The Research of NVIDIA Stock Price Prediction Based on LSTM And ARIMA Model


  • Zhenhao Yang
  • Zhiyang Wang



NVIDIA; stock price prediction; LSTM; ARIMA


The paper explores stock forecasting methods using NVIDIA as the research object. It contrasts how well LSTM and ARIMA models forecast NVIDIA's stock return. According to the study, LSTM surpasses ARIMA in terms of prediction accuracy. However, both models capture the overall trend of the stock. The results suggest that LSTM is better suited for forecasting stock movements due to its ability to handle time series data. The non-stationary nature of the stock market adds complexity to predictions. The significance of stock forecasting is that informed investment decisions can be made to maximise financial returns. Stock forecasting aims to predict the future performance of individual stocks, market sectors or the market as a whole based on various factors such as historical data, market trends, company fundamentals and economic indicators. By analysing and forecasting stock movements, investors can identify opportunities to buy low and sell high, optimise their portfolios and potentially outperform the broader market. Successful stock forecasting can help investors make more informed decisions, reduce risk and improve their chances of achieving their financial goals.


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Guo Changdon00 Research on Stock prediction based on XGBoost Model. Yanbian University, 2022.

Wu Qing. Nvidia trillions of empires behind. China business journal, 2023-07-24 (D04).

Zou Zhichao, Qu Zihao. Using lstm in stock prediction and quantitative trading. CS230: Deep learning, winter, 2020: 1-6.

CK Lee, Y Sehwan, and J Jongdae. Neural network model versus sarima model in forecasting korean stock price index (kospi). Issues in Information System, 2007, 8(2):372–378

Chen, L., Li, Q., & Yi, Y.. A new stock price forecasting model based on SVM regression. Neural Computing and Applications, 2014, 24(7-8), 1711-1718.

Jia-Qian, S., Song-An, L., Tian-Ying, L., & Bo, Z. Research on Support Vector Machine and LSTM in Stock Price Prediction. 2016 IEEE 2nd International Conference on Big Data Analysis (ICBDA), Hangzhou, China.

Hochreiter, S., & Schmidhuber, J. Long short-term memory. Neural computation, 1997, 9(8), 1735-1780.

Gers, F. A., Schmidhuber, J., & Cummins, F. Learning to forget: Continual prediction with LSTM. Neural computation, 2000, 12(10), 2451-2471.

Qiangwei Weng, Ruohan Liu, Zheng Tao. Forecasting. Tesla’s Stock Price Using the ARIMA Model. Proceedings of Business and Economic Studies, 2022, 5(5)

Akaike, Hirotugu. A new look at the statistical model identification. IEEE transactions on automatic control, 1974, 19(6): 716-723.




How to Cite

Yang, Z., & Wang, Z. (2024). The Research of NVIDIA Stock Price Prediction Based on LSTM And ARIMA Model. Highlights in Business, Economics and Management, 24, 896-902.