The Implementation of Artificial Neural Networks for Stock Price Prediction
DOI:
https://doi.org/10.55606/jeei.v3i3.2254Kata Kunci:
Machine Learning, Stock Price Prediction, Neural Network, Multilayer Perceptron, MLP.Abstrak
This research is based on a problem that is difficult to predict stock prices, especially for beginners. Stock prices are hard to predict because they are fluctuating. Users will be easier to predict stock prices through artificial neural networks using Multilayer Perceptron. This MLP is a variant of an artificial neural network and is a development of perceptron. The selection of the Multilayer Perceptron method is based on the ability to solve various problems both classification and regression. The research conducted by the author is a regression problem as the MLP is tasked to predict the close price or closing price of stock after seven days. The results of the model built are able to predict stock prices and produce good accuracy because the resulting RMSE value produced 0.042649862994352014, which is close to 0.
Keywords: Machine Learning, Stock Price Prediction, Neural Network, Multilayer Perceptron, MLP.
Referensi
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Sareen, A., & Sharma, S. (2022). Assessing Financial Distress and Predicting Stock Prices of Automotive Sector: Robustness of Altman Z-score. Vision, 26(1). https://doi.org/10.1177/0972262921990923
Satria, D. (2023). PREDICTING BANKING STOCK PRICES USING RNN, LSTM, AND GRU APPROACH. Applied Computer Science, 19(1). https://doi.org/10.35784/acs-2023-06
Shaheen, H., Agarwal, S., & Ranjan, P. (2020). Minmaxscaler binary pso for feature selection. Advances in Intelligent Systems and Computing, 1045. https://doi.org/10.1007/978-981-15-0029-9_55
Sheth, D., & Shah, M. (2023). Predicting stock market using machine learning: best and accurate way to know future stock prices. In International Journal of System Assurance Engineering and Management (Vol. 14, Issue 1). https://doi.org/10.1007/s13198-022-01811-1
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Yang, C., & Zhou, L. (2016). Individual stock crowded trades, individual stock investor sentiment and excess returns. North American Journal of Economics and Finance, 38. https://doi.org/10.1016/j.najef.2016.06.001
Afifah, S. N., & Fauziyyah, N. (2023). Dampak Resesi 2023 terhadap Harga Saham di Indonesia. MIZANIA: Jurnal Ekonomi Dan Akuntansi, 3(1). https://doi.org/10.47776/mizania.v3i1.612
Awad, A. L., Elkafas, S. M., & Fakhr, M. W. (2021). ROle Of Machine Learning In Predicting Stock Prices: A Literature Survey. Journal of Management Information and Decision Sciences, 24(SpecialIssue1).
Bhattacharjee, J., Singh, R., & Kajol, K. (2021). Risk perception in respect of equity shares: A literature review and future research agenda. In DLSU Business and Economics Review (Vol. 30, Issue 2).
Deepa, B., & Ramesh, K. (2022). Epileptic seizure detection using deep learning through min max scaler normalization. International Journal of Health Sciences. https://doi.org/10.53730/ijhs.v6ns1.7801
Dewan Standar Akuntansi Keuangan IAI. (2018). Standar Akuntansi Keuangan. 934.
Gibbs, H. K., Brown, S., Niles, J. O., & Foley, J. A. (2007). Monitoring and estimating tropical forest carbon stocks: Making REDD a reality. Environmental Research Letters, 2(4). https://doi.org/10.1088/1748-9326/2/4/045023
Huang, B., & Fang, X. (2021). Market Sentiment, Valuation Heterogeneity, and Corporate Investment: Evidence from China’s A-Share Stock Market. Emerging Markets Finance and Trade, 57(8). https://doi.org/10.1080/1540496X.2019.1672531
Husnul, H. M., Hidayat, R. R., & Sulasmiyati, S. (2017). Analisis pengaruh inflasi, kurs (IDR/USD), Produk Domestik Bruto dan harga emas dunia terhadap Indeks Harga Saham Gabungan. Jurnal Administrasi Bisnis, 53(1).
Jaggi, M., Mandal, P., Narang, S., Naseem, U., & Khushi, M. (2021). Text mining of stocktwits data for predicting stock prices. Applied System Innovation, 4(1). https://doi.org/10.3390/asi4010013
Joseph, S., Mduma, N., & Nyambo, D. (2023). A Deep Learning Model for Predicting Stock Prices in Tanzania. Engineering, Technology & Applied Science Research, 13(2). https://doi.org/10.48084/etasr.5710
Napitupulu, R. D. (2021). PENGARUH CR DAN HARGA SAHAM TERHADAP ROA PADA PERUSAHAAN SUB SEKTOR FOOD AND BEVERAGE YANG TERDAFTAR DI BURSA EFEK INDONESIA. Journal of Information System, Applied, Management, Accounting and Research, 5(1). https://doi.org/10.52362/jisamar.v5i1.361
Polamuri, S. R., Srinivas, K., & Krishna Mohan, A. (2019). Stock market prices prediction using random forest and extra tree regression. International Journal of Recent Technology and Engineering, 8(3). https://doi.org/10.35940/ijrte.C4314.098319
Sadorsky, P. (2021). A Random Forests Approach to Predicting Clean Energy Stock Prices. Journal of Risk and Financial Management, 14(2). https://doi.org/10.3390/jrfm14020048
Sareen, A., & Sharma, S. (2022). Assessing Financial Distress and Predicting Stock Prices of Automotive Sector: Robustness of Altman Z-score. Vision, 26(1). https://doi.org/10.1177/0972262921990923
Satria, D. (2023). PREDICTING BANKING STOCK PRICES USING RNN, LSTM, AND GRU APPROACH. Applied Computer Science, 19(1). https://doi.org/10.35784/acs-2023-06
Shaheen, H., Agarwal, S., & Ranjan, P. (2020). Minmaxscaler binary pso for feature selection. Advances in Intelligent Systems and Computing, 1045. https://doi.org/10.1007/978-981-15-0029-9_55
Sheth, D., & Shah, M. (2023). Predicting stock market using machine learning: best and accurate way to know future stock prices. In International Journal of System Assurance Engineering and Management (Vol. 14, Issue 1). https://doi.org/10.1007/s13198-022-01811-1
Sugiyono. (2019). Metode penelitian dan pengembangan (research and development/R&D). Alfabeta.
Yang, C., & Zhou, L. (2016). Individual stock crowded trades, individual stock investor sentiment and excess returns. North American Journal of Economics and Finance, 38. https://doi.org/10.1016/j.najef.2016.06.001
Unduhan
Diterbitkan
2023-10-31
Cara Mengutip
Akbar Maulana, & Enny Itje Sela. (2023). The Implementation of Artificial Neural Networks for Stock Price Prediction. Journal of Engineering, Electrical and Informatics, 3(3), 34–44. https://doi.org/10.55606/jeei.v3i3.2254
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