Year: 2025
Type: article
Abstract: The stock market has grown to be an essential component of China's economic market as the country's economy has developed. And stock prices are closely watched by investors and managers. This paper uses Python to crawl the monthly stock prices of Ping An of China from January 2016 to December 2024 and decides to estimate its closing price using the ARIMA, GM, and LSTM models. Furthermore, this paper mixes the ARIMA model with the GM model to build a hybrid model. The empirical results show that the ARIMA model’s mean square errors is 35.79, while the GM model’s is 125.63. The MSE loss function of the LSTM model is 14.186, and the MSE of the ARIMA-GM hybrid model is 7.575. The results of this paper show that the hybrid model’s prediction accuracy surpasses not only that of a single model but also that of the LSTM model in machine learning, providing a scientific and effective guide for investor and manager behavior.
Author: Xiaochuan Huang
Institution: Beijing Institute of Technology
Language: English
Field: Decision Sciences
Domain: Social Sciences
Open Access status: hybrid
OpenAlex ID: W4412636004