Stock Price Prediction Using Artificial Intelligence: A Literature Review

Stock price prediction remains a critical yet challenging task that has attracted the focus of both researchers and practitioners. The purpose of this study is to present a comprehensive review of recent advancements in the application of artificial intelligence (AI)-based techniques in predicting stock price movements. A systematic analysis of research papers published from 2020 to 2023 was conducted, employing a keyword-based search across two significant databases. Fourteen influential papers were selected, which utilized various AI techniques to forecast stock prices. The literature review reveals a range of predictive approaches, including technical, fundamental, and sentiment analysis, with a significant emphasis on mixed approaches that integrate multiple models. Notably, hybrid models outperform traditional methods by leveraging deep learning algorithms to handle non-linear complexities and temporal dynamics. The paper concludes by emphasizing the significant potential of AI in stock market prediction, highlighting a promising future that involves refining hybrid models and integrating big data analytics. The insights derived from this review provide guidance for future research and practical applications in the financial industry, emphasizing the transformative impact of AI on investment strategies and decision-making processes.