Board of Governors of the Federal Reserve System

May 29, 2020

Monitoring the Liquidity Profile of Mutual Funds

Sirio Aramonte¹, Chiara Scotti², and Ilknur Zer³

This note was updated on July 10, 2020 to extend the analysis through the COVID-19 outbreak.

Policymakers and academics have been particularly attuned to the issues of liquidity transformation and first mover advantage at open-end mutual funds.⁴ Open-end mutual funds engage in liquidity transformation because they promise one-day redemptions on their assets, even when the invested assets have low or uncertain liquidity. If mutual fund investors expect large outflows, they may have an incentive to redeem quickly—in order to benefit from the so-called first mover advantage—because liquid assets may be depleted if they wait too long. During a stress event, these features might raise potential financial stability concerns in that funds, because managers might sell liquid assets first, worsening the liquidity profile, further impairing performance, putting downward pressure on prices, and potentially leading to more fund outflows.

Against this backdrop, monitoring the liquidity profiles of mutual funds—the balance of liquid and illiquid assets held by funds—is particularly important. While stress events unfold quickly, mutual funds portfolio holdings are released with a delay, causing challenges to such monitoring. In this note, we summarize and validate a methodology to monitor funds' liquidity at a higher frequency than possible with regulatory data, focusing on significant market events occurred over the past 15 years.⁵ We study the changes in the liquidity profile of high-yield and bank-loan mutual funds at the onset of the COVID-19 pandemic. We find signs of deterioration in the liquidity profile of high-yield bond and bank loan funds following the historic episodes we analyzed as well as during the COVID-19 pandemic.

How do we estimate a fund's liquidity profile?
Building on the asset-pricing literature, we measure the liquidity profile of a fund using the sensitivity—which we call $$\beta$$ — of its daily portfolio returns to an aggregate liquidity factor.⁶ Changes in the sensitivity of mutual funds to aggregate market liquidity proxy for changes in the liquidity profile of mutual funds. In particular, we interpret an increase in this sensitivity following some relevant events as a deterioration in the fund's liquidity profile.

The rationale behind our approach is that funds with a higher $$\beta$$ are more sensitive to liquidity risk. As illustrated in Figure 1, a nonzero change in such a $$\beta$$ —which we call $$\beta_{\Delta}$$ —implies a change in the slope of the relation between a fund return and the market liquidity factor. Importantly, the fund-specific slope can change even if aggregate liquidity conditions remain the same (moving from the blue circle to the red triangle). At the same time, changes in aggregate liquidity conditions do not necessarily imply a change in the fund's liquidity profile (remaining on the same line but moving from the solid blue circle to the hollow blue circles). What we capture with $$\beta_{\Delta}$$ is a change in the slope, indicating a shift in the sensitivity of fund returns to market liquidity.

Figure 1. The Relation between Expected Returns and Liquidity

We study the change in $$\beta$$ around two types of announcements. First, we focus on scheduled macroeconomic releases and select a set of important real-activity announcements that surprised market participants.⁷ Second, we consider the announcement of significant market events, namely Bill Gross's departure from PIMCO, Third Avenue Focused Credit Fund's suspension of redemptions, and the effect of Lehman Brothers' collapse on Neuberger Berman. We compare changes in the liquidity-factor loading $$\beta$$, between the four weeks before and the four weeks after these announcements—$$\beta_{\Delta}$$.

Empirical analysis
We validate our methodology focusing on U.S. equity, government bond, and investment grade and high-yield corporate bond funds for the period 2004 to 2016.

In the first application, we examine changes in the liquidity profile of funds following scheduled macroeconomic announcements. As shown in Table 1, we find an increase in the sensitivity of less-liquid mutual funds—in particular, those investing in the stocks of small companies and in corporate bonds—following the release of unexpected negative macroeconomic news.⁸ The effect is more pronounced during stress periods—that is, during the 2008 financial crisis and when the Aruoba et al. (2009) Business Conditions index is below its sample median—suggesting that a deterioration in the funds' liquidity could amplify vulnerabilities in situations of already weak macroeconomic conditions. These effects are economically significant: following negative news, a one standard deviation increase in aggregate liquidity implies an increase in the expected return of small-cap funds of about 2 basis points, which is above the 55th percentile of the daily return distribution, corresponding to an annual return of about 5 percent. Similarly, a one standard deviation increase in aggregate liquidity raises daily returns by about 4 (9) basis points for investment-grade (high-yield) corporate bond funds, which is around the 55th (65th) percentile of the category-specific distribution of daily fund returns. As mentioned above, we interpret an increase in this sensitivity as a deterioration in the fund's liquidity profile. While these magnitudes are unlikely to indicate a systemic event, they are average effects estimated over a long period of time; thus, they do not reflect interactions with other vulnerabilities that can emerge at times of market distress.

In the second application, we study fund liquidity around three significant market events: William H. (Bill) Gross's departure from Pacific Investment Co. (PIMCO) on September 26, 2014; the suspension of redemptions from Third Avenue's Focused Credit Fund on December 9, 2015; and the effect of Lehman Brothers' September 15, 2008, collapse on Neuberger Berman, an affiliated asset manager that survived the parent company's bankruptcy. The results presented in Table 2 show that PIMCO fixed-income funds became less liquid after Gross's resignation and that high-yield funds were also less liquid following the suspension of redemptions from Third Avenue's fund. For Third Avenue, the magnitude of the coefficient is similar to that of macroeconomic announcements on high-yield bond funds, whereas the effect of Gross's resignation is much larger than what is reported in Table 1. In contrast, Lehman Brothers' default is associated with an improvement in the liquidity profile of Neuberger Berman funds, most likely because fund managers increased liquidity buffers to navigate a turbulent market.

Monitoring funds' liquidity
Our approach can also be used to estimate liquidity dynamics on a continuous basis without having to acquire holding-level inputs. These dynamics can be helpful to gauge important market developments as they unfold and to monitor financial stability risks. As an illustration, we focus on high-yield corporate bond and bank loan funds, which invest in particularly illiquid assets even though they offer daily redemptions to investors.⁹ Importantly, assets managed by U.S. corporate bond funds increased substantially over the past decade, magnifying the impact of potential disruptions in liquidity transformation.

To this end, we estimate liquidity $$\beta$$s on a sample ending June 8, 2020 by regressing weekly percentage changes in funds’ net asset values on aggregate market liquidity while controlling for other relevant market factors (slope and level of the yield curve, and the CDX spread) using a 20-week rolling windows.¹⁰

Figure 2 depicts the time series of changes in the average rolling $$\beta$$s for high-yield and bank loan funds. In line with the results discussed in the previous section, the liquidity profile of high-yield funds deteriorated during the global financial crisis and following stress episodes in the mutual fund industry, such as the Third Avenue Focused Credit Funds suspension of redemptions in December 2015, which were captured by an increase in change in $$\beta$$s.¹¹ Markets rebounded by mid-2016 and, alongside net inflows in high-yield funds of $2 billion during 2016:Q3, we observe a decrease in $$\beta$$s, suggesting an improvement in the liquidity profile of these funds (Figure 2 top panel).¹²

Focusing on the most recent period, the stress of the COVID-19 pandemic seems to have resulted in a sharp deterioration of the liquidity of both high-yield and bank loan funds, amid changes in investor risk appetite. The magnitude of the $$\beta$$ changes for both types of funds is very large by historical standards, suggesting increased vulnerabilities for these funds.

A key feature of the rolling beta is that it can be used to monitor, in nearly real time, the liquidity profile of funds that engage in significant liquidity transformation. At times of market distress, rapid changes in the liquidity profile of a fund, especially relative to similar funds, could indicate that the fund is selling liquid assets. In this case, remaining investors could rush to redeem, with dynamics similar to a bank run

Figure 2. Liquidity Beta for High-Yield Bond and Bank Loan Funds

Table 1: Changes in Liquidity $$\beta$$s Following Negative Macroeconomic Surprises

Table 2: Case Study Analysis

References

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Aramonte, S., C. Scotti, and I. Zer (forthcoming). Measuring the liquidity profile of mutual funds. International Journal of Central Banking.

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