Leveraging Ant Colony Optimization and Differential Evolution for Stock Price Prediction in Stock Exchange

Stock price prediction has long been a challenging task due to the complex and dynamic nature of financial markets. Traditional methods often struggle to capture the intricate patterns and dependencies inherent in stock data. In recent years, metaheuristic algorithms such as Ant Colony Optimization (ACO) and Differential Evolution (DE) have shown promise in tackling this problem by efficiently exploring the solution space and optimizing predictive models. This paper explores the application of ACO and DE algorithms in predicting stock prices of companies listed in the Stock Exchange. Through a comparative analysis, we investigate the effectiveness of these algorithms in enhancing the accuracy of stock price prediction models, thus providing valuable insights for investors and traders. In this paper, we have investigated the application of Ant Colony Optimization and Differential Evolution algorithms in predicting stock prices of companies listed in the Stock Exchange. Our experimental results demonstrate the effectiveness of these metaheuristic algorithms in optimizing predictive models and improving the accuracy of stock price predictions. Our experimental results demonstrate that both ACO and DE significantly improve the accuracy of stock price prediction models compared to traditional optimization techniques. However, we observe variations in performance across different companies and time periods, highlighting the importance of adaptive algorithms capable of adjusting to changing market dynamics. ACO exhibits faster convergence in some cases but may struggle with local optima, while DE demonstrates better exploration of the solution space but requires more computational resources. Overall, the combination of ACO and DE presents a promising approach for enhancing stock price prediction in the Stock Exchange, providing valuable insights for investors and traders. Future research directions may include exploring ensemble methods combining ACO and DE with other optimization techniques and incorporating additional features such as sentiment analysis and macroeconomic indicators for more comprehensive predictions.

کلیدواژه ها:

Stock price prediction ، Stock Exchange ، Ensemble methods ، Sentiment analysis ، Macroeconomic indicators.