<![CDATA[Purpose- This study aims to develop and evaluate a hybrid approach for forecasting the stock prices of selected banks listed on the Iraq Stock Exchange (ISX) by integrating traditional statistical analysis using autoregressive integrated moving average (ARIMA) with artificial intelligence using long short-term memory (LSTM). Design/methodology/approach- The study uses annual data from Iraqi banks covering the period 2010–2024, including annual closing stock prices, return on assets (ROA), return on equity (ROE), and earnings per share (EPS). The data were divided into training data for 2010–2020 and testing data for 2021–2024. The LSTM model consisted of two layers with 64 units and a dropout rate of 0.2 and was trained using the Adam optimizer. The predictive performance of the hybrid ARIMA–LSTM model was evaluated using mean absolute percentage error (MAPE), root mean square error (RMSE), and the coefficient of determination (R²). Findings- The results indicate that the hybrid ARIMA–LSTM model outperformed the standalone classical models in terms of forecasting accuracy, achieving the lowest MAPE and RMSE values and an R² of 0.99. These findings demonstrate the effectiveness of integrating linear and nonlinear modeling approaches for stock price forecasting. Furthermore, EPS was identified as the most strongly associated factor with stock price movements, with correlation coefficients ranging from 0.956 to 0.985. Originality/value- This study contributes to the literature by applying a hybrid ARIMA–LSTM framework to stock price forecasting in the Iraqi banking sector, an emerging market context that remains relatively underexplored. The findings highlight the potential of combining traditional time-series models with artificial intelligence techniques to improve forecasting accuracy and provide additional insights into the role of profitability indicators in stock price movements.]]>
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