Trading Imbalances around Seasoned Equity Offerings

Transcription

1 Trading Imbalances around Seasoned Equity Offerings Sukwon Thomas Kim School of Business Administration University of California, Riverside Riverside, CA 92521, USA Ronald W. Masulis Australian School of Business University of New South Wales Sydney NSW 2052, Australia Current Draft: August 31, 2014 We would like to thank participants in the conference sessions at the 2011 Finance Down Under conference, the 2011 Financial Institutions Research Society conference and the 2010 Financial Management Association conference and seminar participants at George Washington University for their comments and suggestions. We also wish to thank Ekkehart Boehmer, Amy Edwards, Kingsley Fong, Paul Irvine, Amy Kwan, Craig Lewis, Michelle Lowry, Robert Whaley, Richard Willis and participants in the SEC seminar for their helpful comments. This project began as part of Sukwon Kim s PhD dissertation at the Owen Graduate School of Management, Vanderbilt University. He wishes to especially thank his dissertation chair Hans Stoll and other dissertation committee members for their excellent guidance and generous support. The authors acknowledge the financial support from the Financial Markets Research Center (FMRC) of Vanderbilt University.

2 Trading Imbalances around Seasoned Equity Offerings Abstract To better understand the seasoned equity offering (SEO) process and the economic causes for the associated pricing patterns, we investigate the relationship between daily stock returns and trading imbalances around SEO issue dates. We find that observed trading imbalances are inconsistent with most explanations proposed by prior research. We conclude that the only hypothesis consistent with our evidence is that they are caused by underwriter-affiliated market makers providing price supports. Expected price supports are associated with lower offer prices, producing larger underpricing. This market making support is more costly in shallow markets, indicating that market structure affects equity flotation costs.

3 1. Introduction While a seasoned equity offering (SEO) is a major corporate event that involves a large increase in the supply of company stock to the capital market, there are relatively fewer studies of the relationship between SEOs and its effects on the market microstructure where the stock trades. For example, little information exists about investor trading behavior around SEOs. Furthermore, the influences of market makers, arbitrageurs, SEO investors and underwriters around SEO events are all poorly understood. Nor do we have a clear understanding of how prior secondary market conditions affect major SEO characteristics such as offering size, pricing or flotation costs. We investigate these issues and document new findings on the secondary market trading activity around SEOs and underwriter support of the stock price immediately following the offering and SEO offer price determination. Currently, most empirical evidence addressing these questions is based on an analysis of stock price patterns in the 1990s. However, markets have changed greatly in the interim and price movements can be produced by a variety of economic factors. This is highlighted by the wide array of economic explanations offered by researchers to explain how SEOs affect secondary market conditions. We exploit the fact that SEOs can temporarily create not only price effects and quote changes, but also large trading imbalances in a stock s secondary market. By jointly analyzing trading activities from the vantage point of daily trading imbalances and stock returns, we obtain a much clearer and more comprehensive picture of trading activities and price patterns around SEOs than prior studies and shed new light on the questions of how SEOs affect secondary markets and how prior secondary market conditions affect SEO characteristics. We also examine whether these patterns exist not only in the 1990s, but in the period since 2000 as well. This new evidence enables us to critically re-examine prior explanations for the 1

4 observed impacts of SEOs on secondary markets on both price and non-price dimensions and to assess its stability over a nearly two decades. We use a widely accepted measure of trading imbalances to directly estimate buying/selling pressure around the SEO issue date and examine how both pre and post- SEO order flow is related to major SEO characteristics, including a major component of flotation cost, namely SEO underpricing. 1 To our knowledge, this is the first study to analyze the relation between stock returns, trading imbalances and SEO characteristics, particularly with respect to the setting of offer prices. Our analysis is based on an extensive sample of market microstructure data drawn from an 18 year period ( ), which allows us to assess whether more recent changes in market microstructure and investment banking competition have altered these relationships. In contrast, most prior studies of trading activity around SEOs (and IPOs) are limited to 1 or 2 years of data and measure SEO underpricing based on an ex post metric clearly unobservable by underwriters at the time that SEO offering prices are set, namely a stock s post-seo closing price. We also investigate the roles of lead underwriters and their affiliated market makers in supporting stock prices of weaker issues immediately after an SEO and estimate the ex ante expected cost of such price support activity. We initially explore how the trading activities around SEOs are related to the stock s secondary prices before and after the offering. This addresses questions raised in several prior studies of these trading patterns, such as Kadlec, Loderer, and Sheehan (1994) and Meidan (2005), who argue that pre-issue trading can affect both SEO offer prices and post-issue returns. While these studies infer trading activities from stock returns, we jointly examine price changes, quote changes, trading volume, and order flows. 1 Safieddine and Wilhelm (1996) and Corwin (2003) use stock volume to identify offering dates. 2

5 We also investigate the influence of expected underwriter price support costs in the aftermarket on SEO offer price setting. In documenting a significant relationship, we also uncover evidence that this effect is amplified in a shallow secondary market, which provides an unrecognized link between a stock s secondary market structure and SEO offer price determination. The new findings uncovered in this study are summarized as follows: 1. Trading imbalance patterns around SEOs are distinctively different from stock return patterns, undercutting the reliability of drawing inferences solely from return patterns. 2. There is little support for most of the existing hypotheses that posit particular trading activities around SEOs, such as significant selling activities by arbitrageurs immediately before an issue or institutional investor flipping activity immediately after the issue date in our sample period. 3. Trading activities around SEOs are strongly influenced by underwriteraffiliated market makers through their market making and price support activities. 4. A predictive model of the expected costs of underwriter price support for SEO experiencing poor market receptions is estimated. These price support costs are tangible for weaker issues measured by subsequent stock price performances relative to the offer price, averaging 0.8% of the offer price. 5. Underwriter spreads are positively related to expected price support costs, and these costs explain approximately 25% of cross sectional variability in observed spreads. 3

6 6. Higher underwriter spreads are generally associated with lower offer prices, which lead to higher underpricing, measured on an ex ante basis. Thus, issuers with weaker share demand pay larger flotation costs. 7. Stocks with a shallow secondary market are expected to require greater price support costs and SEO underpricing compared to more liquid stocks. Thus, a stock s secondary market structure has a sizable effect on SEO flotation cost, through its effect on underpricing. 8. Trading activities around equity offerings became a more important determinant of underpricing in the post-2000 period. Prior SEO studies report negative stock returns before an SEO and positive returns thereafter [Kadlec, Loderer, and Sheehan (1994); Corwin (2003); Meidan (2005); and Altinkiliç and Hansen (2006)]. Building on this stock return evidence, we find that average trading imbalances is around zero before an SEO and highly negative afterwards (t-values lower than -10.0). This is a surprising finding given it is well known that trading imbalances and stock returns generally move in the same direction [Chordia and Subrahmanyam (2004)]. More importantly, prior studies of SEOs argue that the observed negative stock returns before the issue date is an indication of strong selling pressure, which leads underwriters to demand a lower SEO offer price. We re-examine these findings and explore their underlying causes in this study. Evaluating a large set of hypotheses proposed in prior studies, we conclude that the trading imbalance pattern around SEOs is mainly generated by underwriter market making activities. We draw this conclusion from tracking trades made by underwriteraffiliated market makers and by documenting that for over 83% of Nasdaq SEOs, 4

7 underwriters have affiliated market makers trading in these SEO stocks. 2 We also find that affiliated market makers place more quotes around SEO issuance dates compared to their typical quote pattern observed than at other times. This is highlighted by an approximate 30% increase in the percentage of quotes placed by underwriter-affiliated market makers around SEO issue dates. We also find that the affiliated market makers of higher ranked underwriters trade the stock more actively post-seo and more often engage in price support activity. In contrast, unaffiliated market makers typically resell most of the shares they buy from primary SEO investors immediately after the offering to minimize their inventory risk level. Sales by unaffiliated market makers can pull down a stock s price in the aftermarket, which creates losses for SEO investors. Thus, highly ranked underwriters more often have their affiliated market makers act as counterparties to these trades, absorbing these sale orders and stabilizing the price. Such dealer behavior is documented in several IPO studies, e.g. see Ljungqvist s (2007) literature survey. However, it remains an unanswered empirical question whether these earlier findings from several IPO studies and based on one or two years of proprietary data, hold for SEOs, which by their nature allow for pre-seo trading activity. Our findings demonstrate that there is a significant amount of price support after SEOs. However, our analysis on the SEO sample shows that price support is not noticeable from publicly available data in the 1990s, as observed trading activities are not significantly correlated with SEO characteristics. On the other hand, after 2000, the observed trading imbalances better reflect trading activity and we find a significant relationship between trading activity and underpricing. In other words, trading activity around equity offerings exhibits stronger effects on trading imbalances in the more 2 We find such pattern from NASTRAQ dataset, which spans the period. 5

8 recent period. Such contrast may be due to the substantial changes in the structure of U.S. equity markets in recent years. For example, Bessembinder (2003) show bid-ask spread declined substantially after decimalization in Thus, in the 1990s, it was more costly for underwriters to engage in aggressive price supports due to large bid-ask spreads. Schultz and Zaman (1994) show underwriters need to place higher bid quotes than other market makers bids to support prices after an SEO. Thus, larger spreads indicate that underwriters must place higher bid quotes and bear greater costs to support prices. Similarly, Cotter, Cao, and Chen (2003) find that the likelihood of price support is decreasing in transaction costs. As bid-ask spreads substantially declined after 2000, price supports became more common in SEOs. An important implication is that the relationship between market structure and equity offering price supports has substantially changed from what existed in the 1990s. We show that many of the existing hypotheses on trading activities are inconsistent with the actual trading imbalance patterns observed around SEOs throughout our sample period. For example, the selling pressure hypothesis suggests that a larger offer size is associated with more negative imbalances before an SEO. We find that average trading imbalances approach zero before an SEO and a larger offer size is sometimes associated with buy-side trades. The information asymmetry hypothesis studied by Safieddine and Wilhelm (1996), Corwin (2003), and Altinkiliç and Hansen (2003) posit that the uncertainty or information asymmetry associated with an offer generates selling activity by current stockholders before the SEO, which puts a downward pressure on the offer price. However, we demonstrate that information asymmetry or uncertainty has little explanatory power for observed trading imbalances. This trading imbalance evidence also rules out the possibility of substantial flipping activity around an SEO. Our result shows that the negative imbalances after an offer can 6

9 proxy for the cost of price support. Underwriters place limit orders at the bid to support prices, and large negative imbalances (trades made at the bid side) indicate that underwriters are buying shares from the public. Similarly, in IPOs, Schultz and Zaman (1994) show underwriters support prices after an offer by placing bid quotes higher than other market makers bids. Ellis (2006) also finds that over 70% of shares sold by investors to market makers after IPOs are traded at the bid quote. Note that these IPO studies are based on a short sample of less than one year in the 1990s, which makes generalizing these SEO results to more recent periods where trading occurs under different market conditions problematic. When underwriters expect a large amount of post-seo market making activity by their affiliates, they have strong incentives to demand a lower offer price to facilitate the distribution of this primary share offering. Consistent with this logic, we find that a larger ex post amount of underwriter-affiliated market making activity (measured by negative imbalances) is associated with a lower offer price, which means higher SEO underpricing measured ex ante using the pre-seo closing stock price as the benchmark. Thus, underwriters appear to demand a lower offer price if they expect to bear large post- SEO affiliated market making costs. We find that affiliated market maker costs rise in shallow markets, indicating that secondary market structure can affect SEO offer prices. The result is consistent with Butler, Grullon, and Weston (2005), who finds underwriter fees are higher for more illiquid stocks. However, they do not specify how illiquidity affects underwriting costs. Underpricing can also be a better measure of illiquidity costs than fees, as underwriters can adjust offer prices after observing the stock liquidity level just prior to an offering. In contrast, underwriter fees are determined prior to the filing date. Also, the underpricing mechanism analyzed here is not empirically evaluated in prior IPO studies, as pre-issue there is generally no observable liquidity in these IPO 7

10 shares. However, there is a theoretical model by Ellul and Pagano (2006) which shows that IPO underpricing is an increasing function of expected stock illiquidity. This study makes several important contributions to the literature. First, we jointly examine how trading patterns around an SEO behave differently from stock returns, which suggest that different economic mechanisms are at play. While price pressure and information asymmetry/uncertainty provide plausible explanations for stock-return movements [see Corwin (2003)], these hypotheses have minimal explanatory power when it comes to observed stock trading imbalance patterns. Second, we document that trading patterns around SEOs are dominated by underwriter-affiliated trading activity, which is influenced by market microstructure effects. These trading and price patterns cannot be explained by the many alternative hypotheses posited in the literature such as the existence of large selling pressure before offer dates, information asymmetry / uncertainty about the offering, or institutional flipping of SEO shares immediately after the offerings. Third, we show that post-issuance trading pressure is related to the size of offer price discounts or SEO underpricing. Specifically, we find a significant portion of the typical SEO offer price discount is positively related to the underwriter affiliated market maker s risk bearing and expected cost of its price support activity. 3 Thus, an important dimension of the equity financing process appears to be a function of the expected costs of price supports, which is itself related to the structure of the secondary market where a stock trades. Our study documents a major channel through which the structure of a stock s secondary market affects firm valuation through its impact on equity flotation cost. In 3 The US is not the only country where underwriter price support activity occurs. For example, we find some anecdotal evidence from China that investment banks frequently support share prices after an SEO. In China, the level of the price support is also influenced by a firm s management connection to government officials. Similarly, Allen, Qian, and Qian (2005) find an important financing channel for Chinese firms during their start-up and subsequent periods are state-owned banks. 8

11 the asset pricing literature, it is recognized that market structure imposes illiquidity costs on stock traders, which affect stock prices (Brennan and Subrahmanyam 1996, Pastor and Stambaugh 2003, and Archarya and Pedersen 2005 for example). Analogously, we show that market structure can also directly affect a stock s economic value through its effect on SEO offer prices, which dilutes the value of a firm s existing shares. The remainder of paper is organized as follows: Section 1 provides a literature review on price patterns and inferred trading pressure around an SEO. Section 2 describes the trading imbalance measure and SEO sample. Section 3 shows how trading imbalance is correlated with important SEO characteristics. Section 4 studies the relation between trading imbalance and SEO underpricing and Section 5 concludes the study. 2. Literature on trading pressure around an SEO Studies of trading activity around seasoned equity offerings, when a large supply shock hits the secondary market, are relatively uncommon. Lease, Masulis, and Page (1991) in an early study report that most SEO offering day transactions occur closer to the bid and that bid-ask spread changes can explain a significant portion of stock returns around SEO issuance dates, suggesting changes in trading activity patterns around SEOs. Corwin (2003) and Mola and Loughran (2004) use SEO characteristics and closing stock price changes to infer trading activity changes around SEOs. Cotter, Chen, and Kao (2004) examine the mode of prices (the most frequent price) after an SEO and argue that the trades being clustered around the mode are evidence of price stabilization activities. Huh and Subrahmanyam (2005) show that around SEOs trading imbalances are negatively correlated with daily stock returns, making it the first study to raise concerns about drawing inferences about trading activity from stock returns alone. 9

12 The prior literature primarily relies on inferences from daily stock-return patterns around SEO issue dates. The typical stock-return pattern is negative before the issue date and positive afterward, so a major branch of research focuses on selling incentives before an SEO and implicitly presumes a resumption of normal trading activity thereafter. Researchers have proposed a number of hypotheses to explain the observed stock price behavior around SEOs, which we will now review, highlighting their various predictions Pre-Offering Selling Incentives An SEO can discourage stock purchases in the secondary market and create selling incentives for many traders, including market makers. Lease, Masulis, and Page (1991) observe that a potential buyer of this stock has an incentive to delay any purchases, while she attempts to buy the stock in the primary market at an expected lower offer price. There can also be incentives to short sell. The offer price is usually lower than the market price before and after an SEO. Gerard and Nanda (1993) show that these offer price-to-market price differentials can create arbitrage opportunities. By short selling a stock before an SEO issue date and then covering the short position by purchasing stock in the primary market, traders can often make a profit. Since this pre- SEO short-selling activity tends to push down the secondary market price, it also puts downward pressure on the future SEO offer price, and as a result it can discourage firms from issuing new shares. To counter this presumed negative effect on capital raising, the SEC imposed restrictions on covering short-selling positions using newly offered shares under Rule 10b However, many investors, especially institutional investors, can 4 From 1988, the SEC imposed Rule 10b-21, which prohibits covering short sales with shares from the primary market. Rule 10b-21 applies to any short sales from the SEO announcement date to issue date. In 1997, the SEC replaced Rule 10b-21 with Rule 105. Rule 105 prohibits short covering with shares from the primary market, if the short-sales position is made within 5 days of issue date. 10

13 easily circumvent this regulation by alternatively covering their short position with other shares they hold. Some studies use stock returns around SEOs as evidence of a price-pressure effect. Kadlec, Loderer, and Sheehan (1994) and Meidan (2005) find that stock returns are negative before the issue date and positive afterward. Corwin (2003) shows that SEO underpricing is increasing in relative offer size, as measured by shares offered divided by shares outstanding. Meanwhile, Altinkiliç and Hansen (2003) argue that the typical stock return pattern around an SEO is inconsistent with the temporary price-pressure hypothesis because the pre-seo price drop is too large relative to the size of the post- SEO price recovery Uncertainty and information asymmetry Since Myers and Majluf (1984), numerous studies have confirmed that uncertainty and information asymmetry have important effects on the equity offering process. Myers and Majluf (1984) focus on a manager s decision to issue equity. They predict that the decision should negatively affect stock price on the announcement date. Since the actual offer size and offer price decisions are determined just prior to the offer date, relatively uninformed investors can be reluctant to purchase the stock between the announcement and issue dates, given their increased uncertainty and information disadvantage. Consistent with this perspective, Safieddine and Wilhelm (1996), Corwin (2003), and Altinkiliç and Hansen (2003) find that stocks with a higher level of uncertainty and information asymmetry have greater SEO underpricing Flipping Investors who acquire shares in an SEO and then sell their shares immediately thereafter are said to be flipping. Since the offer price is typically lower than the market price, SEO investors can generally profit from selling their shares shortly after 11

14 the offering at a higher price, which is a strategy that institutional investors, who receive large allocations of SEO shares, could pursue. Cornelli and Goldreich (2001) find that investment banks allocate more shares to institutional investors who are their regular SEO investors. Underwriters discourage flipping because it puts downward pressure on stock prices and represents a pessimistic signal of an issuer s future financial performance. Flipping can also be costly for underwriters providing price support of the stock. Krigman, Shaw, and Womack (2001) find that the degree of flipping activity is a useful predictor of subsequent weak stock performance. Given the incentives for flipping, there is a distinct possibility that post-seo flipping activity can generate strong selling pressure, especially if institutional investors are better informed than other traders. However, Chemmanur, He, and Hu (2009) argue that institutional investor flipping activity in SEOs is rare. Using a proprietary institutional trading dataset of selective institutions over the period, they find that only about 3% of SEO shares purchased by their sample of institutions are flipped. Thus, evidence on flipping is inconclusive, leaving open the question of who participates in flipping and if it strongly affects market conditions. Table 1 shows a list of prior studies of trading activities around SEOs and their sample periods. Our sample period ( ) is substantially longer than those of most of these earlier studies, and more importantly our sample uses a direct measure of trading activity, rather than inferring trading activity from observed stock-return patterns. Our sample size is also significantly larger than most of IPO studies focusing on trading activities, as these studies typically rely on one or two years of Nasdaq IPOs data in the late 90s (e.g. Ellis, Michaely, and O Hara (2000, 2011) and Ellis (2006)). Thus, we 12

15 expect to obtain a much clearer and more comprehensive picture of trading activity around SEOs. [Table 1] 3. Data description We use the Trade and Quote (TAQ) database for ordinary common shares over the period to construct our trading-imbalance dataset. A majority of our sample period is subject to SEC Rule 105, which was adopted by SEC in April Rule 105 prohibits covering short sales with shares purchased from the primary market, if the short-sale position is taken within 5 days of the issue date. 18% of observations in our sample of SEOs occurring before 1997 are not subject to this rule. However, we find no material difference in the average trading imbalances near SEOs around the 1997 adoption date. The trading imbalance measurement is described in detail in Appendix A and closely follows the method of Chordia, Roll, and Subrahmanyam (2002). Their method is based on Lee and Ready (1991), but it imposes additional filters to reduce infrequent trading problems. The basic idea behind their methodology is to count the number of shares traded each day near the ask quote (buy-side orders) and near the bid (sell-side orders). Subtracting shares traded near the bid from shares traded near the ask quote, we obtain our daily trading-imbalance measure for each issuer s stock. For example, if more shares are traded near the ask quote than the bid quote, then a positive trading imbalance is recorded for that day. Lee and Ready (1991), Chordia, Roll, and Subrahmanyam (2002), and Chordia and Subrahmanyam (2004) find that measuring trading imbalance in number of shares is an effective and accurate measure of price pressure. We normalize the price-pressure measure by dividing share-imbalances by total shares outstanding in 13

16 thousands, excluding the new shares issued. This metric facilitates a cross-sectional comparison of stocks in the sample. In untabulated results, we alternatively measure trading imbalances in daily number of trades or dollar volume and obtain qualitatively similar results. SEOs and their characteristics are extracted from the SDC New Issues database. To be included in the analysis, an SEO must meet the following sample criteria: Have trading data available in the TAQ database for the 180 days around the SEO; Be a public offer of common stock by a U.S. incorporated firm issued over Be a conventional firm commitment underwritten offering (rights offerings, standby offerings, shelf offerings, and other accelerated offerings are excluded); Have stock listed on the NYSE, AMEX, or Nasdaq as its primary exchange; Have an offer price larger than or equal to $5 per share; These criteria are imposed to ensure that our analysis focuses on order flows around typical SEOs by U.S. issuers. The characteristics of order flows are likely to be substantially different for other types of offerings, which would require a separate market microstructure analysis. We also extract stock price data from CRSP and market microstructure data from the Market Microstructure Database at Vanderbilt University, which contains information on all publicly traded stocks of U.S. firms on major exchanges with prices over $5. The database contains daily market microstructure variables such as bid-ask spreads directly compiled from TAQ data. The final data set contains 4,608 SEOs. To identify cases where underwriters have affiliated dealers, we use the NASTRAQ database. NASTRAQ contains quotes and trades of Nasdaq listed stocks, similar to the TAQ database. An additional information of the NASTRAQ is that the 14

17 dataset shows dealer quotes separately and the id dealer who made the quote. Using this dealer quotes data, we match underwriters in the SDC database with specific dealers in the NASTRAQ database. If a dealer is affiliated with a lead underwriter, we code it as an underwriter-affiliated dealer. This matching process is done by hand, since the same institution often uses different names for its underwriting and dealer businesses. The NASTRAQ database begins in 1999 and ends in 2006, so we are limited to analyzing Nasdaq listed SEOs in this subsample period. Many studies, beginning with Lease, Masulis, and Page (1991), point out that SEO issue dates in the SDC database can be inaccurate. The problem cases tend to be offerings launched after the close of daily exchange trading. In these cases, the date when the market is affected by the offering is actually the next trading day. We use the Safieddine and Wilhelm (1996) method to determine the actual SEO event date. Their method uses trading volume surges to detect the effective issue date. If the trading day following the SDC issue date has more than twice the trade volume of the SDC issue date, and if its volume is more than twice the average daily volume in the previous 250 trading days, then the next trade day is treated as the actual SEO issue date. Corwin (2003) uses the same method, and Altinkiliç and Hansen (2003) document that this method yields almost identical results to manually searching newspaper articles. We also check if the time of issue (morning, afternoon, after market close, etc.) is related to our empirical results, and find that the time of day does not have a significant effect. Table 2 reports descriptive statistics for our SEO sample. [Table 2] While our analysis is primarily focused on trading patterns around SEO issue dates, we also examine trading patterns around SEO announcement dates to estimate the relationship between SEO information releases and trading activity. 15

18 [Figure 1] We plot average daily trading imbalances, normalized by daily volume, around SEO announcement dates in Figure 1. The plot shows that SEO announcements have an insignificant effect on stock trading patterns. The average trading imbalance is only mildly negative one day after the SEO announcement date and is on average insignificantly different from zero in the wider (-5, +5) event window. Although SEO announcements may be bad news, trading imbalances need not be negative provided that quotes move fast enough to reflect an SEO announcement s typical negative effect on stock prices. 4. Trading imbalance around SEO issue dates 4.1. Trading imbalances and stock returns Table 3 shows summary statistics of daily trading imbalances and stock returns around SEO issue dates. Daily stock returns are calculated from closing bid-ask midpoints to reduce the effect of bid-ask bounce. We also report daily percentages of positive trading imbalances and stock returns. [Table 3] Figure 2a plots daily trading imbalances and stock returns around SEO issue dates and Figure 2b plots the daily percentage of positive trading imbalances and stock returns for the same periods. We observe evidence of a higher frequency of bid-side transactions in the immediate period around an SEO, with daily trading imbalances particularly significant for trading days -1 to +2, where the SEO issue date is defined as day 0. Large negative imbalances occur from day -1 to day +1, although they continue even after day +1. The pattern of daily percentage positive trading imbalances matches the pattern in Figure 2a before and after the SEO, indicating that the cross-sectional 16

19 average trading imbalance is not driven by a few large outliers. Meanwhile, stock returns are negative from the prior days of SEOs until day +1 and become insignificant after day +2, indicating that returns and imbalances move separately around SEOs. The correlation between daily trading imbalances and stock returns is usually positive and significant [Chordia and Subrahmanyam (2004)]. However, around SEOs, there are days when positive trading imbalances are paired with negative stock returns, as in Huh and Subrahmanyam (2005). We also see that stock returns around SEOs can move without large buying or selling pressure. Thus, determinants of trading patterns can be quite different from determinants of stock returns. [Figure 2] The actual trading imbalance pattern is strikingly different from what prior research has inferred from stock-return patterns. Based on observed stock price patterns, the literature on trading activity around SEOs argues that sellers dominate trading before the issue date and buyers dominate afterwards. Yet, the observed trading-imbalance pattern before an SEO is indistinguishable from zero until one day before the issue. 5 The difference between the stock returns and the trading imbalances shows that combining information from share prices with order flows can yield a valuable new lens to help differentiate among competing hypotheses attempting to explain SEO effects on secondary markets. We also separately report results for sub-periods and major exchanges. Figure 3 and Figure 4 show that large negative trading imbalances are more significant in earlier years, but the pattern is also observable in recent years as well. Since the size of trading imbalances differs by time period, we report subperiod results in the later part of the 5 Even though we observe significant negative imbalances on day -1, the percentage of positive imbalances is 49.6% and the average size of the negative imbalance is small compared to significant imbalances on day +1 ~ + 4. Thus, the degree of sales activity is not very strong on the day before the issue date. 17

20 study. The figures also show that Nasdaq stocks have larger imbalances than NYSE or AMEX stocks, probably because Nasdaq stocks are smaller in size and less liquid in general. [Figure 3] [Figure 4] The mismatch between trading imbalances and stock returns indicates that quotes are intentionally placed above market price around SEOs. We conjecture that this quote pattern is primarily due to underwriter-affiliated dealers. [IPO studies by Aggarwal (2000), Boehmer and Fishe (2004), Lewellen (2006), Ellis (2006), and Ljungqvist (2007), among others, find that underwriters undertake extensive price-support activity for selective IPOs, which often occur through affiliated dealers. 6 Price support, which increases the inventory holdings of underwriters or their affiliated dealers, generates negative trading imbalances. 7 These negative trading imbalances occur because underwriters place bid quotes above other market makers bid quotes. Schultz and Zaman (1994) document such pattern after IPOs. just below the offer price. The negative trading imbalances can be related to market making as well. Ellis (2006) shows non-underwriter affiliated dealers in the Nasdaq market tend to sell a large amount of IPO stock to adjust their inventory positions back to zero. These unaffiliated dealers accumulate inventory initially by providing liquidity to investors who typically place more sell orders after an IPO. Underwriters or their affiliates act as the counterparty for this selling activity, but the underwriters can oversell the IPO (effectively short selling the issue), knowing that later they can use their overallotment 6 These IPO studies share similar sample periods around late 1990s (Aggarwal (2000) and Boehmer and Fishe (2004) use 1997 data, Lewellen (2006) studies data and Ellis (2006) analyzes Underwriters can prepare for such expected inventory accumulation by over-selling the SEO shares in the primary market. They can then cover their short positions by either buying back shares in the secondary market or using overallotment option if prices are above the offer price. 18

21 options from the issuer to adjust their inventory positions if needed from negative to near zero. This type of market making also works as a price support mechanism because underwriter-affiliated dealers are buying the shares from other unaffiliated dealers. To see whether underwriters can effectively undertake substantial price-support or market making activity, we need to first determine if underwriters have affiliated dealers in the SEO stocks. In IPOs, underwriter affiliated dealers are often the primary market makers in the stock and frequently undertake price-support activity [Ellis, Michaely, and O Hara (2000, 2011) and Ellis (2006)]. However, it is more difficult to detect such trading activity after SEOs given the large secondary market activity in these stocks. Non-affiliated underwriters can still influence trading activity, but their role is likely to be more limited relative to underwriters with affiliated dealers who can directly place quotes, monitor trading activity and rapidly execute orders Underwriters as market makers Unfortunately, it is much more difficult to identify underwriter-affiliated dealer trading for NYSE stocks. Each NYSE stock has a single designated market maker called a Specialist, and the designated specialist changes very infrequently. 8 Even if an underwriter engages in price-support activity for a NYSE stock, it generally does not own a NYSE specialist firm. We do not know of any dataset that can identify specific trades by underwriters on the NYSE. [Table 4] Table 4 reports the frequency of underwriter categories and the characteristics of SEO firms in each underwriter category for the Nasdaq listed stocks. Following Altinkilic and Hansen (2003), we measure underwriter reputation by ranking its previous year s SEO market share. The reputation variable has range between 1 and 10, where 1 is 8 There are seven specialists on the NYSE during our sample period, and the list of NYSE specialists does not change over our sample period. 19

22 the lowest and 10 is the highest. If an issue has more than one lead underwriter, we use the highest rank among lead underwriters as our reputation measure, so the reputation measure overall has a tendency to be higher than 5. We find that in over 83% of our Nasdaq sample, underwriters have affiliated dealers in these stocks. This evidence shows that underwriters are heavily involved in market-making activity for Nasdaq listed stocks, which is similar to the findings following IPO of Nasdaq stocks, e.g. Schultz and Zaman (1994), Ellis, Michaely, and O Hara (2000) and Ellis (2006). [Table 5] Table 5 augments the evidence in Table 4 by reporting the monthly ratio of quotes made by underwriter-affiliated dealers on Nasdaq relative to all dealer quotes. Panel A reports SEO stock trading costs by the frequency of quotes of affiliated dealers. We observe that underwriters with higher reputations are associated with a higher frequency of affiliated dealer quotes and larger trading costs, measured in terms of larger bid-ask spreads and lower average trading volume. This relationship indicates that the role of affiliated dealers can be particularly important to the secondary market conditions of stocks with relatively high trading costs. It also reveals that high reputation underwriters are willing to bear greater market making costs, perhaps to increase the likelihood that their SEOs are successful. In Panel B, we report changes in quotes of underwriter-affiliated dealers. We can see that the ratio significantly increases around the SEO issue month. Specifically, the ratio increases by 1.1% compared to one month before the SEO, while six months before the SEO, the ratio is 3.2%. These pieces of evidence indicate that underwriter affiliated dealers are substantially involved in the market making of SEO stocks, and that these 20

23 dealers increase their market making activities around SEO issue dates. 9 This evidence also indicates that market making could be important for a successful offering or a successful SEO underwriting assignment. We examine whether the average increase in quote frequency post-seo varies with underwriter reputation. Similar to Panel A, we find in untabulated results that high reputation underwriters place more quotes around issue dates. These results are available upon request. As far as we know, our study is the first to uncover such evidence for SEOs Determinants of trading imbalances In this section, we use standard regression techniques to identify determinants of trading imbalances, after carefully selecting explanatory variables to distinguish among major hypotheses posited in the SEO literature about associated trading behavior. Our goals are to find the factors affecting trading imbalances and to see how trading imbalances and the underlying factors are correlated with SEO underpricing. Our explanatory variables are offer size, stock idiosyncratic risk, stock volatility, underwriter reputation, a bid-ask spread measure, and a Nasdaq listing indicator. As the relative offer size of the SEO increases, the post-offer number of shares traded in the secondary market substantially increases. Relative offer size is measured by number of shares offered divided by number of shares outstanding before the SEO. 11 Larger offers are likely in the short run to put greater selling pressure on the stock price, causing underwriters to place larger and more frequent bid-side orders to support the price of weak offerings. Thus, when dealers are providing price supports, trading imbalances at or after the issue date become more negative for larger offers. In a similar 9 Underwriter selection may be influenced by whether the underwriter has significant market making capacity. In a similar vein, we check across our sample period whether there is a subperiod when a few underwriters dominate the underwriting market. We do not observe a significant reduction in the number of underwriters throughout our sample. 10 We also examine the quote changes of non-affiliated market makers. We find non-affiliated market makers place more quotes around SEOs, but the size of the increase is statistically insignificant. 11 Average stock volume is also used as a normalizing variable. We find no qualitative difference using this alternative normalizing variable. 21

24 vein, Corwin (2003) shows that offer size is positively correlated with SEO underpricing. Investors are likely to be reluctant to hold shares with high uncertainty or asymmetric information, and this may generate more sell orders than buy orders. Durnev, Morck, Yeung and Zarowin (2003) and Moeller, Schlingemann, and Stulz (2007) all use separate measures of uncertainty and asymmetric information. They argue that investor uncertainty is captured by stock-return volatility measured by the standard deviation of daily stock returns, while information asymmetry is captured by a stock s idiosyncratic risk, measured by the standard deviation of the residuals from regressing daily stock returns on the value-weighted market returns. The estimation period for the two measures is the 6-month period ending 6 months prior to the issue date. There is strong evidence in the IPO and SEO literature that underwriter reputation reduces underpricing (Eckbo, Masulis, and Norli (2007)). This result is interpreted as evidence that better underwriters reduce the uncertainty and information asymmetry associated with equity offerings. We follow Altinkiliç and Hansen (2003) in measuring underwriter reputation by the underwriter s market share of domestic SEOs in the year prior to the offer, measured by gross proceeds. When calculating market share, we only use cases where underwriters serve as lead underwriters. In the case of co-leads, we treat each as having underwritten the issue. We then rank lead underwriters by deciles, going from worst to best (1 to 10) to allow for nonlinear effects. If an underwriter is neither a lead nor a co-lead manager for one or more SEOs in the prior year, then it receives a rank of 1, which is the lowest rank. One characteristic of this ranking variable is that it is strongly positively skewed. Following Bernard and Thomas (1990) and Mendenhall (2004), we divide the raw rank by 10 and then subtract 0.5 to 22

25 shift the midpoint of the variable near zero. If an issue has more than one lead underwriter, we use the highest rank among lead underwriters as our reputation measure. Our bid-ask spread measure captures market making risk. Market makers bear two major risks inventory risk and asymmetric information risk [Stoll (2000)]. Inventory risk is due to stock price uncertainty associated with dealers deviating from their optimal inventory level, which is generally zero. Dealer inventory levels can substantially increase after an SEO because dealers are frequent counterparties to investors selling new shares recently purchased in the primary market [Ellis (2006)]. If inventory risk is high, dealers have a stronger incentive to lower their inventory position before an SEO. Following Stoll (2000), we use daily traded bid-ask spread as a measure of inventory risk. Stoll (2000) shows this spread captures inventory risk after controlling for other determinants of bid-ask spread, such as information risk. 12 Traded spreads are volume-weighted ask-side prices minus volume-weighed bid-side prices. Traded spread is also a common indicator of market depth which measures how costly it is to switch from bid-side to ask-side or vice versa. The spread estimation period is the 6-month period ending 6 months prior to the issue date. To measure the asymmetric information risk of market making, we use the difference between the quoted spread and traded spread, again following Stoll (2000). Finally, we use a Nasdaq listing indicator to capture the effects of underwriteraffiliated dealers, which is very frequent on the Nasdaq market, but infrequent on the NYSE and Amex. As discussed above, it is difficult to identify actual market making activity for NYSE stocks, so we use the Nasdaq indicator to capture the difference in market structure. Also, our data on underwriter-affiliated dealers begins in 1999 and ends 12 Information risk is a component of bid-ask spread that compensates for asymmetric information between informed traders and market makers. An investor may want to trade with a market maker because he has private information about the stock. The market maker may incur loss by trading at a price that does not include the value of the information the informed traded holds. 23

26 in 2006, so trimming our sample period would adversely affect the overall sample size. 13 Masulis and Shivakumar (2002), Altinkiliç and Hansen (2003) and Mola and Loughran (2004) all document that stock price behavior around SEOs is distinctly different for Nasdaq stocks. In addition, firm size, measured by equity capitalization, is included in the regressions as a common control variable in SEO studies. Firm size can account for important cross-sectional variation in SEO characteristics that are not fully captured by other variables. Equity market value is measured 6 months prior to the SEO. We also use Kyle s lambda as a control variable to reflect the sensitivity of a stock s price to trading imbalances. A stock with high sensitivity would be more vulnerable to large trading imbalances. Thus, some informed investors may adjust their trading direction according to the stock s relative sensitivity level, so that their trades are less likely to be detected by the public. The variable is also used as an indicator of market depth, as stock prices are more influenced by trades when market depth is shallow [Brennan and Subrahmanyam (1996)]. Kyle s lambda is estimated by regressing stock returns on trading imbalances. The estimation period is based on 1 year of daily data, ending 6 months before the issue date. Appendix B presents definitions and data sources for the variables used in our statistical analysis. 14 Our basic estimation approach for measuring a stock s daily trading imbalance is a standard panel regression model. TI, α B X Offer Size, Idiosyncratic Risk, Volatility, Reputation, Traded Spread 13 In another regression, we substitute an underwriter-affiliated market maker indicator for the NASDAQ indicator used in the previous section. The subsample from 1999 through 2006 yields similar results, namely that we find a significantly negative indicator coefficient on the affiliated market maker indicator as we do on the Nasdaq indicator. 14 We also check for market fragmentation due to trades being made off the SEO stock s main exchange. We examine the frequency of opening and closing trades made off the main exchange. We find all of the trades are made at the main exchange in our sample. 24

27 (1) C Y Firm Size, Nasdaq, Kyle s Lambda ε, where TI it is the trading imbalance of stock i, on event day t relative to the SEO issue date, and X is the matrix of explanatory variables (offer size, stock idiosyncratic risk, stock price volatility, underwriter reputation, and traded spread) and Y is the matrix of control variables (firm size, Nasdaq indicator, and Kyle s lambda). We use OLS estimation with a heteroskedasticity robust error structure. The error structure is further corrected for firm-level clustering, and year fixed effects to control for temporal changes in equity market conditions. 15 We also estimate these regression models by sub-periods, such as before year 2000 and after to capture the potential effects of changing stock exchange structure. We find similar results in both the earlier and the later period. It is especially noteworthy that our results are stronger in the period after 2005, indicating that our conclusions are not driven by data drawn from an earlier market structure. [Table 6] Table 6 presents regression estimates for equation (1). From panel A, we see that trading imbalances before an SEO are mostly uncorrelated with the explanatory variables suggested by the previous literature. Offer size is positively correlated with trading imbalances on days -4 and -1, while negatively correlated on days -3 and -2. The changing signs indicate that relative offer size is related to the absolute size of imbalances, rather than to the direction of imbalances before an SEO. In unreported results, we take the absolute value of trading imbalances before an SEO and find that the absolute size of imbalances increases with relative offer size. The regression results show that relative offer size has a mixed relationship with trading imbalances before an SEO. However, when we aggregate imbalances over days -4 to -1, relative offer size is 15 In an un-tabulated result, we use median regressions to test if our factor analysis is affected by outliers. We obtain similar results using median regressions. 25

28 negatively associated with imbalances. Stock volatility is negatively correlated with trading imbalances two trading days before an SEO, while asymmetric information risk is positively correlated with imbalances three days before an SEO. These non-stationary results imply that the typical negative stock return preceding an SEO is not the result of underwriter induced selling pressure. On the other hand, two market microstructure variables, both traded spread and Kyle s lambda, have negative correlations with trading imbalances, indicating that less liquid stocks often have sell-side trades before an SEO. Panel B shows the relation between SEO characteristics and trading imbalances on or after SEO issue dates. Importantly, underwriter reputation is associated with significant negative trading imbalances after an SEO, especially on trading days 1 and 2 immediately after the issue date. These results indicate that affiliated dealers of higher ranked underwriters engage in more frequent price support or market making activity. Also, the negative and significant coefficients on Kyle s lambda, a measure of market depth or liquidity, demonstrate that underwriter-affiliated dealer activity tends to rise in shallow markets. Thus, we conclude that an important determinant of the negative trading imbalances observed after SEO issue dates is underwriter affiliated dealer price support and market making activity SEO Market Conditions and Trading Activities In Table 7, we examine the associations of trading imbalances in the two-day SEO announcement period and include prior-week stock returns as an added control for market conditions in addition to the control variables used in Table The approach is to differentiate Hot SEOs and Cold SEOs using prior stock returns. If an SEO is completed in a Hot SEO market, price supports are less likely to be required. Furthermore, underwriters can estimate the expected need for price supports by 16 We vary the length of prior return measurement from one week to one month. Our results are not affected by the choice of length. 26

29 examining other recent SEO underwritten offerings. We take the 2-day post-seo trading imbalances of the most recent other SEO by any underwriter and include it as a further control in the analysis. If there is no previous SEO within 6 months, we do not include the observation in the regression. We also run the test with the average of all the SEOs in the last 3 months and our results are similar. For visual convenience, we aggregate trading imbalances for the 2-days after issue date, when trading imbalances are most prominent, and then estimate equation (1). We also add an indicator variable that takes a value of one when the 2-day post-seo average stock price is lower than offer price plus half the bid-ask spread to allow for a bid-ask bounce effect. The average price is daily volume weighted to minimize the effect of slow trading days, while the bid-ask spread is a 6-month average of the stock s daily spreads, ending 6 months prior to SEO issue dates. Note that underwriters have little incentive to provide price support if the stock price rises above offer price and remains there. In our sample, approximately one third of the cases have post-seo stock prices that fall below offer price during the following 2 trading days. Figure 5 plots the ratio of these cases by year. Falling stock prices are most frequent in 1998 (about 41%) and in 2008 (about 38%), and it indicates that in these periods a large number of SEO offer prices were set too high. [Figure 5] In Table 7 we report estimated associations of two-day post-seo trading imbalances on our control variables. Panel A is based on our full sample. The first model in Table 7 panel A uses the same control variables employed in Table 6 and it shows the explanatory variables that have significant coefficients are similar. Thus, the associations with the aggregated two-day imbalances examined in Table 7 are qualitatively similar to those found earlier for pre-issue daily trading imbalances estimated in Table 6. Model 2 27

30 also includes controls for the cumulative stock return over the 5 trading days prior to the issue date, a post-seo low price indicator, and the post-issue trading imbalances of other recent SEOs. We find the negative trading imbalances immediately after the SEO are uncorrelated with stock s prior returns and with the other recent SEO trading imbalances. On the other hand, the post-seo low price indicator is significantly negative, which reflects the fact that a low post-seo price leads to client pressure for underwriter price support. In model 3 of Table 7, we estimate the model for the subsample of Nasdaq listed stocks to further investigate the effects of market structure. Model 4 estimates the model for both NYSE and AMEX listed stocks. We find the underwriter reputation is negative and significant in both markets, indicating that underwriters are heavily involved market making around SEOs. Table 7, panel B reports separate estimates for the SEO subsamples defined by the low price indicator. The second column shows the estimates for SEOs when price supports are mostly to be required. We observe that underwriter reputation plays a more important role when market price falls below offer price. The size of coefficient on the underwriter reputation variable is twice as large in the low price sample ( vs ). Also, three variables (traded spread. prior return, and Kyle s lambda) exhibit significant coefficients in the low price sample. The prior return has a significant positive coefficient, while traded spread and Kyle s lambda have significant negative coefficients. The findings indicate that underwriter-affiliated market making is particularly important when SEO demand is low (low prior return) and market depth is shallow (high traded spread and Kyle s lambda). [Table 7] 28

31 To further assess the effect of endogeneity, we employ 2-stage least squares (2SLS) estimation in panel C. In the first stage, we estimate a regression between post- SEO imbalances (trading days +1, +2) and pre-seo imbalances (trading days -1, day -2). We then define the dependent variable of the second stage equation as the residual of post-seo imbalances, unexplained part by pre-seo imbalances. Pre-SEO imbalances serve as an instrument variable in this setting by eliminating any common explanatory factors in the pre-seo and post-seo imbalances. Suppose there is an omitted variable that influences imbalances around SEOs. Then the effect of such variable would be controlled by the first stage equation that extracts out the residual from the post-seo imbalances. The results reported in panel C of Table 7 are similar to those in the previous panels. The similarity indicates that the effect of endogeneity is not strong enough to influence our qualitative findings. In Table 7 panel D, we explore the possibility of structural changes in the relationship over time by splitting our sample and estimating the our model in these two subsamples. The first subsample covers the period from 1993 to 2000, while the second subsample covers the period from 2001 to As we show in Figure 3, there is a very large and discernible difference in the magnitude of trading imbalances in these two subperiods. We observe in Panel D that the imbalances in the two subsamples also have quite different determinants. In the 1990s, SEO characteristics have relatively weaker effects on imbalances. After 2000, imbalances are more strongly affected by SEO characteristics, such as offer size. Note that one of the most pronounced results of the Corwin (2003) study is the relationship between offer size and SEO underpricing. This comparison demonstrates that in more recent times SEO characteristics have greater 17 This division of the sample is based on decimalization in We also divide our sample around 1997, when Nasdaq changed from quoting in 8ths to 16ths and introduced a new order handling rule. We find similar patterns as in panel D, that pre-1997 sample shows a weaker relationship between SEO characteristics and imbalances. 29

32 influence on trading activity and underwriter price supports. This contrast is likely to be explained by changing stock liquidity that occurs around year For example, Bessembinder (2003) show that bid-ask spreads in the U.S. substantially fall after decimalization in It also follows that when bid-ask spreads are large, underwriters have less influence over trading activity around SEOs. Overall, we find that trading patterns around SEOs are related to underwriteraffiliated market making and price support activity. Trading activities are influenced more by underwriter-affiliated dealers as the SEO stock price falls below the SEO offer price and when the underwriter has greater reputational capital at risk. Also, underwriteraffiliated dealer influence is more pronounced when SEO demand is low and market depth is shallow. 5. Trading imbalances and SEO underpricing Underwriters have greater incentives to set lower offer prices when they or their affiliated dealer expect to provide more extensive post-offering price support. The preoffering book building process should provide relatively valuable information about these expected price support costs. Similar to IPOs [Ellis (2006)] and as noted earlier, underwriter-affiliated dealers become the likely counterparty in the immediate post-seo period for the inventory rebalancing trades of other market makers. Other market makers generally receive more sell orders from new shareholders than buy orders immediately after an SEO or an IPO. Thus, they typically want to reduce their inventory levels in this period, which suggests that the underwriter-affiliated market maker is going to primarily accommodating this order flow by buying shares. Our discussions with SEC staff and Ellis (2006) reveal that most dealers set their daily inventory target near zero, instead of smoothing out their inventory levels over a 30

33 longer period of time. If market depth is sufficiently robust, then dealer sell orders have only a temporary effect on stock prices. But, if market depth is shallow, then affiliated dealers have to step in to be the major buyers. This situation implies that underwriter affiliated dealers expect to place many buy orders in the immediate post-seo period when market depth is shallow. 18 In a similar context, Ellul and Pagano (2006) argue in their theoretical model that trading costs are associated with IPO underpricing. A major difference between SEOs and IPOs is that SEO underwriters can estimate the cost of share buy backs from the stock s pre-seo trading activity. Rational underwriters recognize that setting lower offer prices allows them to sell more SEO shares, net of the amount bought back from other dealers in the post-seo secondary market. Facing possible buy-backs by their affiliates, underwriters have incentives to over-sell shares in the primary market since they can sell the offering more easily by setting a lower offer price, which implies greater SEO underpricing. Thus, trading imbalances after the issue date, which are mainly driven by underwriter-affiliated market making and stabilization activities, can be affected by the SEO offer price chosen. This is in part because lower offer prices raise customer demand for SEO shares, which can help reduce an underwriter s post-seo inventory level. It can also lower the expected cost of being unable to sell the entire issue at the offer price. We measure the offer price discount as the SEO offer price relative to the previous day closing price. This discount is an ex ante measure of underpricing that can be directly observed by issuers and underwriters, who need to agree on the offer price. This discount is also used as a measure of SEO underpricing in Corwin (2003). On the other hand, Altinkilic and Hansen (2003) use the return calculated from the offer price and offer day closing price. Both of these measures capture a common underpricing 18 Of course, underwriters could demand that the offer size be reduced instead. 31

34 factor, as Smith (1977) and Corwin (2003) document that close-to-offer return, our underpricing measure, has variations similar to that of the return from the SEO offer price to the offer date closing price. The following equation formally defines the SEO underpricing level, which is also termed the offer price discount. Po Underpricing log( ), (2) P c where P o is the SEO offer price and P c is the closing price on the last trading day before SEO. While many factors that affect SEO offer prices also affect underwriter fees (information asymmetry about a firm s performance, for example), the expected cost of market making and price supports would primarily affect offer price. This is because the amount of post-seo buy-back activity can be more accurately estimated through the book building process and the daily trading activity in the stock leading up to the issue date. Underwriters cannot effectively cover their actual market making costs by charging additional ex post fees, because fees are typically determined before the SEO occurs. However, underwriters can cover their expected post-seo market making / price support costs by greater underpricing, i.e. lowering the offer price, which has the added benefit of lowering the expected cost of price supports. In related work, Butler, Grullon and Weston (2005) shows that underwriter fees increases in stock illiquidity. If underwriters are able to fully compensate for the cost of market making by charging higher underwriting fees, then our analysis on SEO underpricing and post-seo trading imbalances should not yield a significant association. The link between the expected cost of market making/price supports and offer price is not limited to the size of possible share buy-backs immediately after the issue date. Because underwriters allocate these typically underpriced shares to favored customers, they also indirectly capture some of this additional financial benefit of this 32

35 pricing decision (Loughran and Ritter 2004, Ljungqvist 2007). Ellis, Michaely and O Hara (2000) find that underwriters on average realize positive returns on their underwritten stock after an IPO and the size of the return is positively correlated with the degree of underpricing. This suggests that the market microstructure of a stock affects a major component of an issuer s equity flotation cost. To test the relation between SEO offer price and the level of underwriter market making activity, we focus on trading imbalances after the SEO issue date. In section 4, we show that there are large negative trading imbalances and this imbalance pattern is consistent with underwriter market making activity. Thus, we estimate underwriter market making cost by the extent of negative imbalances after SEOs and examine the relation between underpricing and these trading imbalances. Corwin (2003) and Eckbo, Masulis, and Norli (2007) document that variables like offer size, idiosyncratic risk, stock volatility, underwriter reputation, firm size, and a Nasdaq listing indicator are significantly related to SEO offer price discounts. We confirm in our sample that the relationships between SEO offer price and these control variables are consistent with the findings of Corwin (2003) and Eckbo, Masulis, and Norli (2007). 19 However, this result poses an econometric problem in interpreting the relation between trading imbalances and offer price discounts because many control variables that explain trading imbalances also explain offer price discounts. One conservative conclusion is that trading imbalances both preceding and following an equity issue date share many of the same explanatory variables that earlier studies find help explain SEO offer prices, suggesting that trading imbalances and offer price discounts are economically related. To measure the relation between offer price discount and expected post-issuance trading imbalances while addressing endogeneity concerns, we estimate a simultaneous 19 The results are available upon request. 33

36 two equation system using GMM. We select trading imbalances on trading days +1 and +2 when large negative imbalances are typically observed (Figure 2a). We specify the following two equation system: Underpricing, α B X Offer Size, Idio_Risk, Volatility, Reputation, Traded Spread C Y Firm Size, Nasdaq, Kyle s Lambda ε, (3) Expected TI Post, α δ Underpricing, δ TI Pre, δ Spread, ε (4) where Underpricing is the offer price discount, X is the matrix of explanatory variables and Y is the matrix of control variable both from Table 7, TI post is the expected aggregate post-seo trading imbalances on trading dates +1 and +2, and TI pre is the aggregate pre-seo trading imbalances for trading dates -1 and -2, where the SEO is day 0. We also add one-month average bid-ask spreads relative to prices measured 6 months before the SEO date to control for market microstructure effects on trading imbalances. Equation (4) takes into account the endogeneously determined underpricing in this two stage regression that predicts the size of price supports, measured in terms of imbalances. If we take out the simultaneous equation structure, equation (4) becomes similar to the estimations in Table 7, which shows the relation between pre-seo variables and post-seo imbalances. Similar to the regression in Table 7 panel C, we use the average pre-seo trading imbalance (trading days 1, 2) as an instrumental variable for estimating expected post-seo trading imbalances (trading days + 1, + 2). This choice of instruments is based on two considerations. First, Chordia, Roll, and Subrahmanyam (2000) and Chordia and Subrahmanyam (2004) report trading imbalances exhibit strong serial correlations. Second, as we can see from Table 6 that pre-seo trading imbalances are not 34

37 significantly correlated with SEO characteristics. We also verify from a single stage underpricing regression that pre-seo trading imbalances have little explanatory power for offer price discounts. On the other hand, we verify that pre-seo trading imbalances are significantly correlated with post-seo trading imbalances (t-stat 2.50) using a single equation regression. [Table 8] Table 8 panel A reports the GMM estimates of Eq. (3) and Eq. (4). The estimates in Table 8 panel A show that SEO offer price discount is positively correlated with expected post-seo trading imbalances (t-stat 3.54). Since negative trading imbalances are associated with underwriter price supports, the result supports the hypothesis that higher expected market making and price support (negative imbalances) costs are associated with larger offer price discounts and greater SEO underpricing. The statistical relation is consistent with underwriters lowering offer prices to reduce their expected price support costs after the SEO. 20 We also conjecture that the relationship between expected price supports and underpricing is stronger when underwriters have affiliated dealers. Although underwriters without affiliated dealers are still able to offer price supports, it is relatively more difficult for them to undertake this price support activity. 21 Table 8 panel B reports the estimates of equation (4) after further controlling for the existence of an underwriter affiliated dealer. We construct an interaction variable between the affiliated dealer indicator (1 for affiliation and 0 otherwise) and underpricing. This interaction term captures the level of underpricing by underwriters with an affiliated dealer. Since Nasdaq 20 We obtain a qualitatively similar result if we use the conventional ex post underpricing measure instead of offer price discount. The conventional measure is the difference between offer price and the closing price of the issue date. Thus, offer price is lowered compared market price before or after issue date, although this is not observable by an underwriter when the offer price is set. 21 Underwriters offering price supports without the benefit of an affiliated dealer must use limit orders through other brokers. This approach opens up the underwriter to greater risk of trading losses because the underwriter cannot monitor these trades on a real time basis or place timely new orders through this indirect channel. 35

38 affiliation information comes from NASTRAQ database and is available only for the 1999 ~ 2006 period, this test is limited to this subsample of our SEOs. Indeed, in Table 8 panel B, we find a much stronger relationship between underpricing and expected price support costs when underwriters have an affiliated dealer. The interaction variable has t- statistics of In Table 8 panel C, we split the sample into subintervals. The first column shows the result for the 1990s and the second column shows the result for the period after We find that the relationship between expected price supports and underpricing is statistically significant in the post-2000 period. Consistent with our earlier result in Table 7 panel D, trading imbalances are more strongly related to trading activity around SEOs after Given simultaneous equation estimates are inherently dependent on model structure, we also report non-parametric tests to supplement the prior parametric estimates. For this purpose, we first estimate the expected price support cost of an affiliated dealer and later compare these expected costs across Nasdaq and NYSE markets. We assume all negative trading imbalances that occur immediately after the offerings are accommodated by underwriter-affiliated dealer purchases. The underlying logic is that negative trading imbalances indicate market makers are purchasing shares from the public. Since underwriters are likely to be the only market maker purchasing stocks, negative imbalances are a form of price support by underwriters. We then estimate the trading imbalance effect on stock returns using the past sensitivity of stock returns to trading imbalances. This relation is based on the Chordia and Subrahmanyam (2004) model, which views stock returns as a product of trading imbalances and a stock return s sensitivity to trading imbalances. In their model, stock return is the dependent variable and trading imbalances is the main explanatory variable 36

39 and the return sensitivity is measured by the estimated trading imbalance coefficient. We estimate the sensitivity using the prior calendar year s daily trading imbalances and stock returns up to trading day 2, where we implicitly assume the sensitivity is similar before and after the SEO. Empirical support for this approach is that this sensitivity is known to be fairly stable over long periods of time such as a year. Brennan and Subrahmanyam (1996), for example, argue that the sensitivity is stable for multiple years. We find a significant 27% correlation between the sensitivity estimates from two adjacent calendar years using our SEO issuer firms. We then take the combined trading imbalances for trading days +1 and +2 and multiply this TI figure by this estimated return sensitivity. [Table 9] Table 9 reports the estimated price support costs for affiliated dealers. We find that this estimated cost averages 0.8% of the stock s offer price. From the average offer size reported in Table 2, we can calculate this expected price support cost to be $1.1 million across the entire sample period and $6.6 million in the more recent post-2007 subperiod. The dollar value of the price support cost will become larger in more recent periods as the size of a typical offer has been consistently rising. Underpricing or offer price discount for our sample averages 3.40%, suggesting that the expected price support cost represents an average 24% (0.8% / 3.40% = 24%) reduction in a typical SEO offer price. We conclude that higher expected underwriter-affiliated dealer price support costs lead underwriters to demand lower SEO offer prices. As a result, we expect to observe that larger SEO offer price discounts are associated with higher expected underwriting costs. Separating the data by market, we find Nasdaq stocks experience higher average underwriting cost of 1.03% and larger offer price discounts of 4.70%. NYSE stocks have average underwriting cost of 0.51% and offer pricing discount of 37

40 2.12%. In both markets, affiliated market maker s expected costs of price supports represents approximately a quarter of the average SEO underpricing cost. The last two rows of each panel in Table 9 show averages by secondary market condition at the time of issue. We rank SEO stocks into quartiles based on their average bid-ask spread over the 6 months prior to the SEO completion date. Then, we take the dealer s expected costs and underpricing in the top and bottom quartiles based on bid-ask spreads. We find expected price support costs and underpricing are significantly larger in the highest spread quartile relative to the lowest quartile (with the difference being significant in 1% level). Specifically, affiliated dealer s expected price support cost and underpricing are twice as large in the high spread group. This is consistent with our previous findings that higher secondary market transaction costs are associated with larger expected price support costs and larger SEO underpricing. In summary, we find that underwriters set lower offer prices when they expect to undertake more intensive market making / price support activity in the immediate postissue period. A less liquid secondary market raises the expected cost of affiliated market making activity, and underwriters appear to demand added compensation for this expected cost by demanding a lower offer price. 22 Thus, this empirical evidence indicates that the microstructure of the secondary market can affect a firm s cost of capital, as expected price support costs fall with a rise in stock liquidity. 6. Conclusions We investigate buying and selling activities around SEO issue dates by analyzing a stock s daily trading imbalances during an 18-year period. We find that trading imbalances are around zero before an SEO and then become highly negative 22 In a separate regression, we verify that market making costs increase when depth is low; small stocks and low volume stocks have significantly higher market making costs. Results are available upon request. 38

41 afterward. The pattern of average trading imbalances is different from what prior research has inferred from stock-return patterns. Prior SEO studies report finding average daily stock returns that are negative before an equity issuance date and positive thereafter. The prior literature posited three hypotheses to explain this observed returns pattern, especially the negative returns before an issue date that could affect the SEO offer price. The three dominant hypotheses in the literature for these observed SEO pricing patterns are selling pressure due to a supply shock from the primary offering, increased asymmetric information or uncertainty around the SEO, and institutional investor flipping behavior following the offering. These hypotheses also relate SEO underpricing to post-seo stock returns. We find, however, that the observed trading imbalance patterns are mostly inconsistent with these hypotheses. Our results highlight the fact that the inferences drawn from analyzing stock-return movements alone can be misleading since they suggest trading activity that is inconsistent with the trading imbalance patterns observed over these same time periods, at least around SEO events. The observed trading imbalance pattern suggests that market makers intentionally place quotes above the market price afterward. Underwriters with better reputations appear to submit more aggressive buy orders post-seo, even when recent stock returns are low. Consistent with this evidence, we find that underwriters of Nasdaq SEOs almost always have affiliated dealers making markets in these stocks and that the level of market making activity by these underwriter affiliates significantly increases in the days before the SEO issue date. Factor analysis of trading imbalances yields further evidence that underwriters use their market making role to aid the SEO flotation process. Underwriter reputation is also associated with highly negative trading imbalances after the SEO issue date. The negative trading imbalances imply that underwriters place bid quotes to provide liquidity and support prices. Non-underwriter affiliated market makers 39

42 actively sell shares to lower their immediate post-seo inventory levels. Thus, without enough traders to absorb these sales, underwriters become the default buyer of these shares. Underwriter-affiliated market making activity is also stronger when SEO demand is low and market depth is shallow. Finally, we find evidence that market microstructure can have a significant influence on SEO offer prices. When underwriters expect to buy back a large proportion of shares through their price support activity, they lower the offer price to make it easier to sell more shares in the primary market, reduce expected price stabilization costs and increase the expected gains to the underwriter s SEO investors. We demonstrate that the post-offer expected cost of market making is associated with larger SEO offer price discounts or underpricing. These market making costs explain a quarter of the offer price discount in both the Nasdaq and the NYSE/Amex markets. 40

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46 Appendix A Construction of Trading Imbalance Data 1. Criteria for stock selection are: The data source comes from Trade and Quote (TAQ) data. The data period is from January 1993 to December We exclude from the data set Certificates, ADRs, shares of beneficial interest, units, Americus Trust components, closed-end funds, preferred stocks, and REITs. We delete the stock from the sample year if the price at any month-end during the year was greater than $999. We eliminate non-synchronous trading issues by marking stock returns as missing if there was no trade today or the previous day. 2. When constructing the trading-imbalance variable, we only use quotes and trades such that: Quotes and trades are in regular market trading times (from 9:30 to 16:00) There are no special settlement conditions All bid-ask spreads are positive. 3. Method to calculate trading imbalance is the Lee and Ready (1991) method: A trade is buyer (seller) initiated if it is closer to the ask (bid) of the prevailing quote. The prevailing quote should be at least 5 seconds old. If the trade is at the midpoint of the quote, the trade is buyer (seller) initiated if prior stock price change was positive (negative). 44

47 Appendix B Explanatory Variables Variable Description Trading Imbalances Daily statistics of bid-side trades (buys) minus offer-side trade (sells), normalized by daily share volume; Data Source: TAQ Underpricing (Offer Price Discount) Log of SEO offer price divided by the closing price of the day before issue date; an SEO underpricing measure in Corwin (2003). Data Source: SDC, CRSP Offer Size Idiosyncratic Risk Return Volatility Information Risk Underwriter Reputation Traded Spread Issuer Equity Market Value Kyle s Lambda Number of shares issued divided by shares outstanding before the SEO. Data Source: SDC, CRSP We estimate idiosyncratic risk by regressing daily stock returns on the value-weighted market returns taken from the CRSP database. The estimation period is the 6-month period ending 6 months prior to the issue date. Data Source: CRSP Uncertainty is measured by the standard deviation of daily stock returns. The estimation period is the same 6-month period as that of idiosyncratic risk. Data Source: CRSP The difference between the quoted and traded spreads. The quoted spread is the difference between inside bid quote and ask quotes. The traded spread is measured by the difference between the daily volume-weighted average bid-side trades and ask-side trades. Data Source: Market Microstructure Database The prior market share of the gross proceeds of domestic SEOs and IPOs, where investment banks serve as lead underwriters for the calendar year prior to the SEO. We then rank lead underwriters into 10 categories from worst to best. Data Source: SDC The traded spread is measured by the difference between the daily volume-weighted average bid-side trades and ask-side trades. Data Source: Market Microstructure Database Market value of a firm is defined as number of shares outstanding multiplied by share price at the end of month. The variable is measured 6 months from SEO issue date. Data Source: CRSP Sensitivity of daily stock prices to daily trading imbalances. The variable is measured in a 1 calendar year period, ending 6 months before an issue date. Data Source: TAQ, CRSP Nasdaq Indicator Takes 1 if a stock is mostly traded on Nasdaq. Takes 0 otherwise. Data Source: Market Microstructure Database. Low Price Indicator Takes 1 if the 2-day post-seo average stock price is lower than offer price plus half the bid-ask spread. Data Source: TAQ, CRSP Shares Filed Number of SEO shares filed; Data Source: SDC Amount Filed Dollar amount of SEO shares filed; Data Source: SDC Estimated Market Making Cost Issuer Stock Trading Volume Daily trading imbalances aggregated over the first two trading days following the SEO issue date (days +1 and +2); Data Source: TAQ Monthly average of the trading volume measured 6 months from the issue date; Data Source: CRSP 45

48 Table 1. Studies on Trading Activities Around SEOs and Their Sample Periods The sample periods of well known U.S. SEO studies are presented below. SEO Study Sample Period Altinkiliç and Hansen (2003) Altinkiliç and Hansen (2006) Corwin (2003) Cotter, Chen, and Kao (2004) Meidan (2005) Mola and Loughran (2004) Safieddine and Wilhelm (1996)

49 Table 2. Summary Statistics Our SEO data contain 4,608 SEOs for the years We report descriptive statistics of the SEO characteristics by issue year. Issuer stock volume is the monthly average of the trading volume measured 6 months from the issue date. The sample includes all firm commitment underwritten SEOs by U.S. nonfinancial, non-utility firms listed on major exchanges that have trading data available in the TAQ database. Variable definitions are presented in Appendix B. Year Obs. Statistic Total 4608 Shares Filed (Millions of Shares) Amount Filed Issuer Market Value ($ Million) ($ Million) Issuer Stock Trading Volume (Millions of Shares) Mean Median Mean Median Mean Median Mean Median Mean Median Mean Median Mean Median Mean Median Mean Median Mean Median Mean Median Mean Median Mean Median Mean Median Mean Median Mean Median Mean Median Mean Median Mean Median

50 Table 3. Summary Statistics of Trading Imbalances We report summary statistics of trading imbalances and stock returns around SEO issue dates. The sample includes all firm commitment underwritten SEOs by U.S. nonfinancial, non-utility firms listed on major exchanges that have trading data available in the TAQ database. Daily event day trading imbalances are measured by daily share imbalances divided by pre-seo shares outstanding (in thousands) where trading imbalances defined as bid-side trades minus offer-side trades. Stock returns are derived from closing bidask mid-point prices. Significant figures are in bold and their significance levels are indicated by a, b, and c subscripts for 1%, 5%, and 10% significance levels respectively. Variable definitions are presented in Appendix B. Panel A: Daily Trading Imbalance (as % of pre-seo shares outstanding) Day - 4 Day 3 Day 2 Day - 1 Issue D Day + 1 Day + 2 Day + 3 Day + 4 Mean b a a a a a Median Std Dev % Positive 49.7% 49.7% 51.0% 49.6% 42.8% 32.8% 38.9% 41.0% 43.0% Panel B: Daily Mid-Quote Stock Return Day - 4 Day 3 Day - 2 Day - 1 Issue D Day + 1 Day + 2 Day + 3 Day + 4 Mean 0.12% b -0.02% -0.15% a -0.46% a -1.52% a -0.52% a 0.32% a 0.17% a 0.17% a Median 0.02% 0.02% -0.08% -0.22% -1.01% -0.27% 0.19% 0.11% 0.11% Std Dev 3.70% 3.80% 3.56% 3.81% 4.70% 4.18% 2.76% 2.60% 2.64% % Positive 50.7% 50.4% 48.3% 45.3% 36.4% 45.7% 55.7% 53.9% 52.5% 48

51 Table 4. Frequency of Underwriter-Affiliated Market Makers In Panel A, we report the frequency of underwriter-affiliated market makers relative to all market makers for a stock in the NYSE and Nasdaq. We use NASTRAQ data to identify Nasdaq market makers for individual SEO issuer stocks and to determine whether they are affiliated with a SEO lead underwriter. For NYSE stocks, we use NYSE panel data on specialists. In Panel B, we report Nasdaq stock characteristics by underwriter affiliation. The sample includes firm commitment underwritten SEOs by U.S. non-financial, non-utility firms listed on major exchanges with trading data available in the TAQ database. Variable definitions are presented in Appendix B. Panel A: Frequency of Underwriter-Affiliated Market Makers Nasdaq Stocks Affiliated? Frequency Percentage of Nasdaq SEOs Yes % No % Panel B: Nasdaq Stock Characteristics for SEO Issuers by Underwriter Affiliation Status Nasdaq Stock Characteristics Affiliated? Pre-SEO Firm Size ($ millions) Offer Size ($ millions) Underwriter Reputation Rank (1 lowest to 10 highest) Trading Imbalances Before SEO Issue Dates (as % of pre-seo shares outstanding) Trading Imbalances After SEO Issue Dates (as % of pre-seo shares outstanding) Yes No

52 Table 5. Ratio of Dealer Quotes Made by Underwriter-Affiliated Market Makers We use NASTRAQ data to identify Nasdaq market makers of an SEO issuer stock and whether a market maker is affiliated with the SEO s lead underwriters. The sample includes firm commitment underwritten SEOs by U.S. nonfinancial, non-utility firms listed on major exchanges with trading data available in the TAQ database. The ratio of underwriter-affiliated market maker quotes relative to total dealer quotes is calculated on a monthly basis. In Panel A, we rank the ratio of underwriter-affiliated dealer quotes every year in quintiles and report average trading costs and underwriter reputation rankings. In Panel B, we separate the reported changes in the ratio in the issue month relative to the ratio 1 and 6 months before SEO issue date. We also report the ratio of underwriter-affiliated quotes in the SEO issue month on the bottom row. Variable definitions are presented in Appendix B. Panel A: Ratio of Underwriter-Affiliated Dealer Quotes and Stocks Trading Costs Ratio of Quotes by Underwriteraffiliated Dealers Underwriter Reputation Rank (1-10) Bid-Ask Spread Relative to Price (%) Daily Share Volume (millions) Number of Trades per Day Rank 5 (Highest) ,299 Rank ,563 Rank ,789 Rank ,966 Rank 1 (Lowest) ,259 Panel B: Changes in Quotes of Underwriter-Affiliated Dealers Mean (%) Median (%) Std Error (%) Observations 1 Month Change in the Ratio of Quotes of Underwriter-Affiliated Dealers (in Dollars) 6 Month Change in the Ratio of Quotes of Underwriter-Affiliated Dealers (in Dollars) Ratio of Quotes Made by Underwriter- Affiliated Dealers Divided by Total Quotes in the SEO Month (in Dollars) 1.10 Increase 0.26 Increase Increase 0.35 Increase

53 Table 6. Determinants of Trading Imbalance The dependent variable, TI, is daily trading imbalance around SEO issue dates. For each business day around SEO issue dates, we estimate a regression with the trading day s trading imbalance as dependent variable. TI, α B X Offer Size, Idiosyncratic Risk, Volatility, Reputation, Traded Spread C Y Firm Size, Nasdaq, Kyle s Lambda ε, (1) The sample includes firm commitment underwritten SEOs by U.S. non-financial, non-utility firms listed on major exchanges in the with trading data available in the TAQ database. The control variables listed in column (1) and denoted in equation (1) above as X, are offer size, idiosyncratic risk, stock volatility, underwriter reputation, and traded spread. Y is the matrix of control variables comprised of firm size, Nasdaq indicator, and stock return sensitivity to trading imbalances (Kyle s lambda). Variable definitions are available in Appendix B. Panel A reports daily trading imbalances immediately before SEO issue dates, while Panel B reports imbalances at and immediately after SEO issue dates. OLS estimates are reported with heteroskedasticity corrected errors clustered by firm. We use year fixed effects to account for time trends. Variable definitions are presented in Appendix B. The coefficient s t-statistics are reported in parenthesis. Significant coefficients are in bold and their significance are indicated by a small a, b, and c subscript for a 1%, 5%, and 10% significance level respectively. Panel A: Daily Trading Imbalance Prior to SEO Issuance Dates Dependent Variable: Daily Trading Imbalances Trading Imbalances on Day -4 Trading Imbalances on Day -3 Trading Imbalances on Day -2 Trading Imbalances on Day -1 Offer Size a (4.84) a (-27.26) a (-6.95) a (13.74) Idiosyncratic Risk (-0.14) (-0.16) (0.40) (0.44) Stock Volatility (0.30) (0.32) c (-1.71) (-0.20) Information Risk (1.53) c (1.84) (1.59) (-0.61) Underwriter Reputation (-0.42) (-1.21) (-0.45) (0.21) Traded Spread (-1.12) a (-3.41) b (-2.67) (-0.77) Log (Issuer Equity Market Value) (1.92) a (3.17) a (3.22) a (4.33) Nasdaq Indicator (-0.97) b (-2.50) a (-3.23) b (-2.55) Kyle s Lambda b (-2.42) a (-3.96) a (-3.77) a (-3.90) Observations 4,595 4,604 4,608 4,608 Adj. R-square 1.2% 11.6% 2.5% 2.7% 51

54 Panel B: Daily Trading Imbalance at and after SEO Issue Dates Dependent Variable: Daily Trading Imbalance Trading Imbalances on the Issue Date Trading Imbalances on Day +1 Trading Imbalances on Day +2 Trading Imbalances on Day +3 Trading Imbalances on Day +4 Offer Size a (-3.15) (-0.13) a (21.48) a (-8.55) a (12.80) Idiosyncratic Risk (-1.36) (0.38) (-0.64) c (1.70) (1.41) Stock Volatility (0.80) (1.03) a (8.41) (0.40) (0.34) Information Risk (-0.80) (0.61) (-1.32) (-1.20) (-1.11) Underwriter Reputation (-1.20) a (-7.19) a (-4.18) (-1.09) (-0.71) Traded Spread (-1.01) (-1.16) (-0.71) (-0.17) (0.27) Log (Equity Market Value) a (5.88) a (8.55) a (4.85) b (2.12) (1.28) Nasdaq Indicator a (-4.31) a (-5.80) a (-3.92) a (-3.44) b (-2.29) Kyle s Lambda a (-2.74) a (-2.57) a (-3.44) a (-3.80) a (-3.28) Observations 4,608 4,608 4,608 4,606 4,603 Adj. R-square 7.5% 6.7% 4.1% 1.7% 1.0% 52

55 Table 7. Trading Imbalance after SEO Issue Dates We replace the dependent variable from Table 6 with daily trading imbalances aggregated over the first two trading days following the SEO issue date (days +1 and +2). TI, α B X Offer Size, Idiosyncratic Risk, Volatility, Reputation, Traded Spread C Y Firm Size, Nasdaq, Kyle s Lambda ε, (1) The sample includes firm commitment underwritten SEOs over the period by U.S. nonfinancial, non-utility firms listed on major exchanges with trading data available in the TAQ database. In panel A, we estimate the equation with full sample and in panel B, we estimate the equation separately for the subsamples of SEOs that have stock prices on trading days +1 and +2. Estimates of trading imbalances after SEO issue dates for two subsample estimates defined by the value of the low price indicator, which is defined to take a value of one when the average of the closing market prices for trading days +1 and +2 is lower than offer price plus half the quoted bid-ask spread and is zero otherwise. In panel C, a two stage estimation procedure is used to address endogeneity concerns. Post-SEO trading imbalances are regressed against pre-seo trading imbalances in the first stage regression. In the second stage, residuals from the post-seo trading imbalance regression are regressed against the previous explanatory variables. In panel D, we report estimates of trading imbalances following SEO issue dates for two subperiods: and Variable definitions are presented in Appendix B. Significant coefficients are in bold and their significance levels are indicated by a small a, b, and c subscript for 1%, 5%, and 10% significance levels respectively. Panel A: Pooled Sample of Immediate Post-Issuance Period Dependent Variable: Trading Imbalances on Days +1 and +2 Offer Size Model 1 Model a (8.98) Idiosyncratic Risk (0.49) Stock Volatility (-1.31) Information Risk (-0.71) a (8.50) (0.03) (-0.92) (-0.47) Model 3 (Nasdaq only) a (3.37) (1.22) (-1.05) (0.22) Model 4 (NYSE & AMEX) a (7.44) (-0.55) (-1.14) b (2.71) Underwriter Reputation a (-7.16) a (-7.11) a (-5.16) a (-5.02) Traded Spread (-0.88) (-1.14) b (-2.66) a (-3.95) Log (Issuer Equity Market Value) a (8.06) a (8.39) a (5.76) a (8.51) Nasdaq Indicator a (-6.46) a (-7.23) Kyle s Lambda (-1.45) c (-1.85) b (-2.00) (-1.38) Stock CAR (-5,-1) (-0.05) (-1.26) b (2.01) Previous SEO Imbalances (1.04) (0.93) (0.85) Low Price Indicator a (-12.52) a (-9.72) a (-8.90) 53

56 Observations 4,608 4,606 2,286 2,320 Adj. R-Square 7.5% 11.4% 11.5% 11.8% Panel B: Subsample Analysis by Status of Stock s Market Price Relative to the SEO Offer Price Dependent Variable: Trading Imbalances over Days +1 and +2 Low Price Indicator = 0 (Market Price Higher than Offer Price) Low Price Indicator = 1 (Market Price Lower than Offer Price) Offer Size a (8.68) b (2.95) Idiosyncratic Risk (0.78) (-0.89) Stock Volatility (-0.79) (-0.79) Information Risk (-0.66) (0.41) Underwriter Reputation a (-4.61) a (-5.53) Traded Spread (0.01) a (-3.54) Log (Issuer Equity Market Value) a (5.01) a (7.88) Nasdaq Indicator a (-5.43) b (-2.55) Kyle s Lambda (-1.33) a (-3.03) Stock CAR (-5,-1) c (-1.98) c (1.99) Previous SEO Imbalances c (1.92) (-0.44) Observations 3,026 1,580 Adj. R-Square 5.3% 18.3% 54

57 Panel C: Two Stage Estimation of Trading Imbalances 1 st Stage Regression: Immediate Post-Issuance Trading Imbalances as the Dependent Variable 2 nd Stage Regression: Immediate Post-Issuance Trading Imbalances Not Explained by Prior Trading Imbalances (Residuals from 1 st Stage Regression) as the Dependent Variable Issuer Stock Average Trading Imbalances on Day -1 and Day a (6.69) Offer Size b (7.93) Idiosyncratic Risk (0.09) Stock Volatility (-0.77) Information Risk (-0.62) Underwriter Reputation a (-7.12) Traded Spread (-1.05) Log (Issuer Equity Market Value) a (8.11) Nasdaq Indicator b (-6.98) Kyle s Lambda c (-1.86) Stock CAR (-5,-1) (-0.50) Previous SEO Imbalances (1.15) Observations 4,606 4,606 Adj. R-Square 0.94% 18.9% 55

58 Panel D: Subperiod Analysis of Trading Imbalances Dependent Variable: Trading Imbalances over Days +1 and Period Period Offer Size (-0.94) a (6.89) Idiosyncratic Risk (0.18) (0.58) Stock Volatility (0.99) c (-1.69) Information Risk (-1.01) b (2.26) Underwriter Reputation a (-3.22) a (-7.02) Traded Spread (-1.33) (-1.16) Log (Issuer Equity Market Value) a (3.85) a (8.95) Nasdaq Indicator a (-4.96) b (-2.55) Kyle s Lambda (0.95) (-1.55) Stock CAR (-5,-1) c (-1.85) c (1.89) Previous SEO Imbalances (-0.45) (-0.23) Low Price Indicator a (-8.05) a (-10.97) Observations 1,235 3,371 Adj. R-Square 11.1% 11.3% 56

59 Table 8. Underpricing and Price Supports We estimate a simultaneous equations model for SEO underpricing using Generalized Method of Moments (GMM). In the first equation denoted as equation (3), the underpricing (offer price discount) is dependent variable and SEO characteristics used as controls in Table 6 are again the control variables. X in equation (3) is the matrix of offer size, idiosyncratic risk, stock volatility, underwriter reputation, and traded spread. Y in the equation (3) represents firm size, Nasdaq indicator, and stock return sensitivity to trading imbalances. In the second equation denoted as equation (4), the dependent variable is average trading imbalances for the first two trading days following the issue date (trading days +1, +2) and underpricing (offer price discount) and trading imbalances in the two days immediately before issue date (trading days -1, -2) are the key explanatory variables. Underpricing, α B X Offer Size, Idiosyncratic Risk, Volatility, Reputation, Traded Spread C Y Firm Size, Nasdaq, Kyle s Lambda ε, (3) Expected TI Post, α δ Underpricing, δ TI Pre, δ Spread, ε (4) Estimates of equation (3) are reported in the first column and estimates of equation (4) are reported in the second column. The associated t-values are in parenthesis. Significant coefficients are in bold and their significance are indicated by a small a, b, and c subscript for a 1%, 5%, and 10% significance level respectively. The sample includes firm commitment underwritten SEOs by U.S. non-financial, non-utility firms listed on major exchanges with trading data available in the TAQ database. In panel B, we report an estimate of the relation between underpricing and trading imbalances after SEOs (equation (4)) for the subsample that has underwriter affiliation data for Nasdaq stocks over the period. In panel C, we report an estimate of the relation between underpricing and post-seo trading imbalances (equation (4)) in subperiods and Variable definitions are available in Appendix B. Significant coefficients are in bold and their significance are indicated by a small a, b, and c subscript for a 1%, 5%, and 10% significance level respectively. Panel A: Full Sample Analysis Dependent Variable Offer Size Underpricing (Offer Price Discount) b (-2.99) Trading Imbalances in Days +1 and +2 Idiosyncratic Risk (0.84) Stock Volatility (-0.79) Information Risk Underwriter Reputation Traded Spread Log (Issuer Equity Market Value) Nasdaq Indicator Kyle s Lambda (-0.84) (-0.91) b (-2.97) (1.15) (-1.58) (-1.58) 57

60 Underpricing (Offer Price Discount) Trading Imbalances on Days -1 and -2 Monthly Average Bid-Ask Spread Measured 6 Months before an SEO a (3.54) (0.98) a (-4.42) Panel B: Subsample Analysis by Market Maker Affiliation Status Dependent Variable Trading Imbalances on Days +1 and +2 Underpricing x Affiliation Indicator Underpricing (Offer Price Discount) Trading Imbalances on Days -1 and -2 Monthly Average Bid-Ask Spread Measured 6 Months before an SEO a (4.31) c (-1.65) a (4.58) a (-3.10) Panel C: Subperiod Analysis of Immediate Post-Issuance Trading Imbalances Dependent Variable Trading Imbalances on Days +1 and +2 over the period Trading Imbalances on Days +1 and +2 over the period Underpricing (Offer Price Discount) Trading Imbalances on Days -1 and -2 Monthly Average Bid-Ask Spread Measured 6 Months before an SEO (0.17) (1.21) a (-5.46) b (2.69) c (1.91) b (-2.08) 58

61 Table 9. Effects of Trading Imbalances on Stock Returns For each stock, we estimate Kyle s lambda, the sensitivity of stock returns to daily trading imbalances. We use 250 business days observations ending on (trading day 1) to estimate the sensitivity, relative to the issue date 0. The market making cost is estimated as 2-day aggregate trading imbalances after the issue date (trading days +1 and +2) multiplied by the estimated Kyle s lambda. The sample includes firm commitment underwritten SEOs by U.S. non-financial, non-utility firms listed on major exchanges with trading data available in the TAQ database. Variable definitions are presented in Appendix B. Estimated Market Making Cost (as a % of the stock price, negative as cost) Size of Underpricing (Offer Price Discount) Mean (%) Interquartile Range (%) Mean (%) Interquartile Range (%) All Observations ~ ~ Year ~ ~ 0.00 Year ~ ~ Year ~ ~ Year ~ ~ Nasdaq Stocks ~ ~ NYSE & AMEX Stocks ~ ~ High Spread Stocks (Upper 25%) Low Spread Stocks (Lower 25%) ~ ~ ~ ~

62 Figure 1. Trading Imbalance around SEO Announcement Dates Imbalances as a % of daily volume 1.0% 0.5% 0.0% 0.5% Trading days around the SEO % 1.5% 2.0% 2.5% 3.0% Figure 2a. Trading Imbalance and Stock Return around SEO Issue Dates Imbalances as a % of shares outstanding 0.1% 0.0% 0.1% Trading days around the SEO Stock Returns 0.5% 0.0% 0.2% 0.3% 0.4% 0.5% 0.6% 0.7% 0.8% Trading Imbalances Midquote Returns 0.5% 1.0% 1.5% 2.0% 60

63 Figure 2b. Percentage of Positive Trading Imbalances and Stock Returns around SEO Issue Dates Percentage of positive trading imbalances 60.0% 50.0% 40.0% 30.0% Trading days around the SEO 20.0% 10.0% 0.0% Trading Imbalances Midquote Returns Figure 3. Trading Imbalance around SEO Issue Dates by Different Sample Periods Imbalances as a % of shares outstanding 0.2% 0.0% 0.2% 0.4% 0.6% 0.8% 1.0% 1.2% 1.4% ~ ~ ~ ~ 2010 Trading days around the SEO 61

64 Figure 4. Trading Imbalances around SEO Issue Dates by Market Imbalances: % of shares outstanding 0.2% 0.0% 0.2% Days around the SEO % 0.6% 0.8% 1.0% 1.2% NASDAQ NYSE&AMEX Figure 5. Ratio of Low Price SEO Cases: Market Price after Issue Dates Is Lower than Offer Price Plus Half the Bid-Ask Spread Percentage 50.0% 45.0% 40.0% 35.0% 30.0% 25.0% 20.0% 15.0% 10.0% 5.0% 0.0% Year 62