Developing an Algorithm for Detecting Suspicious Trades in Tehran Stock Exchange Based on Spoof Trading Model

Stock market manipulation has gained significant attention in recent years. The fact that markets can be manipulated has great implications for designing trading rules and market efficiency. Market manipulation has been developing since the beginning. Thoughtful and accurate manipulation techniques adopted by manipulators may not easily be detected. This research examines stock price manipulation in Tehran Stock Exchange and addresses the identification and detection of stock price manipulation by mathematical algorithm (spoof trading). This study examines transaction-based manipulation in which manipulators place bid and ask orders to control the stock prices. More specifically, this paper studies manipulated stocks by looking at market microstructure (intraday transactions) and aims to introduce an algorithm for detecting suspicious trades so that regulators can detect them beforehand. The ultimate goal of this paper is to help the “Securities and Exchange Organization” (SEO) of Iran to reform market regulations in order to prevent the occurrence of suspicious trades in the market.
This research relies on price data placed by investors in the market. Transaction data consists of two levels; the first level includes executed bid-ask orders available to the public, such as open, close, high, and low prices as well as trading volumes in a specific period. The second level includes the first layer plus all buying and selling orders whether they are executed or not. The latter can only be observed by the regulator and is not available to the public. This paper focuses on the second level in which characteristics and patterns of manipulated stocks are examined by utilizing the intraday transactions of 50 manipulated companies from 2013 to 2016. Panel data analysis and F-limer, Hausman Test, Heteroskedasticity Test, Cointegration Test, Variance Inflation Factors Test, as well as econometric tests relating to price manipulation including stationary, autocorrelation, kurtosis, skewness, run and duration dependence test, were applied to the considered data. Finally, an artificial neural network was used to test the effectiveness of the designed algorithm. The obtained results were illustrated in a confusion matrix.
The results of the mentioned econometric tests were consistent with the results of the designed algorithm. The study’s hypotheses were accepted.
The results of the designed algorithm indicated that the efficiency of the utilized algorithm for detecting suspicious trades is equal to 90.4%, which is an excellent level of performance for accepting an algorithm. Additionally, this research shows that the price and volume of buy and sell orders in the first two lines of the ticker screen are effective in detecting stock price manipulation.