Market Accessibility, Corporate Bond ETFs,
and Liquidity∗
Craig W. Holden
Kelley School of Business
Indiana University
Jayoung Nam
Cox School of Business
Southern Methodist University
March 18, 2019
∗We thank Robert Battalio, Azi Ben-Rephael, Utpal Bhattarcharya, Matthew T. Billett, Dion Bon-gaerts, Brittany Cole, Caitlin Dannhauser, Richard Evans, Thierry Foucault, Vincent Glode, Eitan Gold-man, Ryan D. Israelsen, Sreenivas Kamma, Vincent van Kervel, Pete Kyle, Mina Lee, Katya Malinova,Rabih Moussawi, Christine Parlour, Veronika K. Pool, Uday Rajan, Jonathan Reuter, Jan Schneemeier,Batchimeg Shambalaibat, Philip Strahan, Noah Stoffman, Charles Trzcinka, S. “Vish” Viswanathan, ZhenyuWang, Wenyu Wang, Avi Wohl, Jun Yang, Wei Yang, Scott Yonker and seminar participants at In-diana University, the FIRS 2017 meeting, Eighth Erasmus Liquidity Conference, The Financial TheoryGroup Summer School, the Northern Finance Association, the Financial Management Association Meet-ings, and the 14th WFA-CFAR Corporate Finance Conference. Contact address: Jayoung Nam, SMU CoxSchool of Business, 6214 Bishop Blvd, Dallas, TX 75275; E-mail address: [email protected]; Webpage:https://sites.google.com/view/jnam/home
1
Market Accessibility, Corporate Bond ETFs, and Liquidity
Abstract
We find that market accessibility ex ante plays an important role in how the underlying
assets’ liquidity changes when a basket security is introduced. First, using a multi-market
version of the Kyle model, we show that the less (more) accessible the underlying market
is, the more its liquidity improves (deteriorates) when basket trading becomes available.
Second, we empirically test these predictions using corporate bonds before and after the
introduction of ETFs. Consistent with the model, liquidity improvement is larger for highly
arbitraged, low-volume, high-yield, and long-term bonds and for 144A bonds to which retail
investor access is prohibited by law.
2
1 Introduction
Prior literature on the introduction of basket securities shows that the liquidity of the un-
derlying assets deteriorates because uninformed investors migrate to the basket market in
order to reduce the cost of trading against informed traders. In this paper, we show that if
investor participation in the underlying market is limited, the liquidity of the underlying se-
curities can, in fact, improve after basket securities begin trading. To do so, we first develop
a theoretical model and find that the level of market participation ex ante is an important
determinant of how the underlying assets’ liquidity changes ex post. Second, we empirically
test the predictions of the model by investigating the liquidity of corporate bonds before
and after the introduction of corporate bond exchange traded funds (ETFs) using bond
transaction data from TRACE.
Theories of basket securities trading typically rely on the Kyle model (Kyle, 1985), in
which prices partially reflect informed investors’ information. In the model, liquidity trading
demand is independent of the private information about individual securities. Therefore,
liquidity investors are concerned with their expected losses from trading due to adverse
selection. Since basket securities diversify away the idiosyncratic risks of the underlying
securities, these liquidity investors can reduce their expected losses by trading in basket
securities. Hence discretionary liquidity investors migrate to the basket market and conse-
quently, liquidity in the underlying asset market declines. (Subrahmanyam, 1991; Gorton
and Pennacchi, 1993; Jegadeesh and Subrahmanyam, 1993; Hamm, 2014; Israeli, M. C. Lee,
and Sridharan, 2017)
Largely overlooked is the fact that the basket market and the underlying market may
not be equally accessible for liquidity investors. A large body of literature has shown that
investors’ market participation can be limited due to unfamiliarity (Merton, 1987; Huberman,
2001), home bias (Kang and Stulz, 1997; Pool, Stoffman, and Yonker, 2012), regulatory
constraints (Ellul, Jotikasthira, and Lundblad, 2011), lack of information, high transaction
3
costs, minimum investment requirements, or difficulty in liability matching. This implies
that for liquidity investors, diversification can be quite costly and trading barriers can be
high. As a specific example, for retail investors, the transaction cost associated with trading
an individual junk bond averages 110 basis points (Figure 1). Furthermore, the minimum
trading size for individual bonds is $100,000 at a major brokerage firm. In sharp contrast,
with the introduction of ETFs, investors can trade an entire corporate bond portfolio at a
cost of 1 basis point with an investment as small as $1,000. Additionally, an example of a
fully inaccessible market is 144A bonds, in which trading by retail investors is not legally
permitted.
To derive theoretical predictions, we develop a multi-market, adverse selection model
based on the adverse selection models of Kyle (1985) and Subrahmanyam (1991). In our
model different markets have different levels of accessibility (defined more precisely below).
We show that the introduction of basket securities in a market with limited investor partic-
ipation leads to an improvement in the liquidity of the underlying securities. An important
mechanism that links limited market participation to liquidity is the entrance of new unin-
formed investors into the basket market. This happens because they can achieve a better-
diversified portfolio at a low cost through basket securities. The high liquidity of the basket
securities spills over to the underlying market by arbitrage trading, which we incorporate by
integrating the arbitrage trading model of Holden (1995). This liquidity spillover is greater if
the influx of these new uninformed investors outweighs the departure of uninformed investors
from the underlying market, which is likely to occur when the underlying market ex ante is
less accessible. Conversely, if the underlying market ex ante is highly accessible, then the
departure of uninformed investors from the underlying market exceeds the entrance of new
investors and the liquidity of the underlying securities deteriorates.
Testing whether trading basket securities in a market with limited participation improves
or hurts liquidity of the underlying securities requires that a proper financial instrument be
available. Existing theoretical and empirical studies have focused on the equity market and
4
unanimously find only a one-directional effect: the deterioration of liquidity in the underlying
markets upon the introduction of a basket security. These results are consistent with one of
the predictions of our model since the underlying equity market is already highly accessible.
Yet few studies have been conducted outside the equity market, for example in the bond
market, due to limited data availability of the instruments. One possibility would be open-
end bond mutual funds, which, like bond ETFs, allow investors to achieve a diversified
portfolio at a smaller cost than investing in a portfolio of individual bonds,1 and have long
been available. Although there are no price differences between bond mutual funds and the
underlying assets,to the extent that arbitrageurs primarily utilize trading volume differences
between two correlated markets, the liquidity spillover mechanism would still be present
in bond mutual funds. Therefore, a first environment to test our theoretical predictions
would be around 1986 when Vanguard introduced the first bond index mutual funds.2 Un-
fortunately, the transaction data for corporate bonds are unavailable. A second possibility
would be around 2004 when the TRACE dataset became publicly available. However, newly
launched mutual funds nearly 22 years after the introduction of initial bond mutual funds
may not be an ideal environment in which to assess the effect of market accessibility.
In this paper, we test the predictions of our model using TRACE intraday trading
data for corporate bonds and ETFs since 2007.3 Using the indicator-based regression
methodology (Huang and Stoll, 1997; Bessembinder, Maxwell, and Venkataraman, 2006)
in a difference-in-difference (DD) framework, we compare the changes of the effective spread
of corporate bonds before and after they are included in a newly launched ETF for the first
time. Our control sample consists of corporate bonds that are not included in ETFs but are
similar along other dimensions such as credit rating, time to maturity, issue size, issuer, etc.
1Passively-managed high yield bond mutual funds are non-existent. Actively-managed high yield bondmutual funds cost 138 basis points annually. The annual costs for investment grade bond mutual funds arelower: 88 basis points if actively managed, 33 basis points if passively managed (Cici and Gibson (2012);Chen, Ferson, and Peters (2010)).
2Vanguard total bond market index mutual fund3While equity ETFs started trading in 1993, corporate bond ETFs became available only after 2002 and
have only become popular since 2007.
5
We find that non-investment grade ETF bonds experience improved liquidity (i.e., a
reduction in transaction costs) of 10%, while investment grade ETF bonds experience an
insignificant change in trading costs. ETF bonds with low volume see improved liquidity
once the ETF starts trading, while those with large volume see decreased liquidity. ETF
bonds whose remaining life is greater than 2 years see improved liquidity. As evidence for
the underlying economic mechanism, we estimate the degree of arbitrage between the ETF
market and the underlying bond markets using the creation and redemption activities of
ETFs. We find that bond liquidity improves only when the arbitrage activities are high.
An especially interesting cases is that of 144A bonds, which are privately placed corporate
bonds and are traded among a limited number of qualified investors (i.e., large institutional
investors), such as insurance companies (Carey, Prowse, Rea, and Udell, 1993). Retail
investors are prohibited by law from trading 144A bonds. However, they can gain exposure
to 144A bonds for the first time once bond ETFs start holding them. We find that the
underlying market in 144A bonds experiences a 20% improvement in liquidity once bond
ETFs start holding 144A bonds. This further supports our key theoretical prediction. That
is, if market accessibility is even more limited, then uninformed demand for ETFs can have
an even more positive liquidity spillover to the underlying bonds.
To further support the causality of our results, we also use a quasi-natural experiment
that happened to one of the major high yield bond ETFs, namely, the iBoxx high yield $
corporate bond ETF (HYG). In June 2009, the index composition rules were changed and
were announced only 8 days before the effective date. As a consequence, the number of newly
added bonds dramatically increased to 248 versus only 16 bonds were added previously from
January to May of 2009. Consistent with our predictions, we find an improvement in the
liquidity of those newly added bonds of 3.5%.
Our findings contribute to our understanding of the liquidity of assets by providing new
evidence about the role of market participation, the channel through which it operates, and
the magnitude of its effects on the liquidity of the underlying assets. Both our theoretical
6
results and our empirical tests using bond ETFs are unexpected, given the past evidence
that trading basket securities causes the liquidity of the underlying assets to deteriorate.
However, as previously noted, the existing literature focuses only on the U.S. equity market,
which is a highly accessible market.
Extant literature on the liquidity of the bond market has sidestepped the role of investor
market participation, and has focused on the supply side of the market. While empirical
studies find that the liquidity of corporate bonds can be affected by innovative market mech-
anism design (Biais and Green, 2019; Abudy and Wohl, 2017; Hendershott and Madhavan,
2015), by regulation of post-trade transparency (Schultz, 2001; Bessembinder et al., 2006;
Edwards, Harris, and Piwowar, 2007), and by dealers’ capital commitment (Bessembinder,
Jacobsen, Maxwell, and Venkataraman, 2017; Choi and Huh, 2017), market accessibility is
assumed to be unaffected – perhaps because market participation is hard to change due to
large trading barriers to entry. Our findings suggest that when the underlying market ac-
cessibility is more limited, the liquidity spillover benefits of ETF trading are greater. Thus,
one fundamental driver of corporate bond liquidity appears to be market accessibility.
Our paper also contributes to the growing literature on ETFs. We investigate whether
the introduction of ETFs has any impact on the liquidity of the underlying assets and find
that the effects can be predicted based on long term market accessibility ex ante. Con-
currently, there is active interest in various other impacts of ETFs on market quality: on
market fragility (Bhattacharya and O’Hara, 2016), on the fragility of liquidity spillovers (Pan
and Zeng, 2017), on market volatility (Ben-David, Franzoni, and Moussawi, 2018), on co-
movements of asset returns (Da and Shive, 2018), and on the valuation of the underlying
assets (Dannhauser, 2017). The important aspect of our paper is that market quality can
change in opposite directions depending on long term market accessibility ex ante.
The two papers closest to our study are Dannhauser (2017) and Ye (2019). Dannhauser
(2017) examines how ETFs affect the value of the underlying bonds. Using the same natural
experiment, she shows that corporate bond ETFs improve bond valuations and argues that
7
this is due to the influx of institutional investors into the ETF market. As an auxiliary result,
she also finds that bond market liquidity is not significantly affected overall. A potential
source of the discrepancy between the Dannhauser results and those in this paper stems
from differences in methodology. Our study employs the event study method six months
before and after the introduction of ETFs using the bonds added to the ETF portfolio for
the first time, while her study utilizes a long panel regression over four year periods with
ETF ownership.
In a contemporaneous working paper, Ye (2019) empirically examines the liquidity of
corporate bonds when they are included or excluded from corporate bond ETFs. Using a
liquidity measure which is different from that in this paper, he finds that corporate bond
liquidity improves when a bond is included in an ETF and deteriorates when it is excluded.
His results corroborate our findings and suggest that the argument that the introduction of
ETFs enhances bond liquidity is robust to different measures of liquidity and is symmetric
to exclusions. However, the theoretical contribution of our paper explains why underlying
liquidity is expected to respond differently to the introduction of ETF trading in the stock
and bond markets.
The rest of the paper is organized as follows. In section 2, we develop our theoretical
model and derive empirical predictions. In section 3, we discuss the data and the indicator-
based regression methodology. In section 4, we discuss the empirical results. In section 5,
we conclude.
2 The Model
The first step of our analysis is to model trading of liquid assets as a means of ensuring
uninformed investors’ ability to maintain a diversified portfolio efficiently. Our basic model
is a simple representation of a dynamic problem in which all the investors (uninformed,
informed, arbitrageurs, market makers) optimize their trading decisions and in which new
8
liquid assets increase accessibility of the market. Depending on market accessibility ex ante,
the introduction of new liquid assets may have positive, negative, or neutral impacts on the
liquidity of the assets in place.
2.1 The setup
We consider a security market with two assets and use i ∈ {1, 2} to index the assets. There
are two times. Trading takes place at time 0 and the assets are liquidated at time 1. Let
vi be the time 1 liquidation value, which we assume is normally distributed, vi ∼ N(µi, σ2i ).
For simplicity, we assume that the two assets are uncorrelated, Corr(v1, v2) = 0.4
As in the standard Kyle model, there are (i) informed investors, (ii) uninformed investors,
and (iii) market makers. There are two types of uninformed investors, discretionary or non-
discretionary, as in the Subrahmanyam model.
There are ki risk neutral, informed investors who specialize in the ith security market,
and who have private information about the idiosyncratic risk εi ≡ vi − µi of security i.
They have no interest in speculating in the markets for other securities because ρ = 0.
Informed investor j (j = 1, · · · , ki) in the ith security market optimizes her expected profit,
E [xij (Pi − vi) |εi], from trading on her private information by choosing her optimal demand,
xij, where the price Pi of the security will be determined by the market makers.
There are non-discretionary uninformed investors in each of the markets, who are ob-
ligated to trade a particular security and are not allowed to trade other assets. Their de-
mand for security i is denoted by ui. We assume that this demand is normally distributed,
ui ∼ N(0, σ2ui
).
There are Q risk averse, discretionary liquidity investors who may invest in either of
the assets. They have to trade the assets due to random arrival of liquidity needs. Let lq
denote the demand of investor q (q = 1, · · · , Q), which we assume is normally distributed
lq ∼ N(µl, σ2l ). In order to keep their portfolios diversified, they would prefer to allocate their
4All of the results continue to hold for ρ 6= 0.
9
trading need across the assets with weights αi, to be determined. However, high transaction
costs and high minimum trading requirements may prevent this optimal allocation.
There are multiple risk neutral, market makers in each market. The market makers
observe the demand from all investors – informed, non-discretionary, and discretionary –
and set the price (Pi) of asset i. Competition among the market makers implies that they
set the prices of the securities so as to earn zero excess profit. Following the linear pricing
rule as in the Kyle model, the price of each security i is set by the market makers as
Pi = µi + λiωi = E [vi|ωi]. As in the Subrahmanyam model, the aggregate order flow ωi
is augmented with additional trading demand αilq from discretionary uninformed investors.
The total informed trader demand is xi =∑ki
j=1 xij. The slope of price function λi scales
the price impact of the aggregate order flow submitted by all agents and is determined
endogenously within the model.
We assume discretionary investors have a negative exponential utility function, − 1γ
exp(−γW ),
over their time 1 wealth W , where γ > 0 is the risk aversion parameter. In order to maximize
their diversification benefit, the discretionary investors may choose to split their demand lq
between the assets. But they are concerned about losses due to trading against informed
investors. The expected loss from trading security i will be E [(Pi − vi)αilq] = λiα2i Var(lq).
The uninformed investors wish to maximize their expected utility,
maxα1,α2
E[−1
γexp (−γW )
]= max
α1,α2
{−1
γexp
[−γE(W ) +
γ2
2Var(W )
]}, (1)
W =
(α1W0
µ1
+ α1lq
)v1 +
(α2W0
µ2
+ α2lq
)v2 − (P1 − v1)α1lq − (P2 − v2)α2lq, (2)
E [|α1lqP1|] ≥ B1, for lq 6= 0, and E [|α2lqP2|] ≥ B2, for lq 6= 0, (3)
where 0 ≤ α1, α2 ≤ 1. The objective function (1) is feasible only if the optimal trading
allocation decisions are feasible. We assume in (3) that the expected trading in each security
market must exceed a minimum trading barrier. The minimum trading barriers Bi > 0 are
10
exogenously provided.5 Positive lq indicates buying. Negative lq implies selling.6 If lq = 0,
then no trading occurs, in which case the strategic decision is reduced to the traditional
setting for long term investment.
Definition 1. The equilibrium of the model consists of:
(i) a trading allocation strategy (α1, α2) by discretionary uninformed investors that maintains
the diversification benefits, given the price functions and the trading strategies of informed
investors and non-discretionary uninformed investors,
(ii) a trading strategy (x1, x2) by the informed investors to maximize their expected trading
profits, given the price functions and the trading strategies of the other agents, and
(iii) the price function (P1, P2) set by market makers with zero excess profits.
In order to analyze the effect of the introduction of ETFs on the equilibrium, the baseline
model above is modified as follows. The value of the ETF at t = 1 depends on the values
of the underlying assets, vE = w1v1 + w2v2, where wi is the weight if security i in the
ETF and the weights sum to one. The informed investors in the market i can use their
private information about vi to draw inferences about the value of ETFs, although the
signal is noisy. They trade the ETF to maximize their expected profits. We assume non-
discretionary uninformed investors have an exogenous trading demand uE for the ETF that
follows a normal distribution N(0, σ2uE
). The discretionary uninformed investors reconsider
their strategic decisions on the optimum portfolio trading allocation across securities 1, 2,
and the ETF, i ∈ {1, 2, E}. The trading barrier for ETF is strictly smaller than those for
the individual securities, BE < B1 and BE < B2. For simplicity, we assume the required
minimum trading for the ETF is nonexistent: BE = 0. ETF market makers set the price
with zero excess profits due to competition.
5In reality, the trading barriers Bi take both monetary and non-monetary forms and their magnitudesvary. Trading individual stocks costs little so that Bi is effectively zero. In contrast, the minimum tradingsize for regular corporate bonds is $100,000, while 144A bonds trading are not legally permitted to retailinvestors so that Bi = ∞. Investors are less familiar with high yield bonds than investment grade bondsthat Bi for high yield bonds is higher than that for investment grade bonds.
6The likelihood of selling can be made small by assuming µl > 0, but the results of our paper remain thesame.
11
In addition, arbitrageurs exploit price differences, PE − w1P1 − w2P2, between the un-
derlying securities and the ETF. This is possible because ETFs have their own supply and
demand in the market, independent of those for the underlying securities. The price PE for
the ETF can diverge from the prices of the underlying assets. We assume arbitrageurs’ trad-
ing demand θ is proportional to the trading volume difference of the uninformed investors
uE +αelq−α1(u1 + lq)−α2(u2 + lq). They trade to maximize their arbitrage trading profits.
Finally, the new equilibrium of the model incorporates the optimal trading activities θ∗
by arbitrageurs.
2.2 Analysis
2.2.1 Unconstrained solution. The market is accessible when the uninformed investors
are able to trade at their best levels before and after the ETFs are introduced. In the pre-
ETF period, optimal trading allocations are found to be
α?i =1
γ
(W0 −
σ2l
Ai+ γ
σ4l
Ai
)[W 2
0 σ2i + µ2
iσ2l (σ
2i + 1)− σ2
l µ2iσ
2i
(k2i + ki + 1
k2i + 2ki + 1
)]−1
,
where Ai =[
ki(ki+1)2
µ2i σ2i
σ2u+σ
2l
]−1/2
. See Appendix A for the proof. The assumption of accessibil-
ity means that the investors’ trading strategy α?i satisfies the trading constraints (3). More
explicitly, α?i satisfies |α?i lqPi| ≥ Bi for i = 1, 2. The exogenous parameters that determine
the market accessibility are the investors’ liquidity trading demand lq and a minimum trad-
ing requirement Bi. If investor q’s liquidity demand is large relative to the required trading
minimum, then that investor is not constrained and can access the market. A market is
accessible to all investors if the trading minimum Bi is small enough. One of the important
endogenous parameters is the impact λi of trading by all market participants. Under the
assumption that market i is accessible to all participants,
λi =
√ki
(ki + 1)2µ2iσ
2i
σ2ui
+ α?2i σ2l
,
12
which is smaller than it would be if the market were partially accessible. This implies that
the market is more liquid.
In the post-ETF period, the optimal trading allocation is found to be α̃?i = α?i − α̃?ewi.
This post-ETF trading allocation α̃?i in the accessible markets is smaller than the pre-ETF
trading strategy α?i , i.e. α̃?i < α?i . (See Appendix A for proofs of these statements.) The
ETFs are basket securities where idiosyncratic risks have been diversified away. The cost of
and probability of trading against an informed investor, also known as adverse selection, are
lower in the ETF market. As a consequence, discretionary uninformed investors prefer to
trade ETFs. The endogenous liquidity parameter λ̃i is found as
λ̃i =
√ki
(ki + 1)2µ2iσ
2i
σ2ui
+ α̃?iσ2l + σ2
θ + 2 Cov(θ, ui).
Under the condition that σ2θ + 2 Cov(θ, ui) < (α?i − α̃?i )σ2
l , the post-ETF liquidity declines
as the trading leads to a bigger price impact, i.e. λ̃i > λi. For our purposes, the main
implication of this condition is that if the market is highly accessible, the trading volume σ2l
by discretionary uninformed investors is large and this condition is more likely to hold.
2.2.2 Constrained solution. The market is inaccessible when the discretionary unin-
formed investors are not able to achieve their best trading strategy, α? for i = 1, 2 because
of market accessibility frictions. When the market is inaccessible, the uninformed investors’
optimal trading strategy above may not be implemented. More explicitly, the condition for
the market to be inaccessible is that for some realizations lq, the trading strategy α? above
does not satisfy the condition
α?i =BiAi − (xi + ui)lq
µiAilq + l2q.
We assume that when the optimal trading strategy is infeasible, an uninformed investor q
trades the risk free security. As a consequence, some uninformed investors hold an under-
13
diversified portfolio in the pre-ETF period. In the extreme case that the optimal strategy is
infeasible for all discretionary ‘uninformed investors, the market liquidity in this market is
related to the non-discretionary uninformed investors only,
λi =
√ki
(ki + 1)2µ2iσ
2i
σ2ui
.
We assume the minimum trade constraint Be for the ETF market is zero for simplicity.
The introduction of ETFs allows these uninformed investors to trade the ETFs at their
best level α?e (See Appendix A for the proof). This influx α?elq of the uninformed investors
further increases the ETF liquidity, and increases the arbitrage trading volume σ2θ . The ETF
liquidity spills over to the underlying market through arbitrage. The post-ETF liquidity λ̃
in the underlying market, which is defined as
λ̃i =
√ki
(ki + 1)2µ2iσ
2i
σ2ui
+ σ2θ + 2 Cov(θ, ui)
,
is smaller than its counterpart λi in the pre-ETF market. Hence market liquidity in the
inaccessible market improves after the introduction of ETFs. 7
2.3 Implications
The proposition below states the main implications of our model. A graphical representation
of the proposition is shown in Figure 3.
Proposition 1. Let the market liquidity before and after the introduction of ETFs be λi and
λ̃i, respectively.
1. If the market is sufficiently accessible ex ante, then the market liquidity decreases after
7While we assume the investors in inaccessible market reside in the risk free asset, some of the investorsmay have invested in mutual funds. If these investors migrate to ETFs from mutual funds, then potentiallythe liquidity of the bonds owned by mutual funds, exclusive of those owned by ETFs, may decline. (Changand Yu (2010)) More importantly, our main theoretical predictions on the liquidity of the underlying bondsowned by ETF, exclusive of mutual funds, remain the same.
14
the introduction of ETFs, λ̃i > λi.
2. If the market is inaccessible ex ante, then the market liquidity in the post-ETF periods
increases, λ̃i < λi.
3. As the accessibilities differ more between the basket and underlying markets, the decre-
ment (or the increment) of the market liquidity is larger.
In empirical terms, the proposition above implies that the introduction of ETFs can
have differential impacts on the stock and corporate bond markets. The stock market is
as highly accessible as the ETF market. When stock ETFs are introduced, the liquidity of
the underlying stocks should decrease. This prediction is consistent with the prior litera-
ture (Subrahmanyam (1991)) and has been tested multiple times with stock index futures
and with stock ETFs. In contrast, the liquidity of the bond market should increase when
bond ETFs begin trading. Bond ETF liquidity spills over to the underlying bond market
through arbitrage. Our theoretical predictions are driven by a comparison of accessibility
between the ETF market and the underlying securities markets. Our empirical tests that
follow are focused on less accessible bond market.
A special case of Proposition 1 can be applied to mutual funds. Arbitrageurs may pri-
marily be interested in exploiting trading volume differences between the basket market and
the underlying securities market. As long as the arbitrage trading demand θ is present, the
predictions in Proposition 1 still hold.
3 Research Design
3.1 Identification strategy
To test empirically the impacts of ETF trading on liquidity of constituent securities with
limited accessibility, We identify a source of plausibly exogenous variation in changes in
market accessibility. Most of our analysis focuses on inclusions of corporate bonds in ETF
portfolio as the result of new ETFs inceptions.
15
Identification requires that such corporate bond inclusions correlate with the introduction
of ETFs but not otherwise correlate with the bonds’ expected liquidity. A concern is that
inclusion of a security in a tradable portfolio can be viewed implicitly as a revelation of its
expected liquidity. Managers of passively managed funds would prefer to include more liquid
securities in order to minimize any tracking errors since large observed tracking errors could
deter future investors. Similarly, the managers may drop a security to which tracking errors
are attributed.
We avoid these biases by focusing on introductions of new ETFs, rather than selective
inclusions of individual bonds in existing ETFs. The period since 2002 has seen a gradual
increase in the number of fixed income ETFs. We argue that this growth has been fueled by
unexpected economic events. According to the CEO of Barclays Global Investors (BGI), the
first four corporate bond ETFs8 - LQD, TLT, IEF, SHY - were launched as an experiment.
The hope was that investors would be attracted to financial instruments featuring easy and
inexpensive access to diversified portfolios with simple governance. BGI, being the first
comer, felt that they had a comparative advantage in costs and the offering of new market
access. The bursting of the internet bubble and the Enron accounting scandal in early 2000–
2001 revealed that active fund managers are largely concentrated only in a small number of
securities. This increased investor appetite for diversified portfolios with simple governance,
and hence to survival of the very first bond ETFs. Starting in 2008, the unexpected col-
lapse of Lehman Brothers raised concern about counterparty risk with broker-dealers, upon
which interest in ETFs with more illiquid, instead of liquid, bonds developed. Finally, a
proposed 2016 FINRA fiduciary rule change affecting financial advisors has led to increased
recommendations for ETFs for the benefit of low cost diversification9.
Even so, one may worry that advisory boards for passive ETFs use a random sampling
indexing strategy, which invests in a representative sample of bonds in the underlying index,
8iShares IBoxx $ Investment grade corporate bond ETF (LQD), iShares 20+ Year Treasury BondETF (TLT), iShares 7-10 Year Treasury Bond ETF (IEF), iShares 1-3 Year Treasury Bond ETF (SHY)
9Cite the article with the chart
16
allowing some selection effects. We argue that this concern is largely muted for these new
passively managed ETFs as the composition of the underlying index is predetermined inde-
pendently from the introduction of ETFs. Although the ETF managers wish to minimize
the tracking errors, the lack of their track record in the past makes it difficult to control
for the expected liquidity of the underlying bonds. In addition, according to SEC filings
for passively managed ETFs, the selection is determined by a broad range of criteria. The
liquidity needs are addressed by allowing the funds to invest in derivatives securities or other
liquid assets defined by the advisory board. For instance, the SEC filing for one passively
managed ETF – iShares high yield bond ETF (HYG) – explains the guidelines for inclusion
of a security in the ETF portfolio as follows.10
The selected bonds have, in the aggregate, market capitalization, industry weighting,
return variability, earnings valuation, duration, maturity, credit rating, yield, and
liquidity measures similar to those of the underlying index. Furthermore, to help
ETFs track its underlying index, ETFs invest at least 80% of its assets in compo-
nent securities of their underlying index, and may at times invest up to 20% of its
assets in futures, swap contracts, cash, cash equivalents that are not included in the
underlying index.
One exception is when the benchmark index for an ETF changes its composition. In
section 4.2.2, we investigate the inclusion of corporate bonds in an existing ETF portfolio,
which was caused by the changes of the index composition rule. Since the market segment
covered by this ETF has been accessible since its inception, the empirical results depend on
how much the newly added bonds are related to the existing bonds.
The use of the introduction of new bond ETFs to identify exogenous changes of market
accessibility is new to the literature. The identification strategy behind the addition in ETF
portfolios is similar in spirit to those of ? or Dannhauser (2017). However, we believe that
our identification more cleanly identifies more true effect of ETF inclusion. Dannhauser
10https://www.sec.gov/Archives/edgar/data/1100663/000119312507007582/d485apos.htm
17
includes the bonds added to the ETFs, but, unlike me, she did not condition on those newly
launched. Ye investigates both the inclusion and exclusion effects of ETFs. But it can
capture some endogenous effects as the existing ETFs have observed their tracking errors
and can strategically decide to include more liquid bonds or to exclude illiquid bonds in
order to reduced those observed tracking errors.
3.2 ETF data
Specifically, we use an identification strategy with two main building blocks. First, we focus
on passively managed corporate bond ETFs newly launched during the 2007 and 2014 period.
Second, we require that the bonds included in the newly launched ETFs not be included in
an existing ETF. We do allow them to be held by existing bond mutual funds. Any empirical
results will be additional impact of ETF inclusion beyond that effected by the inclusion in
the existing bond mutual funds. However, if these bond mutual fund and ETFs are newly
launched at the same time, then we exclude the newly added bond from our treatment group
as both instruments improve the market accessibility and the interpretation of our empirical
results would be ambiguous.
To identify ETFs, we use the CRSP mutual fund database with the identifier, et flag.
To find corporate bond ETFs, we first limit the universe of ETF to the fixed income category,
that is, to those ETFs whose lipper asset code (lipper asset cd) is either TX or MB. We
exclude fixed income ETFs that concentrate on Treasury bonds or non-bond assets such as
commodities. From the remaining ETFs, we select those ETFs, in which corporate bonds
comprise at least 20% of the holdings. We identify the subset of the ETF constituent bonds
that are added to ETF portfolio for the first time within 6 months after the inception date
of ETFs.
A potential complication is that for some ETFs, their portfolio holdings were reported to
CRSP with a significant delay, as also noted in Dannhauser. This delayed reporting problem
is more prevalent with ETFs that were launched before 2007. To alleviate this concern, for
18
ETFs offered by BlackRock iShares, we hand collected the monthly portfolio holdings from
the funds’ websites. For other index ETFs with the reporting delay problem, we assume
that the identities of the portfolio holdings do not change significantly between the inception
date and the earliest report date, if made within a year. We take the holdings on the earliest
reported date to be the initial ETF portfolios.
Due to data availability on ETF holdings, our data starts from 2007, which is also the
year in which the number of fixed income ETFs and the aggregate assets under management
increased rapidly (Figure 4). Hence our data are sufficiently large to test the theoretical
predictions in Section 2. However, this will exclude the very first ETFs such as LQD (iBoxx
investment grade corporate bond ETF, launched in 2002) from our analysis.
Table 1 summarizes the 216 corporate bond ETF inceptions. These 216 new ETFs are
sponsored by 24 fund families in total, the largest eight of which are shown in Panel A of
Table 1. The very first corporate bond ETFs were offered by BlackRock in 2002. Corporate
bond ETF inceptions jumped in 2007 and have been spread out fairly evenly over time since
then. Restricting to funds in the CRSP portfolio holdings database reduces the number of
ETFs to 117 as in Panel B of Table 1. In particular, the holdings data for the early ETFs
from 2002 and 2003 are absent due to a long reporting delay. The inception of these 117
ETFs involves 106 unique fixed income securities per fund on average; the maximum is 1840.
On average, these inceptions include 90 securities that appear for the first time.
After the filtering of the bonds described in section 3.1, these 117 newly launched ETFs
are associated with 2,623 corporate bonds that are added to their portfolios for the first time
and whose trades are reported to TRACE (Figure 4). In our data sample, the corporate
bond transaction data starts from 2008 when FINRA started disseminating the buy and sell
indicators (Dick-Nielsen, 2009). Since 2010, ETF portfolios have become larger and a larger
number of bonds have been added.
Identification also requires that ETF introductions increase market accessibility for in-
vestors. To test whether they do, we examine changes in empirical proxies for market
19
accessibility such as fees, share classes, spreads around ETF inclusions. We also examine
characteristics of corporate bonds included in the newly launched ETF portfolios for the first
time. Throughout we use difference-in-difference tests to help remove secular trends that
affect similar bonds at the same time. The purpose of our diff-diff tests is to test whether
bonds that experience exogenous ETF inclusions undergo changes in liquidity that exceed
those expected in the absence of such inclusion.
To remove common influences, diff-diff tests compare the change in liquidity for treated
bonds to the contemporaneous change for a set of control bonds matched to have similar
characteristics but which are unaffected by the treatment. We match bonds on trading
volume before the inclusion in ETF, credit rating at the time of trading, time to maturity,
and issue size based on the prior literature. In addition, we match for mutual fund holdings
and dealers’ inventory positions. Specifically, we choose as controls for each included bond,
one bond based on the observed characteristics, subject to the condition that control bonds
did not experience a new mutual fund inclusion in the six months before or after.
3.3 Corporate bonds intraday trade data
In order to estimate the transaction costs of corporate bonds, we use the enhanced TRACE
intraday trading data for corporate bonds issued by U. S. firms as our primary database.
TRACE covers over 95% of U.S. corporate bonds traded since October 2004 (Rossi, 2014).11
The exact dollar transaction trading volumes are available. We use the masked dealer identity
information available in the enhanced TRACE and control for the dealer inventory changes
in relation to the bond liquidity as a consequence of ETF inclusion. Our dataset consists of
corporate bonds that were added to an ETF within 6 months of its inception date, but never
included in other ETF portfolios previously, and whose trading was reported to TRACE.
We exclude transactions associated with issuing, cancelled orders, sellers’ option settle-
ments, or reversals, following Dick-Nielsen (2009). Inter-dealer transactions may have been
11Corporate bonds traded in NYSE Automated Bond System (ABS) are not reported to TRACE.
20
reported twice by both of the dealers, in which case we keep only one trade. Large changes
of bond price during the day are likely to arise for reasons unrelated to liquidity pressure, for
instance, when an ETF manager possesses private information about the true value of the
bond or has skills to forecast future price changes of the bonds. We therefore remove bond
transactions whose prices at two consecutive time periods deviate more than 10%, following
Campbell and Taksler (2003).
We obtain historical bond credit ratings from DataStream and the issue amount, the issue
date and the issuer information from Bloomberg. For missing data, we use the Mergent Fixed
Income Securities Database (FISD) to complement the information. The three major credit
rating agencies – S&P, Moody’s, Fitch – have different rating categorizations. Following
the convention in the credit rating literature (Becker and Ivashina, 2015; Cornaggia and
Cornaggia, 2013; Bongaerts, Cremers, and Goetzmann, 2012), we assign a numeric score for
the credit rating scale from each rating agency (Becker and Ivashina, 2015) and merge the
scores for each bond as follows: At any date, if three rating scores are available, we take the
middle score. If two rating scores are available, we take the lower score. As the credit rating
upgrades or downgrades for each bond, this combined rating score is adjusted.
We also use a separate database for the 144A bond transactions in TRACE (trace btds144a)
and investigate the liquidity spillover between ETFs and the underlying bond market in sec-
tion 4.3.
3.4 Public information variables
Two of the public information variables – on-the-run Treasury security returns and the yield
spread between BAA bonds and the risk free rate – are obtained from the Federal Reserve
Statistical Release. The third, the percentage returns on the issuing firms’ common stock,
is obtained from the CRSP daily stock returns database.
21
3.5 Corporate Bond Liquidity Measure
Many of the studies on intraday trade execution costs in equity markets rely on pre-trade
bid and ask quotes and construct measures such as quoted spreads or effective spreads.
Alternatively, daily trading cost of securities can also be estimated from daily returns. For
instance, Lesmond, Ogden, and Trzcinka (1999) developed the LOT measure, which has
been applied to the global market by Fong, Holden, and Trzcinka (2017). It assumes that
informed investors trade only when their expected profits outweigh the transaction costs of
the securities. The daily returns themselves implicitly reflect trading costs, which can be
recovered computationally. Chen, Lesmond, and Wei (2007) applied the LOT measure to
corporate bonds and showed that it is correlated to their available quotes.
In contrast to equity market, pre-trade quotes for the majority of corporate bonds are
not available and hence effective or quoted spreads are not viable measures.12 To overcome
this limitation, two alternative measures have been used in the literature. The first method
is estimating transaction costs of corporate bonds by investigating a subset of the bonds
that have both buy and sell orders on each day. The difference between the average buy
and sell prices is used as a measure of the daily transaction costs of the bond. Since it is
common for corporate bonds to have only buy trades or only sell trades on a given day, this
method significantly reduces the number of usable data. Inferring the transaction costs for
other non-traded bonds from it could potentially be limited (Schultz, 2001).
A second method is the indicator-based regression model, which was originally suggested
for equity markets by Huang and Stoll (1997), and then extended to corporate bonds by
Bessembinder et al. (2006). This method decomposes the spreads into adverse selection
costs, inventory costs, and order processing costs. This model does not require that bond
trades are present both on buy and sell sides and is applicable as long as some trades occur.
12Pre-trade quotes are available for corporate bonds traded in NYSE Automated Bond Systems, but arenot reported to TRACE. Reuters Fixed Income Database provides the daily quotes from major bond dealers.Recently, Schestag, Schuster, and Uhrig-Homburg (2016) use the daily bond prices reported in Datastreamand measure the liquidity of bonds.
22
However, very few trades in a short time period or very sparsely traded bonds will make the
cost estimates noisy.
In this paper, we measure corporate bond liquidity by adopting the indicator-based re-
gression model, which consists of two steps. In the first stage, we estimate the serial cor-
relation among the order flows Qt using ordinary linear regression (OLS), Qt = ρQt−1 + εt.
The estimated unexpected order flow is ε̂t = Qt − ρ̂Qt−1, which will be used in the sec-
ond stage. We let Q∗t = ε̂t. In the second stage, we investigate the changes of transaction
costs of bonds with their inclusion in an ETF. To account for a potential parallel trend
present in the bond market, we extend the main model in Bessembinder et al. (2006) to the
difference-in-difference framework,
∆Pt = a+ α1S∆Qt × ETF × Post
+ α2S∆Qt × ETF + α3S∆Qt × Post+ α4S∆Qt + γSQ∗t + wXt + δt. (4)
ETF is a binary variable that takes 1 if the bond is included in ETF and 0 otherwise. Post
is a binary variable that takes 1 if the bond is traded after the inception date of ETF and
0 if the trade occurs prior to the first offer date of the ETF. The coefficient α1S is our
main interest and measures changes of transaction costs of ETF bonds relative to those for
non-ETF bonds, due to the inclusion in an ETF.
Following Bessembinder et al. (2006), Xt includes three components representing public
information available at the time of trading. The first is the yield, on the bond transaction
day, of the on-the-run Treasury security matched for time to maturity with the corporate
bond. Since the maturities of corporate bonds vary, this Treasury yield variable is indica-
tive of changes in the interest rate term structures. The second is the daily default spread
as the difference between the yield rate of aggregate BAA bonds and the risk free rate.
As market sentiment leans negatively (or positively), the risk premium for aggregate BAA
bonds increases (decreases). Hence, this default spread allows us to control for market wide
23
perceptions on the overall economy. The third is the return of the common stock for the
issuing firm since the last transaction date of the bond. All bonds issued by subsidiaries
are considered to have the same parent company. The return on the common stock reflects
firm-specific news. For instance, a positive earnings announcement increases investors’ ex-
pectation of their future earnings, resulting in increases both in the firm’s stock price and
return.
We estimate the second stage regression with the weighted least squared regression (WLS).
Since recent trades are likely to be more serially correlated with today’s trades than are dis-
tant trades, we use as weights the inverse of the number of days elapsed since the last
transaction day. For instance, if the last trade was made two days ago, then all the trades
for today are given the equal weight of 1/2.
To select the non-ETF bonds in the control group, we match on the following charac-
teristics of the bonds, using the propensity score matching algorithm (Wooldridge, 2010):
trading volume before the inclusion in ETF, credit rating at the time of trading, issue size,
age, time to maturity of the bond at the time of trading, ownership by bond mutual funds,
and dealers’ inventory capital. Prior studies (Schultz, 2001; Edwards et al., 2007; Goldstein,
Hotchkiss, and Sirri, 2007; Hendershott and Madhavan, 2015) showed that credit rating and
trading volume are robust explanatory variables for the liquidity of the bonds: bonds with
higher credit rating or with large trading volume are less costly to trade. We include is-
sue size because large issues are more frequently traded due to their availability. Trading
volume also influences the spread because it allows bond dealers to manage their inventory
better (Grossman and Miller, 1987). The time to maturity is relevant to the bond liquidity
because long term bonds are harder to trade in general. The age of the bond matters because
the majority of corporate bonds are known to end up being owned by institutional investors,
who infrequently trade, within two years of issuance. Furthermore, bond ownership by
buyside institutions are important as recent evidences such as in Anand, Jotikasthira, and
Venkataraman (2018) show that they also provide liquidity in bond markets. Therefore, in
24
the pre-ETF period, the two groups of bonds are similar in their liquidity. We are interested
in whether their transaction costs of the bonds diverge in the post-ETF period.
We note that the standard statistical inference with the t-statistic is not valid for our
analysis for the following reasons. The second stage regression has an explanatory variable
Q∗t = ε̂t, which was estimated from the first stage. The correct inference for α1S must include
the adjustment taking into account possible interactions of the noise terms between the first
and second stage regressions (Wooldridge, 2010). Furthermore, some bonds are traded more
than once within a day. This implies that we have multiple observations for the realized
∆Pt for the time t. The number of observations per day is random. Also, if some bonds are
issued by the same parent firm, or their times to maturities are similar, then changes of bond
prices are likely to be correlated. Therefore, the noise term in the second stage regression is
less likely to form the standard t-distribution.
In order to estimate the true distribution of the noise term, δt, we use the block boot-
strapping method. For each bond, since the daily trades are more likely to be correlated
than inter-day trades, we sample with replacement trades from daily trades 100 times. The
correlation structure between the days remains the same. But at each time, the number of
trades occurring is randomly varying. The empirical distribution for the bond’s transaction
cost α̂1S approximates the true population distribution (Wooldridge, 2010). We use this
empirical distribution and test against the null hypothesis that the true transaction cost is
zero, i. e. H0 : α1S = 0. We compute the p-value as the likelihood that the estimate α̂1S
has the sign opposite of the fitted value, given the null hypothesis.
4 The Effect of ETF Inclusion on Bond Liquidity
4.1 Does ETF inclusion increase trading volume?
The theoretical predictions in section 2 imply that inclusion in an ETF portfolio increases
trading volume of the underlying bonds. To examine whether it does, we investigate changes
25
of trading volume around ETF inclusions. In addition, we use difference-in-difference (diff-in-
diff) tests to account for secular trends that affect similar bonds at the same time. The goal
of diff-in-diff analysis is to test whether bonds that are included in ETF portfolios experience
changes of trading volume that exceed those expected in the absence of such inclusion.
To account for contemporaneous trends, our diff-in-diff tests contrast the change in trad-
ing volume of treated bonds to that of control bonds matched to have similar characteristics.
We choose a set of control bonds that were not included in any ETF portfolios during our
sample period, but whose trades were reported to TRACE. We match bonds on credit rat-
ing, issue size, age and time to maturity on the inception date of the ETF, and cumulative
trading dollar volume six months prior to inclusion in the ETF, using the propensity score
algorithm. More specifically, for each treated bond, we choose as a control one bond that
is the closest to the treated bond in terms of the relevant liquidity measure – credit rating,
issue size, age, time to maturity, and cumulative trading volume in the preceding six months
of the ETF inception date.13 These control bonds were not included in any ETF portfolio
in our sample period before or after.
Table 2 describes static characteristics of both ETF and non-ETF bonds in the matched
sample. About 55% of the bonds have the dollar issue size larger than $500M. Bonds with
superior credit quality (AA− and up) compose 12% of the sample. About 63% of the bonds
have investment credit rating of BBB− through A+. Non-investment grade bonds comprise
26% of the sample in both of the groups.
Trading characteristics of the bonds are shown in Table 3. The bonds are traded close
to the par values. The aggregate dollar trading volume decreases slightly for both treatment
and control groups, which is consistent with the findings of Randall (2015) that the overall
trade size of corporate bonds has decreased since 2007. While the total dollar trading volume
of ETF bonds decreases, it increases for low volume, small trade size ETF bonds. The total
number of trades increases for ETF bonds, while it decreases for non-ETF bonds. These
13We also conducted the analysis by selecting multiple bonds with similar characteristics as controls. Theresults remain the same. We report only the results where We choose one matching bond.
26
divergent changes are most prominent for small trade sizes less than $1M.
As a preliminary analysis, we also compare changes in transaction costs of the bonds using
the two methods discussed in section 3.5. Comparison of same-day buy and sell transactions
does not show a significant change in transaction costs, both for ETF bonds and non-ETF
bonds. However this measure is noisy due to the limited sample size. The effective half-
spread using the indicator-based regression method without any control variables does show
a significant decrease in transaction costs. For ETF bonds, the smallest quintile based on
the cumulative trading volume experiences the greatest decrease – about 30% in transaction
cost or 15 basis points. Non-ETF bonds show an increase in transaction costs.
This evidence is consistent with the prediction of the model that inclusion in an ETF
portfolio decreases transaction costs. The benefits of inclusion accrue most to the underlying
bonds with smallest trading volume. Furthermore, Figure 5 shows the parallel trends of the
bonds both for the treatment and control groups. For high yield bonds, the parallel trends
diverge after the inclusion in ETFs. The liquidity of high yield bonds improves significantly,
while that of investment grade bonds remain close to each other.
4.2 Bond liquidity improvement following ETF inclusion
4.2.1 Full sample analysis. To test Proposition 1 more formally, we estimate changes
in transaction costs for bonds included in an ETF for the first time using the indicator-based
regression model in equation (4) which includes all control variables. The coefficients are in
percentages. Table 4 reports the results. The most interesting estimated coefficient is the
one on ∆Q× Post× ETF.
We estimate that the average effective half spread for trading individual corporate bonds
is 42 basis points during our sample period. This is consistent with the findings of Bessem-
binder et al. (2017) that, amid the concern on the overall bond market liquidity after the
financial crisis, the bond liquidity gradually recovered back to its previous level from 63 bps
for the 2009–2010 period to 47 bps for the 2010–2014 period. In addition, this confirms
27
that our bond data samples are mostly traded in the dealers’ market, as Hendershott and
Madhavan (2015) estimated that cost of trading odd lots ($100K∼1M) for corporate bonds
in 2011 was 48 basis points for the dealers’ market and 14 basis points for the electronic
trading platform. Furthermore, in accordance with Proposition 1, the liquidity of corporate
bonds improves significantly following ETF inclusion by 3.5% or 1.3 bps on average, after
taking into account changes in the control bonds.
To test the estimated liquidity improvement of the treated bonds for statistical signifi-
cance, we use the block-boostrapping method to address potential bias due to correlations.
There are two potential souces of bias. When an ETF is newly launched, multiple corpo-
rate bonds are added to the ETF portfolio, which could imply that these bonds are cross-
sectionally correlated in their liquidity. Furthermore, bond price changes are referenced
relative to the last prior-day transaction price. If multiple bond transactions are found in
one day, then they are likely to be correlated in time. Following the standard in the lit-
erature (Wooldridge, 2010), we adopt the block-bootstrapping method while keeping the
correlation matrix the same. Our results are highly statistically significantly different from
zero.
In addition, we find that the coefficient of Qt−1 is positive and statistically significantly
different from zero. This implies that bond orders are positively serially correlated. The
coefficient of the unexpected order flow Q∗t is also positive significant. Since this coefficient
indicates the informational content observed in the order flows of the bonds, this result
implies that some bonds are traded on private information.
The estimated coefficients of the public information variables are significant. Stock re-
turns of the issuing firm have a positive impact on the bond transaction costs. The impact
is larger for non-investment grade bonds than for investment grade bonds. This is consistent
with the finding of Hotchkiss and Ronen (2002) that both stock and bond returns respond to
new information about the value of the issuing firm’s underlying assets. The new information
is more valuable for non-investment grade bonds, because the investors and the firms are
28
more likely to be asymmetrically informed. The coefficient estimates for the default spread
is negative and significant for investment grade bonds, and positive and significant for the
non-investment grade bonds.
4.2.2 Alternative identification strategy: Quasi-Natural Experiment for a high
yield bond ETF. To provide additional support for the causality of our results, we explore
a quasi-natural experiment, where the reason for the ETF portfolio inclusion is manifest.
The same experiment has previously been used by Dannhauser (2017) to show the valuation
effect of corporate bond ETFs. The iShares iBoxx $ High Yield Corporate Bond ETF (HYG)
started trading on April 4, 2007 and is one of the largest high yield bond ETFs by assets
under management – its assets had already reached $3.3 billion as of June 30, 2009. The
HYG portfolio holds about 94% of its assets in non-investment grade corporate bonds and
the remainder in cash. The expense ratio is 0.5 basis points and the portfolio turnover ratio
is about 30%.
HYG is passively managed and follows the aggregate bond index managed by Markit for
their benchmark index. On June 22, 2009, Markit changed the index composition rule by
imposing a 3% cap on the constituent bonds and requiring the index to be computed as a
value-weighted average, effective June 30, 2009. As a consequence, during the second half
of 2009, about 248 bonds were newly added to HYG. While the governing board of HYG
may have known of the change in advance, and may have initiated trading of the relevant
bonds early, these types of trading activities are likely to have been limited due to concerns
about tracking errors. As indirect evidence, the number of newly added bonds increased
dramatically after June 2009: Only 16 were added from January to May, while 31 were
added in June, and 248 were added from July to December. Hence, it seems reasonable that
these bonds were added to HYG due to the rule change rather than to anticipated reduction
in transaction costs. We investigate changes of the liquidity of these bonds using a diff-in-diff
framework and consistently find that ETF inclusion led to a reduction of transaction costs
29
of the bonds by 3.5%. (Table 10)
The fact that we find bond liquidity improves following inclusion in ETF portfolios us-
ing two independent reasons for inclusion – 117 newly launched ETFs and HYG whose
benchmark index underwent a change in the composition rule – supports our predictions
and identification strategies. While we cannot completely rule out a possibility that ETF
managers might have timed the timing of the funds’ inception date in a way that would
spuriously lead to our results, it seems apparent that the bond inclusion in HYG during the
later 6 months of 2009 were involuntary.
4.2.3 ETF arbitrage as the underlying mechanism. As evidence for the underlying
economic mechanism of our results, we estimate ETF arbitrage and investigate whether the
liquidity spillovers are in fact facilitated by arbitrageurs. ETF shares can be created or
redeemed by authorized participants (AP) by exchanging a basket of the securities for ETF
shares. When the net asset value of the ETF portfolio diverges from ETF market prices,
the APs wish to take advantage of these arbitrage opportunities through the creation and
redemption process. Therefore, we measure as a proxy for ETF arbitrage the cumulative
changes in the number of ETF shares outstanding relative to the average ETF shares out-
standing during the preceding 6 months.14 If this ETF arbitrage proxy exceeds 1.25, we
consider the associated ETFs to have high ETF arbitrage. In agreement with our predic-
tions in Proposition 1, we find that the bond liquidity improves only when ETF arbitrage
are high (Table 5). Conversely, when ETF arbitrage is low, the bond liquidity deteriorates.
Furthermore, a potential concern is that ETF arbitrage may become impaired when
the required conditions for ETF arbitrage are not present simultaneously. For instance,
14This ETF arbitrage proxy is likely to be a lower bound on true arbitrage for the following reasons. First,ETF arbitrage can occur entirely in the secondary market without having to create or redeem ETF shares.Second, as Evans, Moussawi, Pagano, and Sedunov (2017) show, not all of ETF creations and redemptionsare completed. Often ETF shares are sold short first, but the underlying securities fail to be delivered withinthe T + 3, up to T + 6, settlement days (“operational-shorting”). Since only the completed creation andredemptions change ETF shares outstanding, our ETF arbitrage proxy is likely to underestimate the truearbitrage. In spite of our conservative estimate of ETF arbitrage, we find that the liquidity spillover by ETFarbitrage are still present.
30
while differences between ETF market prices and the net asset values (NAV) of the ETF
portfolio would signal arbitrage opportunities, a dealers’ inventory imbalance might limit
their arbitrage activities (Pan and Zeng (2017)). To investigate the impact of these two
requirements on ETF arbitrage, we measure as ETF arbitrage opportunities daily differences
of ETF market price and its NAV. To measure the dealers’ inventory imbalance, we calculate
differences of the buy and sell dollar volumes of all the constituent bonds of the ETF portfolio
on each day. We then partition the data into four groups of trading days depending on
arbitrage opportunities and the dealer inventory imbalance (Table 6). We find that the
liquidity spillover from ETFs to the underlying bonds prevails in all four groups. However,
since we require ETFs to have the daily order imbalance and each group relies on a subset
of the total sample days, the sample size is smaller than that for the full analysis.
4.2.4 Interpretation. Our results do not exclude the possibility that the liquidity spillover
mechanism through ETF arbitrage could potentially be fragile at times, especially in the pri-
mary market. The intuition is that ETF authorized participants play a dual role as ETF
arbitrageurs and also as bond dealers. Under the conditions when their inventory is con-
strained, there could be a temporary limit to arbitrage between the ETF and the underlying
corporate bonds, which might hurt the liquidity of the underlying bonds (Pan and Zeng,
2017). The interpretation of our results is that the overall effect of ETF trading in the
long run benefits the liquidity of the underlying bonds due to the effect of the secondary
market. A more recent, but preliminary, analysis suggests that whenever ETF arbitrage
opportunities are high, the trading volume of the underlying bonds increases.
4.2.5 Cross sectional variation of bond liquidity improvement. The effect of ETF
portfolio inclusion on liquidity improvement is likely to vary among bonds that have the
same minimum trading size, but whose market accessibility varies along other dimensions
such as information asymmetry and unfamiliarity. As Corollary 1 predicts, the liquidity
improves more as the trading barrier prior to the introduction of ETFs was higher. Prior
31
evidence suggests that bond market segments with small trading volume, lower credit rating,
or long-term maturity are harder to access for uninformed investors.
Table 7 reports that the underlying bond trading cost prior to the initial trading of
ETF is linearly related to the cumulative trading volume, which is consistent with earlier
findings (Edwards et al. (2007), for instance). The bonds with the smallest dollar trading
volume (VOL 1) have highest trading cost at 92 basis points. As the trading volume increases,
the transaction costs decrease. The most liquid bonds with large dollar trading volume have
lowest trading cost at 41 basis points.
The initiation of ETF trading results in differential changes of trading costs of the under-
lying bonds. The changes are linearly related to the cumulative trading volume. The illiquid
bonds (VOL 1) become less costly to trade and their transaction costs decrease by 9 basis
points, i. e. about a 10% reduction. On the other hand, liquid bonds with median trading
volume become more costly, increasing the trading costs by 2 basis points. The most liquid
bonds (VOL 5) show little impact on the liquidity due to ETF trading.
Table 8 reports transaction cost estimates arranged by the bond credit rating. Before
the introduction of ETFs, non-investment grade bonds are more costly to trade at an esti-
mated 57 basis points, while investment grade bonds cost 37 basis points. This difference
of trading cost can be explained by large information asymmetry between the issuing firm
of non-investment grade bonds and the investors (Holden, Mao, and Nam, 2016). With the
introduction of ETF, the ETF trading from the uninformed investors has an indirect impact
in the underlying junk bond market. The effective spreads of the junk bonds held by ETFs
decrease by 10%, i. e. 5.4 basis point.15 This is consistent with our theoretical predictions
in Proposition 1.16
15As discussed earlier in the introduction, Dannhauser (2017) finds that the relation between ETF tradingactivities and the underlying bond liquidity is not robust and that ETF ownership is correlated with liquiditydeterioration only for investment grade bonds. This is not in direct conflict with our results due to differencesin methodology. We focus on event study with six month window before and after ETF inclusion, whileDannhauser looks at a long panel study over four years with ETF ownership.
16Strategic inventory management by the bond dealers might provide an alternative explanation of ourcross-sectional results. Recent literature (Bessembinder et al. (2017); Choi and Huh (2017); Goldstein andHotchkiss (2017), for instance) has shown that corporate bond dealers manage their inventories strategically
32
Unlike bonds, fixed income ETFs have no maturity. As some incumbent bonds near
maturity, ETF managers may wish to sell off the maturing bonds in order to reduce the
tax burden from realizing the capital gains. This specific trading demand for these bonds
may offset the positive impact of ETFs on improving the liquidity of the underlying bonds.
In Table 9, we show that with the introduction of ETFs, the liquidity improves for the
bonds whose time to maturity is more than two years, while the liquidity deteriorates for
the maturing bonds. ? finds similar results by extrapolating intraday bond quotes from the
Bloomberg quotes available at the end of previous day as an alternative measure of bond
liquidity, which indicates that our results are robust with different definitions of liquidity.
4.2.6 Multivariate Analysis. While all our results so far are univariate, potential cor-
relations among the explanatory variables may weaken our primary results. To alleviate
such concern, in Table 12, we run a multivariate analysis by combining all the explanatory
variables. Our univariate analysis results continue to hold. In addition, as in column (1),
bond trading volume is the dominant factor in determining the transaction cost of the bonds.
The low volume bonds benefit most from the inception of ETF trading. The bonds whose
remaining life is more than 2 years had lower transaction costs by 31 basis points than those
with little time to maturity. With the inception of ETF trading, these bonds near maturity
exhibit an even bigger transaction cost by 2.6 basis points. In columns (2) and (3), we con-
ducted the multivariate analysis based on the bond credit rating. The univariate analysis
from Table 8 remain unchanged.
4.3 The impact of ETF trading on 144A bonds
Liquidity spillover is likely to have the largest effect on 144A bonds, which were offered only
to qualified institutional investors (QIB) until ETFs began to increase their holdings of 144A
by closing out their positions for high yield bonds sooner, with a concern for larger adverse selection andhigher risk capital requirement. However, to the extent that this strategic market making is invariantaround the event of ETF inclusion, this result - the liquidity improvement of the underlying securities - isstill interpreted as an impact of the inclusion in ETF portfolios.
33
corporate bonds in 2010 (Figure 4(B)). One reason is that, since non-institutional investors
are not legally allowed to trade 144A bonds, liquidity reduction due to discretionary investors
leaving the underlying market is limited. More importantly, after the inception of ETFs,
discretionary investors can indirectly access the 144A bond market. As trading volume of
such ETFs increases, the liquidity of these restricted securities is likely to improve. We
empirically test this prediction in this section.
While 144A bonds are restricted securities, they have undergone a series of regulatory
changes that have allowed ETFs to trade them. Unlike public debt, which requires regis-
tration and SEC oversight, firms can privately issue debt if they place it with accredited
investors or a limited number of individual investors who intend to hold it for investment.
This privately placed debt can be resold if the issue is registered with SEC after issuance or
if investors have held it for, typically, at least two years. In 1990, Rule 144A was adopted
to allow investors to immediately resell 144A securities without SEC registration or the two
year holding period, as long as they are sold to qualified institutional buyers (QIB). On
August 15, 2007, NASDAQ offered an electronic trading platform – PORTAL – for these
privately placed securities, the access to which was available only to QIBs and the data of
which were not made available to the public.
Furthermore, in accordance with the Jumpstart Our Business Startups Act (JOBS Act)
section 201(a)(1), SEC finalized a rule in 2012 August that permits the sale of 144A bonds to
persons, other than QIBs, who QIBs believe are QIBs.17 This implied that non-QIBs such as
ETFs could easily trade 144A securities for the first time. As a consequence, the size of 144A
bond market has become large. During the year 2012, firms raised overall capital of $1.2
trillion through regular debt and equity offerings, while $0.636 trillion worth of capital was
raised through 144A offerings.18 To facilitate trading, FINRA started disseminating trans-
action data for 144A bonds as well as certain other private placements.19 As a consequence,
17See https://www.sec.gov/rules/final/2013/33-9415.pdf18See https://www.sec.gov/rules/final/2013/33-9415.pdf19See SEC Release, No. 70345, 16 September 2013. Also, https://www.finra.org/newsroom/2014/finra-
brings-144a-corporate-debt-transactions-light
34
during the third quarter of 2014, transactions of 144A securities exploded, comprising nearly
9% of the average daily volume in investment-grade corporate debt, and nearly 28% of the
average daily volume in high-yield corporate debt (Table 13).
Although 144A securities are liable to be illiquid due to the required holding periods
before resale, they have been popular among debt issuers and institutional investors. U.S.
and foreign issuers take advantage of the speedy issuance in the 144A market.20 As of 2013,
these private placements comprise about 15% of high yield bond issuance (Table 13).21 In
addition, these 144A securities have lower transaction costs than those that are otherwise
equivalent, because trades occur between institutional investors and hence adverse selection
cost is low (Edwards et al., 2007).
Therefore, the increased holdings of the 144A bonds by ETFs likely provide further liq-
uidity in the underlying bond market with limited access. We exploit the TRACE 144A
bond database that has recently become publicly available. Consistent with our theoretical
predictions made earlier in Section 2, we find that with the introduction of ETFs, the trans-
action cost of the 144A bonds owned by ETFs decreases significantly by 5 basis point (Table
14). The reduction of transaction costs is larger for large volume bonds, for high-yield bonds,
and for long-term bonds (not shown).
4.4 Robustness
Since we match the bonds in the treatment group with those in the control group by using
the bond characteristics, one potential concern would be that our results may be driven by
unobservable characteristics of the bonds that are related to their issuing firms. In order
to address this concern, we added firm fixed effects in our analysis. Our results remain the
20Fenn (2000) showed that about 97% of the 144A high-yield securities are subsequently registered withthe SEC. Among high yield 144A debt, 58% was issued by first-time bond issuers, while 29% was issued byprivately owned firms. The credit risk premium for 144A high yield securities is higher than for public debt,but it gradually disappears after issuance. (Chaplisky and Ramchand, 2004)
21In an unreported note, we find that the composition of these privately placed debt is even higher amongthe most risky bonds whose credit rating is below B. As of 2013, they comprise about 50% of the issuanceof the most risky bonds.
35
same (Table 15).
4.5 Discussion
A tenable reading of our results is that retail investors value better diversified portfolios
and react to the newly available exposure to the corporate bond market provided by ETFs.
When individual corporate bonds are included in ETF portfolios, their liquidity improves
as a result. In principle, the exclusion of corporate bonds from any ETF portfolio should
have the opposite effect as exclusion removes the benefit of the liquidity spillover from ETFs.
This prediction has been confirmed by the recent study by Ye (2019).
5 Conclusion
Multi-market trading models often assume that investor market participation is homoge-
neous, but our empirical observation is that some markets are easier to access than others.
How important heterogeneous market accessibility ex ante is in determining response to in-
novation in the financial market is not well understood. This paper provides theoretical and
empirical evidence that market participation plays an important role in how liquidity of the
underlying securities changes when basket securities are introduced. To do so, we exploit
corporate bond ETF inceptions as the events that lower trading barriers.
We first extend the Kyle model to multi-markets where the degree of accessibility varies.
The discretionary investors are uninformed and concerned about having a tradable diversified
portfolio. We show that they prefer to invest in basket securities because of (1) a low expected
loss of trading and (2) ease of accessibility (i.e. lower minimum investment requirement). In
particular, the ease of accessibility in the basket market is critical in order to improve the
liquidity of the underlying securities.
As empirical evidence, first, we exploit TRACE bond transaction data for OTC corporate
bonds and show that in contrast to stocks, trading bond ETFs reduces the transaction cost of
36
the underlying bonds. The reduction of transaction costs is larger for low volume, high-yield,
and long-term bonds, to which the access was limited to retail investors. On the other hand,
transaction costs for high volume, investment grade, or short-term bonds increase. Second,
144A bonds, which were previously inaccessible to non-institutional investors, experience
improved liquidity as ETFs increase their holdings of 144A bonds. The aggregate savings in
trading an ETF bond is as big as −$18.5 million during the six months after inception of a
fixed income ETF containing that bond.22
Our results imply that bond investors benefit from the inclusion of bonds in fixed income
ETFs. This benefit accrues more to small retail bond investors and to junk bond investors,
to which the accessibility is limited previously. We believe the mechanism we proposed in
this paper is general and could potentially be extended in the future to hard-to-trade assets
such as mortgages or hard-to-access markets such as frontier markets.
22=10.259× (−0.0881)+34.897× (−0.0355)+78.447×0.0185+155.002× (−0.0015)+844.282× (−0.0011)=−1.85. This is equivalent to 18.5 million dollars.
37
Appendix A
A.1 Unconstrained solution
In the pre-ETF period, we solve the portfolio trading allocation problem using a constrained
optimization method. The Lagrangian of the objective function is set up with multipliers,
η1 and η2 as
L(α1, α2) = E(W )− γ
2Var(W )− η1 [E(|α1lqP1|)−B1]− η2 [E(|α2lqP2|)−B2] ,
subject to the following conditions
η1 [E(|α1lqP1|)−B1] = 0, η2 [E(|α2lqP2|)−B2] = 0
E(|α1lqP1|) ≥ B1, and E(|α2lqP2|) ≥ B2,
η1 ≥ 0, η2 ≥ 0.
We first look for unconstrained solutions by assuming the minimum trading requirement
Bi is sufficiently small. The constraints E(|αilqPi|) ≥ Bi (i = 1, 2) hold for all q, which
implies that the Lagrange multipliers ηi is zero. Solving the first order condition (FOC) of
the Lagrangian L(α1, α2) above, we obtain
α?i =1
γ
(W0 −
σ2l
Ai+ γ
σ4l
Ai
)[W 2
0 σ2i + µ2
iσ2l (σ
2i + 1)− σ2
l µ2iσ
2i
(k2i + ki + 1
k2i + 2ki + 1
)]−1
,
where Ai =[
ki(ki+1)2
µ2i σ2i
σ2u+σ
2l
]−1/2
.
In the post-ETF period, the discretionary uninformed investors have a expanded oppor-
tunity set for trading. The Lagrangian of the objective function is found as follows.
L(α1, α2, αe) = E(W )− γ
2Var(W )− η1 [E(|α1lqP1|)−B1]− η2 [E(|α2lqP2|)−B2] ,
38
where W =(α1W0
µ1+ α1lq
)v1 +
(α2W0
µ2+ α2lq
)v2 +
(αeW0
µe+ αelq
)ve − (P1 − v1)α1lq − (P2 −
v2)α2lq − (Pe − ve)αelq, depending also on the value ve and price Pe of the ETF. We assume
the ETF market is highly accessible by assuming the minimum trading requirement of ETF
is zero. Hence, the auxiliary conditions remain the same as above.
We let αi = α?i − αewi for i = 1, 2. With these αi, E(W ) and Var(W ) will be the same
as those in the pre-ETF period. To further increase the investor’s utility, we minimize the
loss due to trading by adjusting αe. We let the optimal trading demand of the ETF be
α̃?e. This α̃?e is strictly positive. The optimal trading demand for each security is therefore
α̃?i = α?i − α̃?ewi and is strictly smaller than α?i .
A.2 Constrained solution
Without loss of generality, we focus on the first asset market. In the pre-ETF period, the
optimal trading solution α1 binds with the constraint, E(|α1lqP1|) = B1. Solving for α1,
B1 = E|α1lqP1| = E|α1lq(µ1 + λ1(α1lq + u1 + x1))|
= E|α1||µ1||lq|+ |λ1|α21E(l2q) + E|α1lq||x1 + u1|.
Note that the third term is zero, because lq and x1 + u1 are uncorrelated random variables
with Normal distributions. In addition, E(x1 +u1) = 0. The two variables lq and x1 +u1 are
independent and so are |lq| and |u1+x1|. We also note that |α1λ1| = α1λ1 =√A1/
√σ2u + σ2
l ,
where A1 = σ21k1/(k1 + 1)2. Therefore, we find that
|α1| =B1
|µ1|[σl
√2π
exp(− µ2l
2σ2l
)+ µl
(1− 2Φ
(−µlσl
))]+√
A1
σ2u+σ
2l(σ2
u + σ2l ),
where Φ(·) is the normal cumulative distribution function. For some combinations of µl, σl, σu,
this α1 does not satisfy the FOC. If these infeasible trading solutions happen for all the dis-
cretionary uninformed investors, we call this market inaccessible. We assume these investors
39
trade a risk free security at that time.
On the other hand, in the post-ETF period, as the ETF market is highly accessible, these
uninformed investors reconsider their trading decisions. Their optimal trading demand αe
is strictly positive. The influx of these uninformed investors to the ETF market increases
ETF liquidity, which spills over the underlying market through arbitrage.
40
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Figure 1. Distribution of transaction costs and minimum trading quantities forindividual junk bonds. These bonds are held by the junk bond ETF portfolio (JNK) asof March 30, 2016. For each bond, the bid-ask spreads is divided by their mid point price.The average bid-ask spread is 110 basis points. The minimum trading quantities vary withbid or ask orders. The average minimum bid quantity $133,000, while the average minimumask quantity is $116,000. The median bid or ask quantity is $100,000. Data source: Fidelitycorporate bond database (“Depth of Book”).
46
Figure 2. Model overview for less accessible markets. (A) Before the introductionof ETFs, investors’ portfolios may have limited access to some market segments due to aminimum trading requirement, high transaction costs and other reasons. The cost of havinga diversified portfolio is high. These diversification costs increases as the portfolio is largeor the investors trade over multiple periods. The investors tend to have an under-diversifiedportfolio. (B) ETFs significantly lower the diversification cost than a portfolio of individualsecurities. First, investors flow into ETFs due to low cost of diversification. If the marketwas less accessible ex ante, then the influx of new investors to ETFs is greater. Second, ETFsare liquid. Third, large ETF liquidity is spilled over to the underlying securities througharbitrage mechanism. The liquidity of the underlying securities improves in the previouslyless accessible market.
47
Figure 3. Theoretical predictions after the introduction of ETFs. (A) If the marketwas highly accessible ex ante, discretionary uninformed investors can easily trade a portfolioof individual assets. With the introduction of ETFs, they prefer to trade ETFs in orderto reduce further the adverse selection cost. This migration of the uninformed investorscauses liquidity of underlying securities to decrease. (B) Suppose the market was partiallyaccessible, i.e. only security 1. Then the uninformed investors would prefer to migrate toETF, which will reduce the liquidity of security 1. At the same time, the uninformed investorswho hold the risk free assets would prefer to move investment from the risk free asset to ETF.The large ETF liquidity will spill over the underlying securities 1 and 2. Hence, the liquidityof security 1 is ambiguous, while the liquidity of security 2 improves. (C) If the market isinaccessible, then the liquidities of both security 1 and security 2 improve by applying similarlogic.
48
Figure 4. Newly-launched corporate bond ETFs and the underlying corporatebonds. The data sample is derived from the newly launched corporate bond ETFs and theunderlying corporate bonds over the period 2007 through 2014. The top graph shows theyearly aggregate number of corporate bond ETFs initiated, while the bottom graph showsthe number of the associated corporate bonds, whose trading were reported to TRACE. Theregular corporate bonds and 144A bonds are denoted by the blue and red colors, respec-tively. As of 2014Q4, 1253 distinct corporate bonds were traded and reported to TRACE.144A bonds that were only available to institutional investors through private placement areincreasingly included in ETFs since 2012.
(A)
(B)
49
Figure 5. The parallel trends for the treatment and control groups Each bondin the treatment group is matched with bonds in the control group. The bond liquidityexhibits the parallel trends in the pre-ETF period: (A) Parallel trends for non-investmentgrade bonds, (B) Parallel trends for investment grade bonds. In the post-ETF period, Theliquidity of non-investment grade bonds significantly improves as shown in (A).
(A)
(B)
50
Table
1.
Sum
mary
stati
stic
sof
fixed
inco
me
ET
Fs
Pan
elA
:B
lack
Rock
firs
tin
troduce
dfixed
inco
me
ET
Fs
in20
02,
follow
edby
Sta
teStr
eet,
Van
guar
d,
etc.
The
fund
fam
ily
den
oted
by
”Oth
ers”
incl
ude
AL
PS,C
laym
ore,
Col
um
bia
,D
BX
,E
GA
,F
lexShar
es,
Fra
nklin,
Gra
il,
Gugg
enhei
m,
Pow
erShar
es,
Pro
Shar
es,
and
Sch
wab
.T
he
num
ber
offixed
inco
me
ET
Fs
jum
ped
insi
nce
2007
.P
anel
B:
The
aver
age
num
ber
ofse
curi
ties
incl
uded
ina
fixed
inco
me
ET
Fva
ries
from
42to
137.
The
max
imum
is18
40.
The
num
ber
ofth
ese
curi
ties
firs
ten
tere
din
the
ET
Fp
ortf
olio
dra
mat
ical
lyin
crea
ses.
Pan
elA
.In
cepti
onof
ET
Fs
by
fund
fam
ily
Yea
rB
lack
Rock
Sta
teStr
eet
Van
guar
dO
ther
sW
isdom
Tre
eP
IMC
OA
dvis
orShar
esF
irst
Tru
stF
idel
ity
tota
l20
022
220
032
220
0716
45
530
2008
210
1325
2009
48
82
224
2010
213
11
118
2011
12
141
41
2320
126
315
12
2720
1310
22
85
11
332
2014
71
152
23
333
Tot
al50
2215
8023
125
63
216
Pan
elB
.E
TF
por
tfol
iosi
ze
Yea
rN
Mea
nM
inp10
Med
ian
p90
Max
Fir
sten
tere
d20
076
124
55
152
184
184
319
2009
375
11
9313
013
014
420
1011
429
1044
8191
452
2011
2084
126
4818
744
314
6420
1224
108
129
6522
267
620
3120
1329
175
131
105
417
1840
3804
2014
2413
721
2410
627
046
323
21
51
Table 2. Bond descriptive statistics. For credit rating, We assign a numeric score foreach credit rating by each of the three rating agencies – S& P, Moody’s, Fitch – followingBecker and Ivashina (2015). At each given time, we merge the three credit rating scoresas follows: If the three ratings are available, we take the middle rating. If two ratingsare available, we take the lower. Non-ETF bonds are matched with ETF bonds using issueamount, credit rating, maturity at issuance, age at the inception date of ETF, and cumulativetrading volume during 6 months prior to the inception date of ETF.
ETF bonds non-ETF bonds DiD p-valueDiD=0
Total number of bond issues 2,623 2,623Average issue size ($M) 1,007 1,032 −25 0.23≤ 100M 429 (16%) 517 (20%)100-500M 726 (28%) 692 (26%)> 500M 1,468 (56%) 1,428 (54%)
Average credit quality 19.37 19.20 0.17 0.33AA− and up 283 (11%) 351 (13%)BBB− thru A+ 1,729 (66%) 1,554 (59%)below BBB− 625 (24%) 732 (28%)
Average maturity at issue 7.86 7.71 0.15 0.13(in years)< 2 years 21 (1%) 105 (4%)2-5 years 465 (18%) 597 (23%)5-20 years 2,048 (78%) 1,845 (70%)> 20 years 103 (4%) 90 (3%)
Age at ETF inception date 1.50 1.52 −0.02 0.20(in years)< 2 years 1,955 (74%) 1,959 (74%)2-5 years 663 (25%) 617 (23%)≥ 5 years 19 (1%) 61 (2%)
52
Table 3. Bond trading characteristics. Transaction price, transaction volume, trans-action frequency. To compute average transaction cost per day, we first use a subset ofcorporate bonds whose buy and sell transactions are observed for each day. We computethe average sell price subtracted by the average buy price. To estimate the average tradingcost per transaction, we use the last trading price on the most recent transaction day asa benchmark price and examine the changes of trading prices on the current day relativeto the benchmark price, following the insight of Huang and Stoll (1997); Hendershott andMadhavan (2015). Transaction costs are further examined in details based on the cumulativetrading volume during 6 months prior to the inception date of ETF.
ETF bonds non-ETF bondsbefore after before after
Average trade price(% of par value) 101.11 101.01 100.40 99.93Total trading volume ($B) 1341.73 1122.89 1009.62 788.94Total number of trades (1000) 2304.62 2364.67 2143.66 1913.70< $100K 1291.74 1419.42 1280.89 1194.49$100K-1M 547.10 565.50 466.48 403.93$1-5M 351.69 291.44 321.79 261.44>$5M 114.10 88.31 74.50 53.84
Average two-way transaction cost per day (bp) 65.72 67.21 75.42 76.74Average transaction cost per transaction (bp)
VOL 1 (small) 47.69 32.78 42.75 52.44VOL 2 27.11 22.54 27.64 30.85VOL 3 21.82 22.52 25.08 24.20VOL 4 20.18 20.00 24.15 28.78VOL 5 (large) 20.15 21.45 17.81 19.72
53
Table 4. Full sample analysis. The effective half spreads of ETF-bonds are comparedwith those of non-ETF bonds. Following the methodology of Bessembinder et al. (2006),the first stage is estimated as Qt = a + bQt−1 + εt, where Qt is +1 for the buy order, −1for the sell order, and 0 for the inter-dealer orders. We write the unexpected order flowεt as Q∗
t . The second stage is estimated as ∆Pt = a + γSQ∗t + αS∆Qt + wXt + ωt. To
assess the changes of effective spreads of ETF-bonds relative to the changes of effectivespreads of non-ETF bonds, we interact ∆Qt with Post and ETF . The non-informationalcomponent αS is divided into the changes of the effective spreads in the treatment groupand the changes of the effective spreads in the control group: α1∆Qt + α2S∆Qt × Post +α3S∆Qt × ETF + α4S∆Qt × Post × ETF. Of our main focus is the coefficient α4S. Weinclude three public information variables, each of which is measured on the date of the mostrecent transaction on a day prior to the current transaction. The changes in the interestrate for on-the-run Treasury security is matched to the corporate bond based on maturity.The percentage return on the issuing firm’s common stock. The change in the default spreadbetween long-term indexes of BAA-rated bonds and U. S. Treasury securities. we interactthese variables with investment- and non-investment-grade indicator variables to account forpotential differences in sensitivity based on the bond’s risk. We estimate the first and secondstage regressions using the weighted least squares, where the weight is inversely related tothe number of days elapsed between Qt and Qt−1. The statistical significance is measuredthe probability from block bootstrapping, following Bessembinder et al. (2006).
coeff p-valueFirst stage regressionIntercept 0.1137∗∗∗ 0.000Qt−1 0.0678∗∗∗ 0.000
Second stage regressionIntercept 0.0276∗ 0.060Q∗ 0.0316∗∗∗ 0.000Treasury return −0.0119∗∗∗ 0.000Stock return × invgrd 0.0176∗∗∗ 0.000Stock return × noninvgrd 0.0745∗∗∗ 0.000Default spread × invgrd −0.0074∗∗∗ 0.000Default spread × noninvgrd 0.0386∗∗∗ 0.000∆Q 0.4221∗∗∗ 0.000∆Q× Post 0.0262∗∗∗ 0.000∆Q× ETF −0.0537∗∗∗ 0.000∆Q× Post × ETF −0.0132∗∗∗ 0.000
R2 0.1804RMSE 0.7324N of block boostrapping 50
54
Table 5. By Arbitrage activities To estimate the arbitrage activities of each ETF,we add the magnitude of the daily changes of the ETF shares outstanding in the postevent period, relative to the average ETF shares outstanding. If the arbitrage activitiesfall below (or above) the 40th percentile, i.e. 125%, we consider it low (or high) arbitrageETFs. The effective half spreads of ETF-bonds are compared with those of non-ETF bonds.Following the methodology of Bessembinder et al. (2006), the first stage is estimated asQt = a + bQt−1 + εt, where Qt is +1 for the buy order, −1 for the sell order, and 0 forthe inter-dealer orders. We write the unexpected order flow εt as Q∗
t . The second stage isestimated as ∆Pt = a+γSQ∗
t +αS∆Qt+wXt+ωt. To assess the changes of effective spreadsof ETF-bonds relative to the changes of effective spreads of non-ETF bonds, we interact ∆Qt
with Post and ETF . The non-informational component αS is divided into the changes ofthe effective spreads in the treatment group and the changes of the effective spreads inthe control group: α1∆Qt + α2S∆Qt × Post + α3S∆Qt × ETF + α4S∆Qt × Post× ETF.Of our main focus is the coefficient α4S. We include three public information variablesXt as discussed in 3.5. The statistical significance is measured the probability from blockbootstrapping, following Bessembinder et al. (2006).
coeff p-valueFirst stage regressionIntercept 0.1074∗∗∗ 0.000Qt−1 0.0685∗∗∗ 0.000
Second stage regressionIntercept 0.0372∗∗∗ 0.000Q∗ 0.0325∗∗∗ 0.000Treasury return −0.0157∗∗∗ 0.000Stock return × invgrd 0.0262∗∗∗ 0.000Stock return × noninvgrd 0.0780∗∗∗ 0.000Default spread × invgrd −0.0086∗∗∗ 0.000Default spread × noninvgrd 0.0478∗∗∗ 0.000Arb H × ∆Q 0.4266∗∗∗ 0.000Arb H × ∆Q× Post 0.0227∗∗∗ 0.000Arb H × ∆Q× ETF 0.0227∗∗∗ 0.000Arb H × ∆Q× Post × ETF −0.0300∗∗∗ 0.000Arb L × ∆Q 0.4143∗∗∗ 0.000Arb L × ∆Q× Post 0.0210∗∗∗ 0.000Arb L × ∆Q× ETF −0.0486∗∗∗ 0.000Arb L × ∆Q× Post × ETF 0.0119∗∗∗ 0.000
R2 0.1874N of block bootstrapping 50
55
Table
6.
By
Arb
itra
ge
and
Ord
er
Imbala
nce
inti
me
seri
es
To
esti
mat
eth
ear
bit
rage
opp
ortu
nit
ies
ofea
chE
TF
,w
ees
tim
ate
adiff
eren
ceb
etw
een
the
ET
Fm
arke
tpri
cep
ersh
are
and
its
net
asse
tva
lue
(NA
V)
per
shar
eon
the
pre
vio
us
day
,w
hic
his
then
div
ided
by
the
NA
V.
Ifth
ear
bit
rage
opp
ortu
nit
ies
fall
bel
ow(o
rab
ove)
the
25th
per
centi
le,
we
consi
der
itlo
w(o
rhig
h)
arbit
rage
ET
Fs.
To
esti
mat
eor
der
imbal
ance
,w
eco
nst
ruct
dol
lar
buy
and
sell
volu
mes
per
day
ofal
lth
eco
nst
ituen
tb
onds
ofea
chE
TF
.If
the
diff
eren
ceof
buy
and
sell
dol
lar
volu
mes
isb
elow
(or
abov
e)th
e25
thp
erce
nti
le,
we
consi
der
the
order
imbal
ance
low
(or
hig
h).
Fol
low
ing
the
met
hodol
ogy
ofB
esse
mbin
der
etal
.(2
006)
,th
efirs
tst
age
ises
tim
ated
asQt
=a
+bQ
t−1
+ε t,
wher
eQt
is+
1fo
rth
ebuy
order
,−
1fo
rth
ese
llor
der
,an
d0
for
the
inte
r-dea
ler
order
s.W
ew
rite
the
unex
pec
ted
order
flow
ε tas
Q∗ t.
The
seco
nd
stag
eis
esti
mat
edas
∆Pt
=a
+γSQ
∗ t+αS
∆Qt+wXt+ωt.
The
non
-info
rmat
ional
com
pon
entαS
isdiv
ided
into
the
chan
ges
ofth
eeff
ecti
vesp
read
sin
the
trea
tmen
tgr
oup
and
the
chan
ges
ofth
eeff
ecti
vesp
read
sin
the
contr
olgr
oup:α1S
∆Qt+α2S
∆Qt×ETF.
Of
our
mai
nfo
cus
isth
eco
effici
entα2S
.
Arb
itra
geO
pp
ortu
nit
yH
igh
Hig
hL
owL
owO
rder
Imbal
ance
Hig
hL
owH
igh
Low
coeff
p-va
lue
coeff
p-va
lue
coeff
p-va
lue
coeff
p-va
lue
Fir
stst
age
regr
essi
onIn
terc
ept
0.09
79∗∗
∗0.
000
0.10
13∗∗
∗0.
000
0.09
27∗∗
∗0.
000
0.09
73∗∗
∗0.
000
Qt−
10.
0598
∗∗∗
0.00
00.
0616
∗∗∗
0.00
00.
0643
∗∗∗
0.00
00.
0636
∗∗∗
0.00
0
Sec
ond
stag
ere
gres
sion
Q∗
0.02
60∗∗
∗0.
002
0.01
54∗∗
∗0.
000
0.01
99∗∗
∗0.
002
0.02
59∗∗
∗0.
000
Tre
asury
retu
rn−
0.01
16∗∗
∗0.
000−
0.00
80∗∗
∗0.
000−
0.02
66∗∗
∗0.
000−
0.03
02∗∗
∗0.
000
Sto
ckre
turn×
invgr
d0.
0159
∗∗∗
0.00
00.
0177
∗∗∗
0.00
00.
0157
∗∗∗
0.00
00.
0124
∗∗∗
0.00
0Sto
ckre
turn×
non
invgr
d0.
0584
∗∗∗
0.00
00.
0507
∗∗∗
0.00
00.
0657
∗∗∗
0.00
00.
0732
∗∗∗
0.00
0D
efau
ltsp
read×
invgr
d−
0.01
70∗∗
∗0.
000−
0.01
10∗∗
∗0.
000−
0.01
58∗∗
∗0.
000−
0.00
19∗∗
∗0.
000
Def
ault
spre
ad×
non
invgr
d0.
0126
∗∗∗
0.00
00.
0510
∗∗∗
0.00
00.
0589
∗∗∗
0.00
00.
0308
∗∗∗
0.00
0∆Q
0.42
93∗∗
∗0.
000
0.44
53∗∗
∗0.
000
0.43
22∗∗
∗0.
000
0.42
95∗∗
∗0.
000
∆Q×
ET
F−
0.07
44∗∗
∗0.
000−
0.06
44∗∗
∗0.
000−
0.06
54∗∗
∗0.
000−
0.05
56∗∗
∗0.
000
R2
0.17
880.
1872
0.18
200.
1868
Nof
blo
ckb
oot
stra
ppin
g50
050
050
050
0
56
Table 7. Changes of the effective half spreads by cumulative trading volume.The bonds are segmented into the quintiles based on their cumulative trading volume during6 months prior to ETFs. Following the methodology of Bessembinder et al. (2006), the firststage is estimated as Qt = a + bQt−1 + εt, where Qt is +1 for the buy order, −1 for thesell order, and 0 for the inter-dealer orders. We write the unexpected order flow εt as Q∗
t .The second stage is estimated as ∆Pt = a + γSQ∗
t + αS∆Qt + wXt + ωt. To assess thechanges of effective spreads of ETF-bonds relative to the changes of effective spreads of non-ETF bonds, we interact ∆Qt with Post and ETF . The non-informational component αSis divided into the changes of the effective spreads in the treatment group and the changesof the effective spreads in the control group: α1∆Qt + α2S∆Qt× Post+ α3S∆Qt×ETF +α4S∆Qt×Post×ETF. Of our main focus is the coefficient α4S. We include the three publicinformation variables Xt as discussed in section 3.5. The statistical significance is measuredthe probability from block bootstrapping, following Bessembinder et al. (2006).
57
coeff p-valueFirst stage regressionIntercept 0.1137∗∗∗ 0.000Qt−1 0.0678∗∗∗ 0.000
Second stage regressionIntercept 0.0268∗∗∗ 0.000Q∗ 0.0316∗∗∗ 0.000Treasury return −0.0116∗∗∗ 0.000Stock return × invgrd 0.0175∗∗∗ 0.000Stock return × noninvgrd 0.0745∗∗∗ 0.000Default spread × invgrd −0.0075∗∗∗ 0.000Default spread × noninvgrd 0.0389∗∗ 0.040VOL 1 (Small) ×∆Q 0.9263∗∗∗ 0.000VOL 1 ×∆Q× Post −0.0009 0.300VOL 1 ×∆Q× ETF −0.4695∗∗∗ 0.000VOL 1 ×∆Q× Post × ETF −0.0881∗∗∗ 0.000VOL 2 ×∆Q 0.8714∗∗∗ 0.000VOL 2 ×∆Q× Post 0.0141∗∗ 0.020VOL 2 ×∆Q× ETF −0.5017∗∗∗ 0.000VOL 2 ×∆Q× Post × ETF −0.0355∗∗∗ 0.000VOL 3 ×∆Q 0.7360∗∗∗ 0.000VOL 3 ×∆Q× Post −0.0384∗∗∗ 0.000VOL 3 ×∆Q× ETF −0.3180∗∗∗ 0.000VOL 3 ×∆Q× Post × ETF 0.0185∗∗∗ 0.000VOL 4 ×∆Q 0.4440∗∗∗ 0.000VOL 4 ×∆Q× Post 0.0394∗∗∗ 0.000VOL 4 ×∆Q× ETF −0.1036∗∗∗ 0.000VOL 4 ×∆Q× Post × ETF −0.0015 0.240VOL 5 (Large) ×∆Q 0.4159∗∗∗ 0.000VOL 5 ×∆Q× Post 0.0237∗∗∗ 0.000VOL 5 ×∆Q× ETF −0.0528∗∗∗ 0.000VOL 5 ×∆Q× Post × ETF −0.0011 0.180
R2 0.1822RMSE 0.7316N of block bootstrapping 100
58
Table 8. Changes of the effective half spreads by credit rating of the bonds. Thebonds are divided into two groups based on the bond credit ratings. We assign numericscores to the bond credit ratings from S&P, Moody’s, and Fitch, following Bo and Ivashina(2012). For each bond, if two credit ratings are available, we take the lower. If threecredit ratings are available, we take the middle. Investment grades bonds have credit ratingsBBB− or above based on S&P or Fitch (or equivalently Baa3 from Moody’s). Following themethodology of Bessembinder et al. (2006), the first stage is estimated as Qt = a+bQt−1+εt,where Qt is +1 for the buy order, −1 for the sell order, and 0 for the inter-dealer orders.We write the unexpected order flow εt as Q∗
t . The second stage is estimated as ∆Pt =a + γSQ∗
t + αS∆Qt + wXt + ωt. To assess the changes of effective spreads of ETF-bondsrelative to the changes of effective spreads of non-ETF bonds, we interact ∆Qt with Postand ETF . The non-informational component αS is divided into the changes of the effectivespreads in the treatment group and the changes of the effective spreads in the control group:α1∆Qt + α2S∆Qt × Post + α3S∆Qt × ETF + α4S∆Qt × Post× ETF. Of our main focusis the coefficient α4S. We include the three public information variables Xt as discussed insection 3.5. The statistical significance is measured the probability from block bootstrapping,following Bessembinder et al. (2006).
Investment grade Non-investment gradecoeff p-value coeff p-value
First stage regressionIntercept 0.1190∗∗∗ 0.000 0.1003∗∗∗ 0.000Qt−1 0.0672∗∗∗ 0.000 0.0684∗∗∗ 0.000
Second stage regressionIntercept 0.0072∗∗∗ 0.000 0.1053∗∗∗ 0.000Q∗ 0.0172∗∗∗ 0.000 0.0842∗∗∗ 0.000Treasury return −0.0101∗∗∗ 0.000 −0.0270∗∗∗ 0.000Stock return × invgrd 0.0178∗∗∗
Stock return × noninvgrd 0.0747∗∗∗ 0.000Default spread × invgrd 0.0067∗∗∗
Default spread × noninvgrd 0.0341∗∗∗ 0.000∆Q 0.3728∗∗∗ 0.000 0.5695∗∗∗ 0.000∆Q× Post 0.0194∗∗∗ 0.000 0.0214∗∗∗ 0.000∆Q× ETF −0.0347∗∗∗ 0.000 −0.0200∗∗∗ 0.000∆Q× Post × ETF 0.0001 0.200 −0.0542∗∗∗ 0.000
R2 0.1603 0.2291RMSE 0.5493 0.9049N of block bootstrapping 100 100
59
Table 9. Changes of the effective half spreads of bonds by the time to maturity.The time to maturity is measured as the number of days to the maturity on the first offerdate of ETF. The bonds are segmented into two groups with a threshold of 2 years i.e. 730days. Following the methodology of Bessembinder et al. (2006), the first stage is estimatedas Qt = a + bQt−1 + εt, where Qt is +1 for the buy order, −1 for the sell order, and 0 forthe inter-dealer orders. We write the unexpected order flow εt as Q∗
t . The second stage isestimated as ∆Pt = a+γSQ∗
t +αS∆Qt+wXt+ωt. To assess the changes of effective spreadsof ETF-bonds relative to the changes of effective spreads of non-ETF bonds, we interact ∆Qt
with Post and ETF . The non-informational component αS is divided into the changes ofthe effective spreads in the treatment group and the changes of the effective spreads in thecontrol group: α1∆Qt+α2S∆Qt×Post+α3S∆Qt×ETF +α4S∆Qt×Post×ETF. Of ourmain focus is the coefficient α4S. We include the three public information variables Xt asdiscussed in section 3.5. The statistical significance is measured the probability from blockbootstrapping, following Bessembinder et al. (2006).
Life remaining ≤ 2 years Life remaining ≥ 2 yearscoeff p-value coeff p-value
First stage regressionIntercept −0.0529∗∗∗ 0.000 0.1298∗∗∗ 0.000Qt−1 0.0354∗∗∗ 0.000 0.0667∗∗∗ 0.000
Second stage regressionIntercept −0.0043 0.290 0.0354∗∗∗ 0.000Q∗ 0.0006∗∗∗ 0.000 0.0385∗∗∗ 0.000Treasury return 0.0160∗∗∗ 0.000 −0.0144∗∗∗ 0.000Stock return × invgrd 0.0041∗∗∗ 0.000 0.0194∗∗∗ 0.000Stock return × noninvgrd 0.0713∗∗∗ 0.000 0.0744∗∗∗ 0.000Default spread × invgrd −0.0028∗∗∗ 0.000 −0.0084∗∗∗ 0.000Default spread × noninvgrd −0.0661∗∗∗ 0.000 0.0393∗∗∗ 0.000∆Q 0.1591∗∗ 0.040 0.4547∗∗∗ 0.000∆Q× Post −0.0246∗∗∗ 0.000 0.0353∗∗∗ 0.000∆Q× ETF −0.0539∗∗∗ 0.000 −0.0861∗∗∗ 0.000∆Q× Post × ETF 0.0989∗∗∗ 0.000 −0.0190∗∗∗ 0.000
R2 0.0762 0.1936RMSE 0.3026 0.7270N of block bootstrapping 100 100
60
Table 10. Quasi-natural experiment for HYG. We investigate changes of the liquidityof the constituent bonds for HYG around the index rule change during 2009. The effectivehalf spreads of ETF-bonds are compared with those of non-ETF bonds. Following themethodology of Bessembinder et al. (2006), the first stage is estimated as Qt = a+bQt−1+εt,where Qt is +1 for the buy order, −1 for the sell order, and 0 for the inter-dealer orders.We write the unexpected order flow εt as Q∗
t . The second stage is estimated as ∆Pt =a + γSQ∗
t + αS∆Qt + wXt + ωt.To assess the changes of effective spreads of ETF-bondsrelative to the changes of effective spreads of non-ETF bonds, we interact ∆Qt with Postand ETF . The non-informational component αS is divided into the changes of the effectivespreads in the treatment group and the changes of the effective spreads in the control group:α1∆Qt + α2S∆Qt × Post + α3S∆Qt × ETF + α4S∆Qt × Post× ETF. Of our main focusis the coefficient α4S. We include the three public information variables Xt as discussed insection 3.5. The statistical significance is measured the probability from block bootstrapping,following Bessembinder et al. (2006).
coeff p-valueFirst stage regressionIntercept 0.0485∗∗∗ 0.00Qt−1 0.0686∗∗∗ 0.00
Second stage regressionIntercept 0.1090∗∗∗ 0.00Q∗ 0.0964∗∗∗ 0.00Treasury return −0.0052∗∗∗ 0.00Stock return × invgrd 0.0054∗∗∗ 0.00Stock return × noninvgrd 0.0025∗∗∗ 0.00Default spread × invgrd 0.0195∗∗∗ 0.00Default spread × noninvgrd 0.1943∗∗∗ 0.00∆Q 0.4318∗∗∗ 0.00∆Q× Post 0.0686∗∗∗ 0.00∆Q× ETF 0.2083∗∗∗ 0.00∆Q× Post × ETF −0.0150∗∗∗ 0.00
R2 0.1469RMSE 0.9876N of block boostrapping 100
61
Table 11. ETF by management styles. ETFs can be actively or passively managed. Weseparate the bonds included by active or index ETFs. The bonds can be owned by both stylesof the ETFs, which we exclude from the analysis. The effective half spreads of ETF-bondsare compared with those of non-ETF bonds. Following the methodology of Bessembinderet al. (2006), the first stage is estimated as Qt = a + bQt−1 + εt, where Qt is +1 for thebuy order, −1 for the sell order, and 0 for the inter-dealer orders. We write the unexpectedorder flow εt as Q∗
t . The second stage is estimated as ∆Pt = a+ γSQ∗t +αS∆Qt +wXt +ωt.
To assess the changes of effective spreads of ETF-bonds relative to the changes of effectivespreads of non-ETF bonds, we interact ∆Qt with Post and ETF . The non-informationalcomponent αS is divided into the changes of the effective spreads in the treatment groupand the changes of the effective spreads in the control group: α1∆Qt + α2S∆Qt × Post +α3S∆Qt × ETF + α4S∆Qt × Post × ETF. Of our main focus is the coefficient α4S. Weinclude the three public information variables Xt as discussed in section 3.5. The statisticalsignificance is measured the probability from block bootstrapping, following Bessembinderet al. (2006).
Active ETF Index ETFcoeff p-value coeff p-value
First stage regressionIntercept 0.1490∗∗∗ 0.000 0.0857∗∗∗ 0.000Qt−1 0.0640∗∗∗ 0.000 0.0473∗∗∗ 0.000
Second stage regressionIntercept 0.0092∗∗∗ 0.000 −0.0169∗∗∗ 0.000Q∗ 0.0143∗∗∗ 0.000 0.0518∗∗∗ 0.000Tresury return −0.0084∗∗∗ 0.000 0.0240∗∗∗ 0.000Stock return × Invgrd 0.0150∗∗∗ 0.000 0.0350∗∗∗ 0.000Stock return × Noninvgrd 0.0500∗∗∗ 0.000 0.0890∗∗∗ 0.000Default spread × Invgrd −0.0124∗∗∗ 0.000 0.0024∗∗∗ 0.000Default spread × Noninvgrd 0.0271∗∗∗ 0.000 0.0490∗∗∗ 0.000∆Q 0.4310∗∗∗ 0.000 0.3510∗∗∗ 0.000∆Q× Post 0.0086∗∗∗ 0.000 0.0639∗∗∗ 0.000∆Q× ETF −0.0530∗∗∗ 0.000 0.0083∗∗∗ 0.000∆Q× Post× ETF 0.0080∗∗∗ 0.000 −0.1028∗∗∗ 0.000
R2 0.1813 0.1851RMSE 0.6364 0.6762N of block bootstrapping 100 100
62
Table 12. Multivariate analysis. We evaluate the relative strength of the explanatoryvariables – cumulative trading volume, bond credit rating, age, ETF styles – to the liquidityof the underlying bonds. The effective half spreads of ETF-bonds are compared with those ofnon-ETF bonds. Following the methodology of Bessembinder et al. (2006), the first stage isestimated as Qt = a+bQt−1+εt, where Qt is +1 for the buy order, −1 for the sell order, and 0for the inter-dealer orders. We write the unexpected order flow εt as Q∗
t . The second stage isestimated as ∆Pt = a+γSQ∗
t +αS∆Qt+wXt+ωt. To assess the changes of effective spreadsof ETF-bonds relative to the changes of effective spreads of non-ETF bonds, we interact ∆Qt
with Post and ETF . The non-informational component αS is divided into the changes ofthe effective spreads in the treatment group and the changes of the effective spreads in thecontrol group: α1∆Qt+α2S∆Qt×Post+α3S∆Qt×ETF +α4S∆Qt×Post×ETF. Of ourmain focus is the coefficient α4S. We include the three public information variables Xt asdiscussed in section 3.5. The statistical significance is measured the probability from blockbootstrapping, following Bessembinder et al. (2006).
63
All Invgrad Non-invgrdcoeff p-value coeff p-value coeff p-value
First stage regressionIntercept 0.1137∗∗∗ 0.000 0.1190∗∗∗ 0.000 0.1003∗∗∗ 0.000Qt−1 0.0678∗∗∗ 0.000 0.0672∗∗∗ 0.000 0.0684∗∗∗ 0.000
Second stage regressionIntercept 0.0225∗∗∗ 0.000 0.0024∗∗∗ 0.000 0.1034∗∗∗ 0.000Q∗ 0.0360∗∗∗ 0.000 0.0213∗∗∗ 0.000 0.0858∗∗∗ 0.000Treasury return −0.0088∗∗∗ 0.000 −0.0070∗∗∗ 0.000 −0.0259∗∗∗ 0.000Stock return × Invgrd 0.0176∗∗∗ 0.000 0.0178∗∗∗ 0.000Stock return × Noninvgrd 0.0742∗∗∗ 0.000 0.0746∗∗∗ 0.000Default spread × Invgrd −0.0079∗∗∗ 0.000 −0.0073∗∗∗ 0.000Default spread × Noninvgrd 0.0383∗∗∗ 0.000 0.0336∗∗∗ 0.000VOL 1 (Small) ×∆Q 0.9692∗∗∗ 0.000 0.9814∗∗∗ 0.000 0.6823∗∗∗ 0.000VOL 1 ×∆Q× Post 0.0056∗∗∗ 0.000 0.0087∗∗∗ 0.000 0.0604∗∗∗ 0.000VOL 1 ×∆Q× ETF −0.5092∗∗∗ 0.000 −0.5623∗∗∗ 0.000 −0.1966∗∗∗ 0.000VOL 1 ×∆Q× Post× ETF −0.0902∗∗∗ 0.000 −0.0482∗∗∗ 0.000 −0.1979∗∗∗ 0.000VOL 3 ×∆Q 0.7490∗∗∗ 0.000 0.9482∗∗∗ 0.000 0.3548∗∗∗ 0.000VOL 3 ×∆Q× Post −0.0271∗∗∗ 0.000 0.0028∗∗∗ 0.000 0.0070∗∗∗ 0.000VOL 3 ×∆Q× ETF −0.3178∗∗∗ 0.000 −0.6446∗∗∗ 0.000 −0.3601∗∗∗ 0.000VOL 3 ×∆Q× Post× ETF −0.0150∗∗∗ 0.000 0.0266∗∗∗ 0.000 −0.1078∗∗∗ 0.000VOL 5 (Large) ×∆Q 0.4504∗∗∗ 0.000 0.4019∗∗∗ 0.000 0.5844∗∗∗ 0.000VOL 5 ×∆Q× Post 0.0333∗∗∗ 0.000 0.0276∗∗∗ 0.000 0.0289∗∗∗ 0.000VOL 5 ×∆Q× ETF −0.0895∗∗∗ 0.000 −0.0386∗∗∗ 0.000 −0.0803∗∗∗ 0.000VOL 5 ×∆Q× Post× ETF 0.0091 0.000 −0.0039 0.000 0.2300∗∗∗ 0.000Life ≤ 2yr ×∆Q −0.3139∗∗∗ 0.000 −0.2789∗∗∗ 0.000 −0.0421∗∗∗ 0.000Life ≤ 2yr ×∆Q× Post −0.0589∗∗∗ 0.000 −0.0548∗∗∗ 0.000 −0.1876∗∗∗ 0.000Life ≤ 2yr ×∆Q× ETF 0.0062∗∗∗ 0.000 0.0326∗∗∗ 0.000 0.2370∗∗∗ 0.000Life ≤ 2yr ×∆Q× Post× ETF 0.1351∗∗∗ 0.000 0.0839∗∗∗ 0.000 0.3524∗∗∗ 0.000
R2 0.1910 0.1755 0.2310RMSE 0.6729 0.7774 1.0202N of block bootstrapping 100 100 100
64
Table
13.
144A
vs.
publi
cb
onds
tradin
gch
ara
cteri
stic
s.14
4Aan
dpublic
bon
ds
com
par
ison
ofav
erag
edai
lytr
adin
gvo
lum
ein
million
dol
lars
and
issu
eam
ount
inbillion
dol
lars
.In
vest
men
tgr
ade
bon
ds
hav
ecr
edit
rati
ngsBBB−
and
abov
e.T
he
aver
age
dai
lytr
adin
gvo
lum
eex
plo
ded
duri
ng
the
3rd
quar
ter
of20
14.
This
was
pre
vale
nt
bot
hin
inve
stm
ent
grad
ean
dnon
-inve
stm
ent
grad
eb
onds.
Ave
rage
dai
lytr
adin
gvo
lum
eIs
sue
Am
ounts
Full
Inv
grad
eN
onin
vgr
dIn
vgr
dN
onin
vgr
dY
ear
Qtr
.14
4AP
ublic
144A
Public
144A
Public
144A
Public
144A
Public
2008
41
9262
0%0
01
3386
0%10
9216
636
8%11
6863
7318
%20
104
811
742
0%20
7467
0%2
4274
0%13
5823
941
7%13
7162
8622
%20
114
311
455
0%3
7137
0%0
4318
676
1857
35%
1021
5371
19%
2012
41
9483
0%0
6042
140
880%
597
5304
8%10
3811
633
9%20
134
810
417
0%4
4993
0%8
5611
0%18
812
6915
%65
848
0714
%20
141
412
306
0%4
5360
0%4
7153
0%20
142
5711
339
1%29
4338
1%53
7250
1%20
143
2440
1063
823
%36
637
7110
%21
0971
8429
%20
144
2558
1181
722
%35
240
679%
2261
7953
28%
2015
128
4613
481
21%
351
4549
8%25
3193
3027
%20
152
2896
1285
523
%29
237
798%
2675
9219
29%
2015
327
3511
494
24%
253
3135
8%25
2486
1729
%20
154
2765
1109
625
%23
928
928%
2561
8377
31%
65
Table
14.
Liq
uid
ity
of
144A
bonds.
Hal
f-sp
read
sby
all,
volu
me
and
cred
itra
ting
wit
hth
e14
4Ab
onds.
The
firs
tst
age
ises
tim
ated
asQt
=a
+bQ
t−1
+ε t,
wher
eQt
is+
1fo
rth
ebuy
order
,−
1fo
rth
ese
llor
der
,an
d0
for
the
inte
r-dea
ler
order
s.W
ew
rite
the
unex
pec
ted
order
flow
ε tas
Q∗ t.
The
seco
nd
stag
eis
esti
mat
edas
∆Pt
=a
+γSQ
∗ t+αS
∆Qt+wXt+ωt.
To
asse
ssth
ech
ange
sof
effec
tive
spre
ads
ofE
TF
-144
Ab
onds
rela
tive
toth
ech
ange
sof
effec
tive
spre
ads
ofnon
-ET
F14
4Ab
onds,
we
inte
ract
∆Qt
wit
hPost
andETF
.T
he
non
-info
rmat
ional
com
pon
ent
αS
isdiv
ided
into
the
chan
ges
ofth
eeff
ecti
vesp
read
sin
the
trea
tmen
tgr
oup
and
the
chan
ges
ofth
eeff
ecti
vesp
read
sin
the
contr
olgr
oup:α1∆Qt
+α2S
∆Qt×Post
+α3S
∆Qt×ETF
+α4S
∆Qt×Post×ETF.
Of
our
mai
nfo
cus
isth
eco
effici
entα4S
.W
ein
clude
the
thre
epublic
info
rmat
ion
vari
able
sXt
asdis
cuss
edin
sect
ion
3.5.
The
stat
isti
cal
sign
ifica
nce
ism
easu
red
the
pro
bab
ilit
yfr
omblo
ckb
oot
stra
ppin
g,fo
llow
ing
Bes
sem
bin
der
etal
.(2
006)
.
66
Full
By
volu
me
By
cred
itra
ting
Hig
h/L
owV
olIn
vest
men
tgr
ade
Non
-inve
stm
ent
grad
eco
effp-
valu
eco
effp-
valu
eco
effp-
valu
eco
effp-
valu
eF
irst
stag
ere
gres
sion
Inte
rcep
t−
0.03
89∗∗
∗0.
000−
0.03
89∗∗
∗0.
000−
0.06
70∗∗
0.00
0−
0.01
68∗
0.09
0Qt−
10.
0299
∗∗0.
010
0.02
99∗∗
0.01
00.
0181
∗∗0.
100
0.03
57∗∗
0.01
0
Sec
ond
stag
ere
gres
sion
Inte
rcep
t−
0.04
08∗∗
0.05
0−
0.04
77∗∗
0.03
0−
0.09
74∗∗
∗0.
000−
0.01
480.
036
Q∗
0.10
04∗∗
0.00
00.
0976
∗∗∗
0.00
00.
0247
∗0.
060
0.11
15∗∗
∗0.
000
Tre
asury
retu
rn−
0.00
050.
540
0.00
450.
310
0.05
84∗∗
∗0.
000−
0.01
190.
430
Sto
ckre
turn×
invgr
d0.
0025
0.48
0−
0.00
59∗∗
0.02
0−
0.00
39∗∗
0.02
0Sto
ckre
turn×
non
invgr
d0.
0597
∗∗∗
0.00
00.
0572
∗∗∗
0.00
00.
0600
∗∗∗
0.00
0D
efau
ltsp
read×
invgr
d−
1.55
79∗∗
∗0.
000−
1.35
69∗∗
∗0.
000−
1.23
86∗∗
∗0.
000
Def
ault
spre
ad×
non
invgr
d0.
0675
0.13
00.
0032
0.14
00.
0881
0.23
0∆Q
0.02
31∗
0.07
00.
0321
∗∗∗
0.00
00.
0202
0.11
0∆Q×
Pos
t0.
0267
0.10
00.
0306
∗∗∗
0.00
00.
0266
0.14
0∆Q×
ET
F0.
0716
∗∗0.
000
0.00
520.
230
0.12
97∗∗
∗0.
000
∆Q×
Pos
t×
ET
F−
0.04
92∗∗
0.02
0−
0.03
51∗∗
0.01
0−
0.09
71∗∗
∗0.
000
VO
LL×
∆Q
0.18
28∗∗
∗0.
000
VO
LL×
∆Q×
Pos
t−
0.01
140.
360
VO
LL×
∆Q×
ET
F−
0.12
91∗∗
∗0.
000
VO
LL×
∆Q×
Pos
t×
ET
F0.
0879
∗∗0.
010
VO
LH×
∆Q
−0.
0277
0.11
0V
OL
H×
∆Q×
Pos
t0.
0401
∗∗0.
010
VO
LH×
∆Q×
ET
F0.
1349
∗∗∗
0.00
0V
OL
H×
∆Q×
Pos
t×
ET
F−
0.12
24∗∗
∗0.
000
R2
0.11
910.
1406
0.21
860.
1245
Nof
blo
ckb
oos
trap
pin
g10
010
010
010
0
67
Table 15. Control for firm fixed effect. To control for unobservable bond characteristicsrelated to the bond issuing firms, we include firm fixed effects in the analysis. The effectivehalf spreads of ETF-bonds are compared with those of non-ETF bonds. Following themethodology of Bessembinder et al. (2006), the first stage is estimated as Qt = a+bQt−1+εt,where Qt is +1 for the buy order, −1 for the sell order, and 0 for the inter-dealer orders.We write the unexpected order flow εt as Q∗
t . The second stage is estimated as ∆Pt =a + γSQ∗
t + αS∆Qt + wXt + ωt. To assess the changes of effective spreads of ETF-bondsrelative to the changes of effective spreads of non-ETF bonds, we interact ∆Qt with Postand ETF . The non-informational component αS is divided into the changes of the effectivespreads in the treatment group and the changes of the effective spreads in the control group:α1∆Qt + α2S∆Qt × Post + α3S∆Qt × ETF + α4S∆Qt × Post× ETF. Of our main focusis the coefficient α4S. We include the three public information variables Xt as discussed insection 3.5. The statistical significance is measured the probability from block bootstrapping,following Bessembinder et al. (2006).
coeff p-valueFirst stage regressionIntercept 0.1137∗∗∗ 0.000Qt−1 0.0678∗∗∗ 0.000
Second stage regressionQ∗ 0.0455∗∗∗ 0.000Treasury return −0.0138∗∗∗ 0.000Stock return × invgrd 0.0175∗∗∗ 0.000Stock return × noninvgrd 0.0740∗∗∗ 0.000Default spread × invgrd −0.0069∗∗∗ 0.000Default spread × noninvgrd 0.0383∗∗∗ 0.000∆Q 0.4160∗∗∗ 0.000∆Q× Post 0.0252∗∗∗ 0.000∆Q× ETF −0.0531∗∗∗ 0.000∆Q× Post × ETF −0.0122∗∗∗ 0.000
Firm Fixed Effect YR2 0.1880N of block bootstrapping 50
68