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Stock price prediction is a common topic in finance and economics that investors are interested in. Successful forecasts in stock price can bring significant profits to individuals and communities, thus, it is to be regarded as an important factor that is related to the whole economic society. Over the years, researchers have developed myriad models to figure out the pattern of price change. The autoregressive integrated moving average ARIMA model is one of the well-known statistical models. This paper is going to demonstrate the application of the ARIMA model using real stock data from the China Stock Market & Accounting Research Database CSMAR. The research would be conducted with the assistance of SPSS. In this paper, results obtained not only refer the potential usefulness of the ARIMA model to identify and predict trends in the stock market, but also imply the oversimplification of the model based on a particular stock’s performance in a real financial market.
Ziyue Yu (Fri,) studied this question.