Equity issues and the impact of lead manager affiliation on broker market share and trading volume

Pacific-Basin Finance Journal 38 (2016) 17–33

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Pacific-Basin Finance Journal journal homepage: www.elsevier.com/locate/pacfin

Equity issues and the impact of lead manager affiliation on broker market share and trading volume Peng William He a, Elvis Jarnecic a, Yubo Liu b,⁎ a b

Finance Discipline, The University of Sydney Business School, Sydney, 2006, NSW, Australia The University of Sydney Business School, 4E Wolseley Grove, Zetland, 2017, NSW, Australia

a r t i c l e

i n f o

Article history: Received 16 April 2015 Received in revised form 11 February 2016 Accepted 12 March 2016 Available online 15 March 2016 Keywords: Seasoned equity offerings Equity issues Broker affiliation Broker market share

a b s t r a c t This paper studies the trading volume and market share of brokers around seasoned equity offerings (SEOs) in the Australian equity market based on a unique broker ID dataset. We examine the drivers behind the behaviour of affiliated and unaffiliated brokers around SEOs. The findings contribute to the understanding of how broker affiliation impact SEO trading activity. Results show that SEO affiliation is positively related to broker trading volume and market share on both the announcement day and issuance day. However, there is no evidence suggesting that lead managers or co-manager generate additional trading volume compared to other co-underwriters. Broker reputation, market capitalization and relative SEO size of the offering firm are shown as the primary determinants influencing broker trading activity. © 2016 Elsevier B.V. All rights reserved.

1. Introduction Given that seasoned equity offerings (SEOs) are widely regarded as one of the most important capital structure events for listed companies, a considerable amount of research has been dedicated to examine capital markets around SEOs. Masulis and Korwar (1986) state two major motivations for SEOs: first, to reduce a firm's leverage by increasing the equity component of its capital structure; second, to finance new capital expenditures, such as major expansions or acquisitions. The purpose of SEOs could also reflect the performance or financial health of a company. Therefore, investors may infer information and adjust their expectations regarding the future performance of the firm. As a result, abnormal trading activity is commonly associated with SEO events. Extant literature has documented significant changes in affiliated brokers' market share around various financial events in capital markets, such as SEOs, Initial Public Offerings (IPOs), Mergers and Acquisitions (M&As) and changes in analyst recommendations. For example, short selling activities around both the announcement and issuance dates of SEOs have been examined by Henry and Koski (2010), while Kim and Masulis (2011) observe order imbalance activities after SEOs in the U.S. market, attributing the trading pattern to market-making activities by underwriters. Since underwriters have a significant role in capital markets around SEOs, it is also interesting to investigate their affiliated brokers trading pattern around such offering events.1 The connections between the brokerage and research and advisory services by investment banks have been discussed by Ellis et al. (2000); Irvine (2001) and Niehaus and Zhang (2010). They generally find that investors who receive analyst research from the underwriting firms have an incentive to trade with their affiliated brokerage firms. Their findings suggest that a substantial ⁎ Corresponding author. E-mail addresses: [email protected] (P.W. He), [email protected] (E. Jarnecic), [email protected] (Y. Liu). 1 Affiliated broker is known as the analyst or broker from the same firm as an advisor or underwriter in a SEO.

http://dx.doi.org/10.1016/j.pacfin.2016.03.005 0927-538X/© 2016 Elsevier B.V. All rights reserved.

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proportion of investors tend to trade with affiliated brokers after capital raisings. Ritter and Welch (2002) provide examples of quid pro quo commission arrangements for IPO allocation. They argue that IPO share allocations influence under-pricing, ownership structure, and underwriter compensation. Loughran and Ritter (2002) argue that investors tend to send trading commissions to the underwriter's brokerage in order to gain favourable IPO share allocation on hot deals. This finding is also supported by Goldstein et al. (2011), who also document the linkage between commission payments and hot IPO deals. Reuter (2006) studies mutual fund family's holdings and brokerage commissions in IPO deals and documents that lead underwriters tend to provide profitable IPO share allocations to investors with stable commission revenues. Goldstein et al. (2009) use Abel-Noser dataset and discover similar findings in other institutional investors. They suggest that institutional investors tend to obtain favourable share allocation by focusing on commission payments. Moreover, Nimalendran et al. (2007) suggest that underwriter's commission is one of the important determinants of IPO share allocation. Their empirical results revealed that abnormal trading volumes are most pronounced close to the IPO offer date, which may be indirect compensation for the underwriter's favourable allocation in IPO deals. Similarly, in SEOs, we may also find interesting relationships between investors and underwriter's brokerage arm. Previous studies mainly focus on the trading patterns in the capital markets around SEOs, including order flow imbalance, analyst coverage and short-selling behaviour. Gerard and Nanda (1993) examine the secondary market trading patterns prior to SEO events, while Henry and Koski (2010) and Safieddine and Wilhelm (1996) study short-selling behaviour around SEO events. Lease et al. (1991) and Kim and Masulis (2011) examine order imbalance patterns around SEOs, while Corwin (2003) and Cotter et al. (2004) study trading activities, price stabilization and under-pricing of SEOs by analysing security characteristics of SEO events. However, studies on the trading activities of affiliated brokers in SEOs are very limited. With a unique dataset of broker identification codes provided by the Australian Securities Exchange (ASX), we investigate the trading behaviour of brokers around SEO events in the Australian financial market. Moreover, we examine the abnormal patterns of trading volumes and market share for both affiliated brokers and unaffiliated brokers around SEOs. This study provide evidence that Australian broker trading activities are influenced by broker affiliation around SEO events, while examining the key determinants (such as stock characteristics, market movements, and broker characteristics) that affect affiliated and unaffiliated brokers' abnormal trading behaviour. In this paper, we employ similar methodologies employed by Gerard and Nanda (1993) and Henry and Koski (2010) to examine the impact of SEO affiliation on the trading volume and market share of brokerage firms. We focus on changes in and the determinants of the trading volume and market share of both affiliated and unaffiliated brokers around two types of SEOs (private placements and rights offerings) in the Australian equity market between 2000 and 2009. The characteristics of these offering events are investigated to provide a better understanding of affiliated brokers' and unaffiliated brokers' trading volume and market share behaviour around SEOs. Further regression analysis offer insights into the key determinants that may impact on broker trading behaviour and market share around equity offering events. The findings should be of interest to advisory firms as well as affiliated brokerage firms in deal structuring, as SEO affiliation often lead to possible indirect compensation in the form of increased brokerage market share. While SEO lead and co-managers may assist the company in pricing, marketing and determining the allocations of the shares, no trading by any broker takes place during the share issuance, which is a primary market function. Once the new securities come on to the secondary market, shareholders can trade through any broker, and do not necessarily have to become clients of the underwriters to trade those shares post issuance. The abnormal trading volume by affiliated brokers observed in this study may be a result of the underwriters: 1) providing liquidity, facilitation and market stabilising services using their house account around the time of the issuance; 2) accumulating inventory to conduct the aforementioned actions prior to the issuance; 3) trading on behalf of clients that have subscribed to the SEO in the secondary market after the company issued the shares to the clients in the primary market2; and 4) providing analyst coverage of the company around the SEO, which tends to stimulate trading interest in the security by its clients. The inventory and liquidity offered by the SEO lead and co-managers may attract other brokers and their clients to interact with them on the secondary market.3 While statistical inferences can be drawn about various determinants of broker trading volume and market share around SEOs, there is limited data to allow the examination of possible client migration across brokers or quantify the exact profitability impact of increase market share for underwriters. The elevated trading volume and market share of lead and co-managers around SEOs is likely a natural consequence of their affiliation and service provision, which would result in additional brokerage revenue in addition to underwriting revenue. This additional indirect compensation may be considered by investment banks when pricing SEO management and underwriting services. However, there is a rare possibility that the SEO manager may lose money trading shares for the purpose of liquidity provision, facilitation and market stabilization.

2

This may be more relevant for private placements, where the institutional investor subscribing to the placement is typically also a client of the SEO broker. For example, if the client of another broker is seeking to acquire a large parcel of shares in the company that the underwriter has just managed an SEO for, the first broker may sound out the SEO broker to ask whether any parties that have been allotted the placement wished to offload their stake. 3

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In addition, examining the characteristics of SEOs facilitates a better understanding of the determinants of abnormal trading activities around SEOs. These characteristics include the offer size, the level of underpricing, the reputation of brokers, the purpose of the offering event and the size of the offering firm, which are important factors that advisory firms should consider in searching and structuring deals. The results suggest that broker affiliation has a significant impact on broker trading volume and market share around SEO announcement days and issuance days. Affiliated brokers are shown to outperform other unaffiliated brokers by 147% in terms of trading volume on the SEO announcement day. This number goes up to 179% in the post-announcement period. Around issuance days, affiliated brokers are expected to obtain at least 151% more trading volume, and their abnormal volumes are expected to be 264% higher than that of unaffiliated brokers. Among affiliated brokers, lead managers are expected to attain higher trading volume and market share around SEOs. However, there is no significant evidence showing that lead managers outperform other underwriters around SEO events. Moreover, broker reputation and market capitalization of the offering firm are shown as the two major factors that influence broker market share around SEOs. Broker trading volume is shown to be negatively associated with the market capitalization during announcements. The rest of paper is organized as follows. Section 2 summarizes the existing literature on trading patterns and broker behaviour around equity offering events. Section 3 describes the relevant datasets. Section 4 lists the hypotheses that will be tested in this study. Research design is discussed in Section 5. Section 6 provides a discussion of the results and Section 7 concludes. 2. Literature review 2.1. Broker affiliation and analyst coverage The trading volume and market share of brokerage firms are affected by various factors. There has been a substantial amount of research examining the link between analyst coverage and affiliated broker trading volume. Niehaus and Zhang (2010) study the research coverage and affiliated broker market share in the US. They find that a broker's trading volume in a stock increases by 0.8% on average if that stock is covered by an affiliated analyst. This finding is supported by the empirical evidence in Irvine (2001) and Jackson (2005), who document a positive relationship between analyst research coverage and broker trading volume for individual stocks on the TSE and ASX respectively. Furthermore, both Jackson (2005) and Niehaus and Zhang (2010) suggest that a change in an analyst's recommendation tends to increase the affiliated broker's market share. Given this empirical relationship, it follows that affiliated brokerage firms have an incentive to issue optimistic recommendations. The level of analyst research coverage is considered an important factor of the quality of the underwriter's service. Francis and Philbrick (1993) argue that analysts' earnings forecasts could be influenced by analysts' recommendations, as they need to maintain their relations with the management. A firm tends to be favourable to investors when there is a high level of research coverage on the firm. Hence, this may lead to a higher level of trading volume (Krigman et al., 2001). O'Brien and Bhushan (1990) examine the link between analyst coverage and affiliated brokers' coverage from institutional investors. They find that institutional investors' preferences and analyst coverage preferences are interactive. They suggest that analysts are likely to cover stocks with high levels of institutional ownership. On the other hand, institutional investors tend to buy stocks that are covered by analysts in the brokerage firms. From this point of view, the analyst coverage influences the trading volumes from institutional investors, while these volumes would also impact on the level of analyst coverage. Moreover, Irvine (2001) uses a unique dataset with broker identifications to analyze the connection between the volume and stock coverage of brokerage firms. By comparing brokerage firm volume in covered and uncovered stocks, Irvine (2001) discovers that the broker trading volume in covered stocks is 3.8% higher than the volume in uncovered stocks. This result is consistent with the finding in Bhushan (1989) that the level of analyst research on a firm has a positive relationship with broker trading volume. Jackson (2005) studies the influence of analyst reputation on the trading volume of affiliated brokers. The results suggest that optimistic analysts with a good reputation tend to bring more trade for affiliated brokerage firms. The reputation of analysts is built on their accurate forecasts. Jackson also argues that an upward bias may exist in analyst forecasts and recommendation. However, this argument has been refuted in the literature. Irvine (2004) found no evidence that analyst forecast bias would increase the level of trading volume for brokerage firms. A compelling result is provided by Grant et al. (2010), who find that pessimistic analysts also generate higher levels of trading volume for affiliated brokers. This phenomenon is found to be more significant for retail brokerage firms compared to institutional brokerage firms. In addition, optimistic forecasts tend to generate higher trading volumes for affiliated brokers than pessimistic forecasts. 2.2. Broker affiliation and equity offerings Ellis et al. (2000); Irvine (2001) and Niehaus and Zhang (2010) have shown interactions between the brokerage department and the corporate finance advisory department. Underwriters are likely to provide analyst research coverage for the offering firms to improve the relationships with their clients. On the other hand, investors who receive analyst coverage have the incentive to trade with the underwriter's affiliated brokers in order to build up connections for future analyst research coverage. From this perspective, the connections between broker affiliation and analyst coverage may also appear between broker affiliation and underwriters in equity offering events. Blake and Schack (2002) suggest that increased competition in the market for institutional execution encourages brokers to provide extra long-term and valuable services to maintain business relationships. Goldstein et al. (2009) discover that affiliated underwriters have the incentives to offer their top clients better share allocations of hot

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IPOs as returns to the past business and an inducement for potential business with their brokerage arms. This argument of longterm business relationships between investors and underwriters also explains why Krigman et al. (2001) report that all 15 IPO issuing firms kept their lead underwriter in SEOs even there were large amounts of money left on the table in those IPOs. Benveniste and Spindt (1989) argue that allocations of under-priced IPOs can be used by lead managers to reward valuable clients. Similar situations in managed funds were studies by Gaspar et al. (2006), who argue that mutual funds are willing to pay large brokerage commissions in exchange for better allocations in under-priced IPOs for their funds. Moreover, Reuter (2006) also documents evidence showing that under-priced IPO share allocations from underwriters tend to help the affiliated brokers to earn extra commissions or additional investment banking business. Loughran and Ritter (2002) argue that investors are willing to trade with the brokerage arm of the underwriters and overpay brokerage commissions in order to build a long-term business relationship. This behaviour is further examined in Nimalendran et al. (2007). They suggest that investors tend to offer additional business benefits to underwriters and their affiliated brokers in order to improve their opportunities in getting better share allocations in hot IPOs. This has also been empirically documented in literature. Aggarwal (2000) examines abnormal trading volumes around IPOs in the US market. She shows that underwriters tend to manage the stabilization process in the post-IPO period, which leads to abnormal trading volumes in the market. These post-IPO activities in the US have also been studied by Ellis (2006). Ellis examines the trading volumes in the first two days following an IPO. She finds that a substantial number of trades occur in the first two days after an IPO. The volume of these trades is equivalent to over 70% of the shares issued in the IPO. Ellis argues that the investors who cause these significant trading volumes are those who participate in IPOs and sell their allocated shares after the listing day. There are also other market participants who contribute to the abnormal volumes after IPOs. Day traders play a significant role in the market. They appear to buy and sell their positions in the first few days following IPOs in order to capture volatile price movements (Ellis, 2006, and Geczy et al., 2002). Krigman et al. (1999) study abnormal volume patterns during the post-IPO period in the US market. They suggest that flipping investors were another group that contributed to abnormal volumes following IPOs. These investors tend to sell their allocated shares quickly after the listing day, mainly because of their low expectation of the long-run performance of the new listing firm. However, this phenomenon varies across different IPOs. Krigman et al. (1999) show that 45% of trading volumes were from flipping investors for cold IPOs. In contrast, this percentage goes down to 14% for hot IPOs. Existing literature documents compelling evidence of abnormal trading volumes around equity offering events. Although overall broker trading volume is expected to increase around equity offerings, the market shares of brokerage firms may depend on their affiliations with the underwriters (Ellis, 2006). Based on the existing findings (Aggarwal, 2000, Ellis, 2006, Gerard and Nanda, 1993, Henry and Koski, 2010, and Krigman et al., 1999), we hypothesize that an affiliated broker is expected to obtain abnormal trading volume and market share around SEOs. This issue will be examined in this paper. Lead underwriters provide research recommendations for more than 80% of IPOs after the offer date (Bradley et al., 2003, and Cliff and Denis, 2004). Cliff and Denis (2004) discovered that IPO firms are likely to switch underwriters in SEOs if underwriters do not provide substantial research coverage in the post-IPO period.4 From this perspective, IPO underwriters are likely to issue analyst research coverage for their clients for future potential business opportunities. Investors who receive analyst research from the underwriting firm have the incentive to trade with their affiliated brokerage firms (Ellis et al., 2000, and Niehaus and Zhang, 2010). Moreover, Huang and Zhang (2011) argue that investors who have connections with underwriters are more likely to participate in an SEO. In this case, when underwriters receive SEOs from either their former IPO clients or new clients, they are likely to use their investor networks to promote the SEO events. Hence, the affiliated brokerage firms would receive additional volumes from these investors. As a result, it is also valuable to investigate trading volumes around SEOs. Further, Henry and Koski (2010) use daily data to examine short selling volumes around SEOs in the US market. They find no evidence of informed short selling around SEOs. Henry and Koski suggest that information and motivation are the two main drivers of short selling around SEOs. They argue that traders with privative information before the SEO announcement may decide to short sell on their information. Interestingly, Henry and Koski (2010) also discover that the level of short selling around SEOs increases when there are large discounts on the offering shares.5 Gerard and Nanda (1993) document a possible explanation for this behaviour. They suggest that investors with private information prior to the SEO period tend to sell shares before their information is disclosed in the announcement and repurchase the shares at the discounted issue price to recover their secondary market losses. This argument is also supported by Safieddine and Wilhelm (1996). They discover substantial abnormal sell volumes in both the equity market and the derivatives market around SEOs. Short selling normally improves market price efficiency. However, Gerard and Nanda (1993) and Henry and Koski (2010) document that prices became inefficient prior to the SEO period because of short selling in the market.6

4 Cliff and Denis (2004) found a negative relation between the unexpected amount of post-IPO analyst coverage and the probability of switching underwriters between IPO and SEO. 5 Safieddine and Wilhelm (1996) argue that rational uninformed investors will participate in SEOs only if there is a discount on the offering price in the secondary market. 6 Gerard and Nanda (1993) found that informed traders may attempt to manipulate offering prices by selling shares before the SEO announcement, leading to temporary decreases in the share price.

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2.3. Lead underwriter in equity offerings Ellis et al. (2000) suggest that the lead underwriter always takes large inventory positions in the aftermarket trading, becoming the dominant market maker after IPOs.7 In contrast, other market makers in the market normally have inventory positions close to zero. Apart from the lead underwriter, co-managers do not have significant roles in aftermarket trading. However, the results suggest that underwriters accumulate significant inventory positions for stocks trading below the offer price. The situation is different for stocks trading above offer prices, where underwriters play a significant role in market making. Since the majority of offering shares are allocated by lead underwriters, investors are likely to send their orders to the lead underwriters (Ellis, 2006). Michaely and Womack (1999) disagree with this argument on the basis that institutional investors often trade with a different broker from the one who issues research information. However, Logue et al. (2002) argue that the reputation of underwriters is a significant determinant of trading activities in the market. They also suggest that underwriter reputation influence the aftermarket price stabilization activities and long-run performance of listing firms. Since lead underwriters often have a strong reputation, they are likely to generate more volume in equity offerings. This is supported by Ellis (2006), who shows that lead underwriters have 35% market share in all investor-dealer trades. In contrast, only 19% of customers trade with unaffiliated market makers. Interestingly, besides significant market share, the lead underwriters also obtain 77% of compensation from underwriting fees (Ellis et al., 2000). According to the findings in Logue et al. (2002) and Ellis et al. (2000), we hypothesize that an affiliated lead manager is likely to obtain abnormal trading volume and market share during a SEO event. This hypothesis will be examined in this paper.

3. Data and sample description The broker ID data is obtained from a unique Australian Securities Exchange (ASX) database. ASX is the primary stock exchange group in Australia, ranked as the eighth largest exchange in the world. The major brokerage firms in the ASX consist of global investment banks, which focus their operations on large firms and provide research analysis for institutional investors. Relatively small brokerage firms mainly focus on medium and small firms and target retail traders (Grant et al., 2010, and Jackson, 2005). Jackson (2005) shows that institutional investors tend to submit orders to brokers who provide them with research coverage in order to maintain a good business connection. Moreover, less informed retail investors are also likely to trade based on the research provided by their brokerage firm analysts (Grant et al., 2010).8 The trade data contains ASX stock codes, trade prices, trade volumes, trade values, broker identification codes (including both buyer and seller identifications), trade dates and trade time stamps. There are 129 active brokers in this sample, 26 of which have been affiliated in at least one of the SEO events. Due to the availability of this unique data, the sample covers the period from January 2000 to December 2009. Since every trade contains two parties, the buyer and the seller, a double counting problem becomes an issue if the total trade volume is computed by simply adding the total volume of each broker (Irvine, 2001). To avoid double counting, total trading volume is computed as follows:

VOL ¼

I X T X TBVOLi;t þ TSVOLi;t 2 i¼1 t¼1

ð1Þ

where VOL is the total volume over the sample period for an individual firm, TBVOLi ,t is the total buy volume for an individual firm from broker i on day t, TSVOLi,t is the total sell volume for an individual firm from broker i on day t. Private placement and rights offering datasets are collected from the Thomson Reuters Connect 4 database. The dataset consists of ASX stock codes, announcement dates, listed dates, GICS industry sector codes, equity raising types, issue prices, issue amounts, advisor types, and advisor identifications. The sample in this paper consists of 460 private placements and 67 rights offerings. Previous studies have pointed out that the announcement dates and issuance dates of SEOs in collected datasets could be incorrect (Lease et al., 1991, and Kim and Masulis, 2011). Kim and Masulis (2011) suggested that the announcements could occur after trading hours. In this case, the effective announcement day should be the next trading day. To adjust these dates, the announcement date for all 527 SEOs is re-checked with the announcement documents provided on the ASX. If the announcement is released after trading hours, then the following trading day is set as the announcement day.

7 Ellis et al. (2000) show that the lead underwriters take relatively large inventory positions in the stock. On average, seven percent of shares offered are taken by the lead underwriter. 8 Grant et al. (2010) state that institutional investors often hire a panel of brokerage firms and allocate their trades to those brokers in order to maintain a good business relationship with the brokerage firm analysts and to receive a range of research coverage. On the other hand, retail investors normally deal with one investment advisor and are therefore typically less informed.

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4. Methodology This paper aims to examine the impact of broker affiliation on the trading volumes and market shares of brokerage firms around SEOs following the methodologies in Gerard and Nanda (1993) and Henry and Koski (2010). This section lists the measurement of key variables as well as statistical models used for hypothesis testing. 4.1. Market share measures The market share for broker i on day t is computed as follows (See similar approach in Irvine, 2001): MKTSHAREi;t ¼

DVOLi;t DTVOLt

ð2Þ

where MKTSHAREi,t is the market share for broker i on an individual firm on day t, DVOLi ,t is the daily volume for an individual firm from broker i on day t, and DTVOLi,t is the daily total volume for an individual stock on day t. The abnormal market share for broker i on day t is computed as follows (see similar approach in Irvine, 2001): ABMKTSHAREi;t ¼

MKTSHAREi;t −1 AVEMKTSHAREi

ð3Þ

where ABMKTSHAREi ,t is the abnormal market share for broker i for an individual firm on day t, and AVEMKTSHAREi is the average market share for broker i on an individual firm over the benchmark period. In line with Henry and Koski (2010) the benchmark period used is the 60 trading days preceding the 11 days prior to the event window, excluding days on which the trading volume is zero for the firm. 4.2. Abnormal measures Consistent with Henry and Koski (2010), the measures for abnormal trading volume are defined as below. Firstly, the abnormal trading volume (buy + sell) for an individual firm from broker i on day t is computed as follows: ABVOLi;t ¼

VOLi;t −1 AVEVOLi

ð4Þ

where ABVOLi,t is the abnormal trading volume for an individual firm from broker i on day t, VOLi,t is the total trading volume for an individual firm from broker i on day t, and AVEVOLi is the average trading volume for an individual firm from broker i over the benchmark period. The abnormal buy (sell) volume for an individual firm from broker i on day t is computed as follows:     TBVOLi;t TSVOLi;t ABBV i;t ABSV i;t ¼ −1 AVEBV i ðAVESV i Þ

ð5Þ

where ABBVi ,t(ABSVi ,t) is the abnormal buy (sell) volume for an individual firm from broker i on day t, TBVOLi, t(TSVOLi ,t) is the total buy (sell) volume for an individual firm from broker i on day t, and AVEBVi(AVESVi) is the average buy (sell) volume for an individual firm from broker i over the benchmark period. Broker trading volume and market share can be used to proxy for broker size (Niehaus and Zhang, 2010). Jackson (2005); Grant et al. (2010) and Niehaus and Zhang (2010) used broker market share across all stocks as a proxy for broker size. Following this methodology, the measure for broker size is constructed as follows: BROKERSIZEi;t ¼

TVOLi;t ALLVOLt

ð6Þ

where BROKERSIZEi ,t is the market share for broker i across the whole market on day t, and ALLVOLt is the daily total volume for the whole market on day t. The relative offer size is a proxy to measure the size of a SEO. Following a similar methodology in Henry and Koski (2010), it is computed as follows: RELOF F n ¼

NEWSHAREn PRESHAREn

ð7Þ

where RELOFFn is the relative offer size for SEO event n, NEWSHAREn is the number of new shares offered during SEO event n, PRESHAREnis the number of shares outstanding before SEO event n.

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4.3. Determinants of broker trading volume and market share Regression analyses are conducted in this paper to investigate the determinants of the trading volume patterns and market share of affiliated brokers. Following similar approaches in Grant et al. (2010) and Niehaus and Zhang (2010), the regression models that are employed in this paper are shown as follows:   DEPENDENT i;t ¼ β0 þ β1 AFFILIATIONi;t þ β2 ln BROKERSIZEi;t þβ3 ln ðMKTCAP t Þ þ β4 STDt þ β5 RELSIZEt þ β6 CLOSEOFFERt þ β7 RIGHTS DUMMY t þ εi;t

ð8Þ

  DEPENDENT i;t ¼ β0 þ β1 ln BROKERSIZEi;t þ β2 ln ðMKTCAP t Þ þ β3 STDt þ β4 RELSIZEt þ β5 CLOSEOFFERt þ β6 UNDERWRITERi;t þ β7 LEADMANAGERi;t þ β8 COMANAGERi;t þ β9 RIGHTS DUMMY t þ εi;t :

ð9Þ

BROKERSIZEi,t refers to the total market share of broker i on day t across all stocks. MKTCAPt is the market capitalization of the offering firm on the trading day prior to the issuance day t. Since broker size and market capitalization are not normally distributed, the logarithm forms of these two variables are used in the regression model (see similar approaches in Henry and Koski, 2010). STDt refers to the standard deviation of daily returns over the 30 trading days prior to the offering. RELSIZEt is the relative offer size of the offering firm on day t. CLOSEOFFERt refers to the close-to-offer discount of the offering firm on issuance day t. In Regression 1, AFFILIATIONi,t is a dummy variable equal one if broker i is affiliated in a SEO on day t, and zero otherwise. In Regression 2, UNDERWRITERi , t equals one if broker i for the offering event on day t is an underwriter, and zero otherwise. LEADMANAGERi,t equals one if broker i for the offering event on day t is a lead manager, and zero otherwise. COMANAGERi ,t equals one if broker i for the offering event on day t is a co-manager, and zero otherwise. If the above three dummy variables are all zero, it indicates that the broker is an unaffiliated broker. RIGHTS_DUMMYt equals one if the SEO is a rights issue, and zero otherwise. Both regressions will be estimated against various dependent variables, DEPENDENTi ,t. These dependent variables consist of the abnormal market share for an individual firm from broker i on day t, the raw market share for an individual firm from broker i on day t, the abnormal volume for an individual firm from broker i on day t, and the abnormal market share for an individual firm from broker i on day t. We apply White's heteroscedasticity consistent standard errors to test the statistical significant of the regression coefficients. The regression should provide insights into the characteristics of SEOs that determine broker trading volume and market share around SEOs. 5. Results 5.1. Descriptive statistics Table 1 shows the summary statistics for rights offerings and private placements in the sample period. In total, there are 527 offering events in the 10-year time frame of which private placements constitute the vast majority as compared to rights offerings. In Panel A, the offering events are grouped by industry sectors. Panel A shows that the real estate industry has by far the most offerings, comprising 24.48% of all SEOs among the 23 industry sectors (average less than 5%). The Energy sector has the second most SEOs with 14.04% during the sample period. In contrast, the Chemicals sector and the Telecommunication sector have the lowest proportion of SEOs, each having participated in less than five offerings within the sample period. In terms of private placements more narrowly, the highest proportion is also from the Energy sector. Interestingly, the Diversified Financials sector has the most rights offerings in the sample. Although both the Banks sector and the Insurance sector only constitute around 2% of SEOs, they have the highest average issue value, both over $500 million per offering. Panel B, which groups SEO events according to year, illustrates variation in SEO activity across time, reflecting the influence of market conditions. After 2000, the number of offerings per year and the average issue value remained relatively stable over the sample period, except for 2003, when the average issue value was greater than $100 million. In 2008, the number of SEOs dropped dramatically in the market due to the Global Financial Crisis. However, the average issue value significantly increased to over $260 million. After 2008, the number of SEOs rose again to 75 offerings and the average issue value dropped to $161 million. Table 2 presents the mean values of relevant firm and offering characteristics in this study. The sample is grouped into four quartile subsamples based on market capitalization. In the last two columns, the SEOs are separated into two groups according to their offering type – private placement and rights offering. An interesting point is that the lower three quartiles are relatively small-cap firms. Their volumes tend to be much lower than the more liquid firms with larger market capitalizations. Therefore, it is important to control for market cap in further analysis. Moreover, Table 2 indicates that market capitalization is not normally distributed. Therefore, it is necessary to take the logarithm of market capitalization in further analysis in order to transform this variable from a skewed distribution to a relatively normal distribution. Large-cap firms have higher absolute offer values, providing an average of $290 million per offer. However, their relative offer sizes tend to be lower than the other three quartile groups. The close-to-offer discounts exist for all quartile groups, suggesting that private placements and rights offerings are typically given at discounting prices. In addition, the lowest quartile group has the highest discounting rate of 12.27%. The latter two columns suggest that mean measures for most variables are higher for rights offerings compared to private placements, such as return

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Table 1 Summary statistics for rights offerings and private equity placements. This table summarizes the 527 rights offerings and private equity placements during the period January 2000 to December 2009. In Panel A, the sample is grouped into 23 subsamples according to their GICS sectors. In Panel B, the sample is grouped into 10 subsamples according to the year of offer. Fraction is the number of offerings in each category divided by the total number of offerings. Market cap refers to the average market capitalization measured on the trading day prior to the issuance day. Panel A GICS sector

Automobiles Banks Capital goods Chemicals Commercial & professional Construction Consumer discretionary Diversified financials Energy Food & staples Food beverage Health care Insurance Media Paper & forest Pharmaceuticals Real estate Retailing Software & services Technology hardware Telecommunication Transportation Utilities Total

Rights offerings

Private placements

N

SEOs Fraction

N

Fraction

N

Fraction

5 13 50 2 21 7 13 34 74 5 18 29 10 12 8 24 129 10 9 5 4 24 21 527

0.95% 2.47% 9.49% 0.38% 3.98% 1.33% 2.47% 6.45% 14.04% 0.95% 3.42% 5.50% 1.90% 2.28% 1.52% 4.55% 24.48% 1.90% 1.71% 0.95% 0.76% 4.55% 3.98%

0 1 6 1 5 1 1 11 6 0 7 7 0 0 2 2 10 0 1 1 2 1 2 67

0.00% 1.49% 8.96% 1.49% 7.46% 1.49% 1.49% 16.42% 8.96% 0.00% 10.45% 10.45% 0.00% 0.00% 2.99% 2.99% 14.93% 0.00% 1.49% 1.49% 2.99% 1.49% 2.99%

5 12 44 1 16 6 12 23 68 5 11 22 10 12 6 22 119 10 8 4 2 23 19 460

1.09% 2.61% 9.57% 0.22% 3.48% 1.30% 2.61% 5.00% 14.78% 1.09% 2.39% 4.78% 2.17% 2.61% 1.30% 4.78% 25.87% 2.17% 1.74% 0.87% 0.43% 5.00% 4.13%

Market cap ($ 000s)

$250,499 $9,619,001 $536,289 $3,641,983 $956,945 $1,817,864 $1,255,413 $679,493 $269,682 $1,570,870 $256,135 $505,424 $6,564,323 $1,093,342 $648,215 $1,664,811 $1,155,536 $343,209 $157,323 $187,664 $204,471 $1,806,296 $1,240,928

Average issue value ($ 000s)

$28,032 $594,193 $34,441 $276,126 $78,420 $158,017 $160,234 $63,030 $31,926 $122,490 $52,208 $55,541 $547,458 $80,131 $80,308 $131,683 $83,640 $14,409 $10,600 $25,646 $14,977 $256,710 $166,719

Panel B Year

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 Total

SEOs

Rights offerings

Private placements

N

Fraction

N

Fraction

N

Fraction

25 54 63 45 57 47 61 66 34 75 527

4.74% 10.25% 11.95% 8.54% 10.82% 8.92% 11.57% 12.52% 6.45% 14.23%

3 6 9 5 7 11 8 7 1 10 67

4.48% 8.96% 13.43% 7.46% 10.45% 16.42% 11.94% 10.45% 1.49% 14.93%

22 48 54 40 50 36 53 59 33 65 460

4.78% 10.43% 11.74% 8.70% 10.87% 7.83% 11.52% 12.83% 7.17% 14.13%

Average issue value ($ 000s)

$61,958 $93,181 $62,981 $101,627 $61,939 $79,440 $78,615 $84,525 $266,187 $161,271

Market cap ($ 000s)

$921,514 $1,195,491 $793,438 $1,132,457 $672,700 $898,842 $848,992 $938,172 $3,860,854 $1,589,155

Table 2 Firm and offering characteristics for the SEOs. This table summarizes the sample firms' equity offering characteristics for the period from January 2000 to December 2009. The sample is grouped into four quartiles based on market capitalization. The Total column reports the results for the entire sample. The last two columns show the summary statistics for private placements and rights offerings respectively. The measures of offering characteristics are in line with Henry and Koski (2010). Std Dev Returns refers to the standard deviation of daily returns over the 30 trading days prior to the offering. Relative Offer Size refers to the size of offering divided by the number of shares outstanding prior to the offering. Close-to-Offer Discount is computed as the difference between the offer price and the closing price prior to the issuance day divided by the closing price prior to the issuance day. Pre-Issue Shares and Pre-Issue Market Cap are taken on the trading day prior to the issuance day. Total

Share offered (000s) Offer price Offer value ($ 000s) Pre-issue shares (000s) Pre-issue market cap ($ 000s) Std dev returns Relative offer size Close-to-offer discount

49,862 $3.00 $102,323 374,628 $1,254,667 8.01% 15.33% −7.18%

Market cap

SEO type

Q1

Q2

Q3

Q4

Placement

Rights

43,103 $0.49 $6527 222,553 $45,171 9.04% 19.82% −12.27%

30,086 $1.60 $34,718 193,435 $204,272 7.35% 18.02% −6.37%

66,180 $3.13 $79,366 353,159 $629,142 4.21% 15.13% −5.31%

60,183 $6.77 $290,434 735,433 $4,188,296 11.68% 8.37% −5.02%

43,409 $3.14 $107,154 389,154 $1,370,636 5.13% 13.06% −5.91%

94,363 $2.02 $69,012 274,462 $454,995 27.88% 31.01% −15.90%

P.W. He et al. / Pacific-Basin Finance Journal 38 (2016) 17–33

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volatility, relative offer size, close-to-offer discount. Due to the proportional nature of any dilution resulting from rights offerings vis-à-vis private placements, companies are typically able to place a great discount on the offer prices. Furthermore, the number of rights offerings is only a small proportion of the sample; thus, noting that most of these variables are naturally non-negative, it follows that extreme positive values from small-cap firms substantially bias these mean measures in an upward direction. 5.2. Trading activities around SEOs Table 3 shows daily abnormal trading volume and market share statistics for brokers around SEOs. The event window is split into five periods. (− 20)–(− 10) refers to the period from 20 days prior to the event to 11 days prior to the event. Similarly, (− 10)–(− 6) refers to the period from 10 days prior to the event to six days prior to the event. Day (0) indicates the event day. This table provides insights into trading volume patterns before and after the issuance day. Panel A shows that affiliated brokers experience negative daily abnormal market shares in the first two periods ((−20)–(−11) and (−10)–(−6)), indicating that they underperform during these two periods. Their best performances in competing for market share appear on the issuance day. Significant positive abnormal volumes are observed both in the beginning and at the end of the event window period. The highest abnormal volume and abnormal buy volume are presented prior to the issuance day. In Panel B, significant positive abnormal volume measures for unaffiliated brokers are observed on the issuance day. They are expected to receive over 100% more volume compared to their average level. In the first ten days of the event window period, unaffiliated broker abnormal volumes are not significantly different from zero, indicating their trading activity reflects their average levels. Abnormal market shares of unaffiliated brokers remain significantly negative for most of the event window period, suggesting that affiliated brokers tend to take substantial market shares from unaffiliated brokers in SEOs. 5.3. Determinants of trading volumes and Brokers' market share Table 4 presents the results from regression analyses of various broker activity indicators on broker and market variables that are expected to be important determinants. The affiliation dummy variable is statistically significant at the 1% level in all specifications except those associated with abnormal volume. The coefficient on the affiliation dummy variable is also quite large; for example, affiliated broker volumes are expected to have at least 151% more volumes than unaffiliated brokers around the SEO issuance day. Thus, controlling for potential sources of omitted variable bias, affiliation is clearly an economically significant determinant of broker volume and market share. The abnormal volume for affiliated brokers is expected to be 264% higher. Although the market capitalization of the offering firm has a significant impact on the total volume value, there is no evidence that it increases the likelihood of attaining abnormal volumes. The coefficients of the broker size variable are all positive, and mostly statistically significant at the 1% level, suggesting that brokers with better reputations tend to obtain abnormal volumes. Relative offer size is found to significantly impact broker trading volume. It is unlikely to be an important factor of broker abnormal trading volume. Affiliation is a significant determinant of broker daily market share and abnormal market share in most of the pre- and postissuance periods and on the issuance day. The results indicate that a brokerage firm abnormal market share is expected to be 88% higher than those of unaffiliated brokers on the issuance day. This effect persists into the post-issuance period as well. Broker size and market capitalization are also found to be significant determinants of a broker's daily market share. All coefficients of broker size are positive, which suggests that the level of a broker's reputation has a direct relationship with their market share.

Table 3 Broker trading activity around SEO. This table summarizes broker trading volumes and market share around SEOs. The event window is from 20 days prior to the SEO (−20) to 20 days after the SEO (20). Day 0 refers to the issuance day. Abnormal volume is measured as a broker's daily volume on the offering firm divided by the average daily volume calculated over the benchmark period. Abnormal market share is measured as a broker's daily market share in the offering firm divided by the average daily market share calculated over the benchmark period. Abnormal buy/sell volume is measured as a broker's daily buy/sell volume on the offering firm divided by the average daily buy/sell volume calculated over the benchmark period. The asterisks refer to the level of significance for a t-test of means that the mean value is different from zero. ***, **, * denote statistical significance at the 1%, 5%, and 10% levels respectively for the abnormal measures. (−20)–(−11)

(−10)–(−6)

Panel A: affiliated broker volume statistics around SEOs Abnormal volume 47.06%⁎⁎⁎ 58.67%⁎⁎⁎ Abnormal market share −21.60%⁎⁎⁎ −17.09%⁎⁎⁎ Abnormal buy volume 112.41%⁎⁎⁎ 101.31%⁎⁎⁎ Abnormal sell volume 52.34%⁎⁎⁎ 64.61%⁎⁎⁎

(−5)–(−1)

Day (0)

(1)–(5)

(6)–(10)

(11)–(20)

636.44%⁎⁎⁎ 13.78%⁎⁎⁎ 1794.80%⁎⁎ 522.12%⁎⁎⁎

549.01%⁎⁎⁎ 66.48%⁎⁎⁎ 555.80%⁎⁎⁎ 625.01%⁎⁎⁎

330.04%⁎⁎⁎ 38.98%⁎⁎⁎ 386.30%⁎⁎⁎ 357.59%⁎⁎⁎

223.44%⁎⁎⁎ 18.07%⁎⁎⁎ 269.35%⁎⁎⁎ 242.21%⁎⁎⁎

136.82%⁎⁎⁎ 4.80% 187.32%⁎⁎⁎ 144.84%⁎⁎⁎

187.66%⁎⁎⁎ −23.41%⁎⁎⁎ 216.48%⁎⁎⁎ 236.05%⁎⁎⁎

212.90%⁎⁎⁎ 6.25%⁎ 242.22%⁎⁎⁎ 291.74%⁎⁎⁎

104.28%⁎⁎⁎ −17.71%⁎⁎⁎ 111.53%⁎⁎⁎ 156.38%⁎⁎⁎

69.37%⁎⁎⁎ −21.02%⁎⁎⁎ 88.25%⁎⁎⁎ 105.59%⁎⁎⁎

43.58%⁎⁎⁎ −20.50%⁎⁎⁎ 65.16%⁎⁎⁎ 75.32%⁎⁎⁎

Panel B: unaffiliated broker volume statistics around SEOs Abnormal volume Abnormal market share Abnormal buy volume Abnormal sell volume

31.67%⁎⁎⁎ −28.58%⁎⁎⁎ 79.09%⁎⁎⁎ 45.93%⁎⁎⁎

37.29%⁎⁎⁎ −20.03%⁎⁎⁎ 72.31%⁎⁎⁎ 52.71%⁎⁎⁎

26

Period

Ln (volume)

Abnormal volume

Broker market share

Abnormal market share

(−5)–(−1)

(0)

(1)–(5)

(−5)–(−1)

(0)

(1)–(5)

(−5)-(−1)

(0)

(1)–(5)

(−5)–(−1)

(0)

(1)–(5)

Affiliation

7.314⁎⁎⁎ (1.160) 2.119⁎⁎⁎

8.327⁎⁎⁎ (1.003) 1.697⁎⁎⁎

8.013⁎⁎⁎ (1.090) 1.990⁎⁎⁎

3.378 (8.699) 5.282⁎⁎⁎

−7.458 (5.613) 3.904⁎⁎⁎

1.009 (2.915) 1.613⁎⁎⁎

0.284⁎⁎⁎ (0.050) 0.074⁎⁎⁎

0.467⁎⁎⁎ (0.073) 0.105⁎⁎⁎

0.377⁎⁎⁎ (0.057) 0.095⁎⁎⁎

−0.764 (0.532) 0.425⁎⁎⁎

−3.399⁎⁎ (1.044) 0.883⁎⁎⁎

−1.673⁎⁎ (0.533) 0.792⁎⁎⁎

Ln (broker size)

(0.170) 0.830⁎⁎⁎

(0.156) 0.632⁎⁎⁎

(0.069) 0.269⁎⁎⁎ (0.054) 0.291⁎

(0.063) 0.202⁎⁎⁎ (0.047) 0.179 (0.112) 1.540⁎⁎⁎

(0.173) 0.706⁎⁎⁎ (0.070) 0.220⁎⁎⁎ (0.050) −0.031 (0.127) 1.703⁎⁎⁎

(0.413) −0.502 (0.431) −0.095 (0.184) −1.581 (0.909) 6.967 (4.645) 6.918 (8.901) 1.083 (2.440) 0.038

(0.007) 0.024⁎⁎⁎ (0.003) −0.008⁎⁎⁎ (0.002) −0.000 (0.013) 0.002 (0.012) −0.056 (0.039) −0.013 (0.022) 0.237

(0.010) 0.028⁎⁎⁎ (0.006) −0.017⁎⁎⁎ (0.004) −0.024 (0.012) −0.023 (0.029) −0.120 (0.064) 0.011 (0.034) 0.192

(0.077) 0.206⁎⁎⁎ (0.025) 0.050 (0.025) −0.056 (0.046) 0.558⁎⁎⁎

(0.231) −2.289⁎ (1.109) −1.244⁎⁎⁎ (0.327) 0.387

(1.028) 0.308 (0.513) 0.467 (0.307) −1.235 (0.842) 3.204 (5.260) −4.952 (10.461) −2.572 (2.448) 0.015

(0.008) 0.021⁎⁎⁎ (0.004) −0.013⁎⁎⁎ (0.003) −0.022⁎

(0.229) −3.512⁎⁎⁎ (0.917) −1.851⁎⁎⁎ (0.427) 0.339

(1.572) 2.008 (1.745) 0.233 (0.272) 0.392 (0.575) −2.633 (5.882) −4.963 (8.144) −5.727⁎⁎⁎ (1.462) 0.011

(0.191) 0.079 (0.055) 0.174⁎⁎ (0.053) −0.128⁎ (0.061) −0.179 (0.224) −0.776 (0.793) −0.639⁎⁎ (0.224) 0.047

(0.093) 0.109⁎⁎ (0.037) 0.083⁎⁎⁎ (0.025) −0.150 (0.083) −0.013 (0.148) −0.006 (0.414) −0.358⁎ (0.156) 0.119

Intercept

Ln (market cap) Std dev Relative offer size Close-to-offer Rights dummy R-square

(0.147) 1.779⁎⁎⁎ (0.279) −3.029⁎⁎⁎ (0.878) −1.661⁎⁎⁎ (0.320) 0.438

(0.009) −0.029 (0.017) 0.011 (0.052) 0.012 (0.023) 0.226

(0.148) −0.733 (0.457) −0.398⁎⁎ (0.150) 0.134

P.W. He et al. / Pacific-Basin Finance Journal 38 (2016) 17–33

Table 4 Determinants of broker trading volume and market share around issuance day. This table summarizes the results of the regression analysis on determinants of brokers' trading volumes and market shares around SEO issuance days. Ln (volume), abnormal volume, broker market share and abnormal market share are regressed against dependent variables. (−5)–(−1) refers to the period from five days prior to the event to one day prior to the event. Day (0) refers to the announcement day. (1)–(5) refers to the period from one day after the event to five days after the event. Robust standard errors are used to compute t-statistics, which are reported in parentheses. ***, **, * denote the statistical significance at the 1%, 5%, and 10% levels respectively.

Dependent variable

Ln (volume)

Period

(−5)–(−1)

(0)

(1)–(5)

(−5)–(−1)

(0)

(1)–(5)

(−5)–(−1)

(0)

(1)–(5)

(−5)–(−1)

(0)

(1)–(5)

Intercept Affiliation

10.249⁎⁎⁎ (0.540) 1.133⁎⁎⁎

13.793⁎⁎⁎ (0.466) 1.469⁎⁎⁎

12.129⁎⁎⁎ (0.517) 1.789⁎⁎⁎

8.882 (4.588) 10.505⁎⁎

0.183⁎⁎⁎ (0.021) 0.119⁎⁎⁎

0.144⁎⁎⁎ (0.012) 0.083⁎⁎⁎

−1.438⁎⁎⁎ (0.266) 0.646⁎⁎⁎

(0.134) 0.411⁎⁎⁎

(0.028) 0.062⁎ (0.027) −0.304⁎⁎⁎ (0.035) 0.645⁎

(0.026) −0.082⁎⁎⁎ (0.023) −0.392⁎⁎⁎

(0.109) 0.517⁎⁎⁎ (0.027) −0.004 (0.025) −0.402⁎⁎⁎

(0.005) 0.013⁎⁎⁎ (0.001) −0.003⁎⁎⁎ (0.001) −0.000 (0.001) 0.006 (0.009) −0.003 (0.004) −0.001 (0.003) 0.185

(0.011) 0.013⁎⁎⁎ (0.001) −0.005⁎⁎⁎ (0.001) −0.001 (0.001) −0.008 (0.013) −0.004 (0.006) 0.002 (0.006) 0.198

(0.006) 0.013⁎⁎⁎ (0.001) −0.003⁎⁎⁎ (0.001) 0.001 (0.001) 0.006 (0.008) 0.003 (0.004) −0.002 (0.003) 0.283

−1.692⁎⁎⁎ (0.216) −0.023 (0.041) 0.111⁎⁎⁎

−1.820⁎⁎⁎ (0.450) 0.685⁎⁎⁎

(0.117) 0.492⁎⁎⁎

1.535 (2.244) 3.236 (2.244) 0.791⁎⁎

0.145⁎⁎⁎ (0.012) 0.034⁎⁎⁎

Ln (broker size)

−2.369⁎⁎ (0.768) 0.053 (0.266) 0.141⁎⁎⁎

(0.136) 0.077⁎⁎⁎ (0.023) 0.101⁎⁎⁎ (0.022) −0.004 (0.025) −0.448⁎

(0.082) 0.121⁎⁎⁎ (0.013) 0.076⁎⁎⁎ (0.014) 0.152⁎⁎⁎

Ln (market cap) Std dev Relative offer size Close-to-offer Rights dummy R-square

(0.327) −0.473⁎ (0.204) −0.334⁎ (0.141) 0.164

Abnormal volume

(0.032) 0.320 (0.284) −0.591⁎⁎ (0.208) −0.257 (0.145) 0.172

(0.037) 0.786⁎⁎ (0.299) −0.346 (0.230) −0.002 (0.137) 0.238

(0.027) 0.146⁎⁎⁎ (0.038) −0.170⁎⁎⁎ (0.032) 0.413 (0.221) −0.349⁎⁎ (0.125) −0.267⁎⁎ (0.092) 0.014

(4.039) 0.798⁎⁎ (0.298) −0.095 (0.203) −0.800⁎⁎⁎ (0.154) −2.493 (2.220) −1.134 (1.349) 0.280 (0.912) 0.020

Broker market share

(0.290) 0.128 (0.076) −0.318⁎ (0.139) 4.428 (2.652) −0.550 (0.707) −1.428 (1.006) 0.005

Abnormal market share

(0.010) 0.088⁎⁎⁎ (0.011) 0.022 (0.015) −0.158 (0.093) −0.044 (0.084) −0.001 (0.042) 0.056

(0.182) 0.111 (0.141) 0.078 (0.086) 0.024

(0.042) −0.371⁎⁎ (0.139) −0.077 (0.252) −0.023 (0.045) 0.064

P.W. He et al. / Pacific-Basin Finance Journal 38 (2016) 17–33

Table 5 Determinants of broker trading volume and market share around announcement. This table summarizes the results of the regression analysis on determinants of brokers' trading volumes and market shares around SEO announcements. Ln (volume), abnormal volume, broker market share and abnormal market share are regressed against dependent variables. (−5)–(−1) refers to the period from five days prior to the event to one day prior to the event. Day (0) refers to the announcement day. (1)–(5) refers to the period from one day after the event to five days after the event. Robust standard errors are used to compute t-statistics, which are reported in parentheses. ***, **, * denote the statistical significance at the 1%, 5%, and 10% levels respectively.

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P.W. He et al. / Pacific-Basin Finance Journal 38 (2016) 17–33

Interestingly, the market capitalization of SEO firms appears to be negatively related to the daily market share of a broker. In contrast, market capitalization is expected to have a significant positive impact on broker abnormal market share. Consistent with trading volume and abnormal volume, there is no evidence showing that relative offer size would determine the levels of brokers' market shares and abnormal market shares. Interestingly, close-to-offer discount is only a significant determinant of broker trading volume, indicating that the level of underpricing is unlikely to affect broker market share during SEOs, which is inconsistent with findings in Henry and Koski (2010). Ceteris paribus, rights issues typically generate lower volume and abnormal broker market share than private placements. Unlike rights issues that are spread across the shareholder base and result in proportional dilution, private placements are managed by brokers that facilitate the capital raising from specific large institutional investors, which lead to disproportionate dilution among existing shareholders. There usually would be a greater need to transaction around the placement than rights offerings, as investors rebalance their portfolios and adjust their valuations for the stocks. Table 5 provides the results from the regression analyses relating to the determinants of broker trading volume and market share around SEO announcements. The results are generally in line with that in Table 4. The affiliation dummy variable is significant at the 1% level in the first three equations. The coefficient of the affiliation dummy variable indicates that affiliated broker volumes are expected to be significantly higher than those of unaffiliated brokers in the pre- and post-announcement period and on the announcement day. Affiliated brokers receive 147% more volume than other brokers on average on the announcement days. This percentage increases to 179% even in the post-announcement period. However, affiliated brokers are expected to be the large brokerage firms in the market. For this reason, their volumes are higher than those from other brokerage firms. In the pre-announcement period, the abnormal market share of brokers is unlikely to be influenced by broker affiliation. This is also reflected in Table 3, in which the pre-announcement period appears to be relatively flat. Following the announcement, broker affiliation then becomes a significant determinant. Higher trading volumes prior to both SEO announcements and issuance days reflect potential compensations for underwriters for the purpose of better share allocations in SEO deals, which is consistent with the findings on IPO deals in Nimalendran et al. (2007). Both the broker size and the market capitalization are found to be correlated with broker trading activities around SEOs. The broker size has a positive impact on brokers' trading volumes and market share, indicating that large brokers are likely to take away a substantial market share from smaller brokers. Furthermore, abnormal trading volumes and abnormal market shares are also positively correlated with broker size. This suggests that the percentage changes in broker market share around SEOs

Table 6 Determinants of broker trading volume and market share with interactive variables. This table summarizes the results of the regression analyses related to the determinants of broker trading volume and market share around SEO events. Broker trading volume, abnormal volume, broker market share, and abnormal market share are regressed against dependent variables. (−5)–(−1) refers to the period from five days prior to the event to one day prior to the event. Day (0) refers to the announcement day. (1)–(5) refers to the period from one day after the event to five days after the event. Robust standard errors are used to compute t-statistics, which are reported in parentheses. ***, **, * denote statistical significance at the 1%, 5%, and 10% levels respectively. Dependent variable

Broker market share

Period

(−5)–(−1)

(0)

(1)–(5)

(−5)–(−1)

(0)

(1)–(5)

Intercept

0.272⁎⁎⁎ (0.051) 0.135⁎⁎⁎

0.448⁎⁎⁎ (0.075) 0.233⁎⁎⁎

0.379⁎⁎⁎ (0.059) 0.137⁎⁎⁎

−0.932 (0.531) 0.584⁎

(0.023) 0.114 (0.074) 0.156⁎⁎⁎ (0.037) 0.014⁎⁎⁎

(0.037) 0.125⁎ (0.048) 0.210⁎⁎⁎ (0.054) 0.008⁎

(0.029) 0.117 (0.061) 0.117⁎⁎ (0.038) 0.017⁎⁎⁎

(0.249) 0.102 (0.355) 0.715 (0.407) 0.145⁎⁎⁎

−1.570⁎⁎ (0.526) 0.075 (0.270) −0.496 (0.385) −0.212 (0.313) 0.224⁎⁎⁎

(0.003) −0.010⁎⁎⁎ (0.003) 0.002 (0.016) 0.019 (0.032) −0.061 (0.041) −0.016 (0.024) 0.017⁎⁎ (0.006) −0.027 (0.036) 0.245

(0.004) −0.020⁎⁎⁎ (0.004) −0.010 (0.017) 0.061 (0.042) −0.121 (0.067) 0.017 (0.035) 0.033⁎⁎⁎ (0.009) −0.108 (0.056) 0.206

(0.004) −0.014⁎⁎⁎ (0.003) −0.018 (0.012) 0.075 (0.041) 0.020 (0.055) 0.017 (0.024) 0.008 (0.008) −0.115⁎

(0.029) 0.054 (0.029) −0.007 (0.080) −0.768 (0.629) −0.914 (0.473) −0.446⁎⁎ (0.172) 0.091 (0.055) 1.342⁎

−3.605⁎⁎⁎ (1.066) 0.272 (0.455) −1.115⁎ (0.514) −0.129 (0.545) 0.100 (0.070) 0.207⁎⁎⁎

(0.045) 0.229

(0.660) 0.141

Underwriter Co-manager Lead manager Ln (broker size) Ln (market cap) Std dev Relative offer size Close-to-offer Rights dummy Affiliation ∗ broker size Affiliation ∗ relative offer size R-square

Abnormal market share

(0.059) 0.016 (0.110) −3.652⁎⁎⁎ (1.081) −1.133 (0.818) −0.638⁎⁎ (0.237) −0.059 (0.111) 3.674⁎⁎ (1.117) 0.055

(0.035) 0.100⁎⁎⁎ (0.027) −0.093 (0.102) −0.431 (0.609) 0.002 (0.424) −0.326 (0.170) −0.193⁎⁎ (0.072) 0.539 (0.621) 0.129

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are greater for larger brokerage firms. In comparison, market capitalization is negatively associated with broker trading activities. Brokers are expected to obtain higher trading volumes and market shares in relatively small firms around SEOs. Table 6 presents the results of regression analyses on 11 determinants of broker trading volume and market share around SEO issuance and announcement days. The affiliation dummy variable is replaced by three dummy variables: underwriter, co-manager, and lead manager. Moreover, two additional interactive variables are included in this regression analysis. The use of these interactive variables shows the effect of offer size and broker reputation conditional on affiliation. The key results presented in Table 6 are consistent with the findings in Table 4 and Table 5, which suggest that underwriters are expected to obtain significantly higher daily trading volumes than other brokers in the market. Lead managers are expected to receive a higher market share around SEO issuance days. There is no significant evidence that lead managers are obtaining more trading volume and market share than other underwriters after controlling for broker sizes and market capitalizations. In contrast, co-managers are not expected to gain any abnormal volume or market share around SEO issuance days. These findings are consistent with the findings in Ellis et al. (2000). Similar to the results in previous tables, broker size and market capitalization are shown to be significant determinants of broker trading volume and market share around SEOs. The stock volatility and the level of underpricing are unlikely to impact on broker performance during SEOs. Consistent with the results in Table 4 and Table 5, rights issues are expected to generate lower volume and abnormal market share compared to private placement, which reflects a greater need for investors to rebalance their portfolios around placement than rights issues. The first interactive variable suggests that broker size is expected to be positively associated with broker market share prior to the SEO issuance conditional on affiliation. Moreover, according to the second interactive variable, affiliated brokers tend to obtain significant abnormal market share on the issuance day of bigger

Table 7 Determinants of broker trading volume around issuance day. This table summarizes the results of the regression analysis on determinants of broker trading volume around SEO issuance. Broker trading volume and abnormal volume are regressed against dependent variables. (−5)–(−1) refers to the period from five days prior to the event to one day prior to the event. Day (0) refers to the announcement day. (1)–(5) refers to the period from one day after the event to five days after the event. Robust standard errors are used to compute t-statistics, which are reported in parentheses. ***, **, * denote the statistical significance at the 1%, 5%, and 10% levels respectively. Dependent variable

Ln (volume)

Period

(−5)–(−1)

(0)

(1)–(5)

(−5)–(−1)

(0)

(1)–(5)

Intercept

7.804 (1.058)⁎⁎⁎ 1.572 (0.207)⁎⁎⁎

8.289 (1.070)⁎⁎⁎ 1.161 (0.207)⁎⁎⁎

8.612 (1.046)⁎⁎⁎ 1.661 (0.208)⁎⁎⁎

0.932 (1.131) 0.352 (0.485) 0.814 (0.069)⁎⁎⁎ 0.284 (0.048)⁎⁎⁎ 0.063 (0.273) 1.799 (0.381)⁎⁎⁎

0.243 (1.1659) −0.628 (0.473) 0.612 (0.067)⁎⁎⁎

2.419 (1.123)⁎⁎ 0.278 (0.459) 0.699 (0.067)⁎⁎⁎

15.551 (20.937) 1.795 (4.052) −5.908 (22.603) −4.528 (9.185) 2.264 (1.314)⁎

0.214 (0.049)⁎⁎⁎ 0.028 (0.281) 1.559 (0.382)⁎⁎⁎

0.190 (0.048)⁎⁎⁎ −0.328 (0.271) 2.383 (0.377)⁎⁎⁎

−2.642 (5.724) 1.663 (1.107) 1.555 (6.180) −1.043 (2.511) −0.433 (0.359) −0.068 (0.261) −1.289 (1.501) 11.632 (2.077)⁎⁎⁎

−1.445 (0.815)⁎

−0.582 (0.357) 0.249 (0.182) 0.995 (1.239) 0.852 (0.886) −0.194 (0.259) −1.493 (0.359)⁎⁎⁎ −0.027 (0.321) 1.000 (0.311)⁎⁎⁎

−2.744 (0.839)⁎⁎⁎ 0.320 (0.296) 0.011 (0.299) −0.452 (0.362) 0.108 (0.184) −0.732 (1.277) 0.560 (0.912) 0.077 (0.259) −0.828 (0.355)⁎⁎ −0.269 (0.318) 1.252 (0.320)⁎⁎⁎

−11.869 (10.412) 2.917 (2.015) −1.985 (11.241) −1.558 (4.568) 0.155 (0.653) 0.781 (0.475)⁎ −0.803 (2.732) 2.342 (3.779) −0.621 (8.193) −2.065 (2.883) −1.736 (2.927) −3.224 (3.552) 1.362 (1.807) −3.591 (12.300) 2.798 (10.079) 0.391 (2.529) −0.824 (3.441) 7.681 (3.196)⁎⁎

0.455

0.349

Underwriter Co-manager Lead manager Ln (broker size) Ln (market cap) Std dev Relative offer size Close-to-offer Growth Funding acquisition Working capital Institutional Sophisticated Institutional and professional Institutional and sophisticated Professional and sophisticated Placements and rights Convertible R-square

−2.046 (0.836)⁎⁎ −0.349 (0.293) −0.692 (0.294)⁎⁎

Abnormal volume

0.360 (0.290) 0.331 (0.295) 0.285 (0.356) −0.007 (0.180) −1.440 (1.230) −1.089 (0.879) −0.810 (0.258)⁎⁎⁎

−0.106 (0.955) −0.332 (5.493) −0.266 (7.598) 0.252 (16.474) −0.765 (5.796) −9.749 (5.885)⁎

−1.242 (0.350)⁎⁎⁎ −0.497 (0.319) 0.783 (0.315)⁎⁎⁎

−4.019 (7.142) 4.600 (3.633) −6.863 (24.734) −0.735 (20.268) −3.197 (5.085) −0.183 (6.919) 2.085 (6.427) 19.644 (6.226)⁎⁎⁎

0.418

0.040

−4.969 (3.096) 0.031

11.633 (4.504)⁎⁎ 3.301 (1.584)⁎⁎ 3.399 (1.609)⁎⁎ 3.458 (1.952)⁎ 0.512 (0.993) −4.375 (6.762) 0.115 (5.541) −4.558 (1.390)⁎⁎⁎ −0.255 (1.891) 6.700 (1.757)⁎⁎⁎ −2.166 (1.702) 0.001

30

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SEO deals. We have also conducted a regression analysis on a sub-sample of affiliated brokers only (see Appendix A). The results are consistent with the findings in Table 6. 5.4. Robustness tests In this section, a number of additional factors that may influence brokers trading volume and market share around SEOs are included as control variables in the regression analysis. These qualitative control variables relate to the purpose of the equity offering, the target investor group, the convertibility condition of the SEO, and whether or not there is more than one type of SEO at on particular day. The purpose of the SEO and target investor group is expected to influence trading activities in the market. Firstly, the common reasons for private placements or rights offerings include funding acquisitions, reducing debts, increasing working capital, and growth and expansions. These different purposes may reflect both the performance and health status of a firm. Therefore, it is valuable to investigate the impact of SEO purpose on broker trading activities in regression analyses. Secondly, the SEO underwriters of SEOs may have specific groups of target investors, such as institutional investors, sophisticated investors, professional investors, or retail investors. Since different investor groups are expected to exhibit contrasting trading behaviour, it is important to control for the target investor group on broker trading behaviour. Table 7 and Table 8 present the results of these modified regressions in relation to SEO issuance days. The underwriter is shown to be a significant positive factor, which is consistent with previous results that underwriters are expected to obtain

Table 8 Determinants of broker market share around issuance day. This table summarizes the results of the regression analysis on determinants of broker market share around SEO issuance. Broker market share and abnormal market share are regressed against dependent variables. (−5)–(−1) refers to the period from five days prior to the event to one day prior to the event. Day (0) refers to the announcement day. (1)–(5) refers to the period from one day after the event to five days after the event. Robust standard errors are used to compute t-statistics, which are reported in parentheses. ***, **, * denote the statistical significance at the 1%, 5%, and 10% levels respectively. Dependent variable

Broker market share

Period

(−5)–(−1)

(0)

(1)–(5)

(−5)–(−1)

(0)

(1)–(5)

Intercept

0.359 (0.055)⁎⁎⁎ 0.075 (0.011)⁎⁎⁎

0.493 (0.089)⁎⁎⁎ 0.127 (0.017)⁎⁎⁎

0.427 (0.066)⁎⁎⁎ 0.104 (0.013)⁎⁎⁎

−0.394 (0.579)⁎⁎⁎

−2.860 (1.299)⁎⁎

−1.306 (0.672)⁎

0.061 (0.060) 0.095 (0.024)⁎⁎⁎ 0.026 (0.003)⁎⁎⁎

0.021 (0.096) 0.110 (0.039)⁎⁎⁎ 0.030 (0.006)⁎⁎⁎

0.088 (0.072) 0.092 (0.029)⁎⁎⁎ 0.023 (0.004)⁎⁎⁎

0.292 (0.112) −0.187 (0.626) 0.229 (0.254) 0.212 (0.036)⁎⁎⁎

0.685 (0.130)⁎⁎⁎ 0.128 (0.725) 0.236 (0.295) 0.123 (0.042)⁎⁎⁎

−0.011 (0.003)⁎⁎⁎ −0.002 (0.015) −0.005 (0.020) −0.059 (0.043) −0.036 (0.015)⁎⁎ −0.029 (0.015)⁎

−0.019 (0.004)⁎⁎⁎ −0.010 (0.023) −0.023 (0.032) −0.109 (0.069) 0.002 (0.024) −0.001 (0.025) 0.031 (0.030) −0.004 (0.015) −0.124 (0.106) 0.129 (0.075)⁎

−0.017 (0.003)⁎⁎⁎ −0.017 (0.017) 0.006 (0.024) −0.002 (0.052) 0.020 (0.018) 0.032 (0.019)⁎

0.728 (0.251)⁎⁎⁎ −0.733 (1.403) −0.013 (0.570) 0.097 (0.082) 0.150 (0.059)⁎⁎ −0.062 (0.341) −0.227 (0.471) −0.598 (1.022) 0.175 (0.360) −0.362 (0.365) −0.309 (0.443) 0.401 (0.225)⁎ −1.245 (1.535) −0.564 (1.258) 0.140 (0.316) 0.787 (0.429)⁎ −0.031 (0.399) 0.515 (0.386) 0.076

Underwriter Co-manager Lead manager Ln (broker size) Ln (market cap) Std dev Relative offer size Close-to-offer Growth Funding acquisition Working capital Institutional Sophisticated Institutional and professional Institutional and sophisticated Professional and sophisticated Placements and rights Convertible R-square

−0.007 (0.019) 0.016 (0.010)⁎ −0.028 (0.066) 0.062 (0.047) 0.003 (0.013) 0.052 (0.018)⁎⁎⁎ 0.003 (0.016) 0.017 (0.017) 0.259

Abnormal market share

−0.013 (0.021) 0.052 (0.029)⁎ 0.004 (0.026) −0.026 (0.027) 0.210

0.040 (0.022)⁎ 0.009 (0.011) −0.149 (0.079)⁎ −0.012 (0.057) −0.034 (0.016)⁎⁎ 0.045 (0.022)⁎⁎ −0.023 (0.020) −0.017 (0.020) 0.251

0.034 (0.026) −0.075 (0.152) 0.550 (0.210)⁎⁎⁎ −0.614 (0.456) 0.009 (0.160) −0.235 (0.163) −0.022 (0.198) 0.262 (0.101)⁎⁎⁎ −0.200 (0.685) −0.099 (0.561) 0.066 (0.141) 0.065 (0.192) −0.068 (0.178) 0.461 (0.172)⁎⁎⁎ 0.157

0.058 (0.031)⁎ −0.135 (0.176) 0.211 (0.244) 0.117 (0.528) 0.221 (0.186) 0.090 (0.189) 0.029 (0.229) 0.192 (0.117)⁎ −1.213 (0.793) −0.243 (0.650) −0.130 (0.163) 0.370 (0.222)⁎ −0.042 (0.206) 0.346 (0.200)⁎ 0.145

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greater trading volume and market share over other brokers around SEO issuances. This behaviour differs for co-managers and lead-managers. Similar to the results in previous sections, co-managers are unlikely to outperform other brokers during SEO events. Moreover, lead managers are expected to gain a greater market share, but their trading volumes do not have substantial abnormal patterns during SEOs. Broker size is positively related to broker trading volume and market share. In contrast, market capitalization appears to positively influence broker abnormal market share, but negatively influence broker normal market share. Broker trading volume is more likely to increase when the purpose of the equity offering is growth and expansion. Other purposes are unlikely to affect broker trading activities. If an SEO involves both private placement and rights offering, then brokers are likely to attain higher trading volume and abnormal volume on the issuance day. However, it does not affect their market share. The convertibility of SEOs is found to be positively related to broker trading volume and abnormal volume.

6. Conclusions This paper investigates broker trading volume and market share behaviour around SEO events in the Australian primary market based on a unique broker ID dataset. Multivariate-regressions were conducted to examine the key determinants that influence affiliated and unaffiliated broker behaviour, including stock characteristics, index movements, and broker characteristics. The findings provide insights into the key determinants that may impact on broker trading behaviour and market share around equity offering events. Using evidence from the Australian market, this paper suggests that broker affiliation has a significant impact on broker trading volume and market share around both SEO announcement days and issuance days. Affiliated brokers are shown to outperform unaffiliated brokers by 147% in terms of trading volume on the SEO announcement day, and this number goes up to 179% in the post-announcement period. Around SEO issuance days, affiliated brokers are expected to obtain at least 151% more trading volume, and their abnormal volume is expected to be 264% higher than that of unaffiliated brokers. The results indicate that brokers are able to improve their turnover and performance by participating in SEOs. These findings suggest an amount of indirect compensation for SEO management and underwriting, which is evidenced through increased market share for their brokerage division. This additional indirect compensation may be considered by investment banks when pricing SEO management and underwriting services. This paper explores other the key determinants of broker trading activities around SEOs based on regression analysis. The results show that broker size and the market capitalization of the issuing firm are two primary characteristics affecting broker performance around SEOs. Broker trading volume is shown to be negatively associated with the market capitalization during announcements. Furthermore, relative offer size appears to significantly impact broker trading volume in the postannouncement period, but their trading activities are unlikely to be affected by stock volatility and the level of underpricing of SEOs. Furthermore, SEOs with a purpose of growth and expansion are expected to bring greater trading volume to brokers. These findings should be of interest to advisory firms as well as the affiliated brokerage firms. They imply that market capitalization, relative offer size, and purpose of offering are important factors that they should consider in searching for and constructing deals. Depending on data availability on equity raising income, brokerage commissions and/or facilitation income, future research may potentially examine indirect and cross compensation and subsidisation between different areas of investment banks around capital raisings. Further, during SEOs, the underwriter could be different from the IPO underwriter of the issuing firm. Firms may switch underwriters depending on post-IPO performance, analyst coverage, or change of the firm size. It would also be interesting to examine the factors that influence a firm's decision on the choice of underwriters post IPO, which could shed further light on underwriters' competitiveness and market share.

Acknowledgements This research is funded by the Financial Markets Research Centre under Corporations Regulation 7.5.88(2). We would like to acknowledge the Securities Industry Research Centre of Asia-Pacific for providing the data used in this research. We would also like to thank seminar participants at the 2012 University of Sydney Colloquium and Capital Markets CRC for their helpful comments and suggestions.

Appendix A. Determinants of affiliated broker trading volume and market share around announcement day This table summarizes the results of the regression analysis on determinants of brokers' trading volume around SEO announcements. Broker Trading Volume and Abnormal volume are regressed against dependent variables. Standard errors are listed in brackets. (−5)–(−1) refers to the period from five days prior to the event to one day prior to the event. Day (0) refers to the announcement day. (1)–(5) refers to the period from one day after the event to five days after the event. Robust standard errors are used to compute t-statistics, which are reported in parentheses. ***, **, * denote statistical significance at the 1%, 5%, and 10% levels respectively.

32

(−5)–(−1)

Abnormal volume

Intercept Ln (broker size) Ln (market cap) Std dev Relative offer size Close-to-offer Rights dummy R-square

(0)

9.528⁎⁎⁎

8.697⁎⁎⁎

−1.528 0.815⁎⁎⁎ −0.089 0.266⁎⁎⁎

−1.366 0.604⁎⁎⁎ −0.084 0.265⁎⁎⁎

−0.07 0.274 −0.147 1.662⁎⁎⁎ −0.304 −1.642 −0.92 −1.484⁎⁎⁎

−0.063 0.16 −0.109 1.616⁎⁎⁎ −0.25 −2.957⁎⁎

−0.323 0.323

−0.988 −1.786⁎⁎⁎ −0.434 0.243

(1)–(5) 9.849⁎⁎⁎ −1.339 0.567⁎⁎⁎ −0.088 0.210⁎⁎⁎ −0.06 −0.054 −0.12 1.713⁎⁎⁎ −0.243 −0.761 −1.214 −1.091⁎⁎⁎ −0.329 0.233

(−5)–(−1)

Broker market share

22.941 −22.045 3.316 −3.042 −0.249 −0.551 0.59 −0.778 −5.346 −8.14 −3.579 −9.339 −5.302⁎⁎⁎ −1.393 0.008

(0)

Abnormal market share

−2.675 −7.828 0.317 −0.857 0.43 −0.388 −1.235 −0.87 2.988 −5.823 −3.555 −11.664 −2.401 −2.427 0.004

(1)–(5)

(−5)–(−1)

(0)

(1)–(5)

3.002 −3.709 −1.122 −0.713 −0.202 −0.183 −1.638 −0.925 7.168 −5.121 10.652 −10.018 1.415 −2.434 0.035

0.446⁎⁎⁎ −0.075 0.032⁎⁎⁎ −0.005 −0.011⁎⁎⁎

0.790⁎⁎⁎

0.603⁎⁎⁎

−0.118 0.043⁎⁎⁎ −0.009 −0.025⁎⁎⁎

−0.088 0.026⁎⁎⁎ −0.007 −0.019⁎⁎⁎

−0.003 0.001 −0.014 −0.011 −0.015 −0.054 −0.044 −0.011 −0.022 0.097

−0.005 −0.021 −0.014 −0.055 −0.034 −0.127 −0.074 0.013 −0.034 0.095

−0.004 −0.020⁎ −0.009 −0.046⁎ −0.02 0.025 −0.06 0.014 −0.023 0.072

(−5)–(−1)

(0)

(1)–(5)

0.326 −0.895 0.246⁎⁎⁎ −0.044 0.023 −0.039 −0.055 −0.048 0.527⁎⁎ −0.188 −0.83 −0.518 −0.408⁎⁎

−2.131 −1.463 0.055 −0.083 0.150⁎ −0.07 −0.150⁎ −0.062 −0.037 −0.245 −0.862 −0.902 −0.668⁎⁎

−0.946 −0.8 0.038 −0.061 0.074⁎ −0.035 −0.163⁎ −0.081 0.081 −0.172 0.107 −0.47 −0.361⁎

−0.152 0.098

−0.224 0.02

−0.157 0.024

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