This paper aims to analyze the dynamic evolution of leader–follower relationships among mutual funds. While previous studies generally assume that leadership structures remain constant over time, it proposes a framework that allows these relationships to vary across market conditions and over the sample period. The paper contributes to the literature on herding in delegated portfolio management by examining how leadership dynamics emerge and evolve within the mutual fund industry.
To capture time-varying leadership dynamics, a methodological framework that combines Bayesian and graph-based techniques is developed. First, the evolution of portfolio market exposures is estimated using a Bayesian Capital Asset Pricing Model (CAPM). Second, a rolling Bayesian Vector Autoregressive (VAR) model with time-varying coefficients is used to identify dynamic pairwise leader–follower relationships among funds. Third, graph tools are used to characterize the structure, intensity and topology of the resulting mutual fund network. Finally, the evolution of each fund’s leadership strength over time is estimated and analyzed. The methodology is illustrated using an unbalanced panel of Spanish mutual funds over the period 1999–2016.
The results show that leader–follower relationships among mutual funds are time-varying rather than constant over time. Leadership dynamics intensify during periods of financial stress. In addition, funds exhibiting superior performance and lower portfolio turnover are more likely to emerge as leaders within the network.
It would be necessary to include in the model, not only the market exposures, but also additional risk factors such as size or book-to-market, because multifactor models might allow capturing more general leader-follower relationships. On the other hand, the results obtained suggest the existence of a managerial effect on the leadership capacity of a fund. This suggests the need to extend the two-dimensional framework presented in the paper to a multidimensional framework that describes the joint evolution of the dynamic risk exposures coefficients using sparsity-modeling techniques.
The authors’ approach provides information about which are the leader funds in different periods of time and some possible explanations of their leadership. All this can be very useful for investors in order to decide which funds to invest in.
To the best of the authors’ knowledge, this paper is the first to examine the dynamic evolution of leadership relationships among mutual funds within a time-varying network framework. By extending previous approaches through the combination of Bayesian estimation techniques and graph analysis, the study provides a novel methodology for identifying and visualizing evolving leadership structures in the mutual fund industry.
