Accurate Stock Market Prediction Method Based on Ant Lion Optimization Machine Learning

With the increasing complexity of financial markets, it is gradually becoming a research hotspot in the field of accurate stock market prediction. The ant lion optimization algorithm, due to its excellent global search ability and the advantages of machine learning models in data mining, aims to further improve the accuracy of stock market prediction. Therefore, this paper will explore how to combine the ant lion optimization algorithm with machine learning models to achieve effective prediction of stock market trends. First, a stock relationship graph is constructed using graph neural networks, with stocks as nodes and relationships between stocks as edges. The node embedding technique of graph neural networks is used to extract feature representations of stocks. Then, the ant lion algorithm is used to optimize the parameters of the graph neural network, enabling it to better fit the historical data of the stock market. Finally, the trained model is used to predict the future trend of the stock market. Through experimental verification, it has been found that the proposed method has high fitness during the training process and short training time; the loss value of ant lion’s optimized machine learning algorithm is relatively small, and the model prediction accuracy is high. Applying this method to Apple, Facebook, and Tesla results in higher strategic trading returns. This method improves the accuracy of stock market prediction by optimizing machine learning algorithms with ant lions, providing investors with more reliable decision support.