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Wahyu Febri Ramadhan Sudirman
Univeristas Pahlawan Tuanku Tambusai
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Journal : International Journal of Electrical and Computer Engineering

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Feature selection using non-parametric correlations and important features on recursive feature elimination for stock price prediction Priyatno, Arif Mudi; Ramadhan Sudirman, Wahyu Febri; Musridho, Raja Joko
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 2: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i2.pp1906-1915

Abstract

Stock price prediction using machine learning is a rapidly growing area of research. However, the large number of features that can be used can complicate the learning process. The feature selection method that can be used to overcome this problem is recursive feature elimination. Standard recursive feature elimination carries the risk of producing inaccurate algorithms because the top-ranked features are not necessarily the most important features. This research proposes a feature selection method that combines important features and nonparametric correlation in recursive feature elimination for stock price prediction. The data features used are technical indicators and stock price history. The recursive feature elimination method is modified with important features and nonparametric correlation features. The strategy for combining important features and non-parametric features is average weight, 25:75% weight, 75:25% weight, maximum weight, and minimum weight. The performance evaluation results show that the proposed feature selection method succeeded in obtaining small error values. The proposed method for predicting PT Bank Rakyat Indonesia Tbk (BBRI) stock prices obtains mean squared error, root mean square error, mean absolute error, and mean absolute percentage error evaluation values of 0.0000336, 0.00577, 0.00459, and 1.78%, respectively. This shows that recursive feature elimination with feature selection that combines important features and non-parametric correlation works better than the original recursive feature elimination at predicting stock prices.
Co-Authors Adams, Ravi Afrinis, Nur Alfiatun Zahara Amalia, Fazilla Amalia, Nala AMELIA, NUR Amrina, Dania Hellin Andhini, Bunga Andi Irfan, Andi Andini, Bunga Anggun Pratiwi Anggun Pratiwi Arif Mudi Priyatno Aryadi Aryadi Aryadi Aryadi Aviandita , Reyngga Yusvika Berliana Putri Bunga Andhini Cahyani, Binda Rahma Cholidhazia, Putri Despira, Despira Diany Mairiza Eti, Eti Faridatus Suhadak Firmananda, Fahmi Iqbal Firmanda, Fahmi Iqbal Fitriyana, Rinda Hastuty, Milda Hayu Ardina Hermawanti, Sela Hermawati, Sela Hidayat Hidayat Hidayat Hidayat Irmawanti, Irmawanti Izzah, Aqidatul Jati, Putri Zulia Lasepa, Wanda Lismawati, Lismawati M. Halim, M. M. Zaim Maini, Nur Mardiyah, Suci Maretha Ika Prajawati Martini, Aldini Nofta Maziyah, Putri Maslahatul Mifta Hasda Mohd. Winario Muhammad Arif Muhammad Ikhsan Alif Muhammad Syaipudin Muhammad Zakir Muhammad Zakir Nadhirah, Ayu Nala Amelia Noranisa, Noranisa Nur Afrinis Nuraziza, Sania Nurnasrina Nurnasrina Raden Mohamad Herdian Bhakti Raja Joko Musridho Ramadhan, Al Insani Mutiara Ravi Adams Riani Riani, Riani Rifqil Khairi Rinancy, Hariyet Rinda Fithriyana Riski, Muhamamd Risya Khaerun Nisa Risya Khaerun Nisa Rizki, Syafnur Muhammad Rizky, Syafnur Muhammad Rizqi, Eka Roshifita Rizqon Jamil Farhas Rusnedy, Hidayati Saadah , Nila Sagena, Basir Sari, Efti Novita Saru Reza Sasmitandia, Kienna Candra Siahaan, Nur Halimah Suci Mardiya Suci Mardiyah Supardi Supardi Syafiq, M Rizan Syaipudin, Muhamamd Tanjung, Lailatul Syifa Yuni Siswanti Yusup Yusup Zahara, Alfiatun Zahrah, Alfiatun Zubaidah Assyifa