<![CDATA[This study applies the Holt–Winters method, an exponential smoothing approach incorporating level, trend, and seasonal components, to compare the predictive accuracy of four variants (multiplicative, additive, OR, and average) of Holt-Winter Method in forecasting stock prices of companies listed in the LQ45 index. The dataset consists of stock prices from 2016–2021 for training and January–February 2022 for testing, with forecasting accuracy evaluated using Mean Absolute Percentage Error (MAPE), visualized through boxplots, and assessed using the nonparametric Kruskal–Wallis test. The Holt–Winters computations were performed using Microsoft Excel, while boxplot visualization and the Kruskal–Wallis test were conducted using the R programming language. The results indicate significant differences in predictive performance among the four methods with p-value = 0.04059 in Kruskal-Wallis test. The Additive Holt–Winters method achieves the best performance with the lowest MAPE, while the multiplicative method performs the worst. Among LQ45 stocks, INDF records the lowest forecasting error (1.6799%), whereas TPIA exhibits the highest (83.0783%). These results suggesting that the additive Holt–Winters method is more suitable for forecasting LQ45 stock prices under the observed conditions]]>
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