Reach for Yield in Investment Funds∗

Alexandru Barbu, London Business School†

Christoph Fricke, Deutsche Bundesbank‡

Emanuel Moench, Deutsche Bundesbank§

November 15, 2016

Preliminary, do not cite or circulate without permission

Abstract

Are low yields precipitating higher risk-taking among investment man-

agers? What are the mechanisms through which funds’ risk-taking operates?

And does such behavior in turn affect the pricing of risk? We tackle these

questions using a comprehensive data set on the security-level holdings of over

4500 German retail and institutional investment funds. We document deliber-

ate reach-for-yield across fund categories which intensifies as risk-free rates fall

and credit spreads become compressed. Trading at negative rates only adds

to this tendency, particularly in funds offering capital-protection clauses. In

retail funds, yield-chasing predicts inflows in calm periods, yet can backfire in

stress periods. In institutional funds, reaching for yield lowers the probability

of mandate termination. Finally, we provide evidence suggestive of feedback

loops from funds’ reach for yield to bond returns.

Keywords: Reach for yield, investment funds manager incentives, negative yields,

excess bond returns

JEL Classification: G11, G23, E43

∗We would like to thank Dimitris Georgarakos and Esteban Prieto as well as seminar participantsat Deutsche Bundesbank for valuable comments. We gratefully acknowledge estimation results fromterm structure models provided by Arne Halberstadt. The views stated in this paper are those ofthe authors and do not necessarily reflect the views of the Deutsche Bundesbank or the Eurosystem.†abarbu@london.edu‡christoph.fricke@bundesbank.de§emanuel.moench@bundesbank.de

1 Introduction

In most major developed economies yields on government and corporate bonds have

fallen to unprecedented low levels in recent years. While this decline of rates implies

that governments and enterprises have been able to issue debt at ever more favorable

rates, investors in bond markets have found it increasingly difficult to generate positive

returns on new investments. As a consequence, the profitability of banks, insurance

companies, and other financial intermediaries which hold a sizable portion of their

assets in fixed-income securities has come under pressure.

It is often reported that institutional investors respond to declining rates by shifting

their fixed-income portfolios towards higher-yielding but riskier securities, a behavior

commonly referred to as ”reach for yield”. For most types of intermediaries, regulation

curbs the riskiness of their investments. Since risk is difficult to measure, however, the

reach for yield by such investors may be intensified or even triggered by institutional or

regulatory constraints (see, e.g. Hanson and Stein (2015), Becker and Ivashina (2015),

DiMaggio and Kacperczyk (2016)).

But even in the absence of binding regulatory constraints, incentive problems in the

delegation of investment to asset managers can generate a desire to reach for yield (see,

e.g., Rajan (2005), Morris and Shin (2015)). As a result, the volatility of asset prices

(Guerrieri and Kondor (2012)) and the probability of sharp asset price reversals (Feroli

et al. (2014)) can increase, implying that reach for yield may have a bearing on financial

stability. Moreover, since expectations about future short term interest rates and term

premiums, which can both be heavily influenced by central banks, are two of the main

driving forces behind movements in bond yields, reach for yield may generate a tradeoff

between monetary and financial stability policies.

While asset managers’ incentives to reach for yield have been studied extensively

in the theoretical literature, little is known about how quantitatively important such

1

behavior is in practice. In this paper, we analyze this question using a unique dataset

on the security-level holdings of all German investment funds, retail and institutional.

This dataset, which is available since 2009, allows us to track the monthly changes

in individual funds’ portfolio allocations in response to changing rates, controlling for

fund and time-specific effects. The data further enable us to study the degree to which

the agency problems pointed out in the theoretical literature can explain the observed

reach for yield behavior. Specifically, we define reach for yield as a fund’s propensity to

shift its bond holdings towards higher-yielding securities from one month to the next.

Thus, we treat the fund’s previous month’s portfolio as an implicit benchmark. This

is in contrast to the prior literature, which has commonly defined reach for yield as

a portfolio shift towards higher-yielding securities relative to some benchmark index

portfolio, which however is often not explicitly observed.

Our results can be summarized as follows. We find strong evidence of reach for

yield among German investment funds in response to lower rates. In particular, both

a decline of risk-free government yields as well as a compression of credit spreads,

lead to a statistically and economically significant increase in fund portfolios towards

higher-yielding securities. Decomposing movements in the term structure of interest

rates on government bonds into shifts in expectations about future short rates as well

as term premiums, we find that it is primarily the former which elicit reach for yield

behavior. While we do not directly link these movements in expected future short rates

to monetary policy actions, our results are consistent with the evidence in DiMaggio

and Kacperczyk (2016) who find that money market fund portfolios become more risky

in response to announcements by the Federal Reserve that aimed at maintaining policy

rates low. It also lends empirical support to the notion that monetary policy relying

on forward-guidance can strongly impact the risk-taking of unlevered investors such as

asset managers (Morris and Shin (2015)).

2

The latter part of our sample covers the period when rates on many fixed-income

securities held by the funds in our dataset dropped below zero. We can thus study

whether reach for yield behavior amplifies at negative rates. We find that this is indeed

the case. Quite strikingly, this effect is more pronounced for funds which offer their

investors capital-protection clauses and are thus ostensibly less risky.

The theoretical literature has identified at least two different agency issues which

may give investment fund managers an incentive to reach for yield. The first are in-

centives arising from the agency relationship between a fund and its retail investors.

Specifically, when investors’ decisions to invest or withdraw from a fund depend on

its past returns, the fund managers is more likely to reach for yield. A second source

of implicit incentives stems from agency problems between institutional fund sponsors

and the fund manager. Here, career concerns – for example the prospect of promotion

or termination of the fund – may similarly affect fund managers incentive to reach for

yield. As our dataset covers both retail and institutional funds, we can empirically an-

alyze whether such mechanisms are empirically relevant for the documented reach for

yield behavior. While we find no impact of reach for yield behavior on retail fund flows

in the full sample, this masks an interesting and potentially perilous dynamic: retail

funds which top the reach for yield ranking see strong inflows in normal times, this

effect is reversed in times of market stress albeit at a smaller gradient. Thus, we em-

pirically confirm that retail fund managers reach for yield in order to generate inflows.

We also study the probability of termination for institutional fund mandates, and ask

whether reaching for yield decreases the probability of mandate termination, over and

above what is expected from the regular termination-performance relationship. We find

that funds topping the reach for yield ranking significantly decrease the probability of

being terminated, even after controlling for performance ranking. While this effect also

reverses in time of market stress, the benefit of reaching for yield in normal times again

3

dominates.

Having established that investment funds do reach for yield in response to falling

interest rates, in line with fund managers’ incentives, we finally study whether this

behavior in turn feeds back into bond prices. If it did, falling rates combined with reach

for yield by fund managers would to some extent be self-reinforcing. Focusing on Euro-

Area corporate bonds, we measure the investment fund sector’s buying pressures in a

specific security in a given month. We find that an increase in the fund sectors’ demand

for an individual security indeed leads to higher bond returns, even when controlling

for past returns, past flows, and standard risk factors. Combined, our results thus point

to the existence of feedback loops from lower rates to more bond buying to even lower

rates.

Our paper relates to several strands of the literature. The existence of reach for

yield has been documented for a variety of financial intermediaries. Cox (1967) uses

the term reach for yield to describe banks’ tendencies to lend to riskier borrowers in

order to increase their promised yield. Buch et al. (2014) document that small U.S.

banks increase their risk exposure in response to expansionary monetary policy shocks,

in line with reach for yield. Hanson and Stein (2015) similarly report that banks reach

for yield to manage their reported earnings. The pattern is strongest when the term

spread is widest. Becker and Ivashina (2015) find that U.S. insurance companies engage

in reach for yield within regulatory-confined risk categories. There, reach for yield

is most pronounced in economic expansions, and for companies with poor corporate

governance.

A growing literature argues that reach for yield tendencies may become particularly

relevant in an environment of low and stable interest rates, as market participants be-

come complacent about risk (Rajan (2005), Yellen (2011) and Borio and Zhu (2012)).

Stein (2013) reports “a fairly significant pattern of reaching-for-yield behavior emerging

4

in corporate credit”. Domanski et al. (2015) document a tendency of insurance compa-

nies and pension funds to tilt their portfolios to longer-dated bonds as yields decline.

DiMaggio and Kacperczyk (2016) show how U.S. money market funds shift their port-

folios towards riskier securities in response to Federal Reserve announcements of low

future interest rates. On the theory front, Acharya and Naqvi (2016) propose a model

of financial intermediation where reach for yield behavior can lead to the formation

of asset price bubbles. The paper most closely related to ours is Choi and Kronlund

(2016). Using a sample of U.S. corporate bond mutual funds, they find that reach for

yield tendencies intensify as interest rates fall. They also document a calendar effect -

reach for yield is strongest towards the end of year.

The remainder of this paper is organized as follows. In Section 2 we discuss our

data and the institutional setting in which German investment funds operate, and

introduce the measures of reach for yield used in the empirical analysis. Section 3

documents the empirical results on the relation between movements in bond yields and

reach for yield. Section 4 provides empirical evidence showing that fund managers’

have incentives to reach for yield in order to increase inflows by retail investors and to

reduce the probability of mandate termination by institutional investors. Finally, we

show in Section 5 that bonds which face stronger demand pressures by the investment

fund sector feature higher excess returns. Section 6 concludes.

2 Measuring Reach for Yield by Investment Funds

2.1 Data and Institutional Setting

The German law distinguishes between retail and specialized, or institutional funds.

Retail funds cater to retail and institutional investors alike. Their units can be acquired

by an indefinite number of investors, no single entity having a majority stake. Most

5

retail funds are regulated under the more restrictive UCITS (Undertakings for Collective

Investment in Transferable Securities) Directive, which prohibits, for instance, the use

of leverage for investment purposes, as well as physical short-selling. Specialized funds,

on the other hand, can only be marketed to qualified investors.1 Fund units can be

acquired by a maximum of ten institutional investors, although cases where funds are

set up for a single institutional client are very common. This can lead to potentially

interesting corporate governance structures, where the asset management company that

hires the manager and sets up the fund, as well as the depositary bank that oversees the

securities transactions are themselves subsidiaries of the fund client. Specialized funds

are regulated under the European Alternative Investment Funds Directive (AIF) and

investment restrictions may vary. Generally, though, the use of leverage or short-selling

is very limited.

In our analysis, we use data on security-level holding of all German investment

funds. These data comes from two main sources. Information on fund bond holdings

comes from the Deutsche Bundesbank’s Investment Funds Statistics. We further obtain

bond prices and characteristics from the European System of Central Banks’ (ESCB)

Centralized Securities Database (CSDB). According to these data, as of August 2015,

the German open-ended investment fund sector cumulated some e1.75 Tn in assets

under management distributed over 6013 investment funds. While funds were offered by

99 asset management companies, the industry is relatively concentrated, with the largest

five investment companies overseeing more than half (54.3%) of overall assets.2 In

August 2015, specialized funds accounted for 76% of the sector-wide assets. At the same

time, 83% of the sector-wide assets (e1.41 Tn) was concentrated in securities-based

funds, whose main focus is investment in equities and bonds. Open-ended real estate

1The term qualified includes both semi-professional investors, such as certain high-worth indi-viduals, and professional, e.g. institutional investors.

2See also Bundesbank (2015) on the structure of the German fund industry.

6

funds and funds of funds accounted for 8% and 6% of the sector assets, respectively,

while hedge funds and money market funds remain relatively small, with less than 1%

of the overall AUM. Within securities-based funds, mixed funds accounted for 56% of

the assets, followed by bond funds (27%) and equity funds (17%). Most funds are

actively-managed, with less than 2% reported as indexed funds.

Since September 2009, the Investment Funds Statistics collects highly granular in-

formation on all authorized German open-ended investment funds on a monthly basis.

At issuance, funds must report their name, sponsor, ISIN, type (retail or specialized),

investment focus, as well as various investment clauses, including the utilisation of

earnings (distributive or accumulative fund), the length of the investment mandate

(fixed-term or unlimited), the capital-protection and indexing status. Funds must no-

tify the Bundesbank of any changes in these features, as well as whether they are being

liquidated, have suspended redemptions or have their securities transferred to another

fund. Each month, funds must report the composition of their assets and liabilities,

their security-level holdings, NAV, fund units and gross in- and outflows. The reporting

cut-off date is the end-of-month, and data must be submitted within five business days.

All reportings are mandatory and there are no reporting thresholds.

The CSDB records prices and characteristics for all securities issued by Euro-Area

entities or held by Euro-Area institutional investors. It includes the issuance and ma-

turity dates, ESA 95 instrument type, nominal amounts outstanding, currency, the

coupon type, rate, date and frequency, the yield to maturity, the interest accrued since

last coupon, as well as information on the country and sector of the issuer. We merge

the security holding data from the Investment Fund Statistics with the pricing data

from CSDB at the ISIN-month level.

For the purposes of our analysis, we restrict ourselves to the sample of open-ended,

7

actively-managed bond and mixed funds.3 We remove funds with predefined termi-

nation dates, as the limited investment horizon might justify a different investment

behavior. We eliminate the handful of funds with AUM under e1 Mil to account for

any potential incubation bias (Evans (2010)). We generally exclude funds with capital-

protection clauses or which have reported suspended redemptions, unless these features

are the main focus of the analysis (as is the case in Sections 3.4 and 4). We remove

funds which are currently being liquidated, and exclude entries with improbably high

inflows or outflows (at least 10 times greater than last period NAV). We finally drop

entries where the data collector has concluded the information is unreliable, even after

contacting the reporting party.

Our analysis focuses on the bond holdings of these funds. In practice, German

investment funds are quite heterogenous in their securities’ geographical allocation. To

ensure some commonality, we drop funds which, over their sample lifetime, had never

been exposed to Euro-Area corporate bonds.4 This leaves us with a total of 4,534

funds (3,720 institutional and 814 retail) across 56 fund families. As of August 2015,

our sample of funds accounted for e1.01 Tn in assets under management, representing

roughly 60% of the entire German open-ended investment fund sector. Moving to

their individual bond holdings, we cleanse out entries with missing price and yield

information, or where the price reported in CSDB diverges by more than 10% from

the price implied by the nominal and market values reported in the Investment Funds

Statistics. We restrict ourselves to securities with a minimum residual maturity of 1

year. We finally remove short positions. The filtered sample covers around 83% of funds’

3In practice, mixed funds invest a significant portion of their securities portfolios in bonds.For instance, in August 2015, mixed funds kept, on average, 62% of their securities holdings infixed-income instruments.

4This all-encompassing measure ensures all funds are exposed to a common market (EA corpo-rate bond market) while avoiding cases where funds target only extra-European securities or lackcorporate credit risk. In this sense, a close correspondent to our sample would be the corporatebond mutual funds’ market for the US.

8

actual reported holdings. For our selected fund sample, this amounts to more than 13.8

million individual (fund-month-ISIN) holdings over 44, 667 fixed-income securities. By

August 2015, the cumulative market value for our filtered bond holdings was e615 Bn.

Table I reports the main fund characteristics and their portfolio allocation over the

entire sample period. The average fund invests nearly 80% of its securities in bonds

and allocates 10% each to equities and fund shares. The financial sector is the most

important sector asset allocation (on average 45.4%), followed by the public sector

(38.2%) and corporates (16.4%). Two thirds of assets are from developed economies,

with Germany taking the largest part at 18.6%. The remaining third is invested in

emerging markets or developing countries.

In line with the steady growth of the German fund sector in recent years, funds in our

sample experienced, on average, inflows of 0.66% Total Assets per month, corresponding

to 8.2% per year. However, the distribution of fund flows differs among retail and

institutional funds. Retail funds regularly experience both inflows and outflows, their

net inflows ranging from -0.74% in the lower quartile to 0.68% in the upper quartile.

In contrast, institutional funds rarely exhibit outflows.

2.2 Measuring Reach for Yield

We start by defining two important quantities, the fund’s current end-of-month port-

folio and a hypothetical holding portfolio which the fund would hold absent any active

investments over the course of the last month. If we denote with A the set of n assets

available to fund managers at any time t and with F the set of investment funds in our

sample, fund f ’s asset manager will build a portfolio allocating non-zero weights, wf,i,t

to at least a subset of assets Af ⊂ A. We refer to this allocation as the fund’s current

9

portfolio, and express the associated current portfolio yield to maturity as:

Pytmf,t =n∑

i=1

wf,i,tytmi,t (1)

where wf,i,t denotes the market value weight of asset i at time t in fund f ’s portfolio

allocation. Note this is not the actual yield to maturity that fund f earns on its holdings,

which rather depends on the full sequence of historical yields at which it acquired each

of its assets. Instead, this is the yield to maturity that an investor, willing to replicate

fund f ’s portfolio allocation, will lock-in by purchasing the assets in the market at time

t.

Next, we define a holding portfolio as a hypothetical portfolio that carries forward

the fund’s asset allocation at time t − 1 to current prices. Explicitly, for every fund

f at time t, we create an indicator variable for all the fixed-income securities it has

purchased or sold over the last month and record the number of bond units associated

with each transaction. To build the hypothetical holding portfolio, we take the current

portfolio, and for every bond, subtract from the number of units held the number of

units bought and add the number sold over the reporting period. We then price these

holdings at current market prices and recompute the portfolio yield to maturity as:5

HPytmf,t =n∑

i=1

wf,i,t−1ytmi,t (2)

with weights given by wf,i,t−1 = ui,t−1pit/n∑

i=1

ui,t−1pit, where ui,t−1 denote the number of

bond units held in the previous period. We interpret the holding portfolio as an implicit

fund benchmark, tracking the fund’s historical portfolio allocation. This is in contrast to

5Note that to price this new portfolio, we need to recover the current prices for the securities thefund has already sold. This is done by looking the security up in the CSDB. For virtually all cases,a security which has been sold by any of the funds will still be held by some European counterpartyand thus show up in the CSDB.

10

other studies such as Choi and Kronlund (2016) which use explicit benchmark portfolios

to define reach for yield. Their approach thus implicitly assumes that all funds follow

the same benchmark index. Given the large heterogeneity of fund type and investment

focus in our sample, this assumption appears unduly strong. Let us now compare the

two portfolio yields:

∆Pytmf,t = Pytmf,t −HPytmf,t (3)

Note that in the absence of transactions, the two portfolio yields are identical. More-

over, even in the presence of transactions, the two portfolio yields remain identical,

provided these transactions fully replicate the fund’s portfolio allocation in the previ-

ous month. The difference in these two quantities will thus capture changes in portfolio

yields stemming from those transactions in which the fund manager deviates from her

historical asset allocation.

We coin the term ∆Pytmf,t Reach for Yield from Transactions. This is our baseline

measure of reach for yield. It tracks the marginal change in the fund portfolio yield

as a result of current period transactions. Its magnitude is usually small, in the range

of several basis points, as in any single period the average transaction volume is small

compared to the existing holdings. However, to the extent that reach for yield occurs

persistently, the riskiness of a bond portfolio can change substantially over time.

From an asset pricing perspective, it is interesting not only how funds’ portfolio

yields evolve as a result of transactions, but also what the characteristics of the assets

are that funds are willing to invest or divest in the process of reach for yield. Our data

allows us to delve deeper into a fund’s portfolio transactions and track assets in which

the fund has been a net seller or a net buyer. We can bundle these assets to form a net

purchase portfolio and a net sales portfolio, respectively. The yield to maturity on these

portfolios represents the average yield a fund manager earns by purchasing additional

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securities:

PPytmf,t =n∑

i=1

wpf,i,tytmi,t (4)

or the yield the fund manager forgoes by divesting in existing securities

SPytmf,t =n∑

i=1

wsf,i,tytmi,t (5)

where weights wp and ws capture the market-value of net purchases (sales) a fund f

has made in asset i at time t, as a share of total period net purchases (sales).

In this sense, we can alternatively define Reach for Yield as the spread between the

average yield on a fund’s purchases portfolio and its holding portfolio. Intuitively, a

manager’s investment in securities trading at a higher average yield than that of the

securities she’s currently holding may be indicative of her intention to reach for yield:

RFY phf,t︸ ︷︷ ︸Reach for yield captured by the

spread in the avg. purchase yield

away from the avg. holdings yield

= PPytmf,t︸ ︷︷ ︸avg. yield on

purchase portfolio

− HPytmf,t︸ ︷︷ ︸avg. yield on

holding portfolio

(6)

Last, it seems important to know whether the source of reach for yield lies in funds

investing in higher-yielding securities or merely divesting lower-yielding ones. For funds’

reach for yield to pose additional downward pressures on market spreads - an assumption

commonly taken in models examining the role of funds’ risk-taking in the pricing of risk

and market tantrums (Feroli et al. (2014), Morris and Shin (2015)), we would expect

the effect of purchasing riskier securities to dominate. For this purpose, we compute

12

the average yield spread between the purchase and sales portfolios:

RFY psf,t︸ ︷︷ ︸Reach for yield captured by the

spread in the avg. purchase yield

away from the avg. sales yield

= PPytmf,t︸ ︷︷ ︸avg. yield on

purchase portfolio

− SPytmf,t︸ ︷︷ ︸avg. yield on

sales portfolio

(7)

Mechanically, the difference between equations (6) and (7) is the added effect of secu-

rities sales in funds’ reach for yield. If this difference is small, then securities purchases

dominate.

Figures 1 and 2 plot the time series and the cross-sectional distribution of our first

reach for yield measure, RFY phf,t (eq. (6)), defined as the difference between the yield

on the purchase portfolio and the yield on the holding portfolio.6 Figure 1 suggests funds

were not systematically engaged in reach for yield before mid-2012. The distribution

of funds’ reach for yield was symmetric and centered around zero. Comparatively,

funds’ reach for yield reveals a substantial upward shift after mid-2012, a time when

monetary policy became substantially more accommodative. Such a regime shift is also

underlined by Figure 2 which superimposes the cross-sectional distributions of funds’

reach for yield at the end of the fourth quarter 2009 and the first quarter of 2015.

The distribution shifted substantially into positive territory, indicating that many more

funds were increasing their exposure to higher-yielding securities in 2015 compared to

2009.

6The other two measures reveal comparable time-series and cross-sectional patterns.

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3 Reach for Yield and the Term Structure

In this section, we analyze how changes to the term structure of interest rates affect

the investment behavior of asset managers in our sample. In doing so, we will focus

on movements in interest rates arising from two sources: interest rate and credit risk.7

However, funds in our sample are very heterogenous in their exposure to duration and

credit risk. In fact, the average portfolio residual maturity varies from 2.9 years in

the bottom decile to 9.5 years in the top decile of the monthly funds’ distribution.

And while funds hold on average 80% of their bond holdings in Euro-Area securities,

there remain funds with significant extra-European holdings, as well as a considerable

diversity in terms of intra-European exposures. With this evidence in mind, it seems

unlikely that a single aggregate measure of risk-free slope or corporate credit risk will

be able to summarize funds’ sensitivities to changes in the compensation for risk.8 We

therefore construct fund-specific measures of interest rate and credit risk exposure and

subsequently study how funds’ asset allocation changes in response to exogenous shifts

in the fund-specific risk factors.

3.1 Fund-Specific Interest Rate and Credit Risk

Following Gilchrist and Zakrajsek (2012), we employ securities-level data to derive a

fund-specific measure of slope and credit spread. Specifically, for each bond in our

sample, we construct a hypothetical risk-free security having the same duration as the

7Prices and thus yields of government and corporate bonds can also move due to changes in theliquidity of these bonds. Our core set of results are robust to controlling for measures of market-wide bond illiquidity (The Kspread of Schwarz (2016) i.e. the five year KfW-Bund spread, Bund30 days implied volatility or the root mean square fitting errors from a Nelson Siegel estimation onthe bund curve).

8Moreover, the last few years have witnessed an attrition in the sample of highly rated corporates,as many entities have lost their AAA status. The AAA corporate index has not only been proneto many changes in bond composition, but also an increase in duration (7.5 years, compared to 4.8years on BBB). That led commonly quoted credit spreads, such as the Bank of America MerrillLynch Euro-Area BBB-AAA Corporate spread, to include a non-trivial duration component.

14

original security, yet discounted at the corresponding risk-free rate. This returns a price

and a yield for the hypothetical bond. We then define a security-specific credit spread as

the difference between the yield on the original risky bond and its hypothetical risk-free

counterpart. We aggregate this spread at the fund portfolio level by multiplying the

spreads with their corresponding portfolio weights wf,i,t to obtain a fund-specific credit

spread. Similarly, for each hypothetical risk-free security, we subtract the German

3-month zero coupon rate of the Nelson-Siegel-Svensson model to obtain a security-

specific risk-free slope. As before, we aggregate the slopes at the fund portfolio level

to derive a fund-specific slope. We will refer to our fund-specific slope and spread as

Fslope and Fspread.

To understand this calculation, recall that the holding portfolio yield is the com-

pensation a fund manager will get in the market for purchasing one extra unit of every

single bond she is currently holding (i.e. by replicating her portfolio). The Fspread

and Fslope measure how much of this compensation is due to exposure to credit ver-

sus interest rate risk. Figure 3 provides an illustration of the decomposition for two

different funds whose portfolio yield to maturity is assumed to be identical. Fund i is

assumed to hold a portfolio with lower residual time to maturity but higher credit risk

exposure, whereas fund j is less exposed to credit risk but has higher average residual

time to maturity. As the term structure of interest rates changes, the two Fspread and

Fslope measures will indicate how these changes map into the compensation individual

fund managers expect when replicating their portfolios.

Figure 4 plots the time series and cross-sectional dynamics of our fund-specific inter-

est rate and credit risk factors. The relevant risk-free curve underlying this decompo-

sition is the German Nelson-Siegel-Svensson zero-coupon Bund curve.9 The median of

our fund-specific slope (Fslope) moves closely with the 5Y Bund (correlation coefficient

9The decomposition is robust to various choices of the risk-free curve. Using the Euro-AreaAAA government curve or the Euro-Area OIS curve yields very similar results.

15

at 0.97). The median Fslope slightly exceeds the Bund towards the end of the sample,

indicating a gradual but sustained increase in funds’ portfolio durations. As shown in

the lower graph of Figure 4, our median fund specific spread (Fspread) also correlates

closely with commonly-used measures of corporate credit risk, here the Bank of Amer-

ica Merril Lynch option-adjusted yield spread of Euro-Area AA Corporate Bonds to

the Government, despite more heterogeneity across funds along the credit risk dimen-

sion. Together, our measures of interest rate and credit risk are intended to capture

the exposure of each fund with respect to these two dimensions of risk as precisely as

possible. Using aggregate interest rate and credit risk factors would mask the large

cross-sectional heterogeneity of funds’ risk exposures and thus generate more sampling

uncertainty in the estimation results.

3.2 Do Investment Funds Reach for Yield?

We can now turn to our empirical question. Do changes in the market compensation

for credit and interest rate risk affect the investment behavior of German open ended

bond and mixed funds? To assess this question, we run a fixed effects panel regression

of the form:10

RFYf,t = α + αf + β1L.Fslopef,t + β2L.Fspreadf,t + γL.fund controlsf,t + εf,t (8)

When regressing on the fund-specific risk factors, we control for lagged fund-specific

characteristics such as the funds’ age, assets under management, portfolio shares of

cash, bonds and derivatives, as well as current and past three-month percentage net

flows. 11 We further control for lagged 6-month fund returns, as well as lagged excess

10L represents the lag operator. All independent variables are lagged, to alleviate potentialendogeneity concerns.

11In controlling for past flows, we intend to make sure funds’ reach for yield does not reflectmanagers’ need to modify their portfolio allocation as to accomodate recent inflows or outflows,

16

returns with respect to fund category average (bond or mixed funds) and with respect

to fund family average (see Kempf and Ruenzi (2008)).

If fund managers reach for yield, we would expect that a compression of the fund-

specific risk-free slope and credit-risk factors results in a reallocation of their portfolios

towards higher-yielding and thus likely more risky securities. Specifically, we would

expect the coefficients β1 and β2 in the above regression to be statistically significantly

lower than zero. Table II reports the results using, as dependent variables, the three

measures of reach for yield described in the previous section, each over the sample of re-

tail and institutional funds. The estimated parameters strongly support the hypothesis

of reach for yield among investment managers. Specifically, all but one estimate of β1

and β2 are negative and strongly statistically significant. In our sample, reach for yield

is thus present for all three measures used as well as across both retail and institutional

funds.

We illustrate the economic significance of our results with RFYph, the yield spread

between fund purchases and holdings, and focus on our sample of specialized funds.

Specifically, a one percentage point drop in the Fslope (i.e. compensation funds ex-

tract from exposures to the risk-free slope) prompts managers to invest in securities

yielding, on average, 26bps more than their benchmark portfolio. Results are very

similar for our credit risk measure, Fspread, although the magnitude of the coefficient

is somewhat smaller. The coefficient reveals that a one percentage point lower credit

spread corresponds to an increase of 14.3bps of the funds bond portfolio in the next

month. Thus, our estimates indicate that investment managers tend to be more respon-

sive to changes in the risk-free curve, rather than credit spreads, even after controlling

for their differential exposures to these factors.

Our findings are broadly in line with the results of Choi and Kronlund (2016) who

but rather a deliberate attempt to tilt the existing portfolio towards higher yielding securities.

17

study reach for yield among U.S. corporate bond funds. That said, they differ in the

relative importance of the two risk factors as Choi and Kronlund (2016) document

a slightly higher impact of credit risk (27bps) than slope risk (19bps). These rather

small differences are likely due to differences in the definition of reach for yield, the

construction of risk factors, and the sample period considered.

As we include the funds’ cash, equity, and derivative holdings as controls, we can

rule out that our results are driven by substitution effects within the fund asset portfo-

lio which might exist between the riskiness of the fixed income portfolio and the funds’

equity or derivative holdings or their cash position. In unreported results, we also find

that reach for yield is equally strong for funds which do not have an equity portfo-

lio. Furthermore, funds do not seem to alter their cash positions in response to higher

risk-taking on their bond portfolios. Our results cannot be explained by changes in

bond supply either. It is conceivable that companies increased their issuance or riskier,

subordinate bonds, as they take advantage of the increasingly low financing rates. In

unreported results, we compare the median bond fund against various European corpo-

rate bond indices (Bank of America Merril Lynch Euro-Area BB, B, High Yield) and

find that, with no exception, fund portfolio yields close the gap or even exceed existing

indices. Results are also robust across different sample periods. Moreover, it does not

matter whether the fund-specific risk-free slope is defined in terms of German yields

or Euro-Area OIS swap rates. Finally, results are robust to adding a short rate as an

additional regressor. While a drop in the three-month risk-free short rate is associated

with a statistically significant increase in funds reach for yield, we choose not to include

the short rate in our benchmark specification as it features little variation over most of

our sample which is characterized by a lower bound environment.

These patterns are very similar for our second measure, the yield spread between

purchases and sales, RFY ps. This suggests that investing in higher yielding, riskier

18

securities, rather than divesting safer ones, is the primary choice in managers’ reach

for yield. Finally, the last column in Table II reports results for our third measure of

reach for yield, ∆Pytm, which describes the change in a fund portfolio yield from current

period transactions. On average, fund portfolios’ yields increase by about three basis

points for every one percentage point drop in the slope factor. In sum, these results

provide strong evidence of fund managers reaching for yield in response to exogenous

shifts in the term structure of interest rates.

3.3 Rate Expectations or Term Premia?

Having established that investment managers are particularly sensitive to changes in the

risk-free slope, we next analyze whether it is changes in risk-adjusted rate expectations

or term premia which mainly affect their investment allocation. To this end, we de-

compose the German zero-coupon yield curve into a term structure of rate expectations

and term premia using the Hamilton and Wu (2012) thee-factor Gaussian Affine Term

Structure Model. We will later show that decompositions based on alternative term

structure models provide very similar results. In the following regressions we consider

the estimates at a five-year maturity, which is close to the average residual maturity of

the bond portfolios in our sample.12 The specification of equation (8) becomes:

RFYf,t =α + αf + β1L.Rate5Yf,t + β2L.Term5Yf,t + β3L.Fspreadf,t

+ γL.fund controlsf,t + εf,t

(9)

Table III reports estimation results for term structure components obtained from the

Hamilton and Wu (2012) model for our sample of specialized funds. Looking at our

first measure of reach for yield, the spread between purchases and holdings, we find

12In fact, replacing our Fspread with the five-year Bund slope in equation (9) leads to essentiallyidentical point estimates.

19

that a one percentage point drop in rate expectations over the next five years is associ-

ated with a highly statistically significant 65 basis points rise in reach for yield in the

following month. This compares to only 11 basis points for an equivalent drop in term

premia. While the latter coefficient is statistically significant at the five percent level,

the coefficients on the term premium component are statistically indistinguishable from

zero for the remaining two reach for yield measures. In contrast, the coefficients on the

rate expectations component are strongly statistically significant for all three measures.

In sum, these results thus suggest that changes in rate expectations, and more generally

policies aimed at keeping rates lower for longer, may be particularly powerful in altering

fund managers’ risk-taking behavior.

These results are not model-dependent. As Table IV shows, we find similar estimates

from a suite of affine and shadow rate term structure models estimated on both the

German Bund and the Euro-Area OIS swap curves. Moreover, results are robust to

using model-free measures of either rate expectations or term premia. In particular,

Table V compares the results for the term premium using the Hamilton and Wu (2012)

model with the alternative of measuring the term premium as the one-month forward

rate ten years out. This measure is based on the assumption that expectations about

the future stance of monetary policy far into the future only change slowly over time

and therefore variation of very long-term forward rates mainly reflects variation in term

premiums. Not surprisingly, this term premium measure is highly correlated with the

model-based estimate and thus delivers very similar results when used as an independent

variable in regression (9). Specifically, both model-based and model-free estimates

suggest that changes in the term premium do not trigger a significant reallocation of

fund portfolios towards higher-yielding securities.

Table VI provides a comparison of the model-based rate expectations components

from Hamilton and Wu (2012) with a model-free measure of short rate expectations from

20

the Consensus Economics survey of professional forecasters. Specifically, we use survey

expectations of three-month EURIBOR one year out and compute the risk-adjusted rate

expectation from the Hamilton and Wu (2012) model at the same horizon. The resulting

regression coefficients are very similar across the two measures of rate expectations and

are both highly statistically significant, indicating that a one-percent decline in rate

expectations one year out results in an extension of of funds’ portfolio yield to maturity

of about a quarter of a percent.

Summarizing, the results presented in this section show that it is primarily changes

in expectations about future short-term interest rates and to a lesser extent changes

in term premiums on credit risk-free government bonds that result in fund managers’

reach for yield.

3.4 Do Negative Rates Amplify Reach for Yield?

In the previous section, we have provided evidence that compressions in nominal yields

tend to be associated with strong reach for yield tendencies by investment funds. In re-

cent years, yields on a large amount of outstanding government and also some corporate

bonds in the Euro-Area and other jurisdictions have dropped below zero. Specifically,

July 2012 marks the first month in our sample in which some securities’ yield to ma-

turity turned negative. By April 2015, more than e2Tn in Euro-Area sovereign bonds

were trading at negative rates. This begs two the empirical questions: does reach for

yield further intensify at negative rates? In other words, do fund managers respond

differently when bonds in their investment universe move from, say, 0 to -20 bps, rather

than from 20 to 0 bps? And second: if so, are there any significant differences across

funds? Here, we investigate whether some managers respond more aggressively to the

presence of negative yields than others. In particular, we focus on one particular type

of funds - capital protected funds.

21

Capital protected funds feature, as part of their investment mandate, a capital-

protection clause. While contractual clauses vary across funds, it is most common that

the fund sponsor commits to return the initial invested amount, in full or a percentage of

this amount, at the end of a fixed term. The guarantee clauses of those funds bring their

business model close to the one of constant-NAV money market funds, which promise

the repayment of the full investment at a daily basis. However, the main difference

between both fund types is the promise of capital-protected funds to fulfill guarantees

over a longer maturity. While money market funds are typically able to exit the market

if interest rates turn into negative territory (see ECB (2015)), capital-protected funds

have to fulfill their guarantee to a specific point in the future. Thus, these funds issue a

type of minimum return guarantee that becomes harder to fulfill if yields are negative.

Rajan (2005) suggests that those minimum return guarantees might prompt funds to

chase yields in a low yield environment.

To the best of our knowledge, we are the first to examine the effect of return guaran-

tees at negative rates. We restrict our analysis to specialized funds, since among those

capital-protected funds are relatively more common. As of August 2015, there were 125

active specialized capital-protected funds in our sample, cumulating some e15Bn in

assets under management, representing roughly 2% of the overall German institutional

funds market.

We build our identification strategy in two steps. We start by asking whether funds

which are more exposed to negative rates reach for yield more. Specifically, we proxy

funds’ exposures to negative yields by the market value weighted share of assets in their

holding portfolios currently trading at negative rates. Recall that the holding portfolio

yield is the compensation a fund manager will get in the market for purchasing one extra

unit of every single bond she is currently holding (i.e. by replicating her portfolio). The

intuition is straightforward: if a fund does not deviate from its current investment

22

strategy (i.e. replicates its holding portfolio) and holds assets to maturity, it would

make a certain loss on those transactions where bonds trade at negative rates. Thus,

the greater the share of bonds trading at negative rates, the greater the incentive for

a manager to deviate from its holding portfolio. We run a fund and time fixed-effects

regression with the same control variables as considered in equation (8):

RFYf,t =α + αf + αt + βL.Sharef,t + δ1L.Fslopef,t + δ2L.Fspreadf,t+

γL.fund controlsf,t + εf,t

(10)

The first three constant terms capture differences in the reach for yield levels across

funds and time. δ1 and δ2 capture fund managers’ sensitivity to changes in the risk free

slope and credit spreads. This ensures that the effect we find for negative rates is over

and above what would be expected from the negative relationship between spreads and

reach for yield documented in the previous section. The coefficient β is our coefficient of

interest. It measures the additional effect of a negative rate exposure on reach for yield.

The full effect of the coefficient materializes if the complete fund portfolio is traded at

negative yields (L.Share equals one). Note that in this analysis, we restrict ourselves

to the sample period post July 2012 which captures the period of negative rates.

The left column of Table VII reports the results for this regression. We find that

funds whose portfolio is exposed to negative yields increase their reach for yield. The

coefficient on L.Share shows that a fund for which the share of their current portfolio

trading at negative rates were to increase from 0 to 1 would, on average, increase its

portfolio yield to maturity by 3.3 basis points from their current period transactions.

For comparison, the sample average reach for yield from current period transactions for

March 2015, the period of maximum yield compression in our sample (e.g. BBB-AAA-

spread), amounted to 1.9 basis points. Thus, according to our estimates, the reach for

yield behavior of fund managers further intensifies in an environment of negative rates.

23

This result applies to all bond and mixed funds, and is over and above what would be

expected from the negative relationship between rates and reach for yield documented

above.

The majority of these funds do not report any explicit minimum return guarantees.

We will next study whether this effect is further pronounced for funds which offer

capital-protection clauses to their investors. Capital-protected funds generally earn

lower yields, as they take less credit risk (Fspread of 83bps compared to 110bps for

conventional funds) and invest in bonds with shorter maturities (4.5 years residual

maturity to 5.2 years).

Figure 5 shows the wedge in reach for yield, defined as the spread between the median

portfolio yield change from transactions in conventional and capital-protected funds.

The RFY gap is positive in the first few years of our sample, suggesting conventional

funds have been more actively reaching for yield during this period. Around mid-2012,

however, exactly when negative rates started to become pervasive, the wedge drops

below zero, indicating that capital-protected funds more strongly reached for yield in

that part of the sample. This is striking, as it is capital-protected funds which are

commonly deemed safer by investors.

In what follows, we specify a difference in difference analysis to test whether capital

protected funds, are, all else equal, more responsive to changes in their exposures to

negative yields than conventional funds.

RFYf,t =α + αf + αt + β1L.Sharef,t + β2L.Fslopef,t + β3L.Fspreadf,t+

γ1L.Sharef,t#Wert+ γ2L.Fslopef,t#Wert+ γ3L.Fspreadf,t#Wert+

γ4L.Durationf,t#Wert+ δ3L.fund controlsf,t + εf,t

(11)

As before, the first three constant terms capture differences in levels across funds

and time. The three β coefficients capture the sensitivities from changes in slope,

24

credit spreads and negative rates for all funds. The γ coefficients capture differences in

sensitivities to changes in yields and durations between regular and capital protected

funds, to ensure the parallel trends assumption is satisfied. The coefficient γ1 is our

coefficient of interest. It measures the additional effect negative rates have on capital-

protected funds, compared to regular funds. Wert stands for ”wertgesichert” (German

for capital-protected), a binary variable indicating if the fund is capital protected.

The right column of Table VII reports the results. As before, we identify a positive

and highly significant effect of the relative importance of negative yielding bonds for all

funds. The magnitude of the effect is 3.7 basis points, and thus slightly larger than the

3.3 basis points baseline effect estimated for equation (10). Turning to capital-protected

funds (the dummy variable Wert takes a value of 1), the coefficient on L.Share#Wert,

which captures the additional effect of negative yields on capital-protected funds, shows

that the average capital-protected fund increases its reach for yield by an additional 4

basis points relative to a conventional fund with similar fund characteristics. The size

of the additional effect (4bps) relative to the baseline effect (3.7bps) shows that capital

protected funds appear to be roughly twice as responsive to negative yields compared

to conventional funds, even after controlling for their sensitivities to yield factors.

To summarize, the results in this section document that i) investment funds show

a strong behavior of reach for yield in response to shifts in the risk-free yield curve

and credit risk spreads; ii) it is mainly changes in expectations about future short-term

interest rates that trigger reach for yield in response to shifts in the risk-free yield

curve; and iii) funds’ reach for yield behavior intensifies in a negative rates environ-

ment, particularly those which offer capital-protection clauses and are thus deemed

safer investments.

25

4 Reach for Yield and Fund Manager Incentives

In the previous section, we have documented strong evidence for investment funds’

reaching for yield in response to shifts in the yield curve. In this section, we pro-

vide evidence which suggests that this risk-seeking behavior is consistent with fund

managers’ incentives, as pointed out in the theoretical literature. While the reach for

yield behavior documented for banks and insurance companies has mainly been linked

to frictions arising from regulation and government guarantees, arguably investment

funds are subject to fewer regulatory restrictions than banks and insurance companies,

and should not feature implicit government guarantees. Absent these frictions, what

is the economic mechanism that triggers fund managers to engage in reach for yield

behavior?

Prior research has discussed fund managers’ investment behavior with a focus on

the incentives underlying their mandates. Such incentives may be explicit, as those

embedded in the compensation schemes of fund managers, while others are implicit. The

literature has broadly focused on two sources of implicit incentives. The first examines

incentives arising from the agency relationship between fund companies and investors,

specifically focusing on the question how investors’ decisions to invest or withdraw

from a fund affects fund managers’ investment behavior. A key empirical result is

that flows chase past performance.13 The shape of the flow-performance relationship

may vary, however. Chevalier and Ellison (1997) and Sirri and Tufano (1998), among

others, document a convex relationship in equity funds. By contrast, Goldstein et al.

(2016) find a concave flow-performance relationship in corporate bond funds - that is

- investors are more sensitive to bad performance than to good performance. What

13Why investors chase past performance remains an open question. Carhart (1997) finds littleevidence of performance persistence in equity funds. Berk and Green (2004) show that investorsrationally chase past performance, yet performance does not persist when funds face decreasingreturns to scale. Chen et al. (2010) confirm empirically that fund performance exhibits diseconomiesof scale.

26

is more, investors sensitivity to underperformance sharpens as portfolio and market

liquidity worsens (Chen et al. (2010), Goldstein et al. (2016)).14

A second source of implicit incentives stems from agency problems within institu-

tional asset management companies - between the fund sponsor and the fund manager.

Career concerns, for example the prospect of promotion or termination, may affect fund

managers investment behavior (Fama (1980), Holmstrom (1982)). The empirical liter-

ature studying such agency problems in institutional funds is scarce. Chevalier and

Ellison (1999) document the existence of a termination-performance relationship in US

equity funds. The relationship is convex - the probability of termination increases sig-

nificantly with underperformance, yet falls only marginally with superior performance

- and appears strongest among young managers. Moreover, the prospect of losing their

jobs creates an element of coordination in managers portfolio allocation, giving the

outward appearance of herding (Morris and Shin (2015)).15 Moreover, as Guerrieri

and Kondor (2012) have pointed out, fund managers investment behavior under career

concerns may amplify asset price volatility.

This discussion suggests that different frictions may operate in different types of

funds. Indeed, investor flows are common in retail funds, but not in institutional funds.

Tailored to a few institutional investors having close control over the fund investment

policy and management, German institutional funds rarely exhibit significant flows.

Instead, institutional investors periodically assess fund performance and extend or ter-

minate the fund mandate, either liquidating the holdings or mandating the assets to

a different fund or investment management firm. Since our sample covers retail and

institutional funds, we can thus study the two frictions separately: the role of investor

14Chen et al. (2010) link the liquidity effect with the existence of strategic complementarities(first mover advantage) in investor withdrawals. For a more general discussion of strategic comple-mentarities in mutual funds, see Coval and Stafford (2007).

15Such pattern is not limited to fund managers. Hong et al. (2000), for instance, link careerconcerns to herding behavior among security analysts.

27

flows in retail funds and the role of termination risk in institutional funds.

To study the relationship between fund flows or termination risk and reach for yield

empirically, we think of funds’ reach for yield as a hypothetical relative performance

tournament (Brown et al. (1996)). Specifically, we rank funds by their performance

in a reach for yield tournament and define this continuous variable as Rank∆Y TM ,

which assigns a value of 1 to the fund with the highest reach for yield and 0 to the

fund with the lowest reach for yield. We account for the fact that periods of market

stress are associated with greater investor sensitivity to managers’ investment behavior

(Chen et al. (2010) and Goldstein et al. (2016)).16 We control for such nonlinear rela-

tions between flows and financial stress by including an additional interaction term of

Rank∆Y TM with financial stress. Our preferred measure is the Euro-Area Composite

Indicator of Systemic Stress (CISS) bond subindex of Hollo et al. (2012), expressed in

historical standard deviations. 17

4.1 Reach for Yield and Retail Investor Flows

We begin with our sample of retail funds and ask whether ranking high in the hypo-

thetical RFY tournament predicts future inflows. We regress fund flows on the one

month lagged Rank∆Y TM and a number of fund control variables. We model fund

flows in three different ways. First, we follow the existing literature on fund flow de-

terminants and measure flows as the percentage of last period net asset values of assets

under management (AUM), see Chen et al. (2010), among others. The second measure

is the fund’s ranking according to its contemporaneous net flows. Third, we discretize

the continuous rank measure into rank deciles (see Sirri and Tufano (1998)) and apply

16In their setting, stress periods are associated with higher portfolio and market illiquidity. Illiq-uidy generally worsens investors coordination problem by strengthening the strategic complemen-tarities associated with exiting the fund.

17The results are robust to various other measures of stress (e.g. BBB-AAA spread, 30 daysimplied bond volatilities).

28

a ordered probit model to identify the marginal effect of a higher ranking in the RFY

tournament for the probability of ranking in the top decile of the flow distribution next

period.

The regression specification is:

%Flowf,t = βL.rank∆Y TMf,t + γL.fund controlsf,t + εf,t (12)

The left column of Table VIII reports regression results of the flow as a share of

AUM measure on the rank in the reach for yield tournament by also controlling for

past flows and past performance. Interestingly, we find no significant relation between

fund flows and the ranking in the past reach for yield tournament. The coefficient on

L.Rank∆Y TM is both statistically and economically insignificant. We next focus our

analysis on the question how reach for yield affects fund flows in times of financial stress

and include an interaction term between RFY and market stress in the regression:

%Flowf,t =βL.rank∆Y TMf,t + δL.rank∆Y TMf,t#Stresst+

γ1L.fund controlsf,t + γ2Stresst + εf,t

(13)

The right column of Table VIII reports the results. Interestingly, the main effect,

Rank∆Y TM , and the interaction effect turn out both statistically significant, but with

opposite signs. In the absence of market stress, the average effect of being ranked

first instead of last in the tournament corresponds to an additional 0.2% inflow in the

next month, which amounts to about 2.5% at an annual basis. However, future fund

flows decrease by almost 0.1% (1.2% annually) for every standard deviation increase

in financial stress. This decline equals the average monthly percentage net flow in our

sample and thus shows the economic significance of the outflows of funds with high-

yielding portfolios in times of market stress. This is even more striking as the effect

29

remains after controlling for the regular flow-performance relationship.

To get a sense of the economic significance of this result, it is instructive to consider

some alternative measures of flow sensitivity to managerial reach for yield. We first

rerun the regressions (12) and (13) with the same control variables but using the con-

tinuous rank flow measure discussed above. The right column of Table IX confirms our

findings from above: ranking high in the reach for yield tournament precedes ranking

higher in the flow tournament.

Second, we follow the literature on flow-tournaments and discretize our continuous

[0,1] flow tournament variable into rank deciles (see Jank and Wedow (2013) among

others). We apply an ordered probit model and ask: what is the marginal effect of

ranking well in the reach for yield tournament for the probability of ranking in the top

decile of the flow distribution next period? The left column of Table X shows that

ranking top in the hypothetical reach for yield tournament increases the probability

that an average fund will rank in the top decile of next month’s flow distribution by

0.6%. When we control for stress (right column), the general effect increases to 1.7% in

the absence of financial stress, only to fall by an approximate 0.6% for every standard

deviation increase in stress.18

4.2 Reach for Yield and Institutional Mandate Termination

We next focus on the sample of institutional funds and ask whether funds ranking high

in the reach for yield tournament reduces the probability of being terminated. We run

a random-effects Probit regression of the form:

Pr(term)f,t = βL.RankRFYf,t + γL.fund controlsf,t + εf,t (14)

18For comparison, the predicted probability of the average fund (average in terms of character-istics) to show up in the top decile of the flow tournament next period is 7.5%. Moreover, resultsare robust to looking at marginal effects and the average or average marginal effects.

30

where the dependent variable Pr(term)f,t is a (0, 1) dummy taking value 1 if fund

f is terminated at time t. RankRFYf,t is a continuous variable on [0, 1] and defines

the average rank (with 1 being highest) a fund has attained over the last six months

in a hypothetical reach for yield tournament. We select a time period of six months

to account for the time- and cost-intensive process of awarding investment mandates.

Fund controls include past excess returns and squared excess returns to control for the

(potentially nonlinear) termination-performance relationship.

Results are reported in the left column of Table XII and are expressed as average

marginal effects. The coefficient on the reach for yield rank variable L.RankRFYf,t

suggests a statistically and economically significant relation with the termination prob-

ability of a fund. Specifically, the parameter estimate of −0.006 implies that a fund

which consistently ranks high in the reach for yield tournament reduce its probability

of being terminated in the next month by 0.6% on average. For reference, the aver-

age probability that a fund in our sample will eventually be terminated is 5.5%. The

average monthly rate of termination is much smaller - 0.8%.

We next ask whether this relation strengthens under certain market conditions. We

have previously documented that periods of market stress are associated with a higher

sensitivity of flows to funds’ yield chasing, over and above the regular flow-performance

relationship. We thus run a similar regression for our sample of institutional funds,

interacting our main explanatory variable, lagged RankRFYt, with our stress indicator:

Pr(term)f,t =βL.RankRFYt + δL.RankRFYt#Stresst + γ1L.fund controlsf,t+

γ2Stresst + εf,t

(15)

The right column of Table XII reports the results. Again, we find that the rank in

the reach for yield tournament, L.RankFRYf,t, reduces, on average, the termination

probability. Funds which rank highest in the reach for yield tournament reduce their

31

probability of termination by 1.6% in times of low market stress. Interacting reach

for yield with the CISS indicator of financial market stress reveals that termination

probability increases by 0.5% for every standard deviation increase in stress. We find

somewhat similar dynamics when interacting with measures of term and credit spreads.

In other words, the sensitivity of termination to funds’ yield chasing is strongest when

markets are calm and spreads are compressed.

In sum, our evidence suggests that both the prospect of future inflows and the risk

of termination work as implicit incentives in fostering managerial yield chasing. The

findings thus lend empirical support to the mechanisms which the theoretical literature

has identified as potentially triggering fund managers’ propensity to reach for yield.

However, both mechanisms appear to display an asymmetric behavior over time: funds

that strongly engage in reaching for yield risk attract a steady stream of inflows by

retail investors or are unlikely to have their mandates terminated by their institutional

investors in normal times. However, during spells of market stress, retail and institu-

tional investors alike are more likely to reduce their investment in funds that exhibit a

pronounced reach for yield investment behavior.

5 Fund Demand and Excess Bond Returns

Thus far, we have provided evidence on the existence of reach for yield in German retail

and institutional funds and have shown support for a set of implicit incentives that may

give rise to such behavior. In this section, we provide some evidence that investment

funds’ reaching for yield also has an impact on market risk premiums. This, in turn,

implies that there is a potential for destabilizing feedback loops between exogenous

shocks to the yield curve and asset managers’ investment behavior. Such feedback

loops have been discussed by (Feroli et al. (2014) and Morris and Shin (2015)), among

32

others.

Specifically, we assess whether securities that are strongly sought after by investment

funds feature higher future excess returns. In this analysis, we restrict ourselves to Euro-

Area Euro-denominated sovereign and corporate bonds held by our sample of German

bond and mixed funds. This returns a sample of slightly over 11,000 Euro-Area fixed

income securities. For each security, we compute the one-month realized excess return,

defined as the difference between the bond’s realized one-month holding period return

and the realized one-month German Bund rate.

We specify, for each security, two proxies for fund-sector excess demand which are

scaled to ensure comparability across securities. First, we follow Coval and Stafford

(2007) and measure excess demand of the German fund sector in security i at time t,

ExDemi,t, as the net change in fund holdings over the total value outstanding of that

security:

ExDemi,t =Buyi,t − Selli,t

Amount Outstandingi,t× 100 (16)

Exdem is a continuous variable on [-100,100] where 100 (-100) means the fund sector has

acquired (sold) the entire amount outstanding in a given security. Note that ExDem

can be zero in cases where transactions occur only within the fund sector, or when

transactions with other sectors entirely cancel out.

The second measure, ”ExLSV 92i,t”, defines excess fund sector demand as the net

change in total fund sector holdings over the gross change in fund holdings for security

i at time t (see Lakonishok et al. (1992)):

ExLSV 92i,t =Buyi,t − Selli,tBuyi,t + Selli,t

(17)

ExLSV 92 is a continuous variable on [-1,1], where -1 refers to unanimous selling pres-

sure, 1 stands for unanimous buying pressure, and 0 stands for cases where within sector

33

buy and sell demand completely cancels out. We exclude here the obvious cases where

there is only one fund transacting (hence no cross-sectional disagreement).

Although both measures’ nominator captures net buying, differences in the denom-

inator lead to different interpretations of the two measures. ExDemi,t places more

weight on transactions that are large with respect to the total outstanding stock of a

fixed-income security. In contrast, ExLSV 92 puts more emphasis on how asymmetric

buying pressures are within the fund sector (strong demand pressures vs. strong supply

pressures), regardless of how this pressure compares in magnitude to the overall stock

outstanding. Both measures are computed in terms of market values.

For each measure, we run a regression of one-month bond realized excess returns on

one-month lagged fund demand. The regression includes security fixed effects, lagged

excess returns for the past three months to account for potential momentum trading

(see Timmer (2016)), as well as time fixed-effects, to account for any common factors

driving the cross section of excess returns. The regression is restricted to those securities

for which we record transaction activity in the current month.

Results are reported in Table XIII. The effect of funds’ excess demand on excess

bonds returns is economically large for both measures. The estimated coefficient of

about 0.1 for ExDem in the first column implies that a 1% increase in fund sector

holdings (as % of market value outstanding) is associated, on average, with a 10bps

higher realized return in the next month. The results using the LSV 1992 measure,

shown in the second column, indicate a link between funds’ transactions and future

excess returns. Here, the estimated coefficient of 0.76 means that for any given security,

a shift from ’balanced demand’ (buys and sells are equal and cancel out) to unanimous

buying pressure in the fund sector is associated, on average, with 76bps higher realized

excess returns in the next period.

Our results thus indicate that current fund sector excess demand tends to be as-

34

sociated with higher future excess returns, and the relationship seems stronger when

buying or selling pressures are coordinated. Hence, there appears to be an effect of

fund’s changes in portfolio allocations on bond risk premiums. While there may be

potential alternative interpretations, these findings suggest that funds are among the

marginal investors in fixed income markets - i.e. those investors exercising the buying

and selling pressures, and not merely responding to other sectors’ demand. This would

be in line with the theoretical setting in Morris and Shin (2015) where funds are the ac-

tive investors, while other sectors merely accommodate funds’ demand for risky assets.

Combined with fund managers’ incentives to reach for yield due to the agency frictions

discussed in Section 4, the model in Morris and Shin (2015) implies that bond risk pre-

miums can experience sudden large jumps in reaction to small changes in anticipated

future short term interest rates. Since large reversals in bond yields can have financial

stability implications, such a mechanism thus potentially creates an additional tradeoff

for the central bank.

6 Conclusion

In this paper, we have provided strong empirical evidence that investment fund man-

agers reach for yield in response to shifts in yields on government and corporate bonds.

Specifically, reach-for-yield across fund categories intensifies as risk-free rates fall and

credit spreads become compressed. Decomposing changes in government bond yields

into expectations about future short term rates and term premia, we find that it is

primarily the former which drive reach for yield. Reach for yield further intensifies

when rates become negative, particularly in funds offering capital-protection to their

investors. We find that the reaching for yield is consistent with fund managers’ in-

centives to attract new investors or retain existing ones. In retail funds, yield-chasing

35

predicts inflows in calm periods, yet can backfire in stress periods. In institutional

funds, reaching for yield lowers the probability of mandate termination. Finally, we

show evidence suggesting that funds’ demand for risky securities feeds back to bond

returns, thus generating the potential for feedback loops from falling yields to more risk

taking to further falling yields.

Our results complement the literature on risk-taking of institutional investors in a

low rate environment. While we do not directly link movements in expected future

short rates to monetary policy actions, our results are consistent with the evidence in

DiMaggio and Kacperczyk (2016) who find that money market fund portfolios become

more risky in response to announcements of forward guidance by the Federal Reserve.

Our findings thus also lend empirical support to the notion that monetary policy relying

on forward-guidance can strongly impact the risk-taking of unlevered investors such as

fund managers (Morris and Shin (2015)).

It is often argued that quantitative easing measures by central banks work via a so-

called portfolio balance effect. This entails that the counterparties from which cental

banks purchase government or similar bonds reinvest the proceeds in riskier securities,

thus driving up asset prices on a broader set of securities than those acquired by the

monetary authority. In principle, our dataset allows us to identify at a very granular

level the portfolio shifts by German investment funds in response to market interventions

such as central bank purchase programs. We leave this for future research.

36

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39

Tables and Figures

Figure 1: Visualizing funds’ Reach for Yield

This figure plots the time series and cross-sectional distribution for one of our three measures offunds’ Reach for Yield. Specifically, we measure Reach for Yield as the weighted average yieldspread between a fund’s purchases portfolio and its holding portfolio. Positive values suggest thata fund is purchasing securities at a higher average market yield than the yield of the securities it iscurrently holding. The sample is of actively-managed, specialized German bond and mixed funds.Every period, we winsorize the distribution at 5% and 95% to control for outliers. The dotted linesfollow the median fund. The shaded regions track the interquartile range. Data for the period Nov.2009-Aug. 2015. The frequency is monthly.

-100

-50

0

50

100

-100

-50

0

50

100

Nov-09 Aug-10 May-11 Feb-12 Nov-12 Aug-13 May-14 Feb-15

interquartile range median fund

bps.

40

Figure 2: Funds’ Reach for Yield in the Cross Section

This figure looks at changes in the cross-section of fund’s Reach for Yield for two selected quarters:Q4 2009 (in blue) and Q1 2015 (in red). The cross-sections have been extracted from our selectedsample of actively-managed, specialized German bond and mixed funds. The RFY measure repre-sented here is the RFYph - the weighted average yield spread between a fund’s purchases portfolioand its holding portfolio, and is reported in pp. Distributions are winsorized at 5% and 95%.Kernels are Gaussian, with bandwidth 0.2.

0.0

0.2

0.4

0.6

0.8

1.0

-3.00 -2.00 -1.00 0.00 1.00 2.00 3.00

Q1 2015

0.0

0.2

0.4

0.6

0.8

1.0

-3.00 -2.00 -1.00 0.00 1.00 2.00 3.00

Q4 2009

%

41

Figure 3: Decomposing fund’s portfolio yield

This figure illustrates the Gilchrist and Zakrajsek (2012) decomposition of the portfolio yield intoa fund-specific measure of slope and credit risk. The credit spread, FSpread, is defined as thedifference between the portfolio yield and a synthetically constructed risk-free rate with equivalentmaturity. The fund specific slope, FSlope, is the difference between the risk-free rate of a bondwith equivalent maturity as the fund portfolio and the short rate.

residual maturity0

YTM

Short rate

Bund curve

AA curve

BBB curve

Fslopei

Fslopej

Fspreadi

Fspreadj

Y TMFundi Y TMFundj

Fundi Fundj

1

42

Figure 4: A Fund-specific Risk-Free Slope and Credit Spread

This figure compares our fund specific measures of risk-free slope (fslope) and credit spread (fspread)against some commonly-used market benchmarks. The upper panel compares the distribution offunds’ fslope to the 5Y Bund slope, computed as the difference between the Nelson-Siegel-Svenssonzero coupon 5Y and 3m rates. The lower panel plots the distribution of fund-specific credit spreadsagainst the option-adjusted yield spread to the Government for a selected sample of Euro-Area AACorporate Bonds provided by Bank of America Merrill Lynch. The dotted lines follow the medianfund. The shaded regions depict the interquartile range. Figures are based on our selected sampleof actively-managed specialized German bond and mixed funds for the period Nov. 2009 to Aug.2015. The frequency is monthly.

0.00

0.50

1.00

1.50

2.00

2.50

0.00

0.50

1.00

1.50

2.00

2.50

Nov.09 Nov.10 Nov.11 Nov.12 Nov.13 Nov.14

25/75pct median Fslope Bund5Y - Bund3m

%

0.00

0.50

1.00

1.50

2.00

2.50

0.00

0.50

1.00

1.50

2.00

2.50

Nov.09 Nov.10 Nov.11 Nov.12 Nov.13 Nov.14

25/75pct median Fspread AA Spread

%

43

Figure 5: Reach for Yield Gap between Conventional and Capital-Protected funds

This figure shows the evolution of the difference in the median Reach for Yield of conventional andcapital-protected funds between September 2009 and August 2015. Reach for Yield is defined asthe fund portfolio yield change from current period transactions.

44

Table I: Summary Statistics

This table reports summary statistics of fund characteristics and of portfolio allocations. Portfolioallocations are the sample averages of the market-value weighted averages from monthly cross-sections. ∆Pytm (RFY ph/RFY ps) refers to Reach for Yield from Transactions as defined inequation (3)) (Reach for Yield as the spread between the average yield on a fund’s purchase portfolioand its holding/sales portfolio; equation (6)/(7)). EA stands for the Euro Area and EEA to theEuropean Economic Area. Developed countries are those 39 countries classified as ”advancedeconomies” by the IMF.

Variable Unit 25%-Quantile Median Mean 75%-Quantile Std.

fund characterisitcs

age years 3.59 8.25 10.20 14.52 8.41size EUR mil. 33.85 69.99 249.77 177.14 1051.34net flows % TA[n-1] 0.00 0.00 0.66 0.00 10.52

retail funds % TA[n-1] -0.74 0.00 0.51 0.68 12.06institutional funds % TA[n-1] 0.00 0.00 0.69 0.00 10.18

Reach for Yield∆Y TM % -0.02 0.01 0.01 0.03 0.06RFY ph % -0.23 0.16 0.25 0.63 0.73RFY ps % -0.47 0.21 0.17 0.84 1.02

asset allocation

cash % TA 1.25 3.01 5.65 6.61 8.19securities % TA 91.39 95.28 92.44 97.19 8.88derivatives % TA 0.00 0.00 0.36 0.12 2.35

securities portfolio allocation

bonds % Securities 65.65 85.38 78.93 100.00 23.48equities % Securities 0.00 0.00 10.17 17.57 16.22fund shares % Securities 0.00 0.00 10.71 14.88 17.99

bond portfolio allocation

SectorSovereigns % bonds 16.13 37.70 38.15 55.57 26.31Financials % bonds 29.71 43.46 45.42 59.24 22.71Corporates % bonds 3.57 10.95 16.39 24.75 16.62

LocationGermany % bonds 6.35 10.93 18.59 23.62 19.98EA excl. Germany % bonds 38.63 54.91 50.66 66.83 21.52EEA excl. EA % bonds 4.18 7.93 9.09 12.45 8.23United States % bonds 0.75 4.30 9.05 10.54 14.48Japan % bonds 0.00 0.00 0.15 0.03 1.07Developed % bonds 60.15 70.89 67.78 81.51 21.05Emerging & Rest % bonds 18.49 29.11 32.22 39.85 21.05

bond portfolio characteristics

YTM % p.a 1.49 2.28 2.69 3.33 2.00Time-to-Maturity years 3.42 5.75 8.44 9.26 8.75Duration years 3.27 5.27 6.56 8.00 4.96No. bonds - 16.59 33.84 75.55 79.19 131.89

45

Table II: Funds’ Reach for Yield and Changes in the Term Structure

This table reports the response of funds’ Reach for Yield to changes in the yield curve. RFYph is theweighted average yield spread between a fund’s purchases portfolio and its holding portfolio. RFYpsis the weighted average yield spread between a fund’s purchases portfolio and its sales portfolio.∆YTM is the actual portfolio yield change from current period transactions. The Fspread is a fund-specific risk spread computed as the difference between the average yield on a fund’s bond portfolioand the equivalent-duration Nelson-Siegel Svensson zero coupon Bund rate. The Fslope is a fundspecific risk-free slope computed as the difference between the Nelson-Siegel Svensson zero couponBund rate having the same duration as the fund’s bond portfolio and the 3m zero coupon Bundrate. The fund controls are lagged log(age) log(AUM), cash share, bond share, derivatives share, 6mlagged average returns and 6m excess fund returns with respect to their fund family and fund class.We control for current and past 3m net flows. All series are monthly and reported in percentagepoints. Standard errors are two-way clustered at fund and time levels. t-stats in parantheses.***,**, and * indicate statistical significance at the 1%, 5% and, 10% level, respectively.

Variable RFYph RFYps ∆YTM

Sector Instit. Retail Instit. Retail Instit. Retail

L.Fslope -0.253*** -0.263*** -0.284*** -0.298*** -0.027*** -0.029***

(-4.73) (-4.90) (-3.34) (-3.50) (-2.81) (-3.34)

L.Fspread -0.143*** -0.103*** -0.218*** -0.062 -0.011*** -0.004

(-4.15) (-2.91) (-3.75) (-1.48) (-3.56) (-1.17)

Fund FE Yes Yes Yes Yes Yes Yes

Fund controls Yes Yes Yes Yes Yes Yes

R2 0.029 0.016 0.023 0.015 0.029 0.014

N 108,241 21,593 74,398 14,627 118,735 24,735

46

Table III: Funds’ Reach for Yield and Changes in the Term Structure

Institutional funds. This table reports the response of funds’ Reach for Yield to changes in theterm structure of interest rates. RFYph is the weighted average yield spread between a fund’spurchases portfolio and its holding portfolio. RFYps is the weighted average yield spread betweena fund’s purchases portfolio and its sales portfolio. ∆YTM is the actual portfolio yield changefrom current period transactions. The Rate5Y is the difference between the 5Y and 3m spot rateexpectations from a Hamilton and Wu (2012) term structure decomposition estimated on GermanBund data. Term5Y is the difference between the 5Y and 3m spot term premia from a Hamiltonand Wu (2012) term structure decomposition estimated on German Bund data. F-spread is afund-specific risk spread computed as the difference between the average yield on a fund’s bondportfolio and the equivalent-duration Nelson-Siegel Svensson zero coupon Bund rate. Fund controlsare lagged log(age) log(AUM), cash share, bond share, derivatives share, 6m lagged average returnsand 6m excess fund returns with respect to their fund family and fund class. We control for currentand past 3m net flows. All series are monthly and reported in percentage points. The robuststandard errors are two-way clustered at fund and time levels. ***,**, and * indicate statisticalsignificance at the 1%, 5% and, 10% level, respectively.

Variable RFYph RFYps ∆YTM

L.Rate5Y -0.652*** -0.810*** -0.055***

(-6.41) (-5.93) (-4.42)

L.Term5Y -0.110** -0.134 -0.020

(-2.21) (-1.63) (-1.64)

L.Fspread -0.194*** -0.285*** -0.014***

(-5.56) (-5.11) (-4.84)

Fund FE Yes Yes Yes

Fund controls Yes Yes Yes

R2 0.052 0.042 0.045

N 108241 74398 118735

47

Table IV: Robustness Across Term Structure ModelsInstitutional funds. The following table shows responses of specialized funds’ Reach for Yieldto changes in rate expectations and term premia coming from a range of model-implied termstructure decompositions. The first column posts estimates from a variant of Abrahams et al.(2016) estimated on the German zero coupon Bund curve. The second column shows results froma Hamilton and Wu (2012) 3 factor Gaussian Affine Term Structure Model estimated on EONIAOIS swap rates. The third column presents TBD. The last column shows estimates from a Lemkeand Vladu (2016) Shadow-Rate Term Structure Model based on EONIA OIS swap rates. Fspreadis a fund-specific risk spread computed as the difference between the average yield on a fund’sbond portfolio and the equivalent-duration Nelson-Siegel Svensson zero coupon Bund rate. Fundcontrols are lagged log(age) log(AUM), cash share, bond share, derivatives share, 6m lagged averagereturns and 6m excess fund returns with respect to their fund family and fund class. We controlfor current and past 3m net flows. All series monthly and are reported in pp. Standard errorsare two-way clustered at fund and time levels. T-stats are reported in parantheses.***,**, and *indicate statistical significance at the 1%, 5% and, 10% level, respectively.

AACM(2015)

Hamilton &Wu (2012)

Krippner(2015)

Lemke &Vladu(2014)

Variable ∆YTM ∆YTM ∆YTM ∆YTM

L.Rate10Y -0.048*** -0.030*** -0.031** -0.022

(-6.01) (-5.58) (-2.59) (-0.33)

L.Term10Y -0.009 -0.003 -0.021 -0.011

(-1.35) (-0.42) (-0.67) (-0.35)

L.Fspread -0.013*** -0.012*** -0.008*** -0.008***

(-5.12) (-4.91) (-3.32) (-3.06)

Fund FE Yes Yes Yes Yes

Fund controls Yes Yes Yes Yes

R2 0.033 0.037 0.025 0.022

N 118735 118735 118735 116963

*Lemke and Vladu (2016) series ends in June 2015.

48

Table V: Model-based vs Model-free Term PremiaInstitutional funds. This table compares the responses of funds’ Reach for Yield for two estimatesof term premia. The first column reports the10Y spot term premia implied by the Hamilton and Wu(2012) Gaussian Affine Term Structure Model estimated on EONIA OIS swap rates. The secondcolumn reports the 1m - 10Y ahead Nelson Siegel Svensson Bund forwards as a crude ”model-free”estimate of far-out-into-the-future term premia. Fspread is a fund-specific risk spread computedas the difference between the average yield on a fund’s bond portfolio and the equivalent-durationNelson-Siegel Svensson zero coupon Bund rate. Fund controls are lagged log(age) log(AUM), cashshare, bond share, derivatives share, 6m lagged average returns and 6m excess fund returns withrespect to their fund family and fund class. We control for current and past 3m net flows. All seriesare monthly and reported in percentage points. Standard errors are two-way clustered at fund andtime levels. T-stats are reported in parantheses.***,**, and * indicate statistical significance at the1%, 5% and, 10% level, respectively.

Hamilton &Wu (2012)

NSS 1m10Yahead Bund

forward

Variable ∆YTM ∆YTM

L.Term10Y -0.001 -0.004

(-0.16) (-0.97)

L.Fspread -0.008*** -0.008***

(-2.98) (-3.07)

Fund FE Yes Yes

Fund controls Yes Yes

R2 0.013 0.014

N 118735 118735

49

Table VI: Model-based versus Survey-based Rate Expectations

Institutional funds. This table compares the responses of funds’ Reach for Yield for two estimatesof expectations of future short rates. The first column reports the 3m 1Y-ahead expected OIS rateimplied by a Hamilton & Wu Gaussian Affine Term Structure Model estimated on EONIA OIS swaprates. The second column reports expectations of 3m EURIBOR in 1Y from a panel of Germanfinancial intermediaries and research institutions surveyed by Consensus Economics. Fspread is afund-specific risk spread computed as the difference between the average yield on a fund’s bondportfolio and the equivalent-duration Nelson-Siegel Svensson zero coupon Bund rate. Fund controlsare lagged log(age) log(AUM), cash share, bond share, derivatives share, 6m lagged average returnsand 6m excess fund returns with respect to their fund family and fund class. We control for currentand past 3m net flows. All series are monthly and reported in percentage points. Standard errorsare two-way clustered at fund and time levels. T-stats are reported in parantheses.***,**, and *indicate statistical significance at the 1%, 5% and, 10% level, respectively.

Hamilton &Wu (2012)

GermanConsensus

Survey

Variable ∆YTM ∆YTM

L.Rate1Y -0.027*** -0.023***

(-5.21) (-5.31)

L.Fspread -0.011*** -0.006**

(-4.93) (-2.55)

Fund FE Yes Yes

Fund controls Yes Yes

R2 0.027 0.031

N 118735 118735

50

Table VII: Funds’ Reach for Yield and Negative Yields

Institutional funds. This table reports results from a fixed effects panel regression on the differ-ential response of funds’ RFY in the presence of trading at negative rates. The dependent variable∆YTM measures the fund portfolio yield change from current period transactions. Share is themarket value share of a fund’s holding portfolio currently trading at negative rates. Wert is (0,1)dummy variable taking the value 1 if a fund is capital protected. The Fspread is a fund-specificrisk spread computed as the difference between the average yield on a fund’s bond portfolio and theequivalent-duration Nelson-Siegel Svensson zero coupon Bund rate. The Fslope is a fund specificrisk-free slope computed as the difference between the zero coupon Bund rate having the sameduration as the fund’s bond portfolio and the 3m Bund rate. Duration is a fund portfolio Macaulayduration, computed as the market-weighted average across all individual fund bond holdings’ dura-tions. Fund controls are lagged log(age) log(AUM), 6m lagged average returns and 6m excess fundreturns with respect to their fund family and fund class. In all cases involving interaction terms,we control for the main effects in the regression. The sample period is 07/2012-08/2015. All seriesare monthly and reported in percentage points. T-stats are reported in parantheses. ***,**, and *indicate statistical significance at the 1%, 5% and, 10% level, respectively.

Variable ∆YTM ∆YTM

L.Share 0.033*** 0.037***

(7.65) (8.36)

L.Share # Wert 0.040***

(3.98)

L.Fslope # Wert -0.007

(-1.01)

L.Fspread # Wert -0.010*

(-1.83)

L.Duration # Wert -0.000***

(-2.98)

Fund FE Yes Yes

Time FE Yes Yes

Fund controls Yes Yes

R2 0.075 0.089

N 66983 66983

51

Table VIII: Funds’ Reach for Yield and Flows as % last period AUM

Retail funds. This table shows results from a fixed effects panel regression on the relationshipbetween retail funds’ performance in attracting flows and past Reach for Yield. Flow represents afund netflow as a percentage of last period assets under management. Rank∆YTM is a continuousvariable on [0,1], where the fund ranking highest in hypothetical Reach for Yield tournament ranks1. Reach for Yield is defined as the change in portfolio yields from transactions. Our Stress variableis the Euro-Area Bond Market Composite Indicator of Systemic Stress (CISS), reported in terms ofhistorical standard deviations. Fund controls are lagged fund age, fund size, 6m lagged average fundreturns, 6m lagged average squared returns and 6m excess fund returns with respect to their fundfamily and fund class. We apply Huber-White standard errors. T-stats are reported in parantheses.***,**, and * indicate statistical significance at the 1%, 5% and, 10% level, respectively.

Variable Flow Flow

L.Rank∆YTM 0.030 0.209**

(0.75) (2.47)

L.Rank∆YTM# Stress

-0.098**

(-2.20)

Fund FE Yes Yes

Time FE Yes Yes

Fund controls Yes Yes

R2 0.067 0.067

N 22605 22605

52

Table IX: Funds’ Reach for Yield and Ranked FlowsRetail funds. This table looks at the response of investor flows to funds’ Reach for Yield in retailfunds. RankFlow is a continuous variable on [0,1], where the fund experiencing highest net periodpercentage inflows ranks 1, while the fund experiencing lowest net period percentage inflows ranks0. Rank∆YTM is a continuous variable on [0,1], where the fund ranking highest in hypotheticalReach for Yield tournament ranks 1. Reach for Yield is defined as the change in portfolio yields fromtransactions. Our Stress variable is the Euro-Area Bond Market Composite Indicator of SystemicStress (CISS), reported in terms of historical standard deviations. Fund controls are lagged fundage, fund size, 6m lagged average fund returns, 6m lagged average squared returns and 6m excessfund returns with respect to their fund family and fund class. Standard errors are two-way clusteredat fund and month levels. T-stats are reported in parantheses. ***,**, and * indicate statisticalsignificance at the 1%, 5% and, 10% level, respectively.

Variable RankFlow RankFlow

L.Rank∆YTM 0.001 0.030**

(0.08) (2.11)

L.Rank∆YTM# Stress

-0.016**

(-2.21)

Constant 0.463*** 0.440***

(10.46) (9.62)

Fund FE Yes Yes

Fund controls Yes Yes

R2 0.047 0.047

N 22605 22605

53

Table X: Funds’ Reach for Yield and Flows - An Ordered ProbitRetail funds. This table shows results from an ordered probit analysis on the relationship betweenretail funds’ performance in attracting flows and past Reach for Yield. RankFlow is a categoricalvariable from 0 to 9, taking value 0 if a funds falls in the bottom decile of next period flowsdistribution, and 9 if it falls in the top decile. Rank∆YTM is a continuous variable on [0,1], wherethe fund ranking highest in hypothetical Reach for Yield tournament ranks 1. Reach for Yieldis defined as the change in portfolio yields from transactions. Our Stress variable is the Euro-Area Bond Market Composite Indicator of Systemic Stress (CISS), reported in terms of historicalstandard deviations. Fund controls are lagged fund age, fund size, 6m lagged average fund returns,6m lagged average squared returns and 6m excess fund returns with respect to their fund familyand fund class. Figures report average marginal effects for the case where a fund would fall in thetop decile of next period flows distribution. We apply Huber-White standard errors. T-stats arereported in parantheses. ***,**, and * indicate statistical significance at the 1%, 5% and, 10%level, respectively.

P(top decile) P(top decile)

VariableRankFlow RankFlow

L.Rank∆YTM 0.006 0.017**

(1.70) (2.19)

L.Rank∆YTM# Stress

-0.006

(-1.57)

Fund FE Yes Yes

Fund controls Yes Yes

R2 0.047 0.047

N 22605 22605

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Table XI: Funds’ Reach for Yield and Termination of MandatesInstitutional funds.This table reports results from a random effects probit model on the re-lationship between Reach for Yield and the probability of mandate termination for the sampleof actively-managed, institutional German bond and mixed funds. The dependent variable is a(0,1) dummy taking the value 1 if the fund will be terminated the current period. Rank∆YTMis a continuous variable on [0,1], ranking funds based on their average 6m past performance inthe hypothetical Reach for Yield tournament. Our Stress variable is the Euro-Area Bond MarketComposite Indicator of Systemic Stress (CISS), reported in terms of historical standard deviations.Fund controls are lagged fund age, fund size, fund sponsor size, 6m lagged average fund returns, 6mlagged average squared returns and 6m excess fund returns with respect to their fund family andfund class. In the case of interaction terms, we control for main effects in the regression. Reportedfigures are average marginal effects. ***,**, and * indicate statistical significance at the 1%, 5%and, 10% level, respectively.

VariableP(termination) P(termination)

L.Rank∆YTM -0.006*** -0.016***

(-5.29) (-5.96)

L.Rank∆YTM# Stress

0.005***

(4.15)

Fund RE Yes Yes

Fund controls Yes Yes

N 166263 166263

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Table XII: Reach for Yield and Mandates - A Linear Probability Model

Institutional funds. This table reports results from a linear probability model on the relation-ship between fund Reach for Yield and the termination of mandates. The sample and regressionspecification follow exactly from those from Table XI. In the case of interaction terms, we controlfor main effects in the regression. We apply Huber-White standard errors.***,**, and * indicatestatistical significance at the 1%, 5% and, 10% level, respectively.

Variable P(termination) P(termination)

L.Rank∆YTM -0.005*** -0.018***

(-3.75) (-5.41)

L.Rank∆YTM# Stress

0.008***

(4.13)

Fund FE Yes Yes

Time FE Yes Yes

Fund controls Yes Yes

N 166263 166263

56

Table XIII: Funds’ Demand Pressures and Excess Bond ReturnsThis table shows results from a security-level fixed-effects panel regression on the fund-sector excessdemand as potential determinant for bond-specific excess returns. The dependent variable is the1m bond-specific excess return, computed as the difference between the 1m bond holding periodreturn and the 1m Bund rate. ExDem is a measure of fund-sector excess demand defined, for agiven security, as the difference between gross fund-sector purchases and gross fund-sector sales,divided by the security’s market value outstanding. LSV1992 is inspired from Lakonishok et al.(1992) and defines excess demand, for a given security, as the ratio of total fund-sector net monthlytransactions to total fund-sector gross monthly transactions. We control for the previous 3m bondexcess returns. All series are monthly and reported in percentage points. We apply Huber-Whitestandard errors. t-stats are reported in parentheses. ***,**, and * indicate statistical significanceat the 1%, 5% and, 10% level, respectively.

Variable 1m ExcessReturn

1m ExcessReturn

L.ExDem 0.101***

(3.57)

L.LSV1992 0.762***

(15.32)

L.rx1 0.215*** 0.214***

(38.36) (37.28)

L2.rx1 -0.046*** -0.050***

(-9.52) (-10.40)

L3.rx1 -0.018*** -0.011**

(-3.74) (-2.25)

Constant 6.966*** 6.883***

(19.94) (19.91)

Security FE Yes Yes

Time FE Yes Yes

R2 0.297 0.316

N 125700 116892

57