AI-Based Stock Market Prediction Using Deep Learning and Sentiment Analysis Techniques

This study concludes that Artificial Intelligence-based approaches, particularly the integration of deep learning and sentiment analysis, provide a highly effective framework for stock market prediction. The research demonstrates that traditional forecasting methods are limited in their ability to capture the complex, nonlinear, and dynamic behavior of financial markets. In contrast, hybrid AI models significantly improve prediction accuracy by combining numerical time-series data with qualitative sentiment information derived from financial news and social media platforms. The application of deep learning techniques such as LSTM networks has proven highly efficient in identifying hidden patterns and temporal dependencies in stock price movements. At the same time, sentiment analysis contributes valuable insights into investor behavior, market perception, and psychological factors that influence price fluctuations. The Structural Equation Modeling (SEM) results confirm that both sentiment and deep learning have strong positive relationships with prediction accuracy, while market noise negatively impacts forecasting performance. The model fit indices obtained from AMOS analysis further validate the robustness and reliability of the proposed framework. The findings clearly indicate that hybrid AI models outperform traditional statistical and econometric approaches in terms of accuracy, stability, and predictive power. This confirms the growing importance of artificial intelligence in modern financial decision-making systems. In practical terms, the study suggests that investors, financial analysts, and institutions should adopt AI-driven forecasting models to enhance investment strategies and reduce risk. The integration of real-time sentiment data with deep learning models can significantly improve decision-making efficiency in highly volatile market conditions. In conclusion, the research highlights that the future of stock market prediction lies in intelligent hybrid systems that combine machine learning, natural language processing, and behavioral analytics. Such systems not only improve forecasting accuracy but also provide deeper insights into market behavior. This study contributes to the growing body of knowledge in financial analytics and supports the development of more advanced, reliable, and data-driven investment decision-support systems in the future.

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