Iran Market Manipulation Detection Based On Machine Learning Algorithms Methods

Habibzadeh,  Seyed Mohammad Reza; Gholami-Jamkarani ,   Reza; Rastegar Sorkheh,  Mohammad Ali; Chavoshi,   Seyed Kazem

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year 16, Issue 63 (2025)

fa 2025, 16(63): 1-15

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Iran Market Manipulation Detection Based On Machine Learning Algorithms Methods

Seyed Mohammad Reza Habibzadeh¹

, Reza Gholami-Jamkarani ^*²

, Mohammad Ali Rastegar Sorkheh³

, Seyed Kazem Chavoshi⁴

1- Department of Financial Engineering, Qo.C, Islamic Azad University, Qom, Iran.
2- Department of Accounting, Qo.C. Islamic Azad University, Qom, Iran.
3- Faculty of Industrial and Systems Engineering, Tarbiat Modares University, Tehran, Iran.
4- Department of Insurance, Banking and Customs Management Faculty of Management, Kharazmi University, Tehran, Iran.

Abstract: (12 Views)

One of the most important prerequisites for the expansion and deepening of the capital market as the main economic market of the country is the trust of the actors and beneficiaries of this market in its efficiency and correctness as a basis for determining the price of financial assets fairly and away from fraud.therefore, the purpose of this research is to identify the manipulation of stock prices of companies accepted in the capital market of Iran, and with regard to the development of technology and complex trading algorithms based on artificial intelligence in stock transactions in the capital markets, taking advantage of Developed tools based on machine learning are considered essential by regulatory institutions in order to identify price manipulation in the capital market. In this research, using statistical methods such as sequence, skewness, kurtosis and textual anomaly detection test in order to prepare and label data, from a variety of machine learning methods including decision tree, random forest, support vector machine, network Multilayer neural and logistic regression have been used to identify stock manipulation during the second half of 1398 to the end of 1402 in the Iranian capital market. For this purpose, data on 73 stocks from 19 industries listed on TSE. The total number of trading days was about 71,300, of which manipulation occurred on 537 trading days and no manipulation occurred on the other days. The decision tree algorithm performed better than the other compared methods from the point of view of the balance between accuracy index, readability and F2 index. This result states that one of the most effective ways to identify price manipulation is to use predetermined rules that are extracted by decision tree models and can be updated at different time intervals. Based on the obtained results, the volume variable on the same day of manipulation (vol0) and the volume variable on a trading day before the manipulation (vol1) are the most important in identifying stock manipulation.

Article number: 1

Keywords: Capital Market, Stock Price Manipulation, Artificial Intelligence, Data Labeling, Decision Tree.

Full-Text [PDF 628 kb] (8 Downloads)

Type of Study: Research | Subject: Special

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Habibzadeh S M R, Gholami-Jamkarani R, Rastegar Sorkheh M A, Chavoshi S K. Iran Market Manipulation Detection Based On Machine Learning Algorithms Methods. fa 2025; 16 (63) : 1
URL: http://qfaj.mobarakeh.iau.ir/article-1-2778-en.html

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