Markov average-based weighted fuzzy time series model to predict PT Kimia farma Tbk stock price
Abstract
The COVID-19 pandemic impacted various activities in Indonesia, including the stock market. Despite the declining economic condition, people are increasingly interested in investing. Among other companies available on the Indonesia Stock Exchange, companies in the health sector have a particular appeal to potential investors, one of which is pharmaceutical companies. This research used a Markov Average-Based Weighted Fuzzy Time Series model applied to PT Kimia Farma Tbk stock price data. This model develops the previous Markov chain–Fuzzy Time Series model, which has not calculated the weights for recurring events and used the Sturgess rule to determine the interval length. In this research, each recurring event has given a different weight that provides different probability values for transitions from one state to another. The Average-Based method is used to determine the interval length that can reflect the fluctuation of the data used. The stock price prediction of PT Kimia Farma Tbk using this model is categorized as very accurate with a MAPE of 2.632%.
Keywords
Full Text:
PDFReferences
Aladag, C. H. (2012). Advances in time series forecasting. In Advances in Time Series Forecasting. https://doi.org/10.2174/97816080537351120101
Díaz-Cortés, Margarita-Arimatea, Cuevas, Erik, Rojas, R. (2017). Engineering applications of soft computing. https://doi.org/10.1007/978-3-319-57813-2
Jatipaningrum, M. T., Suryowati, K., & Esti, L. M. (2019). Prediksi kurs rupiah terhadap dolar dengan fts-markov chain dan hidden. Jurnal Derivat, 6(1), 32–41.
Khuat, T. T., & Le, M. H. (2017). An application of artificial neural networks and fuzzy logic on the stock price prediction problem. International Journal on Informatics Visualization, 1(2), 40–49. https://doi.org/10.30630/joiv.1.2.20
Klimberg, R. K., Sillup, G. P., Boyle, K. J., & Tavva, V. (2010). Forecasting performance measures - What are their practical meaning? In Advances in Business and Management Forecasting (Vol. 7). Elsevier. https://doi.org/10.1108/S1477-4070(2010)0000007012
Liu, H., Manzoor, A., Wang, C., Zhang, L., & Manzoor, Z. (2020). The COVID-19 outbreak and affected countries stock markets response. International Journal of Environmental Research and Public Health, 17(8), 1–19. https://doi.org/10.3390/ijerph17082800
Otoritas Jasa Keuangan. (2021). Statistik pasar modal mei 202 minggu 4.
Susilowati, S., & Sulistijanti, W. (2018). Perbandingan metode fuzzy time series dengan metode box-jenkins untuk memprediksi jumlah kunjungan pasien rawat inap (studi kasus: Puskesmas geyer satu). Proceeding of The URECOL, 61–73.
Tsaur, R. C. (2012). A fuzzy time series-markov chain model with an application to forecast the exchange rate between the taiwan and us dollar. International Journal of Innovative Computing, Information and Control, 8(7 B), 4931–4942.
Utami, B. S., & Aliyansah, P. I. (2020). COVID-19: Challenges and opportunities in indonesia health sector. E3S Web of Conferences, 202, 1–7. https://doi.org/10.1051/e3sconf/202020201008
Xihao, S., L. Y. (2008). Average-based fuzzy time series models for forecasting Shanghai compound. World Journal of Modelling and Simulation, 4(2), 104–111.
Yu, H. K. (2005). Weighted fuzzy time series models for TAIEX forecasting. Physica A: Statistical Mechanics and Its Applications, 349(3–4), 609–624. https://doi.org/10.1016/j.physa.2004.11.006
Yunpeng, Sun; Qun, Bao; Zhou, L. (2021). Coronavirus (covid-19) outbreak, investor sentiment, and medical portfolio: Evidence from china, hong kong, korea, japan, and u.s. Pacific-Basin Finance Journal, 65. https://doi.org/https://doi.org/10.1016/j.pacfin.2020.101463
DOI: http://dx.doi.org/10.24042/djm.v4i3.9675
Refbacks
- There are currently no refbacks.
Copyright (c) 2021 Desimal: Jurnal Matematika
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Desimal: Jurnal Matematika is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.