Intelligent Automation in Stock Market Prediction: A Survey of Automated Models

In an era marked by global digitization, the landscape of stock market prediction has undergone a transformative shift, ushering in a new era of technologically-driven trading. With soaring market capitalization, stock trading has emerged as a focal point for financial investment. Analysts and researchers have pioneered tools and methodologies to anticipate stock price movements, empowering investors in making informed decisions. Advanced trading models now harness unconventional textual data from social platforms, leveraging cutting-edge machine learning techniques like text data analytics and ensemble methods to significantly enhance prediction accuracies. Despite these advancements, the dynamic, erratic, and often chaotic nature of stock market data continues to present formidable challenges. This study elucidates the systematic application of machine learning-based approaches and sentimental analysis approaches for stock market prediction, grounded in a versatile framework. A critical analysis of findings from the reputable digital libraries and databases including ACM digital library and Scopus, forms the cornerstone of this inquiry. Moreover, an expansive comparative analysis was conducted to delineate key trends and significant directions. This comprehensive exploration aims to furnish budding researchers with a foundational understanding of this burgeoning domain and pave the way for further inquiry into promising avenues.