A Comparative Study of the Effectiveness of Machine Learning for Predicting Stock Prices in the New Energy Vehicle Industry
DOI:
https://doi.org/10.54097/ytqdec88Keywords:
Deep learning model; CNN; LSTM; Stock prices; New Energy Vehicle Industry.Abstract
This paper investigates the effectiveness of machine learning models in predicting stock prices in the New Energy Vehicle (NEV) industry - a key part of the current transformation of the global transport system and electricity supply. This study applies five models, ARIMA, LSTM, GRU, CNN and TCN using the sample 20 NEV companies, to predict stock prices. And further use the risk metrics such as MSE, RMSE, R2, MAE and MAPE to measure the prediction performance. The results shows that the CNN model stands out for its superior accuracy in predicting stock movements, highlighting its potential as a valuable tool for investors and financial analysts in the fast-growing NEV market. The findings highlight the importance of utilizing advanced neural network algorithms to improve investment strategies and risk assessment in the face of market volatility and economic fluctuations.
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References
Cao, J., & Wang, J. (2019). Stock price forecasting model based on modified convolution neural network and financial time series analysis. International Journal of Communication Systems, 32(12), e3987.
Tsai, C. F., & Wang, S. P. (2009, March). Stock price forecasting by hybrid machine learning techniques. In Proceedings of the international multiconference of engineers and computer scientists (Vol. 1, No. 755, p. 60).
Shahi, T. B., Shrestha, A., Neupane, A., & Guo, W. (2020). Stock price forecasting with deep learning: A comparative study. Mathematics, 8(9), 1441.
Sim, H. S., Kim, H. I., & Ahn, J. J. (2019). Is deep learning for image recognition applicable to stock market prediction? Complexity, 2019.
Fischer, T., & Krauss, C. (2018). Deep learning with long short-term memory networks for financial market predictions. European journal of operational research, 270(2), 654-669.
Chen, M. L., Fan, C., & Wu, Z. P. (2024). Research on stock price prediction of new energy vehicle industry based on machine learning algorithms. Journal of Jilin Business and Technology College, (1), 93-100.
Wang, M. X., Xiao, Z., Peng, H. G., Wang, X. K., & Wang, J. Q. (2022). Stock price prediction for new energy vehicle enterprises: An integrated method based on time series and cloud models. Expert Systems with Applications, 208, 118125.
Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural computation, 9(8), 1735-1780.
Bai, S., Kolter, J. Z., & Koltun, V. (2018). An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. arXiv preprint arXiv:1803.01271.
Makridakis, S. (1993). Accuracy measures: theoretical and practical concerns. International journal of forecasting, 9(4), 527-529.
Ismail Fawaz, H., Forestier, G., Weber, J., Idoumghar, L., & Muller, P. A. (2019). Deep learning for time series classification: a review. Data mining and knowledge discovery, 33(4), 917-963.
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