Mining Longitudinal Network for Predicting Company Value
Yingzi Jin
Longitudinal networks are studied by sociologists to understand network evolution, belief formation, friendship formation, etc. Companies make and receive different impact from other companies in different periods. If one can understand what types of network changes affect a company's value, he/she can predict the future value of the company, grasp industry innovations, and make business more successful. However, it often requires continuous records of network changes, which are often difficult to collect, and the models of mining longitudinal network are quite complicated. In this study, we developed algorithms and a system to infer large-scale evolutionary company networks from public news from 1981 to 2009. Then, on the basis of how networks change over time, as well as the financial information of the companies, we predicted company profit and revenue growth. Herein, we propose feature extraction and selection algorithm for longitudinal networks. This is the first study of longitudinal network-mining-based company performance analysis in the literature. We measured how networks impact company performance and what types of network features are important.