كشف تقلب در بازار بورس اوراق بهادار با استفاده از كاربرد نامساوي چبيشف

Stock Market Fraud Detection, A Probabilistic Approach

يكي از راه­‌هاي مشاركت افراد در توسعه اقتصادي كشور، سرمايه‌­گذاري در بازار سرمايه و به‌خصوص، بورس اوراق بهادار است. به اين منظور، بازارهاي اوراق بهادار بايد مورد اعتماد مردم و فعالان اقتصادي باشند. شفافيت و كارايي بازار مي‌­تواند حقوق و منافع سرمايه‌­گذاران را حمايت كند و باعث رونق بازار شود. در اين ميان، بعضي از افراد با توجه به موقعيت خود از اطلاعات نهاني مربوط به بازار بورس اوراق بهادار سوء استفاده مي‌­كنند و باعث بي‌­اعتمادي افراد به بازار سرمايه مي‌­شود؛ از‌اين‌رو در اين مقاله، با استفاده از كاربرد نامساوي چبيشف، روشي براي شناسايي افرادي كه از اطلاعات نهاني، استفاده شخصي كرده­ و در مدت كوتاهي سود كلاني به‌­دست آورد‌ه‌­اند، ارائه شده است. به‌ منظور استفاده از اين روش دو فيلتر در نظر گرفته شده­ است، به‌طوري‌كه فيلتر نخست تراكنش‌­هاي بزرگ را شناسايي مي‌كند و فيلتر دوم، افرادي كه بيشترين سود حاصل از خريد و فروش سهام در مدت زمان اندك (سه روز)، به‌دست آورده‌­ا‌ند؛ در‌حالي‌كه دست‌كم يك تراكنش بزرگ در اين حد فاصل زماني رخ داده باشد، شناسايي مي­‌كند؛ سپس روش پيشنهادي، بر روي دو دسته از داده­‌هاي واقعي بازار بورس اعمال شده است. با تغيير ضرايب فيلترها، مي‌­توان معيارهاي مورد نظر را تغيير داد.

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.