The Ensemble Model For Long-Term Stock Investment Is Based On Sentiment Analysis And Technical Analysis

Abstract

Investors seek profitable possibilities while reducing risk in the complex stock market. Investors must find high-quality stocks with long-term growth potential. Traditional basic analysis can reveal a company's financial health, but it may not reflect short-term market sentiments. Stock selection using technical analysis and ensemble learning. Technical analysis, which analyses past price and volume data, is integrated with ensemble learning algorithms to improve stock identification. Using neural networks, gradient boosting, random forests, and decision trees, the proposed method makes investment decisions. The ensemble model's feature engineering, data preparation, and model optimization contribute to resilient performance. Moving averages, relative strength indices, and MACD are used to derive input features for the ensemble model from past stock prices and volume. Ensemble learning mitigates the drawbacks of several algorithms and reduces single-model prediction risk. The model uses various technical indicators and learning algorithms to find high-quality stocks with positive price movements and development prospects. The ensemble learning model can identify high-quality shares in various market scenarios. Compared to typical stock selection methods, high-quality commodity identification is more accurate. Technical analysis and ensemble learning are used to create a data-driven stock selection strategy for finance and investment. This approach can help investors and financial professionals make better investment decisions, boosting stock market portfolio performance and risk management.