<![CDATA[This study aims to identify periodic patterns and predict the movement of Coca-Cola (KO) stock prices using Fourier series in a linear regression model. The data utilized includes daily closing stock prices over the 2014-2024 period. A Fourier model with 15 harmonic components was chosen to optimize the balance between prediction accuracy and the risk of overfitting. The analysis results showed an R-squared value of 0.9174, indicating a high capability of capturing stock price variations. The detected price fluctuations reveal significant seasonal cycles and periodic trends. The price forecast for the 2024-2029 period indicates potential higher volatility, influenced by consumer demand dynamics, global economic uncertainty, product innovation, as well as geopolitical factors and climate change. These findings provide insights for investors to develop investment strategies based on the detected stock price fluctuation patterns.]]>
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