DOI: https://doi.org/10.14710/medstat.15.1.36-47

EXTRA TREES METHOD FOR STOCK PRICE FORECASTING WITH ROLLING ORIGIN ACCURACY EVALUATION

*Dani Al Mahkya scopus  -  Actuarial Science Study Program, Institut Teknologi Sumatera, Indonesia
Khairil Anwar Notodiputro  -  Department of Statistics, IPB University, Indonesia
Bagus Sartono  -  Department of Statistics, IPB University, Indonesia
Open Access Copyright (c) 2022 MEDIA STATISTIKA under http://creativecommons.org/licenses/by-nc-sa/4.0.

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Abstract
Stock is an investment instrument that has risk in its management. One effort to minimize this risk is to model and make further forecasts of stock price movements. Time series data forecasting with autoregressive models is often found in several cases with the most popular approach being the ARIMA model. The tree-based method is one of the algorithms that can be used to forecast both in classification and regression. One ensemble approach to tree-based methods is Extra Trees. This study aims to forecast using the Extra Trees algorithm by evaluating forecasting accuracy with Rolling Forecast Origin on BRMS stock price data. Based on the results obtained, it is known that Extra Trees produces a fairly good accuracy for forecasting up to 6 days after training data with a MAPE of less than 0.1%.
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Keywords: Randomized Trees; Extra Trees; Regression; Stock; Forecasting

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Language : EN
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  1. Agustina & Sumartio, F., 2014. Analisis Faktor-faktor yang Mempengaruhi Pergerakan Harga Saham pada Perusahaan Pertambangan. Jurnal Wira Ekonomi Mikroskil, 4(1)
  2. Astutik, S. R. P., Sukestiyarno & Hendikawati, P., 2018. Peramalan Inflasi di Demak Menggunakan Metode ARIMA Berbantuan Software R dan MINITAB. Semarang, s.n
  3. Banarjee, M., Reynolds, E., Andersson, H. B. & Nallamouthu, B., 2019. Tree-Based Analysis: A Practical Approach to Create Clinical Decision Making Tools. Circ Cardiovasc Qual Outcomes, 12(5)
  4. Bumi Resource, 2003. PT BUMI Resources Tbk.. [Online]
  5. Available at: http://www.bumiresources.com/en/about-us/subsidiaries/detail/test
  6. [Accessed 1 Maret 2022]
  7. Fitriyani, F., A, S. F., R, M. I. & T, T. A., 2021. Peramalan Indeks Harga Saham PT Verena Multi Finance Tbk Dengan Metode Pemodelan ARIMA Dan ARCH-GARCH. J Statistika, 14(1), pp. 11-23
  8. Geurts, P., Ernst, D. & Wehenkel, L., 2006. Extremely randomized trees. Mach Learn , Volume 63, pp. 3-42
  9. Hameed, M. M., AlOmar, M. K., Khaleel, F. & Al-Ansari, N., 2021. An Extra Tree Regression Model for Discharge Coefficient Prediction: Novel, Practical Applications in the Hydraulic Sector and Future Research Directions. Hindawi Mathematical Problems in Engineering, Volume 2021
  10. Hyndman, R. J. & Athanasopoulos, G., 2021. Forecasting: Principles and Practice (3rd ed). Melbourne: OTexts
  11. Ispriyanti, D., Prahutama, A. & M., 2019. Klasifikasi Kemiskinan Di Kota Semarang Menggunakan Algoritma Chisquare Automatic Interaction Detection (Chaid) Dan Classification And Regression Tree (Cart). Media Statistika, 12(1), pp. 63-72
  12. Jatmiko, Y. A., Padmadisastra, S. & Chadidjah, A., 2019. Analisis Perbandingan Kinerja Cart Konvensional, Bagging Dan Random Forest Pada Klasifikasi Objek: Hasil Dari Dua Simulasi,. Media Statistika, 12(1), pp. 1-12
  13. Jose, D. M., Vincent, A. M. & Dwarakish, G. S., 2022. Improving multiple model ensemble predictions of daily precipitation and temperature through machine learning techniques. Sci Rep, 12(4678)
  14. Kumar, R., 2013. Decision Tree for the Weather Forecasting. International Journal of Computer Applications, 76(2)
  15. Kurnia, M. T., Nugrahani, E. H. & Sumarno, H., 2014. Analisis Wavelet dan ARIMA untuk Peramalan Harga Emas PT. ANTAM Tbk. Indonesia. Journal of Mathematics and Its Applications, 13(2)
  16. Lazar, C. & Lazar, M., 2015. Using the Method of Decision Trees in the Forecasting Activity. Economic Insights – Trends and Challenges, 4(1), pp. 41-48
  17. Mahkya, D. A. & Anggraini, D., 2019. Forecasting the Number of Passengers from Bakauheni Port during the Sunda Strait Tsunami Period Using Intervention Analysis Approach and Outlier Detection. Lampung Selatan, s.n
  18. Patel, H. H. & Prajapati, P., 2018. Study and Analysis of Decision Tree Based Classification Algorithms. International Journal of Computer Sciences and Engineering, 6(10)
  19. Rady, E. H. A., Fawzy, H. & Fattah, M. A., 2021. Time Series Forecasting Using Tree Based Methods. Journal of Statistics Applications & Probability, 10(1), pp. 229-244
  20. Rezaldi, D. A. & Sugiman, 2021. Peramalan Metode ARIMA Data Saham PT. Telekomunikasi Indonesia. Semarang, s.n
  21. Rupapara, V. et al., 2022. Blood cancer prediction using leukemia microarray gene data and hybrid logistic vector trees model. Sci Rep, 12(1000)
  22. Saham OK, 2011. Saham OK. [Online] Available at: https://www.sahamok.net/emiten/sektor-bei/ [Accessed 22 Maret 2022]
  23. Saini, P., Rai, S. & Jain, A. K., 2014. Decision Tree Algorithm Implementation Using Educational Data. International Journal of Computer-Aided technologies (IJCAx), 1(1)
  24. Sartono, B. & Syafitri, U. D., 2010. Metode Pohon Gabungan: Metode Gabungan untuk Mengatasi Kelemahan Pohon Regresi dan Klasifikasi Tunggal. Forum Statistika dan Komputasi, 15(1), pp. 1-7
  25. Sulia, 2017. Analisis Faktor-faktor yang Mempengaruhi Harga Saham pada Perusahaan LQ45 yang Terdaftar di Bursa Efek Indonesia. Jurnal Wira Ekonomi Mikroskil, 7(2)
  26. Wei, W. W. S., 2006. Time Series Analysis Univariate and Multivariate Method. New York: Pearson Education
  27. Zhang, F. et al., 2019. An Ensemble Cascading Extremely Randomized Trees Framework for Short-Term Traffic Flow Prediction. KSII Transactions on Internet and Information Systems, 13(4)

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