Impact of Board Dynamics in Corporate Bankruptcy Prediction: Application of Temporal Snapshots of Networks of Board Members and Companies
Name
Taavi Ilves
Abstract
Corporate bankruptcy affects significantly a variety of stakeholders, such as investors, creditors, competitors, employees, and is therefore an event, in which there is a serious economic interest to predict it well ahead. Although this topic is widely studied, typically annual financial data is used to make predictions. However, due to significant delay in publication of such data, the predictions are often outdated. At the same time, changes in board membership of companies are made public with significantly shorter delay. This thesis investigates whether usage of network metrics of networks of board members and companies will positively impact accuracy and timeliness of bankruptcy prediction. More specifically, the thesis reveals that network metrics, especially PageRank, degree and eccentricity, indeed improve bankruptcy prediction models. Furthermore, by using random forest learning method and network metrics, the author was able to construct a classification model that was capable of predicting bankruptcy up to nine months in advance.
Graduation Thesis language
English
Graduation Thesis type
Master - Software Engineering
Supervisor(s)
Peep Küngas
Defence year
2014