Application of the Time Series Model in Forecasting Stock Trends

Jiayu Dai ()
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Jiayu Dai: The University of Manchester, School of Natural Sciences

A chapter in Proceedings of the 2025 International Conference on Financial Risk and Investment Management (ICFRIM 2025), 2025, pp 266-273 from Springer

Abstract: Abstract As financial management becomes more technically sophisticated, the prediction of stock price becomes a significant task for investors and traders. The autoregressive integrated moving average (ARIMA) model is a technique commonly employed to predict trends in stock prices. Moreover, it is a time series model, which is more advanced to provide an appropriate rate of daily return with confidence intervals. This paper will utilize the ARIMA model to estimate the share price of Amazon. The stock data for period from January 1, 2020 to April 30, 2020 is chosen, and it includes vital information - the starting price. The research findings indicate that the ARIMA model can capture the dynamic trends of stock prices to a certain extent. The value of MAPE is 2.0088% which is much less than 10%, indicating that the result of the prediction is good. Meanwhile, the valuable reference information it provided for investors and traders can help them better understand the market direction and potential risks.

Keywords: Time Series Analysis; Stock Price Prediction; ARIMA (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:spr:advbcp:978-94-6463-748-9_30

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DOI: 10.2991/978-94-6463-748-9_30

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