Behavior of liquidity and returns around Canadian seasoned equity offerings

Journal of Banking & Finance 34 (2010) 2954–2967

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Behavior of liquidity and returns around Canadian seasoned equity offerings Lawrence Kryzanowski a,*, Skander Lazrak b, Ian Rakita a a b

Finance Department, John Molson School of Business, Concordia University, 1455 de Maisonneuve Boulevard West, Montreal, Que., Canada H3G 1M8 Department of Finance, Operations and Information Systems, Faculty of Business, Brock University, 500 Glenridge Avenue, St. Catherines, Ont., Canada L2S 3A1

a r t i c l e

i n f o

Article history: Received 11 March 2009 Accepted 1 July 2010 Available online 6 July 2010 JEL classifications: G10 G12 G14 G15 Keywords: Seasoned equity offerings Liquidity Lock-up period Asymmetric information Conditional volatility Clustering

a b s t r a c t Spread costs and their adverse selection and temporary components for Canadian SEOs follow an approximate V-shaped pattern with a trough at the closing window. Enhanced ownership diffusion partly explains the decrease in these spread costs post-SEO completion versus pre-SEO announcement. SEO spread costs decrease after the April 1996 TSX decimalization. The adverse selection cost of privatelyplaced Canadian SEOs decreases after Multilateral Instrument 45-102 reduced the lock-up period to four months in 2001. Consistent with results for non-US SEOs, negative abnormal returns (ARs) occur in announcement windows for undifferentiated SEOs. ARs are significantly different for public (significantly negative) versus private (insignificantly positive) SEOs consistent with their associated differential reductions in information asymmetry. Conditional residual volatilities decrease post-announcement, consistent with a diminished temporary spread cost and expected behavior following an unanticipated event. Ó 2010 Elsevier B.V. All rights reserved.

1. Introduction Unlike an initial public offering (IPO) wherein a firm issues equity to the public for the first time, a seasoned equity offering (SEO) occurs when a firm is already publicly traded and is simply selling additional common stock. Although it is much less extensive than the IPO literature, the SEO literature examines strategic behavior around the time of the SEO announcement (e.g., Teoh et al., 1998), the long-run market and operating underperformance of firms following SEO issuance (e.g., Hertzel et al., 2002; Desrosiers et al., 2004), the information content of SEOs (e.g., McLaughlin et al., 1998), and the market behavior of returns (e.g., Asquith and Mullins, 1986; Slovin et al., 1994) and to a much lesser extent liquidity and asymmetric information (e.g., Tripathy and Rao, 1992; Brooks and Patel, 2000) for SEO announcements. Mantecon and Poon (2009) document that firms tend to retain their IPO lead underwriter for their SEOs if the post-IPO liquidity was satisfactory. We extend the existing literature in several ways. We examine the behavior of various liquidity proxies such as dollar volumes, * Corresponding author. Tel.: +1 514 848 2424x2782. E-mail addresses: [email protected] (L. Kryzanowski), [email protected] (S. Lazrak), [email protected] (I. Rakita). 0378-4266/$ - see front matter Ó 2010 Elsevier B.V. All rights reserved. doi:10.1016/j.jbankfin.2010.07.009

spreads (total and components) and depths through all of the various identifiable stages (henceforth, windows) of the SEO issuance cycle. As a result, effects for the post-announcement window are not confounded with the effects of the SEO closing window. This distinction is important because Graham et al. (2006) find that changes in adverse selection and residual volatility differ between a relatively unanticipated event (in our case the SEO announcement) and a relatively anticipated event (in our case the SEO closing).1 By examining four instead of two windows over the SEO issuance cycle, we are able to ensure that the impacts on various metrics such as adverse selection and residual volatility from the unanticipated SEO announcements are not comingled with those from the anticipated SEO closings. Our testable expectation is that overall share liquidity improves over the SEO issuance cycle, that the behavior of the adverse selection component around an SEO announcement will differ in magnitude from that around an SEO closing because the former is unanticipated and the latter is anticipated, and that some of the initial improvement in share liquidity dissipates after the SEO completion window. Unlike the existing literature, we examine the impact of two important regulatory changes on liquidity behavior through the 1 Hwang et al. (2008) find that prices are affected differently for predicted versus unanticipated stock splits.

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SEO issuance cycle.2 To the best of our knowledge, we are the first to directly test the efficacy of a rationale given for the reduction in the required minimum holding period for private placements for qualifying issuers.3 Specifically, the SEC’s (2007, p. 1) stated rationale for shortening the holding period requirement under Rule 144 to six months for ‘‘restricted securities” of issuers on February 15, 2008 was ‘‘[w]e believe that the amendments will increase the liquidity of privately sold securities and decrease the cost of capital for all issuers without compromising investor protection”.4 In this paper, we examine the impact on liquidity through the SEO issuance cycle of the adoption of Multilateral Instrument 45-102 on November 30, 2001 in Canada that reduced the required minimum holding period for private placements to four months for qualifying issuers. This leads to the testable expectation that the asymmetric information associated with privately-placed Canadian SEOs decreased after the adoption of Multilateral Instrument 45-102. To the best of our knowledge, we also are the first to examine the impact on liquidity (including spread components) through the SEO issuance cycle of the reduction of the minimum price increment and introduction of decimal pricing on the TSX in April 1996 (henceforth TSX decimalization). Various papers find that the move to decimalization reduced quoted and effective spreads and quoted depths on the TSX (e.g., Chung et al., 1996). Li and Parker (2005) find that the adverse selection cost is significantly smaller but not economically important in the case of NASDAQ and inversely related to firm size and trading volume after decimalization in US markets. While Gibson et al. (2003) also report that almost all of the reduction in spreads following the conversion to decimal pricing is attributable to a reduction in the order processing component of the spread; they find that the dollar value of spreads attributed to adverse selection and inventory costs do not change significantly. We examine whether these findings for the overall Canadian and US markets can be generalized to an information event such as Canadian SEOs. We do so by testing our expectation that total spreads and their temporary cost components for SEOs decreased after TSX decimalization. To the best of our knowledge, no study conducts a comprehensive comparison of the various types of SEO issuing mechanisms (marketing schemes),5 some of which (e.g., bought deals) are not used in the US to raise new equity financing. As Eckbo et al. (2007) state in their review of the literature on security offerings: ‘‘The very existence of elaborate schemes for marketing security offerings to the public speaks to the importance of information asymmetries in the market for public issues”. Thus, we examine the behavior of liquidity and returns over the SEO issuance cycle for four issuing mechanisms individually and for two of their pairings (bought versus not bought, and private versus public). Bought deals are a widely employed issuing mechanism in Canada and the UK (Pandes, 2010; Slovin et al., 2000). In a bought deal, a single investment dealer contacts an issuing firm with a readymade deal for a firm commitment by the dealer to purchase a fixed number of shares at a fixed price generally before finalizing the 2 Mittoo (2006) documents the impact of the implementation of the Canada and US multijurisdictional disclosure system (MJDS) in 1991 on cross-border SEO issues by Canadian issuers. 3 In concurrent work, Maynes and Pandes (2009) study the effect indirectly by examining the impact on price discounts, and find that offer price discounts are positively and significantly related to the proportional quoted spread pre-SEOannouncement. 4 The US Securities and Exchange Commission (SEC) has often reviewed and revised Rule 144 under the US Securities Act of 1933, which creates a safe harbor for the sale of restricted (and control) securities. In 1997, Rule 144 was amended to shorten the initial holding period from two to one year and the ultimate holding period for nonaffiliates from three to two years. Securities and Exchange Commission, Release No. 33-7390 (February 20, 1997) [62 FR 9242] (‘‘the 1997 Adopting Release”). 5 In concurrent work, Pandes (2010) examines the value of underwriter certification for bought versus underwritten Canadian SEOs.

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placement of the shares or approaching a group of potential (generally institutional) buyers. An abbreviated registration statement (or short-form prospectus) is often used to reduce the normal issuance time frame and associated costs (including lower issuer fees). Since underwriters purchase securities in bought deals and resell such securities to their large clients, bought deals should signal confirmations (or certifications) of issue values to all market participants that should theoretically reduce information asymmetry. This leads to the testable expectation that bought deals are associated with less information asymmetry (and thus lower adverse selection costs) than not bought deals. Unlike their publicly placed counterparts, investors in privatelyplaced SEOs may benefit from reduced issuing costs, more relaxed disclosure requirements, speed of issue closing and collection of issue proceeds, and time-limited trading restrictions (e.g., Brau et al., 2005) that may signal an enhanced alignment of the interests of insiders with new share purchasers. The few studies that examine private placement SEOs find significant positive (low) mean share price effects for the issuing firms over the short (long) run and do not directly examine liquidity effects (e.g., Hertzel et al., 2002). This leads to the testable expectation that private placements as commitment devices reduce information asymmetry as measured by the adverse selection component of the bid-ask spread (and thus, total spreads). The current literature also fails to examine the behavior of liquidity and valuations over the SEO issuance cycle for a resource or commodity based economy such as Canada. Resource issuers may be associated with greater information asymmetry (or degree of opaqueness) due to the need for large initial investments with very uncertain outcomes over a long time horizon, significant environmental obligations, utilization of many complex forms of risksharing structures, the importance of new discoveries to replace depleting reserves, the technical complexity of resource extraction, and the positive relationship between information asymmetry and the intensity of intangibles as measured by the ratio of intangible assets to total assets (as in Gompers, 1995) and idiosyncratic risk (as in Blackwell et al., 1990).6 The right to exploit proved, probable and possible reserves of oil, gas and minerals in various parts of the world (including many countries with weak property rights) is often an important (and risky) intangible asset for the average Canadian natural resource firm. Due to the considerable opaqueness and uncertainty associated with the valuation of reserves, Canadian (and US) regulators have mandated many standards for the disclosure of, for example, proved and probable oil and gas reserve data (e.g., the quarterly assessment of reserve impairment using the ceiling test).7 Furthermore, Booth and Xu (2008) report that the Resource sector (along with Wholesale & Retail) had the highest idiosyncratic risk even for their restricted sample of firms on the CRSP/Compustat merged database that paid regular cash dividends during the 1986–2005 period. This leads to the testable expectation that SEOs for resource issuers are associated with greater information asymmetry (and thus higher spreads) than SEOs for nonresource issuers. 6 The first three rationales are drawn from PriceWaterhouseCoopers (2007). Lin et al. (2007), for example, utilize the following five variables that have been used in past studies to capture the level of information asymmetry between managers and investors: number of forecasts by analysts; analyst forecast dispersion; market model residual standard deviation; institutional percent ownership; and insider percent ownership. 7 For fiscal periods up to December 31, 2003, Accounting Guideline AcG-5 ‘‘Full Cost Accounting in the Oil and Gas Industry” in the CICA Handbook applies a ‘‘ceiling test” under which the net book value of oil and gas properties is compared to (and can not exceed) an estimate, based on constant prices and costs, of undiscounted future net revenue attributable to estimated proved reserves. For subsequent fiscal years, impairment calculations for interims are required at each annual balance sheet date, unlike previously where they were required from the most recent completed fiscal year.

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Finally, the tests reported in the literature for valuation effects around SEO initial and closing announcements, which are reviewed in Section 6 of this paper, rely on unconditional return models. As a result, they provide no explicit controls for the possible effect of time-varying return volatilities on valuation changes for both types of SEO announcements. We provide a formal test of the possible changes in volatilities after each type of SEO announcement to examine if the behavior differs following a relatively unanticipated versus a relatively anticipated event, and in valuations around both SEO announcement and closing dates to determine if their behaviors conform to those found for non-US SEOs. Our testable expectations with regard to valuation effects are that paired differences in abnormal returns (volatility decreases) will favor private over public SEOs, bought over not bought SEOs and resource over not resource SEOs due to expected differences in their adverse selection costs. Before proceeding, a legitimate question to pose is: Why examine the behavior of Canadian SEOs? We argue that an examination of Canadian SEOs allows us to provide a benchmark for issues that have not already been examined in the literature, such as the liquidity behavior for bought deals through the SEO issuance cycle. Similarly, our out-of-sample examinations of some issues previously examined in other markets (primarily, US) that are quite similar in some (but not all) respects to the Canadian market provide us with a useful benchmark for assessing the generalizability of inferences based on research conducted in those markets. The significant differences reported in the literature between Canadian and US IPOs suggest that material differences are likely to exist for Canadian versus non-Canadian SEOs. This literature finds marked differences in various aspects between Canadian and US IPOs (e.g., Chung et al., 2000; Kryzanowski et al., 2005). Our major empirical results are as follows. First, we find that spreads and their components (dollar volumes and depths) follow an approximate (inverted) V-shaped pattern through the SEO issuance cycle. SEOs result in positive liquidity benefits for shareholders that are maximized during closings and extend to a lesser degree beyond SEO closings. This increased liquidity results from a reduction of both adverse selection and temporary costs as SEOs are informative events that result in an increase in the trading activity of investors (including those that are liquidity seeking). While both spread components follow approximate V-shaped patterns, most of the fall in the adverse selection (temporary) cost occurs upon announcement (closing) although both hit their lowest levels around the closing date. Consistent with the predictions of the information processing models (e.g., Kyle, 1985) and the empirical findings of Graham et al. (2006) for unanticipated versus anticipated events, most (but not all) information asymmetry is reduced around the unanticipated SEO announcement when the firm issues a statement declaring its intention to seek new financing. SEOs also increase the potential or actual float and increase investor recognition and subsequent ownership diffusion of their issuers. Second, we find that two important regulatory changes have significant impacts on liquidity through the SEO issuance cycle. The adverse selection spread component after controlling for other determinants is affected significantly downwards by the reduction in the required holding period for private placements in 2001. Similarly, total spreads and its temporary component after controlling for other determinants are reduced by TSX decimalization in 1996. Our findings support our testable expectations that: (1) asymmetric information of privately-placed Canadian SEOs decreased after the adoption of Multilateral Instrument 45-102, and (2) total spreads and its temporary component decreased for SEOs after TSX decimalization. Third, we find that quoted and effective spreads and the two spread components are significantly lower for bought versus not

bought deals, and quoted and effective spreads and the adverse selection costs are significantly higher for private placements after controlling for various firm-specific determinants of spreads. This supports the hypothesis that bought deals are associated with less information asymmetry (and thus lower total spreads) than not bought deals. However, our findings do not support our testable expectations that: SEOs for resource issuers are associated with greater information asymmetry (and thus higher spreads) than SEOs for not resource issuers; and private placements as commitment devices help to alleviate moral hazard problems by reducing information asymmetry as measured by the adverse selection component of the bid-ask spread (and thus, total spreads). Fourth and finally, we find negative abnormal returns in announcement and closing windows (not robust for the latter window) that are related to the reduction in information asymmetry caused by the SEO announcements. Conditional residual return volatilities decrease post-announcement (i.e., after a relatively unanticipated event) indicating reduced uncertainty due to the partial resolution of information asymmetry. Furthermore, only the differences in abnormal returns between public and private SEOs during the announcement window are significant with the former being significantly negative and the latter being insignificantly positive. Thus, our findings only support our testable expectation that abnormal return differences will favor private over public SEOs due to expected differences in their adverse selection costs. The remainder of this paper is organized as follows. In the next section, the SEO sample and data are described. The results of our investigation into short-run liquidity and trade activity, as measured by dollar volume, the proportional effective spread and quoted depth based on univariate and multivariate approaches, are reported and analyzed in Sections 3 and 4, respectively. In Section 5, the components of the bid-ask spread for six types of SEOs are estimated and discussed. A range of return models allowing for conditional volatilities are estimated and discussed in Section 6. Some concluding comments are offered in Section 7. Various tests of robustness are reported at the end of Sections 3–5. 2. Sample and description of the data Canadian SEOs during 1993–2007 are identified using the Financial Post’s Record of New Issues. Debt, initial public offerings, unit offerings, common share offerings simultaneously with flowthrough offerings, flow-through offerings and preferred shares and issues with offer prices below $2 are eliminated. Firms that did not trade for five successive days in either of the pre-announcement or post-closing windows are also eliminated.8 Each of the retained 996 equity issues is classified as a private offering, public offering, bought deal, not bought deal, resource issue and not resource issue.9 All quotes and trades outside normal trading hours on the TSX (9:30 am–4:00 pm ET) are eliminated. Quotes are eliminated if negative or their corresponding spread is less than zero or more than 30% of the midspread. Trades are eliminated if they are for a negative price or number of shares or have special settlement conditions or are for delayed delivery or cancellation or result in trade-by-trade returns exceeding 50%. Table 1 reports an annual account of the number of SEOs and mean issue sizes (not) differentiated by distribution method and issuer type for our final sample of 996 SEOs. Particularly weak 8 All final sample SEOs have trades for at least two of the three days in their announcement and closing windows. 9 Findings are materially unchanged if SEOs issued by utilities (SIC 4900-4949) and financials (SIC 6000-6999) are removed. These two categories together represent less than nine percent of the final sample.

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Table 1 Annual descriptive statistics for SEOs. This table presents statistics on the number and size of Canadian SEOs listed and issued on the TSX based on offering completion date. SEOs are identified using the Record of New Issues database published by the Financial Post. SEOs by firms in the mining or oil and gas sectors are classified as resource. Debt, initial public offerings, unit offerings, common share offering simultaneously with flow-through offerings, flow-through offerings and preferred shares are filtered out. Issues with offer prices below $2 are also deleted. Any firm that did not trade for five consecutive trading days in either the pre-announcement or post-closing window is not retained in the final sample of 996 SEOs. SEO New Financing and Mean Issue Size is in CDN$ times 106. Number is the number of issues. Year

1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 1993–2007

Number

62 62 37 55 102 51 53 62 51 56 89 73 82 74 87 996

SEO New Financing

Private placements

Public offerings

Bought deals

Not bought deals

Resource issues

Not resource issues

Number

Mean issue size

Number

Mean issue size

Number

Mean issue size

Number

Mean issue size

Number

Mean issue size

Number

Mean issue size

3134.41 3810.10 2113.61 5351.39 8760.67 4040.17 3679.64 5904.38 3777.60 4394.09 6917.29 4563.94 6270.52 4946.68 5244.08 72908.55

26 20 12 9 40 14 1 1 2 9 30 21 37 19 16 257

16.91 21.39 14.75 66.86 31.19 64.90 93.77 155.00 129.69 19.20 56.71 76.83 58.97 42.21 32.64 43.99

36 42 25 46 62 37 52 61 49 47 59 52 45 55 71 739

74.85 80.53 77.47 103.25 121.18 84.64 68.96 94.25 71.80 89.82 88.41 56.74 90.86 75.36 66.50 83.36

23 24 13 30 49 36 21 27 30 39 56 51 51 51 68 569

70.67 69.09 37.34 60.06 103.87 90.13 76.19 63.91 93.99 79.03 87.81 70.45 91.57 74.42 52.76 76.79

39 38 24 25 53 15 32 35 21 17 33 22 31 23 19 427

38.69 56.63 67.84 141.98 69.27 53.03 64.99 119.39 45.61 77.18 60.60 44.15 51.63 50.06 87.16 68.41

34 27 17 25 45 24 12 4 7 13 49 36 50 52 57 452

47.75 43.05 54.21 89.26 44.86 53.42 76.18 21.44 31.85 41.66 61.58 79.03 94.34 64.42 45.87 60.95

28 35 20 30 57 27 41 58 44 43 40 37 32 22 30 544

53.96 75.65 59.60 104.00 118.28 102.15 67.45 100.32 80.79 89.59 97.49 46.46 48.54 72.59 87.65 83.38

SEO years in terms of number occur in 1995 for all SEOs, public offerings, bought deals and not resource issues, in 1999–2001 for private placements, in 1998, 2002 and 2007 for not bought deals, and in 2000–2001 for resource issues. A particularly strong SEO year occurs in 1997 for all SEOs in terms of number and total dollars raised. The frequency distribution of SEOs by unique issuer SEO with number of issuers in parentheses is: one (390), two (136), three (61), four (23), five (7) and six SEOs (4). 3. Liquidity behavior around Canadian SEOs The literature examining liquidity behavior around SEOs is not extensive. Tripathy and Rao (1992) examine changes in percentage quoted spreads around announcement and offer dates for US overthe-counter SEO issues. Using issue size as a proxy for the extent of information asymmetry, they find that spreads reach ‘‘normal” levels before the first public disclosure for larger issues and only on the offer date for smaller issues. They conclude that the former finding is consistent with the dealer reducing adverse information risk through information gathering during the underwriting process and that the latter finding is consistent with a reduced information asymmetry effect and with after-market liquidity support by the dealer. Brooks and Patel (2000) report that larger changes in information asymmetry (as proxied by the adverse selection component of bid-ask spreads) at announcements are correlated with larger reductions in wealth for a sample of 48 NYSE and AMEX SEOs issued in 1989. Before proceeding to an examination of liquidity behavior around our sample of SEOs, we need to decide on the windows to be examined. Brooks and Patel (2000) compare the asymmetric component for each day in the announcement window (i.e., the 7 days centered on the announcement day) to a 31-day preannouncement window consisting of days 40 to 10, and for this pre-announcement window to a post-announcement window consisting of days +10 to +40.10 We do not use their delineation since our SEO announcement and closing dates are separated by about 15 trading days (22 calen10 Frijns et al. (2006) report a mean (median) interval between SEO announcements and SEO closings of 41 (31) trading days for their final sample of 1245 US SEOs issued between 1984 and 2000.

dar days) on average with a maximum of 203 days and a standard deviation of 14 trading days. Thus, to avoid a comingling of closing announcements with post-SEO announcement windows and to provide for a better examination of changes over the SEO issuance cycle, our subsequent analysis of liquidity is conducted over four time windows: window 1 (W1) goes from 80 to 20 trading days before the announcement date; windows 2 and 3 (W2 and W3) cover the three trading days centered on the SEO announcement and closing dates, respectively; and window 4 (W4) goes from 20 to 80 trading days after the SEO closing date. According to Eckbo et al. (2007), the existing evidence shows that market making [by underwriters] is very important in the early seasoning of an issue, but typically declines in importance over the first year following listing. Even for IPOs where the need for stabilization may be greater, Chung et al. (2000) find that the relatively low level of market stabilization when trading begins is completely dissipated prior to 20 days after the completion of Canadian IPOs. 3.1. Initial results Table 2 contains the cross-section means and medians of three measures of liquidity for the four SEO windows for the full time period and for pre- and post-TSX-decimalization periods.11 Before proceeding to a discussion of these results, it is important to note that the mean values for the full sample may not lie within the corresponding mean values for the two sub-samples because the full sample includes the SEOs where the start of decimalization (April 15, 1996) is included in one of their four windows (i.e., W1–W4). The first measure is the average of each day’s total traded dollar volume (henceforth, dollar volume). Generally, this measure is highest during the two windows with significantly elevated information flow (i.e., the three-day announcement (W2) and closing (W3) windows). The mean and median dollar volumes for W2 (except for the median for the pre-TSX-decimalization period) are more than double (and significantly different at 1% from) their counterparts preannouncement for the time periods (not) differentiated by TSX decimalization and to whether or not the 66 SEOs with the same announcement and effective dates are included. 11 Since the results for quoted spreads are similar to those for effective spreads, they are not tabulated to save valuable journal space.

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Mean and median dollar volumes decline significantly from W2 to the closing window (W3) but are still significantly higher than those for W1 except for the median for pre-TSX-decimalization period. The mean but not the median dollar volume in the threemonth, post-closing window (W4) declines further but is still consistently higher than during W1. This latter result is expected as dollar trading volumes generally increase with more shares outstanding and the documented increase in trading volumes over time. The second measure is proportional effective spreads, or twice the absolute difference between the transaction price and the prevailing midspread (Mt) scaled by the prevailing midspread based on a one-second lag as in Henker and Wang (2006).12 The statistics reported in Table 2 are obtained by first computing the inter-daily average for each window using the average proportional effective spread for each SEO firm for every day using their intra-daily spreads, and then computing the cross-section mean and median spreads over the SEOs included in our sample for each window. The mean (median) proportional effective spread falls from a preSEO announcement value of 1.63% (1.39%) to 1.31% (1.06%) upon announcement. The spread continues to fall significantly around the SEO closing date to a mean (median) of 1.21% (0.96%) before increasing significantly post-closing to 1.38% (1.16%). Nevertheless, the effective spread for W4 remains significantly lower than for W1 based on univariate paired tests. While the behavior of effective spreads over the SEO issuance cycle are robust to decimalization differentiation, their means and medians are significantly lower postversus pre-decimalization for most like-window comparisons (e.g., their respective means are 1.34% and 1.68%, respectively, for W4). We investigate the impact of decimalization further in the next section using a multivariate approach to account for other mixing factors. The third and final measure is quoted depth or [(bid*bidsize + ask*asksize)/2] for each valid quote where size refers to the number of shares bid or offered. We obtain the statistics reported in Table 2 by first computing daily averages for each SEO for each window, then average successively over the days of each window, and finally over the cross-section of SEOs. The means and medians of the depths increase significantly from W1 to W2 for the three samples, decline during W3 compared to W2 but remain higher than W1, decline further during W4 compared to W3 but remain higher in W4 than W1. A window-to-window comparison of mean and median post- compared to pre-decimalization finds that dollar volumes, proportional effective spreads and depths are significantly higher, lower and lower (at <1%), respectively, post-decimalization for all four windows. Results of similar tests for the six SEO types are also reported in Table 2. All mean and median dollar volumes and quoted depths are significantly higher in W2 versus W1. Except for bought deals, dollar volumes and quoted depths tend to decline from W2 to W3. The only exception is not bought issues whose mean and median quoted depths increase from W2 to W3. While the mean dollar volumes decline significantly in W4 versus W3, the corresponding medians increase marginally but significantly. While all mean and median quoted depths decline in W4 versus W3, the mean declines are not significant for bought, not bought and resources and the median declines are not significant for private offerings. Nevertheless, all mean and median dollar volumes and quoted depths are significantly higher in W4 compared to W1. All mean and median proportional effective spreads are significantly lower in W2 versus W1. While they continue to decline from W2 to W3 and from W3 to W4, the decline is not significant for private, bought

12 Findings reported in Table 2 are materially unchanged if a zero- or five-second lag is used instead (as in Bessembinder; 2003; Lee and Ready, 1991).

and resource issues for W2 to W3. Nevertheless, proportional effective spreads are significantly lower in W4 versus W1. With respect to same-window paired comparisons, private placements have significantly lower dollar volumes and quoted depths and significantly higher proportional effective spreads than public offerings. Not bought deals have significantly lower median dollar volumes in all four windows, significantly lower mean and median quoted depths in W2, and significantly higher mean and median proportional effective spreads in all four windows. With a few exceptions, we find no significant differences in the three metrics in like-window comparisons for resource versus not resource SEOs. 3.2. Test of robustness To examine the possibility that the decimalization results can not be attributed to the April 15, 1996 implementation on the TSX since our time period consists of three-plus years of data in the pre-TSX-decimalization period and 11+ years of data postTSX-decimalization, we conduct a test of robustness on a sample of SEOs that are centered more closely around this implementation date. Specifically, our pre- and post-TSX-decimalization samples consist of 189 and 227 SEOs, respectively, where their four windows are pre- (post-)TSX-decimalization in that the first (last) date in W1 (W4) is at most three years before (after) April 15, 1996. Based on untabulated results, we find that the initial results are generally robust for all three metrics with three exceptions. First, the proportional effective spreads are no longer significantly different post-TSX-decimalization between W4 and W1. Second, the decrease in the median (not mean) quoted depth from W2 to W3 is significant. Third, while a window-to-window comparison of mean and median post- compared to pre-decimalization finds that dollar volumes, proportional effective spreads and depths are higher, lower and lower, respectively, post-decimalization for the four windows, the percentage of significant means and medians drops from 100% to 58% (i.e., for 14 of the 24 comparisons). Two main conclusions are drawn from the findings reported in this section of the paper. First, liquidity changes through the SEO issuance cycle. Liquidity consecutively improves upon issue announcement and closing (if proxied by effective spreads) and deteriorates slightly using quoted depths or trading volumes but remains higher than its pre-announcement level. Depending upon the proxy used, liquidity deteriorates post-closing but remains above its pre-announcement level. Second, firm and issue characteristics (such as firm size) need to be controlled for since they seem to have an impact on liquidity through the SEO issuance cycle. For example, bought deals tend to be more liquid than not bought deals. 4. Multivariate examination of the determinants of SEO spreads We now use a multivariate approach to test for the effect of specific cross-section features on SEO quoted and effective spreads including SEO type, TSX decimalization, and the reduction in the lock-up period for private placements. Since it is used as a control for spreads, the cross-sectional features of dollar traded volume are not examined. We explain the cross-section variation in quoted spreads using the size, trading volume and volatility of returns of the firms issuing the SEOs (as used in a different context by, for example, Bessembinder, 1999). A panel data approach is used to solve for the missing variables problem, quantify spread changes as the SEO issuance cycle unfolds, and to distinguish between the spread effects of particular cross-sectional features such as SEO type. The four windows for each SEO issuance cycle represent the time dimension of our panel. The specific panel model estimated is:

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Table 2 Summary statistics for three liquidity measures and tests of significance (not) differentiated by pre- versus post-TSX-decimalization and by issue type for the period 1993–2007. This table reports various cross-sectional statistics for Dollar Volume, Proportional Effective Spread and Quoted Depth for the sample of 996 SEOs for four different windows surrounding two event dates for each offering. Window 1 (W1) begins 80 trading days before the announcement and ends 20 trading days before the announcement; Window 2 (W2) spans 3 trading days centered on the announcement date; Window 3 (W3) spans 3 trading days centered on the closing date and Window 4 (W4) starts 20 trading days after the closing date and ends 80 days after the closing date. The Proportional Effective Spread (expressed in basis points) is the absolute value of the difference between the transaction price and the midspread divided by the midspread. The Quoted Depth = [(bid*bidsize + ask*asksize)/2]. Each volume (spread and depth) statistic entry is found by averaging the daily volume (spread and depth) for each SEO over the number of days in each window and then taking the cross-sectional average for all SEOs in the sample. Reported results consider the full sample period 1993–2007 and the pre- (post-)decimalization periods resulting from the introduction of decimalization by the Toronto Stock Exchange (TSX) on April 15, 1996. There are 161 and 796 SEOs pre- and post-decimalization, respectively, and one or more windows of 39 SEOs occurred during the decimalization period. Table also reports tests of significance, appearing as superscripts, between statistics for the cross-sectional distributions of the time-series averages for the ratios (for Dollar Volume and Quoted Depth) and for differences (for Proportional Effective Spreads) for each SEO type for each of W4, W3 and W2 compared to that of W1 (appearing as a, b, c and corresponding to significance at the 10%, 5% and 1% levels, respectively); for each of W4 and W3 compared to W2 (appearing as d, e, f and corresponding to significance at the 10%, 5% and 1% levels, respectively); and W4 compared to W3 (appearing as g, h, i and corresponding to significance at the 10%, 5% and 1% levels, respectively). Finally, statistical tests for mean (median) ratios and differences are conducted using a t-test (Mann–Whitney test) for each window (W1 versus W1, W2 versus W2, etc.) for the Post-decimalization period compared to the Pre-decimalization period; for Private Placements compared to Public Offerings; for Not Bought Deals compared to Bought Deals; and for Not Resource issues compared to Resource issues (appearing as j, k, l in the Post-decimalization period, the Private Placements issue type, the Not Bought Deals issue type and the Not Resource issue type corresponding to significance at the 10%, 5% and 1% levels, respectively). Thus, each Post-decimalization (Private Placement, Not Bought Deal and Not Resource) value could be reported with up to four superscript letters. For example, the mean Private Placement Dollar Volume in W4 (at 836 with superscripts c, f, i, l) is significantly different at 1% from the mean Public Offering Dollar Volume in W1, W2 and W3 and is significantly different at 1% from the mean Public Offering Dollar Volume in W4 (at 3214). N is the sample size. Statistic

Dollar volume (‘000s) W1

For full period: (996 SEOs) Mean 1945 Median 552 Std. Dev. 5347

W2 4367c 1301c 10,171

Proportional effective spread (bps) W3

W4

W1

W2

W3

Quoted depth (‘000s) W4

W1

W2

W3

W4

3156c,f 746c,f 8865

2601c,f,i 835c,f,i 5871

163 139 112

131c 106c 97

121c,f 96c,f 92

138c,f,i 116c,f,i 95

37 22 47

72c 35c 100

53c 28c,f 79

40c,f 25c,f,i 46

1666c,f 363c 3684

1139c,f,i 438c,i 1989

190 180 109

171c 155c 95

160c,d 147c,e 98

168c,f,i 165c,i 78

62 40 75

121c 52c 162

91c,d 49c 119

68c 45c,f 72

For post-decimalization period: (796 SEOs) Mean 2142k 4869c,l 3465c,e,k Median 585l 1515c,l 819c,f,l Std. Dev. 5892 11,101 9715

2904c,f,i,l 931c,f,i,l 6434

159l 134l 114

124c,l 98c,l 97

115c,f,l 91c,f,l 90

134c,f,i,l 108c,f,i,l 99

29l 18l 29

58c,l 31c,l 74

40c,d,l 24c,f,l 45

32c,f,l 21c,f,i,l 34

For pre-decimalization period: (161 SEOs) Mean 974 2148c Median 366 641c Std. Dev. 1718 4563

Public offerings (N = 739) Mean 2412 Median 821 Std. Dev. 5969

5311c 1814c 11,409

3956c,f 1135c,f 10,051

3214c,f,i 1200c,f,i 6568

144 121 107

114c 90c 89

103c,f 82c,f 79

119c,e,i 99c,f,i 82

41 25 50

85c 45c 110

60c 34c,f 85

44c,f,i 29c,f,i 48

Private placements (N = 257) Mean 603l 1651c,l Median 224l 494c,l Std. Dev. 2446 4101

854c,l 233c,f,l 2659

836c,f,i,l 277c,i,l 2328

217l 197l 109

181c,l 164c,l 103

175c,l 152c,l 104

193c,e,i,l 180c,e,i,l 108

25l 16l 34

33c,l 20c,l 44

31c,l 18c,d,l 51

29c,i,l 19c,e,l 39

Bought deals (N = 569) Mean 2006 Median 687 Std. Dev. 5503

5142c 1897c 10,807

3134c 929c,f 8532

2769c,i 1114c,f,i 5475

143 124 95

106c 87c 76

103c 86c 76

120c,f,i 99c,f,i 85

35 23 35

83c 43c 103

49c,f 29c,f 59

40c,f, 25c,f,i 42

Not bought deals (N = 427) Mean 1864 Median 345l Std. Dev. 5137

3335c,l 641c,l 9165

3184c,f 493c,d,l 9301

2377c,f,i 538c,f,i,l 6361

189l 164l 127

164c,l 139c,l 111

146c,f,l 122c,f,l 104

162c,i,l 148c,i,l 103

40 21 60

57c,l 27c,l 94

58c,f,j 28c,f 99

41c 24c,f,i 51

Resource issues (N = 452) Mean 1696 Median 602 Std. Dev. 3198

4603c 1381c 10,588

2790c 746c,f 6838

2466b,f,i 880c,f,i 4548

158 139 107

123c 104c 90

123c 99c 93

138c,f,i 116c,f,i 100

33 20 38

65c 32c 88

44c,f 24c,f 62

36c,f 22c,f,i 39

Not resource issues (N = 544) Mean 2152 4171c Median 494k 1226c Std. Dev. 6618 9816

3459c,f 749c,f 10,246

2712c,f,i 798c,i 6779

167 140 117

137c,k 112c,k 102

120c,f 94c,f 90

138c,i 116c,e,i 91

40k 22 54

77c,j 37c 109

60c,e,l 33c,e,l 90

44c,f,i,l 28c,f,I,l 51

Spreadi;t ¼ b1 LnðVolumeÞi;t þ b2 Volatilityi þ b3 LnðSizeÞi þ b4 Decimalizationi þ b5 Priv atei þ b6 PostPriv atei þ b7 Bought i þ b8 Resourcesi þ ai þ c1 W 2 þ c2 W 3 þ c3 W 4 þ ei;t ;

ð1Þ

where Spreadi,t is the proportional quoted or effective spread for SEO i during window t. Ln(Volume)i,t is the natural logarithm of the average daily traded dollar volume for SEO i during window t. Volatilityi is the standard deviation of daily returns extracted from the CFMRC database for SEO i over the four windows. Ln(Size)i is

the natural logarithm of the total assets for SEO i, which is extracted from the last balance sheet disclosed before each SEO announcement from various databases including Compustat, Stockguide, EDGAR and SEDAR (the Canadian equivalent to EDGAR). Decimalizationi is a dummy variable that is equal to one if SEO i is post-decimalization and zero otherwise. Privatei, PostPrivatei, Boughti and Resourcesi are dummy variables that are equal to one if SEO i was, respectively, privately placed, privately placed after the November 2001 lock-up period reduction, issued as a bought deal and issued by a resource company and zero otherwise. ai is the (random) cross-sectional effect for SEO i. W2, W3 and W4 are dummy variables

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that are equal to one, respectively, for windows two (during announcement), three (during closing) and four (post-closing) and zeros otherwise. The c1, c2 and c3 parameters capture the change in the bid-ask spreads as the SEO cycle unfolds and can be thought of as a time dimension random effect. For instance, c1 and c2 measure the incremental effect on each spread measure of announcing and completing the SEO compared to the pre-announcement level. As some of the regressors are time-constant explanatory variables, a random rather than a fixed-effect model is used to estimate the parameters of interest. 4.1. Initial results Summary results of panel regressions for both spread measures with(out) the PostPrivate dummy variable are reported in Table 3.13 White standard errors, which are robust to cross-section correlations, are used in tests of statistical significance. Coefficient estimates for trading volume are negative and significant (<1%) for both spread measures. This conforms to expectations, since higher dollar volumes should result in lower fixed costs per dollar as order processing costs are fixed per transaction. Coefficient estimates for return volatility are positive and significant (<5%) for both spread measures. This conforms to the underlying rationale behind the inventory bid-ask spread models where liquidity providers require higher compensation with higher return volatility. Coefficient estimates for total assets are negative as expected (Bessembinder, 1999) but not significant at conventional levels for both spread measures. Some of the effect of firm size may be subsumed by dollar volumes and return volatility that tend to increase and decrease with firm size. Except for the panel regression for effective spreads with the PostPrivate dummy variable that has the expected negative sign, all of the other coefficient estimates for the effect of TSX decimalization are negative and significant at the 5% level or better. This result is consistent with findings reported earlier in Table 2 where the average effective spreads are lower post-TSX-decimalization for all four windows (e.g., 1.59% versus 1.90% for W1). The estimated coefficient for the private placement dummy variable for spreads is positive and significant (<1%) with (and without) the inclusion of the PostPrivate dummy variable. The coefficient estimates for the PostPrivate dummy variable of 28 and 24 bps are significant at the 5% and 10% levels for the regressions with and without the PostPrivate dummy variable. We conclude that the required holding period reduction in 2001 for private placements reduced trading costs and enhanced liquidity. In Section 5, we test if the new rule affected the issuer’s information asymmetry as measured by the adverse selection spread component. The estimated coefficients in the 14 to 16 bps range for the bought deal dummy variable for spreads with and without the inclusion of the PostPrivate dummy variable are significant (<1%). As underwriters are expected to be better informed than the general public, their willingness to secure each deal sends an indication to the market of their faith in these SEOs and reduces the adverse selection component of trading costs. All of the estimated coefficients for the natural resource dummy variable for quoted and effective spreads are not significant. The remaining parameter estimates reported in Table 3 relate to the change in spreads as the SEO cycle unfolds. The coefficient estimates for W2 or c1 in Table 3 indicate that upon SEO announce13 The reduction in sample size is due to the following reasons with number of observations in parentheses: no spread components (1), no volatility measure (11), no total assets (37), and no volatility and no total assets (3). When a dummy variable for utilities and financials is added, its estimated coefficient is negative and significant for all four regressions reported in this table.

ment quoted spreads are reduced by a significant 14 bps with and 13 bps without the inclusion of the PostPrivate dummy variable and effective spreads are reduced by a significant 10 bps with and without the PostPrivate dummy variable. The coefficient estimates for W3 or c2 in Table 3 indicate that quoted and effective spreads are significantly lower by 40 bps with and 32 bps without the inclusion of the PostPrivate dummy variable upon closing compared to their pre-announcement levels. The coefficient estimates for W3 or c3 in Table 3 indicate that quoted and effective spreads are significantly lower by 15 and 13 bps post-SEO closing compared to pre-announcement. Based on a Wald test for a Chi-square distribution with one degree of freedom, the differences in the estimated coefficients (i.e., c1  c2 in Table 3) around SEO closings versus SEO announcements finds that quoted and effective spreads are significantly lower by 27 and 23 bps around SEO closings versus SEO announcements. The significant estimated coefficients of the differences for both spreads given by c2  c3 in Table 3 indicate that quoted and effective spreads are higher by 25 and 19 bps, respectively, for SEO post-closing versus SEO closing. Spreads achieve lowest values around the SEO completion date and rebound thereafter. However, they remain lower than their pre-SEO announcement levels. In Section 5, we further investigate this approximate V-shaped pattern in trading costs as the SEO cycle unfolds. 4.2. Tests of robustness We first examine if our initial inferences are robust to the use of clustered (often called Huber-White or Rogers) standard errors based on the findings by Petersen (2009). The inferences based on clustered standard errors either remain the same or move from <5% to <1% level. Now all volatility, decimalization, PostPrivate dummy variable and gamma difference estimates are significant at <1% level. Brockman and Yan (2009) find that ownership structure plays a significant role in shaping the firm’s information environment. As a result, we conduct two additional sets of robustness tests that examine whether liquidity improves with greater ownership diffusion. Under asymmetric information models, we expect spreads to increase with a more concentrated share ownership structure due to, for example, less monitoring of the firm’s activities by stock market participants (extension of Kyle’s (1985) model by Holmstrom and Tirole, 1993). Thus, the asymmetric component of spreads (and total spreads) should increase with increasing blockholdings (e.g., Heflin and Shaw, 2000; but not Brockman et al., 2009) and insiders more broadly defined or confined to managers and directors (Chiang and Venkatesh, 1988; but not Kini and Mian, 1995; Rubin, 2007), and with decreasing shares outstanding if it is a satisfactory proxy for number of shareholders (Merton, 1987; Amihud et al., 1999). Our first set of tests examines the robustness of our results to the inclusion of ownership proxies by examining the impact of subsequent SEOs on spreads by adding two dummy variables to Eq. (1). Specifically, M2,i (M3,i) is a dummy variable that is equal to one, if the SEO is the second (third or more) SEO by a specific issuer in our final sample, and zero otherwise. Based on untabulated results, we find that the estimated coefficients of both of these additional dummy variables are negative and significant. Thus, as expected, we find that spreads decrease significantly as ownership becomes more diffused. We also re-estimate Eq. (1) using a sample of 581 SEOs that constitute the earliest SEO for each issuer in the sample and find no qualitative changes in the inferences based on the estimated coefficients for the through the SEO issuance cycle dummy variables W2, W3 and W4. Our second set of tests examines the impact of ownership diffusion when one of four proxies in turn is added to Eq. (1). Given the

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Table 3 Panel data regression results for changes in liquidity against various SEO characteristics. This table presents regression results for regressions between changes in liquidity and various characteristics of our sample of SEOs using a panel data approach. The panel model that is estimated is:

Spreadi;t ¼ b1 LnðVolumeÞi;t þ b2 Volatilityi þ b3 LnðSizeÞi þ b4 Decimalizationi þ b5 Priv atei þ b6 PostPriv atei þ b7 Bought i þ b8 Resourcesi þ ai þ c1 W 2 þ c2 W 3 þ c3 W 4 þ ei;t ; where Spreadi,t is the proportional quoted (columns 3–4 and 7–8) or effective spread (columns 5–6 and 9–10) for SEO i in % in window t. Ln(Volume)i,t is the natural log of the average daily dollar trading volume for SEO i in window t. Volatilityi is the standard deviation of daily returns extracted from the CFMRC database for SEO i over the entire four window period. Ln(Size)i is the natural logarithm of the total assets on the balance sheet for SEO i, where size is proxied by total assets. Total assets from the last balance sheet disclosed before the SEO announcement are extracted from various databases including Compustat, Stockguide, EDGAR and SEDAR (the Canadian equivalent to EDGAR). Decimalizationi is a dummy variable that is equal to one if SEO i is post-decimalization and zero otherwise. Privatei, PostPrivatei, Boughti and Resourcesi are dummy variables that are equal to one if SEO i was, respectively, privately placed, privately placed after the lock-up period reduction, issued as a bought deal and issued by a resource company and zero otherwise. ai is the (random) cross-sectional effect for SEO i. W2, W3 and W4 are dummy variables that are equal to one, respectively, for window two (during announcement), three (during closing) and four (post-closing) and zero otherwise. White standard errors, which are robust to cross-section correlation, are reported. All parameters and standard errors as reported are multiplied by 100. c1  c2 = 0 and c2  c3 = 0 correspond to the linear restrictions. The reported corresponding statistic is the difference in the estimated parameters. Inference is based on a Wald test that follows a Chi-square distribution with one degree of freedom (d.f.). Significance at 10%, 5% and 1% are indicated by superscripts a, b and c, respectively. Regressor

Coefficient

Quoted spread (%)

Effective spread (%)

Quoted spread (%)

Effective spread (%)

Estimate

Estimate

Estimate

Estimate

Standard error

Standard error

0.7093 0.0768 6.0939 0.0225 0.0646 0.0757

5.5232c 0.3064c 13.5042b 0.0202 0.1878c 0.1756c

0.5814 0.0634 5.4740 0.0207 0.0631 0.0435

0.1578c 0.0281 0.1354c 0.4007c 0.1511c 0.5506

0.0383 0.0602 0.0524 0.0200 0.0286

0.1476c 0.0384 0.0970b 0.3247c 0.1312c 0.5580

0.0387 0.0532 0.0433 0.0165 0.0237

Difference test

Estimate

p-Value

Estimate

p-Value

Estimate

p-Value

Estimate

p-Value

c1  c2 = 0 c2  c3 = 0

0.2653c 0.2496c

<0.0001 <0.0001

0.2277c 0.1935c

<0.0001 <0.0001

0.2660c 0.2498c

<0.0001 <0.0001

0.2282c 0.1936c

<0.0001 <0.0001

c b1 b2 b3 b4 b5 b6 b7 b8

c1 c2 c3

paucity of public data on number of shareholders for Canadian SEOs, these proxies are ln(shares outstanding) and the ratios of shares held by management/directors, blockholders and insiders, respectively, pre- and post-SEO. For this test we hand collect data from the various documents filed with SEDAR (primarily management circulars) for SEOs for the most recent five-year period (2003–2007) in our data set. Since ownership data frequency is not daily, we assign the pre-announcement (post-closing) ownership values first to W1 and W2 (W3 and W4), and then to W1 (W2, W3 and W4) to conserve our balanced panel. Both assignment approaches yield similar results. Based on untabulated results, we find that spread measures decrease with more diffuse ownership as measured by either more shares outstanding or less holding concentration. We further examine the relationship between spreads and ownership diffusion by running a regression of the change in the spread measure between W1 and W4 against volume, volatility and size (values for W1) as control variables, the dummies for private, bought and resources and the pre-to-post SEO change in one of the four proxies for ownership diffusion. We find that, although spreads decrease significantly for more diffused ownership for all but the shares outstanding proxy with its expected (but insignificant) negative sign, there is a significant decrease in spreads that is not captured by ownership diffusion and other variables included in this estimation. In summary, we find that both spread measures follow an approximate V-shaped pattern through the SEO issuance cycle. Spreads decrease marginally upon announcement, fall more strongly as the SEO is closed, and increase post-closing to a level that is still significantly lower than pre-announcement. Therefore, we conclude that SEOs result in positive spread cost benefits for shareholders that extend beyond SEO closing, are robust to accounting for within-cluster residual correlations, and are only

0.7004 0.0769 6.1110 0.0226 0.0909 0.1219 0.1387 0.0413 0.0589 0.0524 0.0200 0.0287

5.4979c 0.3076c 13.2203b 0.0214 0.1335 0.3133c 0.2417a 0.1379c 0.0273 0.0962b 0.3244c 0.1308c 0.5615

Standard error

6.8129c 0.3777c 12.7413b 0.0173 0.2812c 0.2418c

Intercept Ln(Volume) Volatility Ln(Size) Decimalization Private Post-private Bought Resources W2 W3 W4 R2

6.7837c 0.3792c 12.4086b 0.0187 0.2174b 0.4034c 0.2837b 0.1463c 0.0412 0.1343b 0.4003c 0.1505c 0.5540

Standard error

0.5698 0.0635 5.4886 0.0207 0.0886 0.0803 0.1293 0.0414 0.0539 0.0433 0.0165 0.0237

partially explained by increasing ownership diffusion postcompletion.14 In the next section, we examine which spread component(s) are the source of the enhanced liquidity as measured by total spreads around SEO issuance. 5. Decomposition of SEO bid-ask spreads The bid-ask spread contains two main components; namely, a temporary cost (composed of order processing and inventory costs) and a permanent or adverse selection component that persistently impacts the value of the traded asset to compensate the uninformed for trading against privately informed traders. As discussed previously, Brooks and Patel (2000) report a significant decline in adverse selection costs for equity SEOs for individual days in the event window centered on the announcement date compared to the pre-event window. Unlike Brooks and Patel (2000), we investigate changes in spread components to address three questions. First, how does each spread component vary over the four windows for each SEO? Second, are these two spread components influenced by the structural breaks caused by the TSX’s move to decimalization on April 15, 1996 and by the required holding period reduction for private placements on November 30, 2001? Third, do the spread components for the six SEO types vary individually across the event windows and with respect to their paired types (i.e., private versus public, bought versus not bought and resource versus not resource) within each event window? 14 Our robustness tests in this and in the previous section do not necessarily rule out the possibility that changes in ownership diffusion have a more substantial impact on the results reported herein, since the proxies employed are rather imperfect gauges of changes in ownership diffusion.

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5.1. Spread decomposition methodology Although we use the Lin et al. (1995), Glosten and Harris (1988) and Neal and Wheatley (1998) models because they are based on quite different assumptions, we report results only for the LSB model due to the similarity of the results. Of the three models related to the realized spread that were considered, we chose the LSB model over the unpublished Masson model used by Brooks and Patel (2000) because the LSB model also considers trades that occur within the bid-ask spread. From the numerous indicator models, we chose the GH model because it has a role for volume, has less parameters to estimate than the Madhavan et al. (1997) model, and due to the findings of Brooks and Masson (1995) that models ‘‘such as Stoll (1989) and the adapted version by George et al. (1991) suffer from a generated regressor problem”. The NW model used by us is a modification of the George et al. model. With all terms as previously defined, the proportion of the spread due to adverse selection or k is estimated for our sample of 996 TSX listed SEOs using stacked intra-day data for each SEO window in the following regression for the LSB model:

DMt ¼ LNðM t =M t1 Þ ¼ kLNðPt =M t Þ þ et :

ð2Þ

Although we expect both spread components to follow an approximate V-shaped pattern over the SEO issuance cycle, we expect the timings of their minimum values to differ. Since temporary costs are affected by various factors, such as portfolio rebalancing and underwriter market stabilization, we expect lower temporary costs due to increased trader activities to occur in the SEO announcement windows and to reach their minimums in the SEO closing windows when the market weights of SEO issuers change. In contrast, we expect minimum adverse selection costs to occur in the announcement windows since SEO announcements are generally more unexpected information events than are SEO completion announcements. 15 5.2. Initial empirical results The spread components for and their differences between event windows for the (non-) differentiated samples are reported in Table 4. A cursory examination of Table 4 suggests that mean/median temporary costs are always between two and four times the corresponding values for adverse selection costs for same-window comparisons within each SEO type. The relatively more liquid types of SEOs (such as public offerings and bought deals due to their larger issue sizes) have consistently lower spread costs compared to same-window levels for their counterparts. The cross-window changes in the mean and median components exhibit several similarities. Both cost components decline significantly in the announcement (W2) versus pre-announcement window (W1). Furthermore, the statistically significant decline of 10 bps from 53 bps in W1 to 43 bps in W2 in the adverse selection cost for the full period is also economically large given that it is the average reduction as a % of price for an average daily dollar volume of $4.4 million for the three days in W2 for 996 SEOs. Further significant declines occur during the closing three-day window (W3) in temporary costs for the full, post-decimalization, public, not bought and not resource samples, and in adverse selection cost for the not bought and not resource samples. Temporary costs rebound significantly for all samples and adverse selection costs for the full and post-decimalization samples during the post-closing window (W4) but remain significantly below their W1 levels for all samples. Like-window temporary and adverse selection cost differences are always significantly lower for post- to pre-TSX-dec15 Frijns et al. (2006) report that about eight and fifteen percent of their initial and final samples of US SEOs issued between 1984 and 2000 were withdrawn.

imalization, public versus private and bought versus not bought offerings. Without further testing using a multivariate framework, this suggests that either bought deals are associated with lower adverse selection as we argued earlier due to strong underwriter signals or that firms that use bought deals already face lower adverse selection due to their larger size. Our multivariate approach, which is similar to that described in Section 4 to account for commonality, is formally stated in the following two separate models:

SpTempi;t ¼ c þ b1 LnðVolumeÞi;t þ b2 Volatilityi þ b4 Decimalizationi þ b5 Priv atei þ b6 PostPriv atei þ b7 Bought i þ b8 Resourcesi þ ai þ c1 W 2 þ c2 W 3 þ c3 W 4 þ li;t ;

ð3Þ

SpAdv ersei;t ¼ c þ b3 LnðSizeÞi þ b4 Decimalizationi þ b5 Priv atei þ b6 PostPriv atei þ b7 Bought i þ b8 Resourcesi þ ai þ c1 W 2 þ c2 W 3 þ c3 W 4 þ ni;t ;

ð4Þ

where SpTempi,t and SpAdversei,t are, respectively, the temporary and adverse selection spread costs for SEO i during window t as estimated using the LSB methodology; and the remaining variables are as described earlier in Section 4.16 Volume and volatility are used as regressors for the temporary component as they relate to the order processing and inventory spread components. Size is used in the adverse selection cost component regression due to its relationship with information asymmetry. As expected and based on columns 3–4 and 7–8 of Table 5, temporary cost is strongly and negatively related to volume.17 Since most of the order processing cost is fixed per trade, its value per traded dollar falls as trading volume increases. Similarly, temporary cost is significantly and positively related to volatility as expected based on the inventory cost component theory. Since their risk of ruin is higher with higher volatility, market makers temporarily increase their spreads and uninformed investors are more likely to place limit over market orders. The temporary cost is significantly lower following TSX decimalization. The temporary costs for privately versus publicly placed SEOs when PostPrivate is not included and for the post- versus pre-window reduction in the holding period in 2001 for private placements are not significant. Significantly lower temporary costs are observed for bought deals. As for changes through the SEO issuance cycle, we find that the temporary cost falls significantly from pre-announcement (W1) by 11 bps upon SEO announcement (W2), continues to fall by an additional and significant 22 bps (at <1%) around SEO closing (W3), increases by a significant 21 bps post-SEO closing but nevertheless remains 11 bps significantly lower post-closing (W4) when compared to the pre-announcement window. Columns 5–6 and 9–10 of Table 5 contain the panel regression results for the adverse selection cost component. As expected, adverse selection costs are significantly lower for larger firms which are generally followed by more financial analysts and investors. The adverse selection cost component is lower by a significant nine bps for bought deals, and is higher by a significant 27 bps (39 with the inclusion of PostPrivate) for private offerings. As was the case for the total spread in Section 4, the coefficient estimate for the PostPrivate dummy for the adverse selection cost is a significant 20 bps. Thus, reducing the lock-up period contributes to a 16 In a different context, Rakowski and Beardsley (2008) examine the behavior of the two liquidity components and their determinants along the limit order book. 17 When a dummy variable for utilities and financials is added, its estimated coefficient is negative and significant for only the two temporary cost regressions. Thus, total spreads are lower for utilities and financials due to lower order processing costs.

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Table 4 SEO bid-ask spread components by period and type of offering and issuer. This table reports estimated spread components using the methodology of Lin et al. (1995). Aside from the full sample period 1993–2007, reported results also consider the pre- (post-)decimalization periods resulting from the introduction of decimalization by the Toronto Stock Exchange (TSX) on April 15, 1996. Offering and issuer types are also considered. In addition, the table reports tests of significance, appearing as superscripts, between statistics for the cross-sectional distributions of the time-series averages and medians for the differences for each of W4, W3 and W2 compared to that of W1, for W4 and W3 compared to W2 and for W4 compared to W3. Finally, like-window comparisons (W1 to W1, W2 to W2, etc.) for each pair of categories (post- versus pre-decimalization, not bought versus bought, etc.) as described in Table 2 are also provided. Statistic

Temporary cost as % of price (bps) W1

W2

Adverse selection cost as % of price (bps) W3

W4

W1

W2

W3

W4

Full period Mean Median Std. Dev.

139 119 92

109c 85c 97

100c,f 77c,f 80

118c,f,i 101c,f,i 77

53 36 57

43c 24c 64

42c 22c 61

45c,f,g 30c,f,i 49

Pre-decimalization Mean Median Std. Dev.

165 158 92

150b 133c 97

135c,d 116c,e 95

152b,h 147b,e,i 71

70 44 75

57b 39c 70

58a 34b 77

54c 40b 44

Post-decimalization Mean Median Std. Dev.

135l 112l 93

101c,l 77c,l 96

93c,e,l 73c,l 76

113c,f,i,l 96c,f,i,l 78

50l 35l 52

41c,l 23c,l 63

39c,l 21c,l 58

44c,h,k 28c,f,i,l 50

Public offerings Mean Median Std. Dev.

126 105 90

96c 73c 89

86c,f 70c,f 66

104c,f,i 90c,f,i 68

43 29 49

33c 19c 48

33c 17c 50

35c 24c,f,i 38

Private placements Mean Median Std. Dev.

178l 169l 88

146c,l 120c,l 108

138c,l 113c,l 103

159c,d,i,l 145c,f,i,l 87

81l 61l 68

72a,l 50c,l 90

67c,d,l 43c,l 79

73b,d,f,l 52c,e,f,l 65

Bought deals Mean Median Std. Dev.

124 107 77

91c 70c 87

86c 70c 64

104c,f,i 90c,f,i 66

45 32 49

32c 20c 51

36c,f 20c,d 50

39c,f 27c,f,i 44

Not bought deals Mean Median Std. Dev.

161l 138l 106

134c,l 105c,l 104

118c,f,l 92c,f,l 96

137c,i,l 123c,e,i,l 86

63l 44l 64

57a,l 34c,l 75

50c,d,l 25c,f,l 72

53c,l 35c,g,l 54

Resources issues Mean Median Std. Dev.

136 117 88

105c 83c 90

101c 79c 79

118c,f,i 102c,f,i 76

54 38 58

42c 24c 64

48e 28c,f 65

48c,e 32c,f 54

Not resource issues Mean Median Std. Dev.

142 121 96

113c 86c 102

99c,f 77c,f 82

119c,i 98c,f,i 77

51 34j 56

44c 25c 64

36c,e,l 18c,f,l 57

42c,i,j 27c,i,k 44

reduction in information asymmetry. As for the changes through the SEO issuance cycle, all adverse selection cost changes are significant. Adverse selection costs fall by 10 bps upon announcement (c1 in Table 5), decrease further by less than 1 bps upon SEO closing (c1-c2 in Table 5) and increase by 3 bps post-closing (c2-c3 in Table 5). Nevertheless, this component remains 8 bps lower during postclosing than during the pre-announcement window (c3 in Table 5). Thus, total spreads and both of its components follow the same approximate V-shaped patterns (see Fig. 1). The smaller relative decline in the adverse selection component is consistent with findings of Autore and Kovacs (2010) who report that firms tend to issue equity over debt instruments when their information asymmetry is low. As our sample is naturally self-selected with low adverse selection since it only contains equity issuers, the decline in the magnitude of adverse selection is expected to be small. 5.3. Tests of robustness In this section, we conduct the same battery of robustness tests that we conducted earlier to examine if our initial results are robust to the use of clustered standard errors and if total liquidity improves with greater ownership diffusion. Based on untabulated

results, we find that the results change for the asymmetric spread component with the use of clustered standard errors. Specifically, the significance level of the estimated coefficients of the PostPrivate dummy variable and of the Bought dummy variable with and without the inclusion of the PostPrivate dummy variable improve from <5% to <1%. However, the decrease (increase) from W2 to W3 (W3– W4) are no longer statistically significant. We examine the impact of ownership diffusion by examining the impact of second and third or more same-issuer SEOs on each spread component by adding two dummy variables to Eqs. (3) and (4). Based on untabulated results, we find that the estimated coefficients of both of these additional dummy variables are negative and significant for both spread components.18 Thus, as expected, we find that both spread components decrease significantly as ownership becomes more diffused. The decrease in the permanent component suggests that the market assesses multiple SEOs as a signal of reduced information asymmetry. We also re-estimate Eqs. (3) and (4) using a sample of 581 SEOs that constitute the earliest SEO for each issuer in the sample and find no qualitative changes in the 18 The three or more SEOs dummy variable becomes insignificant with the use of clustered standard errors.

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Table 5 Panel data regression results for spread components against various SEO characteristics. This table presents results for regressions between each of the two spread components and various characteristics of our sample of SEOs using a panel data approach. The estimated models are:

SpTempi;t ¼ c þ b1 LnðVolumeÞi;t þ b2 Volatilityi þ b4 Decimalizationi þ b5 Priv atei þ b6 PostPriv atei þ b7 Bought i þ b8 Resourcesi þ ai þ c1 W 2 þ c2 W 3 þ c3 W 4 þ li;t SpAdv ersei;t ¼ c þ b3 LnðSizeÞi þ b4 Decimalizationi þ b5 Priv atei þ b6 PostPriv atei þ b7 Bought i þ b8 Resourcesi þ ai þ c1 W 2 þ c2 W 3 þ c3 W 4 þ ni;t where SpTempi,t and SpAdversei,t are, respectively, the temporary (columns 3–4) and adverse selection (columns 5 and 6) spread components for SEO i in % in window t based on the Lin et al. (1995) decomposition method. Ln(Volume)i,t is the natural log of the average daily dollar trading volume for SEO i in window t. Volatilityi is the standard deviation of daily returns extracted from the CFMRC database for SEO i over the entire four windows. Ln(Size)i is the natural logarithm of the total assets on the balance sheet for SEO i, where size is proxied by total assets. Total assets from the last balance sheet disclosed before the SEO announcement are extracted from various databases including Compustat, Stockguide, EDGAR and SEDAR (the Canadian equivalent to EDGAR). Decimalizationi is a dummy variable that is equal to one if SEO i is post-decimalization and zero otherwise. Privatei, PostPrivatei, Boughti and Resourcesi are dummy variables that are equal to one if SEO i was, respectively, privately placed, privately placed after the lock-up period reduction, issued as a bought deal and issued by a resource company and zero otherwise. ai is the (random) cross-sectional effect for SEO i. W2, W3 and W4 are dummy variables that are equal to one, respectively, for window two (during announcement), three (during closing) and four (post-closing) and zero otherwise. White standard errors, which are robust to cross-section correlation, are reported. All parameters and standard errors as reported are multiplied by 100 and are rounded to the nearest four decimal places. c1  c2 = 0 and c2  c3 = 0 correspond to the linear restrictions. The reported corresponding statistic is the difference in the estimated parameters. Inference is based on a Wald test that follows a Chi-square distribution with one degree of freedom (d.f.). Significance at 10%, 5% and 1% are indicated by superscripts a, b and c, respectively. Coefficient

Intercept Ln(Volume) Volatility Ln(Size) Decimalization Private Post-private Bought Resources W2 W3 W4 R2

c b1 b2 b3 b4 b5 b6 b7 b8

c1 c2 c3

Difference test

Adverse selection cost (%)

Temporary cost (%)

Adverse selection cost (%)

Estimate

Estimate

Estimate

Estimate

Standard error

4.9076c 0.2752c 10.6754c

0.4147 0.0402 3.3841

0.2350c 0.0545

0.0544 0.0426

0.0925c 0.0117 0.1055c 0.3230c 0.1135c 0.4596

0.0190 0.0208 0.0274 0.0104 0.0150

Estimate

p-Value

c

c1  c2 = 0 c2  c3 = 0

W1

Temporary cost (%)

0.2175 0.2095c

W2

W3

0.0665

0.0695c 0.0719b 0.2718c

0.0105 0.0303 0.0255

0.0932b 0.0093 0.0998c 0.1044c 0.0758c 0.1409

0.0382 0.0387 <0.0001 <0.0001 <0.0001

Estimate

p-Value

c

<0.0001 <0.0001

0.0046 0.0286c

W4

0

-5 -10 -15 -20 -25 -30 -35 -40 -45 Window in SEO issuance cycle Temp

Perm

Standard error

0.9252c

Cost (bps) relative to window W1

Regressor

Total

Fig. 1. The bid-ask spread and its components over the SEO issuance cycle. This figure plots the bid-ask spread and its temporary and adverse selection (or permanent) components over the four windows of the SEO issuance cycle. The values are normalized to zero for the pre-announcement window. The window periods are W1 for pre-announcement, W2 for announcement, W3 for closing and W4 for post-closing.

inferences based on the estimated coefficients for the through the SEO issuance cycle dummy variables W2, W3 and W4 using either traditional or clustered standard errors. We then examine the impact of ownership diffusion when one of the following four proxies are added in turn to Eqs. (3) and (4): ln(shares outstanding) and the ratios of shares held by management/directors, blockholders and insiders, respectively, pre- and post-SEO. Based on untabulated results, we find that the temporary

<0.0001 <0.0001

Standard error

4.8967c 0.2756c 10.6002c

0.4121 0.0403 3.3980

0.2177c 0.0992a 0.0778 0.0894c 0.0080 0.1052c 0.3229c 0.1134c 0.4601

0.0558 0.0547 0.0491 0.0205 0.0209 0.0275 0.0105 0.0150

Estimate

p-Value

c

0.2177 0.2095c

<0.0001 <0.0001

Standard error

0.8798c

0.0759

0.0696c 0.0296 0.3853c 0.1960b 0.0851b 0.0003 0.0998c 0.1044c 0.0758c 0.1473

0.0104 0.0436 0.0622 0.0784 0.0385 0.0377 <0.0001 <0.0001 <0.0001

Estimate

p-Value

c

0.0046 0.0286c

<0.0001 <0.0001

spread component decreases insignificantly with greater ownership diffusion based on the three ratios and increases significantly with shares outstanding. The asymmetric spread component decreases significantly with greater ownership diffusion based on the three ratios (only at the 10% level for blockholder and management/director ownership ratios) and shares outstanding (<1%). We further examine the relationship between each spread component and ownership diffusion by running a regression of the change in each spread component between W1 and W4 against volume, volatility and size (values for W1) as control variables, the dummies for private, bought and resources and the pre-to-post SEO change in one of the four proxies for ownership diffusion. We find that both spread components decrease with more diffused ownership for all four proxies and only the relationship between the temporary spread component and shares outstanding is not significant. Nevertheless, based on the significant intercept, there is a significant decrease in each spread component that is not captured by ownership diffusion and the other variables included in this estimation.

6. SEO returns In this section, we test the valuation impacts of SEO announcements and closings, including any associated changes in liquidity, and we test for volatility changes upon SEO closing and the impact of any such volatility changes on increased liquidity (as was documented earlier). Hess and Frost (1982) conclude that the negative abnormal returns (ARs) associated with SEO announcements of NYSE-listed firms do not exceed transaction costs. Asquith and Mullins (1986) find that over 80% of the industrial firms in their SEO sam-

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Table 6 SEO return models based on GARCH (1, 1). This table reports the return model estimates for the total SEO sample. The most general form of the four models used is given by: Ri;t ¼ ai þ bi Rm;t þ bi Rm;t  Ia;i;t þ j1;i  Iann;i;t þ j2;i  Ieff ;i;t þ ei;t , whose residuals are assumed to be heteroskedastic. Its conditional volatility is given by the following GARCH (1,1) process: hi;t ¼ xi þ /i  e2i;t1 þ wi  hi;t1 þ j3;i  Ia;i;t and hi;t ¼ Et ðe2i;t Þ. a is the excess return intercept; b and b* measure, respectively, systematic risk and its shift from the announcement window onwards; j1 and j2 are the average daily abnormal returns in, respectively, the announcement and closing windows; / and w are the respective coefficients of the prior-period squared residuals and conditional volatility; j3 is the change in the conditional volatility post-announcement; Ia,i,t is a dummy variable equal to 1 after announcement and zero before; and Iann,i,t and Ieff,i,t are dummy variables equal to 1 for, respectively, the three-day announcement and closing windows, and zero otherwise. The models are run for each SEO separately using returns from one calendar year before SEO announcement to one calendar month after SEO closing, and then cross-sectional statistics are computed. The final sample consists of 935 SEOs with at least 200 daily return observations. For models 1–4, convergence occurs for 839, 903, 854 and 905 SEOs, respectively. Significance at 10%, 5% and 1% is indicated by superscripts a, b, c. Model #

Parameter

Mean

Median

Std. Dev.

Model #

Parameter

Mean

Median

Std. Dev.

1

103  a b b* 103  j1 103  j2 103  x /

1.0596c 0.7643c 0.0994c 3.0580c 0.2153 0.5252c 0.1891c 0.4377c 0.1782c

0.8808c 0.6466c 0.0108 4.8666c 2.0001c 0.2449c 0.1390c 0.5013c 0.0495c

2.2737 0.6493 1.0779 29.4421 19.6595 0.9978 0.2400 0.4243 0.8175

3

103  a b 103  j1 103  j2 103  x /

1.0511c 0.7601c 2.5064c 0.2692 0.5722c 0.1835c 0.4251c 0.1845c

0.8962c 0.6664c 4.8093c 1.7767c 0.2562c 0.1370c 0.4907c 0.0446c

2.3571 0.5887 30.0791 19.9043 1.0637 0.2203 0.4296 0.8404

0.9857c 0.7563c 0.1095c 2.6705c 0.0005c 0.1875c 0.4403c 0.1612c

0.8526c 0.6416c 0.0227a 4.6154c 0.0002c 0.1373c 0.5087c 0.0405c

2.2233 0.6453 1.0927 29.0577 0.0010 0.2373 0.4242 0.9372

4

0.9379c 0.7541c 2.1949c 0.5509c 0.1899c 0.4241c 0.1416c

0.8595c 0.6653c 3.9070c 0.2570c 0.1348c 0.4902c 0.0386c

2.1497 0.5847 28.7312 0.9974 0.2512 0.4331 0.8849

w 3

10  j3 2

103  a b b* 103  j1 103  x /

w 3

10  j3

ple exhibit negative ARs. Slovin et al. (1994) find an average AR of 2.9% for the first SEO after a NASDAQ IPO, and an inverse relationship between ARs and the proportion of shares sold by insiders. Using the unconditional market model, significantly negative ARs are estimated by Mittoo (1997) and Pandes (2010) for Canadian SEO announcements for the 1982–1993 and 1993–2005 time periods, respectively. Unlike these findings for stockholders, Elliott et al. (2009) find that bondholders experience a significant positive return on SEO announcements that is more pronounced for bonds with lower ratings. Traditionally, SEO ARs are examined for the announcement window under the restrictive assumption of constant systematic risks and residual return volatilities through the SEO issuance cycle. Doing so in other contexts has lead to erroneous inferences (e.g., as shown by Kryzanowski and Zhang, 1993; Corhay and Tourani Rad, 1996). Following the methodology of Savickas (2003), we mainly use a GARCH (1,1) specification and allow for shifts in systematic risk (denoted by b*) in two of the four models.19 Allowing for time-varying volatility also provides an opportunity to test whether SEOs directly impact volatility. For tests reported in this section of the paper, our initial sample of 996 SEOs is reduced to a final sample of 935 SEOs after we delete SEOs that do not have at least 200 daily returns in the CFMRC database from one calendar year before SEO announcement to one calendar month after SEO closing. The market portfolio is proxied by the value-weighted global CFMRC index that includes common shares with prices over $2 (consistent with our SEO selection filter). Risk-free interest rates, as proxied by yields on one-month, constant-maturity Canadian Treasury Bills, are extracted from Datastream.

19 Our results are qualitatively unchanged if we allow for an asymmetric volatility effect using both EGARCH and GARCH of Glosten et al. (1993). We find no significant relation between the ARs for the announcement day and the W1 to W2 changes in each of four liquidity proxies (namely, quoted and effective spreads and the permanent and temporary spread components). In an earlier version of the paper, we also ran two models containing a liquidity variable, whose estimated coefficients were not statistically significant. Hence, these results are not tabulated to save valuable journal space.

w 103  j3 103  a b 103  j1 103  x /

w 103  j3

An examination of Table 6 reveals that the mean and median measures of systematic risk (b) for the pre-announcement periods are less than one and consistently significant for all models at <1% level. The change in systematic risk (b*) beginning with the announcement window is positive and significant (see models 1 and 2). Consistent with the literature, average ARs for the announcement window (j1) are negative and significant for all models (e.g., mean daily of 31 bps or around 1% for model 1 over this three-day event window). Similarly, the average ARs during the three-day closing window (j2) from models 1 and 3 are negative and only the medians are significant (at <1%) and smaller in magnitude (20 bps or less daily). These ARs are consistent with the magnitudes of the reductions in information asymmetries caused by these SEO events, as reported in previous sections of this paper. All GARCH model average parameter estimates are significant (at <5%). Thus, prior-period squared residuals (based on the / estimates) and conditional volatilities (based on the w estimates) have significant and positive impacts on the estimations of the contemporaneous conditional volatilities (h). The point estimate for conditional volatility (x) is significantly positive. Conditional volatility decreases, as captured by a significantly negative j3, with the partial resolution of uncertainty post-announcement. This is consistent with our earlier finding that the temporary spread cost is lower after SEO closing versus pre-announcement for all categories. Since volatility is the main driver of the inventory spread component, liquidity improves ceteris paribus as the non-systematic component of volatility falls. Along with the similarity of results across models and their robustness when we confine our sample to the 416 SEOs within the six years centered on April 15, 1996 examined earlier in Section 3.2, this suggests that we need only discuss the results for model 1 (the most general of the four specifications) for the samples differentiated by TSX decimalization and SEO type. Based on Table 7, the estimated betas are less than one and significantly different from zero, and are significantly smaller post-TSX-decimalization and for not resource versus resource offerings. The only significant changes in beta upon announcement are increases, and with no decimalization impact.

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Table 7 Tests of significance for return model 1 for different SEO categories. See Table 6 for the detailed specification of return model 1 that generated the following summary. To facilitate the interpretation of these results, table parameters are defined for easy reference as follows: a is the excess return intercept; b is the measure of systematic risk; b* is the shift in systematic risk from the beginning of the SEO announcement window onwards; j1 is the average daily abnormal return in the announcement period; j2 is the average daily abnormal return in the effective period; x is the point estimate of the conditional volatility; / is the coefficient of the prior-period squared residual; w is the coefficient of the prior-period conditional volatility; and j3 is the change in the conditional volatility post-announcement. Mean (median) parameter values for each SEO category are displayed and superscripts a, b, c indicate significance at 10%, 5% and 1% levels, respectively, for the null H0: mean (or median) parameter = 0 against a suitable alternative. Separately, statistical tests are conducted for mean (median) differences (whose values are not displayed) between paired SEO categories (post- versus pre-decimalization, public versus private, not bought versus bought, and not resource versus resource). Significance at 10%, 5% and 1% levels are indicated by superscripts d, e, f that appear above parameter estimates on post-decimalization, public and not resource rows in the table. This last trio of superscripts (when present) is followed by plus and minus signs to indicate the direction of the difference between respective parameter values. Thus, a post-decimalization parameter estimate for the mean b value of 0.7197 carries the superscripts c, f indicating that this mean parameter value is significantly different from zero at the 1% level, that the difference in the means between the b value post-decimalization versus the same parameter value pre-decimalization is significantly different from zero at the 1% level, and that b values post-TSX-decimalization are smaller than b values pre-TSXdecimalization (hence the minus sign). SEO category

Statistic

103  a

b

b*

103  j1

103  j2

103  x

/

w

103  j3

Pre-decimal

Mean Median Mean Median Mean Median Mean Median Mean Median Mean Median Mean Median Mean Median

1.3146c 0.9138c 1.0315c 0.8809c 1.5806c 1.2052c 0.8898c,f 0.7795c,f 1.0692c 0.8662c 1.0471c 0.8808c 1.0739c 0.8167c 1.0457c 0.9164c

0.9712c 0.7763c 0.7197c,f 0.6227c,f 0.7489c 0.6086c 0.7693c 0.6590c 0.7889c 0.6710c 0.7323c 0.6126c,d 0.9036c 0.8297c 0.6289c,f 0.5368c,f

0.0557 0.0017 0.1134c 0.0192a 0.0064 0.0756 0.1297c 0.0230a 0.0835b 0.0171 0.1200b 0.0649 0.1268b 0.0342 0.0728a 0.0027

3.8046c 5.9337c 3.2985c 5.0448c 2.8932b 0.7756 4.9977c,f 6.7875c,f 2.6668b 5.8996c 3.5647c 3.5609c 2.1270a 4.6766c 3.9625c 5.1336c

0.9356 1.5186 0.1680 2.1781b 0.6878 2.2094 0.0613 1.8467b 1.3423b 2.4607c 1.2440e+ 1.3338 1.3156b 2.9035c 0.8535d+ 1.2979

0.5795c 0.2036c 0.5292c 0.2615c 0.7326c 0.3198c 0.4576c,f 0.2272c,f 0.4784c 0.2247c 0.5858c 0.2824c,d+ 0.5958c 0.2556c 0.4566c,e 0.2416c

0.1363c 0.1000c 0.2015c,f+ 0.1469c,f+ 0.1886c 0.1353c 0.1893c,e+ 0.1411c 0.1835c 0.1250c 0.1965c 0.1591c 0.1455c 0.1014c 0.2316c,f+ 0.1768c,f+

0.4468c 0.5768c 0.4380c 0.4832c 0.4214c 0.5100c 0.4431c 0.4973c 0.4381c 0.4894c 0.4373c 0.5127c 0.4397c 0.5497c 0.4359c 0.4764c

0.3043c 0.0363c 0.1618c,d+ 0.0528c 0.3306c 0.0596c 0.1285c,f+ 0.0472c 0.1791c 0.0520c 0.1771c 0.0454c 0.2287c 0.0480c 0.1292c,d+ 0.0505c

Post-decimal Private Public Bought Not bought Resource Not resource

Except for private placements, the announcement ARs (j1) for the differentiated samples are negative, significant and small in magnitude. Only the j1 for public SEOs are significantly different (smaller) post- versus pre-TSX-decimalization. Most of the average ARs for the SEO closings are negative but not significant. Exceptions include the closing average ARs for bought and resource offerings with mean ARs of 13 bps each.20 Finally, estimated GARCH parameter values are significant at the 1% level individually. Conditional volatility decreases significantly for all the differentiated samples. The paired comparisons based on pre- and post-TSX-decimalization find lower values post-TSX-decimalization for x (the point estimate for conditional volatility) for public versus private issues, and for / (the coefficient of the prior-period squared residuals) for post- versus pre-TSXdecimalization. 7. Concluding remarks Controlling for various firm-specific determinants of spreads, we find that quoted and effective spreads and the two spread components are significantly lower for bought deals, and quoted and effective spreads and the adverse selection cost are significantly higher for private placements. We find that quoted spreads and its temporary cost component decreased post-TSX-decimalization for our sample of SEOs. As expected, the revision of the rule that reduced the lock-up period for privately issued shares to four months reduces the adverse selection cost. This supports the bolded part of the belief of the US SEC that such reductions ‘‘will increase the liquidity of privately sold securities and decrease the cost of capital for all issuers without compromising investor protection”. 20 The numerical output for statistical tests on the sum of the ARs in the announcement and closing windows (i.e., j2 + j2) are not presented since their inference did not differ from that for the announcement window when significance was present.

We find that spreads and their components (dollar volume and depths) follow an approximate (inverted) V-shaped pattern through the SEO issuance cycle. Thus, spreads and their components (dollar volumes and depths) decrease (increase) upon SEO announcement, begin to increase (continue to decrease for temporary costs only) after SEO announcement, and increase (decrease) further after SEO closing to a level that is still lower (higher) than their pre-announcement levels. The positive liquidity benefits for shareholders that extend beyond SEO closing is partly explained by the increase in the actual or potential share float and/or investor following and/or ownership diffusion from the SEO issuance. The negative abnormal returns (ARs) identified for announcement windows appear to be consistent with the magnitudes of the reductions in information asymmetries caused by the SEO events. Only AR differences during the announcement window between public (significantly negative) and private SEOs (insignificantly positive) are significant. Conditional residual return volatilities decrease post-announcement indicating reduced uncertainty. This is consistent with the decline in trade costs that we observe.

Acknowledgements Financial support from the Autorité des Marchés Financiers (AMF), Concordia University Research Chair in Finance and SSHRC (Social Sciences and Humanities Research Council of Canada) are gratefully acknowledged. We thank the editor and anonymous reviewers for helpful comments and suggestions. We are also grateful to the discussant (Joshua Slive) and the participants at the 2008 meetings of the Eastern Finance Association (Saint Pete Beach) for their helpful comments. The usual disclaimer applies in that the views expressed herein are solely our own and do not necessarily reflect the official positions or policies of the providers of financial support of this paper or the views of their staff members.

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References Amihud, Y., Mendelson, H., Uno, J., 1999. Number of shareholders and stock prices: Evidence from Japan. Journal of Finance 54, 1169–1184. Asquith, P., Mullins Jr., D.W., 1986. Equity issues and offering dilution. Journal of Financial Economics 15, 61–89. Autore, D.M., Kovacs, T., 2010. Equity issues and temporal variation in information asymmetry. Journal of Banking and Finance 34, 12–23. Bessembinder, H., 1999. Trade execution costs on Nasdaq and the NYSE: A postreform comparison. Journal of Financial and Quantitative Analysis 34, 387–407. Bessembinder, H., 2003. Issues in assessing trade execution costs. Journal of Financial Markets 6, 233–257. Blackwell, D., Marr, M.W., Spivey, M., 1990. Self-registration and the reduced due diligence argument: Implications of the underwriter certification and the implicit insurance hypotheses. Journal of Financial and Quantitative Analysis 25, 245–259. Booth, L., Xu, Z., 2008. Who smooth dividends? Working Paper, University of Toronto. Brau, J.C., Lambson, V.E., McQueen, G., 2005. Lockups revisited. Journal of Financial and Quantitative Analysis 40, 519–571. Brockman, P., Chung, D.Y., Yan, S., 2009. Block ownership, trading activity, and market liquidity. Journal of Finance and Quantitative Analysis 44, 1403–1426. Brockman, P., Yan, X., 2009. Block ownership and firm-specific information. Journal of Banking and Finance 33, 308–316. Brooks, R., Masson, J., 1995. Performance of Stoll’s spread component estimator: Evidence from simulations, time-series, and cross-sectional data. Journal of Financial Research 28, 459–476. Brooks, R.M., Patel, A., 2000. Information conveyed by seasoned security offerings: Evidence from components of the bid-ask spread. Review of Financial Economics 9, 83–99. Chiang, R., Venkatesh, P.C., 1988. Insider holdings and perceptions of information asymmetry: A note. Journal of Finance 43, 1041–1049. Chung, R., Kryzanowski, L., Zhang, H., 1996. Decimalization’s winners and losers. Canadian Investment Review 9, 35–39. Chung, R., Kryzanowski, L., Rakita, I., 2000. The relationship between overallotment options, underwriting fees and price stabilization for Canadian IPOs. Multinational Finance Journal 4, 5–34. Corhay, A., Tourani Rad, A., 1996. Conditional heteroskedasticity adjusted market model and an event study. Quarterly Review of Economics and Finance 36, 529– 538. Desrosiers, S., L’Her, J.-F., Sauriol, L., 2004. SEOs: Bearers of long-term bad news. Canadian Investment Review 17, 20–31. Eckbo, B.W., Masulis, R.W., Norli, O., 2007. Security offerings. In: Eckbo, B.E. (Ed.), Handbook of Corporate Finance: Empirical Corporate Finance, vol. 1. Elsevier/ North-Holland Handbook of Finance Series, Amsterdam. Elliott, W.B., Prevost, A.K., Rao, R.P., 2009. The announcement impact of seasoned equity offerings on bondholder wealth. Journal of Banking and Finance 33, 1472–1480. Frijns, B., Navissi, F., Tourani Rad, A., Tsai, L., 2006. Stock price performance of seasoned equity offerings: Completed vs withdrawn. Managerial Finance 32, 234–246. George, T., Kaul, G., Nimalendran, M., 1991. Estimation of the bid-ask spread and its components: A new approach. Review of Financial Studies 4, 622–656. Gibson, S., Singh, R., Yerramilli, V., 2003. The effect of decimalization on the components of the bid-ask spread. Journal of Financial Intermediation 12, 121– 148. Glosten, L.R., Harris, L.E., 1988. Estimating the components of the bid-ask spread. Journal of Financial Economics 21, 123–142. Glosten, L.R., Jagannathan, R., Runkle, D.E., 1993. On the relation between the expected value and the volatility of the nominal excess return on stocks. Journal of Finance 48, 1779–1801. Gompers, P.A., 1995. Optimal investment, monitoring and the staging of venture capital. Journal of Finance 50, 1461–1489. Graham, J.R., Koski, J.L., Loewenstein, U., 2006. Information flow and liquidity around anticipated and unanticipated dividend announcements. Journal of Business 79, 2301–2336. Heflin, F., Shaw, K.W., 2000. Blockholder ownership and market liquidity. Journal of Financial and Quantitative Analysis 35, 621–633. Henker, T., Wang, J.X., 2006. On the importance of timing specifications in market microstructure research. Journal of Financial Markets 9, 162–179. Hertzel, M., Lemmon, M., Linck, J.S., Rees, L., 2002. Long-run performance following private placements of equity. Journal of Finance 57, 2595–2617. Hess, A.C., Frost, P.A., 1982. Tests for price effects of new issues of seasoned securities. Journal of Finance 36, 11–25.

2967

Holmstrom, B., Tirole, J., 1993. Market liquidity and performance monitoring. Journal of Political Economy 101, 678–709. Hwang, S., Keswani, A., Shackleton, M.B., 2008. Surprise vs anticipated information announcements: Are prices affected differently? An investigation in the context of stock splits. Journal of Banking and Finance 32, 643–653. Kini, O., Mian, S., 1995. Bid-ask spread and ownership structure. Journal of Financial Research 18, 401–414. Kryzanowski, L., Lazrak, S., Rakita, I., 2005. The behavior of prices, trades and spreads for Canadian IPOs. Multinational Finance Journal 9, 215–236. Kryzanowski, L., Zhang, H., 1993. Market behaviour around Canadian stock-split exdates. Journal of Empirical Finance 1, 57–81. Kyle, A.P., 1985. Continuous auctions and insider trading. Econometrica 53, 1315– 1336. Lee, C.M.C., Ready, M.A., 1991. Inferring trade direction from intraday data. Journal of Finance 46, 733–746. Li, M., Parker, M.E., 2005. Cross-sectional analysis of asymmetric information after decimalization. Journal of Business and Economic Perspectives 30, 68–87. Lin, J.B., Pantzalis, C., Park, J.C., 2007. Corporate use of derivatives and excess value of diversification. Journal of Banking and Finance 31, 889–913. Lin, J.C., Sanger, G.C., Booth, G.G., 1995. Trade size and components of the bid-ask spread. Review of Financial Studies 8, 1153–1183. Madhavan, A., Richardson, M., Roomans, M., 1997. Why do security prices change? A transaction-level analysis of NYSE stocks. Review of Financial Studies 10, 1035–1064. Mantecon, T., Poon, P., 2009. An analysis of the liquidity benefits provided by secondary markets. Journal of Banking and Finance 33, 335–346. Maynes, E., Pandes, J.A., 2009. Private placements of equity and liquidity. Working paper, York University, Toronto. Available from: . McLaughlin, R., Safieddine, A., Vasudevan, G.K., 1998. The information content of corporate offerings of seasoned securities: An empirical analysis. Financial Management 27, 31–45. Merton, R.C., 1987. A simple model of capital market equilibrium with incomplete information. Journal of Finance 42, 483–511. Mittoo, U.R., 1997. Seasoned equity offerings and cost of equity in the Canadian market. In: Halpern, P.J.N. (Ed.), Financing Growth in Canada. University of Calgary Press. Mittoo, U.R., 2006. The multijurisdictional disclosure system and value of equity offerings. Journal of International Financial Management and Accounting 17, 52–83. Neal, R., Wheatley, S.M., 1998. Adverse selection and bid-ask spreads: Evidence from closed-end funds. Journal of Financial Markets 1, 121–149. Pandes, J.A., 2010. Bought deals: The value of underwriter certification in seasoned equity offerings. Journal of Banking and Finance 34, 1576–1589. Petersen, M.A., 2009. Estimating standard errors in finance panel data sets: Comparing approaches. Review of Financial Studies 22, 435–480. PriceWaterhouseCoopers, 2007. The potential impact of international financial reporting standards (IRFS) on the Canadian oil and gas sector. PriceWaterhouseCoopers LLP, Canada, Toronto. Available from: . Rakowski, D., Beardsley, X.W., 2008. Decomposing liquidity along the limit order book. Journal of Banking and Finance 32, 1687–1698. Rubin, A., 2007. Level, ownership concentration and liquidity. Journal of Financial Markets 10, 219–248. Savickas, R., 2003. Event-induced volatility and tests for abnormal performance. Journal of Financial Research 16, 165–178. Securities and Exchange Commission (SEC), 2007. Revisions to Rules 144 and 145, 17 CFR Parts 230 and 239 [Release No. 33-8869, File No. S7-11-07]; RIN 3235AH13, December 6, 2007. Available from: . Slovin, M.B., Sushka, M.E., Bendeck, Y.M., 1994. Seasoned common stock issuance following an IPO. Journal of Banking and Finance 18, 207–226. Slovin, M.B., Sushka, M.E., Lai, K.W., 2000. Alternative flotation methods, adverse selection, and ownership structure: Evidence from seasoned equity issuance in the UK. Journal of Financial Economics 57, 157–190. Stoll, H.R., 1989. Inferring the components of the bid-ask spread: Theory and empirical tests. Journal of Finance 44, 115–134. Tripathy, N., Rao, R.P., 1992. Adverse selection, spread behavior, and over-thecounter seasoned equity offerings. Journal of Financial Research 15, 39–56. Teoh, S.H., Welch, I., Wong, T.J., 1998. Earnings management and the underperformance of seasoned equity offerings. Journal of Financial Economics 50, 63–99.