16,955
Views
25
CrossRef citations to date
0
Altmetric
Articles

S_I_LSTM: stock price prediction based on multiple data sources and sentiment analysis

, , &
Pages 44-62 | Received 30 Jan 2021, Accepted 03 Jun 2021, Published online: 14 Jun 2021

References

  • Achkar, R., Elias-Sleiman, F., Ezzidine, H., & Haidar, N. (2018). Comparison of BPA-MLP and LSTM-RNN for stocks prediction. In 2018 6th International Symposium on Computational and Business Intelligence (ISCBI) IEEE, 48–51. https://doi.org/10.1109/ISCBI.2018.00019.
  • Ariyo, A. A., Adewumi, A. O., & Ayo, C. K. (2014, March 26-28). Stock price prediction using the ARIMA model. 2014 UKSim-AMSS 16th International Conference on Computer Modelling and Simulation, Cambridge, 106–112. https://doi.org/10.1109/UKSim.2014.67.
  • Brockwell, P. J., & Davis, R. A. (2015). Time series: Theory and methods. Springer-Verlag.
  • Checkley, M. S., Higón, D. A., & Alles, H. (2017). The hasty wisdom of the mob: How market sentiment predicts stock market behavior. Expert Systems with Applications, 77, 256–263. https://doi.org/10.1016/j.eswa.2017.01.029
  • Chen, S., & He, H. (2018). Stock prediction using convolutional neural network. IOP Conference Series: Materials Science and Engineering, 435(1), 012026. https://doi.org/10.1088/1757899X/435/1/012026
  • Eapen, J., Bein, D., & Verma, A. (2019, January 7-9). Novel deep learning model with CNN and bi-directional LSTM for improved stock market index prediction. 2019 IEEE 9th Annual Computing and Communication Workshop and Conference (CCWC) IEEE, 0264–0270. https://doi.org/10.1109/CCWC.2019.8666592.
  • Fama, E. F. (1964). The distribution of the daily differences of the logarithms of stock prices. Unpublished Ph. D Dissertation, University of Chicago.
  • Hinton, G. E., Srivastava, N., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. R. (2012). Improving neural networks by preventing co-adaptation of feature detectors. arXiv preprint arXiv:1207.0580.
  • Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735
  • Idrees, S. M., Alam, M. A., & Agarwal, P. (2019). A prediction approach for stock market volatility based on time series data. IEEE Access, 7, 17287–17298. https://doi.org/10.1109/ACCESS.2019.2895252
  • Jiawei, X., & Murata, T. (2019, March 13-15). Stock market trend prediction with sentiment analysis based on LSTM neural network. Proceedings of the International MultiConference of Engineers and Computer Scientists, 475–479.
  • Jin, Z., Yang, Y., & Liu, Y. (2019). Stock closing price prediction based on sentiment analysis and LSTM. Neural Computing and Applications, 1–17. https://doi.org/10.1007/s00521-019-04504-2
  • Kim, Y. (2014). Convolutional neural networks for sentence classification. arXiv preprint arXiv,1408.5882. https://doi.org/10.3115/v1/D14-1181.
  • Kraus, M., & Feuerriegel, S. (2017). Decision support from financial disclosures with deep neural networks and transfer learning. Decision Support Systems, 104, 38–48. https://doi.org/10.1016/j.dss.2017.10.001
  • Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017). Imagenet classification with deep convolutional neural networks. Communications of the ACM, 60(6), 84–90. https://doi.org/10.1145/3065386
  • Lee, C. Y., & Soo, V. W. (2017, December 1-3). Predict stock price with financial news based on recurrent convolutional neural networks. 2017 Conference on Technologies and Applications of Artificial Intelligence (TAAI) IEEE, 160–165. https://doi.org/10.1109/TAAI.2017.27.
  • Li, J., Bu, H., & Wu, J. (2017, June 16-18). Sentiment-aware stock market prediction: A deep learning method. Proceedings of 2017 International Conference on Service Systems and Service Management IEEE, 1–6. https://doi.org/10.1109/ICSSSM.2017.7996306.
  • Li, X., Xie, H., Chen, L., Wang, J., & Deng, X. (2014). News impact on stock price return via sentiment analysis. Knowledge-Based Systems, 69, 14–23. https://doi.org/10.1016/j.k-nosys.2014.04.022
  • Li, M., Yang, C., Zhang, J., Puthal, D., Luo, Y., & Li, J. (2018, January 29-February 2). Stock market analysis using social networks. Proceedings of the Australasian Computer Science Week Multiconference, 1–10. https://doi.org/10.1145/3167918.3167967.
  • Liang, W., Xiao, L., Zhang, K., Tang, M., He, D., & Li, K. C. (2021). Data fusion approach for collaborative anomaly intrusion detection in blockchain-based systems. IEEE Internet of Things Journal, 1–1. https://doi.org/10.1109/JIOT.2021.3053842
  • Liang, W., Xie, S., Zhang, D., Li, X., & Li, K. (2020a). A mutual security authentication method for RFID-PUF circuit based on deep learning. ACM Transactions on Internet Technology, 1–20. https://doi.org/10.1145/3426968
  • Liang, W., Zhang, D., Lei, X., Tang, M., Li, K., & Zomaya, A. (2020b). Circuit copyright blockchain: Blockchain-based homomorphic encryption for IP circuit protection. IEEE Transactions on Emerging Topics in Computing, 1–1. https://doi.org/10.1109/TETC.2020.2993032
  • Lin, S. J., & Hsu, M. F. (2014). Enhanced risk management by an emerging multi-agent architecture. Connection Science, 26(3), 245–259. https://doi.org/10.1080/09540091.2014.908821
  • Liu, Y., Zeng, Q., Yang, H., & Carrio, A. (2018, August 28-29). Stock price movement prediction from financial news with deep learning and knowledge graph embedding. In Pacific rim Knowledge acquisition workshop (pp. 102–113). Springer, Kenichi Yoshida, Maria Lee. https://doi.org/10.1007/978-3-31997289-3_8.
  • Malkiel, B. G. (1973). A random walk down wall street. W. W. Norton.
  • Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013). Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781.
  • Minh, D. L., Sadeghi-Niaraki, A., Huy, H. D., Min, K., & Moon, H. (2018). Deep learning approach for short-term stock trends prediction based on two-stream gated recurrent unit network. IEEE Access, 6, 55392–55404. https://doi.org/10.1109/ACCESS.2018.2868970
  • Mohan, S., Mullapudi, S., Sammeta, S., Vijayvergia, P., & Anastasiu, D. C. (2019, April 4-9). Stock price prediction using news sentiment analysis. 2019 IEEE Fifth International Conference on Big Data Computing Service and Applications (BigDataService) IEEE, 205–208. https://doi.org/10.1109/BigDataService.2019.00035.
  • Oliveira, N., Cortez, P., & Areal, N. (2016). Stock market sentiment lexicon acquisition using microblogging data and statistical measures. Decision Support Systems, 85, 62–73. https://doi.org/10.1016/j.dss.2016.02.013
  • Porshnev, A., Redkin, I., & Shevchenko, A. (2013, December 7-10). Machine learning in prediction of stock market indicators based on historical data and data from twitter sentiment analysis. 2013 IEEE 13th International Conference on Data Mining Workshops IEEE, 440–444. https://doi.org/10.1109/ICDMW.2013.111.
  • Ritter, J. R. (2003). Behavioral finance. Pacific-Basin Finance Journal, 11(4), 429–437. https://doi.org/10.1016/S0927-538X(03)00048-9
  • Sayavong, L., Wu, Z., & Chalita, S. (2019, August 10-11). Research on stock price prediction method based on convolutional neural network. 2019 International Conference on Virtual Reality and intelligent Systems (ICVRIS) IEEE, 173–176. https://doi.org/10.1109/ICVRI-S.2019.00050.
  • Selvin, S., Vinayakumar, R., Gopalakrishnan, E. A., Menon, V. K., & Soman, K. P. (2017, September 13-16). Stock price prediction using LSTM, RNN and CNN-sliding window model. 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI) IEEE, 1643–1647. https://doi.org/10.1109/ICACCI.2017.8126078.
  • Shah, D., Isah, H., & Zulkernine, F. (2018, December 10-13). Predicting the effects of news sentiments on the stock market. 2018 IEEE International Conference on Big Data, 2018. (Big data), Seattle, WA, USA, 4705–4708. https://doi.org/10.1109/BigData.2018.8621884.
  • Sohangir, S., Wang, D., Pomeranets, A., & Khoshgoftaar, T. M. (2018). Big data: Deep learning for financial sentiment analysis. Journal of Big Data, 5(1), 3–28. https://doi.org/10.1186/s40537-017-0111-6
  • Srivastava, V., & Biswas, B. (2020). CNN-based salient features in HSI image semantic target prediction. Connection Science, 32(2), 113–131. https://doi.org/10.1080/0954009-1.2019.1650330
  • Statman, M. (2011). Investor sentiment, stock characteristics, and returns. The Journal of Portfolio Management, 37(3), 54–61. https://doi.org/10.3905/jpm.2011.37.3.054
  • Sun, T., Wang, J., Zhang, P., Cao, Y., Liu, B., & Wang, D. (2017, August 10-11). Predicting stock price returns using microblog sentiment for Chinese stock market. 2017 3rd International Conference on Big Data Computing and Communications (BIGCOM) IEEE, 87–96. https://doi.org/10.1109/BIGCOM.2017.59.
  • Sundermeyer, M., Schlüter, R., & Ney, H. (2012, September 1). LSTM neural networks for language modeling. Thirteenth Annual Conference of the International Speech Communication Association.
  • Vargas, M. R., Anjos, C. D., Bichara, G. L., & Evsukoff, A. G. (2018, July 8-13). Deep leaming for stock market prediction using technical indicators and financial news articles. 2018 International Joint Conference on Neural Networks (IJCNN) IEEE, 1–8. https://doi.org/10.1109/IJCNN.2018.8489208.
  • Vargas, M. R., De Lima, B. S., & Evsukoff, A. G. (2017, June 26-28). Deep learning for stock market prediction from financial news articles. 2017 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA) IEEE, 60–65. https://doi.org/10.1109/CIVEMSA.2017.7995302.
  • Wang, Y. (2017). Stock market forecasting with financial micro-blog based on sentiment and time series analysis. Journal of Shanghai Jiaotong University (Science), 22(2), 173–179. https://doi.org/10.1007/s12204-017-1818-4
  • Wang, Q., Li, X., & Liu, Q. (2020). Empirical research of accounting conservatism, corporate governance and stock price collapse risk based on panel data model. Connection Science, 1–16. https://doi.org/10.1080/09540091.2020.1806204
  • Wang, F., Shieh, S. J., Havlin, S., & Stanley, H. E. (2009). Statistical analysis of the overnight and daytime return. Physical Review E, 79(5), 056109. https://doi.org/10.1103/PhysRevE.79.056109
  • Wei, D. (2019, October 17-19). Prediction of stock price based on LSTM neural network. 2019 International Conference on Artificial Intelligence and Advanced Manufacturing (AIAM) IEEE, 544–547. https://doi.org/10.1109/AIAM48774.2019.00113.
  • Wu, H., Zhang, W., Shen, W., & Wang, J. (2018, October 22-26). Hybrid deep sequential modeling for social text-driven stock prediction. Proceedings of the 27th ACM International Conference on Information and Knowledge Management, 1627–1630. https://doi.org/10.1145/3269206.3269290.
  • Xu, Y., & Keselj, V. (2019, December 9-12). Stock prediction using deep learning and sentiment analysis. 2019 IEEE International Conference on Big Data (Big Data) IEEE, 5573–5580. https://doi.org/10.1109/BigData47090.2019.9006342.
  • Yadav, R., Kumar, A. V., & Kumar, A. (2019). News-based supervised sentiment analysis for prediction of futures buying behaviour. IIMB Management Review, 31(2), 157–166. https://doi.org/10.1016/j.iimb.2019.03.006
  • Ying, L., Qian Nan, Z., Fu Ping, W., Tuan Kiang, C., Keng Pang, L., Heng Chang, Z., Lu, C., Jun, L. G., & Nam, L. (2021). Adaptive weights learning in CNN feature fusion for crime scene investigation image classification. Connection Science, 2021(5), 1–16. https://doi.org/10.1080/09540091.2021.1875987
  • Yun, H., Sim, G., & Seok, J. (2019, September 11-13). Stock prices prediction using the title of newspaper articles with Korean natural language processing. 2019 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) IEEE, 019–021. https://doi.org/10.1109/ICAIIC.2019.8668996.
  • Zhang, K., Zhong, G., Dong, J., Wang, S., & Wang, Y. (2019). Stock market prediction based on generative adversarial network. Procedia Computer Science, 147, 400–406. https://doi.org/10.1016/j.procs.2019.01.256
  • Zhao, B., He, Y., Yuan, C., & Huang, Y. (2016, July 24-29). Stock market prediction exploiting microblog sentiment analysis. 2016 International Joint Conference on Neural Networks (IJCNN) IEEE, 4482–4488. https://doi.org/10.1109/IJCNN.2016.7727786.
  • Zhou, Z., Gao, M., Liu, Q., & Xiao, H. (2020). Forecasting stock price movements with multiple data sources: Evidence from stock market in China. Physica A: Statistical Mechanics and its Applications, 542, 123389. https://doi.org/10.1016/j.physa.2019.123389