TY - EJOU AU - Zulkifley, Mohd Asyraf AU - Munir, Ali Fayyaz AU - Sukor, Mohd Edil Abd AU - Shafiai, Muhammad Hakimi Mohd TI - A Survey on Stock Market Manipulation Detectors Using Artificial Intelligence T2 - Computers, Materials \& Continua PY - 2023 VL - 75 IS - 2 SN - 1546-2226 AB - A well-managed financial market of stocks, commodities, derivatives, and bonds is crucial to a country’s economic growth. It provides confidence to investors, which encourages the inflow of cash to ensure good market liquidity. However, there will always be a group of traders that aims to manipulate market pricing to negatively influence stock values in their favor. These illegal trading activities are surely prohibited according to the rules and regulations of every country’s stock market. It is the role of regulators to detect and prevent any manipulation cases in order to provide a trading platform that is fair and efficient. However, the complexity of manipulation cases has increased significantly, coupled with high trading volumes, which makes the manual observations of such cases by human operators no longer feasible. As a result, many intelligent systems have been developed by researchers all over the world to automatically detect various types of manipulation cases. Therefore, this review paper aims to comprehensively discuss the state-of-the-art methods that have been developed to detect and recognize stock market manipulation cases. It also provides a concise definition of manipulation taxonomy, including manipulation types and categories, as well as some of the output of early experimental research. In summary, this paper provides a thorough review of the automated methods for detecting stock market manipulation cases. KW - Artificial intelligence; machine learning; convolutional neural network; recurrent neural network; stock market manipulation DO - 10.32604/cmc.2023.036094