Discovery of Profitable Stock Price Movement Patterns from Various High Utility Pattern Mining

Mohammad Iqbal; Edwina Simanungkalit; Safira Nur Latifa; Nurul Hidayat; Imam Mukhlash

doi:10.1007/978-3-031-22356-3_5

Discovery of Profitable Stock Price Movement Patterns from Various High Utility Pattern Mining

Mohammad Iqbal^*, Edwina Simanungkalit, Safira Nur Latifa, Nurul Hidayat, Imam Mukhlash

^*Corresponding author for this work

Laboratory of Machine Learning and Big Data

Institut Teknologi Sepuluh Nopember

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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.

Original language	English
Title of host publication	Ambient Intelligence—Software and Applications—13th International Symposium on Ambient Intelligence
Editors	Vicente Julián, João Carneiro, Ricardo S. Alonso, Pablo Chamoso, Paulo Novais
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	44-53
Number of pages	10
ISBN (Print)	9783031223556
DOIs	https://doi.org/10.1007/978-3-031-22356-3_5
Publication status	Published - 2023
Event	13th International Symposium on Ambient Intelligence, ISAmI 2022 - L´Aquila, Italy Duration: 13 Jul 2022 → 15 Jul 2022

Publication series

Name	Lecture Notes in Networks and Systems
Volume	603 LNNS
ISSN (Print)	2367-3370
ISSN (Electronic)	2367-3389

Conference

Conference	13th International Symposium on Ambient Intelligence, ISAmI 2022
Country/Territory	Italy
City	L´Aquila
Period	13/07/22 → 15/07/22

Keywords

Data mining
High utility mining
Profit
Stock price

Access to Document

10.1007/978-3-031-22356-3_5

Cite this

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 V. Julián, J. Carneiro, R. S. Alonso, P. Chamoso, & P. Novais (Eds.), Ambient Intelligence—Software and Applications—13th International Symposium on Ambient Intelligence (pp. 44-53). (Lecture Notes in Networks and Systems; Vol. 603 LNNS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-22356-3_5

Iqbal, Mohammad ; Simanungkalit, Edwina ; Latifa, Safira Nur et al. / Discovery of Profitable Stock Price Movement Patterns from Various High Utility Pattern Mining. Ambient Intelligence—Software and Applications—13th International Symposium on Ambient Intelligence. editor / Vicente Julián ; João Carneiro ; Ricardo S. Alonso ; Pablo Chamoso ; Paulo Novais. Springer Science and Business Media Deutschland GmbH, 2023. pp. 44-53 (Lecture Notes in Networks and Systems).

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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.",

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Iqbal, M, Simanungkalit, E, Latifa, SN, Hidayat, N & Mukhlash, I 2023, Discovery of Profitable Stock Price Movement Patterns from Various High Utility Pattern Mining. in V Julián, J Carneiro, RS Alonso, P Chamoso & P Novais (eds), Ambient Intelligence—Software and Applications—13th International Symposium on Ambient Intelligence. Lecture Notes in Networks and Systems, vol. 603 LNNS, Springer Science and Business Media Deutschland GmbH, pp. 44-53, 13th International Symposium on Ambient Intelligence, ISAmI 2022, L´Aquila, Italy, 13/07/22. https://doi.org/10.1007/978-3-031-22356-3_5

Discovery of Profitable Stock Price Movement Patterns from Various High Utility Pattern Mining. / Iqbal, Mohammad; Simanungkalit, Edwina; Latifa, Safira Nur et al.
Ambient Intelligence—Software and Applications—13th International Symposium on Ambient Intelligence. ed. / Vicente Julián; João Carneiro; Ricardo S. Alonso; Pablo Chamoso; Paulo Novais. Springer Science and Business Media Deutschland GmbH, 2023. p. 44-53 (Lecture Notes in Networks and Systems; Vol. 603 LNNS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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Iqbal M, Simanungkalit E, Latifa SN, Hidayat N, Mukhlash I. Discovery of Profitable Stock Price Movement Patterns from Various High Utility Pattern Mining. In Julián V, Carneiro J, Alonso RS, Chamoso P, Novais P, editors, Ambient Intelligence—Software and Applications—13th International Symposium on Ambient Intelligence. Springer Science and Business Media Deutschland GmbH. 2023. p. 44-53. (Lecture Notes in Networks and Systems). doi: 10.1007/978-3-031-22356-3_5