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Tock Market Forecasting Based on Machine Learning Approach of ARIMA Model

In: Proceedings of the 2022 2nd International Conference on Economic Development and Business Culture (ICEDBC 2022)

Author

Listed:
  • Lingfeng Ren

    (SWJTU-Leeds Joint School, Southwest Jiaotong University)

  • Chenhao Zhao

    (SWJTU-Leeds Joint School, Southwest Jiaotong University)

Abstract

As an important manifestation of national economic and financial activities, the stock market plays an important role in the economic development of various countries. If we can grasp the trend of the stock market in advance, it will be beneficial to both investment institutions and investors. By training a good ARIMA model, this paper applies this model to the stock prediction of five different listed companies: Alibaba, Baidu, Tencent, Pinduoduo, and WangYi. The predicted results are evaluated by MSE, MAE, RMSE, and MAPE. The results show that the accuracy of this model in the stock forecast of Alibaba, Baidu, Tencent, PingDuoDuo, and WangYi is between 96.0% and 99.4%, among which WY has the highest accuracy of 99.4% and PDD has the worst accuracy of 96.0%. The ARIMA model has high accuracy for stock prediction, but it needs to rely on the reliability of its AR and MA models, and it is highly dependent on the judgment of residual sequence. At the same time, because the stock market is also affected by other factors, a framework that may be accurate for all types of stock markets will be included in the future development plan, thus providing investors and related investment institutions with reference for stock investment decisions.

Suggested Citation

  • Lingfeng Ren & Chenhao Zhao, 2022. "Tock Market Forecasting Based on Machine Learning Approach of ARIMA Model," Advances in Economics, Business and Management Research, in: Yushi Jiang & Yuriy Shvets & Hrushikesh Mallick (ed.), Proceedings of the 2022 2nd International Conference on Economic Development and Business Culture (ICEDBC 2022), pages 233-237, Springer.
    • Handle: RePEc:spr:advbcp:978-94-6463-036-7_34
    DOI: 10.2991/978-94-6463-036-7_34
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