The Market for New Issues of Municipal Bonds: The Roles of Transparency and Limited Access to Retail Investors

Transcription

1 The Market for New Issues of Municipal Bonds: The Roles of Transparency and Limited Access to Retail Investors Paul Schultz University of Notre Dame Abstract I examine how transparency and interdealer trading affects prices investors pay in municipal bond offerings. Real-time trade reporting for municipal bonds started January 31, The dispersion of purchase prices fell sharply at that time, but there was little impact on average markups. Markups do increase with the amount of interdealer trading before an investor purchase. Past interdealer trading and past markups with the same lead underwriter predict markups and interdealer trading in current offerings. This suggests that inadequate distribution networks, not hard-to-sell bonds, are behind the relation between interdealer trading and markups. September, 2011 Preliminary, do not quote. I am grateful for the comments of Rich Ryffel and Sophie Shive.

2 1. Introduction Municipal bond issues are a vital source of funds for cities, states and other government entities. Municipal offerings are common. In the period from 1999 through June 2010, more than 100,000 municipal bond offerings were conducted, and more than 1,000,000 individual bonds were issued. Municipal bond offerings are very different from equity offerings. Because most municipal bonds are tax exempt, the IRS regulates their sale and requires that underwriters sell at least 10% of the bonds at the reoffering price. They are then free to sell the rest of the bonds at whatever price the market will bear. Green, Hollifield, and Schürhoff (GHS) (2007) provide several interesting facts about the prices at which municipal bonds are sold. First, small investors pay more. Large purchases generally take place at or near the reoffering price, while small purchases typically involve markups of 1%, 2%, or even 5% over the reoffering price. Second, different investors pay very different prices to buy similar numbers of the same bond on the same day. The dispersion of purchase prices is typically small for large trades, and much greater for small trades. Third, the prices investors pay for municipal bonds are generally lowest on the offering date and increase in the following days. In this paper, I explore two factors that influence the markup of purchase prices over reoffering prices and the dispersion of purchase prices. The first is transparency. Pre-trade transparency, that is firm dealer quotes prior to trades, is non-existent. During the period of the GHS study, post-trade transparency was also minimal. Trades were reported to the Municipal Securities Rulemaking Board (MSRB) at the end of the day, but only for bonds that traded four or more times during that day. Trades were reported at the end of the month for bonds that traded less frequently. With minimal transparency, small investors may pay more to buy bonds because they do not know the contemporaneous prices of other trades in the same bond. Larger investors may receive more quotes from more dealers or may be aware of prices that other institutions or large investors are paying for a bond. Harris and Piwowar (2006) suggest that the lack of transparency, rather than fixed costs, is the primary reason that trading costs per municipal bond decline with 1

3 trade size. Lack of transparency in the municipal market can also explain the dispersion of prices, and the tendency for purchase prices to increase after the offer date. On the offer date it may be difficult to mark up bonds very much over par, but as time passes and trading diminishes it may become easier for dealers to exploit the ignorance of small investors. I capitalize on a dramatic regulatory change to examine the effects of post-trade transparency on municipal bond prices. On January 31, 2005, the MSRB began requiring all municipal trades to be reported within 15 minutes. Trade prices, the number of bonds traded, and whether the trade was a sale to a dealer, a purchase from a dealer, or an intradealer trade are then immediately made available on the internet. This abrupt increase in post-trade transparency provides a natural experiment that allows me to examine the impact of transparency on trading costs and price dispersion. I find that increased post-trade transparency did sharply reduce the dispersion of purchase prices, but that the effect on markups was small. In fact, large trades appear to pay more relative to the reoffering price following the increase in post-trade transparency. A second explanation for the difference in purchase prices is differences in the costs of selling bonds. The fixed costs of paperwork, reporting, advice, and so forth make costs much lower on a per bond basis for large trades. In particular though, I focus on the costs of finding investors. There is a well-known adage in the municipal business that bonds are not bought, they are sold. Underwriters without good distribution networks will resell bonds to dealers who can reach additional investors. These dealers may in turn resell bonds to other dealers. These interdealer trades are costly, and the costs are passed on to the eventual purchasers of the bonds. Hence prices paid by investors will be higher for bonds that pass through several dealers on their way to an investor who will hold them. Consistent with this, I find that municipal bonds often pass through a sequence of interdealer trades at successively higher prices before reaching investors. Markups over the reoffering price that are paid by investors increase with the amount of interdealer trading that precedes the bond purchase. This relation between interdealer trading and markups holds both before and after the increase in post-trade transparency, suggesting that costs, not market opaqueness are behind it. The rest of the paper is organized as follows. Section 2 discusses municipal bond 2

4 offerings and the municipal bond market. A literature review of work on the municipal bond market and on the impact of transparency on bond markets in general is provided in Section 3. The date used in the paper is introduced in Section 4. Section 5 examines the impact of realtime trade reporting. Section 6 presents evidence on the relation between interdealer trading and price markups. I summarize findings and draw conclusions in Section Municipal bond offerings and the municipal bond market A city, state, or other government entity that wishes to issue municipal bonds first chooses between a competitive or negotiated offering. In a competitive offering, potential underwriters submit sealed bids for a bond offering with specified characteristics. Individual underwriters may handle entire issues if the offering is small. Syndicates of underwriters will bid for larger offerings. The issuer uses the underwriter or underwriting syndicate that promises to issue the bonds at the lowest cost. In a negotiated offering, the municipality negotiates with one underwriter (or group of underwriters) about the costs and characteristics of the bond offering. In the negotiation process, complex features of a bond can be tailored to the needs of the issuer. The bonds are then presold to investors in the when issued market. Underwriters do not act as fiduciaries in a negotiated offering and can raise the interest rate on the bonds to clear the market. This minimizes risk for the underwriter. Customers receive the bonds at closing. In recent years, negotiated offerings have been more common than competitive offerings. Competitive offerings, on average, raise smaller amounts of money. A great advantage of competitive offerings though is that their transparency minimizes possible abusive practices. In either type of offering, the underwriter is compensated by purchasing the bonds from the issuer at the takedown price and reselling to the public at a higher reoffering price. Bonds that are sold by the underwriting syndicate to underwriters or dealers who are not part of the syndicate are sold at the concession price, which lies between the price paid by the underwriting syndicate and the reoffering price. Municipal bond offerings differ from equity offerings in that the entire offering need not 3

5 be sold to investors at the reoffering price. IRS rules require that a substantial fraction (interpreted as 10% or more) must be sold at the reoffering price, but additional bonds may be sold for higher prices. In many cases, a significant proportion of the bonds are sold with markups of 1%, 2%, or more above the reoffering price. These markups may vary substantially for purchases of the same bond. Small investors in particular may pay very different prices for near simultaneous purchases of bonds in an offering. Sometimes hedge funds or other institutions ( flippers ) will buy blocks at the reoffering price or close to it, and resell to smaller brokers. 1 The secondary market for municipal bonds is over-the-counter. The municipal bond market is highly fragmented with many bonds issued by thousands of entities through many dealers. For most states, interest on bonds issued in that state, and that state only, are exempt from state taxes. The municipal market may be thought of as numerous loosely integrated state markets for municipal bonds. Most municipal bonds are purchased by investors who intend to hold them to maturity. Hence, municipal bonds trade infrequently in the secondary markets, and trading costs in the secondary market are high. 2.1 The Introduction of Real-Time Trade Reporting The municipal bond market is a dealer market, and a highly fragmented one. Pre-trade transparency is non-existent. That is, it is impossible to find firm quotes or even indicative quotes displayed for a bond. A potential bond buyer must contact dealers for quotes. Post trade transparency has improved steadily in recent years. In 1995 the MSRB first required interdealer transactions to be reported. Four years later, customer trades began to be reported to the MSRB, and both customer and dealer trades were reported the next day for bonds that traded four or more times. Individual trade data started to be reported in In 2003, the 2 four trade threshold for next day reporting was abandoned and all trades began to be reported. The municipal market took a quantum leap forward in transparency on January 31, In the 1920's, municipal bonds traded on the New York Stock Exchange. Biais and Green (2007) find that municipal bond trading costs were significantly lower before trading moved to the over-the-counter market. 2 See Ryon (2004) 4

6 Starting on this date, municipal dealers were required to report all trades in real time, that is within 15 minutes. The trade reports, which include number of bonds traded, CUSIP, price, and whether the trade was a purchase or sale for the investor, are available online almost immediately. They are currently posted on the Electronic Municipal Market Access (EMMA) website run by the MSRB. When real time trade reporting was introduced in 2005, they were displayed on a website run by The Bond Market Association. On the first day of 15 minute trade reporting, The Bond Market Association reported that the site averaged about 10,000 visits per minute. Clearly, the introduction of 15 minute trade reporting meant that investors had a much better idea of a bond s market value before buying or selling. If the opacity of the municipal bond market allowed dealers to exploit investors, we might expect to observe three changes in trade prices following 15 minute trade reporting. First, we could see smaller markups to reoffering prices in the first few days after an offering. Second, we might expect to see smaller markups over prices of interdealer trades. Finally, a smaller standard deviation of purchase prices across trades might be observed if investors can learn what other investors are paying. 3. Studies of the Bond Market and the Impact of Transparency An important study of the market for new issues of municipal bonds is Green, Hollifield, and Schürhoff (GHS) (2007). They examine 190,300 trades in 12,493 bonds issued by 833 municipal entities over February 15, 2000 through May 1, They find that some investors pay the reoffering price for municipal bonds, but many pay a markup over the reoffering price. Markups increase steadily over the days following the offer date with median markups reaching about 120 basis points after a week. They attribute this to larger proportion of small trades as the offering date recedes. In some cases, investors pay as much as 5% over the reoffering price for small trades. They also find a wide range of trade prices on days with multiple trades. These price ranges decline with trade size. For 21% of the bond-days, the intraday price range is 100 or more basis points. For trades of $250,000 or more, the price range is 100 basis points or more on just 5

7 4% of bond-days. During the period of the GHS study, trades were required to be reported the next day only if there were four or more trades in the bond. GHS note (page 644) that the dispersion of prices... appears to be sustainable because of the institutional mechanisms through which the bonds are issued, and the decentralized, opaque market setting in which bonds are issued. Green (2007) provides a theoretical basis for the GHS findings of high prices for small purchases and dispersion of prices across similar size trades. His model provides a simple but realistic rendering of the municipal bond market. Municipal bonds are issued through syndicates of underwriters. There is limited transparency. Each underwriter has access to a limited number of retail investors who are willing to pay top dollar for new municipal bonds. Underwriters can sell any remaining bonds to institutional investors for a lower price. A key assumption is that the combined retail capacity of all the underwriters is not sufficient to sell the entire issue. Some must be sold to institutions. Green shows that there is a range of equilibrium quantity allocations in which each dealer receives at least enough bonds to meet his retail demand. Bonds are priced above the reoffering price in sales to retail investors, while each dealer sells remaining bonds to institutional investors. Green, Li, and Schürhoff (GLS) (2010) examine prices in the secondary market for municipal bonds. They demonstrate that ask prices that dealers charge quickly rise to reflect higher values for municipals. When the values of these bonds fall, ask prices adjust slowly. A similar but less dramatic affect is found with bid prices. In effect, municipal dealers change prices quickly when it is in their interest to do so, but slowly when it means reducing the price they receive or increasing the price that they pay on municipal trades. In a competitive market, prices charged by dealers would adjust quickly to changes in the value of the municipals. GLS attribute their results to an opaque market in which individual municipal dealers have local monopoly power. This paper is closely related to the GHS study, and builds on it in two ways. First, I study the impact of interdealer trading on pricing and price dispersion. Interdealer trading can proxy for the costs of reaching potential bond buyers. I find that more interdealer trading is associated with higher markups relative to reoffering prices. Second, this paper includes bond offerings before 6

8 and after the January 31, 2005 rule change that required all trades to be reported within 15 minutes. This allows me to test whether opacity and local monopoly power explain the dispersion of bond prices, or whether the differences in prices are also a function of differences in costs. More post-trade transparency is associated with a decline in price dispersion, but its impact on markups over the reoffer price is small. Several papers study similar changes in post-trade transparency in the corporate bond market. The TRACE (Transaction Reporting and Compliance Engine) system was introduced into the corporate bond market in July Initially, trades were only reported in investment grade bonds with issue sizes of $1 billion or more, as well as 50 representative non-investmentgrade bonds. There was concern that public dissemination of trades in smaller, less active bonds would harm liquidity. The universe of bonds subject to TRACE reporting expanded over time with trades in all publicly issued bonds reported starting January 9, Maximum trade report delays were initially 75 minutes, and were reduced gradually to 15 minutes on July 1, Goldstein, Hotchkiss, and Sirri (2007) conduct a controlled experiment to assess the impact of dissemination of trade information on corporate bond trading costs. Trade information was publicly disseminated for a sample of 90 actively traded and 30 inactive BBB rated corporate bonds over April 14, 2003 to February 27, Trading costs for bonds with publicly disseminated trade information fell relative to trading costs for similar BBB bonds for transactions of 250 bonds or less. Despite the decrease in trading costs, they are unable to detect an increase in trading volume. Bessembinder and Maxwell (2008) review evidence on the impact of TRACE on the corporate bond market. Bessembinder, Maxwell, and Venkataraman (2006), Edwards, Harris, and Piwowar (2007), and Goldstein, Hotchkiss and Sirri (2007) all report large declines in trading costs following the adoption of TRACE. Other effects of TRACE reporting were less desirable. Dealers became less willing to hold inventory and switched to being primarily brokers. There is also some evidence that the initiation of TRACE reporting was accompanied by a shift from publicly traded to privately placed bonds. 7

9 4. Data The data used in this paper come from two sources. The Municipal Securities Rulemaking Board (MSRB) provides information on all municipal bond trades from January 1999 through June Each trade record includes the bond s CUSIP number, the par value of the bonds exchanged in the trade, the trade price, the trade date, the trade time, and whether the trade was a dealer sale to an investor, a dealer purchase from an investor, or an interdealer trade. The second source of data is the Mergent/FISD Municipal Bond dataset. This dataset provides bond characteristics. Data items include the size of the offering, the type of bond (general obligation, revenue bond, etc.), the offering date of the bond, the maturity date of the bond, the bond issuer s state, whether the bond is insured, if it is callable or putable, as well as sinking fund provisions. The Mergent/FISD dataset also contains bond ratings, but this is a snapshot of the ratings as of the time the dataset was cut, not at the time the bond was issued. Hence, the ratings are of limited use. This data is matched with the MSRB dataset by CUSIP. Table 1 describes the data. I include only trades that occurred between 25 days before the offering date and ten days afterwards. I include trades before the offering date to incorporate when-issued trading. In total, 11,856,078 trades occurred within this offering date window. These trades occurred in 123,897 bond issues. Typically, a municipal bond issue consists of several different bonds with different maturities, so the trades are distributed across 1,056,985 different bonds. In contrast, the number of stocks listed simultaneously on the NYSE, Amex, and Nasdaq together has never reached 10,000. Municipal bond market trading is fragmented and diffuse. Both individual investors and institutions participate in the municipal bond market. The minimum municipal bond trade is $5,000. A large number of the trades in my sample, about 4.6 million, are for face values of $25,000 or less. Almost 3.6 million of the trades are of $100,000 or more. Most volume is from these larger trades. The majority of trades, 8.8 million of 11.3 million are sales of bonds from dealers to investors. This is not surprising. These trades include the first sale of newly underwritten bonds to the public. More surprising is the number of interdealer trades. Almost a quarter of the trades, 3.0 million, are interdealer trades. Over 4.9 million trades are of bonds issued in negotiated offerings while 2.1 million 8

10 trades are in bonds issued in competitive underwritings. Unfortunately, data on offering type is missing in Mergent for most offerings before November 2004, so 4.7 million trades cannot be classified by offering type. The five states with bonds with the largest number of trades include California, New York, Texas, Florida, and Pennsylvania. There are more than 4.7 million trades in bonds from these states. Figure 1 shows the total number of small purchases, large purchases, and interdealer trades across all bond offerings for the offer date and the ten succeeding days. Large purchases are defined as more than $100,000 par value of bonds, while small purchases are $25,000 or less in par value. There are almost 700,000 large purchases and about 700,000 interdealer trades on the offering days. The total number of small purchases is a little less than 600,000. The day after the offering date, the number of small purchases reaches about 850,000, while the number of large purchases and the number of interdealer trades remain little changed. In the following days, the number of trades of all types declines, but the number of large purchases falls far faster than the number of small purchases. Big investors buy municipal bonds on the offer date or shortly thereafter. Small investors are the purchasers several days later. Figure 2 shows the number of trades around offerings by year. In many of the tests to follow, I will compare prices investors paid for bonds in 2003 and 2004 before the introduction of 15 minute trade reporting to the prices investors paid in March 2005 though I use this subperiod to minimize the impact of other changes to the municipal market on trading costs and to avoid using prices from the financial crisis. Figure 2 shows that there are about 22 million trades in and about 18 million in The Impact of 15 Minute Trade Reporting 5.1 Real-Time Reporting and Markups on Sales to Investors One way to measure markups is to compare the prices that investors pay for bonds with the prices of interdealer trades. For each bond each day around the offer date, I calculate the mean price for interdealer trades and the mean price of investor purchases from dealers for small, medium, and large trades. I then calculate the ratio of the mean price for investor purchases to the 9

11 mean interdealer price for each bond each day that has one or more interdealer trades and at least one investor purchase from a dealer in the size category. To examine the impact of post-trade transparency, grand average ratios of purchase prices to interdealer trade prices are calculated across bond-days for and for March 2005 through I omit a two-month window around the transparency change to allow market participants to adjust to the new regime. T-statistics that test whether the mean ratios are equal across subperiods are calculated. Municipal bonds are usually part of an issue of bonds with several maturities. So, t-statistics are calculated clustering on the issue and on the trade date. Table 2 reports results. The top numbers in each row are the mean ratios of price of investor purchases to interdealer prices. A ratio of 1.01 means that investors pay 1% more for a bond than dealers pay in an interdealer trade on the same day. The smaller numbers in brackets below the ratios are the number of observations, or bond-days used to calculate the ratios. Each of these observations has at least one investor purchase and one interdealer trade, but may be based on the average of several investor and several dealer trades. Two clear patterns emerge in this table. First, for every day around the offer date, and for both subperiods, small investor purchases occur at higher prices than medium size purchases, which in turn occur at higher prices than large purchases. So, for example, prices for small investor purchases averaged 74 basis points more than interdealer trades on the offer dates during , while medium size trades occurred at a 54 basis point premium and large trade prices were only 31 basis points more than interdealer trade prices. Similarly, on the day after the offering date during , prices for small investor purchases averaged 86 basis points more than interdealer trades, while the premium was 64 basis points for medium purchases and 36 basis points for large purchases. It is not surprising that small purchases of bonds by investors take place at higher prices than large trades. There is a fixed cost component to bond trading that can be spread across more bonds for large trades. Differences in fixed costs are not diminished by increased transparency. On the other hand, the evidence on Table 2 makes it difficult to argue that the opacity of the market is the main reason small investors pay higher prices for municipals. 10

12 A second pattern that emerges clearly in Table 2 is that the ratio of investor purchase prices to interdealer prices increases from the offer date through Day 5. This was first discussed by GHS in their 2007 paper. As an example, municipal bonds are marked up by 74 basis points over interdealer trade prices for small trades on the offer date during The markup increases to 1.27% for small customer purchases on Day 5 during In general, most of the increase in ratios of purchase price to dealer price occurs in the first couple of days following the offer date. A somewhat surprising result is that ratios of purchase prices to interdealer trade prices does not change much with the increase in post-trade transparency. The largest and most significant decrease in the ratio of purchase prices to interdealer trade prices is for small trades on the offer date. In this case, the ratio of purchase prices to interdealer trade prices was before the 15 minute trade reporting requirement and afterwards. The t-statistic for the difference is a highly significant The ratio of purchase price to interdealer trade price falls from to for large trades on the offer day. Although the results are economically weak, the t-statistics of indicates statistical significance at the 5% level. For each trade size on all days after the offering date, the ratio of purchase price to interdealer trade price falls slightly with the increase in post-trade transparency, but is never statistically significant. An advantage of this technique for measuring trading costs is that interdealer trade prices represent a current price of the bond, and reflect changes in interest rates. A disadvantage is that bonds are only included if there is both an interdealer trade and a sale to investors on the same day. On the one hand, this means that the bonds are relatively active. On the other hand, an interdealer trade implies an additional level of costly intermediation. So, it is not clear that the prices that investors pay for these bonds are representative of the prices paid for municipal bonds in general, nor whether these trading costs are higher or lower than costs for trading other bonds. As an alternative, I calculate the ratio of the average price of an investor purchase from a dealer to the reoffering price for each bond each day. I then average the ratios for each day relative to the offer date for small, medium, and large investor purchases, for the and subperiods. Results are reported in Table 3. Panel A presents results for all offerings, while Panels B and C report results for negotiated and competitive offerings separately. 11

13 Because an interdealer trade is no longer required, there are now far more observations. For example, in Table 2 there are 15,867 bonds with small investor purchases and interdealer trades on the offer date during In Table 3, there are 23,385 bonds with small investor purchases and reoffering prices during The patterns observed in Table 2 are also observed in Panel A of Table 3. Ratios of investor purchase prices to reoffering prices are highest for small purchases, smaller for medium size purchases, and smallest for the large purchases. We also observe that the ratios of investor purchase prices to reoffering prices increase from the offer date through the next five days. For example, during , small investor purchases on the offer date take place at 20 basis points above the reoffering price on average. Five days later, small investor purchase prices average 115 basis points above the reoffering price. When the two subperiods are compared, the ratio of purchase prices to reoffering prices for small trades decline by seven basis points on the offer date and by four basis points on the day after. Both of these declines are statistically significant at the 1% level. For two or more days after the offer date, the ratio of purchase to reoffering price for small trades barely changes following the increase in post-trade transparency. For medium-size trades, the difference in ratios between subperiods is never significant. For large trades, the ratio of purchase price to reoffering prices actually increases when trades are required to reported within 15 minutes. Furthermore, these differences are significant at the 1% level for the offering day and the next three days. It is possible that dealers benefitted as much or more from the trade reporting than did institutional investors. Price transparency may have allowed dealers to coordinate their pricing in ways that they could not when trades were reported the next day. It is possible, that with delayed trade reporting, institutional investors who check prices with multiple dealers will know more about market prices than many of the dealers themselves. Alternatively, comparing results in Table 2 and Table 3 indicate that large purchase prices increase relative to offer prices but not relative to interdealer trade prices. If differences in the sample are unimportant, this means that interdealer trade prices have risen relative to offer prices, and dealers are passing on their costs to large traders. 12

14 Mergent/FISD provides the offering type for almost all issues from November 2004 on, but only sporadically before that date. I examine the ratio of purchase price to reoffering price separately for offerings designated by Mergent as negotiated or competitive. Some caution is needed in interpreting these results since it is not clear which biases, if any, are imparted by the incomplete coverage of offering type in the subperiod. Results for negotiated offerings are reported in Panel B of Table 3. In some ways the results for bonds issued in negotiated offerings are similar to the results for municipal bond offerings in general. As in Panel A, the mean ratio of purchase prices to reoffering prices decreases with the size of the purchase. Likewise, the ratio of purchase prices to reoffering price is low on the offer date and increases in the following days. What is different is that ratios of purchase price to reoffering price increase from to for all trade sizes and all days around the offering date. For small purchases on the offer date, the ratio of purchase price to reoffering price increases from in to in The t-statistic for the difference is For medium size trades, the mean ratio of purchase price to reoffering price on the offering date increases from in to in This difference is significant at the 1% level with a t-statistic of Panel C shows the results for competitive offerings. As before, the mean ratio of purchase price to reoffering price is lower for medium size purchases than small purchases, and lower still for purchases of more than $100,000 in par value. Ratios of purchase price to reoffering price are low on the offer date and increase over the succeeding days as was true of negotiated offerings. A striking difference between competitive offerings and negotiated offerings is that for competitive offerings, the mean ratios of purchase price to reoffering price fall for small and medium size purchases with increased post-trade transparency. For small purchases, the decline is statistically significant at the 1% level for the offer date and the following four days. For medium-size trades, the decline is significant at the 1% level for the first, fourth, and fifth day after the offering date. The difference is statistically significant at the 5% level on the second and third day after the offering date. In contrast, for large trades the mean ratios of purchase price to reoffering price increase from to In this way, the results for competitive offerings are similar to the results for negotiated offerings. The increases in ratios are smaller for competitive 13

15 offerings than negotiated offerings though, and the increases are only statistically significant on the offer date and the day after. A concern in comparing purchase prices of municipal bonds in with prices in is that the trades may have changed in other ways. Trade sizes may be larger within the size categories, maturities may be longer or shorter, or offering sizes may have changed. To see if the change in post-trade transparency affected purchase prices after adjusting for other potential changes, I regress the ratios of purchase price to reoffering price for individual trades on a dummy variable that takes a value of one after 15 minute trade reporting was introduced. Trade size and log of trade size are included in the regression because the ratio of purchase price to reoffering price tends to fall with trade size. Both variables are included to account for potential non-linear relations between prices and trades sizes. Similarly, I include both the offer size and log of offer size and the time to maturity and log of time to maturity in the regressions. Additional dummy variables are used for general obligation bonds, bonds with call provisions, bonds with put provisions, insured bonds and bonds from small states. Small states are defined as all states except California, New York, Texas, Florida, and Pennsylvania. Standard errors for coefficients are estimated after clustering on both issue and trade date. Regression results are shown in Table 4. Panel A reports regressions for purchases of bonds with par values of $100,000 or less from negotiated offerings. Regressions are run separately for trades on the offering date and on succeeding days. For these small and medium size purchases from negotiated offerings, the coefficient on the dummy variable for 15 minute trade reporting is positive but insignificant each day. Posttrade transparency has had almost no impact on markups over reoffering prices for these trades. Markups do increase with the time to maturity. Perhaps investors are more willing to pay a greater premium for bonds if they expect to amortize it over several years. Markups over the reoffering price are larger for insured bonds and smaller for puttable bonds. Perhaps insured bonds are purchased by less sophisticated investors while puttable bonds are purchased by more sophisticated investors who are better able to negotiate purchase prices. Panel B reports results for purchases of $100,000 or less from competitive offerings. For these regressions, the coefficients on the dummy variable for 15 minute trade reporting are 14

16 negative and significant. For competitive offerings, increased post-trade transparency resulted in lower markups over the reoffering price. Panel C provides results for trades of more than $100,000 for bonds issued in negotiated offerings. Now, coefficients on the 15 minute trade reporting dummy are positive and significant. For these large purchases, markups increased with more post-trade transparency. It is possible that dealers may have been at an informational disadvantage to large investors who talk to several dealers before purchases. In this case, increased post-trade transparency may have improved the negotiating position of dealers. Panel D shows regression estimates for large trades of bonds issued in competitive offerings. The coefficient on 15 minute trade reporting is positive and significant at the 5% level for the offer date and the following day. On other days it is negative and insignificant. As a whole, the regression results, which adjust bond characteristics, confirm the findings in Table 3. Post trade transparency decreased markups of small trades in competitive offerings but had little effect on markups of negotiated offerings. Markups seem to have increased for large trades. 5.2 Real-time reporting and trade price dispersion Green, Hollifield, and Schurhoff (2007) observe that municipal bond purchasers often pay very different prices for similar size purchases of the same bond on the same day. This seems especially likely to disappear with greater transparency. To test this, I calculate the standard deviation of bond purchase prices in small, medium, and large trade size categories for each bond on each day around its offering. I then calculate the average standard deviation across all bonds for days and trade size categories for and for March Results are reported in Table 5. The first number in a row is the mean standard deviation of purchase prices, stated as a percentage of the bond s par value. The smaller number below in brackets is the number of observations: bond offerings with two or more trades in the size category on a day around the offering date. The number of bond observations is somewhat smaller in this table than in earlier tables 15

17 because of the requirement that bonds have two or more trades in a given size category on the trade date. Despite this, results are clear. The standard deviation of prices is much greater for small or medium size trades than for large trades. Standard deviations of purchase prices tend to be lowest on the offering date and to increase steadily over the next few days. For example, the mean standard deviation of purchase prices for small trades in is on the offer date, on the first day after, on the second day after, and reaches on five days after the offering date. The important result in Table 5 is that the standard deviation of purchase prices falls sharply and significantly with the introduction of real-time trade reporting. For example, the mean standard deviation of small purchases on the offering day was in , and for March With 12,434 bonds in the first period and 9,479 bonds in the second period, the t-statistic for the difference, after clustering on issue and trade date, is The standard deviation of purchase prices declines significantly every day from the offer date on for small purchases. This is expected as small investors are thought to be least able to obtain information in an opaque market. Standard deviations of trade prices, however, also decline for medium size and large trades. The decline in standard deviations for large trades is economically meaningful and significant for the offer date and the two following days. For example, the mean standard deviation of prices for large trades on the day after the offer date is in and in The t-statistic for the difference is To summarize, increased post-trade transparency has led to a sharp decline in the dispersion of prices paid by investors. Investors are now less likely to pay wildly different prices for the same amount of the same bond on the same day. Average markups over reoffering prices are not, however, affected much by the increased transparency. Post-trade transparency allows investors to see what other investors pay for bonds, but it also allows bond dealers to see what their competitors charge. Large investors pay higher prices following the introduction of realtime trade reporting. This is not entirely surprising. If large investors routinely spoke to a number of bond dealers, they might negotiate favorable prices. If all dealers see trade prices in real-time, it may be easier to hold the line on prices. Patterns in markups persist after the introduction of real-time reporting. Small trades still 16

18 pay higher prices than large trades. Purchasers pay more for bonds several days after the offering date than they do on the offering date itself. These patterns are apparently not a result of the opacity of the market. They instead suggest difference in the costs of selling bonds. 6. Interdealer trades and price markups One of the costs borne by municipal bond underwriters is the cost of locating potential buyers. The market is fragmented. Most states exempt bonds issued in state from state income tax, but exclude bonds issued by government entities from other states. Hence residents of the state in which the issuer is located form a natural clientele for the issuers bonds. In addition, there are many bond issues and most are relatively small. The number of issues suggests that potential buyers may not be aware of a specific issue. Small issues are less likely to be sold to institutions and must instead be sold to retail investors. Because of the tax advantage of municipal bonds, individual investors can be expected to purchase a large proportion of municipal bonds. The costs of getting a municipal offering into the hands of investors will be lower for underwriters with good distribution networks and an established customer base. Underwriters with poor distribution networks will have more difficulty placing bonds. One indicator of this may be that bonds pass through a number of dealers through interdealer trades before reaching the investors who will purchase them. The dealers incur costs each time they trade with each other, and they will only complete these trades if they believe the costs can be passed on to bond buyers. The MSRB has expressed concern about price differentials that arise from what they refer to as transaction chains. The securities involved in these chains are often small issues that are relatively unknown to most market participants. The bonds are passed through a number of dealers before reaching an investor. The MSRB notes that individual dealers in a chain do not make excessive profits on their trades but the total markup can be large. 3 3 See Nazareth (2004) and MSRB Notice

19 6.1 Prices and trade sizes for interdealer trade chains In Table 6, I examine the prices of sequences of interdealer trades. Panel A reports means, across bonds, of ratios of the price of interdealer trades to the price of the first interdealer trade of an offering. Only trades that occur within the first ten days of an offering are included. On average, the second interdealer trade takes place at a price 6.6 basis points greater than the price of the first interdealer trade. With over 400,000 observations, the t-statistic of allows us to easily reject a null hypothesis that the ratio of second to first interdealer trade prices is one. The ratio of mean trade price to first interdealer trade price increases monotonically with trades, and th reaches 32.1 basis points with the 12 trade. The second to the last column of the table reports mean ratios of trade prices to the previous trade price. These ratios decrease from for the th th ratio of the second to first interdealer trade prices to for the ratio of the 12 to the 11 trade price. They remain statistically significant, however. Panel B shows ratios of interdealer trade prices across days, rather than individual trades. For each bond, I calculate the mean interdealer trade price for every day with interdealer trades. I then calculate the ratios of interdealer prices to prices on the first day after the offer date (t+1), and ratios of daily mean interdealer prices to the mean interdealer price on the previous day. The day after the offer date is used for comparison because there are far more interdealer trades than on the offer date itself. Ratios of more than 1.2 or less than 0.8 are discarded as likely data errors. The mean ratio of interdealer trade prices on day t+2 to day t+1 is With over 58,000 observations, it is not surprising that the t-statistic testing whether the ratio is one is over 100. Ratios of subsequent day prices to the interdealer price on t+1 are generally slightly higher. In all cases interdealer trade prices exceed interdealer trade prices on day t+1 by 23 to 29 basis points. The second to the last column of the table reports ratios of mean interdealer trade prices to interdealer trade prices the previous day. In each case, the mean ratio is significantly greater than one. If interdealer trades take place on two consecutive days, the trade price will likely be higher on the second day. The evidence in Table 6 is consistent with each dealer in a chain of transactions adding a markup to the municipal bond price. If a bond passes through a number of dealers before reaching an investor, the markup will be larger to reflect the profits made by every dealer in the 18

20 chain. The first five columns of table 7 describe the distribution ratio of the size of interdealer trades to the size of the first interdealer trade of an offering. I do not report mean trade size ratios because, even with thousands of observations, a small number of very small trades followed by large trades results in a large mean ratio. Instead, I report the median ratio across bonds, along th th with the 25 and 75 percentile. The median ratio of number of bonds in the second interdealer trade to number in the first interdealer trade is one. The second trade tends to be as large as the first. Thereafter, the median ratios decline to by the sixth trade and to by the 12 th th th trade. The 25 and 75 percentiles also decline over time. In the second five columns of Table 7, I report the distribution of ratios of daily mean interdealer trade sizes to mean interdealer trade sizes on the day after the offering date. (t+1) each day. The median ratio of mean trades sizes on t+2 to mean trade sizes on t+1 is The median ratio declines monotonically over time and reaches when the size of trades on the th tenth day are compared with the size of trades on the first day after the trade date. The 25 and th 75 percentiles of ratios also fall over the succeeding days. The results in Table 7 suggest that larger dealers sell a portion of the bonds they receive to investors and sell the remaining bonds to other, probably smaller, dealers. A sequence of interdealer trades could be symptomatic of a bond that is difficult to sell, or an underwriter with an inadequate distribution network. 6.2 Interdealer trades and markups paid by investors I now turn to the issue of whether a chain of interdealer trades is associated with higher prices for the bond s ultimate investors. For each investor purchase of a municipal bond, I calculate the proportion of the total bond offering that had been traded between dealers up until the bond purchase. Table 8 shows mean ratios of purchase price to reoffering price for trades with different levels of interdealer trading on the offer day and the following five days. The entire period from 1999 through June of 2010 is used. Panel A provides results for small purchases of $25,000 or less. For small purchases that 19

21 occurred on the offering date and were preceded by less than 25% of the value of the offering traded between dealers, the mean ratio of purchase price to reoffering price is In other words, a bond with an reoffering price of $1,000 was purchased for $1, on average. The number in parentheses, 275,651, is the number of trades that fit this category. Ratios of purchase price to reoffering price in Panel A indicate that, holding interdealer trading constant, ratios of purchase price to reoffering price are lowest on the offering date and increase thereafter. The ratios increase primarily from the offer day over the next two days. Increases between the second and fifth day after the offering are relatively small. Panel A also demonstrates that average markups over reoffering prices increase as the percentage of the issue traded between dealers prior to the purchase increases. For example, on the day after the offering day, the mean ratio of purchase price to reoffering price is if less than 25% of the bonds traded between dealers, but if 200% to 300% of the bonds were traded between dealers. Most of the increase in the ratio occurs when interdealer trading goes from less than 25% of the offering to 25% to 50%. Panel B is similar to Panel A but reports ratios of purchase price to reoffering price for medium size trades of $25,000 to $100,000. Again, the ratios of purchase price to reoffering price increase as days pass after the offering date and as the proportion of the offering traded interdealer increases. Now, price ratios rise when interdealer trading increases above 25%, as before, and also as it increases over 100% of the offering. Panel C provides ratios of purchase price to reoffering price for purchases of more than $100,000 in par value. As with smaller trades, ratios increase for the first two days following the offer date. Ratios also increase as the proportion of interdealer trades passes 25% and as the proportion increases over 100%. In Table 9, I test for the significance of the interdealer volume in determining ratios of purchase price to reoffering price. I regress ratios for individual purchases on dummy variables for each of the five days after the offer date and dummy variables for six categories of interdealer volume. Panel A reports regression results for small trades. T-statistics are calculated after clustering on both issue and trade date. The first column provides the regression estimate when all small trades are used. The 20