Mutual fund flight-to-liquidity

Transcription

1 Mutual fund flight-to-liquidity Aleksandra Rzeźnik February 2015 Abstract This paper shows that active utual fund anagers actively anage the liquidity of their portfolio. Specifically, I use onthly holdings for a large saple of US equity active utual funds. Consistent with flight-to-liquidity phenoenon, utual fund anagers increase the relative liquidity of their portfolio in ties of arket uncertainty. They reduce illiquid stocks and purchase liquid ones. Fund anagers are also concerned about liquidity risk. When the arket volatility is high, they decrease the liquidity risk of their portfolio and trade stocks with lower liquidity uncertainty. Mutual fund anagers value the flexibility derived fro cash. Therefore, they increase their cash holdings in anticipation of arket turoil. This appears to affect return and liquidity volatility. The idiosyncratic return and liquidity risk, of stocks experiencing utual fund deand pressure, decreases, whereas the systeatic part reains unaffected. Keywords: Market uncertainty, financial crisis, liquidity, flight-to-liquidity, utual funds, institutional investors, price pressure, idiosyncratic volatility, systeatic risk JEL classification: G01, G11, G12, G14, G20 Departent of Finance, Copenhagen Business School, Solbjerg Plads 3 A5.24, 2000 Frederiksberg, Denark; Phone: ; E-ail: ar.fi@cbs.dk 1

2 The fear about disappearing of liquidity fro financial arkets, when it is ost needed, has been becoing ore and ore pronounced aong investors since the collapse of Long-Ter Capital Manageent hedge fund. According to Myron Scholes, one of the reasons for arket liquidity failure is the risk-anageent of financial institutions. 1 Scholes [2000] stresses the iportance of liquidity anageent by financial institutions to itigate the risk of evaporating liquidity. The finance literature provides soe echaniss explaining the liquidity dry-ups (e.g. Brunnereier and Pedersen [2009], Bernardo and Welch [2004], Malherbe [2014]) and describes investors behaviour - flight-to-liquidity (e.g. Vayanos [2004], Beber et al. [2009], Chalers et al. [2013]) in those periods. Moreover, soe of the arket anoalies (e.g. oentu and reversal - Lou [2012], return predictability - Coval and Stafford [2007]) or features (e.g. return cooveent - Anton and Polk [2014], Greenwood and Thesar [2011], liquidity cooveent - Koch et al. [2009]) have been partially explained by investors in- and out-flows to utual fund industry and fund anagers correlated trading. With a constantly growing utual fund industry, it sees natural to analyse liquidity anageent of active utual funds, when the arket liquidity is about to disappear, and test the ipact of utual fund liquidity preferences on stock arket return and liquidity volatility. This paper presents an epirical analysis that describes utual fund liquidity preferences in ties of arket uncertainty. It is also concerned with a uch ore fundaental question: How do utual fund liquidity preferences influence stock return and liquidity volatility? I use Vayanos [2004] theoretical odel as a foundation of y epirical analysis. Understanding the ipact of utual fund liquidity deand on stock return and liquidity riskiness is iportant for general coprehension of echanis in financial arkets. I focus on active US utual funds, investing in US equity. I use onthly frequency of holdings obtained fro Morningstar fro January 1999 to Deceber I assign illiquidity rank to all coon stocks every onth and copute the ean value-weighted illiquidity rank of utual fund holdings. I assue that the periods of arket uncertainty coincide with the top 10% onths with highest VIX observations. I show that utual funds actively anage the liquidity of their portfolio. They rebalance their holdings towards ore liquid ones ahead of arket turoil. Fund anagers reduce the nuber of shares held of illiquid stocks, whereas they increase the holdings of liquid assets, so that the relative liquidity of the portfolio is enhanced. The liquidity risk plays an iportant role as well. The ean value-weighted liquidity volatility rank of utual fund holdings decreases in ties of arket uncertainty, eaning that fund anagers decrease the liquidity risk of their portfolio when arket volatility is high. They also choose to trade stocks with lower liquidity uncertainty in ties of high arket volatility. Another evidence on utual fund flight-to-liquidity is that utual funds enhance their flexibility by increasing their cash holdings by 10%, when they expect high arket volatility. One of y ain epirical finding is that utual fund deand for liquid assets affects stock return and liquidity riskiness. Return and liquidity volatility of stocks experiencing utual fund deand pressure decreases (relative to other stocks in the 1 When the sea dries up, The Econoist, Septeber 23,

3 arket) in ties of arket uncertainty. It is the idiosyncratic risk that is reduced, whereas systeatic risk (arket and liquidity betas) stays unchanged. Section 1 discusses related literature, Section 2 describes the data and variable construction. Section 3 reports on y epirical results and Section 4 concludes. 1 Related Literature This study is related to two strands of finance literature. First, y analysis focuses on the iportance of liquidity in the financial arkets. The level of liquidity and liquidity risk can change over tie affecting stock prices [Aihud and Mendelson [1986], Aihud [2002]] and their riskiness [Acharya and Pedersen [2005]]. According to Pástor and Stabaugh [2003] the possibility that an asset drops in its liquidity exactly when investors want to exit their position has a significant ipact on security prices. Brunnereier and Pedersen [2009], Bernardo and Welch [2004] or Malherbe [2014] odel different echaniss driving liquidity dry-ups. Malherbe [2014] coes up with an adverse selection liquidity odel, where holding cash iposes a negative externality, reducing future arket liquidity and causing liquidity dry-ups. Bernardo and Welch [2004] odel liquidity runs, where an investor fears future liquidity shocks and prefers to sell today in order to receive an average price. Brunnereier and Pedersen [2009] link the arket liquidity with the funding liquidity and predict that arket liquidity is related to arket volatility. Higher arket volatility reduces arket-aker s ability to provide liquidity causing liquidity dry-ups. An epirical evidence on flight-to-liquidity is provided by Acharya and Pedersen [2005]. They show that illiquid stocks have higher coonality in liquidity, higher return sensitivity to the arket liquidity, and higher liquidity sensitivity to the arket returns. Beber et al. [2009] focus on the iportance of the quality and the liquidity for the deterination of sovereign yield spreads. They show changes in credit quality can explain sovereign yield spreads to large extend. However, in ties of arket uncertainty liquidity of sovereign bonds is the ain driver of the yield spreads. My analysis is also related to the literature on utual funds. There is a vast aount of research on ipact of institutional ownership netflows on asset prices and their riskiness. (e.g. Coval and Stafford [2007], Frazzini and Laont [2008] or Lou [2012]). My paper, however, goes one step ahead. It reports on active liquidity anageent of fund anagers in anticipation of potential future withdrawals and its asset pricing iplications. My analysis is also related to the literature on the ipact of utual fund correlated trading on the stock arket (e.g. Koch et al. [2009] and Anton and Polk [2014]). My study shows that utual fund deand for liquidity changes over tie, what influences stock return and liquidity volatility. Whereas, Greenwood and Thesar [2011] use a utual fund ownership data for US stocks to construct a fragility easure. Fragility ebodies non-fundaental shifts in deand that can be caused by collective liquidity shocks. They show in their paper that stocks fragility can predict the volatility of returns. To y knowledge, there is a scarce research focusing on utual fund liquidity 3

4 anageent and its arket iplications. Massa and Ludovic [2005] concentrates on the liquidity level of utual fund s portfolio and find that portfolio liquidity increases with portfolio size, concentration and trading frequency. In y paper I cobine the liquidity literature with the evidence of utual fund ipact on stock prices and their riskiness. This epirical study is closely related to the theoretical odel of Vayanos [2004]. He proposes a odel where withdrawals can happen randoly but also when utual fund perforance falls below a given benchark. The withdrawals are extree in his odel. When they take place, all assets are liquidated and the fund size is reduced to zero. For the siplicity, Vayanos [2004] assues that after the liquidation, the fund anager starts a new fund with the fund size equal to the old fund less the transaction costs incurred fro liquidation and fro setting up new positions. Moreover, fund anager is entitled to an exogenous fee that is proportional to a fund size. His optiization proble is deterined by fund s portfolio price discount, which is, in turn, influenced by arket volatility. Stock risk and liquidity preia as well as the probability of the perforance falling below the threshold increase with arket uncertainty. Illiquid stocks experience, however, greater price discount than liquid assets in ties of high arket volatility. Consequently, utual fund anagers require higher risk preiu per unit of volatility and they are less willing to keep illiquid stocks in their portfolio. Vayanos [2004] suggests a few iplications of utual fund preferences for liquid stocks in ties of high arket volatility. He predicts, inter alia, that stocks held by utual funds becoe less sensitive to the arket volatility, thus the arket and liquidity betas of those stocks decrease in volatile ties. The ipact of utual fund liquidity preferences in ties of arket uncertainty on stock return and liquidity volatility is the ain focus of y study. In that anner I extend the previous work of Huang [2014] and Ben-Rephael [2014]. I show a clear evidence on utual fund flight-to-liquidity, where anagers actively sell illiquid stocks and purchase liquid ones in order to enhance the liquidity of their portfolio in the anticipation of high arket volatility. I also show that unlike Vayanos [2004] predictions, systeatic risk is not influenced by utual fund flight-to-liquidity. It is the idiosyncratic risk that is affected by anagers liquidity preferences. The residual return and liquidity risk decreases for stocks that experience an above average deand fro utual funds when the arket volatility is high. 2 Data and Variable Construction In this section, I introduce the source of y data and the screening procedures. I also explain the construction of the variables used for y further analysis and I discuss their ain features by eans of descriptive statistics. 2.1 Mutual fund and stock data My epirical analysis focuses on US equity active utual funds. The source of the data is Morningstar. I use the onthly holdings of active utual funds, doiciled in US and investing in US equity. In order to copute the nuber of shares traded 4

5 of a stock in a given onth, I presuppose that no trade can take place between two portfolio dates. It can only occur on the portfolio date. Morningstar provides, as well, onthly data on the estiated net flows and total net assets at the fund level. The stock data (daily returns, prices, trading volues and share outstanding) for coon shares (share code 10 and 11) is obtained fro the Center for Research in Security Prices (CRSP). I require a stock price at the beginning of the onth to be between 1 and 1000 USD. I use CUSIP identification nuber to erge utual fund holdings inforation with CRSP stock dataset. In y analysis I only keep those utual funds with 70% of their holdings identified as coon US equity and erged with CRSP dataset. I also require 24 onths of consecutive utual fund holdings inforation, though the presented results stay robust if I use 12 onths as a threshold. The daily VIX observation I download fro Chicago Board Options Exchange (CBOE). 2 The saple period is fro January 1999 to Deceber After applying these screening procedures, I finally arrive with 77,462 fund-onth observations and 1,069 different utual funds. The table (1) shows soe suary statistics of utual funds ain characteristics at the end of the year. The nuber of utual funds in the saple increases fro 25 in Deceber 1999 to 869 in Deceber There is also a significant increase in both total net assets and aggregated utual fund ownership. The ean total net asset (T NA) has doubled fro 1999 ($ Mio.) to 2013 ($ 1, Mio.). At the beginning of the analysis, the entire utual fund industry fro y saple holds on average less than 1% of stock s shares outstanding, whereas in 2013 the ean aggregated utual fund ownership increases to 6.1%. The nuber of stocks that utual funds include in their portfolio increases fro 913 in 1999 to 3,964 in There is soe skewness in the nuber of stocks held by utual funds, eaning that there are few utual funds with nuerous stocks in their portfolio. The edian nuber of stocks per fund is between 61 and 80. The last colun in table 1 shows the percentage of the holdings value that has been successfully erged with coon stocks fro CRSP dataset. 2.2 Variable construction Illiquidity, arket capitalization rank and return I concentrate on liquidity anageent by utual funds in ties of high arket volatility and its arket iplications. Stock s liquidity is not an observable variable and can be only estiated. Therefore I use log-aihud easure [Aihud, 2002]. 3 Following Aihud [2002], I define the liquidity of a stock s on the day d as: ( R ILLIQ s s d = log d V s d ), (1) where Rd s s is a return of stock s on the day d and Vd is its dollar volue. I use onthly frequency of utual fund holdings, therefore I need to estiate onthly Hasbrouck [2009] shows that out of liquidity easures, which he tested, Aihud easure is the ost strongly correlated liquidity proxy with TAQ-based price ipact coefficient. 5

6 stock liquidity, by averaging daily Aihud easure in the onth : ILLIQ s = 1 Days s Days s d=1 ILLIQ s d, (2) where Days s is the nuber of observation for stock s in the onth. One of the ain focuses of this research is to test the way utual fund actively anage their portfolio liquidity in ties of high arket volatility. However, the arket volatility can affect not only utual fund anagers actions but also stock s liquidity. 4 Therefore, I need to control for the ipact of VIX on stock s liquidity, in order to identify utual fund portfolio liquidity anageent. I proceed with assigning illiquidity ranks to all stocks in y saple. Each onth I sort all stocks on their onthly liquidity and set the illiquidity rank between zero and one for the stock s in the onth, RANK ILLIQ s. Aihud easure is increasing in illiquidity, thus an illiquidity rank of one denotes the least liquid stock in the saple in the onth. Then, I copute the value weighted illiquidity rank for the utual fund f in the onth as: RANK ILLIQ f = Stocks f s=1 ω s,f RANK ILLIQ s, (3) where Stocks f is the nuber of stock in the utual fund f in the onth and ω s,f the weight of the stock s in the utual fund f in the onth. The purpose of y analysis is to describe utual fund s active anageent, for that reason I split utual fund holdings into three groups (in the sae anner as Coval and Stafford [2007] did it): expanded (stocks that were part of utual fund s portfolio in a previous onth and their nuber of shares held has increased in the current onth, as well as stocks that did not belong to the portfolio before but they have been incorporated to the portfolio in the current onth), reduced (stocks that were part of utual fund s portfolio before and their nuber of the shares held by the utual fund has decreased in the current onth or they have been copletely eliinated fro the portfolio) and aintained (stocks that were not traded by the utual fund anager and the nuber of shares held in the portfolio did not change between the previous and current onth). This classification allows e to separately analyse the effect of utual fund anager s sales and purchases on the portfolio liquidity in ties of high arket volatility. For each of the group I calculate the value-weighted illiquidity rank, with the weights ω s,g,f equal to the value of shares traded (for reduced and expanded class) or the value of shares held (for the aintained stocks). The value-weighted illiquidity rank for the group g in the utual fund f in 4 Chung and Chuwonganant [2014] show that the liquidity of a single stock is strongly related both to its own risk and to the level of uncertainty in the arket as a whole. In their theoretical odel, Brunnereier and Pedersen [2009] also predict that increases in VIX coincide with drops in arket liqudity, because arket-aker s liquidity provision is liited when the arket volatility is high. 6

7 the onth is: Stocks g,f RANK ILLIQ g,f = s=1 ω s,g,f RANK ILLIQ s, (4) where Stocks g,f is the nuber of stocks in the group g of the fund f in the onth. In an exactly analogous way I copute the value-weighted arket capitalization rank for the utual fund f (RANK MCAP f ) and for the group g in the utual fund f (RANK MCAP g,f ). The return on a fund s holdings (R) f is obtained as the value-weighted ean of all stock returns in the portfolio, where the weight is the nuber of shares held by utual fund f in the onth Cash Cash is the ost liquid asset in a utual fund portfolio, thus I integrate cash into y analysis. Morningstar provides inforation about utual fund cash holdings, however it is not uniquely identified in the dataset. I include to y cash easure, aong others, cash, cash and equivalents, cash and oney funds, cash and treasury bills, cash currency, cash account, and cash oney arket. I exclude cash options, cash forwards, cash offset, and cash collateral. The cash weight in a utual fund is: CASH f = ite f i=1 CASH ITEM i,f MV (HOLDINGS f ) 100%, (5) where ite f is the nuber of different cash entries for the utual fund f in the onth and MV (HOLDINGS f ) is the arket value of holdings of the fund f in onth the Fund flows and arket uncertainty There is broad literature on the influence of utual fund flows on fund anagers trading and the stock arket (e.g. Lou [2012] or Frazzini and Laont [2008]). In y analysis I want to control for the ipact of investors flows on utual fund active anageent and ainly focus on fund anagers trading driven by arket uncertainty. Morningstar provides estiated fund-level net flow (MSTAR FLOW f ) at the onthly frequency. 5 I copute the relative net flow in order to capture the percentage of oney flowing in and out of utual fund relative to its total assets: FLOW f = MSTAR FLOW f TNA f 1 (6) where TNA f 1 is a total net asset of the fund f in the previous onth. 5 Estiated fund-level net flow is coputed fro aggregated share-class-based flow if available, otherwise estiated fro surveyed fund size. 7

8 Following previous research (e.g. Ben-Rephael [2014]) I proxy arket uncertainty with CBOE Volatility Index VIX. 6 For every onth I copute the onthly VIX (VIX ) as an average across daily VIX observations in the onth. I a interested in periods of high arket volatility ties of flight-to-liquidity. I assue that flightto-liquidity periods coincide with the top 10% of onths with the highest ean onthly VIX observations. The relative net flows and onthly VIX see to be negatively correlated. The figure 1 shows the plot of z-scored onthly VIX and z-scored onthly edian relative net flow. 7 The shaded areas depict periods of flight-to-liquidity. In the figure one can see that heavy outflows fro utual fund industry coincide with ties of high arket volatility. For that reason, I include the fund flows in y analysis to control for this part of trades that is driven by liquidity needs due to institutional flows Mutual fund ownership In the second part of y analysis I test how anagers trading decision in periods of high arket uncertainty affect the stock arket. Therefore I need a easure capturing utual funds preferences towards liquid stocks in ties of high arket volatility. I use the detrended aggregated ownership following DeVault et al. [2014]. Firstly I define utual fund aggregated ownership as the su of all shares of the stock s held by utual funds in y saple in the onth relative to the nuber of shares outstanding: funds s OWN s f=1 Shares s,f =, (7) Shares Outstanding s where funds s is the nuber of funds holding the stock s in the onth and Share s,f is the nuber of shares of the stock s held by the utual fund f in the onth. I a interested in active portfolio anageent, thus the passive aggregated ownership is not very useful. This is why, I define the change in the aggregated utual fund ownership as: OWN s = OWN s OWN s 1. (8) However, the change in the aggregated utual fund ownership is increasing over tie. With the growing utual fund industry also the utual fund ownership in a stock increases. Therefore, I subtract the onthly ean change in ownership across all the stocks and coe up with a deeaned aggregated utual fund ownership: Deean. OWN s = OWN s 1 Stocks a Stocks a s=1 OWN s, (9) 6 CBOE Volaatility Index was introduced by the Chicago Board Options Exchange in It was designed to easure the arket s expectation of 30-day volatility iplied by at-the-oney S&P100 option prices. In 2003, a new VIX easure was launched, which is based on the S&P500 Index and is estiated as a weighted average of call and put prices for a wide range of strike prices (source: 7 I use the edian flow in order to avoid the influence of outliers, but the plot is siilar if I use the ean flow. 8

9 where Stocks a is a nuber of stocks that is part of the aggregated utual fund portfolio. I a interested in utual fund extra deand pressure for a stock, Therefore, I define a positive detrended change in aggregated utual fund ownership Detrend. OWN s capturing the above average utual fund deand for a given stock in the onth : { Detrend. OWN s Deean. OWN s =, if Deean. OWN s > 0 (10) 0, if Deean. OWN s Descriptive statistics The table 2 with descriptive statistics for the constructed variables deonstrates soe interesting insights into utual funds liquidity preferences. The ean (edian) cross-sectional fund s illiquidity rank is (0.118), eaning that utual funds invest in top 11% ost liquid stocks. They also prefer larger stocks in their portfolio, with the ean (edian) arket capitalization rank of (0.882). Additionally, utual funds tend to choose stocks with lower volatility of liquidity (the botto 28%). Moreover, on average utual funds hold 2.5% of their holdings in for of cash. Fro the last three coluns one can see that on onthly basis utual funds purchase ore stocks (37) than they sell (31). However, the biggest part of a portfolio is not traded. On average the nuber of shares held of 82 stocks reain unchanged over a onth. 3 Epirical Results 3.1 Flight-to-liquidity In his theoretical odel, Vayanos [2004] predicts that utual funds tilt their portfolio towards ore liquid assets in ties of arket volatility. The anagers are afraid of investors withdrawals as their perforance is linked to a benchark. In ties of arket uncertainty withdrawals are ore likely, because the probability of lower than benchark fund perforance increases. As a consequence utual fund anagers choose ore liquid stocks for their portfolio when they face high arket uncertainty. In the first part of y epirical analysis I test the theoretical predictions of Vayanos [2004] about utual funds flight-to-liquidity. I hypothesize that utual fund anager tilt their portfolio towards ore liquid assets when the arket volatility is high, eaning that they fly to liquidity. I a interested in portfolio anagers decisions, in trades following those decisions and the overall liquidity of the portfolio. Therefore I carry out the sae test for the entire portfolio, for the reduced, expanded and aintained groups in order to capture each aspect of liquidity anageent in the volatiles ties. I run the following regression: RANK ILLIQ f = b 0 + b 1 HIGH VIX 1 + b 2 RANK ILLIQ f 1 + b 3 R f + b 4 QUINTILE f + b 5 RANK MCAP f 1 + Tie trend + F und FE, (11) 9

10 where HIGH VIX 1 is a lagged duy variable equal to one if a given onth belongs to top 10% onths with highest VIX observations, otherwise zero. The ipact of investor flows on the liquidity anageent is captured by QUINTILE f {1, 2, 3, 4, 5}: one stands for the botto 20% of utual funds in the onth with ost outflows, whereas five indicate the top 20% of utual funds with ost inflows. RANK ILLIQ f increases over tie, eaning that utual funds add ore and ore illiquid stocks to their portfolio. Tie trend variable is supposed to capture the increasing behaviour of RANK ILLIQ f. The table 3 shows the estiated coefficient fro the panel regression. The colun (2) shows the effect of high volatility onth on the portfolio liquidity. The coefficient is negative (-0.987) and highly significant (t-stat: ), eaning that in ties of arket uncertainty utual fund anagers prefer a ore liquid portfolio. A high previous return increases the liquidity of the portfolio as well (the coefficient is negative and significant). An extra return generated in the previous onth allows anagers to extend their portfolio with ore liquid assets (a liquidity cushion) that will be easy to liquidate in any convenient tie. The arket capitalization rank (RANK MCAP f ) is highly correlated with the illiquidity rank, thus I include it into y regression as well. Larger stocks are on average ore liquid, therefore it is not surprising that past high arket capitalization rank predicts higher next onth portfolio liquidity. The colun (1) repeats the sae analysis but in differences. The effect of arket uncertainty stays negative and significant. The colun (3) shows the coefficient estiates fro the regression of illiquidity rank for the reduced stocks on high volatility duy and other control variables (I include additionally lagged group illiquidity rank RANK ILLIQ f,g 1 and lagged group arket capitalization rank RANK MCAP f,g 1). The coefficient on HIGH VIX is significantn(t-stat: 5.98) and positive (3.387), eaning that utual funds sell their illiquid stocks in anticipation of the arket volatility. On the other hand, they enhance portfolio liquidity by purchasing ore liquid stocks (colun (4)). This show an evidence that utual fund actively anage their portfolio liquidity. In anticipation of high arket uncertainty, they get rid of illiquid stocks and load up on liquid ones, so that in overall their portfolio becoes ore liquid. The last colun (5) shows that this results are not driven by utual funds non-traded holdings. Stocks, which are kept untraded in the portfolio, becoe less liquid when the arket volatility is high. This ight be one of the ain reasons, why utual fund do not trade the. The anagers are afraid of facing high trading costs and rather keep those stocks unchanged waiting for the better ties to coe. Mutual funds are not only concerned about liquidity level of their portfolio but also about the liquidity uncertainty of their holdings when the arket volatility is high. I run the sae regression as in (11), but substitute the illiquidity rank RANK ILLIQ f with the illiquidity volatility rank RANK σ(illiq) f. The regression results are in the table 4. The analysis shows that utual fund are cautious about stock s volatility of liquidity. In ties of high arket uncertainty, they tilt their portfolio towards lower illiquidity volatility stocks (the coefficient is negative and significant in colun (2)). In face of turbulent periods, utual funds ostly trade those stocks, that have lower liquidity risk. Coluns (3) and (4) shows negative and significant coefficients 10

11 for reduced stocks ( ) and for expanded stocks ( ). This eans that utual fund anagers tend to iniize the liquidity volatility of their portfolio when they expect high arket volatility. They are also afraid of getting stuck with high transaction cost trades when the arket is uncertain. For that reason, they choose stocks to trade with lower liquidity volatility in ties of high arket volatility. Further, I want to be sure, that y result are not caused by soe other liquidity needs. Therefore, I divide the utual funds every onth into two groups: those with positive and negative netflows. I want to test whether there is any difference in deand for liquidity between utual funds experiencing in- and out-flows. I reran the regression (11) on these two sub-saples and present the estiated coefficients in table 5. The results reain unchanged regardless of the netflow direction. Nevertheless, the coefficient on HIGH VIX 1 for positive flow funds (5.667) is twice of the size of the coefficient for negative flow funds (2.172). This ight ean that positive flow fund anagers are ore flexible and they can adjust their portfolio to arket uncertainty by selling ore of illiquid stocks. However, stock holdings constitute only one part of a utual fund s portfolio. A skilful cash anageent can contribute to the fund s flexibility. Managers with disposable cash can react quickly to new inforation purchasing an attractive stock or avoid costly fire sales by eeting redeptions with their cash buffer [Edelen [1999], Coval and Stafford [2007], Siutin [2013]]. Cash holdings is the ost liquid asset in fund s portfolio. This is why I expect utual funds to increase their cash holdings in anticipation of arket uncertainty. The table 6 shows the estiated coefficient fro the panel regression of cash holding weight CASH f on lagged high VIX duy variable HIGH VIX and on lagged cash holding weight CASH f (in the second regression). The HIGH VIX coefficient is positive (0.239) and significant (t-stat: 4.83), eaning that utual funds increase the aount of cash in their holdings by about 10%, when they expect high arket volatility. Adding lagged cash holdings into regression does not change the results, the HIGH VIX coefficient slightly decreases to 0.148, but stays significant (t-stat: 3.42). The above analysis provide a strong evidence that utual fund anagers actively tilt their portfolio towards ore liquid stock when they expect high arket volatility. They sell illiquid stocks and replace the with liquid ones, transforing their portfolio into a ore liquid one. Fund anagers are also aware of the trading costs uncertainty. Mutual funds anager do not want to be surprised by high transaction costs in the iddle of the trade, this is why they buy or sell stocks with lower liquidity risk when the arket uncertainty is high. Finally, funds anagers adjudt the aount of cash holdings to the arket uncertainty. They increase their cash buffer in anticipation of high arket volatility. These results give support to theoretical predictions of Vayanos [2004] concerning utual funds flight-to-liquidity in ties of arket volatility. The so far analysis poses a question about the ipact of utual funds liquidity preferences in ties of uncertainty on stock arket. 11

12 3.2 Return and Liquidity Volatility In the previous section I showed that utual funds anagers see to adjust their portfolio liquidity to arket uncertainty. They tend to buy ore liquid stocks and discard the illiquid ones. In that anner utual fund industry creates an extra deand pressure for liquid assets that can affect stock s return and liquidity volatility. Vayanos [2004] predicts that stocks held by utual funds becoe less sensitive to the arket volatility, iplying a decrease in arket and liquidity betas of those stocks in volatile ties. I will begin the second part of y analysis with siple return and liquidity volatility ratios, then continue with betas - systeatic risk easure and finish with ean squared error ratios, representing idiosyncratic risk. The purpose of y test is to exaine the ipact of utual fund deand pressure for liquid stocks on their return and liquidity volatility. I use the return RETURN RATIO s (liquidity ILLIQ RATIO s ) volatility ratio of stock s in the onth, which is defined as the standard deviation of daily stock returns (Aihud easure) in the onth relative to the ean standard deviation of daily returns (Aihud easure) for all stock available in the arket: RETURN RATIO s = ILLIQ RATIO s = StDev(R s ) 1 Stocks Stocks s=1 StDev(R) s StDev(ILLIQ s ) 1 Stocks Stocks s=1 StDev(ILLIQ s ), where Stocks is the nuber of stocks in the arket in the onth. In y analysis I test the ipact of utual funds deand for liquid stocks in ties of high arket volatility on stock s return volatility relative to the ean arket volatility. I run the following regression: RET URN RATIO s = b 0 + b 1 Detrend OWN s 1 + b 2 HIGH VIX 1 + b 3 HIGH VIX 1 OWN s 1 + b 4 RETURN RATIO s 1 + b 5 ILLIQ RAT IO s 1 + b 6 LOG(MCAP) s 1 + b 7 ILLIQ s 1. (12) Detrend OWN s 1 is supposed to capture the ipact of utual fund deand for the stock s, HIGH VIX 1 controls for the high volatility periods. Of the ain interest is an interaction ter HIGH VIX 1 OWN s 1 representing the effect of utual fund extra deand for liquid stocks when the arket volatility is high. RETURN RATIO s 1 controls for persistence in return volatility, ILLIQ RATIO s 1 corresponds with the part of return volatility that coes fro liquidity risk, LOG (MCAP) s 1 and ILLIQ s 1 take into account stock s arket capitalization and its liquidity, respectively. The table 7 shows the regression results. The Detrend OWN s 1 is positive and in ajority significant, eaning the stocks experiencing high utual fund deand have higher return volatility. This result is consistent with the evidence of utual fund s aversion to low variance stocks shown by Falkenstein [1996]. The coefficient on the interaction ter HIGH VIX 1 OWN s 1 provides 12

13 a very interesting results. Mutual fund extra deand for liquid stocks in ties of arket volatility reduces returns relative volatility, all coefficients are significant and negative (ranging for (-0.49) to (-0.57)). Moreover, the interaction ter coefficients are greater in absolute ters than coefficients on Detrend OWN s 1, eaning that in the periods of high arket volatility, utual fund high deand and ownership of liquid asset iunise the fro uncertainty in the arket. The rest of the control variables see to be eaningful. A high return volatility ratio in a previous onth eans that one can expect high RETURN RAT IO s in a current period. Larger (LOG(MCAP) s 1) and ore liquid stocks (ILLIQ s 1) tend to have lower relative return volatility. Siilar results can be found in the table 8, where I test the ipact of utual fund deand for liquid stocks in ties of arket uncertainty on a stock s relative liquidity volatility. Stocks that experience extra deand fro utual fund industry tend to have higher liquidity risk. That can be caused by utual fund anager willingness to include a ore illiquid stock with greater illiquidity risk in order to earn a liquidity risk preiu. The situation is, however, reversed when the arket uncertainty is high. In ties of high volatility at the arket, utual fund extra deand for liquid stocks reduces their relative liquidity risk and ake the iune to arket turoils. The return risk can be decoposed into systeatic and non-systeatic risk. In the last part of y analysis I want to test whether high deand stocks, in ties of arket uncertainty, becoe less responsive to the arket or whether it is the idiosyncratic risk that decreases. In the next step, I want to test Vayanos [2004] predictions that stocks, held by utual funds in ties of high arket volatility, becoe less sensitive to the arket, and thus have lower return and liquidity betas. I estiate the arket beta BETA s R, for stock s in onth in a siple regression: where R s d,, Rkt d, R s d, = a 0 + BETA s R, R kt d, + η s R,d,, (13) is return on the stock s / equally-weighted arket on the day d in the onth. The liquidity beta BETA s Liq, is coputed in two steps approach, following Haeed et al. [2010]. Firstly, I regress the daily Aihud easure on its lagged value and the day of the week duy variable: ILLIQ s d, = b 0 + b 1 ILLIQ s d 1 + T hedayoft hew eekduy + ε s d,. (14) and regress the on the equally-weight inno- I keep the innovations in liquidity ε s d, vations in arket liquidity ε kt d, : ε s d, = a 0 + BETA s Liq, ε kt d, + η s Liq,d,. (15) I repeat the sae regression as in (12), but replace the RETURN RATIO s with the arket and liquidity betas. The results are presented in table 9 and 10. The interaction ter of utual fund extra deand and the arket uncertainty Detrend OWN s 1 is insignificant. This eans that the sensitivity to the arket is not reduced for the stocks in high deand by utual funds in highly volatile ties. As a consequence, it ust be the non-systeatic risk that decreases. For a check-up test I run again the 13

14 sae regression fro (12), but I use return and liquidity ean squared error ratio as a easure for idiosyncratic risk and as a dependent variable. The return ean squared error ratio coes fro the regression (13): MSE RATIO s R, = 1 Days s Days s 1 Days kt d=1 (η s R,d, )2 Days kt. (16) d=1 (ηr,d, kt )2 The liquidity ean squared error is coputed an analogous way but using the residuals fro the regression (14). The estiated regression coefficients are available in the table 11 and 12 for return and liquidity ean squared error ratios, respectively. The results confir y earlier expectations, that the utual fund high deand for liquid stocks in ties of arket volatility reduces non-systeatic risk of those stocks. All coefficients of Detrend OWN s 1 are negative and significant. 4 Conclusion In this paper, I analyse utual fund liquidity preferences in ties of arket uncertainty using a large saple of US equity active utual funds with onthly holdings inforation fro January 1999 to Deceber I provide evidence on utual fund flight-to-liquidity consistent with theoretical predictions of Vayanos [2004]. Mutual funds actively adjust their portfolio to the arket volatility. In the anticipation of arket turoil, fund anagers reduce aount of shares of illiquid stocks and increase the nuber of shares of liquid securities. In that anner, the overall liquidity of the portfolio increases. This behaviour is, however, not driven by flow-induced liquidity needs. Mutual fund anagers are also concerned about liquidity risk. They reduce the liquidity uncertainty of their portfolio in ties of high arket volatility. Additionally, they are cautious about liquidity volatility of their trades. When the arket uncertainty is high, they buy and sell stocks with lower liquidity risk. I also show that fund anagers value the flexibility provided by cash holdings. With rising arket volatility, they increase their cash buffer. My paper focuses, as well, on arket iplications of utual fund flight-to-liquidity. The increased deand for liquid stocks in ties of arket uncertainty has an ipact on asset return and liquidity volatility. Return and liquidity risk, of stocks experiencing deand pressure fro utual funds, decreases relative to other stocks in the arket. It is the idiosyncratic coponent of risk that becoes saller. The arket and liquidity betas reain unchanged by utual fund liquidity deand. My findings show the iportance of utual fund tie-varying liquidity preferences, which influences the non-systeatic return and liquidity volatility. 14

15 Figures Figure 1: Median onthly VIX, high VIX duy and a utual fund edian percentual net flow. 15

16 Tables Table 1: Mutual Fund Suary Statistics. This table shows suary statistics for utual fund ain characteristics at the end of a year. No. Funds is the nuber of actively anaged US utual funds investing in US equity at the end of the year; TNA is a total net assets at the end of the year; Aggregated Ownership(%) is a percentage nuber of shares a given stock held by all utual funds in the saple relative to the nuber of shares outstanding; No. Stocks per Fund is a nuber of stocks held by a fund on average; No. Stocks is a nuber of distinct stocks that are held by all utual funds in the saple; Matching Rate is the percentage of the holdings value that has been successfully erged with CRSP stock data (utual funds with atching rate lower than 70% are discarded fro the saple). Month- No. Aggregated No. Stock No. Matching TNA ($ Million) Year Funds Ownership (%) per Fund Stocks Rate Mean Median Mean Median Mean Median , , , , , , , , , , , , , , , , , , , , , , , ,

17 17 Table 2: Cross-sectional Descriptive Statistics. This table shows onthly cross-sectional eans, edians, standard deviations, inius and axius for the constructed variables: fund s illiquidity rank RANK ILLIQ f, fund s liquidity standard deviation rank RANK σ(illiq) f, fund s arket capitalization rank RANK MCAP f, fund s holdings return in percent RET f, a relative net flow FLOW f, onthly VIX easure VIX f, fund s cash holdings in percent CASH f and stock s change in aggregated utual fund ownership OWN s. Every onth stocks are sorted on their illiquidity and assigned ranks between 0 (ost liquid) and 1 (least liquid). RANK ILLIQ f is a value-weighted illiquidity rank for utual fund portfolio, with a percentage dollar investent in a stock as a weight. RANK σ(illiq) f and RANK MCAP f are constructed in an analogous anner. RET f is the value-weighted return of a portfolio holdings. FLOW f is a ratio of estiated fund-level net flows to one-onth lagged total net assets. VIX f is an average VIX observation in a given onth. CASH f is a percentage weight of the portfolio kept in cash. OWN s is the change, between current and previous onth, in the nuber of shares held by all utual funds in the saple relative to the nuber of shares outstanding. I split utual fund holdings into three groups: expanded (stocks that were part of utual fund s portfolio in a previous onth and their nuber of shares held has increased in the current onth, as well as stocks that did not belong to the portfolio before but they have been incorporated to the portfolio in the current onth), reduced (stocks that were part of utual fund s portfolio before and their nuber of the shares held by the utual fund has decreased in the current onth or they have been copletely eliinated fro the portfolio) and aintained (stocks that were not traded by the utual fund anager and the nuber of shares held in the portfolio did not change between the previous and current onth). The last three coluns show the distribution of the nuber of stocks that are expanded, aintained and reduced by utual fund anager. ILLIQ RANK MCAP RET f FLOW f VIX CASH f OWN s Ex- Main- Re RANK f σ(illiq) f RANK f pan. tain. duce Mean Median Std. Dev Min Max

18 Table 3: Fund s Illiquidity Rank. This table shows panel regressions of funds illiquidity rank RANK ILLIQ f on the lagged arket volatility duy HIGH VIX 1 and other control variables. HIGH VIX 1 is equal to one if a given onth belongs to the top 10% of onths with highest VIX observations, otherwise zero. HIGH VIX 1 is the difference between two consecutive arket volatility duy variables. RANK ILLIQ f f,g 1 and RANK ILLIQ 1 stand for lagged illiquidity rank for fund f in the previous onth and for the lagged illiquidity rank for group g in fund f in the previous onth. R f 1 and R f 1 is the lagged return and the difference in returns for fund f. Mutual fund flows are split into flow QUINTILE f {1, 2, 3, 4, 5}. The QUINTILE f of one signifies the botto 20% of utual funds with ost outflows, whereas the QUINTILE f of five corresponds with the top 20% of utual funds with ost inflows. The QUINTILE f is the difference between current onth flow quintile and past onth flow quintile for fund f. The RANK MCAP f f,g 1 and RANK MCAP 1 denote the lagged arket capitalization rank for utual fund f and the lagged arket capitalization rank for the group g in utual fund f. The RANK MCAP f 1 is the difference between two arket capitalization rank for a given fund. A Tie trend variable is included in the regressions as well as fund fixed effect. The regressions in coluns (3) (5) test separately the effect of high arket volatility on the illiquidity rank of expanded RANK ILLIQ e, reduced RANK ILLIQ r and aintained RANK ILLIQ stocks. The errors are corrected for heteroscedasticity. HIGH VIX (-3.65) (1) (2) (3) (4) (5) RANK RANK RANK RANK RANK ILLIQ f ILLIQ f ILLIQ r ILLIQ e ILLIQ HIGH VIX 1 (-11.80) (5.98) (-5.05) (2.79) RANK ILLIQ f (168.61) (17.97) (16.64) (18.80) RANK ILLIQ f,g (15.76) (9.89) (15.02) R f (-3.58) (0.93) (5.39) (-0.66) R f 1 QUINTILE f QUINTILE f RANK MCAP f 1 RANK MCAP f 1 RANK MCAP f,g 1 Tie trend Intercept (6.26) (-2.38) (-321.8) (-0.77) (2.50) (-5.51) (-0.91) (-50.43) (-9.06) (-4.38) (-2.48) (-2.22) (-8.55) (-5.40) (16.91) (5.53) (17.25) (2.67) (1.16) (44.53) (8.03) (3.20) (4.07) Fund FE No Yes Yes Yes Yes NOBS

19 Table 4: Fund s Illiquidity Uncertainty Rank. This table shows panel regression of funds illiquidity volatility rank RANK σ(illiq) f on the lagged arket volatility duy HIGH VIX 1 and other control variables. HIGH VIX 1 is equal to one if a given onth belongs to the top 10% of onths with highest VIX observations, otherwise zero. HIGH VIX 1 is the difference between two consecutive arket volatility duy variables. RANK σ(illiq) f 1 and RANK σ(illiq)f,g 1 stand for lagged volatility illiquidity rank for fund f in the previous onth and for the lagged volatility illiquidity rank for group g in fund f in the previous onth. R f 1 and Rf 1 is the lagged return and the difference in returns for fund f. Mutual fund flows are split into flow QUINTILE f {1, 2, 3, 4, 5}. The QUINTILE f of one signifies the botto 20% of utual funds with ost outflows, whereas the QUINTILE f of five corresponds with the top 20% of utual funds with ost inflows. The QUINTILE f is the difference between current onth flow quintile and past onth flow quintile for fund f. The RANK MCAP f f,g 1 and RANK MCAP 1 denote the lagged arket capitalization rank for utual fund f and the lagged arket capitalization rank for the group g in utual fund f. The RANK MCAP f 1 is the difference between two arket capitalization rank for a given fund. A Tie trend variable is included in the regressions as well as fund fixed effect. The regressions in coluns (3) (5) test separately the effect of high arket volatility on the liquidity volatility rank of expanded RANK σ(illiq) e, reduced RANK σ(illiq) r and aintained RANK σ(illiq) stocks. The errors are corrected for heteroscedasticity. HIGH VIX (-18.72) (1) (2) (3) (4) (5) RANK RANK RANK RANK RANK σ(illiq) f σ(illiq) f σ(illiq) r σ(illiq) e σ(illiq) HIGH VIX 1 (-27.91) (-7.88) (-8.19) (-7.85) RANK σ(illiq) f (108.72) (26.19) (17.71) (27.99) RANK σ(illiq) f,g (7.59) (7.44) (9.04) R f (11.16) (2.60) (4.65) (5.37) R f 1 QUINTILE f QUINTILE f RANK MCAP f 1 RANK MCAP f 1 RANK MCAP f,g 1 Tie trend Intercept (-2.14) (0.57) (-29.40) (1.31) (7.12) (-9.62) (0.50) (-52.58) (-13.07) (-5.28) (-11.55) (-5.75) (-11.29) (-13.47) (52.96) (21.16) (30.43) (19.47) (2.66) (10.48) (2.66) (-5.25) (4.35) Fund FE No Yes Yes Yes Yes NOBS

20 Table 5: Positive and Negative Fund Flows - Illiquidity Rank. This table shows panel regression of funds illiquidity rank RANK ILLIQ f on the lagged arket volatility duy HIGH VIX 1 and other control variables. The test is ran separately for utual funds with positive fund flows in onth - coluns (1) (5) and for negative flows utual funds - colun(6) (10). HIGH VIX 1 is equal to one if a given onth belongs to the top 10% of onths with highest VIX observations, otherwise zero. HIGH VIX 1 is the difference between two consecutive arket volatility duy variables. RANK ILLIQ f 1 and RANK ILLIQf,g 1 stand for lagged illiquidity rank for fund f in the previous onth and for the lagged illiquidity rank for group g in fund f in the previous onth. R f 1 and Rf 1 is the lagged return and the difference in returns for fund f. FLOW f and FLOW s is a percentage net flow and the difference between the current and previous onth for the utual fund f. The RANK MCAP f f,g 1 and RANK MCAP 1 denote the lagged arket capitalization rank for utual fund f and the lagged arket capitalization rank for the group g in utual fund f. The RANK MCAP f 1 is the difference between two arket capitalization rank for a given fund. A Tie trend variable is included in the regressions as well as fund fixed effect. The regressions in coluns (3) (5) and (8) (10) test separately the effect of high arket volatility on the illiquidity rank of expanded RANK ILLIQ e, reduced RANK ILLIQr and aintained RANK ILLIQ stocks. The errors are corrected for heteroscedasticity. POSITIVE FLOW MUTUAL FUNDS NEGATIVE FLOW MUTUAL FUNDS (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) RANK RANK RANK RANK RANK RANK RANK RANK RANK RANK ILLIQ f ILLIQ f ILLIQ r ILLIQe ILLIQ ILLIQ f ILLIQ f ILLIQ r ILLIQe ILLIQ HIGH VIX 1 (-1.27) (-3.67) HIGH VIX 1 (-5.95) (5.23) (-3.42) (3.14) (-10.40) (3.45) (-3.91) (0.61) RANK ILLIQ f (107.48) (8.93) (10.85) (10.40) (120.58) (17.27) (11.28) (16.84) RANK ILLIQ f,g 1 R f t 1 R f t 1 FLOW f FLOW f RANK MCAP f 1 RANK MCAP f 1 RANK MCAP f,g 1 Tie trend Intercept (10.46) (7.76) (12.06) (9.85) (5.29) (5.80) (-4.64) (1.31) (2.15) (-0.92) (-1.38) (0.12) (4.84) (-0.08) (4.33) (4.53) (-3.36) (5.49) (-4.14) (17.43) (1.62) (-2.09) (-1.07) (-19.11) (-2.61) (0.94) (-193.2) (-258.4) (-35.22) (-5.59) (-3.94) (-1.86) (-37.26) (-5.75) (-3.13) (-1.29) (-0.38) (-3.24) (-1.62) (-2.88) (-8.06) (-6.14) (10.91) (4.09) (9.17) (2.65) (13.20) (5.69) (14.42) (2.24) (1.13) (31.71) (4.36) (3.15) (1.88) (0.98) (26.93) (4.59) (1.67) (2.52) Fund FE No Yes Yes Yes Yes No Yes Yes Yes Yes NOBS