Is fund performance driven by flows into connected funds? spillover effects in the mutual fund industry

The relationship between fund flows and financial performance is one of the most intensely researched topics in the mutual fund literature. Warther (1995), Edelen and Warner (2001), Goetzmann and Massa (2003), and Ben-Rephael et al. (2011) provide clear evidence that fund flows into equity mutual funds can exert price pressure on aggregate stock market returns. More recently, Coval and Stafford (2007), Frazzini and Lamont (2008), and Lou (2012) document how individual stock returns can be affected by fund flows into equity mutual funds. In addition, mutual fund flows themselves have been suggested as a potential source for the well-documented underperformance of mutual funds relative to their benchmarks as extreme inflows or outflows force mutual funds to engage in costly transactions (Grinblatt and Titman 1989; Wermers 2000). Edelen (1999) and Rakowski (2010) provide direct evidence for the hypothesis that fund flow-induced transaction costs are a cause of the underperformance of mutual funds.

In this paper, we test whether the performance of individual mutual funds is subject to spillover risk caused by fund flows into connected mutual funds. We measure the degree of connectedness between two mutual funds by their share of overlapping portfolio holdings. The interconnectedness among entities from investing in common assets is considered an important channel for the propagation of systemic risk (see (Elliott et al. 2014a; Lin and Guo 2019) and many others). However, the majority of the literature uses the network analysis method and emphasizes the position of an entity inside the network (see for example (Eisenberg and Noe 2001; Gai and Kapadia 2010; May and Arinaminpathy 2010; Lin and Guo 2019)). Only few studies focus on network externalities. Antón and Polk (2014) provide evidence of excessive co-movement in individual stock returns caused by the shared ownership of active mutual funds. Blocher (2016) studies the impact of overlapping portfolio holdings on the co-movement in the returns of mutual funds. Different from previous literature, this paper studies the characteristics that can affect a firms’ vulnerability to network externalities. Building on the price pressure effects documented in the mutual fund literature, we hypothesize that flows into connected funds can affect individual mutual fund performance. Moreover, we examine whether there is a predictive relationship, by testing the spillover hypothesis not only based on actual flows, but also using expected fund flows, which are predicted from past fund returns and flows. Our empirical study is based on a global sample of 3,010 US-focused equity mutual funds over the January 2005 to December 2014 period.

Building up on the works of Lou (2012) and Blocher (2016), we confirm that flows into connected mutual funds impact the abnormal performance of individual mutual funds in a predictable way. We find that a one standard deviation increase in monthly expected flows to connected funds is associated with an annualized excess return of 0.22%. When splitting the sample of connected funds into three equal groups, we find that a one standard deviation increase in flows to the most connected funds is associated with an annualized spillover effect of 1.13%.

We make several contributions to the literature. First, we contribute to the literature on liquidity-based price pressure in the mutual fund industry. When mutual funds experience strong outflows, they are reliant on the liquidity provided by other market participants and may be forced to sell assets at ‘fire-sale’ prices (Coval and Stafford 2007). Our results provide evidence that spillover effects are significantly more pronounced during periods of constrained market liquidity.¹ During crisis periods, a one standard deviation increase in expected flows to the tertile of funds with the highest overlap is associated with a monthly excess return of 1.50%.

Second, we document that the spillover effect is more pronounced for small cap stock mutual funds relative to large cap mutual funds. During periods with strong outflows, the spillover effect to small cap stock funds is 1.07% per month, while only 0.02% for large cap stock funds. Several papers have challenged the suitability of the open-end fund structure as an investment vehicle. Stein (2005) argues that the risk of sudden withdrawals in case of short-term underperformance makes open-end funds managers unlikely to bet on profitable long term opportunities, where convergence to fundamental values is unlikely to be either smooth or rapid (see also Shleifer and Vishny (1997)). Cherkes et al. (2009) emphasize the suitability of closed-end funds to hold illiquid securities, because they are not subject to large-scale creation or redemption of shares, which can lead to potentially large transaction costs, as is the case with open-end funds. Chen et al. (2010) document that funds investing in less liquid stocks exhibit a stronger sensitivity of outflows to poor past performance than funds with liquid assets. The authors argue an investor’s tendency to withdraw increases when there is a concern for the damaging effect of other investors’ redemptions, due to higher trading costs. Our findings contribute to this strand of the mutual fund literature by showing that the spillover risk-factor is predominantly a concern for small cap stock funds. In contrast, the open-end mutual fund structure is more suitable for large cap stock funds, as suggested by their robustness with respect to the spillover risk factor.

The third contribution we make is related to a recent strand of the financial literature that focuses on the dark side of diversification. While the diversification is generally perceived positively due to well-documented reduction for the risk of individual financial entities, Slijkerman et al. (2013) argue that it can lead to increased systemic risk. Allen et al. (2010) show that diversification results in more overlap and more similarities among the portfolios of financial entities, thereby increasing the probability of coinciding failures with other similar institutions. However, thus far only very little empirical evidence is provided to support this argument. Most of the analyses are from theoretical or analytical perspective (see for example (Allen and Gale 2000; Upper and Worms 2004; Acemoglu et al. 2015; Elliott et al. 2014b, October)). In this paper, we empirically test whether similarity in portfolio positions results in increased spillover effects. For this purpose, we group funds into highly and less diversified funds. During periods with strong outflows, the spillover effect to the 25 percentile of most diversified funds rises to 2.6% per month, while the effect is only 0.1% for the 25 percentile of least diversified funds. These results confirm that funds with more diversified assets are most seriously affected by the spillover effect. This finding provides empirical evidence that diversification can increase the transfer of risks between financial institutions. The reduction of the risks at the individual portfolio level does hence come at the cost of increased risk sharing between entities with similar portfolio positions.

The remainder of this paper is organized as follows. Section 2 introduces the dataset and descriptive statistics. Section 3 introduces the methodology. The empirical results are provided in Section 4. Section 5 contains the conclusion.

To estimate potential spillover effects, we examine how the excess returns of mutual funds are affected by flows to other mutual funds. The excess return \(\widetilde {R}\) of fund i in period t is defined as the difference between the fund return and fund size-weighted average return of all US-focused equity funds in our sample: \(\widetilde {R}_{i,t}=R_{i,t}-\bar {R}_{t}\). In our sample, the average excess return is close to zero, and the standard deviation is 1.72% (Table 1).

There are two reasons why we opt for the simple excess return rather than alternative performance measures such as raw fund returns or three or four-factor model alphas. (1) By using excess returns, we remove the return component of the general stock market. The literature documents that fund flows and contemporaneous returns are highly correlated (see for example (Warther 1995), or (Ben-Rephael et al. 2011)). Thus, it would not be clear whether positive fund returns are due to spillover effects from flows to connected mutual funds, or due to high returns in the stock market in general. (2) Three or four-factor alphas aim to capture risk exposure of the fund to small cap, value, or momentum stocks, but these risk factors might themselves be driven by above-average flows to their respective subgroups. For example, Lou (2012) finds evidence that flow-driven return effects can at least partially account for mutual fund performance persistence and stock price momentum. Hence, risk-adjusted returns are not ideally suited to isolate any fund flow-driven spillover effects.⁵

Our base model to estimate spillover effects of fund flows on the performance of other mutual funds is represented by the following equation:

where ρ is the coefficient for the spillover effect of flows to connected funds on fund returns. Flows to connected funds are measured by the term \( \sum\limits_{j=1,j\neq i}^{N_{t}}{w_{i,j,t}{Flow_{j,t}}}\), where Flow is either the actual or expected flow to fund j in period t, as described in the previous section.⁶

The list of control variables (Control_i,t) is consistent with the literature on fund performance and includes fund flows, lagged fund returns, fund size, the size of the fund family, fund age, the expense ratio, the fund’s cash ratio, and the turnover ratio. u_i stands for the firm fixed effects, which captures time-invariant firm characteristics, such as the country.

A special emphasis needs to be placed on how flows to connected funds are weighted. The strength of the total spillover effect from the flows of all connected funds is likely to be related to the number of connections and the degree of connectedness between each pair of funds. Thus, perhaps the most intuitive approach is to weight the total flows to all connected funds by the respective total share of overlapping portfolio positions between each pair of funds, as \( \sum\limits_{j=1,j\neq i}^{N} {S_{i,j,t}{Flow_{j,t}}}\), where S_i,j,t is the total share of overlapping portfolio positions between fund i and fund j in period t. This weighted sum of all fund flows to connected funds can then be interpreted as the total fund flow relevant to fund i. A disadvantage of this approach is, however, that it is no longer feasible to differentiate whether the spillover effect is due to a high number of connected funds, a high degree of overlapping portfolio holdings, or due to strong flows to only a few connected funds.

Bell and Bockstael (2000) suggest to row-standardize the weight matrix in order to allow for the appealing interpretation that the sum of the effects of neighbors in different bands can vary over bands. In our setting, we are interested in whether the same dollar amount of fund flows causes a stronger spillover effect for funds with a higher degree of overlapping portfolio holdings. Thus, we row-standardize the weight matrix w_i,j,t, by transforming the total share of overlapping portfolio holdings between each pair of funds, so that the total weights sum up to one for each fund:

This approach implicitly assumes that the spillover effect from the total flows to all connected funds remains constant, irrespective of the number of the connections of a fund or the share of overlapping portfolio holdings.

In order to test whether the spillover effect of funds flows is related to the degree of connectedness, we split the sample of connected funds into three groups:

where Q1 (Q3) denotes the tertile of funds with the lowest (highest) total share of overlapping portfolio holdings S_i,j,t.

Building on the literature on flow-induced price pressure, this paper addresses a gap in the literature by providing a systematic assessment of how the relative performance of mutual funds is affected by fund flows to connected mutual funds. Using a sample of 3010 US-focused equity mutual funds, we provide strong empirical evidence for the fund flow spillover hypothesis. Moreover, our analysis provides the following three key findings: 1) the spillover effect is more pronounced noticeable during asset fire sale periods; 2) the spillover effect is particularly strong for small cap stocks funds; and 3) more diversified funds are more seriously affected by the spillover effect. Finding 1 links the spillover effect to the degree of liquidity in the market, whereas finding 2 relates the spillover effect to the degree of liquidity in the underlying assets held by the finds. Finding 3 lends empirical support to the dark side of diversification. Diversification goes hand in hand with a stronger connectivity to other funds in the industry. The resulting level of connectivity leads to an increased transfer of risks among mutual funds. Moreover, our findings on the dark side of diversification are also consistent with recent research by (Fulkerson and Riley 2019), who document higher risk-adjusted returns for less diversified mutual funds. Overall, our research demonstrates how individual mutual fund performance is affected by investor behaviour in connected mutual funds. Our results suggest that stakeholders in the open-end mutual fund industry should not only monitor industry-level fund flows, or flows to individual funds, but also consider the interrelation of individual mutual funds.