Lely Hiryanto
Fakultas Teknologi Informasi Universitas Tarumanagara Jakarta - Indonesia

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Journal : Jurnal Teknik Informatika (JUTIF)

Forecasting Indonesian Banking Stock Prices Using Prophet, XGBoost, and Ridge Regression: A Comparative Analysis Tony, Tony; Ratchagit, Manlika; Hiryanto, Lely
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 4 (2025): JUTIF Volume 6, Number 4, Agustus 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.4.4973

Abstract

This study investigates the efficacy of Prophet, XGBoost, and Ridge Regression in forecasting stock prices of four major Indonesian banks—Bank Central Asia (BBCA.JK), Bank Negara Indonesia (BBNI.JK), Bank Rakyat Indonesia (BBRI.JK), and Bank Mandiri (BMRI.JK)—using daily historical data from January 2020 to March 2025, sourced from Yahoo Finance. The banking sector's volatility, evidenced by year-to-date declines ranging from 7.59% (BBCA) to 22.69% (BMRI) as of May 1, 2025, underscores the need for accurate predictive models. Performance was evaluated using Mean Absolute Error (MAE) and Root Mean Square Error (RMSE), revealing Ridge Regression as the superior method, consistently achieving the lowest errors (i.e., MAE of 23.81 for BBNI.JK and RMSE of 55.75 for BBCA.JK). Prophet exhibited the highest errors, suggesting its seasonal focus is less suited to stock price unpredictability, while XGBoost performed moderately better but lagged behind Ridge Regression. The results highlight Ridge Regression’s effectiveness in handling multicollinearity and noise in financial data. Our discussions emphasize the importance of model selection based on data characteristics, with implications for investment decision-making in the Indonesian market. This research contributes to the field of computational finance by providing a comparative analysis that not only identifies Ridge Regression as a superior method for forecasting stock prices but also illuminates the limitations of popular models like Prophet and XGBoost in handling financial data's unique characteristics, guiding future model selection and development. Future research could explore hybrid models to enhance accuracy across varied market conditions, addressing the study’s 60-day forecasting horizon limitation.
Co-Authors Alfine Candra Cuaca Anak Agung Gede Sugianthara Andre Widjaya Andri Firnandius Andri Muliawan Ardhytia Satria Nugraha Arnold Pramudita Tjiawi Aurelia Bagus Mulyawan bagus Mulyawan Bobby Tumbelaka Bobby Tumbelaka Brando Dharma Saputra Chairisni Lubis Chandra Wijaya Chandra Wijaya Chandra Wijaya Chintia Yusnita Violetta Darius Andana Haris Dedi Trisnawarman Desi Arisandi Dya Erny Herwindiati Dyah Erny Herwindiati Dyah Erny Herwindiati Elizabeth Erlsha Elizabeth Erlsha, Elizabeth Ericko Satyagraha Ericko Satyagraha Farenco Farenco Farenco Farenco Ferryanto Ferryanto Ferryanto Ferryanto Fika Alfiani Frankie Frankie Frankie Frankie, Frankie Fransisca Regina Fransisca Regina, Fransisca Gabriel Fransisco Gabriel Fransisco, Gabriel Grimaldi Suryadi Grimaldi Suryadi Gunadi Gan Gunadi Gan Harprori Patti Irawati Djajadi Irawati Djajadi Isa Iskandar Jacklin Sinthia Thio James Ariel Gunawan James Ariel Gunawan, James Ariel Janson Hendryli Jason Djatmiko Josselyn Sinthia Thio Karendef Karendef Kristianto, Hans Kurniawan Sulianto Lee, Viciano Lina Lina Listovie Cavito Mariana - Mariana Mariana Martono Darsono Martono Darsono Mishelle Tirtajaya Winartha Nadia Yanitra Nadya Yanitra, Nadya Pharadya Ajeng Swari Sukmawati Ratchagit, Manlika Renaldo Ali Renaldo Ali, Renaldo Riki Yohanes Hendriyanto Rionaldy Trisaputra Rosalinda . Rosalinda Rosalinda Satrya N. Ardhytia Stephanie Budianto Stephen Yan Putra Halim, Stephen Yan Putra Stevy Lie Stevy Lie, Stevy Sufisan Sufisan TATI NURHAYATI Teny Handhayani Timothy Reynaldi Tony Tony Tony Tony TRI SUTRISNO Vina Tandean Viny Christanti M Wirawan, Andhika Putra Yunita Yunita Yunita Yunita