Neural Network on Stock Prediction using the Stock Prices Feature and Indonesian Financial News Titles
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Abstract
Stocks are the most popular investments among entrepreneurs or other investors. When investing in stocks these investors tend to learn how to invest stocks correctly and when is the right time. For the problem of how to invest shares correctly can be used a variety of basic theories that already exist, but for the problem when the right time needs further learning. In this paper will purpose about stock price prediction using stock data indicators and financial headline data in Bahasa Indonesia. The machine learning model used is a multi-layer perceptron neural network (MLP-NN) with the highest accuracy produced by 80%.
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[2] Malkiel, Burton G. "A random walk down Wall Street. W. W." (1973).
[3] White, Halbert. "Economic prediction using neural networks: The case of IBM daily stock returns." ICNN. Vol. 2. 1988.
[4] Zhang, G. Peter. "Time series forecasting using a hybrid ARIMA and neural network model." Neurocomputing 50 (2003): 159-175.
[5] Vanstone, Bruce, and Gavin Finnie. "An empirical methodology for developing stockmarket trading systems using artificial neural networks." Expert systems with Applications 36.3 (2009): 6668-6680.
[6] J. Bollen, H. Mao, and X. Zeng, Twitter mood predicts the stock market, Journal of Computational Science, 2(1), 1-8 (2011).
[7] Y. Zhai, A. Hsu and S. Halgamuge, Combining news and technical indicators in Daily stock price trends prediction, Advances in Neural Networks, 1087-1096 (2007).
[8] G. Rachlin, M. Last, D. Alberg and A. Kandel, ADMIRAL: A data mining based financial trading system, IEEE Symposium on Computational Intelligence and Data Mining, 720-725 (2007).
[9] J. J. Wang, J. Z. Wang, Z. G. Zhang and S. P. Guo, Stock index forecasting based on a hybrid model, Omega, 40(6), 758-766 (2012).
[10] R. P. Schumaker and H. Chen, A quantitative stock prediction system based on financial news, Information Processing and Management, 45(5), 571-583 (2009).
[11] M. Khashei and M. Bijari, A novel hybridization of artificial neural networks and ARIMA models for time series forecasting, Applied Soft Computing, 11(2), 2664-2675 (2011).
[12] A. K. Nassirtoussi, S. Aghabozorgi, T. Y Wah and D. C. L. Ngo, Text mining of news-headlines for FOREX market prediction: A Multi-layer Dimension Reduction Algorithm with semantics and sentiment, Expert Systems with Applications, 42(1), 306-324 (2015).
[13] Vargas, Manuel R., et al. "Deep leaming for stock market prediction using technical indicators and financial news articles." 2018 International Joint Conference on Neural Networks (IJCNN). IEEE, 2018.
[14] Y. Kim, Convolutional neural networks for sentence classification, arXiv preprint arXiv:1408.5882, (2014).
[15] X. Zhang and Y. LeCun, Text understanding from scratch, arXiv preprint arXiv:1502.01710, (2015).
[16] P. Blunsom, E. Grefenstette and N. Kalchbrenner, A convolutional neural network for modelling sentences, Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, (2014).
[17] R. Collobert and J. Weston. A unified architecture for natural language processing: Deep neural networks with multitask learning, Proceedings of the 25th International Conference on Machine Learning, ICML, New York, NY, USA, 160–167 (2008).
[18] T. Mikolov, M. Karafiát, L. Burget, J. Cernocký and S. Khudanpur, Recurrent neural network based language model, INTERSPEECH 2010, 11th Annual Conference of the International Speech Communication Association, Makuhari, Chiba, Japan, September 26-30, 1045–1048 (2010).
[19] O. Irsoy and C. Cardie, Opinion Mining with Deep Recurrent Neural Networks, EMNLP, 720-728 (2014).
[20] C. Zhou, C. Sun, Z. Liu and F. Lau, A C-LSTM neural network for text classification, arXiv preprint arXiv:1511.08630, (2015).
[21] S. Lai, L. Xu, K. Liu and J. Zhao, Recurrent Convolutional Neural Networks for Text Classification, AAAI, 2267-2273 (2015).
[22] Yu, Pengfei, and Xuesong Yan. "Stock price prediction based on deep neural networks." Neural Computing and Applications 32.6, 1609-1628, (2020).
[23] Gardner, Matt W., and S. R. Dorling. "Artificial neural networks (the multilayer perceptron)—a review of applications in the atmospheric sciences." Atmospheric environment 32, 14-15, 2627-2636, (1998).
[24] Yahoo Finance, https://finance.yahoo.com/quote/BMRI.JK/history?p=BMRI.JK, Accessed December 10, 2020.
[25] Yahoo, https://id.search.yahoo.com/, Accessed December 10, 2020.
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