References
[1]. Chen, K., Zhou, Y., & Dai, F. (2015, October). A LSTM-based method for stock returns prediction: A case study of China stock market. In 2015 IEEE international conference on big data (big data) (pp. 2823-2824). IEEE.
[2]. Zhuge, Q., Xu, L., & Zhang, G. (2017). LSTM Neural Network with Emotional Analysis for prediction of stock price. Engineering letters, 25(2).
[3]. Zhao, Z., Rao, R., Tu, S., & Shi, J. (2017, November). Time-weighted LSTM model with redefined labeling for stock trend prediction. In 2017 IEEE 29th international conference on tools with artificial intelligence (ICTAI) (pp. 1210-1217). IEEE.
[4]. Hansson, M. (2017). On stock return prediction with LSTM networks.
[5]. Liu, S., Liao, G., & Ding, Y. (2018, May). Stock transaction prediction modeling and analysis based on LSTM. In 2018 13th IEEE Conference on Industrial Electronics and Applications (ICIEA) (pp. 2787-2790). IEEE.
[6]. Baek, Y., & Kim, H. Y. (2018). ModAugNet: A new forecasting framework for stock market index value with an overfitting prevention LSTM module and a prediction LSTM module. Expert Systems with Applications, 113, 457-480.
[7]. Li, H., Shen, Y., & Zhu, Y. (2018, November). Stock price prediction using attention-based multi-input LSTM. In Asian conference on machine learning (pp. 454-469). PMLR.
[8]. Naik, N., & Mohan, B. R. (2019, May). Study of stock return predictions using recurrent neural networks with LSTM. In International conference on engineering applications of neural networks (pp. 453-459). Springer, Cham.
[9]. Li, X., Li, Y., Yang, H., Yang, L., & Liu, X. Y. (2019). DP-LSTM: Differential privacy-inspired LSTM for stock prediction using financial news. arXiv preprint arXiv:1912.10806.
[10]. Eapen, J., Bein, D., & Verma, A. (2019, January). Novel deep learning model with CNN and bi-directional LSTM for improved stock market index prediction. In 2019 IEEE 9th annual computing and communication workshop and conference (CCWC) (pp. 0264-0270). IEEE.
[11]. Qian, F., & Chen, X. (2019, April). Stock prediction based on LSTM under different stability. In 2019 IEEE 4th International Conference on Cloud Computing and Big Data Analysis (ICCCBDA) (pp. 483-486). IEEE.
[12]. Borovkova, S., & Tsiamas, I. (2019). An ensemble of LSTM neural networks for high‐frequency stock market classification. Journal of Forecasting, 38(6), 600-619.
[13]. Moghar, A., & Hamiche, M. (2020). Stock market prediction using LSTM recurrent neural network. Procedia Computer Science, 170, 1168-1173.
[14]. Yadav, A., Jha, C. K., & Sharan, A. (2020). Optimizing LSTM for time series prediction in Indian stock market. Procedia Computer Science, 167, 2091-2100.
[15]. Mehtab, S., Sen, J., & Dutta, A. (2020, October). Stock price prediction using machine learning and LSTM-based deep learning models. In Symposium on Machine Learning and Metaheuristics Algorithms, and Applications (pp. 88-106). Springer, Singapore.
[16]. Lu, W., Li, J., Li, Y., Sun, A., & Wang, J. (2020). A CNN-LSTM-based model to forecast stock prices. Complexity, 2020.
[17]. Zou, Z., & Qu, Z. (2020). Using LSTM in Stock prediction and Quantitative Trading. CS230: Deep Learning, Winter.
[18]. Ding, G., & Qin, L. (2020). Study on the prediction of stock price based on the associated network model of LSTM. International Journal of Machine Learning and Cybernetics, 11(6), 1307-1317.
[19]. Jin, Z., Yang, Y., & Liu, Y. (2020). Stock closing price prediction based on sentiment analysis and LSTM. Neural Computing and Applications, 32(13), 9713-9729.
[20]. Wu, J. M. T., Li, Z., Herencsar, N., Vo, B., & Lin, J. C. W. (2021). A graph-based CNN-LSTM stock price prediction algorithm with leading indicators. Multimedia Systems, 1-20.
[21]. Lin, Y., Yan, Y., Xu, J., Liao, Y., & Ma, F. (2021). Forecasting stock index price using the CEEMDAN-LSTM model. The North American Journal of Economics and Finance, 57, 101421.
[22]. Gao, Y., Wang, R., & Zhou, E. (2021). Stock Prediction Based on Optimized LSTM and GRU Models. Scientific Programming, 2021.
[23]. Zhang, R. (2022, March). LSTM-based Stock Prediction Modeling and Analysis. In 2022 7th International Conference on Financial Innovation and Economic Development (ICFIED 2022) (pp. 2537-2542). Atlantis Press.
[24]. Shi, Z., Hu, Y., Mo, G., & Wu, J. (2022). Attention-based CNN-LSTM and XGBoost hybrid model for stock prediction. arXiv preprint arXiv:2204.02623.
Cite this article
Qian,H. (2023). Review of Stock Price Predicting Method Based on LSTM. Advances in Economics, Management and Political Sciences,3,479-488.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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References
[1]. Chen, K., Zhou, Y., & Dai, F. (2015, October). A LSTM-based method for stock returns prediction: A case study of China stock market. In 2015 IEEE international conference on big data (big data) (pp. 2823-2824). IEEE.
[2]. Zhuge, Q., Xu, L., & Zhang, G. (2017). LSTM Neural Network with Emotional Analysis for prediction of stock price. Engineering letters, 25(2).
[3]. Zhao, Z., Rao, R., Tu, S., & Shi, J. (2017, November). Time-weighted LSTM model with redefined labeling for stock trend prediction. In 2017 IEEE 29th international conference on tools with artificial intelligence (ICTAI) (pp. 1210-1217). IEEE.
[4]. Hansson, M. (2017). On stock return prediction with LSTM networks.
[5]. Liu, S., Liao, G., & Ding, Y. (2018, May). Stock transaction prediction modeling and analysis based on LSTM. In 2018 13th IEEE Conference on Industrial Electronics and Applications (ICIEA) (pp. 2787-2790). IEEE.
[6]. Baek, Y., & Kim, H. Y. (2018). ModAugNet: A new forecasting framework for stock market index value with an overfitting prevention LSTM module and a prediction LSTM module. Expert Systems with Applications, 113, 457-480.
[7]. Li, H., Shen, Y., & Zhu, Y. (2018, November). Stock price prediction using attention-based multi-input LSTM. In Asian conference on machine learning (pp. 454-469). PMLR.
[8]. Naik, N., & Mohan, B. R. (2019, May). Study of stock return predictions using recurrent neural networks with LSTM. In International conference on engineering applications of neural networks (pp. 453-459). Springer, Cham.
[9]. Li, X., Li, Y., Yang, H., Yang, L., & Liu, X. Y. (2019). DP-LSTM: Differential privacy-inspired LSTM for stock prediction using financial news. arXiv preprint arXiv:1912.10806.
[10]. Eapen, J., Bein, D., & Verma, A. (2019, January). Novel deep learning model with CNN and bi-directional LSTM for improved stock market index prediction. In 2019 IEEE 9th annual computing and communication workshop and conference (CCWC) (pp. 0264-0270). IEEE.
[11]. Qian, F., & Chen, X. (2019, April). Stock prediction based on LSTM under different stability. In 2019 IEEE 4th International Conference on Cloud Computing and Big Data Analysis (ICCCBDA) (pp. 483-486). IEEE.
[12]. Borovkova, S., & Tsiamas, I. (2019). An ensemble of LSTM neural networks for high‐frequency stock market classification. Journal of Forecasting, 38(6), 600-619.
[13]. Moghar, A., & Hamiche, M. (2020). Stock market prediction using LSTM recurrent neural network. Procedia Computer Science, 170, 1168-1173.
[14]. Yadav, A., Jha, C. K., & Sharan, A. (2020). Optimizing LSTM for time series prediction in Indian stock market. Procedia Computer Science, 167, 2091-2100.
[15]. Mehtab, S., Sen, J., & Dutta, A. (2020, October). Stock price prediction using machine learning and LSTM-based deep learning models. In Symposium on Machine Learning and Metaheuristics Algorithms, and Applications (pp. 88-106). Springer, Singapore.
[16]. Lu, W., Li, J., Li, Y., Sun, A., & Wang, J. (2020). A CNN-LSTM-based model to forecast stock prices. Complexity, 2020.
[17]. Zou, Z., & Qu, Z. (2020). Using LSTM in Stock prediction and Quantitative Trading. CS230: Deep Learning, Winter.
[18]. Ding, G., & Qin, L. (2020). Study on the prediction of stock price based on the associated network model of LSTM. International Journal of Machine Learning and Cybernetics, 11(6), 1307-1317.
[19]. Jin, Z., Yang, Y., & Liu, Y. (2020). Stock closing price prediction based on sentiment analysis and LSTM. Neural Computing and Applications, 32(13), 9713-9729.
[20]. Wu, J. M. T., Li, Z., Herencsar, N., Vo, B., & Lin, J. C. W. (2021). A graph-based CNN-LSTM stock price prediction algorithm with leading indicators. Multimedia Systems, 1-20.
[21]. Lin, Y., Yan, Y., Xu, J., Liao, Y., & Ma, F. (2021). Forecasting stock index price using the CEEMDAN-LSTM model. The North American Journal of Economics and Finance, 57, 101421.
[22]. Gao, Y., Wang, R., & Zhou, E. (2021). Stock Prediction Based on Optimized LSTM and GRU Models. Scientific Programming, 2021.
[23]. Zhang, R. (2022, March). LSTM-based Stock Prediction Modeling and Analysis. In 2022 7th International Conference on Financial Innovation and Economic Development (ICFIED 2022) (pp. 2537-2542). Atlantis Press.
[24]. Shi, Z., Hu, Y., Mo, G., & Wu, J. (2022). Attention-based CNN-LSTM and XGBoost hybrid model for stock prediction. arXiv preprint arXiv:2204.02623.