Revisiting Corporate Political Connections Using Social Networks and Prediction of Post-IPO Performance

ABSTRACT

This paper studies political connections from the view of the social networks. We build social networks annually from 2009 to 2017, where firm leaders serve or served in the same government institutions are linked together. Empirically, the social network each year shares a similar structure and shows a small-world effect. Furthermore, we apply four network topologies, namely, degree centrality, betweenness centrality, closeness centrality, and the clustering coefficient, to measure the strength of political connections and utilize these new proxies to predict the post performance of initial public offerings (IPOs). We observe political network centrality has negative effects on the short-term and long-term post-IPO stock returns in that the leaders’ position on the political hierarchy is inversely related to the post-performance of IPO firms. This finding may be attributable to the argument of “grabbing hand” that the capability of an IPO firm on acquiring resources or information on the political network is positively correlated to the extent that government officials using their power to reap the private or political benefits instead of firm value maximization.

KEY WORDS:

• Network topology
• political connections
• post-IPO performance
• social networks

JEL:

• G10
• G30

Acknowledgments

None of the authors of this paper has a financial or personal relationship with other people or organizations that could inappropriately influence or bias the content of the paper. This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. This paper has not been published elsewhere and it has not been submitted simultaneously for publication elsewhere.

Declaration of interest statement

No potential conflict of interest was reported by the authors.

Notes

1. Because of the limited space, we only provide two network graphs.

2. A giant component of a network is the maximal subset of vertices in the network such that there exists at least one path between any pairs of vertices (Newman 2010).

3. To define $d_{ij}$ mathematically, we first should know the adjacency matrix $A$. In an unweighted network, the element $A_{ij}=1$ if there is an edge between vertex $i$ and v $j$, otherwise $A_{ij}=0$. Then, we can define $d_{ij}$ as $d_{ij}={min}_r\left(r\left|\left[A_{ij}^r\right]>0\right.\right)$ (Newman 2010).