Abstract
Securities trading has always been a high-risk, high-return domain. Investors seek high returns while endeavoring to minimize risks as much as possible. Therefore, stock price prediction has become a popular and immensely valuable research topic. This paper will use the ARMA model to forecast stock prices. Firstly, an analysis was conducted on selected stock, determining that the price sequence exhibits no seasonal effects but does display volatility effects. The trend is essentially linear, and the relationship between volatility effects and trends fits an additive model. Based on this, preprocessing was conducted by taking the three-day moving average sequence of the series to eliminate the volatility effects, yielding a clean sequence trend. Then, the trend was differenced once to obtain a stationary sequence. Subsequently, the appropriate ARIMA model order was determined by the (partial) autocorrelation plot of this stationary sequence, and the model was fitted to the stock for prediction, yielding satisfactory results. This indicates that the model can accurately forecast long-term trends, but the filtering of volatility effects prevents the prediction results from sensitively reflecting short-term fluctuations.
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