Use ARIMA (Autoregressive Integrated Moving Average) Model to Study the Future Development Trend of Google’s Stock

Zimeng Wang ()
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Zimeng Wang: University of New South Wales

A chapter in Proceedings of the 2025 International Conference on Financial Risk and Investment Management (ICFRIM 2025), 2025, pp 568-578 from Springer

Abstract: Abstract This study using the data from June 20, 2024, to December 19, 2024 Google, investigates the application of the AMIRA model to predict future trends in Google’s stock prices. Google, a subsidiary of Alphabet Inc., is a global technology leader whose stock performance reflects market dynamics influenced by innovation and advertising revenue. Traditional statistical models often struggle with the complexity of stock data, which includes non-linearity and volatility. To address these challenges, this study employed the ARIMA (2,2,1) model, it can better predict this volatility data. The research involved testing stationarity( the ADF (Augmented Dickey-Fuller) test and the white noise test), determining model parameters through ACF (Autocorrelation Function) and PACF (Partial Autocorrelation Function) analyses, and evaluating model performance using metrics like AIC (Akaike Information Criterion), BIC (Bayesian Information Criterion), and residual analysis. As result, the results demonstrate that the ARIMA model effectively captures trends in Google’s stock prices, with a Mean Absolute Percentage Error (MAPE) of less than 1%. The prediction accuracy is very high. This study highlights the advantages of the ARIMA model in accurately predicting future volatility data, emphasizing its potential for broader applications in the financial sector.

Keywords: Time Series Model; ARIMA Model; Google’s Stock (search for similar items in EconPapers)
Date: 2025
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DOI: 10.2991/978-94-6463-748-9_64

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