Document Type : Research Article
Authors
Department of Statistics and Informatics, University of Mosul, Mosul, Iraq
10.22054/jmmf.2026.90440.1248
Abstract
Stock price forecasting poses significant challenges due to non-stationarity, nonlinearity, and noise in financial markets, particularly for the Iraqi stock exchange. This study proposes an enhanced interval-valued forecasting model for daily prices of the Al Mansour Pharmaceutical Industries (MPI) company (2020–2025) using v-support vector regression (VSVR) with hyperparameters optimized via the waterwheel plant algorithm (WWPOA). The WWPOA approach tunes key VSVR parameters through population-based exploration and exploitation phases inspired by WWPOA, outperforming grid search (GS-VSVR) and cross-validation (CV-VSVR). Forecasting performance is evaluated using four criteria, namely mean absolute error , root mean squared error , direction accuracy and coefficient of determination . The empirical results show that the proposed model achieves lower values, compared to and indicating superior accuracy, robustness, and directional forecasting capability. On training data (637 days), WWPOA-VSVR achieves superior metrics for center and radius compared to baselines; testing results (308 days) confirm robustness. Further, Diebold-Mariano tests validate center-based WWPOA-VSVR superiority over radius-based at 95% confidence (p<0.05). On the training set, the center based WWPOA VSVR achieves MAE of about 0.18, RMSE of about 0.28, DA around 0.63, and R2 close to 0.93, while the radius based model attains MAE near 0.19, RMSE around 0.29, DA about 0.61, and R2 near 0.92. On the test set, center forecasts retain strong performance, with MAE around 0.20, RMSE about 0.30, DA near 0.60, and R2 approximately 0.92, and radius forecasts achieve MAE close to 0.17, RMSE near 0.27, DA about 0.57, and R2 around 0.88.
Keywords
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