A Hybrid Price Forecasting Model for the Stock Trading Market Based on AI Technique

Artificial Intelligence, specifically, Boost-base methods, has successfully revolutionized the area of stock price forecasting in recent years. This article integrates the idea of the hybrid stacking ensemble learning to forecast the trend of stocks. In the utilized Google stock price dataset, the market features “Open,” “High”, “Low,” and “Volume” are utilized as input variables for a proposed hybrid stacking model in line with various Artificial Intelligence (AI) techniques. Unlike traditional stock price forecasting methods, this hybrid stacking approach incorporates multiple AI models, including XGBoost, CatBoost, and Light Gradient-Boosting Machine (LightGBM), with Lasso regression serving as the meta-learner. By employing the intrinsic pattern hidden in the real-world stock market and the inherent stacking mode of the proposed methodology, the hybrid algorithm in this paper endeavors to enhance the prediction capability and capture the real-world operation mode in financial market. By leveraging distributed stock price data inputs and the stacking methodology, the model aims to enhance forecasting accuracy and better reflect actual stock market conditions. The multi-state algorithm outperformed on the widely-employed Google datasets with several typical prediction evaluation criteria, including some classical format. The benchmark experiments on various baseline models demonstrate the effectiveness and the advantages of the developed multi-stage algorithm and enjoy the significant improvement on the Google standard market competition.