Stock Price Prediction based on the AMIAR Model

Stock price prediction is widely used in the financial industry and holds significant relevance for nations, businesses, and investors. According to research conducted both domestically and internationally, several methods are currently available for forecasting stock prices. This paper focuses on the A-share stock Zhongyuan Env. Protection for research purposes. This paper employs the ARIMA model to predict changes in stock prices. The original data used in this paper is sourced from CSMAR and consists of stock closing prices, with a time range spanning from August 31, 2018, to August 30, 2023. The results indicate that the closing price of the stock follows the ARMA (1,1) model. Furthermore, a comparison between the predicted and actual data for the last three weeks of trading days reveals a consistent trend, affirming the effectiveness of the model. The research findings can provide valuable insights for financial investments. However, this paper also observes that accurate predictions may not be achievable for data with longer time intervals. Therefore, the model needs to consider the timeliness of the data and the influence of other factors.