Machine Learning Insights for Stock Market Trend Identification
Keywords:
Stock Market Trend Analysis, Machine Learning, Gradient Boosting, LSTMs, ARIMAAbstract
The stock market is characterized by its complexity, dynamism, and sensitivity to a multitude of factors, making accurate trend analysis a paramount concern for investors and traders. This research investigates the application of machine learning techniques for stock market trend analysis, providing a comprehensive study of historical stock prices, economic indicators, and advanced machine learning algorithms. Ensemble methods, particularly Gradient Boosting, outperformed other models in accuracy, precision, recall, and F1-Score. Technical indicators and lag features play a pivotal role in capturing trends, providing actionable insights. The analysis emphasizes the significance of time horizons in model performance, emphasizing the necessity to align model choices with investment strategies. This research advances stock market analysis, demonstrating the value of machine learning predictions for investors and traders.
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