Abstract
Understanding stock price movements in a few moments consider a pivotal step for investors to decide their next actions. To read the movements easily, we can offer stock price movement patterns to the investor. Existing studies on mining stock price movement patterns mostly focused on the frequent ones. In this work, we aim to assist the investors to see stock price movement patterns that bring profit. Additionally, we consider profitable stock price movements pattern that also frequently occurs in the dataset. To be specific, this work applies two different data mining methods; fuzzy high utility itemset mining and skyline frequent utility pattern mining. Further, this work formulates profit stock price returns to help us recognise the benefit ones. Also, this work addresses the uncertainty movements from the fuzzy set. In this work, we analyzed fifteen companies in Indonesia. We listed a few suggestions like portfolios including the company, the company sector, and the whole listed company that is most profitable.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
Note that we follow the range value of each item based on the given data.
- 2.
Due to the limitation space, we neglect the details information of the results.
References
Arafah, A.A., Mukhlash, I.: The application of fuzzy association rule on co-movement analyze of Indonesian stock price. Proc. Comput. Sci. 59, 235–243 (2015). https://doi.org/10.1016/j.procs.2015.07.541. International Conference on Computer Science and Computational Intelligence (ICCSCI 2015)
Chen, Y., Wu, J., Wu, Z.: China’s commercial bank stock price prediction using a novel k-means-lstm hybrid approach. Expert Syst. Appl. 202, 117370 (2022). https://doi.org/10.1016/j.eswa.2022.117370
Cheng, K.C., Huang, M.J., Fu, C.K., Wang, K.H., Wang, H.M., Lin, L.H.: Establishing a multiple-criteria decision-making model for stock investment decisions using data mining techniques. Sustainability 13(6) (2021). https://doi.org/10.3390/su13063100
Fournier-Viger, P., Chun-Wei Lin, J., Truong-Chi, T., Nkambou, R.: A Survey of High Utility Itemset Mining, pp. 1–45. Springer International Publishing, Cham (2019). https://doi.org/10.1007/978-3-030-04921-8_1
Fournier-Viger, P., Wu, C.W., Zida, S., Tseng, V.S.: Fhm: Faster high-utility itemset mining using estimated utility co-occurrence pruning. In: Andreasen, T., Christiansen, H., Cubero, J.C., Raś, Z.W. (eds.) Foundations of Intelligent Systems, pp. 83–92. Springer International Publishing, Cham (2014). https://doi.org/10.1007/978-3-319-08326-1_9
Kocian, A., Chessa, S.: Auto regressive integrated moving average modeling and support vector machine classification of financial time series. In: Bucciarelli, E., Chen, S.H., Corchado, J.M. (eds.) Decision Economics. Designs, Models, and Techniques for Boundedly Rational Decisions, pp. 1–8. Springer International Publishing, Cham (2019)
Krishnamoorthy, S.: Hminer: efficiently mining high utility itemsets. Expert Syst. Appl. 90, 168–183 (2017). https://doi.org/10.1016/j.eswa.2017.08.028
Lan, G.C., Hong, T.P., Lin, Y.H., Wang, S.L.: Fuzzy utility mining with upper-bound measure. Appl. Soft Comput. 30, 767–777 (2015). https://doi.org/10.1016/j.asoc.2015.01.055
Lin, J.C.W., Yang, L., Fournier-Viger, P., Hong, T.P.: Mining of skyline patterns by considering both frequent and utility constraints. Eng. Appl. Artif. Intell. 77, 229–238 (2019). https://doi.org/10.1016/j.engappai.2018.10.010
Liu, H., Du, H.: Stock sequence pattern mining method based on swi-gsp algorithm. In: Proceedings of the 2017 International Conference on Data Mining, Communications and Information Technology. DMCIT ’17. Association for Computing Machinery, New York, USA (2017). https://doi.org/10.1145/3089871.3089875
Liu, M., Qu, J.: Mining High Utility Itemsets Without Candidate Generation, pp. 55–64 (2012). https://doi.org/10.1145/2396761.2396773
Mukhlash, I., Iqbal, M., Wulandari, M.N., Arafah, A.A.: Mining fuzzy time intervals of fuzzy stock price co-movements patterns. ICIC Express Lett. B: Appl. 10, 859–866 (2019). https://doi.org/10.24507/icicelb.10.10.859
Raval, H.R., Kaushik, D.: Efficient Mining of Fast Frequent Itemset Discovery from Stock Market Data (2015)
Syu, J.H., Yeh, Y.R., Wu, M.E., Ho, J.M.: Self-management portfolio system with adaptive association mining: a practical application on Taiwan stock market. Mathematics 9(10) (2021). https://doi.org/10.3390/math9101093
Acknowledgements
This research is partially supported by the Institute for Research and Community Development of Institut Teknologi Sepuluh Nopember, Surabaya through Lab Based Education scheme with Funding Agreement Letter of 1102/PKS/ITS/2019.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Iqbal, M., Simanungkalit, E., Latifa, S.N., Hidayat, N., Mukhlash, I. (2023). Discovery of Profitable Stock Price Movement Patterns from Various High Utility Pattern Mining. In: Julián, V., Carneiro, J., Alonso, R.S., Chamoso, P., Novais, P. (eds) Ambient Intelligence—Software and Applications—13th International Symposium on Ambient Intelligence. ISAmI 2022. Lecture Notes in Networks and Systems, vol 603. Springer, Cham. https://doi.org/10.1007/978-3-031-22356-3_5
Download citation
DOI: https://doi.org/10.1007/978-3-031-22356-3_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-22355-6
Online ISBN: 978-3-031-22356-3
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)