To Detect Irregular Trade Behaviors In Stock Market By Using Graph Based Ranking Methods

Abstract

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 semisupervised ranking methods outperform the random walk Laplacian based semi-supervised ranking method.