Open Access

ARTICLE

A Survey on Stock Market Manipulation Detectors Using Artificial Intelligence

by Mohd Asyraf Zulkifley^1,*, Ali Fayyaz Munir², Mohd Edil Abd Sukor³, Muhammad Hakimi Mohd Shafiai⁴

1 Department of Electrical, Electronic and Systems Engineering, Universiti Kebangsaan Malaysia, Bangi, 43600, Malaysia
2 Faculty of Management, Virtual University of Pakistan, 54000, Lahore, Punjab, Pakistan
3 Department of Finance, Faculty of Business and Economics, Universiti Malaya, 50603, Kuala Lumpur, Malaysia
4 Center of Sustainable and Inclusive Development, Universiti Kebangsaan Malaysia, 43600, Selangor, Malaysia

* Corresponding Author: Mohd Asyraf Zulkifley. Email:

Computers, Materials & Continua 2023, 75(2), 4395-4418. https://doi.org/10.32604/cmc.2023.036094

Received 16 September 2022; Accepted 30 December 2022; Issue published 31 March 2023

Abstract

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.

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Keywords

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