Forecasting Malaysia Shipping Logistic Company Stock Market Using Machine Learning

In an era where automation is rapidly transforming transportation and logistics, accurate forecasting has become crucial for enhancing supply chain responsiveness, reducing operational risks, and improving decision-making. The integration of machine learning (ML) into predictive systems offers new possibilities for modeling complex market behaviors, particularly in financial markets that influence investment and planning in logistics operations. This study explores the application of machine learning models in forecasting stock prices within Malaysia’s stock market, utilizing historical data from 2013 to 2023. Four prominent ML models, Linear Regression, Decision Trees, Support Vector Machines, and Neural Networks, were evaluated using performance metrics such as Mean Squared Error (MSE), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R-squared (R<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>). The dataset was split into a 70% training set and 30% test set to ensure reliable validation. Among the models tested, Linear Regression consistently outperformed the others, achieving the lowest error rates and highest predictive accuracy. This model was subsequently employed to generate forecasts for the 2023–2024 period. The forecasts displayed stable trends with minimal variance, reflecting both the robustness of the model and the consistency of the underlying market inputs. These findings underscore the potential of machine learning, particularly linear models, in supporting strategic automation and planning in logistics sectors that rely on financial indicators for resource allocation and investment decisions.