Prediksi Harga Saham Bank BCA Menggunakan XGBoost
DOI:
https://doi.org/10.47065/arbitrase.v3i2.495Keywords:
Prediction; Stock Price; XGBoost; Technical Indicator; Feature EngineeringAbstract
This study aims to determine the prediction of Bank BCA's stock price using XGBoost. XGBoost is an open source implementation of Gradient Boosting for generating forecasts based on historical data that is fast and scalable. The data in this study is stock price data of Bank BCA for 4 (four) years, namely from 01-01-2017 to 31-12-2020. The technical indicators used in this study are the Simple Moving Average (SMA), Exponential Moving Average (EMA), Moving Average Convergence/Divergence (MACD) and Relative Strength Index (RSI). The results show that the Exponential Moving Average technical indicator greatly influences the prediction results. The results of this study also show a fairly good prediction accuracy with MAPE of 4.01 percent with hyper parameter settings; but the predictions are slightly less good in March 2020 due to the Covid-19 pandemic case.
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References
Beyaz, Erhan & Tekiner, Firat & Zeng, Xiao-jun & Keane, John A. (2018). Stock price forecasting incorporating market state. 2018 IEEE 20th International Conference on High Performance Computing and Communications; IEEE 16th International Conference on Smart City; IEEE 4th International Conference on Data Science and Systems (HPCC/SmartCity/DSS). IEEE, 1614-1619.
Chen, Tianqi & Guestrin, Carlos (2016). XGBoost: A scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, 785-794.
Friedman, Jerome H. (2001). Greedy function approximation: a gradient boosting machine. Annals of statistics, 1189-1232.
Jange, Beno. (2021). Prediksi Harga Saham Bank BCA Menggunakan Prophet. Journal of Trends Economics and Accounting Research, 2(1), 1-5.
Jange, Beno. (2022). Prediksi Indeks Harga Saham Gabungan Menggunakan Prophet. JOTIKA Journal In Management and Entrepreneurship, 1(2), 53-59.
Kim, HyoungDo (2018). Prediction of the Movement Directions of Index and Stock Prices Using Extreme Gradient Boosting. The Journal of the Korea Contents Association, 18(9), 623-632.
Malkiel, Burton G. (2003). The Efficient Market Hypothesis and Its Critics. Journal of Economic Perspectives, 17(1), 59-82.
Nabipour, M., Nayyeri, P., Jabani, H., Mosavi, A., & Salwana, E. (2020). Deep learning for stock market prediction. Entropy, 22(8), 840.
Nobre, João & Neves, Rui Ferreira (2019). Combining principal component analysis, discrete wavelet transform and XGBoost to trade in the financial markets. Expert Systems with Applications, 125, 181-194.
Oriani, Felipe Barboza & Coelho, Guilherme P. (2016). Evaluating the impact of technical indicators on stock forecasting. 2016 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE. 1-8.
Stankovi?, Jelena & Markovi?, Ivana & Stojanovi?, Miloš (2015). Investment Strategy Optimization Using Technical Analysis and Predictive Modeling in Emerging Markets. Procedia Economics and Finance, 19, 51–62.
Taran-Morosan, Adrian (2009). Some Technical Analysis Indicators. Revista Economica, 46(3), 116-121.
Yun, Kyung Keun & Yoon, Sang Won & Won, Daehan (2021). Prediction of stock price direction using a hybrid GA-XGBoost algorithm with a three-stage feature engineering process. Expert Systems with Applications, 186, 115716.
Zhang, Yifan (2022). Stock Price Prediction Method Based on XGBoost Algorithm. 2022 International Conference on Big Data Blockchain and Economy Management (ICBBEM 2022), 595-603.
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