—We investigated two popular scenarios of stock price manipulations: pump-and-dump and spoof trading. Pump-and-dump is a procedure to buy a stock and push its price up. Then, the manipulator dumps all of the stock he holds to make a profit. Spoof trading is a procedure to trick other investors that a stock should be bought or sold at the manipulated price. We proposed mathematical definitions based on level 2 data for both scenarios, and used them to generate a training set consisting of buy/sell orders in an order book of 10 depths. Order cancellations, which are important indicators for price manipulation, are also visible in this level 2 data. In this paper, we considered a challenging scenario where we attempted to use less-detailed level 1 data to detect manipulations even though using level 2 data is more accurate. First, we implemented feedforward neural network models that have level 1 data, containing less-detailed information (no information about order cancellation), but is more accessible to investors as an input. The neural network model achieved 88.28% accuracy for detecting pump-and-dump but it failed to model spoof trading effectively. Therefore, we further investigate the two-dimensional Gaussian model and show that it can detect spoof trading using level 2 data as input.
—Stock price manipulation, pump-and-dump, spoof trading, neural network.
Teema Leangarun and Poj Tangamchit are with the Department of Control Systems and Instrumentation Engineering, King Mongkut’s
University of Technology Thonburi, Bangkok, Thailand (e-mail: email@example.com, firstname.lastname@example.org).
Suttipong Thajchayapong is with the National Electronic and Computer Technology Center, National Science and Technology Development Agency (NSTDA), Thailand (e-mail: email@example.com).
Cite: Teema Leangarun, Poj Tangamchit, and Suttipong Thajchayapong, "Stock Price Manipulation Detection Based on Mathematical Models," International Journal of Trade, Economics and Finance vol.7, no.3, pp. 81-88, 2016.