A Deep Learning Approach to Stock Trend Forecasting Using CNNs with Technical Indicators

The approach encompassed under this model introduces stock trend forecasting using a new approach that involves Convolutional Neural Networks (CNNs) supplemented with technical indicators like the Exponential Moving Average (EMA), Moving Average Convergence Divergence (MACD), and Relative Strength Index (RSI). This study reacts to the limitations of traditional methods like ARIMA and GARCH due to their shortcomings with nonlinear dependence on stock data. The robust solution for trend prediction closely follows the hierarchical learning of CNN. This model attains a remarkable accuracy of 99% in stock trend prediction using Convolutional Neural Networks, fine-tuning crucial but elaborate patterns for stock price movements. The forecast results categorize stock trends as Neutral, Bullish, and Bearish, with validation shown from high precision, recall, and Fl-scores computed from complete classification reports and confusion matrices. The capability of forecasting provides tremendous assistance in making informed investment decisions. These results prove that CNNs more efficiently predict stocks if combined with technical analysis and give portions of insight to traders. This study shows the capacity to apply various deep learning techniques in finance to enhance stock trend predictions and shape on-the-ground investment strategies. The study fills an existing gap in research with the introduction of combining CNN with technical indicators in the prediction models for improvement with predictive power and accuracy.