To Detect Irregular Trade Behaviors In Stock Market By Using Graph Based Ranking Methods
This work addresses illegal trade detection for financial regulators, but it is incremental as it applies existing graph-based methods to a specific domain.
The paper tackled the problem of detecting irregular trade behaviors in the stock market by employing three graph Laplacian-based semi-supervised ranking methods, finding that un-normalized and symmetric normalized methods outperformed the random walk Laplacian method.
To detect the irregular trade behaviors in the stock market is the important problem in machine learning field. These irregular trade behaviors are obviously illegal. To detect these irregular trade behaviors in the stock market, data scientists normally employ the supervised learning techniques. In this paper, we employ the three graph Laplacian based semi-supervised ranking methods to solve the irregular trade behavior detection problem. Experimental results show that that the un-normalized and symmetric normalized graph Laplacian based semi-supervised ranking methods outperform the random walk Laplacian based semi-supervised ranking method.