<![CDATA[Accurate stock price forecasting is critical for investment decision-making, yet the nonlinear and complex nature of time series data poses significant challenges. This study investigates the application of the Nonlinear Autoregressive Neural Network (NARNN) for modeling the monthly stock price time series of PT Bank Mandiri (Persero) Tbk (BMRI) from January 2011 to December 2023. The model is constructed by exploring combinations of feedback delays and hidden neurons to identify the optimal configuration based on the root mean squared error. The dataset is divided into training, validation, and testing. Evaluation results show that the configurations 8–12 and 8–13 yield the best testing accuracy with a MAPE of 4.71%. An ensemble mean strategy is also employed, producing competitive and stable performance. These findings demonstrate that the NARNN approach effectively captures nonlinear patterns in stock data and holds promise for financial forecasting applications.]]>
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