  In an illiquid stock, traders can collude and place orders on a predetermined
price and quantity at a fixed schedule. This is usually done to manipulate the
price of the stock or to create artificial liquidity in the stock, which may
mislead genuine investors. Here, the problem is to identify such group of
colluding traders. We modeled the problem instance as a graph, where each
trader corresponds to a vertex of the graph and trade corresponds to edges of
the graph. Further, we assign weights on edges depending on total volume, total
number of trades, maximum change in the price and commonality between two
vertices. Spectral clustering algorithms are used on the constructed graph to
identify colluding group(s). We have compared our results with simulated data
to show the effectiveness of spectral clustering to detecting colluding groups.
Moreover, we also have used parameters of real data to test the effectiveness
of our algorithm.

Trading and Market Microstructure;Computational Engineering, Finance, and Science;Machine Learning