Investigating the Determinants of Stock Prices and Comparing the Performance of Advanced Nonlinear Methods in Forecasting
Keywords:
Stock price forecasting, Fuzzy regression, Machine learning, Genetic algorithm, Support vector machine, Decision treeAbstract
Objective: The objective of this study was to compare the predictive accuracy and performance of fuzzy regression, genetic algorithm, support vector machine, decision tree, and machine learning methods in forecasting stock closing prices using real out-of-sample observations.
Methodology: This applied quantitative study utilized daily stock market data of Isfahan Steel Company from January 1, 2024, to November 19, 2025. After removing highly collinear variables, selected predictors were incorporated into the forecasting models. The final 30 observations were reserved as real out-of-sample data to evaluate forecasting performance. Fuzzy regression, genetic algorithm, interpretable machine learning, decision tree, and support vector machine models were developed and compared. Prediction accuracy was assessed using Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE).
Findings: The results demonstrated significant differences in predictive performance among the examined models. Fuzzy regression achieved the highest forecasting accuracy, producing the lowest error values (MSE = 26, RMSE = 31, MAE = 996). Interpretable machine learning ranked second (MSE = 32, RMSE = 40, MAE = 1,567). Decision tree models showed moderate performance (MSE = 53, RMSE = 71, MAE = 5,027). In contrast, support vector machines and genetic algorithms generated substantially larger forecasting errors and exhibited weaker generalization ability on out-of-sample data. Furthermore, opening price, highest price, and lowest price emerged as the most influential predictors across most forecasting models.
Conclusion: The findings indicate that interpretable forecasting approaches, particularly fuzzy regression, provide a superior balance between predictive accuracy and economic interpretability. These models not only deliver reliable forecasts but also facilitate a clearer understanding of the relationships among financial variables. Consequently, they can serve as effective tools for short-term stock price forecasting and evidence-based investment decision-making in capital markets.
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Copyright (c) 2025 Parasto Azami, Najmeh Kargar Kamvar, Hoda Hemmati (Author)
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