“liquidity management by asset managers”...4th luxembourg asset management summit october 22,...

“Liquidity Management By Asset Managers”

4th Luxembourg Asset Management Summit October 22, 2015

Russ Wermers University of Maryland

and Office of Financial Research, U.S. Treasury Department

http://wwwen.uni.lu/universite/actualites/evenements/

4th_luxembourg_asset_management_summit_21_23_october_2015_final_program

Caveat

•  My opinions expressed today do not necessarily reflect those of the Office of Financial Research (OFR), or the Financial Stability Oversight Council (FSOC) in the United States.

2October26,2015 LiquidityManagementbyAssetManagers

Motivation for this Discussion •  A key focus since the crisis has been asset management liquidity •  Driving this is the perception of these vehicles as “shadow banks”

October 26, 2015 Liquidity Management by Asset Managers Slide #3

AssetManagementSector

U.S.Size($Trillions)

Regulator(s) Ac:onsTakenorConsidered

Money-marketmutualfunds(MMMF)

2.7 SecuriFesandExchangeCommission(SEC)

2010and2014AmendmentstoRule2A-7

ETFs 2.5 SECandCFTC None

Mutualfunds(non-MMMF)

15 SEC 2014DataEnhancement;2015ProposedLiquidityRules

Hedgefunds

3 SEC FormPF(PrivatelyfiledwithSEC)

SeparateAccounts 15-20(?) Managed:SECReFrement:DeptofLabor

None

Money Market Mutual Funds

Background on MMMFs

5

What are money market funds (MMMFs)? •  Mutual funds that hold only high-quality, short-term, fixed-income securities.

(~fonds monetaires / fonds dynamiques) •  Highly regulated but not insured •  Additional reforms added in 2010 Types of MMMFs •  Government and Prime Types of Shareholders •  Retail investors = you and me, our personal retirement savings (IRAs). •  Institutional investors = nonfinancial and financial companies, company

retirement plans (401k), state and local govts, other mutual funds, non profits.

•  “hot money” = shareholders of MMMFs that move large amounts of money in and out of MMMFs during both periods of calm and crisis. •  Who? Economic reasoning suggests large-scale investors where traders have

incentives to maximize yield. For example, the cash desk of a large, integrated financial institution such as Goldman-Sachs or JP Morgan

Despite yielding almost nothing, investors still choose MMMFs to the tune of >$2.5 trillion

6

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

5.0

2005 2006 2007 2008 2009 2010 2011

Money Market Fund Yields and Total Net Assets Monthly, 2005-2011

* Simple average of net yields on taxable money market funds. Sources: Investment Company Institute and iMoneyNet

Total net assets ($ trillions)

Net yield* (percent)

Why MMMFs can have a Liquidity Crisis •  Money market funds have many bank-like features, but no explicit

protections of investor capital (e.g., FDIC insurance) –  Virtually all US money market funds have fixed NAV of $1 per share –  Risk of runs since portfolio market value can diverge from NAV –  Structure resembles a demand deposit contract

•  Two (not mutually exclusive) mechanisms can lead to runs: –  Deterioration in fundamentals / bad future returns –  Externalities (payoff complementarities) induced by the behavior of other

investors. Runs become self-fulfilling prophesies •  During the days following Lehman bankruptcy (Sep 15, 2008):

large-scale withdrawals from prime money market funds

October 26, 2015 Runs on Money Market Mutual Funds Slide #7

MMMFs & the 2008 Liquidity Crisis –  9/15/2008: Lehman Brothers declared bankruptcy –  9/16/2008: Reserve Primary Fund held 1% Lehman securities

•  Reserve allowed redemptions at $1 per share prior to 3 pm; closing 4 pm NAV = $0.97 per share.

–  9/17/2008: Other funds close or require support (Putnam, Wachovia). –  9/19/2008: Treasury announces that it will guarantee certain money fund

assets •  Sets up voluntary insurance program for money funds with a NAV of at least $0.995 as

of 9/19/2008 •  Insurance is triggered when NAV falls below $0.995

–  9/19/2008: Fed announced “The Asset-Backed Commercial Paper Money Market Mutual Fund Liquidity Facility”

•  Fed funding of banks buying asset-backed CP from money funds –  October 7, 2008: Fed announced “The Commercial Paper Funding Facility”

•  Provide credit to a special purpose vehicle that would purchase three-month commercial paper from U.S. issuers

–  October 21, 2008: the Federal Reserve announced “The Money Market Investor Funding Facility”

•  Provide credit to a special purpose vehicle to purchase CDs, paper, etc., from MMMFs

8

Date in 2008

Dai

ly f

low

(%

of

prev

ious

day

TN

A)

Panel A: Daily flows (% of previous day TNA) by category

09/01 09/08 09/15 09/22 09/29 10/06 10/13 10/20 10/27−0.12

−0.1

−0.08

−0.06

−0.04

−0.02

0

0.02

0.04

0.06

0.08

Prime Institutional

Prime Retail

Govt Institutional

Govt Retail

Date in 2008

Cum

ulat

ive

flow

($

Bil

)

Panel B: Cumulative flows ($ Bil) by category

09/01 09/08 09/15 09/22 09/29 10/06 10/13 10/20 10/27−500

−400

−300

−200

−100

0

100

200

300

400

500

Figure 1: Daily Flows to/from money fund categories in September-October 2008

This figure plots summary statistics for flows to/from money market mutual funds in di↵erentcategories during September and October 2008. The top panel displays the daily percentagechange in total assets under management for each category, while the bottom panel plots thecumulative change in assets under management for each category, in billions of dollars, over thetwo-month period. September 15-22, 2008, the week following the failure of Lehman Brothers isindicated by blue shading.


Date in 2008

Dai

ly f

low

(%

of

prev

ious

day

TN

A)

Panel A: Daily flows (% of previous day TNA) by category

09/01 09/08 09/15 09/22 09/29 10/06 10/13 10/20 10/27−0.12

−0.1

−0.08

−0.06

−0.04

−0.02

0

0.02

0.04

0.06

0.08

Prime Institutional

Prime Retail

Govt Institutional

Govt Retail

Date in 2008

Cum

ulat

ive

flow

($

Bil

)

Panel B: Cumulative flows ($ Bil) by category

09/01 09/08 09/15 09/22 09/29 10/06 10/13 10/20 10/27−500

−400

−300

−200

−100

0

100

200

300

400

500

Figure 1: Daily Flows to/from money fund categories in September-October 2008

This figure plots summary statistics for flows to/from money market mutual funds in di↵erentcategories during September and October 2008. The top panel displays the daily percentagechange in total assets under management for each category, while the bottom panel plots thecumulative change in assets under management for each category, in billions of dollars, over thetwo-month period. September 15-22, 2008, the week following the failure of Lehman Brothers isindicated by blue shading.

Daily % Flows

Cumulative $ Flows


Outflows were very heterogeneous

Quan:lesofCumula:veFund-LevelInst.Flows9/15-9/19

Q1 -59%

Q5 -40%

Q10 -37%

Q25 -20%

Q50 -6%

Q90 +5%

Net

flo

w (

%)

Date

Quantiles of daily flows − Prime Institutional funds

09/01 09/08 09/15 09/22 09/29 10/06 10/13 10/20 10/27−0.2

−0.15

−0.1

−0.05

0

0.05

Q10Q50Q90

Net

flo

w (

%)

Date

Quantiles of daily flows − Prime Retail funds

09/01 09/08 09/15 09/22 09/29 10/06 10/13 10/20 10/27

−0.04

−0.03

−0.02

−0.01

0

0.01

0.02

0.03

0.04

Figure 2: Quantiles of Daily Flow Distributions by Category in September-October 2008 (in %)

This figure plots the 10th, 50th, and 90th quantiles of fund-level flows (as a fraction of fundassets) for September-October 2008. The top panel corresponds with Prime Institutional funds,and the bottom panel corresponds with Prime Retail funds. September 15-22, 2008, the weekfollowing the failure of Lehman Brothers is indicated by blue shading.

Daily % Flows for Prime Institutional Funds

Net

flo

w (

%)

Date

Quantiles of daily flows − Prime Institutional funds

09/01 09/08 09/15 09/22 09/29 10/06 10/13 10/20 10/27−0.2

−0.15

−0.1

−0.05

0

0.05

Q10Q50Q90

Net

flo

w (

%)

Date

Quantiles of daily flows − Prime Retail funds

09/01 09/08 09/15 09/22 09/29 10/06 10/13 10/20 10/27

−0.04

−0.03

−0.02

−0.01

0

0.01

0.02

0.03

0.04

Figure 2: Quantiles of Daily Flow Distributions by Category in September-October 2008 (in %)

This figure plots the 10th, 50th, and 90th quantiles of fund-level flows (as a fraction of fundassets) for September-October 2008. The top panel corresponds with Prime Institutional funds,and the bottom panel corresponds with Prime Retail funds. September 15-22, 2008, the weekfollowing the failure of Lehman Brothers is indicated by blue shading.

Daily % Flows for Prime Retail Funds

What Did We Learn from 2008? •  Prime MMMFs, as a sector, can have a liquidity

crisis •  More money left on Tuesday, relative to Monday,

indicating: –  Investors extrapolated troubles with Lehman

commercial paper to all financial commercial paper – Uncertainty about who held what contributed to a

run in a significant minority of Prime MMMFs – Management companies play key role in providing

implicit or explicit insurance for Prime MMMFs (so, their depth of pockets and incentives are important!) 11

Schmidt, Timmermann, and Wermers (2015) take a deeper look at the crisis week

•  Study Prime MMMF daily flows, for each share class of each fund

•  Identification: – Each share class has the same pro-rata claims on

cash flows from assets – We can identify strategic behavior among different

investors (different share classes), while keeping portfolio fundamentals constant

– With many MMMFs having this structure, we can independently vary fundamentals and investor type

12

Institutional investors in low expense ratio shareclasses had larger redemptions


n  Evidence of a nonlinearity: extremely low EXPR shareclasses had extremely large outflows, all else constant

Quantile Regression Results

15

16

2010 Reforms Detail

October26,2015 LiquidityManagementbyAssetManagers

17

2008 Crisis and Resulting Reforms Key 2010 Reforms

1.   liquidity provisions (reduced credit and interest rate risk)

2.   “know your investor” rules (prepared for redemptions)

3.   transparency (increase oversight and scrutiny of their portfolios)

4.   orderly closing process (reduce incentive to run)

5.   reducing the amount of lower-rated commercial paper (reduced credit and interest rate risk)

October26,2015 LiquidityManagementbyAssetManagers

18

MMMFs & Summer of 2011 Taxable Money Market Fund FlowsSum of rolling 5-business day change in assets, $ billions

Source: iMoneyNet

188

37-59

-369

-56

-61

4 8 10 12 16 18 22 24 26 30 2 6 8 10 15 2 6 8 10 14 16 20 22 24 28 30 5 7 11 13 15 19 21 25 27 29 2 4 8 10 12 16 18 22 24 26 30 1 6 8

Sep

2008

TotalPrimeGovernment

Oct2011

AugJulJun

U.S. Debt Ceiling (August 1, 2011)(June 30, 2011)(Sep 19, 2008)

Gallagher, Schmidt, Timmermann, and Wermers (2015) are first to examine actual investor “types”

in a “bank-run” scenario

•  In Schmidt, Timmermann, and Wermers (2015), investor “types” are assumed homogeneous within a given share class – Lower expense share classes are found to be more

likely to exhibit run behavior during the crisis

•  GSTW (2015) use a unique database that identifies, at a more granular level, investor types

19

Money Fund Data Source #1

–  Extensive panel dataset (iMoneyNet) of the vast majority of US money market mutual funds – Daily total net assets (TNA) of individual share classes

•  Money funds that predominantly cater to institutional investors

•  Money funds that predominantly cater to retail investors

– Some holdings statistics: •  % Maturing within 7 days •  % Treasury •  % Commercial paper •  Weighted average maturity

– Multiple share classes per fund


–  Portfolio holdings data (monthly) from Form N-MFP •  Detailed data on issuer and security type (commercial paper,

repo, Treasury, CD) •  Available since November 2010


–  Unique additional database from the Investment Company Institute (collected from its members)

•  Year-end shareholdings from different types of investors

within each MMMF shareclass: –  Corporations, non-financial –  Corporations, financial –  Retirement accounts –  Nonprofit accounts –  Fiduciary accounts –  State and local governments –  Retail brokerage-directed accounts –  Retail individual-directed accounts

Some Results of GSTW (2015)

23

Ownership of All Prime Funds


Distribution of True Institutional Ownership Across Prime Funds (Year-end 2013)


Table 9


Flows are a Non-Linear Function of True Institutional Ownership

27Control variables: gross yield, port. liquidity, WAM, TNA, minimum investment

Certain Institutions are Most Likely to Run


Retail Investors Don’t Run (or, Even “Walk”)!


Effect of IO on Portfolio Holdings

30Interpretation: Q5 spends more time in Repo—reaching for yield, and in Banks

Non-MMMF Mutual Funds (e.g., equity funds, debt funds,

liquid-alt funds)

Funding Liquidity

•  Several papers, e.g., Sirri and Tufano (1998) find that retail investors exhibit a convex flow-performance relation (chase winners, do not disinvest from losers)

•  Del Guercio and Tkac (2000) find that institutions exhibit a more linear flow-performance relation

•  Overall, analogous to what we find in MMMFs

October26,2015 LiquidityManagementbyAssetManagers 32

Asset Liquidity •  SEC found that funds sell most liquid assets upon

redemptions, not a strip of everything •  Equity funds—no worries (so far) •  Bond funds--???—could be a problem if massive

outflows follow rate hikes –  Empirical evidence: Goldstein, Jiang, and Ng (2015) paper

on flows in corporate bond funds –  Finds that bond funds exhibit a concave flow-performance

relation, and more concave when more institutions own the fund

•  Liquid alt mutual funds: anybody’s guess, but, so far, this segment is small


New SEC Proposals

•  1. Investment Company Reporting Modernization (Implemented)

Monthly holdings disclosure (without delay to SEC)

Monthly risk metrics to SEC •  2. Liquidity Management Rules (Proposed)

– Assessment of %-age of portfolio in each liquidity bucket (e.g., 1-day liquidity, 2-3 days, 4-7 days)

– Swing pricing October26,2015 LiquidityManagementbyAssetManagers 34

•  ETFs represent 25-40% of total market volume in the U.S. !!! (Great reference: CFA monograph on ETFs published online about a month ago, authored by Joanne Hill of ProShares)

•  Growing concern that speculative (noise) trading in ETFs is creating liquidity problems in the underlying cash securities

•  Massouwi, et al. (2015) find cross-sectional relation between volatility of daily returns and institutional ownership


•  Wermers and Xue (2015) are examining intraday impulse-response functions, a la Hasbrouck (2003) – Response of cash index prices when high noise

trader buying in ETF, vs. informed trader buying in ETF

•  Results: – 1. When noise traders heavily purchase ETF, there is

a temporary price dislocation of cash index (~1 minute)

– 2. When informed traders heavily purchase ETF, there is a more permanent price impact (at least 10 minutes, and maybe more)


Hedge Funds

Extracts from Form PF


Hedge Fund Liquidity

•  OFR researchers are studying the relationship between investor share liquidity and portfolio liquidity in private funds, using Form PF

•  Early empirical results suggest that portfolio liquidity and investor liquidity are strongly correlated across funds. This suggests that funds specialize in strategies with different liquidity profiles and investors select funds based on their anticipated liquidity needs. In such models, funds that expect to trade more heavily in less liquid assets impose tighter share restrictions on their investors


Some Lessons Learned


A Model of Investor Behavior 𝑓𝑙𝑜𝑤↓1,𝑡 =𝑓(𝐸[𝑓𝑢𝑛𝑑𝑙𝑖𝑞↓𝑡+1 ], 𝐸[𝑎𝑠𝑠𝑒𝑡𝑙𝑖𝑞↓𝑡+1 ]) 𝑓𝑙𝑜𝑤↓2,𝑡 =𝑔(𝐸[𝑓𝑢𝑛𝑑𝑙𝑖𝑞↓𝑡+1 ], 𝐸[𝑎𝑠𝑠𝑒𝑡𝑙𝑖𝑞↓𝑡+1 ]) 𝑓𝑢𝑛𝑑𝑙𝑖𝑞↓𝑡+1 =ℎ(𝑓𝑢𝑛𝑑𝑙𝑖𝑞↓𝑡 ,𝑓𝑙𝑜𝑤↓1,𝑡 ,𝑓𝑙𝑜𝑤↓2,𝑡 ) 𝑤ℎ𝑒𝑟𝑒 𝑓𝑢𝑛𝑑𝑙𝑖𝑞=𝑓𝑢𝑛𝑑𝑖𝑛𝑔 𝑙𝑖𝑞𝑢𝑖𝑑𝑖𝑡𝑦↓  𝑎𝑛𝑑 𝑎𝑠𝑠𝑒𝑡𝑙𝑖𝑞=𝑝𝑜𝑟𝑡𝑓𝑜𝑙𝑖𝑜 ℎ𝑜𝑙𝑑𝑖𝑛𝑔𝑠 𝑙𝑖𝑞𝑢𝑖𝑑𝑖𝑡𝑦 So, these relationships are interconnected, and feedback on each other (a la Brunnermeier and Pedersen, 2009)


Goals of Liquidity-Based Regulations

•  The goal of any liquidity-based regulation should be to make it easy for all investors to determine a fund’s liquidity each day, but to make it hard for some investors to have advantageous information and/or higher attentiveness; alternatively, to impose a time-varying redemption cost that takes away any “first-mover” advantage (e.g., swing pricing)


But, Really!

•  However, this is too idealistic •  Realistic constraints on funds and investors help

to reduce the potential for harmful “run-like” behavior


Regulations that Constrain Funds Should Be Procyclical

•  Force increased liquidity during up-market periods


Regulations that Constrain Investors Should Be Countercyclical

•  Redemption fees should increase in down-markets

•  Redemption gating should be possible in down-markets

•  Swing-pricing should be more severe in down-markets


Where is Research Needed? •  MMMFs: Effect of new regime, starting Oct

2016, on liquidity and potential for investor runs •  Mutual Funds: Effect of new monthly

disclosure, new liquidity rules •  ETFs: effect of growing scale and scope on

cash securities markets •  Hedge Funds: New Form PF data (but, must

spend significant visit at SEC or OFR) •  Separate Accounts: Made possible by new

disclosure rules 48October26,2015 LiquidityManagementbyAssetManagers

Conclusions •  Many changes in the MMMF landscape

–  2008 MMMF crisis, 2010 Amendments to Rule 2A-7, 2011 Eurozone and Debt-Ceiling crises, 2014 Amendments to Rule 2A-7

–  Liquidity seems to have improved, but no big crisis to stress test

•  Mutual funds, especially bond funds and “liquid alts,” present liquidity risks in a stress event

•  Hedge funds


Conclusions (continued)

•  Truly exciting and opportune time to study asset management issues, especially liquidity and systemic risks


“liquidity management by asset managers”...4th luxembourg asset management summit october 22,...

Documents