Apoorva JavdekarHow Does Mutual Fund Reputation Affect Subsequent Fund Flows?

How Does Mutual Fund Reputation Affect Subsequent Fund Flows?

Apoorva Javadekar

Boston University

February 8, 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 InvestmentManage 15 Tr $Mutual funds owns 30% US equities Vs 20% direct holdings 46% of US household own mutual funds

2 Understand Behavioral Patterns:Investors learn about managerial ability through returns⇒ fund flows shed light on learning, information processing capacities etc.

3 Fund Flows Affect Managerial Risk Taking

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 performance and fund flows (flow-schedule)Finds and rationalizes evidence of return chasing and convexity in fund flowsBut not much is known about the importance of performance history (reputation)

2 This Paper:Explore the role of reputation for fund flowsHow history up to t − 1 affect link between time t performance and time t + 1 flowsCan we explain the evidence?


Introduction

Role of Reputation

Better understanding of managerial incentives:High reputation ⇒ Low P(Getting Fired) (Khorana; 1996, Kostovetsky; 2011)My sample: 30% of the fired managers belong to bottom 20% reputation rankBut compensation too determine the incentives and flows affect compensation⇒ Important to know how reputation affect flows

But can reputation affect flows?Investor Heterogeneity ⇒ investor composition is history-specific⇒ subsequent reactions to fund performance become history-specific


Introduction

Agenda

Empirical Evidence ModelTesting model predictions in dataTests to check validity of model mechanism


Introduction

Literature Review

Return Chasing and flow convexity:Ippolito (1992), Sirri & Tuffano (1998), Chevallier & Ellison (1997)Lack Performance Persistence:Carhart (1997), Bollen & Busse (2004) test short and medium term persistenceRisk shifting due to convex flows:Brown, Harlow, Starks (1996), Basak (2012)Theoretical Models:

Berk & Green (2004): rationalizes lack of persistence and return chasing simultaneously using decreasing returns and competitive capital supplyLynch & Musto (2003): explains convexity using manager replacement

Berk & Tonks (2007): repeat losers have insensitive flows to the left of flow-schedule


Empirical Evidence

VariablesFund Flows:

FLOW i t =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:

Ranks within same ’investment objective’ based on 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 ): Based upon 5 year window ending with current year t .Apoorva Javadekar (Boston University) Reputation and Fund Flows February 8, 2016 7 / 34

Empirical Evidence

Summary Statistics

Reputation Excess αLT Exp Front Turn σLT Size AgeRetLT Ratio Load over Mn$ Years

LowMean -0.042 -0.038 0.013 0.038 0.886 0.186 670.933 17.268Median -0.041 -0.037 0.013 0.041 0.700 0.176 122.750 12.000

MedMean -0.003 -0.001 0.012 0.038 0.715 0.172 1329.879 17.335Median -0.007 -0.004 0.012 0.043 0.550 0.167 208.500 12.000

TopMean 0.042 0.041 0.012 0.035 0.702 0.175 2019.931 16.014Median 0.031 0.032 0.012 0.038 0.520 0.170 351.650 11.000

Full SampleMean 0.000 0.002 0.012 0.037 0.743 0.175 1368.062 17.027Median -0.005 -0.002 0.012 0.042 0.570 0.169 211.475 12.000


Empirical Evidence

Basic Regression Framework

FLOW i t +1

Objective: Asses impact of reputation starting at time t on flow-schedule for the period t + 1Regression:

5

5j=2j=2

= a +

φj Qj i t +

ψj (Qj i t × reputeit−1)+(γ × reputeit−1) + controlsit + εit+1

Qj i t denotes dummy for j th quantile of Perfit

Regression of t + 1 flows on time t recent performance given reputation starting at time tRegression for each quantile of Perfit to account for non-linearity (Chevallier & Ellison; 1997)


Empirical Evidence

Regression Output: Fund Flows

Panel A: 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 )

repute t−1 × (Q3t −

Q1t ) repute t−1 × (Q4t

− 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

Result 1: Significant return chasing effect ignoring reputation interactions and even after controlling for reputation

Result 2: Return chasing effect is reduced by more than half after including reputation interactions

Result 3: All the interaction terms are large and significantSignificant =⇒ (Q j − Q1|repute = high) > (Q j − Q1|repute = low ).Large =⇒ Interaction effect more important than return chasing effect

Result 4: Coefficients on Interaction term rise monotonically with performance

⇒ Flow-Schedule more sensitive for higher reputed fundsFlow-schedule sensitive even at the lower end for high reputation fund.


Empirical Evidence

Flow-Schedule Graph


Empirical Evidence

Example

Best Fund: Q5t = 1 and reputet−1 = 0.90

Worst Fund: Q1t = 1 and reputet−1 = 0.10

∆FLOW ≡ FLOW (Best) − FLOW (Worst ) = 40.8%

Break-Up:Source Contribution∆FLOW Due to Return Chasing Effect 10.7%

∆FLOW Due to 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

Results valid across age and size categories

Result

Results valid even if recent performance is computed over a longerhorizon

Result


Empirical Evidence

Model


Model

Set-Up

Manager with unknown skill α and generates gross return as

R t = α + εtwith

tε ∼ N 0, σ

2ε

. .

Convex cost of active management: C (x ) = ηx 2

Net Return Process:rt = ht−1Rt − f − η

.(h t 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 investorsµ fraction of Always Attentive (AA)1 − µ fraction Occasionally Attentive (OA) Each period, P(attention|OA) = δ < 1 Have infinitely deep pockets

Beliefs About Managerial Skill: At the end of time t

t2tα ∼ N(φ , σ )

⇒E t (α) = E t (R t+ 1) = φt


Model

Mechanism IEquilibrium Condition When δ = 1: (Berk & Green; 2004)

E t (rt+1|ht, φt )=0

Deep pockets ensure that fund receive required inflowsFull attention ensures that no investor invests in negative NPVmanager.

Equilibrium Condition When δ < 1:E t (rt+1|ht, φt )≤0

Deep pockets ensure that no positive expected NPV project existsInattentive investors ⇒ capital outflows could be less than required to attain zero NPV condition

Inattention =⇒ Over-Sized funds relative to competitive benchmark.


Model

Mechanism II

In equilibrium: Low reputation funds predominantly owned by OA-types

Because AA-types are fast to move out of poor performing funds

Implications For Flows:Dampened outflows after yet another bad performance by low reputation fundsOver-Sized ⇒ Low required inflows after a good performance

Implications for Persistence:Over-Sized ⇒ Low reputation funds must under-perform


Model

Solution With δ < 1Initial Investor Composition: A investor’s ownership at t = 0 is

λ0 =µ

µ + (1 − µ)δs

F ¸̧

EcAttentive raction In

x onomy

Competitive Size and Flows: qt∗

satisfytE t [rt+1|ht, q∗] =

0and required flows

e∗∗

t = qt − qt−1(1 + rt )Attentive

Capital:zt = λt −1

t−1 + (1 − λ ) δA

sttentive Fraction¸̧

Within xFund

t −1 t q (1 + r )


Model

Investor Composition

Outflows ⇒ λt < λt−1

If fund has enough attentive capital:

λt−1

AA’s Contribution To Outflows = >λ λt−1 + (1 − λt−1)δ

t −1

tIf zt < |e∗| ⇒ λt = 0 as every attentive investor liquidates

Inflows⇒ λt > λt−1

AA-type contribute λ0 of new capital and outflows reduce λ ⇒ λ0 is upper limit of λt−1

λt is a weighted average of λ0 and λt−1

⇒ λt ∈ (λt−1, λ0)

Persistent outflows ⇒ High fraction of Inattentive Investors


Model

Learning and Fund FlowsBelief Updates:

φt = φt−1 + h

.2

σt −1σ2 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

FF t =− 2f

2

.1 + ωt 1

. r t + ψ t − 1

. . 2

(1 + ψt −1 t)(1 + r ) − 1

In case zt is not enough to 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-Size Effect: Low reputed fund ⇒ ψt−1 > 0 =⇒ requiredt tinflows e∗ = q∗ − qt−1(1 + rt ) are smaller compared to

competitivelysized 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 reasonable parameter values, Over-Size effect dominates Learning effect


Model

Flows With Various Parameter Values


Model

Performance Persistence

Reputation Decile

Market Beta

SMBBeta

HMLBeta

Momentum Beta

4-factor Alpha

N Adj 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 2t−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 Distortion ψt

:tt +1 ¸̧ xs−1.64%

2 ∗t t tE (r ) = −ηh q ψ = −

fs¸¸x1.76%

×ψt


Model

Experiments To Validate Model Mechanism

Heterogeneity in Investors ⇒ Heterogeneity in Flows What events damp this heterogeneity?

Managerial Replacement:⇒ media news, and other soft information⇒ higher investor attention even from otherwise inattentive investors⇒ dampened investor heterogeneityLarge Front Loads Large front loads ⇒ potentially more attention by investors

In both these cases, interaction between reputation and recent performance must lose its importance.

Replacement front loads


Model

Concluding Remarks

Return chasing gets stronger with reputation Persistence in poor performance for low-reputation fundsSimple model with inattentive investors explains the heterogeneity in flow-scheduleInteresting to study risk shifting conditional on reputation


Model

Thank You !


Model

Regression With Change in Market Share


Panel B: 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


reputet−1 × (Q3t −

Q1t ) reputet−1 × (Q4t


− Q1t )

-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

Panel A: Age Bins

Panel B: Size Bins

Q2t − Q1t

Q3t − Q1t

Q4t − Q1t

Q5t − Q1t

reputet−1




− Q1t ) repute t−1 × (Q5t

− Q1t )

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 B: 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

repute t−2

repute t−2 × (Q2t − Q1t )




− Q1t )

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 B: CAPM-αReplacement Yes No Yes No

Perft 0.135** 0.123*** 0.169*** 0.146***

-0.052 -0.029 -0.061 -0.033reputet−1 0.007 0.034 -0.013 -0.036

-0.046 -0.024 -0.047 -0.023Perft× reputet−1 0.196* 0.313*** 0.104 0.280***

-0.102 -0.05 -0.099 -0.052Intercept -0.123 0.008 -0.084 -0.009

-0.087 -0.043 -0.088 -0.045N 1136 7014 1136 7014Adj R2 0.158 0.21 0.152 0.208

Back to Experiments


Model

Regressions Across Fee Structures


Panel B: CAPM-AlphaFront Load 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 2785Adj R2 0.239 0.169 0.223 0.164

Back to Experiments


Apoorva JavdekarHow Does Mutual Fund Reputation Affect Subsequent Fund Flows?

Economy & Finance

Transcript of Apoorva JavdekarHow Does Mutual Fund Reputation Affect Subsequent Fund Flows?