comparative analysis of machine learning algorithms in predicting jumps in stock closing price: case study of iran khodro using nearmiss and smote approaches

Predicting stock price fluctuations has always been one of the most important financial challenges due to the complexities of financial data and nonlinear market behavior. this research aimed to analyze and compare the performance of machine learning algorithms in predicting the closing price jump of iran khodro company shares. two different methods of managing unbalanced data, nearmiss and smote, were used to overcome the challenge of unbalanced data. the results showed that the nearmiss method outperformed smote by balancing precision and recall in machine learning models. the catboost model was recognized as the best machine learning model in this study due to its stable performance in nearmiss and smote methods. the catboost model showed a perfect balance between evaluation indicators in the nearmiss method, with an accuracy of 91.46% and an f1 score of 91.29%. this model also had high precision (93.18%) and acceptable recall (89.52%), which showed the ability to detect jumps and avoid wrong predictions correctly. on the other hand, in the smote method, the random forest model was superior, with an accuracy of 85.08%. these results show that a combination of unbalanced data management methods and advanced machine learning algorithms can significantly improve the accuracy of price volatility prediction. the results of this research can help investors and financial analysts make better decisions in risk management and optimizing investment strategies.