Abstract
Based on the quarterly data of mutual funds in China from the fourth quarter of 2004 to the fourth quarter of 2019, this paper constructs a series of complex bipartite networks based on the overlapped portfolios of mutual funds and then explores the influences of fund network position on mutual fund’s investment behavior and performance. This paper finds that a mutual fund with shorter information transmission path to other entities in the fund network (i.e., having higher closeness centrality) or with stronger ties with those entities in important information positions (i.e., having higher eigenvector centrality) will achieve better investment performance. However, a stronger mediating role over the potential information flow of the fund network (i.e., having higher betweenness centrality) cannot help a mutual fund increase performance. The empirical results also indicate that a mutual fund holding stock portfolios with high valuation difficulties caused by the market or fundamental information uncertainty will achieve better investment performance, while holding hard-to-value portfolios caused by limited public information will reduce the performance of the fund. Furthermore, high closeness centrality or eigenvector centrality can help mutual funds deal with the disclose problems of public information, thus reducing the likelihood of a mutual fund holding hard-to-value portfolios caused by limited public information to achieve worse performance. Eigenvector centrality brings information advantages about company fundamentals, so it is easier for a mutual fund with high eigenvector centrality to profit from holding hard-to-value portfolios caused by the fundamental information uncertainty. The conclusions of this paper can enhance our understanding of the fund network and its information mechanism and shed new light on mutual fund’s information advantages and related asset allocation strategies.
Highlights
Investors need to rely on information to make investment decisions
High closeness centrality can improve the breadth of the information acquired by a mutual fund and enable the fund to access to richer information sources [41]. erefore, mutual funds with high closeness centrality can break through the limitations of poor disclosure of public information. e coefficient of Eigenvector × HTV_info is significantly positive in Model (3-9) as well (β > 0; p < 0.01), indicating that eigenvector centrality can reduce the negative impacts of holding hard-to-value portfolios caused by limited public information
The results support Hypothesis 3 to some extent, indicating that the information breadth brought by closeness centrality have positive influences on the returns of hard-to-value portfolios caused by limited public information, and the information depth brought by eigenvector centrality have positive influences both on the returns of hard-to-value portfolios caused by limited public information or fundamental information uncertainty
Summary
Investors need to rely on information to make investment decisions. Since mutual funds are main institutional investors in the capital market, how they obtain and use information advantages have attracted scholars’ attention. Closeness centrality is the reciprocal of the average shortest path of a focal node’s access to other nodes directly or indirectly in the network [20] It shows the connectivity of a node in the network and can reflect the efficiency of the node to obtain information by using its network location. Using the quarterly data of China’s mutual funds from the fourth quarter of 2004 to the fourth quarter of 2019, this paper explores three important questions: (i) how closeness centrality, betweenness centrality, and eigenvector centrality in fund network impact mutual fund’s investment performance, respectively; (ii) how holding hard-to-value portfolio impacts mutual fund’s investment performance; (iii) how the information advantages brought by the three network centrality indicators impact mutual fund’s return from the hard-to-value portfolio
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