Payoff Complementarities and Financial Fragility: Evidence from Mutual Fund Outflows. Qi Chen (Duke) Itay Goldstein (Wharton) Wei Jiang (Columbia)

Transcription

1 Payoff Complementarities and Financial Fragility: Evidence from Mutual Fund Outflows Qi Chen (Duke) Itay Goldstein (Wharton) Wei Jiang (Columbia)

2 Payoff Complementarities: Theory and Evidence Payoff complementarities are thought to generate financial fragility. Bank Runs (Diamond and Dybvig, 1983). Currency Attacks (Morris and Shin, 1998). Empirical evidence? One would like to see how differences in the degree of complementarities affect the level of fragility. Hard to show in the above settings.

3 Mutual Funds: A Testing Ground Why mutual funds? Rich and detailed data. Payoff structure exhibits complementarities. Literature shows that investors who redeem their shares impose negative externalities on remaining shareholders (e.g., Edelen, 1999, Johnson, 2004). Incentive to redeem increases in amount of redemptions. More institutional details below. Goal: Use data available for mutual funds to identify link between payoff complementarities and financial fragility.

4 Institutional Background: Costly Redemptions Redemptions impose two types of costs on mutual funds: Direct costs: commissions, bid-ask spreads, and price impact. Indirect costs: Unexpected flows move fund managers away from their optimal portfolios. liquidity-based trading generates average negative return. These costs are higher when the funds assets are more illiquid.

5 Institutional Background: Who Bears the Cost? Investors have the right to redeem at the fund s Net Asset Value (NAV) on any business day. This is based on the last trades of the securities for the day. But, in many cases, redemption-induced trades take place after, and are not reflected in the NAV. Funds often don t know the amount of redemptions at a given day. Redemption requests accepted until 4pm, either directly to the fund or to brokerage houses. Most funds do not know aggregate redemptions until the next day. For strategic reasons, funds often prefer to delay trades. Cost falls on remaining shareholders, not on the redeeming ones.

6 Institutional Background: Example Day 0: A fund has 20 equal shareholders, investing in 1,000 shares of XYZ, at $1/share. Day 1: Shareholder #1 redeems his 5% at 3:59pm. XYZ closes at $1 at 4:00pm, and the redeemer gets $50. No trading takes place. Day 2: Fund need to sell in order to raise $50. XYZ drops to $.95 due to selling pressure. Additional trading cost is $.04/share. Need to sell ($50/0.91=) 55 shares. Each remaining shareholder has (0.95*945/19=) $ Day 3: Redemption turns out to be uninformed. Price rebounds to $1. Each remaining shareholder has ($1*945/19 =) $49.7 < $50.

7 Institutional Background: The Extent of the Problem 76% of outflows lead to additional trading (Edelen, 1999). Average trading cost for institutional investors can be high (85bp according to Jones and Lipson, 2001). Much higher for illiquid stocks. Redemption-based trading reduces fund return by 1.4% annually (Edelen, 1999). This is higher for funds holding illiquid assets (Coval and Stafford, 2006). Tufano (2005): market timing at Putnam cost shareholders $4.4 million in 7 years; unusually high redemptions following the public allegation cost investors $ 48.5 million in 3 months. Major concern for funds and the SEC Redemption fee allowed by the SEC in 2005.

8 Back to Our Question: Designing the Empirical Tests Two Premises: Complementarities arise when funds experience net outflows, i.e., when the performance is low. Conditional on net outflows, complementarities are stronger when funds hold more illiquid assets. Based on a global-game model: H1: Conditional on low performance, funds that hold illiquid assets will experience more outflows. i.e., fragility increases in complementarities. Sharpen the test (based on Corsetti et al., 2004): H2: Pattern weakens when fund is held by large investors. i.e., large investors internalize the externality.

9 Model: Illustration More complementarities Low High Always run Run Do not run Never run Maybe Depend on what others do

10 Data Most data from CRSP mutual fund database. Hand-collect data from Morningstar when necessary. Include 3,185 equity funds from Main analysis conducted on fund-share basis. Overall: 7,777 unique fund shares and 640,219 fund-share-month observations. Distinguish between liquid funds and illiquid funds. Illiquid funds are: small-cap & mid-cap equity funds (domestic or international), or single-country funds excluding US, UK, Japan and Canada. Obtain finer liquidity measures based on holdings (Thomson s Spectrum database). Distinguish between institutional shares and retail shares.

11 Regression Specification Flow = α + β Alpha + β Illiq Alpha + β Control + Flow Alpha it, t 0 it, 1 1 i it, 1 2 it, it, it, 1 Illiq i Control β Control Alpha + ε 3 it, it, 1 it, ( 1 ) TNAit, TNAit, 1 + RETit, = TNAit, 1 = avg. monthly Alpha estimate from 1-factor market model over prior 6-months = 1 if the fund is an illiquid fund, 0 otherwise = Ln(FundSize), Ln(FundAge), Load, Expense, Dummy for institutional share class, past flow. All regressions include year fixed-effects; standard errors adjust for within-cluster correlation clustered at the fund level.

12 Liquidity and Outflows: Semi-Parametric Approach

13 Liquidity and Outflows: Regression Analysis

14 Some Discussion The results show that illiquid funds exhibit stronger sensitivity of outflow to poor performance. In illiquid funds, the negative externality imposed by redeeming investors on the remaining ones amplifies outflows after low performance. Importantly, there is a difference from bank runs: the outflow events that we capture are not total runs. Our story applies mostly to the marginal investor making a redemption decision, not to the average investor. Evidence by Johnson (2006) and Agnew, Pierluigi, and Sunden (2003) show that most investors do not trade much. Total runs on mutual funds are very uncommon (examples provided by Bannier, Fecht, and tyrell, 2006).

15 When Funds are Held Primarily by Large Investors Institutional-oriented funds with %Large Investor>=75% Retail-oriented funds with %Large Investor<=25% Inst=1 MinPur250k=1 Inst=1 MinPur250k=1 Alpha1 Illiq*Alpha * ** ** 0.20 ** ** 0.16 **

16 Alternative Explanations: Fundamentals? Lag Performance Quintiles Q1 Q2 Q3 Q4 Q5 Q5-Q1 t-stat Q1 Q2 Q3 Q4 Q5 Q5-Q1 t-stat Liq(Q5-Q1) - Illiq(Q5-Q1) t-stat Alpha Liquid Funds Illiquid Funds Difference Alpha

17 Alternative Explanations: Clientele? (Analyzing Only Large Investors) Institutional-oriented funds with %Large Investor>=75% Retail-oriented funds with %Large Investor<=25% Inst=1 MinPur250k=1 Inst=1 MinPur250k=1 Alpha ** ** ** Illiq*Alpha * * 1.94

18 Alternative Measures of Liquidity Obtain mutual funds detailed securities holding data from Thomson s Spectrum database. For each stock held, calculate: average trading volume (in $). Amihud (2002) liquidity measure: T $ TradingVolumet / RETURN T = t 1 Value-weight average these measures across all stocks held in the fund to obtain fund-specific holdingbased liquidity measures. t

19 Alternative Measures of Liquidity - Results Ln(trade_vol) Amihud All observations %INST>=75% All observations %INST>=75% Alpha 0.24 ** ** ** ** 5.02 Liq_Holding* Alpha ** **

20 Outflow, Liquidity, and Performance Outflow negatively affects funds future performance above and beyond the moment effect, significantly so for illiquid funds.

21 Fund Policies More illiquid funds are more likely to 1. hold larger cash reserve, 2. implement redemption fee, with more stringent conditions (higher fee and/or longer restriction period).

22 Main Contributions Mutual fund literature. e.g., Brown, Harlow, and Starks (1996), Chevalier and Ellison (1997), Sirri and Tufano (1998), and Zheng (1999). We show that interaction among investors matters for mutual fund flows. Financial fragility literature. Demonstrate that payoff complementarities contribute to financial fragility. Show the vulnerability of open-end financial institutions (Stein, 2005; Cherkes, Sagi, and Stanton, 2006). Policy implications. Testing predictions from models with strategic complementarities. e.g., Manski (1993), Glaeser, Sacerdote, and Scheinkman (2003), and Matvos and Ostrovsky (2006). We show that global games prove to be a useful tool to provide empirical implications that can be taken to the data.