1,329
Views
0
CrossRef citations to date
0
Altmetric
Research Article

A multi-feature stock price prediction model based on multi-feature calculation, LASSO feature selection, and Ca-LSTM network

, , ORCID Icon &
Article: 2286188 | Received 04 Dec 2022, Accepted 16 Nov 2023, Published online: 19 Jan 2024

References

  • Alam, W., Ray, M., Kumar, R. R., Sinha, K., Rathod, S., & Singh, K. (2018). Improved ARIMAX modal based on ANN and SVM approaches for forecasting rice yield using weather variables. The Indian Journal of Agricultural Sciences, 88(12), 1909–1913. https://doi.org/10.56093/ijas.v88i12.85446
  • Bali, T. G., & Hovakimian, A. (2009). Volatility spreads and expected stock returns. Management Science, 55(11), 1797–1812. https://doi.org/10.1287/mnsc.1090.1063
  • Bas, E., Egrioglu, E., & Kolemen, E. (2022). Training simple recurrent deep artificial neural network for forecasting using particle swarm optimization. Granular Computing, 7(2), 411–420. https://doi.org/10.1007/s41066-021-00274-2
  • Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31(3), 307–327. https://doi.org/10.1016/0304-4076(86)90063-1
  • Bontempi, G., Ben Taieb, S., & Borgne, Y.-A. L. (2012). Machine learning strategies for time series forecasting. In European business intelligence summer school (pp. 62–77). Springer.
  • Chang, P.-C., Liu, C.-H., Lin, J.-L., Fan, C.-Y., & Ng, C. S. (2009). A neural network with a case based dynamic window for stock trading prediction. Expert Systems with Applications, 36(3), 6889–6898. https://doi.org/10.1016/j.eswa.2008.08.077
  • Chen, K., Le, C., Zhong, S., Guo, L., & Xu, G. (2022). NNNPE: Non-neighbourhood and neighbourhood preserving embedding. Connection Science, 34(1), 2615–2629. https://doi.org/10.1080/09540091.2022.2133082
  • Chen, S., & Ge, L. (2019). Exploring the attention mechanism in LSTM-based Hong Kong stock price movement prediction. Quantitative Finance, 19(9), 1507–1515. https://doi.org/10.1080/14697688.2019.1622287
  • Coelho, F., Costa, M., Verleysen, M., & Braga, A. P. (2020). Lasso multi-objective learning algorithm for feature selection. Soft Computing, 24(17), 13209–13217. https://doi.org/10.1007/s00500-020-04734-w
  • 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. https://doi.org/10.1007/s13042-019-01041-1
  • Fu, L., Ding, X., & Ding, Y. (2022). Ensemble empirical mode decomposition-based preprocessing method with multi-LSTM for time series forecasting: A case study for hog prices. Connection Science, 34(1), 2177–2200. https://doi.org/10.1080/09540091.2022.2111404
  • Gao, Y., Wang, R., & Zhou, E. (2021). Stock prediction based on optimized LSTM and GRU models. Scientific Programming, 2021(4), 1–8. https://doi.org/10.1155/2021/4055281
  • 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
  • Hollis, T., Viscardi, A., & Yi, S. E. (2018). A comparison of LSTMs and attention mechanisms for forecasting financial time series. Preprint arXiv:1812.07699.
  • Hoseinzadeh, S., Sohani, A., & Ashrafi, T. G. (2022). An artificial intelligence-based prediction way to describe flowing a Newtonian liquid/gas on a permeable flat surface. Journal of Thermal Analysis and Calorimetry, 147(6), 4403–4409. https://doi.org/10.1007/s10973-021-10811-5
  • Kazem, A., Sharifi, E., Hussain, F. K., Saberi, M., & Hussain, O. K. (2013). Support vector regression with chaos-based firefly algorithm for stock market price forecasting. Applied Soft Computing, 13(2), 947–958. https://doi.org/10.1016/j.asoc.2012.09.024
  • Kumar, K., Haider, M., & Uddin, T. (2021). Enhanced prediction of intra-day stock market using metaheuristic optimization on RNN–LSTM network. New Generation Computing, 39(1), 231–272. https://doi.org/10.1007/s00354-020-00104-0
  • Li, J., Zhou, T., & Hu, X. (2022). Prediction algorithm of stock holdings of Hong Kong-funded institutions based on optimized PCA-LSTM model. International Journal of Innovative Computing, Information and Control, 18(3), 999–1008.
  • Li, Q., Tan, J., Wang, J., & Chen, H. (2020). A multimodal event-driven LSTM model for stock prediction using online news. IEEE Transactions on Knowledge and Data Engineering, 33(10), 3323–3337. https://doi.org/10.1109/TKDE.2020.2968894
  • Li, S., Tian, Z., & Li, Y. (2023). Residual long short-term memory network with multi-source and multi-frequency information fusion: An application to China's stock market. Information Sciences, 622, 133–147. https://doi.org/10.1016/j.ins.2022.11.136
  • Li, Y., Dai, H.-N., & Zheng, Z. (2022). Selective transfer learning with adversarial training for stock movement prediction. Connection Science, 34(1), 492–510. https://doi.org/10.1080/09540091.2021.2021143
  • Mahmoudan, A., Esmaeilion, F., Hoseinzadeh, S., Soltani, M., Ahmadi, P., & Rosen, M. (2022). A geothermal and solar-based multigeneration system integrated with a TEG unit: Development, 3E analyses, and multi-objective optimization. Applied Energy, 308, 118399. https://doi.org/10.1016/j.apenergy.2021.118399
  • Mahmoudan, A., Samadof, P., Hosseinzadeh, S., & Garcia, D. A. (2021). A multigeneration cascade system using ground-source energy with cold recovery: 3E analyses and multi-objective optimization. Energy, 233, 121185. https://doi.org/10.1016/j.energy.2021.121185
  • Matias, J. M., & Reboredo, J. C. (2012). Forecasting performance of nonlinear models for intraday stock returns. Journal of Forecasting, 31(2), 172–188. https://doi.org/10.1002/for.v31.2
  • Niu, H., Xu, K., & Wang, W. (2020). A hybrid stock price index forecasting model based on variational mode decomposition and LSTM network. Applied Intelligence, 50(12), 4296–4309. https://doi.org/10.1007/s10489-020-01814-0
  • Piramuthu, S. (2004). Evaluating feature selection methods for learning in data mining applications. European Journal of Operational Research, 156(2), 483–494. https://doi.org/10.1016/S0377-2217(02)00911-6
  • Qiu, J., Wang, B., & Zhou, C. (2020). Forecasting stock prices with long-short term memory neural network based on attention mechanism. PLoS One, 15(1), e0227222. https://doi.org/10.1371/journal.pone.0227222
  • Rounaghi, M. M., & Zadeh, F. N. (2016). Investigation of market efficiency and financial stability between S&P 500 and london stock exchange: Monthly and yearly forecasting of time series stock returns using ARMA model. Physica A: Statistical Mechanics and Its Applications, 456, 10–21. https://doi.org/10.1016/j.physa.2016.03.006
  • Sohani, A., Hoseinzadeh, S., Samiezadeh, S., & Verhaert, I. (2021). Machine learning prediction approach for dynamic performance modeling of an enhanced solar still desalination system. Journal of Thermal Analysis and Calorimetry, 147, 3919–3930. https://doi.org/10.1007/s10973-021-10744-z
  • Sohani, A., Pedram, M. Z., Berenjkar, K., Sayyaadi, H., Hoseinzadeh, S., Kariman, H., & Assad, M. E. H. (2021). Techno-energy-enviro-economic multi-objective optimization to determine the best operating conditions for preparing toluene in an industrial setup. Journal of Cleaner Production, 313, 127887. https://doi.org/10.1016/j.jclepro.2021.127887
  • Song, Y., Lee, J. W., & Lee, J. (2019). A study on novel filtering and relationship between input-features and target-vectors in a deep learning model for stock price prediction. Applied Intelligence, 49(3), 897–911. https://doi.org/10.1007/s10489-018-1308-x
  • Soni, P., Tewari, Y., & Krishnan, D. (2022). Machine learning approaches in stock price prediction: A systematic review. Journal of Physics: Conference Series, 2161, 012065.
  • Su, Z., Xie, H., & Han, L. (2021). Multi-factor RFG-LSTM algorithm for stock sequence predicting. Computational Economics, 57(4), 1041–1058. https://doi.org/10.1007/s10614-020-10008-2
  • Sun, L., Xu, W., & Liu, J. (2021). Two-channel attention mechanism fusion model of stock price prediction based on CNN-LSTM. Transactions on Asian and Low-Resource Language Information Processing, 20(5), 1–12. https://doi.org/10.1145/3453693
  • Tang, M., Chen, W., & Yang, W. (2022). Anomaly detection of industrial state quantity time-series data based on correlation and long short-term memory. Connection Science, 34(1), 2048–2065. https://doi.org/10.1080/09540091.2022.2092594
  • Tibshirani, R (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society: Series B (Methodological), 58(1), 267–288.
  • Wang, J., Cheng, Q., & Dong, Y. (2022). An XGBoost-based multivariate deep learning framework for stock index futures price forecasting. Kybernetes, 52(10), 4158–4177.
  • Wang, J., Cui, Q., Sun, X., & He, M. (2022). Asian stock markets closing index forecast based on secondary decomposition, multi-factor analysis and attention-based LSTM model. Engineering Applications of Artificial Intelligence, 113, 104908. https://doi.org/10.1016/j.engappai.2022.104908
  • Wang, J., & Zhu, S. (2022). A multi-factor two-stage deep integration model for stock price prediction based on intelligent optimization and feature clustering. Artificial Intelligence Review, 56, 7237– https://doi.org/10.1007/s10462-022-10352-9
  • Wang, J.-Z., Wang, J.-J., Zhang, Z.-G., & Guo, S.-P. (2011). Forecasting stock indices with back propagation neural network. Expert Systems with Applications, 38(11), 14346–14355. https://doi.org/10.1016/j.eswa.2011.04.222
  • Wu, S., Liu, Y., Zou, Z., & Weng, T.-H. (2022). S_i_lstm: Stock price prediction based on multiple data sources and sentiment analysis. Connection Science, 34(1), 44–62. https://doi.org/10.1080/09540091.2021.1940101
  • Xia, Y., Liu, Y., & Chen, Z. (2013). Support vector regression for prediction of stock trend. In 2013 6th International conference on information management, innovation management and industrial engineering (Vol. 2, pp. 123–126). IEEE.
  • Yu, Y., & Kim, Y.-J. (2019). Two-dimensional attention-based LSTM model for stock index prediction. Journal of Information Processing Systems, 15(5), 1231–1242.
  • Yu, Z., Qin, L., Chen, Y., & Parmar, M. D. (2020). Stock price forecasting based on LLE-BP neural network model. Physica A: Statistical Mechanics and Its Applications, 553, 124197. https://doi.org/10.1016/j.physa.2020.124197
  • Yujun, Y., Yimei, Y., & Wang, Z. (2021). Research on a hybrid prediction model for stock price based on long short-term memory and variational mode decomposition. Soft Computing, 25(21), 13513–13531. https://doi.org/10.1007/s00500-021-06122-4
  • Zahedi, J., & Rounaghi, M. M. (2015). Application of artificial neural network models and principal component analysis method in predicting stock prices on Tehran stock exchange. Physica A: Statistical Mechanics and Its Applications, 438, 178–187. https://doi.org/10.1016/j.physa.2015.06.033
  • Zhao, Y., & Yang, G. (2023). Deep learning-based integrated framework for stock price movement prediction. Applied Soft Computing, 133, 109921. https://doi.org/10.1016/j.asoc.2022.109921