Acknowledgement
This research was supported by 2021 Baekseok University Research Fund.
References
- S. H. Shin. (2013). Analysis on the Relation between Foreign Investors in Korean Stock Markets and Macroeconomic Variables. INTERNATIONAL BUSINESS REVIEW, 17(2), 89-107 https://doi.org/10.21739/IBR.2013.06.17.2.89
- M. F. Elhusseiny. (2017). Industries Stock Return Reactions To Risk Factors: An Empirical Investigation On The G-7 Countries. Journal of Financial and Monetary Economics, 4(1), 196-204.
- K. Guru-Gharan, K. M. Rahman & S. Parayitam. (2009). Influences of selected macroeconomic variables on US stock market returns and their predictability over varying time horizons. Academy of Accounting and Financial Studies Journal, 13(1), 13.
- Y. Qian, Y. Fan, W. Hu & F. K. Soong. (2014, May). On the training aspects of deep neural network (DNN) for parametric TTS synthesis. In 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). (pp. 3829-3833). IEEE.
- J. Mao, W. Xu, Y. Yang, J. Wang, Z. Huang & A. Yuille. (2014). Deep captioning with multimodal recurrent neural networks (m-rnn). arXiv preprint arXiv:1412.6632.
- R. Pascanu, C. Gulcehre, K. Cho & Y. Bengio. (2013). How to construct deep recurrent neural networks. arXiv preprint arXiv:1312.6026.
- I. Goodfellow, Y. Bengio, A. Courville & Y. Bengio. (2016). Deep learning (Vol. 1, No. 2). Cambridge : MIT press.
- L. R. Medsker & L. C. Jain. (2001). Recurrent neural networks. Design and Applications, 5.
- S. Selvin, R. Vinayakumar, E. A. Gopalakrishnan, V. K. Menon & K. P. Soman. (2017, September). Stock price prediction using LSTM, RNN and CNN-sliding window model. In 2017 international conference on advances in computing, communications and informatics (icacci). (pp. 1643-1647). IEEE.
- D. H. Shin, K. H. Choi & C. B. Kim. (2017). Deep Learning Model for Prediction Rate Improvement of Stock Price Using RNN and LSTM. Korean Institute of Information Technology, 15(10), 9-16.
- T. J. Hsieh, H. F. Hsiao & W. C. Yeh. (2011). Forecasting stock markets using wavelet transforms and recurrent neural networks: An integrated system based on artificial bee colony algorithm. Applied Soft Computing, 11(2), 2510-2525. doi:10.1016/j.asoc.2010.09.007
- M. S. Mahmud & P. Meesad. (2014). Time series stock price prediction using recurrent error based neuro-fuzzy system with momentum. 2014 International Electrical Engineering Congress (iEECON). (pp. 1-4) doi:10.1109/ieecon.2014.6925866
- G. P. C. Fung, J. X. Yu & W. Lam. (2003, March). Stock prediction: Integrating text mining approach using real-time news. In 2003 IEEE International Conference on Computational Intelligence for Financial Engineering, 2003. Proceedings. (pp. 395-402). IEEE.
- S. Hong. (2020). Research on Stock price prediction system based on BLSTM. Journal of the Korea Convergence Society, 11(10), 19-24. https://doi.org/10.15207/JKCS.2020.11.10.019
- S. Hong. (2020). A study on stock price prediction system based on text mining method using LSTM and stock market news. Journal of Digital Convergence, 18(7), 223-228. https://doi.org/10.14400/jdc.2020.18.7.223