Can Mutual Fund Managers Time Liquidity During Market Turbulence?

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Contents

Abstract

Introduction

Literature review

Data

Market liquidity measure

Time period

Empirical Analysis

1) Test of liquidity timing: Benchmark Model

2) Asymmetric liquidity timing model

3) Market timing, volatility timing and liquidity timing

Summary

Discussion/Possible Explanation

Liquidity-timing ability in stable market environment

Possible Explanation for one-sided liquidity timing ability

Possible Explanation for reverse liquidity timing behavior during turbulence

Conclusion

Reference

Abstract

1.    Introduction

Liquidity squeeze usually follows and exacerbates market declines during stock market crash. Therefore, if mutual fund managers could predict market-wide liquidity and adjust its exposure accordingly, they could protect investors’ capital from huge losses and achieve better performance. Cao, Simin et al. (2013) documents that mutual fund managers in U.S. demonstrated the ability to time market liquidity both on the aggregate level and the individual level using data from 1974-2009. In this research, we want to investigate whether mutual fund managers in emerging market (China) also possess liquidity-timing ability. Since emerging markets are more volatile and noisy than mature markets such as U.S., we will look specifically into how well mutual fund managers could time market-wide liquidity during market turbulence and whether mutual fund managers could protect its investors against significant market declines during market turmoil.

To study fund managers’ liquidity-timing behavior in both stable and turbulent environment, we use mutual fund data from 2006-2017, covering two major market crashes in China’s stock markets. We then divide the sample into three periods: 1) 2007-2008 market bubble and crash (2006-2009); 2) Stable market (2009-2013); 3) 2015-2016 Stock bubble and crash (2013-2017). We use the Amihud (2002) illiquidity measure as a proxy for market liquidity condition in our model. We also separate the funds’ liquidity timing behavior in liquidity tightening periods from that in liquidity loosening periods to see whether fund managers exhibit asymmetric liquidity-timing ability. We also control for return timing and volatility timing in the estimate.

We find that in a stable market environment mutual fund managers exhibit significant one-sided liquidity-timing ability. In particular, funds with more illiquid holdings display stronger liquidity-timing ability and funds with more liquid holdings exhibit weaker liquidity-timing ability.

However, during the recent market turbulence in 2015-2016, we find that mutual fund managers display significant reverse liquidity-timing behavior. More specifically, funds with more liquid holdings tend to reduce market position as liquidity environment improves; while funds with more illiquid holdings increase market exposure as market liquidity worsens.

We also try to provide possible explanations for the reverse liquidity-timing behavior fund managers demonstrate during market turbulence. Our hypotheses include: limits to arbitrage (Shleifer and Vishny 1997), flight to liquidity (Brunnermeier and Pedersen 2009), passive beta exposure and risk shifting.

The remainder of the paper is organized as follows: Section 2 reviews the relevant literature on market timing and liquidity timing. Section 3 introduces the methodology we use including data description and market liquidity measure. Section 4 presents the empirical analysis at aggregate level of liquidity timing. In Section 5 we proposed several possible explanations for the patterns we find in our empirical analysis. Section 6 is the conclusion.

2.    Literature review

There is an extensive literature on fund managers’ market-timing ability. Traditional research on timing ability of mutual fund managers focus mostly on timing market excess return and volatility. Treynor and Mazuy (1966) develop a framework to measure market timing ability by studying whether fund managers adapt their market exposure based on their forecast of future market return. Other measures were proposed for identifying return-timing and volatility-timing ability, e.g., Henriksson and Merton (1981), Kosowski, Timmermann et al. (2006), Jagannathan, Malakhov et al. (2010), Grinblatt and Titman (1989), Ferson and Schadt (1996), Busse (1999).

Cao, Simin et al. (2013) is the first one to study liquidity timing and find mutual fund managers in U.S. would increases their portfolio exposure as market liquidity condition improves. Cao, Chen et al. (2013) applies the same approach to study hedge fund performance and find hedge fund managers also adapt their portfolio exposure to market liquidity. Stefanova and Siegmann (2014) finds that there is little relation between market liquidity and hedge fund’s market beta before 2003, however hedge fund started to show liquidity timing after 2003. Bodson, Cavenaile et al. (2013) integrates market return timing, volatility timing and liquidity timing into one model to study the timing ability of U.S. mutual fund managers. The result shows that around 14% of funds in their sample displays liquidity-timing ability.

In this paper, we explore fund managers’ ability to time market liquidity, especially during crisis and market turbulence. In particular, we want to answer the following questions: Can mutual fund managers in China time market liquidity by predicting future liquidity environment and adjusting their portfolio market betas accordingly? Do mutual fund managers exhibit different liquidity-timing abilities in liquidity-tightening and liquidity loosening environment? How well could mutual fund managers time liquidity during market turmoil?

Market-wide liquidity is an important dimension of market conditions. Several researches show that market liquidity, is a priced market state variable for asset pricing since it captures the ease of trading a large amount of assets in a short time without incurring high transaction costs, e.g., Amihud and Mendelson (1986), Amihud and Mendelson (1986), Amihud, Mendelson et al. (1990), Amihud (2002), Amihud, Mendelson et al. (2006), Pástor and Stambaugh (2003), Acharya and Pedersen (2005).

The reason we are interested in fund managers’ liquidity timing behavior during crisis is that it is widely acknowledged that during crisis market declines is a source for illiquidity, e.g. Chordia, Roll et al. (2001), Lesmond (2005), Liu (2006), Yeyati, Schmukler et al. (2008), Hameed, Kang et al. (2010), Hegde and Paliwal (2011). As a result, the ability to time liquidity is especially important during crisis for open-end mutual funds.

In regard to the relationship between market liquidity and returns during market turbulence, Amihud, Mendelson et al. (1990) were among the first to demonstrate a relationship between market liquidity and returns in 1987 stock market crash. Chordia, Roll et al. (2001) discover that bid-ask spreads respond asymmetrically to market returns as they significantly increase in down markets and only marginally decrease in up markets. Liu (2006) shows that market liquidity in the U.S. stock market is impaired following large economic and financial events such as the 1972-1974 recession, the 1987 crash, the Asian financial crisis in 1997, the 1998 Russian default, the collapse of the LTCM hedge fund in 1998 and the terrorist attacks on September 11, 2001.

The same pattern also exists in emerging market. Specifically, Lesmond (2005) shows that bid-ask spreads and several other liquidity measures sharply increase during the periods of the Asian and Russian crisis. Yeyati, Schmukler et al. (2008) also focus on emerging markets and use a sample from seven countries over the period from April 1994 to June 2004 to demonstrate that crisis periods are associated with higher liquidity costs.

3.    Methodology

3.1 Data

 

Mutual fund performance

In this research, we study open-end mutual funds listed on China’s market investing in companies public listed on Chinese stock exchange. The mutual fund data and stock return/trading volume data we use come from WIND database. We use monthly data from May 2006 to March 2017 to cover the two recent major stock crashes (one starting from February 2007 and another starting from 2015). Our sample includes 2946 equity funds, among which there are 525 value funds, 781 balanced funds, 167 growth funds, 1300 large-cap funds, 173 medium/small-cap funds. Our factor model data come from RESSET database.

We construct the aggregate portfolio of each category (value, balanced, growth, large-cap, medium/small-cap) by taking the equal-weighted average of all the individual funds under the category.

Table 1 Summary statistics of fund characteristics

Portfolio Obs. Number of funds Average Monthly return(%) Std. Dev.
Value 131 525 1.301623 7.19887
Balanced 131 781 1.457255 7.366518
Growth 131 167 1.51566 7.993425
Large-cap 131 1300 1.402039 7.289993
Medium-cap 131 173 1.475062 7.424903

3.2 Market liquidity measure

We use the Amihud (2002) illiquidity measure as a proxy for market liquidity condition in our model. Amihud measures the illiquidity of stock

iin month

t,

ILLIQti, as follows

ILLIQti=1Di,t∑d=1Di,tRtdiVtdi

Where

Di,tis the number of days stock

iis traded in month

t,

Rtdi,

Vtdiare the return and trading volume (in millions RMB) of stock

ion day

din month

t.

We construct the monthly market illiquidity measure as follows

Market_ILLIQtm=1Nt∑i=1NtILLIQti

Nt

is the number of all trading stocks listed on Shanghai and Shenzhen Stock Exchange in month

t.

Table 2 Summary statistics of market liquidity measures

Panel A: Summary statistics
Obs Mean Std. Dev. Min Max
Amihud illiquidity 131 0.2832558 0.4036498 0.0289666 2.744457
Panel B: Pearson correlations
Market return SMB HML UMD Amihud illiquidity Volatility
Market return 1
SMB 0.1682 1
HML 0.0391 -0.5227* 1
UMD 0.2254* 0.0473 -0.1067 1
Amihud illiquidity -0.0642 -0.0581 -0.1247 -0.1506 1
Volatility -0.3231* -0.1113 -0.0175 -0.2873* 0.1678 1

*Significant at the 5% level

3.3 Time periods

To study the liquidity timing behavior during market turbulence, we divide our sample into three periods: period 1 (2007 market crash): May 2006-April 2009; period 2 (stable): April 2009-March 2013; period 3 (2015 market crash): April 2013-March 2017.

Table 3 Sub-sample periods

Period 1 Period 2 Period 3
Time May 2006-April 2009 April 2009-March 2013 April 2013-March 2017
Market state Turbulent Stable Turbulent
(2007-2008 stock bubble + crash) (2014-2016 Stock bubble + crash)
Duration 3 years 4 years 4 years

 

 

 

 

 

 

4.    Empirical Analysis

1) Test of liquidity timing: Benchmark Model

We first use a regression model to evaluate how a fund’s beta in month t changes with market liquidity realized in month t (e.g., proxied by the Amihud (2002) liquidity measure), while controlling for the fund’s exposures to other relevant factors. If fund beta moves in the opposite direction with market illiquidity, it indicates the fund has positive effective liquidity timing, i.e., the fund chooses to have relatively high (low) market exposure in anticipation of conditions where market liquidity is good (poor).

We start with Carhart (1997) four-factor model to analyze the time series of mutual fund returns:

Ri,t=αi+βm,iRm,t+βSMB,iSMBt+βHML,iHMLt+βUMD,iUMDt+εi,t

Ri,t

is the monthly excess return of the aggregate portfolio over monthly risk-free rate,

Rm,tis the excess return of the value-weighted market portfolio return of all tradable stocks listed on Shanghai and Shenzhen Stock Exchange.

SMBt,

HMLtand

UMDtare factor portfolios on size, book-to-market ratio and 12-month momentum. Our data on monthly risk-free rate and factor portfolios come from RESSET dataset.

We follow Cao, Simin et al. (2013) and express the portfolio beta as a linear function of the deviation of current market liquidity from its time-series average as follows

βm,i=β0,m,i+γilliq,i(Market_ILLIQtm-Market_ILLIQtm̅)

where

Market_ILLIQtm̅is the time series mean of

Market_ILLIQtmfrom last 12 months (from t-13 to t-1). Substituting this equation into Carhart (1997) four-factor model we get the four-factor timing model:

Ri,t=αi+β0,m,iRm,t+βSMB,iSMBt+βHML,iHMLt+βUMD,iUMDt

+ γilliq,i(Market_ILLIQtm-Market_ILLIQtm̅)Rm,t+εi,t

The coefficient

γm,imeasures the liquidity-timing ability of the fund manager. A significant and negative

γm,iindicates the fund managers could predict the market will become more (less) illiquid and reduce (increase) its market exposure. If

γm,iis significant and positive, the fund manager either are constantly wrong about future market liquidity condition or choose to increase market exposure when market liquidity dries up (reverse liquidity timing).

Table  reports the regression results for the equal-weighted portfolios of all funds (Total), Value funds, Balanced funds, Growth funds, Large-cap funds, and Medium-cap funds during each period. As can be seen from the table, no aggregate portfolio shows significant liquidity-timing ability from 2006-2009 during the 2007 market crash. However, after market went back to normal during 2009-2013, all but value funds have negative and significant coefficient (

γilliq,i) on liquidity timing term, meaning funds under these categories could anticipate future liquidity environment and reduce their market exposure when market liquidity condition worsens. The positive and significant liquidity timing coefficients during 2009-2013 show that fund managers could time market liquidity when market is calm and stable, but their liquidity-timing ability was very limited and almost non-existent during the 2007 crash as we observe negative liquidity timing coefficients (but not significant) on all portfolios during 2006-2009.

In particular, during 2009-2013, the liquidity timing coefficients of Value, Balanced and Growth funds are -0.149(not significant), -0.624 and -0.638 respectively, meaning that Growth fund managers has the most significant liquidity-timing ability where liquidity timing behavior is almost non-existent among Value fund managers.

Between Large-cap and Medium/small cap funds, the coefficients are -0.412 and -0.646 respectively. The liquidity timing behavior of Medium/small cap fund managers is stronger than Large-cap funds and is comparable to that of the Growth fund managers.

The most puzzling part of the regression results is that during the 2015 crash (2013-2017) the coefficients on liquidity timing term turn positive and are significant for all aggregate portfolios which indicates that fund managers engaged in reverse liquidity timing behavior which means fund managers increase their market exposure as market liquidity environment declines.

The liquidity timing coefficients of Value, Balanced and Growth funds are 0.0829, 0.141 and 0.172 respectively, indicating that Growth funds display the most significant reverse liquidity timing behavior while Value funds are the least likely to increase market exposure as market liquidity declines.

The coefficients of Large-cap and Medium/small-cap funds are 0.120 and 0.152. Medium and small cap funds (Funds investing in medium and small-cap companies) displays more significant reverse liquidity timing behavior than Large-cap funds.

If we compare the regression results in the stable (2009-2013) and the turbulent (2013-2017) market environment, not only can we find that all funds abandoned liquidity timing and started engaging in reverse liquidity timing during market upheaval, we also find that the one who was the most skilled at liquidity timing in stable environment becomes the one who displays the most significant reverse liquidity timing behavior during market turbulence.

Table 4 Liquidity timing: Benchmark model

Fund Total Value Balanced Growth Large-cap Medium-cap
Panel A: 2006/5 – 2009/4
β0,m,i 0.691*** 0.709*** 0.684*** 0.686*** 0.690*** 0.670***
(27.97) (28.38) (27.52) (22.48) (28.11) (23.71)
βSMB,i -0.315*** -0.358*** -0.298*** -0.285*** -0.313*** -0.336***
(-5.68) (-6.37) (-5.34) (-4.16) (-5.67) (-5.29)
βHML,i -0.286** -0.222* -0.317** -0.249 -0.292** -0.174
(-2.79) (-2.15) (-3.08) (-1.97) (-2.87) (-1.49)
βUMD,i 0.0123 0.0102 0.0120 0.0360 0.0157 -0.0173
(0.22) (0.18) (0.21) (0.52) (0.28) (-0.27)
γilliq,i -0.109 -0.0982 -0.114 -0.0871 -0.111 -0.0888
(-1.28) (-1.14) (-1.33) (-0.83) (-1.31) (-0.91)
αi 0.828** 0.800* 0.827** 0.929* 1.101*** 0.877*
(2.84) (2.71) (2.82) (2.58) (3.80) (2.63)
Panel B: 2009/4 – 2013/3
β0,m,i 0.771*** 0.834*** 0.741*** 0.733*** 0.778*** 0.716***
(31.68) (47.90) (26.31) (20.17) (32.04) (25.48)
βSMB,i 0.00982 -0.0970* 0.0580 0.0926 -0.00486 0.0525
(0.16) (-2.17) (0.80) (0.99) (-0.08) (0.73)
βHML,i -0.331*** -0.216*** -0.389*** -0.404*** -0.322*** -0.374***
(-5.49) (-5.00) (-5.57) (-4.49) (-5.35) (-5.37)
βUMD,i -0.0287 -0.0262 -0.0333 -0.00857 -0.0293 -0.0263
(-1.18) (-1.51) (-1.19) (-0.24) (-1.21) (-0.94)

γilliq,i

-0.461** -0.149 -0.624*** -0.638** -0.412** -0.646***
(-3.18) (-1.44) (-3.72) (-2.94) (-2.85) (-3.86)
αi -0.190 -0.0953 -0.237 -0.274 0.131 -0.282
(-1.28) (-0.90) (-1.38) (-1.24) (0.88) (-1.64)
Panel C: 2013/4 – 2017/3
β0,m,i 0.768*** 0.765*** 0.759*** 0.822*** 0.773*** 0.749***
(42.19) (46.24) (33.51) (27.01) (42.94) (31.24)
βSMB,i 0.0525 -0.0293 0.102 0.0827 0.0318 0.188**
(1.10) (-0.68) (1.72) (1.04) (0.68) (3.00)
βHML,i -0.270*** 0.0335 -0.369*** -0.731*** -0.247*** -0.435***
(-4.80) (0.66) (-5.29) (-7.79) (-4.45) (-5.89)
βUMD,i 0.0889** 0.0159 0.116** 0.201*** 0.0823** 0.150***
(3.10) (0.61) (3.26) (4.20) (2.90) (3.96)

γilliq,i

0.123** 0.0829* 0.141** 0.172* 0.120** 0.152**
(2.99) (2.21) (2.74) (2.50) (2.94) (2.80)
αi -0.111 -0.0605 -0.142 -0.148 0.239 -0.223
(-0.68) (-0.40) (-0.69) (-0.54) (1.47) (-1.03)

t statistics in parentheses

* p < 0.05, ** p < 0.01, *** p < 0.001

2) Asymmetric liquidity timing model

To further study whether fund managers would apply different strategy when anticipating improving and declining liquidity environment, we modify our model as follows:

Ri,t=αi+β0,m,iRm,t+βSMB,iSMBt+βHML,iHMLt+βUMD,iUMDt

+ γ1illiq,i max(MarketILLIQtm-MarketILLIQtm̅,0 )Rm,t

+ γ2illiq,i min(MarketILLIQtm-MarketILLIQtm̅,0 )Rm,t+εi,t

where

γ1illiq,tmeasures manager’s liquidity timing behavior when they predict market dries up,

γ2illiq,tmeasures the liquidity timing behavior as market liquidity improve. Table  is the specific interpretation of signs on each coefficient.

Table 5 Interpretation of signs of coefficient on asymmetric liquidity timing variable

Scenario Positive(+)/significant

Reverse liquidity timing

Negative(-)/significant

Positive liquidity timing

 γ1illiq,i

Liquidity condition worsens Increase market exposure Reduce market exposure
 γ2illiq,i Liquidity condition improves Reduce market exposure Increase market exposure

 

Table 6  is the regression result of the asymmetric liquidity timing model.

Now that we separate the liquidity tightening periods from the liquidity loosening periods, we find that during the first market turbulence (2006-2009), all funds display weak tendencies to apply asymmetric strategies on liquidity timing.

The coefficients measuring timing behavior during liquidity-tightening periods (

γ1illiq,i) are all positive but not significant for all six portfolios, indicating weak tendencies to increase market exposure as liquidity tightens; The coefficients measuring timing behavior during liquidity-loosening periods (

γ2illiq,i) are all negative, among which the coefficients of Growth and Large-cap funds and all funds as a portfolio (“Total”) are significant. Specifically, the total funds portfolio and Large-cap portfolio exhibit statistically significant liquidity timing behavior when market liquidity loosens at 5% level of significance. The Growth fund portfolio has the most significant liquidity-timing ability among all portfolios as liquidity environment improves at 1% level of significance.

During 2009-2013, the coefficients measuring liquidity-timing ability in both liquidity tightening and loosening environments are negative. This result tells us that after the market has calmed down after 2009, all funds started to exhibit symmetric liquidity timing behaviors which is different from 2006-2009.

The regression result is in line with what we find in the benchmark regression where all portfolios show significant liquidity-timing ability except for value fund portfolio during this period. However, after separating the liquidity loosening from the tightening, we find that most of the liquidity timing behaviors took place when market liquidity environment improves since the coefficients measuring liquidity timing in liquidity loosening environment (

γ2illiq,i) of all portfolios are significant except for the value fund portfolio where the coefficients measuring liquidity timing in liquidity drying-up environment (

γ1illiq,i) are not significant.

In 2013-2017, the regression result shows different patterns of liquidity timing for each portfolio. The Value fund portfolio displays significant reverse liquidity timing behavior when liquidity environment loosens up and weak positive liquidity timing tendency when liquidity condition worsens. On the contrary, the Growth fund portfolio displays significant reverse liquidity timing behavior as illiquidity increases and weak positive liquidity timing when liquidity environment improves. In other words, it is statistically significant that Value fund managers reduce their market exposure when liquidity environment improves and Growth fund managers increase market exposure when liquidity environment worsens as well as Medium/small-cap funds.

In conclusion, during first market turbulence (2006-2009), all funds display slight asymmetric liquidity timing tendencies. In this sub-period, it is statistically significant that the Total, Growth and Large-cap portfolio increase their market exposure when market liquidity improves.

After market has cooled down (2009-2013), all fund managers shows positive but asymmetric liquidity-timing ability, meaning that they adjust (increase) their market exposure during the liquidity loosening rather than tightening.

During the second market turbulence (2013-2017), our regression result show that Value funds reduce their market exposure as liquidity improves where Growth funds and Medium/Small-cap funds increase market exposure as liquidity dries up.

Table 6 Asymmetric liquidity timing

Fund Total Value Balanced Growth Large-cap Medium-cap
Panel A: 2006/5 – 2009/4
β0,m,i 0.657*** 0.676*** 0.653*** 0.621*** 0.658*** 0.636***
(20.77) (21.08) (20.35) (17.07) (20.86) (17.40)
βSMB,i -0.318*** -0.361*** -0.301*** -0.290*** -0.316*** -0.339***
(-5.88) (-6.59) (-5.49) (-4.67) (-5.86) (-5.43)
βHML,i -0.326** -0.262* -0.354** -0.327** -0.331** -0.215
(-3.18) (-2.52) (-3.40) (-2.77) (-3.24) (-1.82)
βUMD,i 0.0326 0.0304 0.0305 0.0753 0.0353 0.00333
(0.58) (0.54) (0.54) (1.17) (0.63) (0.05)
γ1illiq,i 0.0474 0.0570 0.0281 0.214 0.0399 0.0698
(0.38) (0.45) (0.22) (1.48) (0.32) (0.48)
γ2illiq,i -0.296* -0.284 -0.284 -0.449** -0.292* -0.279
(-2.10) (-1.99) (-1.99) (-2.77) (-2.08) (-1.71)
αi 0.913** 0.885** 0.905** 1.093** 1.183*** 0.963**
(3.17) (3.03) (3.09) (3.30) (4.12) (2.89)
Panel B: 2009/4 – 2013/3
β0,m,i 0.760*** 0.831*** 0.726*** 0.717*** 0.770*** 0.696***
(20.57) (31.41) (16.99) (13.00) (20.88) (16.37)
βSMB,i 0.0115 -0.0966* 0.0604 0.0950 -0.00368 0.0556
(0.18) (-2.13) (0.82) (1.01) (-0.06) (0.76)
βHML,i -0.325*** -0.214*** -0.380*** -0.396*** -0.318*** -0.363***
(-5.18) (-4.76) (-5.25) (-4.23) (-5.08) (-5.02)
βUMD,i -0.0294 -0.0264 -0.0344 -0.00967 -0.0298 -0.0277
(-1.20) (-1.50) (-1.21) (-0.26) (-1.22) (-0.98)
γ1illiq,i -0.101 -0.0617 -0.138 -0.119 -0.168 -0.00370
(-0.11) (-0.10) (-0.13) (-0.09) (-0.19) (-0.00)
γ2illiq,i -0.505** -0.160 -0.683** -0.700* -0.442* -0.724**
(-2.79) (-1.23) (-3.26) (-2.59) (-2.44) (-3.47)
αi -0.184 -0.0937 -0.228 -0.265 0.135 -0.270
(-1.22) (-0.87) (-1.31) (-1.17) (0.90) (-1.55)
Panel C: 2013/4 – 2017/3
β0,m,i 0.786*** 0.827*** 0.761*** 0.778*** 0.797*** 0.726***
(25.15) (31.82) (19.45) (14.98) (25.92) (17.61)
βSMB,i 0.0518 -0.0315 0.102 0.0842 0.0309 0.189**
(1.08) (-0.79) (1.70) (1.06) (0.66) (3.00)
βHML,i -0.286*** -0.0230 -0.372*** -0.691*** -0.269*** -0.414***
(-4.69) (-0.45) (-4.86) (-6.82) (-4.49) (-5.15)
βUMD,i 0.0910** 0.0229 0.117** 0.196*** 0.0851** 0.147***
(3.14) (0.95) (3.22) (4.08) (2.98) (3.85)
γ1illiq,i 0.0860 -0.0435 0.136 0.262* 0.0702 0.199*
(1.30) (-0.79) (1.64) (2.38) (1.08) (2.28)
γ2illiq,i 0.213 0.387*** 0.153 -0.0431 0.240 0.0383
(1.62) (3.55) (0.93) (-0.20) (1.86) (0.22)
αi -0.123 -0.100 -0.144 -0.119 0.224 -0.209
(-0.74) (-0.72) (-0.69) (-0.43) (1.37) (-0.95)

3) Market timing, volatility timing and liquidity timing

Because market liquidity is positively correlated with market returns (illiquidity measure is negatively correlated with market returns) and negatively correlated with market volatility (illiquidity measure is positively correlated with market returns) (Pástor and Stambaugh 2003), it is possible that we erroneously capture the return timing and volatility timing ability into the liquidity timing coefficient. In addition, during market turbulence, both market volatility and market illiquidity could increase. Therefore, we need to see if liquidity timing still exists when return timing and volatility timing is controlled for.

3.1) Benchmark model after controlling for market timing and liquidity timing:

Ri,t=αi+β0,m,iRm,t+βSMB,iSMBt+βHML,iHMLt+βUMD,iUMDt

+ γilliq,iMarketILLIQtm-MarketILLIQtm̅Rm,t +λm,i Rm,t2+δm,i (Vm,t-Vm̅)Rm,t+εi,t

Where

Vm,tis the market estimate for volatility in month t, and

Vm̅is the time series mean of market volatility estimate. In calculation, we calculate

Vm,tusing realized market volatility. For each month t, we use the average realized volatility in the past 12 months as a proxy for the time series mean of volatility (

Vm̅). Coefficient

λm,iand

δm,imeasures the market return timing ability and volatility timing.

Table  is the regression result of the benchmark model controlled for market timing, volatility timing. For all three sub-periods, there is no significant evidence that fund managers have market timing and volatility timing skills. However, what we find baffling is the fact that during the first market turbulence (2006-2009) and stable period (2009-2013), several funds display reverse volatility timing behavior. In particular, in 2006-2009, there is statistically significant evidence that Value, Balanced and Large-cap fund managers increase market exposure when market volatility goes up and reduce exposure when market volatility decreases; In 2009-2013, the Large-cap and Medium/Small-cap funds exhibit the same reverse volatility timing behavior. There is no statistically significant evidence suggesting that fund under any category at aggregate level engage in positive or reverse volatility timing in 2013-2017.

In conclusion, adding market return timing and volatility timing into the benchmark model doesn’t materially affect the significance of liquidity timing coefficients. Therefore, the result indicate that the significant liquidity timing coefficients are not the results of correlation between market returns and liquidity or that between volatility and liquidity.

Table 7 Benchmark model controlled for market timing, volatility timing

Fund Total Value Balanced Growth Large-cap Medium-cap
Panel A: 2006/5 – 2009/4
β0,m,i 0.652*** 0.673*** 0.642*** 0.661*** 0.650*** 0.635***
(23.45) (23.75) (23.20) (17.78) (23.74) (19.37)
βSMB,i -0.333*** -0.376*** -0.316*** -0.298*** -0.331*** -0.355***
(-6.38) (-7.09) (-6.08) (-4.27) (-6.45) (-5.78)
βHML,i -0.346** -0.282** -0.380*** -0.290* -0.354*** -0.234
(-3.54) (-2.83) (-3.90) (-2.22) (-3.68) (-2.03)
βUMD,i 0.0306 0.0263 0.0320 0.0472 0.0348 -0.00224
(0.58) (0.49) (0.61) (0.67) (0.67) (-0.04)
γilliq,i -0.134 -0.123 -0.140 -0.105 -0.137 -0.114
(-1.68) (-1.52) (-1.76) (-0.98) (-1.74) (-1.22)
λm,i

(r2)

0.00157 0.00190 0.00144 0.00129 0.00157 0.00211
(1.24) (1.47) (1.14) (0.76) (1.26) (1.41)
δm,i

(V)

7.997* 7.525* 8.494* 5.206 8.300* 7.338
(2.53) (2.34) (2.70) (1.23) (2.67) (1.97)
αi 0.848* 0.742 0.889* 0.893 1.132** 0.774
(2.38) (2.05) (2.51) (1.88) (3.23) (1.85)
Panel B: 2009/4 – 2013/3
β0,m,i 0.770*** 0.835*** 0.739*** 0.729*** 0.777*** 0.717***
(31.80) (48.63) (26.19) (20.10) (32.60) (26.27)
βSMB,i 0.0154 -0.0948* 0.0649 0.102 0.000407 0.0570
(0.25) (-2.18) (0.91) (1.11) (0.01) (0.82)
βHML,i -0.353*** -0.229*** -0.414*** -0.439*** -0.345*** -0.399***
(-5.84) (-5.33) (-5.87) (-4.83) (-5.79) (-5.85)
βUMD,i -0.0309 -0.0277 -0.0358 -0.0119 -0.0317 -0.0290
(-1.30) (-1.64) (-1.30) (-0.34) (-1.35) (-1.08)
γilliq,i -0.605** -0.230 -0.787*** -0.861** -0.559** -0.801***
(-3.54) (-1.90) (-3.96) (-3.36) (-3.32) (-4.16)
λm,i

(r2)

0.00260 0.00105 0.00313 0.00436 0.00246 0.00217
(1.04) (0.60) (1.08) (1.17) (1.00) (0.77)
δm,i

(V)

10.17 6.964 11.11 14.89 11.01* 12.91*
(1.90) (1.84) (1.78) (1.86) (2.09) (2.14)
αi -0.215 -0.0679 -0.281 -0.344 0.126 -0.244
(-1.18) (-0.53) (-1.33) (-1.26) (0.70) (-1.19)
Panel C: 2013/4 – 2017/3
β0,m,i 0.741*** 0.768*** 0.715*** 0.770*** 0.744*** 0.704***
(34.20) (36.00) (27.70) (21.39) (34.91) (25.40)
βSMB,i 0.0349 -0.0307 0.0761 0.0530 0.0141 0.162**
(0.77) (-0.69) (1.41) (0.71) (0.32) (2.81)
βHML,i -0.271*** 0.0408 -0.375*** -0.741*** -0.249*** -0.444***
(-5.00) (0.76) (-5.80) (-8.21) (-4.66) (-6.39)
βUMD,i 0.110*** 0.0228 0.146*** 0.233*** 0.104*** 0.178***
(3.73) (0.78) (4.13) (4.74) (3.56) (4.69)
γilliq,i 0.162** 0.0996* 0.192** 0.226** 0.159** 0.199**
(3.40) (2.11) (3.37) (2.85) (3.37) (3.26)
λm,i

(r2)

-0.00175 -0.00103 -0.00218 -0.00218 -0.00172 -0.00190
(-1.04) (-0.62) (-1.09) (-0.78) (-1.04) (-0.89)
δm,i

(V)

2.287 -0.748 3.780 4.679 2.342 4.094
(1.33) (-0.44) (1.85) (1.64) (1.39) (1.87)
αi 0.106 -0.0389 0.176 0.216 0.458* 0.0947
(0.61) (-0.23) (0.85) (0.75) (2.67) (0.42)

3.2) Asymmetric liquidity timing model after controlling for market timing and liquidity timing:

Ri,t=αi+β0,m,iRm,t+βSMB,iSMBt+βHML,iHMLt+βUMD,iUMDt

+ γ1illiq,t max(MarketILLIQtm-MarketILLIQtm̅,0 )Rm,t

+ γ2illiq,tmin⁡MarketILLIQtm-MarketILLIQtm̅,0 Rm,t

+λm,i Rm,t2+δm,i (Vm,t-Vm̅)Rm,t+εi,t

Table  is the regression result of the asymmetric liquidity timing model controlled for market timing, volatility timing. There is still no evidence suggesting fund managers could time market return on aggregate level. However, during 2006-2009, all portfolios exhibit reverse volatility timing behavior while during 2009-2013 only Large-cap and Medium/Small-cap fund managers reversely time volatility.

After including market timing and volatility timing, all portfolios during 2006-2009 present statistically significant and negative coefficients on liquidity timing term in liquidity loosening environment (

γ2illiq,i). Compared with the regression result without controlling volatility and return timing, this result indicates that during the first market turbulence, all five portfolios present statistically significant positive liquidity-timing ability as market liquidity condition improves, meaning that fund managers at aggregate level increase market exposure as market liquidity improves. However, this one-sided positive liquidity-timing ability is muddled up by fund managers’ inability to time liquidity as liquidity worsens on the other side.

During 2013-2017, after controlling for return and volatility timing, we find that in addition to Value funds, Balanced and Large-cap fund portfolio also presents statistically significant reverse liquidity-timing ability as liquidity environment loosens. In other words, it is statistically significant that Value, Balanced and Large-cap fund managers reduce their market exposure as liquidity improves. And we still see evidence suggesting that Growth and Medium-cap fund managers increase market exposure as liquidity dries up.

Table 8 Asymmetric liquidity timing controlled for market timing and volatility timing

Fund Total Value Balanced Growth Large-cap Medium-cap
Panel A: 2006/5 – 2009/4
β0,m,i 0.590*** 0.613*** 0.584*** 0.564*** 0.589*** 0.574***
(17.65) (17.78) (17.31) (13.36) (17.90) (13.98)
βSMB,i -0.340*** -0.384*** -0.323*** -0.310*** -0.339*** -0.363***
(-7.25) (-7.94) (-6.83) (-5.23) (-7.34) (-6.30)
βHML,i -0.422*** -0.356*** -0.452*** -0.409** -0.429*** -0.308*
(-4.59) (-3.75) (-4.87) (-3.52) (-4.74) (-2.73)
βUMD,i 0.0667 0.0617 0.0664 0.104 0.0705 0.0334
(1.36) (1.22) (1.34) (1.68) (1.46) (0.56)
γ1illiq,i 0.0948 0.100 0.0782 0.254 0.0888 0.111
(0.87) (0.89) (0.71) (1.84) (0.83) (0.83)
γ2illiq,i -0.421** -0.404** -0.413** -0.555** -0.420** -0.398*
(-3.34) (-3.12) (-3.25) (-3.49) (-3.39) (-2.57)
λm,i

(r2)

0.00176 0.00209 0.00162 0.00159 0.00176 0.00230
(1.54) (1.77) (1.41) (1.10) (1.56) (1.64)
δm,i

(V)

10.19** 9.669** 10.58** 8.643* 10.46** 9.500*
(3.46) (3.19) (3.56) (2.32) (3.61) (2.63)
αi 1.024** 0.914* 1.057** 1.169** 1.306*** 0.948*
(3.14) (2.72) (3.22) (2.84) (4.07) (2.37)
Panel B: 2009/4 – 2013/3
β0,m,i 0.765*** 0.836*** 0.731*** 0.724*** 0.777*** 0.705***
(21.03) (32.40) (17.25) (13.27) (21.67) (17.21)
βSMB,i 0.0160 -0.0950* 0.0658 0.103 0.000444 0.0585
(0.26) (-2.15) (0.91) (1.10) (0.01) (0.83)
βHML,i -0.351*** -0.230*** -0.409*** -0.435*** -0.345*** -0.392***
(-5.51) (-5.10) (-5.53) (-4.56) (-5.51) (-5.47)
βUMD,i -0.0312 -0.0276 -0.0363 -0.0123 -0.0317 -0.0297
(-1.29) (-1.61) (-1.29) (-0.34) (-1.33) (-1.10)
γ1illiq,i -0.462 -0.285 -0.541 -0.672 -0.549 -0.407
(-0.51) (-0.45) (-0.51) (-0.50) (-0.62) (-0.40)
γ2illiq,i -0.619** -0.225 -0.812*** -0.880** -0.560** -0.842***
(-3.17) (-1.62) (-3.57) (-3.00) (-2.91) (-3.83)
λm,i

(r2)

0.00254 0.00107 0.00303 0.00429 0.00246 0.00202
(1.00) (0.60) (1.02) (1.13) (0.98) (0.71)
δm,i

(V)

10.04 7.014 10.89 14.72 11.00* 12.55*
(1.84) (1.81) (1.71) (1.79) (2.04) (2.04)
αi -0.210 -0.0695 -0.274 -0.338 0.126 -0.232
(-1.13) (-0.53) (-1.26) (-1.21) (0.69) (-1.11)
Panel C: 2013/4 – 2017/3
β0,m,i 0.774*** 0.826*** 0.743*** 0.759*** 0.784*** 0.708***
(26.20) (30.48) (20.70) (14.96) (27.35) (18.12)
βSMB,i 0.0306 -0.0381 0.0725 0.0544 0.00905 0.161**
(0.69) (-0.94) (1.35) (0.72) (0.21) (2.76)
βHML,i -0.310*** -0.0246 -0.407*** -0.728*** -0.294*** -0.449***
(-5.34) (-0.46) (-5.77) (-7.30) (-5.22) (-5.85)
βUMD,i 0.117*** 0.0344 0.152*** 0.231*** 0.112*** 0.178***
(4.01) (1.28) (4.26) (4.60) (3.94) (4.61)
γ1illiq,i 0.0838 -0.0332 0.128 0.252* 0.0665 0.188*
(1.25) (-0.54) (1.57) (2.19) (1.03) (2.13)
γ2illiq,i 0.362** 0.437*** 0.356* 0.163 0.392** 0.227
(2.79) (3.67) (2.26) (0.73) (3.12) (1.32)
λm,i

(r2)

-0.00181 -0.00114 -0.00223 -0.00215 -0.00180 -0.00191
(-1.11) (-0.76) (-1.12) (-0.77) (-1.13) (-0.88)
δm,i

(V)

2.909 0.302 4.290* 4.481 3.070 4.180
(1.69) (0.19) (2.05) (1.52) (1.84) (1.84)
αi 0.116 -0.0221 0.184 0.213 0.470** 0.0961
(0.68) (-0.14) (0.89) (0.73) (2.83) (0.43)

Summary

To sum up, in the regression result of benchmark model, we don’t find any statistically significant evidence suggesting fund managers have liquidity-timing ability during the first market upheaval (2006-2009). However, after the market returned calm during 2009-2013, all but value portfolio show positive liquidity-timing ability. Among these portfolios, Growth funds present stronger liquidity-timing ability than Balanced funds while Medium/Small-cap funds display better liquidity timing skills than Large-cap funds.

We also find that during the most recent market turbulence, all aggregate portfolios exhibit reverse liquidity timing behaviors. In addition, funds that were the most skilled at liquidity timing in a stable market environment (2009-2013) becomes the ones that are most likely to engage in reverse liquidity timing behavior during recent market turbulence and evidence suggests that it is also true the other way around.

After separating the liquidity timing behavior of fund managers in the liquidity tightening environment from that in a loosening environment, we find all fund managers present asymmetric liquidity timing behavior in all sub-periods. Specifically, during the first and second sub-periods, most portfolios have positive liquidity-timing ability as market liquidity improves; However, when liquidity worsens, they show very limited, even negative liquidity timing.

During the most recent market turbulence (2013-2017), we find that the Value fund portfolio reduce market exposure as liquidity improves (reverse liquidity timing) while the Growth and Medium/Small-cap portfolio increase market exposure as liquidity declines (reverse liquidity timing).

Including return timing and volatility timing variables in the benchmark model doesn’t materially affect the previous estimates of liquidity timing coefficient. Also, there is no evidence suggesting fund managers have return timing and volatility timing ability. In addition, several portfolios exhibit negative volatility timing behavior, indicating that these fund managers increase market exposure as market becomes more volatile.

On the contrary, after controlling for return and volatility timing in the asymmetric liquidity timing model, we find that during the first market upheaval all portfolios have significant one-sided liquidity-timing ability as market liquidity improves. In addition, we find that during the most recent market crash, the Value, Balanced and Large-cap fund managers reduce their market exposure as liquidity improves (reverse liquidity timing) while Growth and Medium-cap fund managers increase market exposure as liquidity dries up (reverse liquidity timing). Including control variables doesn’t affect our previous estimates on fund managers’ liquidity-timing ability during stable period (2009-2013).

5.    Discussion/Possible Explanation

Liquidity-timing ability in stable market environment

Cao, Simin et al. (2013) find that at the portfolio level, U.S. mutual fund managers have significant and positive liquidity-timing ability. They also find that among funds with different investment style, Aggressive growth funds have the most significant liquidity-timing ability, followed by Growth and Growth-and-income funds, and Income funds do not exhibit liquidity timing ability. Their explanation for this pattern is that Aggressive growth funds tend to invest more heavily in small-cap stocks and thus hold more illiquid assets, and funds with more illiquid holdings are more likely to time market liquidity.

We find the same pattern in our second sub-period during which market is stable. Specifically, when market is stable, fund managers at portfolio level exhibit positive liquidity-timing ability. We also find that funds with more illiquid holdings are more likely to present liquidity-timing ability. In particular, Growth funds have the most significant liquidity-timing ability, followed by Balanced fund, and Value funds doesn’t present statistically significant liquidity-timing ability; In our research, we find that Medium/Small-cap funds exhibit stronger liquidity timing ability than large-cap funds, which is in line with Cao, Simin et al. (2013) interpretation that fund managers with more illiquid holdings tend to possess stronger liquidity-timing ability.

Possible Explanation for one-sided liquidity timing ability

In 2006-2013, we observe that fund managers at aggregate level only exhibit significant liquidity-timing behavior as liquidity improves. This could be explained by the fact that market liquidity affects transaction cost (Demsetz 1968). Specifically, as market becomes more liquid, transaction cost of trading stocks decreases as trading activity will not cause as much price impact as in an illiquid environment.

During 2007 market turbulence, we find that the pattern of fund managers with more illiquid holdings have better liquidity-timing ability still exist for Growth, Balanced and Value funds, but Large-cap funds presents stronger one-sided liquidity timing capacity than Medium/Small-cap funds.

The difference in liquidity timing behavior of fund managers between 2007 market turbulence and the following stable market environment is that during 2007 market turbulence, fund managers exhibit weak tendencies to increase market exposure as liquidity worsens (reverse liquidity timing). We observe the same behavior pattern during the 2015 market crash where Growth and Medium/Small-cap funds increase market exposure as liquidity worsens.

Possible Explanation for reverse liquidity timing behavior during turbulence

Our finding in 2013-2017 sub-sample period further confirms that mutual fund managers’ liquidity-timing behavior could change fundamentally during market turbulence.

During market turbulence, the coefficients on liquidity timing ability during liquidity-tightening suggest that fund managers have tendencies to engage in negative/reverse liquidity timing, meaning that they tend to increase their market exposure as liquidity worsens.

This pattern becomes more puzzling in the most recent 2015-2016 market upheaval. In 2013-2017 we find that all aggregate portfolios engage in one-sided reverse liquidity-timing behaviors. In particular, we find that fund managers with more illiquid holdings (Growth funds, Medium/Small-cap funds) show statistically significant tendencies to increase market exposure as market liquidity worsens; while fund managers with more liquid holdings (Value funds, Balanced funds and Large-cap funds) reduce their market exposure as liquidity environment improves.

The reverse liquidity timing behavior during market turbulence could be explained by “flight-to-quality/liquidity” behavior of fund managers during volatile times. When market becomes more volatile, investors become more risk-averse and prefer liquid assets. Vayanos (2004) theoretically shows that “flight-to-quality/liquidity” behavior is a result of the capital constraint faced by fund managers. Since fund managers are subject to withdrawal that depend on the past performance, they prefer more liquid assets in times of uncertainty. Brunnermeier and Pedersen (2009) also noted that “in times of crisis, reductions in market liquidity and funding liquidity are mutually reinforcing, leading to a liquidity spiral” which means during market turbulence, outside investors are more likely to withdraw their capital, subject to equity withdrawal fund managers tend to liquid their market position to meet outside investors’ demand. This is in line with the reverse liquidity timing behavior we observed during 2015 market turbulence.

The fact that fund managers with more liquid holdings (Value funds, Balanced funds and Large-cap funds) tend to reduce their market position when liquidity environment improves could be the result of such funds’ managing transaction cost for liquidating positions to meet their withdrawal demands. The reason that other funds show less tendency to liquidate market position as market liquidity improves might be because these funds, with mostly illiquid holdings, don’t have the same flexibility to manage the transaction cost of meeting withdrawal demand since liquidity premia increases during market turbulence (Vayanos 2004).

In other words, part of the reverse liquidity timing behavior we observe during market turbulence might be the result of managers’ “withdrawal-timing” behavior, meaning that fund managers expect higher outside withdrawal are more likely to occur during market turbulence and therefore they try to predict future liquidity environment and lower their transaction cost of liquidating market position by selling their assets when market liquidity improves during which period the price impact of their selling is less severe. The irrational reverse liquidity timing behavior of fund managers during market turbulence could be a result of the typical limit-to-arbitrage faced by open-end fund where outside investors allocate funds based on fund performance (Shleifer and Vishny 1997).

The other side of reverse liquidity-timing behavior we observe during recent turbulence is that fund managers with more illiquid holdings (Growth funds, Medium/Small-cap funds) show statistically significant tendencies to increase market exposure as market liquidity worsens; There are several possibilities for this pattern.

The first is that these funds have difficulties liquidating their illiquid holdings during market upheaval, so they take on debt or payout existing cash to meet outside withdrawal demand, as a result, their market betas increase not out of fund managers active choices to buy stocks but as a result of passively meeting increasing demand for withdrawal by reducing cash or taking debt due to higher illiquidity friction as market becomes more volatile.

The second possibility is that by buying in more of the illiquid stocks they have already owned, fund managers could utilize the “positive price impact” in an illiquid environment to drive up the Net Asset Value of their existing holdings. Specifically, to manage mutual fund’s performance in an illiquidity environment, funds with illiquid holdings could manually create a price bubble on these assets by further buying in these illiquid assets to increase the value of their assets (Net Asset Value).

6.    Conclusion

In this paper, we investigate the liquidity-timing ability of open-end mutual funds in China both in stable and turbulent market environment. We find that when market is stable, fund managers demonstrate the ability to predict market liquidity and adjust its position accordingly. More specifically, they increase(reduce) their market exposure when they anticipate liquidity environment will improve(decline). We also find that funds with more illiquid holdings are more likely to present stronger liquidity-timing ability. In particular, Growth funds have the most significant liquidity-timing ability, followed by Balanced fund, and Value funds doesn’t present statistically significant liquidity-timing ability; we also find that Medium/Small-cap funds exhibit stronger liquidity timing ability than Large-cap funds, which is in line with Cao, Simin et al. (2013)’s observation that fund managers with more illiquid holdings tend to exhibit higher liquidity-timing ability.

We also find that the liquidity-timing behaviors of mutual funds reverse during the most recent market upheaval. Specifically, funds with more liquid holdings (Value funds, Balanced funds and Large-cap funds) reduce their market exposure as liquidity environment improves; Funds with more illiquid holdings (Growth funds, Medium/Small-cap funds) increase market exposure as market liquidity worsens.

The possible explanations for the reverse liquidity-timing behavior during market upheaval are 1) “Withdrawal timing”: Funds with liquid assets expect higher demand for withdrawal during market turbulence and try to lower the transaction cost of liquidating their assets by selling stocks when market liquidity is better thus the price impact of their selling is smaller; 2) Passive Beta exposure: Funds with illiquid assets will have difficulties selling illiquid assets to meet withdrawal demand, so they need to take on debt or pay out existing cash thus passively increase the market beta of their overall portfolio; 3) Risk-shifting: funds with illiquid assets try to manipulate their performance by utilizing the positive price impact in an illiquid environment.

Our paper makes several contributions to the literature on fund managers’ liquidity-timing ability. First, by adding Large-cap and Medium/Small-cap funds into analysis, we confirm that funds with more illiquid holdings tend to exhibit stronger liquidity-timing ability in a stable market environment.

Second, we find that funds’ liquidity-timing behavior could change fundamentally during market upheaval in emerging market. This is particularly important in times of crisis when liquidity risk is a major concern for investors. Our finding suggests that even though Growth and Medium/Small-cap funds present the most significant liquidity-timing ability in a stable environment, they also suffer the most from reverse liquidity timing during crisis. Therefore, during crisis, mutual fund investors need to adjust their asset allocation between funds to avoid the potential reverse liquidity-timing when market becomes more volatile.

Finally, we propose several possible explanations for mutual fund managers’ reverse liquidity-timing behavior during crisis including limits to arbitrage (Shleifer and Vishny 1997), flight to liquidity (Brunnermeier and Pedersen 2009), passive beta exposure and risk shifting.

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