhattacharyya, and J. K. Kalita. 2021. Uifdbc: Effective density based clustering to find clusters of arbitrary shapes without user input. Expert Systems with Applications 186:115746. doi:10.1016/j.eswa.2021.115746.
   Web of Science ®Google Scholar
• Cox, C. P. 1987. A handbook of introductory statistical methods. Washington DC: John Wiley & Sons.
   Google Scholar
• Das, T., and G. Saha. 2019. Addressing big data issues using rnn based techniques. Journal of Information and Optimization Sciences 40 (8):1773–85. doi:10.1080/02522667.2019.1703268.
   Web of Science ®Google Scholar
• Data, I. B., and A. Hub. 2016. Extracting business value from the 4 v’s of big data. Retrieved July 19:2017.
   Google Scholar
• Day, T. E., and C. M. Lewis. 1992. Stock market volatility and the information content of stock index options. Journal of Econometrics 52 (1–2):267–87. doi:10.1016/0304-4076(92)90073-Z.
   Web of Science ®Google Scholar
• Dempster, A. P., N. M. Laird, and D. B. Rubin. 1977. Maximum likelihood from incomplete data via the em algorithm. Journal of the Royal Statistical Society: Series B (Methodological) 39 (1):1–22. doi:10.1111/j.2517-6161.1977.tb01600.x.
   Web of Science ®Google Scholar
• Deodhar, M., J. Ghosh, G. Gupta, H. Cho, and I. Dhillon. 2008. Hunting for coherent co-clusters in high dimensional and noisy datasets. 2008 IEEE International Conference on Data Mining Workshops, Pisa, Italy, 654–63. IEEE.
   Google Scholar
• Dinandra, R., G. Hertono, and B. Handari. 2019. Implementation of density-based spatial clustering of application with noise and genetic algorithm in portfolio optimization with constraint. AIP Conference Proceedings, Depok, Indonesia, 2168, 020026. AIP Publishing LLC.
   Google Scholar
• D’Urso, P., L. De Giovanni, R. Massari, R. L. D’Ecclesia, and E. A. Maharaj. 2020. Cepstral-based clustering of financial time series. Expert Systems with Applications 161:113705. doi:10.1016/j.eswa.2020.113705.
   Web of Science ®Google Scholar
• Erdős, P., M. Ormos, and D. Zibriczky. 2011. Non-parametric and semi-parametric asset pricing. Economic Modelling 28 (3):1150–62. doi:10.1016/j.econmod.2010.12.008.
   Web of Science ®Google Scholar
• Esfahanipour, A., and W. Aghamiri. 2010. Adapted neuro-fuzzy inference system on indirect approach tsk fuzzy rule base for stock market analysis. Expert Systems with Applications 37 (7):4742–48. doi:10.1016/j.eswa.2009.11.020.
   Web of Science ®Google Scholar
• Ester, M., H.-P. Kriegel, J. Sander, X. Xu. 1996. A density-based algorithm for discovering clusters in large spatial databases with noise. Kdd 96 (34):226–31.
   Google Scholar
• Estévez, P. A., M. Tesmer, C. A. Perez, and J. M. Zurada. 2009. Normalized mutual information feature selection. IEEE Transactions on Neural Networks 20 (2):189–201. doi:10.1109/TNN.2008.2005601.
   PubMed Web of Science ®Google Scholar
• Fan, J., and Q. Yao. 2017. The elements of financial econometrics. Cambridge, United Kingdom: Cambridge University Press.
   Google Scholar
• Fayyad, U., P. S. Bradley, and C. Reina. 2001. Scalable system for expectation maximization clustering of large databases. US Patent 6,263,337.
   Google Scholar
• Fifield, S., D. Power, and D. Knipe. 2008. The performance of moving average rules in emerging stock markets. Applied Financial Economics 18 (19):1515–32. doi:10.1080/09603100701720302.
   Google Scholar
• Fukunaga, K., and L. Hostetler. 1975. The estimation of the gradient of a density function, with applications in pattern recognition. IEEE Transactions on Information Theory 21 (1):32–40. doi:10.1109/TIT.1975.1055330.
   Web of Science ®Google Scholar
• Gupta, V., V. Kumar, Y. Yuvraj, and M. Kumar. 2023. Optimized pair trading strategy using unsupervised machine learning. 2023 IEEE 8th International Conference for Convergence in Technology (I2CT), Lonavla, India, 1–5. IEEE.
   Google Scholar
• Halkidi, M., Y. Batistakis, and M. Vazirgiannis. 2001. On clustering validation techniques. Journal of Intelligent Information Systems 17 (2–3):107–45. doi:10.1023/A:1012801612483.
   Web of Science ®Google Scholar
• Han, C., Z. He, and A. J. W. Toh. 2023. Pairs trading via unsupervised learning. European Journal of Operational Research 307 (2):929–47. doi:10.1016/j.ejor.2022.09.041.
   Web of Science ®Google Scholar
• Heiberger, R. H. 2014. Stock network stability in times of crisis. Physica A: Statistical Mechanics and Its Applications 393:376–81. doi:10.1016/j.physa.2013.08.053.
   Web of Science ®Google Scholar
• Horn, D., and A. Gottlieb. 2002. The method of quantum clustering. Advances in Neural Information Processing Systems 14:769–76.
   Google Scholar
• Hyrien, O., and A. Baran. 2016. Fast nonparametric density-based clustering of large datasets using a stochastic approximation mean-shift algorithm. Journal of Computational and Graphical Statistics 25 (3):899–916. doi:10.1080/10618600.2015.1051625.
   PubMed Web of Science ®Google Scholar
• Iorio, C., G. Frasso, A. D’Ambrosio, and R. Siciliano. 2018. A p-spline based clustering approach for portfolio selection. Expert Systems with Applications 95:88–103. doi:10.1016/j.eswa.2017.11.031.
   Web of Science ®Google Scholar
• Kim, K., and J. W. Song. 2020. Analyses on volatility clustering in financial time-series using clustering indices, asymmetry, and visibility graph. IEEE Access 8:208779–95. doi:10.1109/ACCESS.2020.3037240.
   Web of Science ®Google Scholar
• Kurban, H., M. Jenne, and M. M. Dalkilic. 2017. Using data to build a better em: Em* for big data. International Journal of Data Science and Analytics 4 (2):83–97. doi:10.1007/s41060-017-0062-1.
   Google Scholar
• Lee, A. J., M.-C. Lin, R.-T. Kao, and K.-T. Chen. 2010. An effective clustering approach to stock market prediction. PACIS 54:54.
   Google Scholar
• Mahbobi, M., S. Kimiagari, and M. Vasudevan. 2021. Credit risk classification: An integrated predictive accuracy algorithm using artificial and deep neural networks. Annals of Operations Research 330 (1–2):609–37. doi:10.1007/s10479-021-04114-z.
   Web of Science ®Google Scholar
• Maldonado, R., and A. Saunders. 1981. International portfolio diversification and the inter-temporal stability of international stock market relationships, 1957-78. Financial Management 10 (4):54–63. doi:10.2307/3665219.
   Web of Science ®Google Scholar
• McDaid, A. F., D. Greene, and N. Hurley. 2011. Normalized mutual information to evaluate overlapping community finding algorithms. arXiv Preprint arXiv 1110:2515.
   Google Scholar
• Metghalchi, M., J. Marcucci, and Y.-H. Chang. 2012. Are moving average trading rules profitable? evidence from the European stock markets. Applied Economics 44 (12):1539–59. doi:10.1080/00036846.2010.543084.
   Web of Science ®Google Scholar
• Nagy, L., and M. Ormos. 2018. Friendship of stock market indices: A cluster-based investigation of stock markets. Journal of Risk and Financial Management 11 (4):88. doi:10.3390/jrfm11040088.
   Google Scholar
• Nair, B. B., P. S. Kumar, N. Sakthivel, and U. Vipin. 2017. Clustering stock price time series data to generate stock trading recommendations: An empirical study. Expert Systems with Applications 70:20–36. doi:10.1016/j.eswa.2016.11.002.
   Web of Science ®Google Scholar
• Nakagawa, K., M. Imamura, and K. Yoshida. 2019. Stock price prediction using k-medoids clustering with indexing dynamic time warping. Electronics and Communications in Japan 102 (2):3–8. doi:10.1002/ecj.12140.
   Web of Science ®Google Scholar
• Nanda, S., B. Mahanty, and M. Tiwari. 2010. Clustering Indian stock market data for portfolio management. Expert Systems with Applications 37 (12):8793–98. doi:10.1016/j.eswa.2010.06.026.
   Web of Science ®Google Scholar
• Pandey, A., and S. Sehgal. 2017. Volatility effect and the role of firm quality factor in returns: Evidence from the Indian stock market. IIMB Management Review 29 (1):18–28. doi:10.1016/j.iimb.2017.01.001.
   Web of Science ®Google Scholar
• Parvatha, L. S., D. N. V. Tarun, M. Yeswanth, and J. S. Kiran. 2023. Stock market prediction using sentiment analysis and incremental clustering approaches. 2023 9th International Conference on Advanced Computing and Communication Systems (ICACCS), Coimbatore, India, 1, 888–93. IEEE.
   Google Scholar
• Prabhu, C., A. S. Chivukula, A. Mogadala, R. Ghosh, and L. J. Livingston. 2019. Big data analytics for financial services and banking. In Big data analytics: Systems, algorithms, applications, ed. Aneesh S. C., A. Mogadala, R. Ghosh, L. M. J. Livingston, C. S. R. Prabhu, and A. S. Chivukula, 249–56. Springer, Singapore: Springer.
   Google Scholar
• Rahmah, N., and I. S. Sitanggang. 2016. Determination of optimal epsilon (eps) value on dbscan algorithm to clustering data on peatland hotspots in Sumatra. IOP conference series: earth and environmental science, Bogor, Indonesia, 31, 012012. IOP Publishing.
   Google Scholar
• Rasekhschaffe, K. C., and R. C. Jones. 2019. Machine learning for stock selection. Financial Analysts Journal 75 (3):70–88. doi:10.1080/0015198X.2019.1596678.
   Web of Science ®Google Scholar
• Ren, Y., C. Domeniconi, G. Zhang, and G. Yu. 2014. A weighted adaptive mean shift clustering algorithm. Proceedings of the 2014 SIAM International Conference on Data Mining, Philadelphia, PA, USA, 794–802. SIAM.
   Google Scholar
• Rukmi, A. M., A. Wahid. 2019. Role of clustering based on density to detect patterns of stock trading deviation. Journal of Physics: Conference Series, Surabaya, Indonesia, 1218, 012053. IOP Publishing.
   Google Scholar
• Scitovski, R., and K. Sabo. 2020. Dbscan-like clustering method for various data densities. Pattern Analysis and Applications 23 (2):541–54. doi:10.1007/s10044-019-00809-z.
   Web of Science ®Google Scholar
• Sivarajah, U., M. M. Kamal, Z. Irani, and V. Weerakkody. 2017. Critical analysis of big data challenges and analytical methods. Journal of Business Research 70:263–86. doi:10.1016/j.jbusres.2016.08.001.
   Web of Science ®Google Scholar
• Song, D.-M., M. Tumminello, W.-X. Zhou, and R. N. Mantegna. 2011. Evolution of worldwide stock markets, correlation structure, and correlation-based graphs. Physical Review E 84 (2):026108. doi:10.1103/PhysRevE.84.026108.
   Web of Science ®Google Scholar
• Starczewski, A., and A. Cader. 2019. Determining the eps parameter of the dbscan algorithm. International Conference on Artificial Intelligence and Soft Computing, Chennai, India, 420–30. Springer.
   Google Scholar
• Tan, P.-N., M. Steinbach, and V. Kumar. 2016. Introduction to data mining. Noida, Uttar Pradesh, India: Pearson Education India.
   Google Scholar
• Verma, N. K., S. Dwivedi, and R. K. Sevakula. 2015. Expectation maximization algorithm made fast for large scale data. 2015 IEEE workshop on computational intelligence: Theories, applications and future directions (WCI), Kanpur, India, 1–7. IEEE.
   Google Scholar
• Wagner, S., and D. Wagner. 2007. Comparing clusterings: An overview. Karlsruhe, Germany: Universität Karlsruhe, Fakultät für Informatik Karlsruhe.
   Google Scholar
• Weglarczyk, S. 2018. Kernel density estimation and its application. ITM Web of Conferences, Aviemore, Scotland, 23, 00037. EDP Sciences.
   Google Scholar
• Weinstein, M., and D. Horn. 2009. Dynamic quantum clustering: A method for visual exploration of structures in data. Physical Review E 80 (6):066117. doi:10.1103/PhysRevE.80.066117.
   Web of Science ®Google Scholar
• Weinstein, M., F. Meirer, A. Hume, P. Sciau, G. Shaked, R. Hofstetter, E. Persi, A. Mehta, and D. Horn. 2013. Analyzing big data with dynamic quantum clustering. arXiv Preprint arXiv 1310:2700.
   Google Scholar
• Yang, Y. 2016. Temporal data mining via unsupervised ensemble learning. Amsterdam, Netherlands: Elsevier.
   Google Scholar
• Yeung, K. Y., and W. L. Ruzzo. 2001. Details of the adjusted rand index and clustering algorithms, supplement to the paper an empirical study on principal component analysis for clustering gene expression data. Bioinformatics 17 (9):763–74. doi:10.1093/bioinformatics/17.9.763.
   PubMed Web of Science ®Google Scholar
• Yue, S.-H., P. Li, J.-D. Guo, and S.-G. Zhou. 2004. Using greedy algorithm: Dbscan revisited ii. Journal of Zhejiang University-Science A 5 (11):1405–12. doi:10.1631/jzus.2004.1405.
   Google Scholar
• Zillner, S., T. Becker, R. Munné, K. Hussain, S. Rusitschka, H. Lippell, E. Curry, and A. Ojo. 2016. Big data-driven innovation in industrial sectors. In New horizons for a data-driven economy, ed. Cavanillas J. M., C. Edward, and W. Wolfgang, 169–78. Cham: Springer.
   Google Scholar