Machine Learning Model for Stock Market Trend Prediction Building

The financial markets are highly dynamic and nonlinear, thus, making predicting of stock market trends a very difficult task. This paper presents an ensemble machine learning framework of stock market forecasting through various sophisticated algorithms. The framework combines the XGBoost Beijing lightgbm and Gradient Boosting using the complementary algorithms of Random Forest, Support Vector Machine, K-Nearest Neighbors, and Logistic Regression. Past data of stock prices together with technical analysis data that includes moving averages, Relative Strength Index, and Moving Average Convergence Divergence are used as input features in training models. The ensemble learning approach allows addressing the complex patterns and dependencies of financial time-series data in a better way. Evidence of experimental analysis shows that the offered framework is highly accurate in prediction, precise, and more stable than individual machine learning models. The findings provide evidence that the ensemble methodology can help to make informed investment choices and enable financial risk management to a great extent.