Apoorva JavdekarHow Does Mutual Fund Reputation Affect Subsequent Fund Flows?

How Does Mutual Fund Reputation Affect Subsequent
Fund Flows?
Apoorva Javadekar
Boston University
February8, 2016
Apoorva Javadekar (Boston University) Reputation and Fund Flows February 8, 2016 1 / 34

Introduction
Motivation I:Why Study Mutual Funds?
1 Mutual Funds: Important Vehicle of Investment
Manage 15Tr $
Mutual funds owns30% USequities Vs20%direct holdings
46% of US householdown mutual funds
2 Understand BehavioralPatterns:
Investors learn about managerial ability through returns
⇒ fund flows shedlight on learning, information processingcapacities
etc.
3 Fund Flows AffectManagerial RiskTaking
90% funds managers paid as a % of assets
⇒ flow patterns can affect risk taking
⇒ impacts on asset prices

Introduction
Motivation II:The Paper
1 Existing Literature:
Studies link between fund performanceand fund flows (flow-schedule)
Finds andrationalizes evidenceof return chasing andconvexity in fund
flows
But not muchis known about the importance of performancehistory
(reputation)
2 This Paper:
Explore the role of reputation for fund flows
How history up to t − 1 affect link between time t performanceand
time t + 1flows
Can we explain the evidence?

Introduction
Role of Reputation
Betterunderstanding ofmanagerial incentives:
High reputation ⇒ Low P(Getting Fired) (Khorana; 1996, Kostovetsky;
2011)
My sample:30% of the fired managersbelong to bottom 20%
reputation rank
But compensation too determine the incentives andflows affect
compensation
⇒ Important to know how reputation affect flows
But can reputationaffect flows?
Investor Heterogeneity ⇒ investor composition is history-specific
⇒ subsequentreactions to fund performancebecome history-specific

Introduction
Agenda
Empirical Evidence
Model
Testing model predictions in data
Tests to check validity of model mechanism

Introduction
Literature Review
Return Chasing and flowconvexity:
Ippolito (1992), Sirri & Tuffano (1998), Chevallier & Ellison (1997)
Lack Performance Persistence:
Carhart (1997), Bollen & Busse(2004) test short andmedium term
persistence
Risk shifting due to convexflows:
Brown, Harlow, Starks (1996), Basak (2012)
Theoretical Models:
Berk & Green (2004): rationalizes lack of persistence andreturn chasing
simultaneously using decreasingreturns and competitive capital supply
Lynch & Musto (2003): explains convexity using manager replacement
Berk& Tonks(2007): repeat losershaveinsensitive flows to the
left of flow-schedule

Empirical Evidence
Variables
Fund Flows:
FLOWit =
qit − [qit−1 × (1 + rit )]
it−1 × itq (1 + r )
where rit denotes net of expense fund returns during time t and qit
denotes fund assets at the end of time t.
Fund Performance:
Rankswithin same’investmentobjective’ basedon raw net returns
(Sirri & Tuffano; 1998)
Ranks based upon ’CAPM-Alpha’ (Berk & Binsbergen; 2014)
Ranks are normalized to lie between [0, 1]interval.
Current Performance (Perfit ): Based upon current year t
Reputation(reputeit): Basedupon 5yearwindow ending with
current yeart.

Empirical Evidence
Summary Statistics
Reputation Excess αLT Exp Front Turn σLT Size Age
RetLT Ratio Load over Mn$ Years
Low
Mean -0.042 -0.038 0.013 0.038 0.886 0.186 670.933 17.268
Median -0.041 -0.037 0.013 0.041 0.700 0.176 122.750 12.000
Med
Mean
-0.003 -0.001 0.012 0.038 0.715 0.172 1329.879 17.335
Median -0.007 -0.004 0.012 0.043 0.550 0.167 208.500 12.000
Top
Mean
0.042 0.041 0.012 0.035 0.702 0.175 2019.931 16.014
Median 0.031 0.032 0.012 0.038 0.520 0.170 351.650 11.000
FullSample
Mean
0.000 0.002 0.012 0.037 0.743 0.175 1368.062 17.027
Median -0.005 -0.002 0.012 0.042 0.570 0.169 211.475 12.000

Empirical Evidence
Basic Regression Framework
FLOWit+1
Objective:Assesimpact of reputation starting at time t on
flow-schedule for the period t + 1
Regression:
5 5
j=2 j=2
= a + φjQjit + ψj (Qjit × reputeit−1)
+(γ × reputeit−1) + controlsit + εit+1
Qjit denotes dummyfor jth quantile of Perfit
Regressionof t + 1flows on time t recent performancegiven
reputation starting at time t
Regressionfor eachquantile of Perfit to account for non-linearity
(Chevallier & Ellison; 1997)

Empirical Evidence
Regression Output: Fund Flows
PanelA: Raw Returns
0.034***
(0.006)
0.084***
(0.007)
0.124***
(0.007)
0.241***
(0.010)
0.037***
(0.006)
0.090***
(0.007)
0.130***
(0.007)
0.246***
(0.010)
0.202***
(0.013)
Q2t − Q1t
Q3t − Q1t
Q4t − Q1t
Q5t − Q1t
reputet−1
reputet−1 × (Q2t − Q1t )
reputet−1 × (Q5t − Q1t)
0.013
(0.011)
0.032***
(0.012)
0.050***
(0.014)
0.107***
(0.018)
0.083***
(0.015)
0.043**
(0.019)
0.108***
(0.021)
0.149***
(0.026)
0.261***
(0.033)
Intercept 0.056
(0.037)
-0.067*
(0.035)
-0.008
(0.035)
Adj R2 0.176 0.208 0.215
Results

Empirical Evidence
Main Results Regression Table
Result1: Significant return chasing effectignoring reputation
interactions andeven after controlling for reputation
Result2: Return chasing effectis reducedby morethan half after
including reputation interactions
Result 3: All the interaction terms are large and significant
Significant =⇒ (Qj − Q1|repute = high) > (Qj − Q1|repute = low ).
Large=⇒ Interaction effectmoreimportant than return chasing
effect
Result4: Coefficients on Interaction term risemonotonically with
performance
⇒ Flow-Schedule more sensitive for higher reputed funds
Flow-schedule sensitive even at the lower end for high reputation fund.

Empirical Evidence
Flow-Schedule Graph

Empirical Evidence
Example
Best Fund: Q5t = 1and reputet−1 = 0.90
WorstFund: Q1t = 1andreputet−1 = 0.10
∆FLOW ≡ FLOW (Best) − FLOW (Worst) = 40.8%
Break-Up:
Source Contribution
∆FLOW Due to Return Chasing Effect 10.7%
∆FLOW Dueto Reputation Effect 6.6%
∆FLOW Due to Interaction Effect 23.50%

Empirical Evidence
Robustness Checks
Change in Market Share as dependent variable (Spiegel & Zhang;
2012) Result
Resultsvalid across age andsize categories Result
Results valid even if recent performance is computed over a longer
horizon Result

Empirical Evidence
Model

Model
Set-Up
Manager with unknown skill α andgenerates gross return as
Rt = α + εt
with
tε ∼ N 0,σ2
ε
. .
Convexcostofactivemanagement: C (x ) = ηx2
Net Return Process:
rt = ht−1Rt − f −η
.
(ht 1 × qt− −
qt −1
1)2
.
where ht−1 denotes actively managed share of assets during time t

Model
Investors and Beliefs
Investors:
Unit mass of risk neutral investors
µ fraction of Always Attentive (AA)
1 − µ fraction Occasionally Attentive (OA)
Eachperiod, P(attention|OA) = δ< 1
Haveinfinitely deep pockets
Beliefs AboutManagerial Skill: At the end of time t
t
2
tα ∼ N(φ , σ)
⇒
Et (α) = Et (Rt+1) = φt

Model
Mechanism I
Equilibrium ConditionWhenδ = 1: (Berk & Green; 2004)
Et (rt+1|ht, φt)=0
Deep pockets ensure that fund receive required inflows
Full attention ensures that no investor invests in negative NPV
manager.
Equilibrium Condition Whenδ <1:
Et (rt+1|ht, φt)≤0
Deep pockets ensure that no positive expected NPV project exists
Inattentive investors ⇒ capital outflows could belessthan required to
attain zero NPV condition
Inattention=⇒ Over-Sized funds relative to competitive
benchmark.

Model
Mechanism II
Inequilibrium:Low reputation funds predominantly owned by
OA-types
Because AA-types are fast to move out of poor performing funds
Implications ForFlows:
Dampenedoutflows after yet another bad performanceby low
reputation funds
Over-Sized ⇒ Low required inflows after a good performance
Implications for Persistence:
Over-Sized⇒ Low reputation funds must under-perform

Model
Solution With δ < 1
Initial Investor Composition: A investor’s ownership at t = 0 is
λ0 =
µ
µ + (1 − µ)δ
s
F
¸¸
EcAttentive raction In
x
onomy
Competitive Size and Flows: qt
∗satisfy
tEt [rt+1|ht,q∗] = 0
andrequired flows
e∗ ∗
t = qt − qt−1(1 + rt )
Attentive Capital:
zt = λt −1 t−1+ (1 − λ )δ
A
s
ttentive Fraction
¸¸
Within
x
Fund
t −1 tq (1 + r )

Model
Investor Composition
Outflows⇒ λt <λt−1
If fund has enoughattentive capital:
λt−1
AA’s Contribution To Outflows = >λ
λt−1 + (1 − λt−1)δ
t −1
tIf zt < |e∗|⇒ λt = 0 as everyattentive investor liquidates
Inflows⇒λt >λt−1
AA-type contribute λ0 of newcapital andoutflows reduceλ ⇒ λ0 is
upper limit of λt−1
λt is a weighted average of λ0 and λt−1
⇒ λt ∈(λt−1, λ0)
Persistent outflows ⇒ Highfraction ofInattentive Investors

Model
Learning and Fund Flows
Belief Updates:
φt = φt−1+
h
.
2σt −1
σ2 2
t−1 + σε
..
rt − Et−1(rt )
.
t−1
s
=ω
¸¸ x
t − 1
⇒ ∆φt bigger for over-sized funds as Et−1(rt ) < 0
t −1Fund Flows: Let qt−1 = q∗ × (1 + ψt−1)
If capital adjustment is complete
FFt =
− 2f 2
.
1 + ωt 1
.
rt + ψt − 1
. . 2
(1 + ψt −1 t)(1 + r)
−1
In case zt is not enoughto support outflows
tFF = −
zt
t t +1q (1 + r )

Model
Fund Flows Continued
=⇒ FlatLimited Outflows: Low reputed funds ⇒ low λt−1
flow-schedule on the left tail
Dampened Inflows:
Over-SizeEffect: Low reputed fund ⇒ ψt−1 > 0 =⇒ required
t t
inflows e∗ = q∗ − qt−1(1 + rt ) aresmaller comparedto competitively
sized fund
tLearning Effect: Et−1(rt ) < 0 ⇒ q∗ itself is pushed up for a given rt
t
⇒ e∗ is higher for a given rt
For reasonableparametervalues, Over-Size effectdominatesLearning
effect

Model
Flows With Various Parameter Values

Model
Performance Persistence
Reputati
on
Decile
Marke
t
Beta
SMB
Beta
HML
Beta
Momentu
m Beta
4-
facto
r
Alpha
N Ad
j
R2
D1 (Low) 1.00426*** 0.16568*** -0.02126 0.00836 -0.00137*** 420 0.968
(0.01232) (0.01845) (0.02147) (0.01435) (0.00045)
D2 1.00323*** 0.17559*** -0.00004 0.02108 -0.00138*** 420 0.976
(0.00988) (0.01873) (0.01886) (0.01535) (0.00039)
D3 1.01012*** 0.14140*** 0.02330 0.01872 -0.00118*** 420 0.976
(0.01136) (0.01883) (0.02081) (0.01400) (0.00040)
D4 0.98307*** 0.13459*** 0.03731** 0.00185 -0.00060* 420 0.978
(0.01017) (0.01757) (0.01775) (0.01180) (0.00035)
D5 0.97228*** 0.13435*** 0.02788 0.00757 -0.00059 420 0.975
(0.01108) (0.02109) (0.01739) (0.01116) (0.00037)
D6 0.96283*** 0.08781*** 0.00442 -0.00417 -0.00039 420 0.972
(0.01688) (0.02009) (0.01763) (0.01291) (0.00045)
D7 0.96463*** 0.13536*** 0.01433 0.00991 -0.00022 420 0.974
(0.01140) (0.01836) (0.02146) (0.01302) (0.00040)
D8 0.97028*** 0.16909*** -0.01974 0.01421 -0.00048 420 0.977
(0.01387) (0.01493) (0.01666) (0.01190) (0.00041)
D9 0.94807*** 0.17254*** -0.02423 -0.00728 0.00023 420 0.972
(0.01533) (0.01826) (0.02095) (0.01340) (0.00044)
D10(Top) 0.98846*** 0.20101*** -0.00393 -0.01694 -0.00018 420 0.969
(0.01092) (0.02160) (0.01902) (0.01344) (0.00044)

Model
Calibration Exercise
Parameter Value Source
f
ψlow
ωt =
.
t − 1
.
σ2
σ2 2
t−1
+σε
δlow (1 − λlow ) + λlow
δhigh(1 − λhigh) + λhigh
1.76% Data (including loads)
0.93 See below
0.0955 Berk, Green (2004)
0.18 Moment Fitting
0.49 Moment Fitting
Size Distortionψt:
t t +1¸¸ x
s
−1.64%
2 ∗
t t tE (r ) = −ηh q ψ = − fs¸¸x
1.76%
×ψt

Model
Experiments To Validate Model Mechanism
HeterogeneityinInvestors⇒ HeterogeneityinFlows
What events damp this heterogeneity?
Managerial Replacement:⇒media news, and other soft information
⇒ higher investor attention even from otherwise inattentive investors
⇒ dampened investor heterogeneity
LargeFront LoadsLargefront loads ⇒ potentially moreattention by
investors
In both thesecases,interaction between reputation and recent
performancemust lose its importance.
Replacement front loads

Model
Concluding Remarks
Returnchasinggetsstronger with reputation Persistence
in poor performance for low-reputation funds
Simple model with inattentive investors explains the heterogeneity in
flow-schedule
Interesting to study risk shifting conditional on reputation

Model
Thank You !

Model
Regression With Change in Market Share
PanelA: Raw Returns PanelB: CAPM-Alpha
0.042
(0.026)
0.107***
(0.032)
0.258***
(0.033)
0.510***
(0.046)
0.061**
(0.026)
0.131***
(0.036)
0.276***
(0.035)
0.490***
(0.047)
Q2t − Q1t
Q3t − Q1t
Q4t − Q1t
Q5t − Q1t
reputet−1
-0.125***
(0.046)
-0.186**
(0.079)
-0.158***
(0.051)
-0.167**
(0.070)
-0.048
(0.060)
0.326***
(0.098)
0.577***
(0.169)
0.811***
(0.124)
1.309***
(0.186)
-0.085*
(0.044)
-0.130*
(0.071)
-0.110**
(0.053)
-0.149**
(0.069)
-0.023
(0.066)
0.297***
(0.088)
0.517***
(0.166)
0.753***
(0.121)
1.195***
(0.186)
Intercept -0.189
(0.240)
-0.217
(0.223)
-0.220
(0.231)
-0.305
(0.221)
Adj R2 0.062 0.088 0.055 0.077
Back to Robustness

Model
Age And Size Robustness With Raw Returns
PanelA: Age Bins PanelB: Size Bins
Q2t − Q1t
Q3t − Q1t
Q4t − Q1t
Q5t − Q1t
reputet−1
Young=1
0.004
(0.024)
0.029
(0.026)
0.057*
(0.030)
0.157***
(0.039)
0.075***
(0.028)
0.042
(0.038)
0.126***
(0.042)
0.177***
(0.052)
0.268***
(0.066)
Young=0
0.011
(0.012)
0.035***
(0.013)
0.046***
(0.014)
0.087***
(0.019)
0.095***
(0.018)
0.048**
(0.021)
0.089***
(0.024)
0.125***
(0.026)
0.237***
(0.036)
Small=1
-0.001
(0.015)
0.016
(0.017)
0.041*
(0.023)
0.116***
(0.028)
0.056**
(0.024)
0.058*
(0.031)
0.164***
(0.036)
0.214***
(0.053)
0.323***
(0.057)
Small=0
0.034**
(0.016)
0.045***
(0.017)
0.048***
(0.017)
0.086***
(0.021)
0.093***
(0.020)
0.014
(0.026)
0.070**
(0.028)
0.122***
(0.028)
0.246***
(0.037)
Intercept 0.096
(0.122)
-0.076**
(0.039)
0.024
(0.057)
-0.044
(0.041)
Adj R2 0.209 0.234 0.181 0.268
Back to Robustness

Model
Longer Horizon For Recent Performance
Panel A: Raw Returns PanelB: CAPM-Alpha
0.019**
(0.008)
0.060***
(0.009)
0.101***
(0.009)
0.217***
(0.013)
0.039***
(0.008)
0.058***
(0.008)
0.123***
(0.010)
0.212***
(0.013)
0.008
(0.008)
0.042***
(0.009)
0.074***
(0.009)
0.177***
(0.013)
0.158***
(0.014)
0.029***
(0.008)
0.041***
(0.008)
0.097***
(0.010)
0.173***
(0.013)
0.156***
(0.014)
Q2t − Q1t
Q3t − Q1t
Q4t − Q1t
Q5t − Q1t
reputet−2
0.005
(0.015)
0.021
(0.016)
0.024
(0.018)
0.048*
(0.028)
0.066***
(0.022)
0.022
(0.029)
0.063**
(0.030)
0.117***
(0.031)
0.230***
(0.043)
0.001
(0.014)
0.017
(0.017)
0.035*
(0.018)
0.034
(0.028)
0.040*
(0.022)
0.079***
(0.027)
0.076**
(0.030)
0.144***
(0.031)
0.257***
(0.044)
Intercept 0.035
(0.036)
-0.035
(0.036)
-0.005
(0.036)
0.002
(0.036)
-0.074**
(0.036)
-0.037
(0.037)
Adj. R2 0.329 0.343 0.347 0.326 0.339 0.344
Back to Robustness

Model
Regression With Managerial Replacement
Panel A: RawReturns Panel B: CAPM-α
Replacement Yes No Yes No
Perft 0.135** 0.123*** 0.169*** 0.146***
-0.052 -0.029 -0.061 -0.033
reputet−1 0.007 0.034 -0.013 -0.036
-0.046 -0.024 -0.047 -0.023
Perft× reputet−1 0.196* 0.313*** 0.104 0.280***
-0.102 -0.05 -0.099 -0.052
Intercept -0.123 0.008 -0.084 -0.009
-0.087 -0.043 -0.088 -0.045
N 1136 7014 1136 7014
Adj R2 0.158 0.21 0.152 0.208
Back to Experiments

Model
Regressions Across Fee Structures
Panel A: RawReturns Panel B: CAPM-Alpha
FrontLoad Low High Low High
Perft 0.171*** 0.153*** 0.167*** 0.166***
(0.042) (0.039) (0.049) (0.039)
reputet−1 0.054 0.096*** 0.058 0.098***
(0.036) (0.035) (0.037) (0.032)
Perft× reputet−1 0.268*** 0.140** 0.222*** 0.102
(0.071) (0.066) (0.081) (0.067)
Intercept 0.106 -0.057 0.108 -0.092
(0.085) (0.066) (0.085) (0.066)
N 2581 2785 2581 2785
Adj R2 0.239 0.169 0.223 0.164
Back to Experiments

Apoorva JavdekarHow Does Mutual Fund Reputation Affect Subsequent Fund Flows?

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