Abstract. Accurate stock price prediction is important for financial investors, as it directly influences investment strategies and decision-making. In this study, the author focuses on evaluating the effectiveness of the Elastic Net model in forecasting the closing prices of Apple Inc.'s stock. The dataset, spanning from 2014 to 2023, is sourced from Kaggle and includes both historical prices and a range of technical indicators, thereby ensuring the accuracy and reliability of the research data. To enhance the predictive accuracy of the model, this paper carefully selects the 14-day moving average of the closing prices along with a set of relevant technical indicators as the key input variables. This selection aims to capture the underlying trends and patterns in the stock's price. The predictive performance of the Elastic Net model is evaluated using multiple evaluation metrics, including Mean Squared Error (MSE) and R-squared values, which quantify the model's accuracy and explanatory power. Additionally, this paper utilizes the Autocorrelation Function (ACF) plot to analyze the residuals and further validate the model's effectiveness. The results demonstrate that the Elastic Net model is well-suited for handling high-dimensional financial data, exhibiting strong performance in predicting Apple Inc.'s stock prices. However, it also underscores the model's potential problem as a valuable tool for investors seeking to make proper decisions in real-life conditions.