Stock Market Fraud Detection, A Probabilistic Approach

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Kazemi tabar Seyed Javad; Shahbazzadeh Majid

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Journal: SIGNAL AND DATA PROCESSING Year:2020 | Volume:17 | Issue:1 (43) Page(s): 3-14

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Information Journal Paper

Title

Stock Market Fraud Detection, A Probabilistic Approach

Author(s)

Kazemi tabar Seyed Javad | Shahbazzadeh Majid | Issue Writer Certificate

Keywords

Stock ExchangeQ1

Market manipulationQ1

Front runningQ1

Fraud detectionQ1

Chebyshev inequalityQ1

Abstract

In order to have a fair market condition, it is crucial that regulators continuously monitor the stock market for possible fraud and Market manipulation. There are many types of fraudulent activities defined in this context. In our paper we will be focusing on "Front running". According to Association of Certified Fraud Examiners, Front running is a form of insider information and thus is very difficult to detect. Front running is committed by brokerage firm employees when they are informed of a customer's large transaction request that could potentially change the price by a substantial amount. The fraudster then places his own order before that of the customer to enjoy the low price. Once the customer's order is placed and the prices are increased he will sell his shares and makes profit. Detecting Front running requires not only statistical analysis, but also domain knowledge and filtering. For example, the authors learned from Tehran's Over The Counter (OTC) Stock Exchange officials that fraudsters may use cover-up accounts to hide their identity. Or they could delay selling their shares to avoid suspicion. Before being able to present the case to a prosecutor, the analyst needs to determine whether predication exists. Only then, can he start testing and interpreting the collected data. Due to large volume of daily trades, the analyst needs to rely on computer algorithms to reduce the suspicious list. One way to do this is by assigning a risk score to each transaction. In our work we build two filters that determine the risk of each transaction based on the amount of statistical abnormality. We use the Chebyshev inequality to determine anomalous transactions. In the first phase we focus on detecting a large transaction that changes market price significantly. We then look at transactions around it to find people who made profit as a consequence of that large transaction. We tested our method on two different stocks the data for which was kindly provided to us by Tehran Exchange Market. The officials confirmed we were able to detect the fraudster.

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APA: Copy

Kazemi tabar, Seyed Javad, & Shahbazzadeh, Majid. (2020). Stock Market Fraud Detection, A Probabilistic Approach. SIGNAL AND DATA PROCESSING, 17(1 (43) ), 3-14. SID. https://sid.ir/paper/377583/en

Vancouver: Copy

Kazemi tabar Seyed Javad, Shahbazzadeh Majid. Stock Market Fraud Detection, A Probabilistic Approach. SIGNAL AND DATA PROCESSING[Internet]. 2020;17(1 (43) ):3-14. Available from: https://sid.ir/paper/377583/en

IEEE: Copy

Seyed Javad Kazemi tabar, and Majid Shahbazzadeh, “Stock Market Fraud Detection, A Probabilistic Approach,” SIGNAL AND DATA PROCESSING, vol. 17, no. 1 (43) , pp. 3–14, 2020, [Online]. Available: https://sid.ir/paper/377583/en

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