Trading around Disclosure of Municipal Bond Defeasance

Donald Monk and Ke Xu∗

January 2017

ABSTRACT

Using a novel sample of municipal bond defeasance disclosures we examine the effi-ciency of the secondary market in impounding the information related to the defeasanceevent. In support of market efficiency we find that quickly after the disclosure of de-feasance, customer buys of defeased bonds occur at expected yield after the event.Interestingly, we find trades occurring a month before the disclosure that indicate in-formation leakage related to the event. We provide evidence that links the leakage oflucrative defeasance information to the timing of financial disclosures related to thenewly issued bonds intended to refund the old bonds. Moreover, those investors witha greater incentive and potential to use defeasance information (those trading largeramounts) trade in ways consistent with having superior knowledge relative to other in-vestors. We recommend that policymakers consider amending Rule 15c2-12 to mitigatethis leakage.

JEL classification: G18, H74, M48.

Keywords: Municipal Bonds, Defeasance, Regulation, Disclosure, Rule 15c2-12, Advance Refunding

∗Monk is at Rutgers Business School at Rutgers University, and Xu is affiliated with Southwestern University ofFinance and Economics (SWUFE) and Rutgers University. Monk (email: dmonk@business.rutgers.edu) would liketo give special thanks to DPC Data, especially Peter Schmitt, for allowing him to use their continuing disclosure dataand for hours of conversations about the municipal bond market. Xu (kx22@scarletmail.rutgers.edu) appreciatesfinancial support from the Dean’s Ph.D. Summer Research Funding at Rutgers Business School.

I. Introduction

When significant events related to municipalities are revealed, such as imminent or likely

bankruptcy in Detroit or Puerto Rico, the press, bond markets, politicians, and constituents react.

Since there is not usually a single disclosure that indicates a potential bankruptcy, studying the

reaction of market participants is intractable. Moreover, using bankruptcy events to gain a better

understanding of the informational efficiency of the bond market has been limited due to a dearth

of actual bankruptcies in the municipal bond market (Bergstresser and Cohen, 2011) and the lack

of a central repository for the disclosure of other events that may impact the market. We over-

come these limitations by using a database of disclosures required by the SEC to examine whether

the municipal bond market reaction to bond defeasance, which we link to advance refunding, is

commensurate with the activity disclosed.

The particular event we study is the defeasance of an outstanding municipal bond. In a legal

sense, the “defeasance” of a bond means that certain rights and obligations for the parties in

the debt contract have been met. A defeasance is typically the result of the municipality issuing

additional bonds (called “refunding bonds” or “new bonds”) to fund the creation of a refunding

escrow to make the required payments of the existing bonds (called “refunded bonds” or “old

bonds”). Once a bond is defeased the municipality can remove it from its liabilities and reap any

budgetary benefit of the refinancing. For the investors holding a defeased bond, future interest

payments and principal repayment are secured by an escrow to maturity, which is typically the

next call date.

Due in part to the substantial changes this event causes to the terms of the security, the

Securities and Exchange Commission (SEC) has acted to make investors aware of their exposure

to it. First, the SEC included defeasance as one of the 11 events required to be disclosed when

it amended §240.15c2-12 (“Rule 15c2-12”) in 1994 to include certain disclosures. Second, in 2010

the regulator went further by removing the qualification of “if material” for some of the continuing

disclosures, including defeasance. Recently the SEC stepped up enforcement of the continuing

disclosure requirements of Rule 15c2-12 by offering obligated parties that come forward with failures

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to comply with the disclosures the benefit of favorable settlement terms. As of December 2016 the

SEC has taken enforcement action on over 140 underwriters and issuers and assessed penalties

totaling nearly $18 million. Clearly, regulators are convinced that these disclosures have an effect

on the market and investors should know about them in a timely manner.

Using the disclosure of defeasance as an event has notable advantages over other event studies

in bond markets. The yield for a defeased bond should be equal to or very near the yield of the

securities placed in escrow to make the interest payments and refund the principal. Even more

useful in this context is the fact that the U.S. Treasury Department constructs State and Local

Government Securities (SLGS) specifically for this refunding purpose, and these securities must be

used in most cases to fund the escrow to prevent tax arbitrage. SLGS are not available for trade

and their rates are set daily by the U.S. Treasury Department. Using SLGS rates as a reliable

estimate of the expected after-event yield allows us to test market efficiency more easily compared

to bond events that result in an unknown after-event yield, such as ratings changes.1

To empirically test the impact of defeasance disclosures we create a database of defeasance

events and merge it with bond characteristics and bond trades. From a database of over 7 million

total continuing disclosures over the period 1998–2012 provided by DPC Data, we identify 225,825

defeasance disclosures associated with 189,252 unique nine-digit CUSIPs (bonds) and 9,786 unique

six-digit CUSIPs (issuers). After merging these data with bond characteristics from the Mergent

Municipal Bond Database and bond trades from the Realtime Transaction Reporting System from

the Municipal Securities Rulemaking Board (MSRB), our sample includes 93,069 unique bonds.

Using this sample we test whether the municipal bond market reacts to the disclosure of defea-

sance and how quickly the reaction takes place. We show that after the disclosure of defeasance,

customer buys of defeased bonds occur at a yield that is not statistically different from matched

SLGS rates. Before the disclosure, yields of customer buys are steadily decreasing, yet they are

statistically greater than the SLGS rate. These results support the claim that the municipal bond

market efficiently impounds the defeasance information and it does so rather quickly. Moreover,

1? survey the literature on abnormal (corporate) bond returns and show that many of the inferences from previousstudies are not robust. They offer a method that performs better than existing abnormal bond return measures, butsome debate remains as to the appropriate calculation of abnormal returns (Ederington, Guan, and Yang, 2013).

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the designation of defeasance as a relevant continuing disclosure in regulatory filings appears to be

justified by the substantial movement of the defeased bond yields to matched SLGS yields.

Interestingly, there is a marked decrease in yields and an increase in trading that occur ap-

proximately four weeks prior to the defeasance disclosure. One potential explanation for the early

movement in yields is that there is leakage of information about the defeased bonds in a filing for

the refunding bonds intended to refund the defeased bonds. Though data limitations make an exact

match of old bonds to new bonds intractable, we match the old bonds to new bonds at the six-digit

CUSIP and require the matched new bond to be designated as a “refunding” issue in Mergent.

Using these matches we show that the vast majority of settlement dates for the matched refund-

ing bonds is during the 20-30 days prior to the defeasance disclosure. Upon manual examination

of a few preliminary offering statements for refunding bonds we note that the bonds intended to

be defeased are listed, sometimes at the nine-digit CUSIP level and often at the series level. We

continue to investigate ways to better determine if information leakage via other issuer disclosures

causes this marked decrease in yields prior to the public dissemination of the defeasance disclosure.

For the early information to have value, some investors must have preferential access to it

or have better ability to process it. If there are traders with superior (early) information about

the impending defeasance of a bond, they can earn a substantial, short-term gain in value by

buying the bonds before the defeasance and then selling them after the bond yields decrease to

the SLGS rate after defeasance disclosure or simply holding the defeased bonds to the call date.

If all market participants have access to the information at the same time and can process it, the

market reaction to defeasance would be immediately impounded in prices upon the release of the

indicative information, not upon the defeasance disclosure as we show.

To determine whether some investors benefit from early knowledge of the defeasance we investi-

gate differences in the direction, frequency, and size of trades surrounding the defeasance disclosure.

We follow existing literature by attributing large trades to institutions while small trades are at-

tributed to retail investors, though we do not have the necessary data to determine exactly what

type of investor made a trade. Cuny (2016) provides support for institutional traders possessing

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superior information in this market by showing that traders she classifies as retail investors pay a

premium relative to what institutional investors pay, and the premium declined as a result of the

introduction of increased financial disclosures.2

Using trade frequency and par value we show that larger buy transactions tend to increase

more than small transactions in the month prior to the defeasance disclosure. Also, larger sell

transactions occur disproportionally just after the defeasance disclosure. While this is consistent

with larger, more knowledgeable traders benefiting from superior information, it is impossible to

determine with our data if this is truly institutional investors engaging in these activities. Taken

together, however, our results and the potential for certain market participants to benefit from

preferential information provide support for further investigation into the timing of Rule 15c2-12

defeasance disclosures and how that information is disseminated to market participants.

In recent years there has been an enhanced academic interest in the municipal bond market,

primarily due to access to new data. Schultz (2012), Green, Hollifield, and Schurhoff (2007),

and Harris and Piwowar (2006) reveal characteristics of the market microstructure of the municipal

bond market using municipal bond trading data that were not readily available beforehand. Similar

advancements happened in the corporate bond market once TRACE was available (Edwards, Harris,

and Piwowar, 2007). More generally, Hildreth and Zorn (2005) provide a historical review of the

municipal market with a specific focus on disclosure requirements.3

Our work extends our understanding of municipal bond market information efficiency, particu-

larly with respect to regulatory disclosures intended to ameliorate opacity in the secondary market

for municipal bonds. There are two closely related studies that examine advance refunding, but

neither combines continuing disclosures from Rule 15c2-12 with market reactions as we do. Fis-

cher (1983) shows that there is a swift market reaction around the announcement date of advance

2Since the municipal bond market is dealer-based and over-the-counter, dealers could also have superior informa-tion and the ability to benefit from it. Schmitt (2009) provides evidence consistent with informational discrepancybetween dealers and investors in this market. Our data do not allow for us to address this possibility.

3Many studies analyze the primary market impact of various characteristics of municipalities such as use of GAAPreporting (Gore, 2004; Baber and Gore, 2008), use of GASB 34 reporting (Plummer, Hutchison, and Patton, 2007;Reck and Wilson, 2014), religious fractionalization (Bergstresser, Cohen, and Shenai, 2011), corruption (Butler,Fauver, and Mortal, 2009), financial restatements (Baber, Gore, Rich, and Zhang, 2013), and disclosure timing(Edmonds, Edmonds, Vermeer, and Vermeer, 2015).

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refunding: For a sample of 50 issues in 1977 that announced advance refunding, the mean differ-

ence between quoted yield and predicted yield (based on various market, bond, and issuer factors)

changes significantly around the announcement date. Ang, Green, and Xing (2015) investigate the

efficiency of advance refunding and provide evidence that the practice is costly and ill-advised for

issuers, but do not analyze the secondary market.

The paper continues as follows. In Section II we develop our hypotheses and research methodol-

ogy using references to relevant literature. We follow that with a description of the development of

our dataset in Section III. We present our results in Section IV. Finally, the conclusion in Section V

summarizes our findings.

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II. Hypotheses Development and Research Methodology

Government Accountability Office (2012) provides a municipal bond market assessment that

finds limitations of timeliness, frequency, and completeness of continuing disclosures. The “effect

of these limitations on individual investors largely is unknown because limited information exists

about the extent to which individual investors use disclosures to make investment decisions.” We

investigate the timeliness and completeness of a particular continuing disclosure called “defeasance”

using the secondary market reaction to the disclosure as a measure of market efficiency. Moreover,

trading activity around the event date is indicative of the timeliness of the disclosure relative to

the knowledge of the event entering the market.

Market reaction to a particular disclosure has often been the measure of usefulness of a disclosure

and has been studied thoroughly in equity markets, less so in corporate bond markets, and rarely

in municipal bond markets. One reason for the differing levels of research and attention among the

markets are the vast differences in the data available. The disclosure amendments to §240.15c2-12

(“Rule 15c2-12”) in 1994 and trade transparency rules promulgated by the Municipal Securities

Rulemaking Board (MSRB) in the mid-1990s resulted in the construction of databases that would

not become widely available until much later.

Once bond market trade data became available a number of studies were capable of looking

deeper into municipal bond market efficiency by studying market microstructure. For example,

Schultz (2012) and Green, Hollifield, and Schurhoff (2007) use transactions data to examine the

market at or near the time of bond issuance. Schwert (2015) uses the bond transaction data

to estimate state and local credit spreads and decomposing those spreads into default risk and

liquidity premium. Cuny (2016) examines the effect that disclosure has on the premium paid by

small investors relative to large investors using both the transactions data and aspects of continuing

disclosures. Reck and Wilson (2006) find that market reactions to information have moved from

just around new issue public information release to times during the year when other information

is released. Our study contributes to this literature by providing direct evidence of a secondary

market reaction to a particular disclosure required by Rule 15c2-12.

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Using a novel dataset we are capable of combining actual disclosures with the transactions data

to examine market reaction to a specific disclosure. In particular, we investigate whether there

is a market reaction to the disclosure of a bond defeasance, a disclosure that is required by Rule

15c2-12. Additionally, we are able to examine whether trading patterns are consistent with other

information available earlier in the defeasance process that can be used to deduce a forthcoming

defeasance.

A. Market Reaction to Defeasance

An advance refunding is effectively a refinancing of an outstanding bond, but a primary differ-

ence is that the existing bond is not paid off with the new bonds that are issued. Instead, money

from the new issue is placed into an escrow in an amount that equals future principal and interest

payments up to the call date or maturity. Once the escrow is established and verified, the refunded

bond is “defeased” and its associated liability is removed from the financial obligations of the mu-

nicipality.4 It is at this point that a continuing disclosure is required per Rule 15c2-12 to alert

bondholders that a particular bond has been defeased.

Decades ago, studies of the municipal bond market were limited to analyses of general measures

such as accounting rule changes, state-level analyses, or hand-collected data on a limited sample

of issues. One study from the last category that is particularly relevant to our work is the Fischer

(1983) examination of the secondary market reaction to a sample of bonds that “are announced to

be advance refunded.” Comparing the quotes of this hand-collected sample of 50 bonds from 1977

to a sample of nonadvance refunded bonds, Fischer (1983) finds that the market reacts quickly and

in the direction predicted by the decrease in default risk associated with an advance refunding.

Advance refunding of a bond results in drastic changes to the payoff stream of a bond, and

thus offers a good opportunity to test how the market impounds the event into trades. Similar

to Fischer (1983), we use this event to examine the efficiency of the secondary market.5 One

4A municipality can also report any budgetary gain due to dropping the old bond from its financial statementsand adding the new bond, which likely has a lower interest rate. However, as shown in Ang, Green, and Xing (2015)this potential budgetary benefit in the short run is at the expense of greater total costs of the financing, primarilydue to fees associated with underwriting the new bonds and the option value lost by committing to call the old bond.

5Fischer (1983) uses the hand-collected announcement date of advance refunding as the event, whereas we use the

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contribution that we make is our use of a methodology that is not contingent on a yield prediction

model, as the use of such models leads to results that are not robust (?) and the debate as to

a proper model to examine bond market returns is ongoing (Ederington, Guan, and Yang, 2013).

Specifically, surrounding the event we compare yields on trades of bonds to the maturity matched

rate for State and Local Government Securities (SLGS), which are securities sold by the U.S.

Treasury Department to municipalities for the purpose of funding advance refunded escrows. Since

the default risk and call option value of a defeased bond are erased by the establishment of an

escrow holding payments and interest until the first call date or maturity, the yield on a defeased

bond should be equal to the maturity-matched risk-free yield that has been adjusted for the tax

benefits of municipals, which should be closely approximated by the SLGS yield.

Hypothesis 1. At the event date of a bond defeasance and thereafter, the yield on defeased bonds

is equal to the yield on matched SLGs securities.

B. Timeliness of the Disclosure

Next we concentrate on the timeliness of the regulatory disclosure. If the disclosure of defeasance

is the first public disclosure of the advance refunding, there should be no discernible movement in

defeased bond yields toward the SLGS rate before the public disclosure nor should there be a

significant increase in trading activity absent confounding information disclosures. An indication

to the contrary would support some traders having superior information about the event before

it is disclosed. As noted above, the advance refunding process results in substantial changes in

the refunded bond payoff stream, and any trader with knowledge of a forthcoming refunding and

defeasance stands to gain substantially as the bond yield decreases to the SLGS rate and the bond

price increases. Notably, Rule 15c2-12 requires the disclosure of defeasance, yet the disclosure of

an intention to advance refund a bond is not required.

Hypothesis 2. Prior to the defeasance announcement there is no significant movement in yields

toward the expected yield after the announcement.

defeasance disclosure that is required per SEC regulation.

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Hypothesis 3. Prior to the defeasance announcement and absent other information disclosure

there is no significant increase in trading activity for the bond to be defeased.

There is ample support in the literature for differences in access to information or the ability

to process information between certain investors in the municipal bond market. Many studies,

such as Harris and Piwowar (2006), Green, Hollifield, and Schurhoff (2007), and Schultz (2012),

show that small trades (usually categorized as made by “retail” investors) are more expensive than

large trades (usually categorized as made by “institutional” investors) which is consistent with a

market structure in which large trades are made by more informed investors. Cuny (2016) finds

that the availability of trade transactions via a website (EMMA) diminished the premium that

small investors pay over large investors which is consistent with large investors having more ready

access to the trade information before the website disclosure was available.6

Our tests investigate whether there is leakage of the defeasance prior to the disclosure date

by examining the change in yield on traded bonds in the weeks before the public disclosure of

defeasance. If Hypothesis 1 holds and there is no leakage, we expect a sudden and complete change

in the yield of a defeased bond to the SLGS rate at the defeasance announcement. If Hypothesis 1

holds and there is leakage, we expect changes in yield to be significant and in the direction of the

SLGS rate in the time before the defeasance announcement. This latter case is consistent with

some traders having knowledge of the forthcoming defeasance.

Likewise, we expect the timing of trading activity to follow a similar pattern. If there is no

leakage, those traders interested in trading on the defeasance disclosure will trade at the disclosure

date or soon thereafter. If there is a significant increase in trading before the announcement,

it is indicative of some investors trading based on the forthcoming defeasance event. To limit

the possibility of confounding events we limit our sample to those defeased bonds without other

continuing disclosures in the 12 weeks surrounding defeasance.

6A notable weakness of these studies is that the data do not allow for identification of traders as “retail” or“institutional.” We note that the categorization of trade sizes into types of investors is not ideal as demonstratedin Cready, Kumas, and Subasi (2014), but all of our findings are not necessarily connected to the type of investor.Rather, any findings we present that attempt to delineate investors are associated with trade size.

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C. Regulatory Implications

One potential cause for leakage is that information disclosed publicly before the defeasance can

be used to deduce the defeasance event. In particular, bond offering statements that are required

for the issuance of the new money that will refund the old money may contain information about

the advance refunded bonds. Since the new money must be issued to fund the escrow that must

be in place before the old bonds can be defeased, any information on the use of proceeds within

the offering statements of the new money may be useful in deducing a forthcoming defeasance.

However, there is no regulatory requirement for the underwriter or issuer of the new money to

notify the holders of the bonds to be refunded that a defeasance is imminent.

In an attempt to determine whether this leakage is occurring, we attempt to find the offering

statements for the new money that may contain information about the old money to be refunded.

After matching new money to the old money it is intended to refund, we compare the number of

days between the new money offering date and the defeasance of the old money to the trading

activity in the old bonds that occurs before the defeasance date. We also examine a few offering

statements to see what information, if any, is available about the old bonds to be refunded.7

Additionally, some investors must have more ready access to this information or have better

abilities to determine its impact than others. Otherwise, if all traders received and processed the

information early, all trades would be at or very close to the fully defeased yield (the matched

SLGS rate) as soon as the pre-defeasance information is disclosed. We examine the frequency, size,

and direction of trades as a way to see if a certain group of investors is trading in ways consistent

with having prior knowledge of the defeasance event. We categorize investors as “institutional” if

they have large trades and as “retail” otherwise. Since institutional investors are more likely than

retail investors to discover the information indicative of a defeasance and be able to process it, we

expect to see large trades indicative of forthcoming defeasance.

7For reasons made clear in the data section, these tests are limited in their ability to discern whether this is thecause of any information leakage. We continue to consider alternative tests to allow for better identification of thisissue.

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III. Data

A. Sample Creation

The event data that we use are available due to the adoption of amendments to Municipal

Securities Disclosures, 17 C.F.R. §240.15c2-12 in 1994 with an effective date of July 3, 1995. In

particular, paragraph (b)(5)(i)(C) of “Rule 15c2-12” added a requirement that the Participating

Underwriter verify that the bond issuer or obligated person discloses 11 events in a timely manner,

among other requirements. In 2010 the rule was amended to remove the materiality condition from

some events and to add four new events.8 Among these events we focus our attention on the

disclosure of bond defeasance, an event that is a direct result of an advance refunding.

Our source for the disclosures data is DPC Data (DPC), a New Jersey company that was

designated as a Nationally Recognized Municipal Securities Information Repository (NRMSIR)

from 1992 until a change to the filing requirements made the Municipal Securities Rulemaking Board

(MSRB) the sole repository as of July 1, 2009. Since that change, DPC obtains municipal market

disclosures via a real-time feed directly from the Electronic Municipal Market Access (EMMA)

system maintained by the MSRB. DPC rigorously processes filings to ensure proper allocation

to individual bonds, issuers, and obligors. For filings from long ago, DPC has reviewed actual

filing documents, which are often scanned versions of faxed documents that cannot be read using

a computer, using employees trained on the municipal bond market to create a relational database

of millions of filings and accompanying documents.

We start with the continuing disclosure data from DPC for the period 1998–2012. There are

a total of 7,245,605 continuing disclosures for the 15 events listed in Paragraph (b)(5)(i)(C) of

Rule 15c2-12 and three other events from other sections of the rule. We extract all of the 237,374

defeasance disclosures, which are linked to 189,252 individual municipal bonds at the nine-digit

8The current rule can be found in the Code of Federal Regulations at www.ecfr.gov searching Title 17, Part240 whereas the final rule for the 1994 amendments by the SEC is Securities and Exchange Commission (1994).In March of 2014, the SEC increased its efforts to enforce the disclosure requirements of the rule by offering theMunicipalities Continuing Disclosure Cooperation Initiative (MCDC), a program that offers favorable settlementterms to parties obligated to make the disclosures if they self report violations. This program resulted in a total ofover 140 enforcement actions against underwriters and issuers and millions in penalties.

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CUSIP (“bond”) level that were issued by 9,786 unique six-digit CUSIP identifiers (“issuers”) as

shown in Table I.9 The primary reason that there are more defeasance disclosures than the number

of municipal bonds is because some bonds are partially refunded at the first refunding and then

refunded again at a later date. To mitigate the effect of previous refunding disclosure on current

refunding disclosure for those bonds that were partially refunded, we keep only the first refunding

disclosure if a bond was refunded multiple times, leaving 189,252 observations.

Our next step is to merge the disclosures data with bond characteristics from the Mergent

Municipal Bond Database. As the announcement of defeasance is also a commitment to call the

bond, we restrict the sample to callable bonds. However, as noted in Brown (2011), almost all

municipal bonds are callable, a fact that distinguishes the municipal market from high-rated cor-

porate bonds. We use the first call date from Mergent to calculate days-to-call, which allows us to

determine whether the defeasance is an advance refunding or a current refunding. At this juncture

we restrict our sample to those announcements of defeasance that happen at least 90 days before

the first available call date to ensure that our sample includes advance refundings and not current

refundings.10 After merging bond characteristics data, 102,132 unique bond-level observations

remain.

The next dataset that we incorporate includes bond-level transaction data from the historical

data report from the Municipal Securities Rulemaking Board (MSRB). Since 1995 the MSRB has

collected data reported by dealers for municipal securities transactions. Each trade is identified

as a dealer’s sell to a customer (customer buy), a dealer’s purchase from a customer (customer

sell), or an inter-dealer trade. For customer buys and sells each transaction observation generally

includes the yield, price, and par value traded. We use observations with positive yield only, and

transactions with inconsistent yield values that trade at the same price on the same day are given

the consistent yield value from the other trade on the same day.

We further restrict our analysis to transactions of customer buys and customer sells. Doing so

9Municipal Securities Rulemaking Board (2013) shows that defeasances equal about 2% of continuing disclosures.Our numbers are similar.

10According to https://www.irs.gov/pub/irs-tege/part2d02.pdf an issue is an “advance” refunding if any ofits proceeds are used to pay debt service on refunded bonds after 90 days from its issuance. The threshold numberof days is 180 for bonds issued prior to 1986.

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focuses our attention on the prevailing market conditions for investors rather than dealers. Also,

dealers are not required to report the yield for interdealer transcactions, making it difficult to

find an appropriate benchmark to which a defeased bond yield can be compared. Table I shows

that total transactions for customer buys (customer sells) for our sample at this stage is 5,338,357

(2,096,022 million) for an associated number of unique bonds of 93,069 (79,806). Fig. 1a shows the

yearly distribution of defeasance disclosures dates for the 93,440 unique bonds overall between buys

and sells, while Figures 1b and 1c show the distribution of buy transactions and sell transactions,

respectively.11

Finally, we use State and Local Government Securities (SLGS) rates as the yield benchmark to

calculate abnormal yield for refunded municipal bonds. SLGS are issued by the U.S. Treasury for

the sole purpose of establishing escrows to make interest and principal payments of refunded bonds

until their redemption at the call date. As such, the yield on defeased bonds should be very close, if

not equal to, the yield on matched maturity SLGS. SLGS are regarded as risk-free, and their yield

rates are determined by the US Treasury on a daily basis for maturities from 1 month to 30 years.

In order to compute the abnormal yield we follow the approach in Ang, Green, and Xing (2015) and

match refunded bonds to corresponding SLGS rates (downloaded from www.treasurydirect.com)

by months to maturity as calculated by the number of months to the next possible call date.12

B. Summary Statistics

We make a number of adjustments to the data to create a relevant and reliable sample. Since

municipal bonds do not trade often enough to use daily transaction measures reliably, we aggregate

trades by week. We provide summary statistics for the sample at this point in the screening process

in Appendix B. Table B1 shows that on average defeasance disclosures occur about seven years after

the bond has been issued, nine years prior to original maturity, and approximately 2.5 years to the

next possible call date. About 50% of the defeased bonds are general obligation bonds. The vast

majority are state tax exempt (84%) and almost all are federal tax exempt (98%).

11Of the 94% of our 102,132 observations with available Mergent data on redemption type, 80% are “pre-refunded,”11% are “partially refunded,” and 7.5% are “called.”

12Our data do not allow us to match to the exact securities that are placed in escrow.

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The statistics for the full defeased sample are similar when split into samples of defeased bonds

that have buy and sell trades. Table B2 shows that defeased bonds trade very rarely with an

average of less than five trades per year in the one, two, or three years prior to the defeasance

disclosure date. There tend to be fewer sells transactions than buy transactions in the time leading

up to a defeasance. Table B3 provides information on use of proceeds, state of issuance, and rating

that generally agrees with typical samples in municipal studies.

We focus our attention on the 12 weeks before and after the defeasance event. To do so, we

require that refunded bonds must have at least one customer-based trading record in the 12 weeks

before or after the disclosure date.13 To mitigate the effect of outliers, we trim outliers of yield

and price at the 1% level on both sides. Our final sample contains 35,865 (35,452) bond-level

observations and 111,222 (104,333) total observations for customer buy (customer sell).

Table II provides summary statistics of characteristics associated with the defeased bonds in

our final sample. Since we restrict the sample to defeased bonds that trade with [-12,12] weeks

surrounding the defeasance event date, each type of trade has greater trading activity. The average

number of buy trades for a defeased bond in our sample is approximately nine in each of the three

years prior to the defeasance event, which is more than double the trading activity for the full

sample shown in Table B2. On the sell side, defeased bonds trade about five times per year, which

again is about double the frequency in the full sample. The other bond characteristics are similar

between the full sample and the final sample, except Issue Size is considerably larger for the final

sample.

13Although Table I shows that a considerable number of observations could be saved using a 52-week window, weshow in Fig. 2 that the abnormal market activity surrounding the defeasance disclosure happens within a 12-weekwindow.

15

IV. Tests

A. Market Reaction at the Event Date

We first present evidence to determine if there is a market reaction to the defeasance disclosure.

More formally, Hypothesis 1 states that at the event date of a bond defeasance and thereafter, the

yield on defeased bonds is equal to the yield on matched SLGs securities. Figure 2(a) confirms this

hypothesis, showing that the abnormal yield on customer buy trades (measured as the yield on

the customer buy trade minus the matched SLGS rate) apparently goes to zero at the event date

(defeasance disclosure). Moreover, the abnormal yield remains at zero or very slightly negative

after the event date.

Results from a test of the statistical validity of these claims is presented in Table III. While

there is a slightly positive and significant abnormal yield of twelve basis points the week prior to

the event, the abnormal yield decreases to three basis points in the week after and then down to

an insignificant negative one basis point in the second week after the event. There appears to be

a slight negative trend in the abnormal yield in the weeks following week one, but many of these

values are not statistically different from zero. As our matches between defeased bonds and SLGS

rates are not perfect, we expect slight error in the abnormal yield. In sum, our results indicate that

the market fully impounds the defeasance information as of the event date.

B. Trading Before the Event

In the following section we present tests to determine if there is a pre-disclosure market reaction

that is indicative of the forthcoming event. Our first test examines the change in market abnormal

yields on trades in the weeks prior to the defeasance disclosure date. As Hypothesis 2 states,

before the disclosure we expect there to be no significant movement in yields toward the expected

post-disclosure yield, which is proxied by the matched SLGS rate.

Graphical evidence appears to reject this hypothesis. A drastic early movement in abnormal

yields is evident in Fig. 2(a) and Fig. 3, which presents the same data shown over a 52-week

week span and a 12-week span surrounding the defeasance, respectively. Clearly, there is a general

16

decrease in yields and the decrease steepens about four to six weeks prior to the disclosure event.

It is also clear from Fig. 2(b) and Fig. 3 that trading activity increases well before the event date

giving the indication that Hypothesis 3 is rejected as well.

Table IV provides a clearer indication of early movement in yields indicative of traders knowing

the defeasance event is coming. Decreases in abnormal yields start in earnest seven weeks before the

defeasance disclosure and become significantly different from zero three weeks prior to the event.

Over the 12 weeks prior to the defeasance disclosure the abnormal yield decreases from about 50

basis points to 7 basis points. Also, trading activity increases significantly in the weeks leading up

to the defeasance event. The number of trades increases from 3,386 in the twelfth week before the

event to 5,282 in the week of the event.

C. Disclosure Timing

C.1. New Money Timing Relative to Old Money Defeasance

Since there appears to be trading before the event date that is indicative of a forthcoming

defeasance, we attempt to discern how the information could be discovered early. After discussions

with industry specialists, we learned that offering statement disclosures associated with refunding

bonds whose proceeds are intended to refund old bonds often contain some mention of the bonds

to be refunded. Upon manual examination of a few offering statements we confirmed that bonds

to be refunded (and defeased) are mentioned exactly (at the CUSIP9 level) or at least at the series

level.

While our anecdotal evidence supports the hypothesis that information relevant to bond defea-

sance is being disclosed before the defeasance disclosure itself, we develop a test to examine this

issue on a broader scale. First, we match old money (refunded bonds) to the new money (refunding

bonds). Next, we compare the closing date for the new money, which is an approximation of the

date any offering statement is disclosed, to the defeasance disclosure date. We then examine how

many days before the defeasance disclosure that the refunding bonds closed and compare that to

the number of days before the defeasance disclosure when the bond trading indicates information

17

leakage relevant to defeasance. Specifically, we do the following:

• Find issuers of old bonds

– 256,865 advance refunding announcement (CUSIP9-date)

– 116,541 cases of first announcement of advance refunding (CUSIP9)

– 10,756 cases of first announcement of advance refunding (CUSIP6)

• Find issuers of new bonds

– 1,211,382 new issues of bonds with “refunding” capital purpose (CUSIP9-date)

– 89,964 new issues (CUSIP6-date)

– 16,362 new issues with only one time of refunding capital purpose (CUSIP6)

• Merge CUSIP6, requiring that the gap between old money defeasance disclosure date and

new bond settlement date is less than 90 days

Figure 4 shows the distribution of new bond settlement date relative to old bond defeasance

disclosure date for the 1,363 observations that result from this process. Obviously the matching

process above is not flawless since some observations have a negative gap, indicating that the

refunding money settlement happened AFTER the defeasance, which is not possible. However,

it is apparent that new bond issuance is normally before or at the date of old bond refunding

disclosure, and more specifically it is approximately 20-30 days prior to the defeasance date. This

gap is consistent with the trading data showing that trading activity is indicative of defeasance

approximately 3-4 weeks prior to the defeasance event. Further analysis is under way to better

match new money to old money.

C.2. Investors with Superior Information

For the prescient defeasance information to have value, some investors must have access to

it while others do not. Using trade size we segregate trades into large (greater than $100k par

value traded) and small categories, assuming that large trades are invoked by traders with superior

knowledge about their investment or better ability to use information in their favor. If investors

making large trades tend to have superior information, we expect to see the pre-event movement

18

in yields and trade frequency occur more often in large trades.

We examine the frequency, size, and direction of trades as a way to see if a certain group of

investors is trading in ways consistent with having prior knowledge of the defeasance event. Fig. 5

provides the abnormal yield and unique bonds transacted in the 12 weeks surrounding the event

divided into small and large trades for customer buys and sells. Though the steepness of the increase

in large-trade buying before the event may be slightly greater than small-trade buying, this figure

is not convincing of any significant differences. On the other hand, Fig. 6 and Fig. 7 show that

the par amount bought by large traders jumps drastically about four weeks prior to the defeasance

event while the par amount sold peaks just after the defeasance event. Here again, further analysis

is needed to determine whether these trades are indicative of traders of large amounts of defeased

bonds are trading based on the defeasance event.

19

V. Conclusion

We examine the defeasance event for municipal bonds and find some rather predictable results

and some rather interesting results. On the predictable side, we find that the municipal bond

market, despite how opaque and illiquid it is, incorporates the defeasance information into bond

trades very quickly after the event. What is interesting is our finding that trades well before the

event seem to signal that a defeasance is coming, and not everyone gets that information at the

same time.

We propose explanations as to why the trading activity before the defeasance event is occurring

and then develop tests for those explanations. First, we show that there may be an unintended

consequence of the inclusion of specific use of proceeds information in the filing of a preliminary

offering statement for new money to refund old money in an advance refunding. Since the bonds

that are to-be-defeased are often mentioned in a preliminary offering statement of the new money,

a knowledgeable investor could benefit from buying them before the event. Our analysis, though

in need of deeper statistical rigor, indicates that this could be happening. Moreover, we show that

traders buying in large amounts, which is attributed to institutional investors, perform prescient

buying in much larger amounts than other traders.

We believe that policymakers should evaluate the defeasance event and adapt Rule 15c2-12 to

accommodate any early disclosure of bonds that are going to be defeased. As part of this evaluation,

a deeper study of the possible incentives to trade a bond that is going to be defeased soon or is

already defeased is warranted. We leave this for future study.

20

References

Ang, Andrew, Richard C Green, and Yuhang Xing, 2015, Advance Refundings of Municipal Bonds, Workingpaper, .

Baber, William R, and Angela K Gore, 2008, Consequences of GAAP Disclosure Regulation: Evidence fromMunicipal Debt Issues, The Accounting Review 83, 565–591.

Baber, William R, Angela K Gore, Kevin T Rich, and Jean X Zhang, 2013, Accounting restatements,governance and municipal debt financing, Journal of Accounting and Economics 56, 212–227.

Bergstresser, Daniel, and Randolph Cohen, 2011, Why fears about municipal credit are overblown, Workingpaper, Harvard University and MIT.

Bergstresser, Daniel, Randolph Cohen, and Siddharth Shenai, 2011, Fractionalization and the municipalbond market, Working Paper pp. 1–43.

Brown, David T, 2011, Callable Bonds: What is Special About Munis?, Working Paper.

Butler, Alexander W, Larry Fauver, and Sandra Mortal, 2009, Corruption, Political Connections, and Mu-nicipal Finance, Review of Financial Studies 22, 2673–2705.

Cready, William, Abdullah Kumas, and Musa Subasi, 2014, Are trade size-based inferences about tradersreliable? Evidence from institutional earnings-related trading, Journal of Accounting Research 52, 877–909.

Cuny, Christine, 2016, Municipal Disclosure and the Small Trade Premium, SSRN Electronic Journal.

Ederington, Louis, Wei Guan, and Zongfei (Lisa) Yang, 2013, Bond Market Event Study Methods, SSRNElectronic Journal.

Edmonds, Christopher T, Jennifer E Edmonds, Beth Y Vermeer, and Thomas E Vermeer, 2015, DoesTimeliness of Financial Information Matter in the Governmental Sector?, Working paper, .

Edwards, Amy K, Lawrence E Harris, and Michael S Piwowar, 2007, Corporate Bond Market TransactionCosts and Transparency, Journal of Finance 62, 1421–1451.

Fischer, P J, 1983, Note, advance refunding and municipal bond market efficiency, Journal of Economicsand Business 35, 11–20.

Gore, Angela K, 2004, The effects of GAAP regulation and bond market interaction on local governmentdisclosure, Journal of Accounting and Public Policy 23, 23–52.

Government Accountability Office, 2012, Municipal Securities, .

Green, Richard C, Burton Hollifield, and Norman Schurhoff, 2007, Dealer intermediation and price behaviorin the aftermarket for new bond issues, Journal of Financial Economics 86, 643–682.

Harris, Lawrence E, and Michael S Piwowar, 2006, Secondary Trading Costs in the Municipal Bond Market,Journal of Finance 61, 1361–1397.

Hildreth, W Bartley, and C Kurt Zorn, 2005, The Evolution of the State and Local Government MunicipalDebt Market over the Past Quarter Century, Public Budgeting and Finance 25, 127–153.

21

Municipal Securities Rulemaking Board, 2013, Continuing Disclosure Statistical Summary, Working paper,.

Plummer, Elizabeth, Paul D Hutchison, and Terry K Patton, 2007, GASB No. 34’s Governmental FinancialReporting Model: Evidence on Its Information Relevance, The Accounting Review 82, 205–240.

Reck, Jacqueline L, and Earl Wilson, 2006, Information transparency and pricing in the municipal bondsecondary market, Journal of Accounting and Public Policy 25, 1–31.

Reck, Jacqueline L, and Earl Wilson, 2014, The Relative Influence of Fund-Based and Government-WideFinancial Information on Municipal Bond Borrowing Costs, Journal of Governmental and Nonprofit Ac-counting forthcoming, 1–43.

Schmitt, Peter J, 2009, The Consequences of Poor Disclosure Enforcement in the Municipal Securities Market,.

Schultz, Paul, 2012, The market for new issues of municipal bonds: The roles of transparency and limitedaccess to retail investors, Journal of Financial Economics 106, 492–512.

Schwert, Michael, 2015, Municipal Bond Liquidity and Default Risk, Working paper, .

Securities and Exchange Commission, 1994, Final Rule: Municipal Securities Disclosure, .

22

Appendices

A. Variable Descriptions Listed Alphabetically

Variable Definition

Abyield Yield of transaction minus corresponding SLGS rate matched on trade week andmaturity

Age Years since the bond settlement date at the defeasance disclosure dateCoupon The current rate applicable for interest payments on a bondFTE Indicator variable for a bond that’s interest payments are exempt from federal taxesGO Bond Indicator variable for a general obligation bondIssue Year Year of the bond settlement dateIssue Size Principal amount of the original bond offering in dollarsParTraded Par value of trades in dollarsPrice Price of transactionRating Current bond rating when not missing, in order of preference from S&P, Moody, or

Fitch. Ratings are re-coded to the common lettering groups AAA, AA, A, BBB,BB, and B as needed.

SLGS SLGS rate corresponding to refunded bond with the same time to callSTE Indicator variable for a bond that’s interest payments are exempt from state taxesTF4w Number of transactions in the four weeks prior to the defeasance disclosure dateTF13w Number of transactions in the 13 weeks prior to the defeasance disclosure dateTF1y Number of transactions in the year prior to the defeasance disclosure dateTF2y Number of transactions in the second year prior to the defeasance disclosure dateTF3y Number of transactions in the third year prior to the defeasance disclosure dateYield Yield of transaction (see further description below)YrsTC Years from defeasance disclosure date until the first possible call dateYrsTM Years from defeasance disclosure date until the original maturity date

Per MSRB Rule G-33 yield is based on “yield to worst” that the customer may realize onan investment in the bonds based on the principal amount of the trade, the interest rate on thebonds, and the remaining period until maturity or earlier redemption. Dealers are required byRule G-14 to accurately report each transaction within 15 minutes after the transaction, and theyields reported by dealers are recorded in the Real-time Transaction Reporting System (RTRS)system. Adjustments are made by the MSRB when errors are detected. Any missing yield valuesare generally due to the variable rate feature of certain bonds and bond defaults.

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B. Summary Statistics for Sample with No Trade Restrictions

The following tables provide summary statistics for the 93,440 municipal bonds remaining afterscreening defeasance disclosures and Mergent data according to Table I and merging with theMSRB RTRS data.

Table B1: Bond Characteristics for Defeasance Sample with No Trade Restrictions

Issuance Year is the year of the bond settlement date. Age, YrsTC, and YrsTM are all measures of time in years fromthe defeasance disclosure date to the settlement date, to the first call date, and to original maturity, respectively.GO is an indictor variable that equals one for a general obligation bond as categorized in Mergent as “Limited Go”or “Unlimited GO” and equals zero otherwise. FTE and STE are indicator variables for federal tax exemption andstate tax exemption, respectively. Issue Size is the principal amount in dollars of the bond at issuance. More detailson the variable descriptions are available in Appendix A.

Variable N Mean St. Dev. Min. p25 p50 p75 Max.

Issuance Year 91,115 2001.8 3.2 1986 2000 2002 2004 2012Age 91,115 6.5 2.3 0.0 4.9 7.1 8.4 18.2YrsTC 93,440 2.9 2.2 0.3 1.2 2.2 4.2 18.9YrsTM 93,440 9.4 5.2 0.3 5.6 8.6 12.0 40.6Coupon 93,431 4.84 0.83 0 4.5 5 5.25 13GO bond 93,440 0.52 0.5 0 0 1 1 1FTE 93,440 0.98 0.12 0 1 1 1 1STE 93,440 0.84 0.36 0 1 1 1 1Issue Size 92,245 4,567,299 16,138,871 35 400,000 1,075,000 3,330,000 1,700,000,000

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Table B2: Summary Statistics for Full Sample by Type of Trade

This table presents summary statistics for the full sample as described in Table I before any trade restrictions. Atransaction is labeled as “buy” or “sell” using the indicator in the Real-time Reporting System of trades from theMunicipal Securities Rulemaking Board. Panel A (B) shows the statistics for trades marked as a buy (sell) transaction.Issuance Year is the year of the bond settlement date. Age, YrsTC, and YrsTM are all measures of time in years fromthe defeasance disclosure date to the settlement date, to the first call date, and to original maturity, respectively. GOis an indictor variable that equals one for a general obligation bond as categorized in Mergent as “Limited Go” or“Unlimited GO” and equals zero otherwise. FTE and STE are indicator variables for federal tax exemption and statetax exemption, respectively. Issue Size is the principal amount in dollars of the bond at issuance. Trade frequenciesfor the 1, 2, and 3 years prior to the defeasance disclosure date are represented by TF1, TF2, and TF3, respectively.More details on the variable descriptions are available in Appendix A.

Panel A: Buy Transactions

N Mean STD Min P25 Median P75 Max

Issuance Year 90,770 2001.8 3.2 1986 2000 2002 2004 2012Age 90,770 6.5 2.3 0.0 4.9 7.1 8.4 18.2YrsTC 93,069 2.9 2.2 0.2 1.2 2.2 4.2 18.9YrsTM 93,069 9.4 5.2 0.3 5.6 8.6 12.0 40.6Coupon 93,060 4.84 0.82 0 4.5 5 5.25 13GO 93,069 0.52 0.5 0 0 1 1 1FTE 93,069 0.98 0.12 0 1 1 1 1STE 93,069 0.84 0.36 0 1 1 1 1Issue Size 91,891 4,576,538 16,164,096 35 400,000 1,075,000 3,335,000 1,700,000,000TF1 91,370 3.89 20.59 0 0 0 2 2,567TF2 89,210 4.25 24.87 0 0 0 2 3,144TF3 85,611 4.37 23.9 0 0 0 2 1,467

Panel B: Sell Transactions

N Mean STD Min p25 Median p75 Max

Issuance Year 77,547 2001.53 3.1959246 1986 2000 2002 2004 2012Age 77,547 6.58 2.32 0 4.98 7.14 8.44 18.18YrsTC 79,806 2.93 2.21 0.25 1.19 2.23 4.26 18.94YrsTM 79,806 9.57 5.32 0.25 5.65 8.77 12.22 40.62Coupon 79,798 4.88 0.82 0 4.5 5 5.25 13GO 79,806 0.5 0.5 0 0 1 1 1FTE 79,806 0.98 0.12 0 1 1 1 1STE 79,806 0.85 0.36 0 1 1 1 1Issue Size 78,639 5,095,457 17,365,163 35 475,000 1,245,000 3,835,000 1,700,000,000TF1 78,816 2.84 11.82 0 0 0 2 979TF2 77,595 2.57 12.18 0 0 0 2 1,716TF3 75,667 2.33 11.05 0 0 0 2 1,262

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Table B3: Use of Proceeds, State of Issue, and Rating Listed by Proportion

This table reports the first 20 items from in reverse order of their contribution to the distribution of defeased bonds by the intended use of the proceedsand the state of issue. Also, the table reports the entire distribution of the current bond rating for the sample.

Use of Proceeds N % Total % State N % Total % Rating N % Total %

Gen Purpose/Pub Improvement 28,066 30 30 CA 10,115 11 11 AAA 17,434 19 19Primary/Secondary Education 22,257 24 54 TX 9,976 11 22 AA 28,140 30 49Water and Sewer 9,869 11 64 NY 5,183 6 27 A 6,571 7 56Higher Education 6,850 7 72 PA 4,704 5 32 BBB 1,353 1 57Other Education 4,309 5 76 WI 4,126 4 37 BB 73 0 57Hospitals 2,328 2 79 OH 4,106 4 41 B 17 0 57Govt/Public Buildings 2,252 2 81 WA 3,678 4 45 No Rating 33,901 36 94Other Healthcare 1,976 2 83 FL 3,428 4 49 Withdrawn 4,019 4 98Public Power 1,500 2 85 VA 3,331 4 52 Missing 1,932 2 100Multiple Public Utilities 979 1 86 CT 3,287 4 56Mass/Rapid Tran 972 1 87 MO 2,924 3 59Toll Road and Highway 833 1 88 CO 2,480 3 61Correctional Facilities/Jails 639 1 89 NC 2,333 3 64Economic Development 635 1 89 NJ 2,027 2 66Other Transportation 627 1 90 IN 2,024 2 68Redevelopment/Ld Clearance 622 1 91 MI 1,976 2 70Stadiums/Sports Complex 608 1 91 MN 1,942 2 72Other Housing 585 1 92 KS 1,905 2 74Airports 486 1 92 TN 1,904 2 76

26

Table I: Sample Selection

The following table provides details on the sample selection beginning with all defeasance disclosure events for the period 1998–2012. the sourcesof the data are provided in parentheses. Observation totals are allocated into three columns: one at the issuer level (six-digit CUSIP), one at thebond level (nine-digit CUSIP), and a final one at the individual observation level to allow for multiple observations within a time period. Once theMSRB Real-time Transaction Reporting System (RTRS) data are incorporated, we use the included designation of a trade type to follow two differentsamples. Customer buy (customer sell) transactions are a customer purchase (sell) of a bond from (to) a dealer.

Issuer level Bond level Total observations

Defeasance disclosure sample (from DPC, bond-date level) 9,786 189,252 237,374Remove bond-date duplicates 9,786 189,252 225,825First case of defeasance 9,786 189,252 189,252

Merging with bond characteristics (from Mergent) 9,346 177,558 177,558Bonds with available subsequent call date and defeasance 90 days before call date 6,835 102,132 102,132

Buy Sell Buy Sell Buy SellMerge with trading sample (from MSRB RTRS) 6,760 6,599 93,069 79,806 5,338,357 2,096,022

Collapsing trading data from daily to weekly 6,760 6,599 93,069 79,806 1,862,382 1,476,798Traded within [-52, +52] weeks around advance refunding disclosure 6,026 6,017 57,127 56,417 388,739 354,072Traded within [-12, +12] weeks around advance refunding disclosure 5,183 5,182 36,276 35,916 113,641 106,419

Other data screening requirementsTrim certain RTRS outliers at 1% & 99% 5,138 5,139 35,865 35,452 111,222 104,333

27

Table II: Summary Statistics by Type of Trade

This table presents summary statistics for the final sample as described in Table I. A transaction is labeled as “buy”or “sell” using the indicator in the Real-time Reporting System of trades from the Municipal Securities RulemakingBoard. Panel A (B) shows the statistics for trades marked as a buy (sell) transaction. Issuance Year is the year of thebond settlement date. Age, YrsTC, and YrsTM are all measures of time in years from the defeasance disclosure dateto the settlement date, to the first call date, and to original maturity, respectively. GO is an indictor variable thatequals one for a general obligation bond as categorized in Mergent as “Limited Go” or “Unlimited GO” and equalszero otherwise. FTE and STE are indicator variables for federal tax exemption and state tax exemption, respectively.Issue Size is the principal amount in dollars of the bond at issuance. Trade frequencies for the 1, 2, and 3 years priorto the defeasance disclosure date are represented by TF1, TF2, and TF3, respectively. More details on the variabledescriptions are available in Appendix A.

Panel A: Buy Transactions

N Mean STD Min P25 Median P75 Max

Issuance Year 34,971 2001.64 3.3674209 1986 2000 2002 2004 2012Age 34,971 6.59 2.38 0 5.04 7.23 8.44 18.18YrsTC 35,865 3.01 2.26 0.25 1.25 2.28 4.31 15.58YrsTM 35,865 10.61 5.83 0.65 6.31 9.77 13.54 40.62Coupon 35,864 4.92 0.78 0 4.6 5 5.25 12.5GO 35,865 0.45 0.5 0 0 0 1 1FTE 35,865 0.99 0.11 0 1 1 1 1STE 35,865 0.88 0.32 0 1 1 1 1Issue Size 35,225 8,664,670 24,599,817 5,000 1,000,000 2,580,000 7,550,000 1,700,000,000TF1 34,866 9.11 32.17 0 1 3 8 2,567TF2 33,994 9.52 34.28 0 0 2 7 2,192TF3 32,631 9.92 37.09 0 0 2 7 1,467

Panel B: Sell Transactions

N Mean STD Min p25 Median p75 Max

Issuance Year 34,560 2001.61 3.3247428 1986 2000 2002 2004 2012Age 34,560 6.65 2.3 0 5.12 7.27 8.45 18.18YrsTC 35,452 2.96 2.2 0.25 1.25 2.27 4.25 15.58YrsTM 35,452 10.62 5.83 0.65 6.32 9.78 13.55 40.62Coupon 35,450 4.93 0.77 0 4.6 5 5.25 12.5GO 35,452 0.45 0.5 0 0 0 1 1FTE 35,452 0.99 0.11 0 1 1 1 1STE 35,452 0.88 0.32 0 1 1 1 1Issue Size 34,819 8,716,583 24,716,440 5,000 1,010,000 2,600,000 7,615,000 1,700,000,000TF1 34,835 5.77 17.21 0 1 2 5 979TF2 34,175 4.98 17.91 0 0 1 4 1,716TF3 33,175 4.51 16.24 0 0 1 4 1,262

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Table III: Abnormal Yield of Customer Buys Surrounding Defeasance Disclosure

This table shows statistics for the average abnormal yield for customer buy transactions, calculated as the yield for each customer buy trade minus thebond’s matched SLGS rate and then averaged by bond over each week, occurring during the period one week prior to the defeasance announcementand for 11 weeks thereafter. The weekly change in the average abnormal yield is also presented. P-values from tests of statistical significance for themean, median, and weekly change are presented in separate columns.

Event Trading Std P-value P-value Weekly P-valueWeek Frequency Mean Dev. (%) p5 p25 Median p75 p95 (mean=0) (median=0) Change (change=0)

-1 4648 0.12% 1.07% -1.27% -0.72% 0.16% 0.58% 2.05% <0.01 <0.01 -0.10% <0.0010 5282 0.07% 0.99% -1.26% -0.70% 0.15% 0.50% 1.80% <0.01 <0.01 -0.05% 0.011 5866 0.03% 0.91% -1.27% -0.69% 0.16% 0.47% 1.51% 0.03 <0.01 -0.04% 0.032 5789 -0.01% 0.85% -1.28% -0.68% 0.15% 0.44% 1.29% 0.49 <0.01 -0.03% 0.043 5724 -0.02% 0.86% -1.29% -0.71% 0.15% 0.44% 1.28% 0.14 <0.01 -0.01% 0.574 5594 -0.03% 0.87% -1.33% -0.74% 0.14% 0.45% 1.29% <0.01 <0.01 -0.02% 0.365 5371 -0.03% 0.90% -1.32% -0.74% 0.13% 0.44% 1.28% <0.01 <0.01 0.01% 0.696 5273 -0.02% 0.92% -1.31% -0.74% 0.13% 0.44% 1.35% 0.17 <0.01 0.01% 0.687 4991 -0.03% 0.90% -1.32% -0.73% 0.13% 0.43% 1.30% <0.01 <0.01 -0.01% 0.588 4857 -0.03% 0.94% -1.35% -0.78% 0.11% 0.44% 1.36% 0.02 <0.01 0.00% 0.839 4701 -0.04% 0.90% -1.33% -0.78% 0.11% 0.43% 1.30% <0.01 <0.01 -0.01% 0.510 4663 -0.03% 0.91% -1.31% -0.77% 0.12% 0.44% 1.32% 0.01 <0.01 0.01% 0.5611 4498 -0.05% 0.91% -1.32% -0.78% 0.09% 0.45% 1.30% <0.01 <0.01 -0.01% 0.56

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Table IV: Abnormal Yield Before Defeasance Disclosure

This table shows statistics for the average abnormal yield, calculated as the yield for each trade minus the matchedSLGS rate and then averaged by bond over each week, for customer buy trades occurring during the period twelveweeks prior to the defeasance announcement and one week immediately thereafter. The weekly change in the averageabnormal yield is also presented. P-values reflect the statistical significance of the change in weekly abnormal yield.

Event Trading Std Weekly P-valueWeek Frequency Mean Dev. (%) p5 p25 Median p75 p95 Change (change=0)

-12 3386 0.51% 1.25% -1.16% -0.44% 0.39% 1.17% 2.88%-11 3391 0.47% 1.25% -1.20% -0.50% 0.36% 1.16% 2.81% -0.04% 0.19-10 3362 0.50% 1.25% -1.16% -0.48% 0.38% 1.16% 2.89% 0.03% 0.31-9 3356 0.46% 1.21% -1.15% -0.49% 0.37% 1.10% 2.77% -0.04% 0.06-8 3468 0.47% 1.23% -1.18% -0.48% 0.38% 1.12% 2.76% 0.01% 0.46-7 3418 0.44% 1.25% -1.23% -0.52% 0.34% 1.11% 2.77% -0.03% 0.39-6 3453 0.42% 1.23% -1.23% -0.54% 0.33% 1.07% 2.78% -0.02% 0.52-5 3649 0.38% 1.22% -1.23% -0.57% 0.30% 1.01% 2.63% -0.05% 0.11-4 3804 0.36% 1.20% -1.23% -0.56% 0.27% 0.99% 2.62% -0.02% 0.52-3 3967 0.28% 1.16% -1.23% -0.62% 0.23% 0.83% 2.43% -0.08% <0.01-2 4257 0.22% 1.14% -1.25% -0.66% 0.19% 0.73% 2.39% -0.06% 0.01-1 4648 0.12% 1.07% -1.27% -0.72% 0.16% 0.58% 2.05% -0.10% <0.010 5282 0.07% 0.99% -1.26% -0.70% 0.15% 0.50% 1.80% -0.05% <0.011 5866 0.03% 0.91% -1.27% -0.69% 0.16% 0.47% 1.51% -0.04% 0.03

30

(a) Defeasance Announcements

(b) Customer Buys (c) Customer Sells

Figure 1: The top figure (a) shows the distribution of the sample of defeasance disclosures beforeany trading restrictions. The lower two figures show the number of transactions designated asCustomer Buy (b) and Customer Sell (c) after all sample selection restrictions. The sample periodis 1998–2012. Refer to Table I for details on the sample selection procedure.

31

(a) Abnormal Yield and Unique Bonds

(b) Trade Frequency

Figure 2: The upper figure (a) shows the abnormal yield over the SLGS rate (left axis) andthe number of unique bonds traded (right axis) during the 52 weeks around advanced refundingdisclosure (denoted by 0 on x-axis). The lower figure (b) shows the trading frequency during the52 weeks around advanced refunding disclosure (denoted by 0 on x-axis).

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Figure 3: This figure shows the abnormal yield over the SLGS rate (left axis) and the numberof unique bonds traded (right axis) during the 12 weeks around advanced refunding disclosure(denoted by 0 on x-axis).

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Figure 4: New Bond vs Old Bond. This figure shows the distribution of refunding (“new”) bond settlement date relative to refunded (“old”)bond advance refunding disclosure date for the period 1998–2012. New bonds are matched to old bonds at the six-digit CUSIP level. The sampleof old bonds is required to have an advance refunding announcement (denoted as time zero on the x-axis) and be the first such disclosure for thatparticular bond at the nine-digit CUSIP level. The sample of new bonds is required to be marked as a “refunding” bond in Mergent.

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(a) Customer Buys

(b) Customer Sells

Figure 5: These figures show the abnormal yield and number of unique bonds traded for small andlarge customer buy transactions (a) and customer sell transactions (b) during the 12 weeks aroundadvanced refunding disclosure (denoted by 0 on x-axis).

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(a) Frequency of Buys

(b) Total Par Value of Buys

Figure 6: The upper figure (a) shows the frequency of customer buy transactions by the size ofthe trade during the 52 weeks around advanced refunding disclosure (denoted by 0 on x-axis). Thelower figure (b) shows the total par value traded for customer buy transactions by the size of thetrade during the 52 weeks around advanced refunding disclosure (denoted by 0 on x-axis).

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(a) Frequency of Sells

(b) Total Par Value of Sells

Figure 7: The upper figure (a) shows the frequency of customer sell transactions by the size of thetrade during the 52 weeks around advanced refunding disclosure (denoted by 0 on x-axis). Thelower figure (b) shows the total par value traded for customer sell transactions by the size of thetrade during the 52 weeks around advanced refunding disclosure (denoted by 0 on x-axis).

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