Unveiling Future Trends for Predicting Online Smart Market Stock Prices using Ensemble Neural Network

Abstract

Predicting stock prices in the online smart market is a complex task, and leveraging advanced data mining techniques has become essential for accurate forecasting. This study proposes a novel approach utilizing an ensemble neural network combined with swarm optimization for enhanced predictive accuracy. The ensemble neural network, a robust machine learning approach, is adept at capturing complex patterns in stock market data. Concurrently, swarm optimization further refines the model's predictive capabilities, optimizing parameters for superior performance. By incorporating these techniques, the study unveils future trends in predicting online smart market stock prices, providing investors and traders with invaluable insights for informed decision-making. Existing algorithms are limited. The ensemble neural network integrates diverse models to capture intricate patterns in financial data, while swarm optimization refines the model parameters for optimal performance. The experimental results showcase an impressive accuracy of 92.5%, highlighting the efficacy of the proposed methodology. This research not only contributes to the field of stock price prediction but also provides valuable insights into future trends in the online smart market.