APPLICATION OF FUZZY TIME SERIES WITH FIBONACCI RETRACEMENT FOR FORECASTING STOCK PRICE PT. BANK RAKYAT INDONESIA

  • Anggel Dwi Miranda Department of Mathematics, Faculty of Mathematics and Natural Sciences, Bengkulu University, Indonesia
  • Siska Yosmar Department of Mathematics, Faculty of Mathematics and Natural Sciences, Bengkulu University, Indonesia
  • Septri Damayanti Department of Mathematics, Faculty of Mathematics and Natural Sciences, Bengkulu University, Indonesia
Keywords: Fuzzy time series, Fibonacci retracement, Fibonacci forecast

Abstract

Stock can be defined as securities that indicate the ownership of a person or legal entity to the company issuing the shares. Good stocks for long-term investment are stocks that have good fundamentals and large market capitalization. The purpose of investing is to make a profit. In investing in stocks, investors need to know the risk management that can affect the ups and downs of a stock. Forecasting or forecasting is an analysis to predict everything related to the production, supply, demand, and use of technology in an industry or business. One of the forecasting methods is using fuzzy time series. The primary purpose of fuzzy time series is to predict time series data that can widely use on any real-time data, including capital market data. In this study, we will discuss the evolution of the time series model in overcoming fluctuations that often occur in stock prices by using a fuzzy time series that combines a stock analysis approach, namely Fibonacci retracement. The stock data used in this study is the close price of BBRI for October 2021 to March 2022. Forecasting results for 1 April 2022 are IDR 4660.49 with a Mean Absolute Percentage forecasting accuracy value of 1.034%.

Downloads

Download data is not yet available.

References

M. Simatupang, “Pengetahuan Praktis Investasi Saham dan Reksa Dana,” Jakarta: Mitra Wacana Media, 2010.

A. Sofia, “Profil PT. Bank Rakyat Indonesia ,” Investing.com, 2022.

S. Kusumadewi and H. Purnomo, “Aplikasi Logika Fuzzy untuk pendukung keputusan,” Yogyakarta: Graha Ilmu, pp. 33–34, 2010.

S.-H. Cheng, S.-M. Chen, and W.-S. Jian, “Fuzzy time series forecasting based on fuzzy logical relationships and similarity measures,” Inf Sci (N Y), vol. 327, pp. 272–287, 2016.

Muhammad Alfin Royyin, “Analisis Forecasting Harga Saham PT. Bank Central Asia Tbk (BBCA) Menggunakan Model Cheng pada Metode Fuzzy Time Series (Studi Kasus: Harga Saham PT. Bank Central Asia Tbk (BBCA) Periode 1 Juli 2019 – 31 Desember 2019),” 2020.

B. Siswoyo and A. Zaenal, “Model Peramalan Fuzzy Logic,” Jurnal Manajemen Informatika (JAMIKA), vol. 8, no. 1, 2018.

S. M. Vita Virgianti and N. Imro’ah, “Penerapan Fuzzy Time Series Chen Average Based Pada Peramalan Curah Hujan,” Bimaster: Buletin Ilmiah Matematika, Statistika dan Terapannya, vol. 10, no. 4.

T. L. Chen, C. H. Cheng, and H. Jong Teoh, “Fuzzy time-series based on Fibonacci sequence for stock price forecasting,” Physica A: Statistical Mechanics and its Applications, vol. 380, no. 1–2, pp. 377–390, Jul. 2007, doi: 10.1016/j.physa.2007.02.084.

N. Khairina, S. Kom, and M. Kom, “LOGIKA FUZZY.”

S. Fitria Eka, “Pengaplikasian Fuzzy Time Series Chen dan Fuzzy Time Series Cheng Dalam Memprediksi Kurs Rupiah Terhadap Dollar Singapura,” Universitas Islam Negeri Jakarta, Jakarta, 2019.

S. Utomo, Trading Saham dengan Menggunakan Fibonacci Retracement. Elex Media Komputindo, 2016.

Muhammad Nasir, “Probabilitas Profit Dari Penggunaan Fibonacci Retracement dan Stochastic Oscillator Pada Perdagangan Foreign Exchange,” Universitas Islam Negeri (UIN) Alaudin, Makassar, 2020.

P. Mahadma, “Penerapan Fuzzy Time Series Dengan Prinsip Gelombang Elliot Untuk Peramalan Harga Saham,” Universitas Brawijaya, Malang, 2014.

Aswi and Sukarna, Analisis Data Deret Waktu : Teori dan Aplikasi. 2016.

S. Solikhin and U. Yudatama, “Fuzzy Time Series dan Algoritme Average Based Length untuk Prediksi Pekerja Migran Indonesia,” Jurnal Teknologi Informasi dan Ilmu Komputer, vol. 6, no. 4, pp. 369–376, 2019.

S.-M. Chen, “Forecasting Enrollments Based on High-Order Fuzzy Time Series.,” Cybern Syst, vol. 33, pp. 1–16, Jan. 2002, doi: 10.1080/019697202753306479.

Published
2023-06-11
How to Cite
[1]
A. Dwi Miranda, S. Yosmar, and S. Damayanti, “APPLICATION OF FUZZY TIME SERIES WITH FIBONACCI RETRACEMENT FOR FORECASTING STOCK PRICE PT. BANK RAKYAT INDONESIA”, BAREKENG: J. Math. & App., vol. 17, no. 2, pp. 0787-0796, Jun. 2023.