Stock Prediction Using Optimized LightGBM Based on Cost Awareness

The application of machine learning in stock market prediction has generated widespread attention. This paper proposes a new method for short-term stock investment using optimized LightGBM based on cost awareness. By cost awareness here we define that the decision process is more aware of the false-positive errors, or is more aware of `fake chances', and the cost for investment can be reduced. The main contribution of the research is to design an investment method for stock price prediction. Based on the principles of short-term investment and current quantitative investment research, we select a series of technical indicators that meet the requirements of this research to improve the reliability of the prediction results. We propose the concept of cost awareness to improve the accuracy of prediction. By comparing with the previous results, we demonstrated that the performance of the optimized model based on cost awareness could be improved significantly. we eventually examined and compared results with XGBoost and Random Forest, and found that LightGBM provides better performances in prediction accuracy, profitability, and risk control ability.