Stock Market Index Prediction using Bi-directional Long Short-Term Memory

Muhammad Althaf Majid; Prilyandari Dina Saputri; Soehardjoepri Soehardjoepri

doi:10.30871/jaic.v8i1.7195

Muhammad Althaf Majid Institut Teknologi Sepuluh Nopember
Prilyandari Dina Saputri Institut Teknologi Sepuluh Nopember
Soehardjoepri Soehardjoepri Institut Teknologi Sepuluh Nopember

DOI: https://doi.org/10.30871/jaic.v8i1.7195

Keywords: bi-LSTM, Global Index, IHSG, LSTM

Abstract

The IHSG (Indonesia Stock Exchange Composite Index) is a stock price index in the Indonesia Stock Exchange (BEI) that serves as an indicator reflecting the performance of company stocks through stock price movements. Therefore, IHSG becomes a reference for investors in making investment decisions. Advanced stock exchanges generally have a strong influence on other stock exchanges. Several studies have proven the influence of one global index on another. Global index is a term that refers to each country's index to represent the movement of its country's stock performance. Forecasting IHSG can be one of the analyses that help investors make wise decisions when investing. To obtain an IHSG forecasting model, an appropriate and suitable method is needed, especially for data that has a large amount. LSTM is a development of Recurrent Neural Network (RNN) which has the ability to remember information in a longer period of time, while Bi-LSTM is a development of LSTM which has the ability to remember information longer and can understand more complex patterns than LSTM. This research provide the IHSG forecasting based on global index factors. The results showed that the best Bi-LSTM model (6-9-1) had a better performance in predicting and forecasting JCI movements with a MAPE value of 0.572314% better than the best LSTM model (4-10-1) which had a MAPE of 0.74326%. With forecasting based on the Bi-LSTM model, it is expected to help investors in making decisions on the Indonesia Stock Exchange (IDX).

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References

D. Bery, “Pengaruh Indeks Harga Saham Global Terhadap Indeks Harga Saham Gabungan (IHSG),” Universitas Brawijaya, 2018.

S. D. Prahesti, “Pengaruh Indeks SSEC, N225, STI, dan Faktor Makroekonomi terhadap IHSG,” J. Ilmu Manaj. Vol., vol. 8, no. 3, pp. 878–893, 2020.

M. Jonathan, “Pengaruh Pergerakan Indeks Global Terhadap Pergerakan IHSG Periode 2015-2021,” Universitas Atma Jaya Yogyakarta, 2022.

S. Siami-Namini, N. Tavakoli, and A. S. Namin, “The Performance of LSTM and BiLSTM in Forecasting Time Series,” 2019.

K. Moharm, M. Eltahan, and E. Elsaadany, “Wind speed forecast using LSTM and Bi-LSTM algorithms over gabal el-zayt wind farm,” Proc. - 2020 Int. Conf. Smart Grids Energy Syst. SGES 2020, pp. 922–927, 2020, doi: 10.1109/SGES51519.2020.00169.

A. Nilsen, “Model LSTM, dan Model GRU dalam Memprediksi Harga Saham-Saham LQ45.,” J. Stat. dan Apl., vol. 6, no. 1, pp. 137–147, 2022.

U. Ugurlu, I. Oksuz, and O. Tas, “Electricity price forecasting using recurrent neural networks,” Energies, vol. 11, no. 5, pp. 1–23, 2018, doi: 10.3390/en11051255.

S. Gangwar, V. Bali, and A. Kumar, “o n Scalable Information Systems EAI Endorsed Transactions Comparative Analysis of Wind Speed Forecasting Using LSTM and SVM,” vol. 7, no. 25, pp. 1–9, 2020.

S. T. Lammoreno, “Peramalan IHSG Berdasarkan Faktor Indeks Global Menggunakan Metode Long Short-Term Memory dan Gated Recurrent Unit,” Institut Teknologi Sepuluh Nopember, 2023.

S. Zaheer et al., “A Multi Parameter Forecasting for Stock Time Series Data Using LSTM and Deep Learning Model,” Mathematics, vol. 11, no. 3, pp. 1–24, 2023, doi: 10.3390/math11030590.

C. Choi, “Time Series Forecasting with Recurrent Neural Networks in Presence of Missing Data,” UiT The Arctic University of Norway, 2018.

D. I. Puteri, “Implementasi Long Short Term Memory (LSTM) dan Bidirectional Long Short Term Memory (BiLSTM) Dalam Prediksi Harga Saham Syariah,” EULER J. Ilm. Mat. Sains dan Teknol., vol. 11, no. 1, pp. 35–43, 2023.