Clustering and visualization of bankruptcy trajectory using self-organizing map

Bankruptcy trajectory reflects the dynamic changes of financial situation of companies, and hence make possible to keep track of the evolution of companies and recognize the important trajectory patterns. This study aims at a compact visualization of the complex temporal behaviors in financial statements. We use self-organizing map (SOM) to analyze and visualize the financial situation of companies over several years through a two-step clustering process. Initially, the bankruptcy risk is characterized by a feature self-organizing map (FSOM), and therefore the temporal sequence is converted to the trajectory vector projected on the map. Afterwards, the trajectory self-organizing map (TSOM) clusters the trajectory vectors to a number of trajectory patterns. The proposed approach is applied to a large database of French companies spanning over four years. The experimental results demonstrate the promising functionality of SOM for bankruptcy trajectory clustering and visualization. From the viewpoint of decision support, the method might give experts insight into the patterns of bankrupt and healthy company development.

► A SOM clustering approach to analyze and visualize the temporary evolution of company.
► Feature SOM clusters the financial vectors and characterizes the bankruptcy risk.
► Map the financial situation to a 2-d space and observe the evolution as a trajectory.
► Trajectory SOM clusters the produced trajectories to extract the patterns.
► Give experts insight into the patterns of bankrupt and healthy company development.