The Benefits and Costs of Concentrated Ownership of Firms in East Asia and Western European Economies: A Simultaneous Equations Approach

This study uses a simultaneous equations model to examine the impact of ownership structure on corporate policies and performance for listed firms in East Asia and Western Europe. The policy areas examined include dividend, diversification, leverage, and earnings management. Both accounting performance and market valuation are used as performance measures. The empirical analysis reveals that the policy choices are interrelated and have joint impact on firm performance. There exist some regional differences with respect to how ownership structure affects the largest shareholder’s policy decisions. These differences are related to the difference in capital market developments and possibly reflect the nonlinear effects of ownership structure as well. For a sample of 927 listed firms from eight East Asian economies, I find that (1) the level of cash flow rights held by the largest owner is positively related to subsequent dividend payment, diversification, leverage, operating efficiency, and firm value, and negatively related to earnings management; (2) efficiency gains and expropriation costs coexist in firms with concentrated ownership; (3) the expropriation costs increase with the control stake held by the largest owner; (4) firms located in countries with better investor protection pay higher dividends, are less engaged in earnings management, and have superior performance. For a sample of 1,757 listed firms from 13 Western European economies, I find that (1) the level of cash flow rights held by the largest owner has negative effects on leverage and firm value; (2) the excess control rights are negatively related to dividend payment, diversification, leverage, and firm value; (3) strong investor protection is beneficial to minority shareholders. Taken together, this study provides some insights regarding how controlling shareholders choose corporate policies for expropriation purposes. The extant literature largely ignore the interrelationships among firm policies and their joint impact on firm performance. In addition, the empirical results for the listed Asian firms suggest that efficiency gains and expropriation costs coexist in firms with concentrated ownership structure. Some of the expropriation costs born by minority shareholders may be viewed as a price paid for the efficiency gains, supporting the view of Grossman and Hart (1980). This study contributes to the growing body of literature on the efficiency of corporate governance mechanisms around the globe

The Effect of Bank Activity Restriction on Life Insurers’ Efficiency: Evidence from European Markets

This paper examines the relation between bank entry restrictions into insurance operations and life insurers’ operating efficiency for a sample of 21 European countries over 1995-2003. Controlling for insurance market penetration, insurance risk retention, legal environment, and the economic development of the hosting country, we document that insurers operate more efficiently in markets with lower bank entry restrictions. Our results suggest that financial deregulation has positive spill-over effect, supporting the deregulation efforts in the global financial markets

Hypergraph Neural Networks

In this paper, we present a hypergraph neural networks (HGNN) framework for data representation learning, which can encode high-order data correlation in a hypergraph structure. Confronting the challenges of learning representation for complex data in real practice, we propose to incorporate such data structure in a hypergraph, which is more flexible on data modeling, especially when dealing with complex data. In this method, a hyperedge convolution operation is designed to handle the data correlation during representation learning. In this way, traditional hypergraph learning procedure can be conducted using hyperedge convolution operations efficiently. HGNN is able to learn the hidden layer representation considering the high-order data structure, which is a general framework considering the complex data correlations. We have conducted experiments on citation network classification and visual object recognition tasks and compared HGNN with graph convolutional networks and other traditional methods. Experimental results demonstrate that the proposed HGNN method outperforms recent state-of-the-art methods. We can also reveal from the results that the proposed HGNN is superior when dealing with multi-modal data compared with existing methods.Comment: Accepted in AAAI'201