Amazon’s Stock Trends Prediction based on ARIMA Model
DOI:
https://doi.org/10.54097/g3yrh896Keywords:
Stock price forecasting; ARIMA model; autoregression; prediction accuracy.Abstract
The rapid development of e-commerce in recent years has made stock market prediction a challenging yet important task for investors and traders. The ARIMA model has been extensively applied for forecasting in financial time series data. This study applied the ARIMA model to predict the Amazon stock price, utilizing historical stock price data from a period of a decade. The results of the analysis indicated that the ARIMA model can effectively predict short-term stock price movements with a certain level of accuracy. The model managed to encapsulate the predominant trend in the variations of Amazon’s stock prices, providing insights into potential future price movements. The predicted stock prices closely matched the actual prices, with a reasonable level of precision. Furthermore, the ARIMA model can be useful for investors and traders in making informed decisions. By utilizing the model’s predictions, investors can better assess the potential risks and returns associated with their investment strategies. It should be noted that the ARIMA model is not without its limitations. The model may not perform well during periods of structural changes or when there are significant events affecting the stock price. Besides, it can also be influenced by other factors such as policies, market sentiment and corporate fundamentals. In conclusion, the ARIMA model can assist in predicting short-term Amazon stock price movements, serving as a valuable tool for investors and traders. However, it is essential to integrate the model's forecasts with other pertinent information and analysis to form comprehensive investment decisions.
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