Penerapan Metode Peramalan Arima Box-Jenkins Pada Harga Penutupan Harian Saham Alphabet Inc

Authors

  • Eva Zulfa N Bahfein Administrasi Bisnis, Administrasi Niaga, Politeknik Negeri Jakata, Depok, 10430, Indonesia, UP2M, Politeknik Negeri Jakrta, Depok, 10430, Indonesia
  • Ni Made Widhi Sugianingsih Administrasi Bisnis, Administrasi Niaga, Politeknik Negeri Jakata, Depok, 10430, Indonesia, UP2M, Politeknik Negeri Jakrta, Depok, 10430, Indonesia
  • Mawar Onida Sinaga Administrasi Bisnis, Administrasi Niaga, Politeknik Negeri Jakata, Depok, 10430, Indonesia, UP2M, Politeknik Negeri Jakrta, Depok, 10430, Indonesia

Keywords:

ACF, ARIMA, EACF, PACF, Saham

Abstract

Abstract

Forecasting stock prices is important information needed by companies in the capital market. With the forecasting of Alphabet Inc.'s stock price, the company can prepare to face the market for the investment it takes. One method that can be used in forecasting is the time series analysis method. Time series data is one type of data that is collected according to a time sequence within a certain time span. The rationale for the series is that the current observation (Zt) is influenced by one or more previous observations (Zt-k). In other words, a time series model is created because of the correlation between series observations. The objectives of time series analysis include understanding and explaining certain mechanisms, predicting a future value and optimizing control systems (Hamilton, 2020). Therefore, researchers are interested in conducting research with the title "Application of Box Jenkins ARIMA (Autoregressive Integrated Moving Average) Forecasting Method on Alphabet Inc's Daily Closing Prices."

Keywords ACF, ARIMA, EACF, PACF, Stock

References

Ayu Rezaldi, D. (2021). PRISMA, Prosiding Seminar Nasional Matematika Peramalan Metode ARIMA Data Saham PT. Telekomunikasi Indonesia. Peramalan Metode ARIMA Data Saham PT. Telekomunikasi Indonesia. PRISMA, Prosiding Seminar Nasional Matematika, 4, 611–620. https://journal.unnes.ac.id/sju/index.php/prisma/

Adebiyi, A. A., Adewumi, A. O., & Ayo, C. K. (2014). Comparison of ARIMA and artificial neural networks models for stock price prediction. Journal of Applied Mathematics, 2014.Bao, W., Yue, J., & Rao, Y. (2017). A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one, 12(7), e0180944.

Cao, J., & Wang, J. (2019). Stock price forecasting model based on modified convolution neural network and financial time series analysis. International Journal of Communication Systems, 32(12), e3987.Cryer, Jonathan D. dan Kung-Sik Chan. (2002). Time Series Analysis: With Applications in R Second Edition. New York: Springer Science+Business Media.

FAUZI, A. (2019). Forecasting saham syariah dengan menggunakan LSTM. Al-Masraf: Jurnal Lembaga Keuangan Dan Perbankan, 4(1), 65–69.

Hamilton, J. D. (2020). Time series analysis. Princeton university press.

Hilal, Samsul. (2003) Analisis Peramalan Konsumsi Energi Primer di Indonesia dengan Metode ARIMA, tesis. Depok: Universitas Indonesia.

Khodijah, Z. S., & Sirodj, D. A. N. (2021). Analisis Trend dalam Meramalkan Harga Saham pada Indeks Saham Syariah Indonesia (ISSI) Tahun 2021. Prosiding Statistika, 441–448.

Latumahina, H., & Radjabaycolle, J. E. T. (2021). PERAMALAN INFLASI KOTA AMBON TAHUN 2021 MENGGUNAKAN METODE ARIMA BOX JENKINS FORECASTING INFLATION AMBON CITY IN 2021 USING THE ARIMA BOX JENKINS METHOD.

Layla, N. N., Kurniati, E., & Suhaedi, D. (2021). Peramalan Indeks Harga Saham dengan metode Autoregressive Moving Average Generelized Autoregressive Conditional Heteroscedasticity (ARMA GARCH).

Marcellino, M., Stock, J. H., & Watson, M. W. (2006). A comparison of direct and iterated multistep AR methods

for forecasting macroeconomic time series. Journal of econometrics, 135(1-2), 499-526.

Mehtab, S., & Sen, J. (2020). A time series analysis-based stock price prediction using machine learning and deep learning models. International Journal of Business Forecasting and Marketing Intelligence, 6(4), 272-335.

Montgomery, D. C., Jennings, C. L., & Kulahci, M. (1998). Introduction To Time Series Analysis. United State Of

America: Wiley-Intersciense.

Mondal, P., Shit, L., & Goswami, S. (2014). Study of effectiveness of time series modeling (ARIMA) in forecasting stock prices. International Journal of Computer Science, Engineering and Applications, 4(2), 13.

Mulyono, Sri. (2000). Peramalan Harga Saham dan Nilai Tukar: Teknik Box-Jenkins. Ekonomi dan Keuangan Indonesia Volume XLVIII Nomor 2, 125-142.

Musdholifah, A., & Sari, A. K. (2019). Optimization of ARIMA forecasting model using firefly algorithm. IJCCS (Indonesian Journal of Computing and Cybernetics Systems), 13(2), 127–136.

Nurulita. (2010). Penerapan Metode Peramalan ARIMA (Autoregressive Integrated Moving Average) Untuk

Penentuan Tingkat Safety Stock Pada Industri Elektronik, skripsi. Depok: Universitas Indonesia.

Pai, P. F., & Lin, C. S. (2005). A hybrid ARIMA and support vector machines model in stock price forecasting. Omega, 33(6), 497-505.

Pamungkas, M. B., & Wibowo, A. (2019). Aplikasi Metode Arima Box-Jenkins Untuk Meramalkan Kasus Dbd Di Provinsi Jawa Timur. The Indonesian Journal of Public Health, 13(2), 183.

Perawati, L., & Muhardi, M. (2018). Analisis Peramalan Penjualan Kopi (Kapal Api) Menggunakan Metode Arima Box-Jenkins pada PT Fastrata Buana Bandung. Prosiding Manajemen, 604–610.

Perihatini, D. I., Lestari, I. F., & Primandari, A. H. (2018). Peramalan Harga Cabai Merah Besar Keriting Kabupaten Banyumas Menggunakan Metode ARIMA Box-Jenkins. Prosiding KNPMP III.

Pitaloka, R. A., Sugito, S., & Rahmawati, R. (2019). PERBANDINGAN METODE ARIMA BOX-JENKINS DENGAN ARIMA ENSEMBLE PADA PERAMALAN NILAI IMPOR PROVINSI JAWA TENGAH. Jurnal Gaussian, 8(2), 194–207.

Purnaningrum, E. (2020). Pendekatan Metode Kalman Filter untuk Peramalan Pergerakan Indeks Harga Saham Terdampak Pandemi Coronavirus. Majalah Ekonomi, 25(2), 103–109.

Tsaih, R. (2000). Forecasting stock markets using wavelet transforms and recurrent neural networks: An integrated system based on artificial bee colony algorithm. Expert Systems with Applications, 39(1), 150-165.

Yahoo Finance. Alphabet Inc. (GOOG) Historical Data, sumber data. https://finance.yahoo.com/quote/GOOG/history?p=GOOG (11 Agustus 2022)

Zhang, J., Liu, Y., Zhang, J., & Zhu, Q. (2018). Stock price prediction with ARIMA, LSTM, and hybrid models using news sentiment analysis. Complexity, 2018.

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Published

2023-08-05

How to Cite

Bahfein, E. Z. N., Sugianingsih, N. M. W., & Sinaga, M. O. (2023). Penerapan Metode Peramalan Arima Box-Jenkins Pada Harga Penutupan Harian Saham Alphabet Inc. Seminar Nasional Inovasi Vokasi, 2, 394–405. Retrieved from https://prosiding.pnj.ac.id/sniv/article/view/439