A Machine Learning Framework for Stock Trading: Integrating Technical and Economic Indicators
This study presents a machine learning-based framework designed to predict stock price movements by integrating technical and macroeconomic indicators. Utilizing models such as neural networks, softmax logistic regression, and decision forests, the framework aims to optimize buy and sell triggers to maximize trading profits. The approach emphasizes medium to long-term profitability, leveraging data from the S&P 500 index and various economic indicators to inform trading decisions.
