Predicting Market Trends: Ant Colony Optimization Algorithms to Stock Price Clustering in stock Istanbul

This study investigates the application of Ant Colony Optimization (ACO) algorithms for stock price clustering in the Istanbul Stock Exchange (ISE). By leveraging computational intelligence techniques, ACO algorithms offer a promising approach to segmenting stocks based on their price dynamics and uncovering underlying market structures. The research employs historical stock price data from the ISE and implements ACO-based clustering algorithms to partition stocks into cohesive groups. Through empirical analysis and performance evaluation, the study demonstrates the effectiveness of ACO algorithms in identifying coherent market segments and enhancing predictive modeling capabilities. The results highlight the significance of ACO-based clustering in market trend prediction, portfolio optimization, and risk management strategies. Furthermore, the study provides insights into future research directions, including hybrid modeling approaches, integration of alternative data sources, and ethical considerations in financial analysis. Overall, this research contributes to the ongoing discourse on leveraging computational intelligence for informed decision-making in dynamic financial markets.

کلیدواژه ها:

Ant Colony Optimization (ACO) ، Stock Price Clustering ، Istanbul Stock Exchange (ISE) ، Computational Intelligence ، Market Segmentation ، Risk Management ، Financial Analysis And Hybrid Modeling.