Stock weighted average price prediction based on feature engineering and Lightgbm model

作者：Hongyi Shui, Xinye Sha, Baizheng Chen, Jiajie Wu · 年份：2024 · DOI：10.1145/3677892.3677945 · 被引用次数：28 · 研究领域：Stock Market Forecasting Methods、Grey System Theory Applications、Energy Load and Power Forecasting

Stock price prediction is essential yet challenging in financial markets, guiding investment decisions for stakeholders. While traditional methods rely on technical and fundamental analysis, the integration of big data and machine learning presents new opportunities for enhanced accuracy. This paper introduces a novel approach for stock price prediction, utilizing feature engineering and the LightGBM model. Feature engineering, a crucial step in machine learning, is employed to select or create relevant features, focusing on capturing the weighted average price from historical trading data. The LightGBM model, known for its efficiency and accuracy, is harnessed to improve prediction accuracy. Our study contributes by offering a comprehensive framework that integrates advanced machine learning techniques with domain-specific feature engineering. Real-world stock market data validates the effectiveness of our approach, outperforming traditional methods. Our contributions include leveraging LightGBM with Optuna fine-tuning for enhanced prediction accuracy, introducing a novel feature engineering approach for WAP incorporation, and demonstrating superior performance through extensive experiments, marking a significant advancement in stock price prediction methodologies.