DESIGNING AN ADVANCED DEEP LEARNING MODEL FOR PREDICTING THE STOCK MARKET GROWTH

Abstract

Stock market prediction has long been a challenging task due to its highly volatile, nonlinear, and dynamic nature influenced by economic indicators, market sentiment, and global events. This study presents the design of an advanced deep learning model for predicting stock market growth by integrating multiple neural network architectures to capture both temporal dependencies and complex feature interactions. The proposed framework combines Long Short-Term Memory (LSTM) networks for timeseries forecasting, Convolutional Neural Networks (CNN) for feature extraction, and attention mechanisms to enhance pattern recognition in historical price data and technical indicators. The model incorporates preprocessing techniques such as data normalization, noise filtering, and feature engineering to improve predictive accuracy and stability. Additionally, sentiment analysis from financial news and social media data is integrated to enrich the model’s contextual understanding of market movements. The performance of the proposed model is evaluated using standard metrics such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and directional accuracy. Experimental results demonstrate that the advanced hybrid deep learning approach outperforms traditional statistical models and standalone machine learning techniques in predicting stock market growth trends. The study highlights the potential of deep learning-driven financial forecasting systems to assist investors, analysts, and financial institutions in making informed and data-driven investment decisions.