Application of Stock Price Prediction during the "Double 11" Period Based on the LSTM Model

Haozhe Wu; Zhenshen Pan; Jiaying Zhai

doi:10.62051/mfp24f92

Authors

Haozhe Wu
Zhenshen Pan
Jiaying Zhai

DOI:

https://doi.org/10.62051/mfp24f92

Keywords:

LSTM Model; Stock Price Prediction;

Abstract

Studying stock price changes during special periods such as the "Double 11" shopping festival using the Internet E-commerce Industry Index can aid investors and e-commerce companies in making informed decisions. This paper focuses on stock price fluctuations during the "Double 11" period, using the "Internet E-commerce" Industry Index from January 20, 2016, to August 16, 2024, as the research object. The study employs ARIMA, LR, LGBM, and LSTM models to predict stock prices in the "Internet E-commerce" sector and uses RMSE, MAE, and R² as evaluation metrics to compare and assess model performance. The results confirm that the LSTM model demonstrates superior effectiveness and accuracy in predicting nonlinear stock price fluctuations, especially during special periods like "Double 11," compared to other models. Unlike traditional models, LSTM excels in capturing long-term dependencies and complex patterns in time series data. Its ability to adapt to nonlinear relationships and temporal variations makes it a more robust tool for stock price forecasting, particularly in volatile periods such as "Double 11".

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References

[1] Gong, X. (2024). ARIMA-BPNN based stock price prediction model based on fusion news sentiment analysis. Scalable Computing: Practice and Experience, 25 (3), 2062 - 2075.

[2] Wang, Y., Zhang, Y., & Wei, G. (2023). Stock price prediction based on BP neural network and higher-order fuzzy cognitive map. Intelligent Computing and Applications, 13 (08), 100 - 106+113. (In Chinese)

[3] Wang, P., Zhou, X., & Chen, X. (2024). Stock price and sentiment multi-feature nested attention fusion stock price prediction research. Systems Engineering, 1-14. Retrieved August 21, 2024. (In Chinese).

[4] Zheng, J., Xin, D., Cheng, Q., Tian, M., & Yang, L. (2024, February 27). The random forest model for analyzing and forecasting the US stock market in the context of smart finance. arXiv preprint arXiv:2402.17194.

[5] Saha, S., Gao, J., & Gerlach, R. (2022). A survey of the application of graph-based approaches in stock market analysis and prediction. International Journal of Data Science and Analytics, 14 (1), 1 - 15.

[6] Wang, G., et al. (2019). Application of a long short-term memory neural network: A burgeoning method of deep learning in forecasting HIV incidence in Guangxi, China. Epidemiology and Infection, 147, e194, 1 - 7.

[7] Zhao, C., Wu, M., Liu, J., Duan, Z., Shen, L., Shangguan, X., Liu, D., & Wang, Y. (2023). Progress and prospects of data-driven stock price forecasting research. International Journal of Cognitive Computing in Engineering, 4, 100 - 108.

[8] Li, H., Huang, X., Zhou, D., Cao, A., Su, M., Wang, Y., & Guo, L. (2022). Forecasting carbon price in China: A multimodel comparison. International Journal of Environmental Research and Public Health, 19, 6217.

[9] Li, Y., & Wei, Z. (2022). Regional logistics demand prediction: A long short-term memory network method. Sustainability, 14 (13478).

[10] Chen, J., Li, Y., Zhang, C., Tian, Y., & Guo, Z. (2023). Urban flooding prediction method based on the combination of LSTM neural network and numerical model. International Journal of Environmental Research and Public Health, 20, 1043.

[11] Shahrel, M. Z., Mutalib, S., & Abdul-Rahman, S. (2021). PriceCop–Price monitor and prediction using linear regression and LSVM-ABC methods for e-commerce platform. International Journal of Information Engineering and Electronic Business (IJIEEB), 13 (1), 1 - 14.

[12] Ji, C., Zhang, C., Hua, L., Ma, H., Nazir, M. S., & Peng, T. (2022). A multi-scale evolutionary deep learning model based on CEEMDAN, improved whale optimization algorithm, regularized extreme learning machine and LSTM for AQI prediction. Environmental Research, 215 (Pt 1), 114228.

[13] Wang, W., Shao, J., & Jumahong, H. (2023). Fuzzy inference-based LSTM for long-term time series prediction. Scientific Reports, 13 (1), 20359.

[14] Salehi, W., Baglat, P., Gupta, G., Khan, S. B., Almusharraf, A., Alqahtani, A., & Kumar, A. (2023). An approach to binary classification of Alzheimer's disease using LSTM. Bioengineering (Basel), 10 (8), 950.

[15] Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 12 (9), 1735 - 1780.