A Critical Analysis of Artificial Intelligence in Stock Market Prediction: A Literature Review

Abstract

Stock market prediction, a vital responsibility for investors and financial institutions, enables them to make knowledge-driven investment decisions. Artificial Intelligence (AI) techniques, such as deep learning (DL) and machine learning (ML), have exhibited impressive outcomes in the field of stock market prediction due to their ability to decode complicated and nonlinear correlations in financial data. This research paper offers an exhaustive examination of the literature related to AI methodologies for stock market prediction, including machine learning, deep learning, and hybrid models. It also discusses the diverse types of data utilized for stock market prediction, namely historical price data, news articles, social media inputs, and financial statements. Moreover, it includes various evaluation metrics critical for assessing the effectiveness of AI models in stock market prediction. In addition, the paper draws attention to the existing limitations and challenges in the field while highlighting potential avenues for future research. Providing an insightful understanding of the cutting-edge AI techniques for stock market prediction, this paper is a useful resource for researchers and practitioners in the finance industry to make well-informed decisions.